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Sample records for crust-mantle system inferred

  1. 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.

  2. 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

  3. 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

  4. What Hf isotopes in zircon tell us about crust-mantle evolution

    NASA Astrophysics Data System (ADS)

    Iizuka, Tsuyoshi; Yamaguchi, Takao; Itano, Keita; Hibiya, Yuki; Suzuki, Kazue

    2017-03-01

    The 176Lu-176Hf radioactive decay system has been widely used to study planetary crust-mantle differentiation. Of considerable utility in this regard is zircon, a resistant mineral that can be precisely dated by the U-Pb chronometer and record its initial Hf isotope composition due to having low Lu/Hf. Here we review zircon U-Pb age and Hf isotopic data mainly obtained over the last two decades and discuss their contributions to our current understanding of crust-mantle evolution, with emphasis on the Lu-Hf isotope composition of the bulk silicate Earth (BSE), early differentiation of the silicate Earth, and the evolution of the continental crust over geologic history. Meteorite zircon encapsulates the most primitive Hf isotope composition of our solar system, which was used to identify chondritic meteorites best representative of the BSE (176Hf/177Hf = 0.282793 ± 0.000011; 176Lu/177Hf = 0.0338 ± 0.0001). Hadean-Eoarchean detrital zircons yield highly unradiogenic Hf isotope compositions relative to the BSE, providing evidence for the development of a geochemically enriched silicate reservoir as early as 4.5 Ga. By combining the Hf and O isotope systematics, we propose that the early enriched silicate reservoir has resided at depth within the Earth rather than near the surface and may represent a fractionated residuum of a magma ocean underlying the proto-crust, like urKREEP beneath the anorthositic crust on the Moon. Detrital zircons from world major rivers potentially provide the most robust Hf isotope record of the preserved granitoid crust on a continental scale, whereas mafic rocks with various emplacement ages offer an opportunity to trace the Hf isotope evolution of juvenile continental crust (from εHf[4.5 Ga] = 0 to εHf[present] = + 13). The river zircon data as compared to the juvenile crust composition highlight that the supercontinent cycle has controlled the evolution of the continental crust by regulating the rates of crustal generation and intra

  5. 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

  6. 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.

  7. Mapping the crust/mantle boundary with the Moon Mineralogy Mapper instrument data

    NASA Astrophysics Data System (ADS)

    Martinot, M.; Besse, S.; Flahaut, J.; Blanchette-Guertin, J.-F.; Isaacson, P.; van Westrenen, W.

    2015-10-01

    Determining the composition and structure of the lunar crust is crucial to study its origin and evolution[5]. Several space missions were sent to the Moon in order to study its gravity field, which can be used to derive information about its crustal thickness [9]. Results from the Clementine mission (1990s) suggested a lunar crustal thickness varying between 20 and 120 km[6], and enabled studies of the mineralogy of the lunar crust [8]. From September 2011 to December 2012, the Gravity Recovery and Interior Laboratory (GRAIL) mission acquired more precise gravimetric data from the Moon. GRAIL data suggest a significantly smaller average lunar crustal thickness between 34 and 43 km depending on the model considered [9].This survey aims to study the crust/mantle boundary region, and evaluate the lunar crustal thickness using impact craters as natural drill holes. To this end, the proximity to the mantle was calculated for all craters in the lunar crater database, using the method described in [4]. Craters that fall within a specific proximity range, diagnostic of the crust/mantle boundary region, and with preserved central peaks are selected for further investigations. The mineralogy of the selected craters central peaks is derived from the Moon Mineralogy Mapper (M3) data [7] to evaluate the presence or absence of mantle material. One ultimate goal is to place constraints on the crust/mantle boundary depth and mineralogy, and assess which GRAIL model(s)best describe the Moon crust.

  8. Ca isotope fingerprints of early crust-mantle evolution

    NASA Astrophysics Data System (ADS)

    Kreissig, K.; Elliott, T.

    2005-01-01

    isotope ratios in some mafic samples reflect anomalous K/Ca ratios as a result of intense K-metasomatism ˜3.6 Ga. Thus Ca isotope measurements are not a robust tracer of crustal growth in the presence of intense tectono-metamorphic processes. Coupled with other isotope data, however, the degree of overprint can be estimated and the 40Ca/ 44Ca ratio of a little disturbed sample hints at a small contribution of Hadean protocrust in the coastal part of the Godthåbsfjord area (Southwest Greenland). In the majority of Zimbabwe TTG samples, unradiogenic initial Ca isotope ratios point to very little prior crustal history and minor subsequent disturbance. We thus infer that the modest initial ɛ Nd ˜0.8 of the Zimbabwean samples is representative of the depleted mantle at ˜3.6 Ga. Furthermore, Ca isotope systematics provide little support for a "steady state" model of crustal growth.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. Geothermal profile and crust-mantle transition beneath east-central Queensland: Volcanology, xenolith petrology and seismic data

    NASA Astrophysics Data System (ADS)

    Griffin, W. L.; Sutherland, F. L.; Hollis, J. D.

    1987-04-01

    Geothermobarometry of garnet granulite and garnet websterite xenoliths in basalts from numerous localities in east-central Queensland gives P-T points that fall along the geotherm previously defined for southeastern Australia. This elevated geotherm is ascribed to the advective transport of heat by Tertiary-Recent magmas ponded at the crust-mantle boundary. The lower crust in this region consists dominantly of mafic granulites, representing frozen basaltic melts and cumulates. Spinel lherzolite becomes a dominant rock type at depths of ca. 30 km, and persists, interlayered with pyroxenites, to depths of ca. 55 km. Seismic reflection profiles show a "layered lower crust" between depths of 20 and 36 km depth. The lithologically defined crust-mantle boundary lies within this zone, at least 6 km above the seismically defined Moho. This interpretation is consistent with the observed velocity ( Vp) gradient downward through the layered zone. The constructed geotherm implies that the bottom of the lithosphere beneath eastern Australia is shallower than ca. 100 km. This makes it unlikely that the diamonds of eastern Australia are derived from local intrusions, unless these are > 200 Ma old.

  18. The world turns over: Hadean-Archean crust-mantle evolution

    NASA Astrophysics Data System (ADS)

    Griffin, W. L.; Belousova, E. A.; O'Neill, C.; O'Reilly, Suzanne Y.; Malkovets, V.; Pearson, N. J.; Spetsius, S.; Wilde, S. A.

    2014-02-01

    We integrate an updated worldwide compilation of U/Pb, Hf-isotope and trace-element data on zircon, and Re-Os model ages on sulfides and alloys in mantle-derived rocks and xenocrysts, to examine patterns of crustal evolution and crust-mantle interaction from 4.5 Ga to 2.4 Ga ago. The data suggest that during the period from 4.5 Ga to ca 3.4 Ga, Earth's crust was essentially stagnant and dominantly mafic in composition. Zircon crystallized mainly from intermediate melts, probably generated both by magmatic differentiation and by impact melting. This quiescent state was broken by pulses of juvenile magmatic activity at ca 4.2 Ga, 3.8 Ga and 3.3-3.4 Ga, which may represent mantle overturns or plume episodes. Between these pulses, there is evidence of reworking and resetting of U-Pb ages (by impact?) but no further generation of new juvenile crust. There is no evidence of plate-tectonic activity, as described for the Phanerozoic Earth, before ca 3.4 Ga, and previous modelling studies indicate that the early Earth may have been characterised by an episodic-overturn, or even stagnant-lid, regime. New thermodynamic modelling confirms that an initially hot Earth could have a stagnant lid for ca 300 Ma, and then would experience a series of massive overturns at intervals on the order of 150 Ma until the end of the EoArchean. The subcontinental lithospheric mantle (SCLM) sampled on Earth today did not exist before ca 3.5 Ga. A lull in crustal production around 3.0 Ga coincides with the rapid buildup of a highly depleted, buoyant SCLM, which peaked around 2.7-2.8 Ga; this pattern is consistent with one or more major mantle overturns. The generation of continental crust peaked later in two main pulses at ca 2.75 Ga and 2.5 Ga; the latter episode was larger and had a greater juvenile component. The age/Hf-isotope patterns of the crust generated from 3.0 to 2.4 Ga are similar to those in the internal orogens of the Gondwana supercontinent, and imply the existence of plate

  19. 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.

  20. System Support for Forensic Inference

    NASA Astrophysics Data System (ADS)

    Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan

    Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.

  1. The lithosphere-asthenosphere and crust-mantle boundaries in the region of the Upper Rhine Graben as seen by S-wave receiver functions

    NASA Astrophysics Data System (ADS)

    Ritter, J. R. R.; Seiberlich, C.; Wawerzinek, B.

    2012-04-01

    The Upper Rhine Graben is a branch of the European Cenozoic Rift System and is characterised by a clear rift structure which stretches more than 300 km from Basel to Frankfurt. Since 2004 we study the deep structure of the Upper Rhine Graben within the TIMO project, using the mobile seismic broadband stations of the KArlsruhe BroadBand Array (KABBA). The data are complemented with recordings from permanent stations (BFO, ECH, STU, TNS and WLF). Here we present the results from shear wave receiver function (S-RF) modelling. S-RF are waveforms which should contain only S-to-P converted phases which were generated at seismic discontinuities inside the Earth. The stacked S-RF contain clear signals from the crust-mantle boundary (Moho) under the study region. After a depth migration the Moho topography varies between 25 km and 28 km underneath the Upper Rhine Graben region; within the error limits of 5 km there is no difference between the graben itself and its shoulders. In the southern part of the graben there is an indication for a thinning of the crust to about 23 km. After the Moho signals there is a second phase with opposite polarity in the S-RF. We interpret this signal as conversion from the lithosphere-asthenosphere boundary (LAB). A depth migration results in LAB depths of 70-80 km under the Upper Rhine Graben; the graben itself does not show a specific anomaly. The most shallow LAB depths are found in the region of the Eifel (about 60 km), where a small mantle plume is active.

  2. 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).

  3. 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

  4. 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.

  5. Decoupled crust-mantle accommodation of Africa-Eurasia convergence in the NW Moroccan margin

    NASA Astrophysics Data System (ADS)

    JiméNez-Munt, I.; Fernã Ndez, M.; VergéS, J.; Garcia-Castellanos, D.; Fullea, J.; PéRez-Gussinyé, M.; Afonso, J. C.

    2011-08-01

    The extent of the area accommodating convergence between the African and Iberian plates, how this convergence is partitioned between crust and mantle, and the role of the plate boundary in accommodating deformation are not well-understood subjects. We calculate the structure of the lithosphere derived from its density distribution 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 model is based on the integration of gravity, geoid, elevation, and heat flow data and on the crustal structure across the NW Moroccan margin derived from reflection and wide-angle seismic data. The resulting mantle density anomalies suggest important variations of the lithosphere-asthenosphere boundary (LAB) topography, indicating prominent lithospheric mantle thickening beneath the margin (LAB > 200 km depth) followed by thinning beneath the Atlas Mountains (LAB ˜90 km depth). At crustal levels the Iberia-Africa convergence is sparsely accommodated in a ˜950 km wide area and localized in the Atlas and Gorringe regions, with an inferred shortening of ˜50 km. In contrast, mantle thickening accommodates a 400 km wide region, thus advocating for a decoupled crustal-mantle mechanical response. 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 crustal shortening estimates and with the accommodation of part of the Iberia-Africa convergence farther NW of the Gorringe Bank and/or off the strike of the profile.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. Single board system for fuzzy inference

    NASA Technical Reports Server (NTRS)

    Symon, James R.; Watanabe, Hiroyuki

    1991-01-01

    The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Implementation of Fuzzy Inference Systems Using Neural Network Techniques

    DTIC Science & Technology

    1992-03-01

    rules required to implement the system, which are usually supplied by ’experts’. One alternative is to use a neural network -type architecture to implement...the fuzzy inference system, and neural network -type training techniques to ’learn’ the control parameters needed by the fuzzy inference system. By...using a generalized version of a neural network , the rules of the fuzzy inference system can be learned without the assistance of experts.

  16. 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.

  17. 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…

  18. Crustal magnetization and temperature at depth beneath the Yilgarn block, Western Australia inferred from Magsat data

    NASA Technical Reports Server (NTRS)

    Mayhew, M. A.; Wasilewski, Peter J.; Johnson, B. D.

    1991-01-01

    Variations in crustal magnetization along a seismic section across the Archean Yilgarn block of Western Australia inferred from Magsat data are interpreted as a subtle thermal effect arising from variations in depth to the Curie isotherm. The isotherm lies deep within the mantle of the eastern part of the province, but transects the crust-mantle transition and rises well into the crust on the western side. The model is consistent with heat flow variations along the section line. The mean crustal magnetization implied by the model is approximately 2 A/m. The temperature variation implied by the model is consistent with the hypothesis that the crust-mantle transition seen seismically corresponds to the mafic granulite-eclogite phase transition within a zone of igneous crustal underplating.

  19. Parameter Inference for Biochemical Systems that Undergo a Hopf Bifurcation

    PubMed Central

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

    2008-01-01

    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. PMID:18456830

  20. 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.

  1. 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.

  2. 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.

  3. INFeRS: Interactive Numeric Files Retrieval System. Final Report.

    ERIC Educational Resources Information Center

    Chiang, Katherine; And Others

    In 1988 Mann Library at Cornell University proposed to develop a computer system that would support interactive access to significant electronic files in agriculture and the life sciences. This system was titled the Interactive Numeric Files Retrieval System (INFeRS). This report describes how project goals were met and it presents the project's…

  4. 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.

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

    PubMed

    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.

  6. 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.

  7. 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.

  8. 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.

  9. A fuzzy inference system to evaluate contract service provider performance.

    PubMed

    Cruz, Antonio Miguel; Denis, Ernesto Rodriguez

    2005-01-01

    This paper puts forward a fuzzy inference system for evaluating the quality performance of service contract providers. An Application Service Provider was designed and put online, featuring surveys to establish the most useful indicators to evaluate the quality of the service. This model was implemented in 10 separate hospitals. As a result, the service cost-acquisition cost ratio in these cases was reduced from 16.14% to 6.09% in the period 2001-January 2003.

  10. Topological augmentation to infer hidden processes in biological systems

    PubMed Central

    Sunnåker, Mikael; Zamora-Sillero, Elias; López García de Lomana, Adrián; Rudroff, Florian; Sauer, Uwe; Stelling, Joerg; Wagner, Andreas

    2014-01-01

    Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index. Contact: mikael.sunnaker@bsse.ethz.ch; andreas.wagner@ieu.uzh.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24297519

  11. Post-collisional high-Mg granitoids from the Paleoproterozoic East Sarmatian Orogen (East European Craton): Evidence for crust-mantle interaction

    NASA Astrophysics Data System (ADS)

    Terentiev, R. A.; Santosh, M.

    2017-03-01

    The East Sarmatian Orogen (ESO) is located along the southwestern domain of the East European Craton and occupies a key tectonic link between the Sarmatian and Volgo-Uralian domains. Here we investigate the Paleoproterozoic Novaya Melovatka pluton and its mafic-ultramafic xenoliths to gain insights into the role of interaction between intermediate-felsic crustal melt with mantle rocks as a mechanism for the generation of high-Mg granitoids at crustal pressures. The pluton is composed of biotite-orthopyroxene quartz dioritic and monzodioritic porphyrites (Phase 1) and medium-grained biotite-amphibole quartz diorite, tonalite and granodiorite and commingled Phase 1 mafic magmatic enclaves (MME) (Phase 2). The general geochemical characteristics of these rocks are similar to those of Late-Archean high-Mg sanukitoids. The TDM (model) ages for intermediate Phase 1 and granitoid Phase 2 are similar and show a range of 2324-2439 and 2284-2519 M, respectively. The εNd(t) values are grouped around subchondritic values (=+1.4-+1.9 and + 1.1-+2.2) and the initial 87Sr/86Sr ratios are in the range of 0.70202-0.70390. The complex compositional zoning of minerals suggests that the rocks crystallized as synchronous but discrete magma pulses, with limited to significant mixing. Based on the geochemical features we infer that the Phase 1 rocks formed from partial melting of a mantle wedge metasomatized to different degrees by fluids/melts. The presence of MMEs, compositional zoning of minerals including reversely zoned amphiboles, plagioclases with thin calcic overgrowths, and acicular apatite, as well as the whole-rock geochemical features are consistent with a hybrid origin of the high-Mg granitoids belonging to Phase 2. Geobarometry indicates crystallization at upper-crustal depths (i.e. 1.7-2.4 kbar). The igneous suite evolved by fractional crystallization of orthopyroxene, hornblende, plagioclase and biotite. Here we propose a tectonic model involving partial melting of the

  12. 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

  13. 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

  14. Statistical inference for noisy nonlinear ecological dynamic systems.

    PubMed

    Wood, Simon N

    2010-08-26

    Chaotic ecological dynamic systems defy conventional statistical analysis. Systems with near-chaotic dynamics are little better. Such systems are almost invariably driven by endogenous dynamic processes plus demographic and environmental process noise, and are only observable with error. Their sensitivity to history means that minute changes in the driving noise realization, or the system parameters, will cause drastic changes in the system trajectory. This sensitivity is inherited and amplified by the joint probability density of the observable data and the process noise, rendering it useless as the basis for obtaining measures of statistical fit. Because the joint density is the basis for the fit measures used by all conventional statistical methods, this is a major theoretical shortcoming. The inability to make well-founded statistical inferences about biological dynamic models in the chaotic and near-chaotic regimes, other than on an ad hoc basis, leaves dynamic theory without the methods of quantitative validation that are essential tools in the rest of biological science. Here I show that this impasse can be resolved in a simple and general manner, using a method that requires only the ability to simulate the observed data on a system from the dynamic model about which inferences are required. The raw data series are reduced to phase-insensitive summary statistics, quantifying local dynamic structure and the distribution of observations. Simulation is used to obtain the mean and the covariance matrix of the statistics, given model parameters, allowing the construction of a 'synthetic likelihood' that assesses model fit. This likelihood can be explored using a straightforward Markov chain Monte Carlo sampler, but one further post-processing step returns pure likelihood-based inference. I apply the method to establish the dynamic nature of the fluctuations in Nicholson's classic blowfly experiments.

  15. 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.

  16. 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.

  17. ANUBIS: artificial neuromodulation using a Bayesian inference system.

    PubMed

    Smith, Benjamin J H; Saaj, Chakravarthini M; Allouis, Elie

    2013-01-01

    Gain tuning is a crucial part of controller design and depends not only on an accurate understanding of the system in question, but also on the designer's ability to predict what disturbances and other perturbations the system will encounter throughout its operation. This letter presents ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatically tuning controller parameters in real time. ANUBIS is based on the Bayesian brain concept and modifies it by incorporating a model of the neuromodulatory system comprising four artificial neuromodulators. It has been applied to the controller of EchinoBot, a prototype walking rover for Martian exploration. ANUBIS has been implemented at three levels of the controller; gait generation, foot trajectory planning using Bézier curves, and foot trajectory tracking using a terminal sliding mode controller. We compare the results to a similar system that has been tuned using a multilayer perceptron. The use of Bayesian inference means that the system retains mathematical interpretability, unlike other intelligent tuning techniques, which use neural networks, fuzzy logic, or evolutionary algorithms. The simulation results show that ANUBIS provides significant improvements in efficiency and adaptability of the three controller components; it allows the robot to react to obstacles and uncertainties faster than the system tuned with the MLP, while maintaining stability and accuracy. As well as advancing rover autonomy, ANUBIS could also be applied to other situations where operating conditions are likely to change or cannot be accurately modeled in advance, such as process control. In addition, it demonstrates one way in which neuromodulation could fit into the Bayesian brain framework.

  18. Order restricted inference for oscillatory systems for detecting rhythmic signals

    PubMed Central

    Larriba, Yolanda; Rueda, Cristina; Fernández, Miguel A.; Peddada, Shyamal D.

    2016-01-01

    Motivation: Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist's choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic. Availability and Implementation: A user friendly code implemented in R language can be downloaded from http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm. Contact: peddada@niehs.nih.gov PMID:27596593

  19. 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.

  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. 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.

  2. Prediction of Earth rotation parameters by fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Akyilmaz, O.; Kutterer, H.

    2004-09-01

    The short-term prediction of Earth rotation parameters (ERP) (length-of-day and polar motion) is studied up to 10 days by means of ANFIS (adaptive network based fuzzy inference system). The prediction is then extended to 40 days into the future by using the formerly predicted values as input data. The ERP C04 time series with daily values from the International Earth Rotation Service (IERS) serve as the data base. Well-known effects in the ERP series, such as the impact of the tides of the solid Earth and the oceans or seasonal variations of the atmosphere, were removed a priori from the C04 series. The residual series were used for both training and validation of the network. Different network architectures are discussed and compared in order to optimize the network solution. The results of the prediction are analyzed and compared with those of other methods. Short-term ERP values predicted by ANFIS show root-mean-square errors which are equal to or even lower than those from the other considered methods. The presented method is easy to use.

  3. Perturbation biology: inferring signaling networks in cellular systems.

    PubMed

    Molinelli, Evan J; Korkut, Anil; Wang, Weiqing; 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.

  4. Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system.

    PubMed

    Mandal, Indrajit; Sairam, N

    2013-05-01

    This work presents more precise computational methods for improving the diagnosis of Parkinson's disease based on the detection of dysphonia. New methods are presented for enhanced evaluation and recognize Parkinson's disease affected patients at early stage. Analysis is performed with significant level of error tolerance rate and established our results with corrected T-test. Here new ensembles and other machine learning methods consisting of multinomial logistic regression classifier with Haar wavelets transformation as projection filter that outperform logistic regression is used. Finally a novel and reliable inference system is presented for early recognition of people affected by this disease and presents a new measure of the severity of the disease. Feature selection method is based on Support Vector Machines and ranker search method. Performance analysis of each model is compared to the existing methods and examines the main advancements and concludes with propitious results. Reliable methods are proposed for treating Parkinson's disease that includes sparse multinomial logistic regression, Bayesian network, Support Vector Machines, Artificial Neural Networks, Boosting methods and their ensembles. The study aim at improving the quality of Parkinson's disease treatment by tracking them and reinforce the viability of cost effective, regular and precise telemonitoring application.

  5. Classification of Microarray Data Using Kernel Fuzzy Inference System.

    PubMed

    Kumar, Mukesh; Kumar Rath, Santanu

    2014-01-01

    The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function.

  6. An integrated fuzzy inference based monitoring, diagnostic, and prognostic system

    NASA Astrophysics Data System (ADS)

    Garvey, Dustin

    To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable "grab bag" of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1

  7. Automatic Road Gap Detection Using Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.

    2011-09-01

    Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

  8. 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.

  9. Dynamical Inference from a Kinematic Snapshot: The Force Law in the Solar System

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

    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 ar = -A [r/r 0]-α, where r is the distance from the Sun. Using a probabilistic inference technique, we infer 1.989 < α < 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.

  10. HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

    PubMed

    Kim, J; Kasabov, N

    1999-11-01

    This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

  11. 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.

  12. 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.

  13. Seizure detection in intracranial EEG using a fuzzy inference system.

    PubMed

    Aarabi, A; Fazel-Rezai, R; Aghakhani, Y

    2009-01-01

    In this paper, we present a fuzzy rule-based system for the automatic detection of seizures in the intracranial EEG (IEEG) recordings. A total of 302.7 hours of the IEEG with 78 seizures, recorded from 21 patients aged between 10 and 47 years were used for the evaluation of the system. After preprocessing, temporal, spectral, and complexity features were extracted from the segmented IEEGs. The results were thresholded using the statistics of a reference window and integrated spatio-temporally using a fuzzy rule-based decision making system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11 s. The results from the automatic system correlate well with the visual analysis of the seizures by the expert. This system may serve as a good seizure detection tool for monitoring long-term IEEG with relatively high sensitivity and low false detection rate.

  14. Bayesian Inference Networks and Spreading Activation in Hypertext Systems.

    ERIC Educational Resources Information Center

    Savoy, Jacques

    1992-01-01

    Describes a method based on Bayesian networks for searching hypertext systems. Discussion covers the use of Bayesian networks for structuring index terms and representing user information needs; use of link semantics based on constrained spreading activation to find starting points for browsing; and evaluation of a prototype system. (64…

  15. New developments of a knowledge based system (VEG) for inferring vegetation characteristics

    NASA Technical Reports Server (NTRS)

    Kimes, D. S.; Harrison, P. A.; Harrison, P. R.

    1992-01-01

    An extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).

  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. Conditional Inference and Logic for Intelligent Systems: A Theory of Measure-Free Conditioning

    DTIC Science & Technology

    1991-08-01

    4 TITLE AND SUBTITLE 5 FUNDING NUMBERS CONDITIONAL INFERENCE AND LOGIC FOR INTELLIGENT SYSTEMS PR: ZE90 PR: ZW40 A Theory of Measure-Free Conditioning...200 UNCLASSIFIED tf F I CONDIT[ONAL INFERENCE AND LOGIC FOR INTELIUGENT SYSTEMS: I, A THEORY OF MEASURE-FREE CONDTONING F by L R. Goodman Command and...complete and satisfactory theory of "measure-free" conditioning. If the concept of "conditional event" can be formalized and a suitable algebra of

  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. 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.

  1. Asymptotic inference in system identification for the atom maser.

    PubMed

    Catana, Catalin; van Horssen, Merlijn; Guta, Madalin

    2012-11-28

    System identification is closely related to control theory and plays an increasing role in quantum engineering. In the quantum set-up, system identification is usually equated to process tomography, i.e. estimating a channel by probing it repeatedly with different input states. However, for quantum dynamical systems such as quantum Markov processes, it is more natural to consider the estimation based on continuous measurements of the output, with a given input that may be stationary. We address this problem using asymptotic statistics tools, for the specific example of estimating the Rabi frequency of an atom maser. We compute the Fisher information of different measurement processes as well as the quantum Fisher information of the atom maser, and establish the local asymptotic normality of these statistical models. The statistical notions can be expressed in terms of spectral properties of certain deformed Markov generators, and the connection to large deviations is briefly discussed.

  2. Isotopic abundances - Inferences on solar system and planetary evolution

    NASA Astrophysics Data System (ADS)

    Wasserburg, G. J.

    1987-12-01

    For matter that has been removed from a region of nucleosynthetic activity and the effects of interactions with nuclear active particles, the only changes in nuclear abundances that can occur in an isolated system derive from the decay of radioactive nuclei of an element to yield the nucleus of another element. These two related nuclei furnish the absolute chronometers of geologic and cosmic time, through the decay of spontaneously radioactive parent nuclei and the accumulation of daughter nuclei. For systems related to such cosmic processes as the formation of the solar system from the precursor interstellar medium, and involving the very early evolution of the sun, there may arise considerable complexity, due to the intrinsic isotopic heterogeneity of the medium and the presence of short-lived nuclei.

  3. Data-driven sensitivity inference for Thomson scattering electron density measurement systems

    NASA Astrophysics Data System (ADS)

    Fujii, Keisuke; Yamada, Ichihiro; Hasuo, Masahiro

    2017-01-01

    We developed a method to infer the calibration parameters of multichannel measurement systems, such as channel variations of sensitivity and noise amplitude, from experimental data. We regard such uncertainties of the calibration parameters as dependent noise. The statistical properties of the dependent noise and that of the latent functions were modeled and implemented in the Gaussian process kernel. Based on their statistical difference, both parameters were inferred from the data. We applied this method to the electron density measurement system by Thomson scattering for the Large Helical Device plasma, which is equipped with 141 spatial channels. Based on the 210 sets of experimental data, we evaluated the correction factor of the sensitivity and noise amplitude for each channel. The correction factor varies by ≈10%, and the random noise amplitude is ≈2%, i.e., the measurement accuracy increases by a factor of 5 after this sensitivity correction. The certainty improvement in the spatial derivative inference was demonstrated.

  4. 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

  5. Field Demonstration of a Broadband Acoustical Backscattering System Mounted on a REMUS-100 for Inferences of Zooplankton Size and Abundance

    DTIC Science & Technology

    2012-09-30

    Backscattering System Mounted on a REMUS-100 for Inferences of Zooplankton Size and Abundance Andone C. Lavery Department of Applied Ocean Physics and...SUBTITLE Field Demonstration of a Broadband Acoustical Backscattering System Mounted on a REMUS-100 for Inferences of Zooplankton Size and Abundance 5a...of this REMUS- mounted broadband backscattering system with regards to inferring fish and zooplankton distribution, size and abundance in comparison

  6. PLY: A System of Plausibility Inference with a Probabilistic Basis,

    DTIC Science & Technology

    1982-12-01

    a hard time estimating. This estimation problem has been encountered in both the PROSPECTOR project [Hart 77], which deals with mineral exploration consulting...PROSPECTOR [Hart 77, Duda 79] is a computer system designed to aid mineral exploration . It takes in user information and then tells what and where...1976. [Duda 79] Duda, R., Hart, P., Konollge, K., Reboh, R. A Computer-Based Consultant for Mineral Exploration . Final Report, SRI Project 6415, SRI

  7. 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.

  8. Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations.

    PubMed

    Vrettas, Michail D; Opper, Manfred; Cornford, Dan

    2015-01-01

    This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.

  9. 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.'

  10. 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.

  11. 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

  12. The state of the atmosphere as inferred from the FGGE satellite observing systems during SOP-1

    NASA Technical Reports Server (NTRS)

    Halem, M.; Kalnay, E.; Baker, W. E.; Atlas, R.

    1981-01-01

    Data assimilation experiments were performed to test the influence of different elements of the satellite observing systems. Results from some of the experiments are presented. These findings show that the FGGE satellite systems are able to infer the three-dimensional motion field and improve the representation of the large-scale state of the atmosphere. Preliminary results of the forecast impact of the FGGE data sets are also presented.

  13. 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

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

    PubMed

    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.

  15. 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.

  16. Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

    PubMed Central

    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. PMID:25089832

  17. 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.

  18. 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.

  19. Assessing water quality in rivers with fuzzy inference systems: a case study.

    PubMed

    Ocampo-Duque, William; Ferré-Huguet, Núria; Domingo, José L; Schuhmacher, Marta

    2006-08-01

    In recent years, fuzzy-logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental problems. In the present study, a methodology based on fuzzy inference systems (FIS) to assess water quality is proposed. A water quality index calculated with fuzzy reasoning has been developed. The relative importance of water quality indicators involved in the fuzzy inference process has been dealt with a multi-attribute decision-aiding method. The potential application of the fuzzy index has been tested with a case study. A data set collected from the Ebro River (Spain) by two different environmental protection agencies has been used. The current findings, managed within a geographic information system, clearly agree with official reports and expert opinions about the pollution problems in the studied area. Therefore, this methodology emerges as a suitable and alternative tool to be used in developing effective water management plans.

  20. Perceptual inference.

    PubMed

    Aggelopoulos, Nikolaos C

    2015-08-01

    Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience.

  1. Inference system using softcomputing and mixed data applied in metabolic pathway datamining.

    PubMed

    Arredondo, Tomás; Candel, Diego; Leiva, Mauricio; Dombrovskaia, Lioubov; Agulló, Loreine; Seeger, Michael

    2012-01-01

    This paper describes the development of an inference system used for the identification of genes that encode enzymes of metabolic pathways. Input sequence alignment values are used to classify the best candidate genes for inclusion in a metabolic pathway map. The system workflow allows the user to provide feedback, which is stored in conjunction with analysed sequences for periodic retraining. The construction of the system involved the study of several different classifiers with various topologies, data sets and parameter normalisation data models. Experimental results show an excellent prediction capability with the classifiers trained with mixed data providing the best results.

  2. Inference of biological S-system using the separable estimation method and the genetic algorithm.

    PubMed

    Liu, Li-Zhi; Wu, Fang-Xiang; Zhang, W J

    2012-01-01

    Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations (ODEs) is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity. In this paper, a pruning separable parameter estimation algorithm (PSPEA) is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method (SPEM) and a pruning strategy, which includes adding an l₁ regularization term to the objective function and pruning the solution with a threshold value. Then, this algorithm is combined with the continuous genetic algorithm (CGA) to form a hybrid algorithm that owns the properties of these two combined algorithms. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The results show that the proposed algorithm with the pruning strategy has much lower estimation error and much higher identification accuracy than the existing method.

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

    SciTech Connect

    Harmandaris, Vagelis; 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.

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. Development of rainfall runoff models using Takagi Sugeno fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Jacquin, Alexandra P.; Shamseldin, Asaad Y.

    2006-09-01

    SummaryThis study explores the application of Takagi-Sugeno fuzzy inference systems to rainfall-runoff modelling. The models developed intend to describe the non-linear relationship between rainfall as input and runoff as output to the real system using a system based approach. Two types of fuzzy models are proposed, where the first type is intended to account for the effect of changes in catchment wetness in the rainfall-runoff transformation and the second type incorporates seasonality as a source of non-linearity in this relationship. The models developed are applied to data from six catchments of diverse climatic characteristics. The results of the fuzzy models are compared with those of the Simple Linear Model, the Linear Perturbation Model and the Nearest Neighbour Linear Perturbation Model, which use similar input information. The results of this study indicate that fuzzy inference systems are a suitable alternative to the traditional methods for modelling the non-linear relationship between rainfall and runoff.

  9. Use of fuzzy inference system for condition monitoring of induction motor

    NASA Astrophysics Data System (ADS)

    Janier, Josefina B.; Zaim Zaharia, M. F.; Karim, Samsul Ariffin Abd.

    2012-09-01

    Three phase induction motors are commonly used in industry due to its robustness, simplicity of its construction and high reliability. The tasks performed by these motors grow increasingly complex because of modern industries hence there is a need to determine the faults. Early detection of faults will reduce an unscheduled machine downtime that can upset production deadlines and may cause heavy financial losses. This paper is focused in developing a computer based system using Fuzzy Inference system's membership function. An unusual increase in vibration of the motor could be an indicator of faulty condition hence the vibration of the motor of an induction motor was used as an input, whereas the output is the motor condition. An inference system of the Fuzzy Logic was created to classify the vibration characteristics of the motor which is called vibration analysis. The system classified the motor of the gas distribution pump condition as from 'acceptable' to 'monitor closely'. The early detection of unusual increase in vibration of the induction motor is an important part of a predictive maintenance for motor driven machinery.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference System

    PubMed Central

    Ahn, DaeHan; Park, Homin; Hwang, Seokhyun; Park, Taejoon

    2017-01-01

    Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrance by leveraging built-in smartphone sensors only. The results of our comprehensive evaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8% accuracy regardless of smartphone positions and vehicle types. PMID:28208795

  15. 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).

  16. Urban area mapping from polarimetric SAR data using fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Ahluwalia, Asmeet; Manickam, Surendar; Bhattacharya, Avik; Porwal, Alok

    2016-05-01

    In this work, we present urban area mapping from full-polarimetric synthetic aperture radar (SAR) data using fuzzy inference system (FIS). In particular, our aim is to utilize the profound knowledge available about scattering mechanism from urban targets to delineate urban environment. In this approach, we have utilized the recently developed polarimetric SAR scattering power decomposition technique (SD-Y4O) given in Bhattacharya et. al. The improved powers along with some other polarimetric parameters were used in this study. A suitable normalization procedure was adapted to handle the skewness in the estimated parameters. The fuzzy if-then rules were constructed from the in-depth knowledge of scattering mechanisms from an urban environment. Suitable methods were introduced to define the fuzzy inference system. The defuzzified membership values were thresholded using an unsupervised clustering method (k-means). The pixels lying in the range [μmax-σ, μmax+σ] corresponds to urban areas where µmax is the largest cluster center and σ is the standard deviation of the cluster corresponding to µmax. The extracted urban area is in visually good agreement with the high resolution optical image. ALOS PALSAR full-polarimetric L-band SAR data has been used in this study.

  17. 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.

  18. Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models.

    PubMed

    Erguler, Kamil; Stumpf, Michael P H

    2011-05-01

    The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.

  19. Another expert system rule inference based on DNA molecule logic gates

    NASA Astrophysics Data System (ADS)

    WÄ siewicz, Piotr

    2013-10-01

    With the help of silicon industry microfluidic processors were invented utilizing nano membrane valves, pumps and microreactors. These so called lab-on-a-chips combined together with molecular computing create molecular-systems-ona- chips. This work presents a new approach to implementation of molecular inference systems. It requires the unique representation of signals by DNA molecules. The main part of this work includes the concept of logic gates based on typical genetic engineering reactions. The presented method allows for constructing logic gates with many inputs and for executing them at the same quantity of elementary operations, regardless of a number of input signals. Every microreactor of the lab-on-a-chip performs one unique operation on input molecules and can be connected by dataflow output-input connections to other ones.

  20. 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.

  1. 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.

  2. 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.

  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. A Statistical Framework to Infer Delay and Direction of Information Flow from Measurements of Complex Systems.

    PubMed

    Schumacher, Johannes; Wunderle, Thomas; Fries, Pascal; Jäkel, Frank; Pipa, Gordon

    2015-08-01

    In neuroscience, data are typically generated from neural network activity. The resulting time series represent measurements from spatially distributed subsystems with complex interactions, weakly coupled to a high-dimensional global system. We present a statistical framework to estimate the direction of information flow and its delay in measurements from systems of this type. Informed by differential topology, gaussian process regression is employed to reconstruct measurements of putative driving systems from measurements of the driven systems. These reconstructions serve to estimate the delay of the interaction by means of an analytical criterion developed for this purpose. The model accounts for a range of possible sources of uncertainty, including temporally evolving intrinsic noise, while assuming complex nonlinear dependencies. Furthermore, we show that if information flow is delayed, this approach also allows for inference in strong coupling scenarios of systems exhibiting synchronization phenomena. The validity of the method is demonstrated with a variety of delay-coupled chaotic oscillators. In addition, we show that these results seamlessly transfer to local field potentials in cat visual cortex.

  5. 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

  6. 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…

  7. Eccentricity Inferences in Multi-planet systems with Transit Timing: Degeneracies and Apsidal Alignment

    NASA Astrophysics Data System (ADS)

    Jontof-Hutter, Daniel; Van Laerhoven, Christa L.; Ford, Eric B.

    2016-05-01

    Hundreds of multi-transiting systems discovered by the Kepler mission show Transit Timing Variations (TTV). In cases where the TTVs are uniquely attributable to transiting planets, the TTVs enable precise measurements of planetary masses and orbital parameters. Of particular interest are the constraints on eccentricity vectors that can be inferred in systems of low-mass exoplanets.The TTVs in these systems are dominated by a signal caused by near-resonant mean motions. This causes the well-known near-degeneracy between planetary masses and orbital eccentricities. In addition, it causes a degeneracy between the eccentricities of interacting planet pairs.For many systems, the magnitude of individual eccentricities are weakly constrained, yet the data typically provide a tight constraint on the posterior joint distribution for the eccentricity vector components. This permits tight constraints on the relative eccentricity and degree of alignment of interacting planets.For a sample of two and three-planet systems with TTVs, we highlight the effects of these correlations. While the most eccentric orbital solutions for these systems show apsidal alignment, this is often due to the degeneracy that causes correlated constraints on the eccentricity vector components. We compare the likelihood of apsidal alignment for two choices of eccentricity prior: a wide prior using a Rayleigh distribution of scale length 0.1 and a narrower prior with scale length 0.02. In all cases the narrower prior decreased the fraction of samples that exhibited apsidal alignment. However, apsidal alignment persisted in the majority of cases with a narrower eccentricity prior. For a sample of our TTV solutions, we ran simulations of these systems over secular timescales, and decomposed their eccentricity eigenmodes over time, confirming that in most cases, the eccentricities were dominated by parallel eigenmodes which favor apsidal alignment.

  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.

  9. Shape Descriptions of Nonlinear Dynamical Systems for Video-based Inference.

    PubMed

    Venkataraman, Vinay; Turaga, Pavan

    2016-02-23

    This paper presents a shape-theoretic framework for dynamical analysis of nonlinear dynamical systems which appear frequently in several video-based inference tasks. Traditional approaches to dynamical modeling have included linear and nonlinear methods with their respective drawbacks. A novel approach we propose is the use of descriptors of the shape of the dynamical attractor as a feature representation of nature of dynamics. The proposed framework has two main advantages over traditional approaches: a) representation of the dynamical system is derived directly from the observational data, without any inherent assumptions, and b) the proposed features show stability under different time-series lengths where traditional dynamical invariants fail. We illustrate our idea using nonlinear dynamical models such as Lorenz and Rossler systems, where our feature representations (shape distribution) support our hypothesis that the local shape of the reconstructed phase space can be used as a discriminative feature. Our experimental analyses on these models also indicate that the proposed framework show stability for different time-series lengths, which is useful when the available number of samples are small/variable. The specific applications of interest in this paper are: 1) activity recognition using motion capture and RGBD sensors, 2) activity quality assessment for applications in stroke rehabilitation, and 3) dynamical scene classification.We provide experimental validation through action and gesture recognition experiments on motion capture and Kinect datasets. In all these scenarios, we show experimental evidence of the favorable properties of the proposed representation.

  10. Shape Distributions of Nonlinear Dynamical Systems for Video-Based Inference.

    PubMed

    Venkataraman, Vinay; Turaga, Pavan

    2016-12-01

    This paper presents a shape-theoretic framework for dynamical analysis of nonlinear dynamical systems which appear frequently in several video-based inference tasks. Traditional approaches to dynamical modeling have included linear and nonlinear methods with their respective drawbacks. A novel approach we propose is the use of descriptors of the shape of the dynamical attractor as a feature representation of nature of dynamics. The proposed framework has two main advantages over traditional approaches: a) representation of the dynamical system is derived directly from the observational data, without any inherent assumptions, and b) the proposed features show stability under different time-series lengths where traditional dynamical invariants fail. We illustrate our idea using nonlinear dynamical models such as Lorenz and Rossler systems, where our feature representations (shape distribution) support our hypothesis that the local shape of the reconstructed phase space can be used as a discriminative feature. Our experimental analyses on these models also indicate that the proposed framework show stability for different time-series lengths, which is useful when the available number of samples are small/variable. The specific applications of interest in this paper are: 1) activity recognition using motion capture and RGBD sensors, 2) activity quality assessment for applications in stroke rehabilitation, and 3) dynamical scene classification. We provide experimental validation through action and gesture recognition experiments on motion capture and Kinect datasets. In all these scenarios, we show experimental evidence of the favorable properties of the proposed representation.

  11. Ecological Inference

    NASA Astrophysics Data System (ADS)

    King, Gary; Rosen, Ori; Tanner, Martin A.

    2004-09-01

    This collection of essays brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half-decade has witnessed an explosion of research in ecological inference--the process of trying to infer individual behavior from aggregate data. Although uncertainties and information lost in aggregation make ecological inference one of the most problematic types of research to rely on, these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, by business in marketing research, and by governments in policy analysis.

  12. 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.

  13. 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.

  14. 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.

  15. The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection

    NASA Astrophysics Data System (ADS)

    Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti

    2014-06-01

    In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.

  16. 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

  17. Condition monitoring of distributed systems using two-stage Bayesian inference data fusion

    NASA Astrophysics Data System (ADS)

    Jaramillo, Víctor H.; Ottewill, James R.; Dudek, Rafał; Lepiarczyk, Dariusz; Pawlik, Paweł

    2017-03-01

    In industrial practice, condition monitoring is typically applied to critical machinery. A particular piece of machinery may have its own condition monitoring system that allows the health condition of said piece of equipment to be assessed independently of any connected assets. However, industrial machines are typically complex sets of components that continuously interact with one another. In some cases, dynamics resulting from the inception and development of a fault can propagate between individual components. For example, a fault in one component may lead to an increased vibration level in both the faulty component, as well as in connected healthy components. In such cases, a condition monitoring system focusing on a specific element in a connected set of components may either incorrectly indicate a fault, or conversely, a fault might be missed or masked due to the interaction of a piece of equipment with neighboring machines. In such cases, a more holistic condition monitoring approach that can not only account for such interactions, but utilize them to provide a more complete and definitive diagnostic picture of the health of the machinery is highly desirable. In this paper, a Two-Stage Bayesian Inference approach allowing data from separate condition monitoring systems to be combined is presented. Data from distributed condition monitoring systems are combined in two stages, the first data fusion occurring at a local, or component, level, and the second fusion combining data at a global level. Data obtained from an experimental rig consisting of an electric motor, two gearboxes, and a load, operating under a range of different fault conditions is used to illustrate the efficacy of the method at pinpointing the root cause of a problem. The obtained results suggest that the approach is adept at refining the diagnostic information obtained from each of the different machine components monitored, therefore improving the reliability of the health assessment of

  18. A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson's Disease.

    PubMed

    Camara, Carmen; Warwick, Kevin; Bruña, Ricardo; Aziz, Tipu; del Pozo, Francisco; Maestú, Fernando

    2015-11-01

    Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7% in 70% of the patients.

  19. Smartphone-Based System for Learning and Inferring Hearing Aid Settings

    PubMed Central

    Aldaz, Gabriel; Puria, Sunil; Leifer, Larry J.

    2017-01-01

    Background Previous research has shown that hearing aid wearers can successfully self-train their instruments’ gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the “untrained system,” that is, the manufacturer’s algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The “trained system” first learned each individual’s preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). Purpose To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. Research Design An experimental within-participants study. Participants used a prototype hearing system—comprising two hearing aids, Android smartphone, and body-worn gateway device—for ~6 weeks. Study Sample Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Intervention Participants were fitted and instructed to perform daily comparisons of settings (“listening evaluations”) through a smartphone-based software application called Hearing Aid

  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. Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.

    PubMed

    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.

  2. Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level

    NASA Astrophysics Data System (ADS)

    Thakar, Juilee; Albert, Réka

    The following sections are included: * Introduction * Boolean Network Concepts and History * Extensions of the Classical Boolean Framework * Boolean Inference Methods and Examples in Biology * Dynamic Boolean Models: Examples in Plant Biology, Developmental Biology and Immunology * Conclusions * References

  3. Inferring the Architectures of Planetary Systems from Kepler Results with SysSim

    NASA Astrophysics Data System (ADS)

    Ford, Eric

    . Without a method to interpret and debias the Kepler planet candidates on a system-by- system basis, it is not possible to rigorously address critical NASA-relevant science questions like: 1) What fraction of stars have planets? What fraction of stars have solar system analogs? 2) What is the planetary system environment of potentially habitable planets? 3) What is the expected yield of future NASA exoplanet missions? 4) Are there different populations of planetary systems? What are their architectures? and many other valuable questions that are critical for understanding the origins of solar systems. To fill this critical gap, we have developed the Planetary System Simulator or SysSim, which empirically determines the underlying debiased distribution of planetary properties (e.g., planet size, orbital period, etc.) and planetary system architecture (e.g., relative inclinations, number of planets per star) simultaneously. The earliest version of SysSim measured the exoplanetary inclination distribution for the first time, a finding of major consequence for planet formation theorists (LR+11). We propose to extend SysSim to include new planetary architecture parameters and new observational constraints from the growing Kepler dataset. We will produce rigorously-debiased exoplanetary populations that will improve the understanding of the frequency, architecture, and origins of planetary systems. Our team is uniquely qualified to fulfill these tasks based on our extensive experience with Kepler data, metadata, multi-transiting systems, statistical inference, and previous work (LR+11, B12, Ragozzine & Holman 2010).

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. The Gaia astrophysical parameters inference system (Apsis). Pre-launch description

    NASA Astrophysics Data System (ADS)

    Bailer-Jones, C. A. L.; Andrae, R.; Arcay, B.; Astraatmadja, T.; Bellas-Velidis, I.; Berihuete, A.; Bijaoui, A.; Carrión, C.; Dafonte, C.; Damerdji, Y.; Dapergolas, A.; de Laverny, P.; Delchambre, L.; Drazinos, P.; Drimmel, R.; Frémat, Y.; Fustes, D.; García-Torres, M.; Guédé, C.; Heiter, U.; Janotto, A.-M.; Karampelas, A.; Kim, D.-W.; Knude, J.; Kolka, I.; Kontizas, E.; Kontizas, M.; Korn, A. J.; Lanzafame, A. C.; Lebreton, Y.; Lindstrøm, H.; Liu, C.; Livanou, E.; Lobel, A.; Manteiga, M.; Martayan, C.; Ordenovic, Ch.; Pichon, B.; Recio-Blanco, A.; Rocca-Volmerange, B.; Sarro, L. M.; Smith, K.; Sordo, R.; Soubiran, C.; Surdej, J.; Thévenin, F.; Tsalmantza, P.; Vallenari, A.; Zorec, J.

    2013-11-01

    The Gaia satellite will survey the entire celestial sphere down to 20th magnitude, obtaining astrometry, photometry, and low resolution spectrophotometry on one billion astronomical sources, plus radial velocities for over one hundred million stars. Itsmain objective is to take a census of the stellar content of our Galaxy, with the goal of revealing its formation and evolution. Gaia's unique feature is the measurement of parallaxes and proper motions with hitherto unparalleled accuracy for many objects. As a survey, the physical properties of most of these objects are unknown. Here we describe the data analysis system put together by the Gaia consortium to classify these objects and to infer their astrophysical properties using the satellite's data. This system covers single stars, (unresolved) binary stars, quasars, and galaxies, all covering a wide parameter space. Multiple methods are used for many types of stars, producing multiple results for the end user according to different models and assumptions. Prior to its application to real Gaia data the accuracy of these methods cannot be assessed definitively. But as an example of the current performance, we can attain internal accuracies (rms residuals) on F, G, K, M dwarfs and giants at G = 15 (V = 15-17) for a wide range of metallicites and interstellar extinctions of around 100 K in effective temperature (Teff), 0.1 mag in extinction (A0), 0.2 dex in metallicity ([Fe/H]), and 0.25 dex in surface gravity (log g). The accuracy is a strong function of the parameters themselves, varying by a factor of more than two up or down over this parameter range. After its launch in December 2013, Gaia will nominally observe for five years, during which the system we describe will continue to evolve in light of experience with the real data.

  10. 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.

  11. Inference of S-system models of genetic networks by solving one-dimensional function optimization problems.

    PubMed

    Kimura, S; Araki, D; Matsumura, K; Okada-Hatakeyama, M

    2012-02-01

    Voit and Almeida have proposed the decoupling approach as a method for inferring the S-system models of genetic networks. The decoupling approach defines the inference of a genetic network as a problem requiring the solutions of sets of algebraic equations. The computation can be accomplished in a very short time, as the approach estimates S-system parameters without solving any of the differential equations. Yet the defined algebraic equations are non-linear, which sometimes prevents us from finding reasonable S-system parameters. In this study, we propose a new technique to overcome this drawback of the decoupling approach. This technique transforms the problem of solving each set of algebraic equations into a one-dimensional function optimization problem. The computation can still be accomplished in a relatively short time, as the problem is transformed by solving a linear programming problem. We confirm the effectiveness of the proposed approach through numerical experiments.

  12. 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.

  13. 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

  14. 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.

  15. Astrophysical Site of the Origin of the Solar System Inferred from Extinct Radionuclide Abundances

    NASA Astrophysics Data System (ADS)

    Harper, Charles L., Jr.

    1996-08-01

    Extinct radionuclides in the solar abundance distribution (SAD) provide a basis with which to characterize the molecular cloud environment in which the solar system formed 4566±2 Ma ago. The low abundance of the longer-lived r-process radionuclide 129I(T½ = 16 Ma) indicates a long (˜ 102 Ma) isolation time from energetic interstellar medium (ISM) reservoirs containing most of the Galaxy's budget of freshly-synthesized Type II supernova products. However, the abundances of the shorter-lived species 60Fe (T½ = 1.5 Ma), 53Mn (T½ = 3.7 Ma), and 107Pd (T½ = 6.5 Ma) are consistent with late admixture of freshly synthesized Type II supernova products. The fit for these species is based on an average yield distribution obtained by decomposition of the SAD. The apparent timescale contradiction is resolved in a simple two timescale molecular cloud self-contamination model consistent with formation of the Sun in an old evolved stellar complex at the eroding boundary of a molecular cloud interacting with an adjacent OB association. Admixture of an ˜10-5 to ˜10-6 mass fraction of Type II supernova ejecta into the presolar cloud dominates the shorter-lived species and 107Pd, whereas longer- lived 129I preserves information on the longer timescale constraining the mean isolation/condensation/ accretion age of the molecular material in the protosolar reservoir. The inferred model age of nucleosynthetic isolation in the long timescale is consistent with cyclicity in the nucleosynthesis rate in an orbiting ISM parcel controlled by galactic spiral structure and beads-on-a-string organization of star formation in "stellar complexes" in arms. Abundant 26Al (T½ = 0.7 Ma) in the early solar system at ˜102 times the model prediction may point to 26Al/27Al ratio of ˜0.2 in the source, or an ˜102 times greater mixing fraction for pre-explosion winds over postexplosion ejecta. A mass-losing low-mass asymptotic giant branch (AGB) star model can be tuned to account for 41Ca, 26Al

  16. 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

    2016-07-05

    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.

  17. 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

  18. 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

  19. Crustal deformation in the central California coast region inferred from Global Positioning System data

    NASA Astrophysics Data System (ADS)

    Murray-Moraleda, J. R.; Thatcher, W. R.; Onishi, C. T.; Svarc, J. L.

    2011-12-01

    The Central California Coast Region (CCCR), defined here as the area from north of Point Piedras Blancas (36°N) south to Point Arguello (34.6°N) and west of the Rinconada and East Huasna faults, is a structurally complex region cut by several subparallel, late Quaternary faults. Despite relatively low rates of deformation inferred from geologic studies of the CCCR, the occurrence of the 2003 Mw 6.5 San Simeon earthquake southeast of Point Piedras Blancas highlights the need to better understand the ongoing patterns of deformation here as a means for assessing the seismic hazard. Geological and geophysical data from this region have been interpreted as evidence for ongoing transpression due to the clockwise rotation of the Transverse Ranges which would predict crustal contraction normal to the plate boundary. However an alternative interpretation concludes that the region instead experiences the active westward transfer of right-lateral strike-slip motion in a left-stepping fashion which would result in northwest-southeast contraction. Geodetic data can be used to elucidate how strain is currently partitioned between shear parallel to the San Andreas Fault (SAF) and contraction within the CCCR and to identify actively deforming structures. We use a newly compiled Global Positioning System (GPS) secular velocity field for the CCCR as well as GPS velocities for the greater southern California region from the SCEC Crustal Motion Map v.4 and the EarthScope Plate Boundary Observatory velocity solution to constrain block models of deformation. We solve for the rotation of fault-bounded blocks, fault slip rates, and internal strain within blocks. Results thus far indicate that the data do not require substantial slip on the Rinconada fault (for which the estimated slip rate is ~2 mm/yr) or on the Oceanic and West Huasna faults that bound the eastern edge of the CCCR in an alternative block configuration (for which the estimated slip rate is <1 mm/yr). The data also do

  20. An application of adaptive neuro-fuzzy inference system to landslide susceptibility mapping (Klang valley, Malaysia)

    NASA Astrophysics Data System (ADS)

    Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan

    2010-05-01

    Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient

  1. Systems Biology of Dehalococcoides: Using Network Inference Modeling to Integrate Omics Datasets Under Varied Conditions

    DTIC Science & Technology

    2012-01-13

    demonstrating a wide range over which they are informative. These RNA-respiration trends were also tested in an additional strain of...with Bayesian inference algorithms to generate data-driven models of gene networks and gene-metabolite interactions. Summary of the most important...hypothesis that electrons stripped from H2 are first passed to Nuo as the first step in the electron transport chain in these organisms. Biochemical assays

  2. 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.

  3. 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.

  4. Statistical Inference

    NASA Astrophysics Data System (ADS)

    Khan, Shahjahan

    Often scientific information on various data generating processes are presented in the from of numerical and categorical data. Except for some very rare occasions, generally such data represent a small part of the population, or selected outcomes of any data generating process. Although, valuable and useful information is lurking in the array of scientific data, generally, they are unavailable to the users. Appropriate statistical methods are essential to reveal the hidden "jewels" in the mess of the row data. Exploratory data analysis methods are used to uncover such valuable characteristics of the observed data. Statistical inference provides techniques to make valid conclusions about the unknown characteristics or parameters of the population from which scientifically drawn sample data are selected. Usually, statistical inference includes estimation of population parameters as well as performing test of hypotheses on the parameters. However, prediction of future responses and determining the prediction distributions are also part of statistical inference. Both Classical or Frequentists and Bayesian approaches are used in statistical inference. The commonly used Classical approach is based on the sample data alone. In contrast, increasingly popular Beyesian approach uses prior distribution on the parameters along with the sample data to make inferences. The non-parametric and robust methods are also being used in situations where commonly used model assumptions are unsupported. In this chapter,we cover the philosophical andmethodological aspects of both the Classical and Bayesian approaches.Moreover, some aspects of predictive inference are also included. In the absence of any evidence to support assumptions regarding the distribution of the underlying population, or if the variable is measured only in ordinal scale, non-parametric methods are used. Robust methods are employed to avoid any significant changes in the results due to deviations from the model

  5. Statistical Inference

    NASA Astrophysics Data System (ADS)

    Khan, Shahjahan

    Often scientific information on various data generating processes are presented in the from of numerical and categorical data. Except for some very rare occasions, generally such data represent a small part of the population, or selected outcomes of any data generating process. Although, valuable and useful information is lurking in the array of scientific data, generally, they are unavailable to the users. Appropriate statistical methods are essential to reveal the hidden “jewels” in the mess of the row data. Exploratory data analysis methods are used to uncover such valuable characteristics of the observed data. Statistical inference provides techniques to make valid conclusions about the unknown characteristics or parameters of the population from which scientifically drawn sample data are selected. Usually, statistical inference includes estimation of population parameters as well as performing test of hypotheses on the parameters. However, prediction of future responses and determining the prediction distributions are also part of statistical inference. Both Classical or Frequentists and Bayesian approaches are used in statistical inference. The commonly used Classical approach is based on the sample data alone. In contrast, increasingly popular Beyesian approach uses prior distribution on the parameters along with the sample data to make inferences. The non-parametric and robust methods are also being used in situations where commonly used model assumptions are unsupported. In this chapter,we cover the philosophical andmethodological aspects of both the Classical and Bayesian approaches.Moreover, some aspects of predictive inference are also included. In the absence of any evidence to support assumptions regarding the distribution of the underlying population, or if the variable is measured only in ordinal scale, non-parametric methods are used. Robust methods are employed to avoid any significant changes in the results due to deviations from the model

  6. 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.

  7. 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).

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. Simulation of SiGe:C HBTs using neural network and adaptive neuro-fuzzy inference system for RF applications

    NASA Astrophysics Data System (ADS)

    Karimi, Gholamreza; Banitalebi, Roza; Babaei Sedaghat, Sedigheh

    2013-07-01

    In this article, the small-signal equivalent circuit model of SiGe:C heterojunction bipolar transistors (HBTs) has directly been extracted from S-parameter data. Moreover, in this article, we present a new modelling approach using ANFIS (adaptive neuro-fuzzy inference system), which in general has a high degree of accuracy, simplicity and novelty (independent approach). Then measured and model-calculated data show an excellent agreement with less than 1.68 × 10-5% discrepancy in the frequency range of higher than 300 GHz over a wide range of bias points in ANFIS. The results show ANFIS model is better than ANN (artificial neural network) for redeveloping the model and increasing the input parameters.

  13. TDSDMI: Inference of time-delayed gene regulatory network using S-system model with delayed mutual information.

    PubMed

    Yang, Bin; Zhang, Wei; Wang, Haifeng; Song, Chuandong; Chen, Yuehui

    2016-05-01

    Regulatory interactions among target genes and regulatory factors occur instantaneously or with time-delay. In this paper, we propose a novel approach namely TDSDMI based on time-delayed S-system model (TDSS) model and delayed mutual information (DMI) to infer time-delay gene regulatory network (TDGRN). Firstly DMI is proposed to delete redundant regulator factors for each target gene. Secondly restricted gene expression programming (RGEP) is proposed as a new representation of the TDSS model to identify instantaneous and time-delayed interactions. To verify the effectiveness of the proposed method, TDSDMI is applied to both simulated and real biological datasets. Experimental results reveal that TDSDMI performs better than the recent reconstruction methods.

  14. The use of microsatellite variation to infer population structure and demographic history in a natural model system.

    PubMed

    Goldstein, D B; Roemer, G W; Smith, D A; Reich, D E; Bergman, A; Wayne, R K

    1999-02-01

    To assess the reliability of genetic markers it is important to compare inferences that are based on them to a priori expectations. In this article we present an analysis of microsatellite variation within and among populations of island foxes (Urocyon littoralis) on California's Channel Islands. We first show that microsatellite variation at a moderate number of loci (19) can provide an essentially perfect description of the boundaries between populations and an accurate representation of their historical relationships. We also show that the pattern of variation across unlinked microsatellite loci can be used to test whether population size has been constant or increasing. Application of these approaches to the island fox system indicates that microsatellite variation may carry considerably more information about population history than is currently being used.

  15. 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.

  16. 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

  17. 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…

  18. European Cenozoic rift system

    NASA Astrophysics Data System (ADS)

    Ziegler, Peter A.

    1992-07-01

    The European Cenozoic rift system extends from the coast of the North Sea to the Mediterranean over a distance of some 1100 km; it finds its southern prolongation in the Valencia Trough and a Plio-Pleistocene volcanic chain crossing the Atlas ranges. Development of this mega-rift was paralleled by orogenic activity in the Alps and Pyrenees. Major rift domes, accompanied by subsidence reversal of their axial grabens, developed 20-40 Ma after beginning of rifting. Uplift of the Rhenish Shield is related to progressive thermal lithospheric thinning; the Vosges-Black Forest and the Massif Central domes are probably underlain by asthenoliths emplaced at the crust/mantle boundary. Evolution of this rift system, is thought to be governed by the interaction of the Eurasian and African plates and by early phases of a plate-boundary reorganization that may lead to the break-up of the present continent assembly.

  19. 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

  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. 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.

  2. Optical Inference Machines

    DTIC Science & Technology

    1988-06-27

    de olf nessse end Id e ;-tl Sb ieeI smleo) ,Optical Artificial Intellegence ; Optical inference engines; Optical logic; Optical informationprocessing...common. They arise in areas such as expert systems and other artificial intelligence systems. In recent years, the computer science language PROLOG has...cal processors should in principle be well suited for : I artificial intelligence applications. In recent years, symbolic logic processing. , the

  3. Bayesian inference for functional response in a stochastic predator-prey system.

    PubMed

    Gilioli, Gianni; Pasquali, Sara; Ruggeri, Fabrizio

    2008-02-01

    We present a Bayesian method for functional response parameter estimation starting from time series of field data on predator-prey dynamics. Population dynamics is described by a system of stochastic differential equations in which behavioral stochasticities are represented by noise terms affecting each population as well as their interaction. We focus on the estimation of a behavioral parameter appearing in the functional response of predator to prey abundance when a small number of observations is available. To deal with small sample sizes, latent data are introduced between each pair of field observations and are considered as missing data. The method is applied to both simulated and observational data. The results obtained using different numbers of latent data are compared with those achieved following a frequentist approach. As a case study, we consider an acarine predator-prey system relevant to biological control problems.

  4. 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.

  5. From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

    PubMed

    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.

  6. 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

  7. Recharge Regimes of the Saq Aquifer System, Saudi Arabia: Inferences from Geochemical and Isotopic Compositions

    NASA Astrophysics Data System (ADS)

    Abouelmagd, A.; McCabe, M. F.; Castro, M. C.; Sultan, M.; Jana, R. B.; Al-Mashharawi, S.

    2014-12-01

    One of the most valuable groundwater reserves in Saudi Arabia is the Saq aquifer system (SAS), a thick (400-1200 meters) sandstone unit that extends across 300,000 km2 in Saudi Arabia and neighboring Jordan. Due to its high productivity and high water quality, current pumping and overexploitation of the aquifer has significantly lowered the groundwater level over the years. Understanding the recharge regimes of the SAS is critical for the development of sustainable exploitation of water resources in the region and for the establishment of appropriate management practices. In this study, we investigate the hydrologic setting of the SAS and seek to differentiate the degree of paleo versus modern contributions using a range of geochemical approaches. Multiple groundwater samples were collected from deep production wells tapping the SAS at depths between 375-1800 m and across a range of locations. Samples were analyzed for their chemical concentrations, stable isotopic compositions (δ18O and δ2H), and dissolved noble gas concentrations and isotopic ratios. Examining these data identifies unmixed pools of fossil groundwater at deeper depths as well as mixed shallower systems that indicate contributions from modern precipitation. Through isotopic and noble gas analyses, the relative age and timing of these recharge events was examined and show contributions from both glacial and inter-glacial periods, with some modest contributions from modern meteoric sources.

  8. Genetic diversity in the Homosporous Fern Ophioglossum vulgatum (Ophioglossaceae) from South Korea: inference of mating system and population history.

    PubMed

    Chung, Mi Yoon; López-Pujol, Jordi; Chung, Jae Min; Moon, Myung-Ok; Chung, Myong Gi

    2013-03-01

    It is generally believed that the members of Ophioglossaceae have subterranean, potentially bisexual gametophytes, which favor intragametophytic selfing. In Ophioglossaceae, previous allozyme studies revealed substantial inbreeding within Botrychium species and Mankyua chejuense. However, little is known about the mating system in species of the genus Ophioglossum. Molecular marker analyses can provide insights into the relative occurrence of selfing versus cross-fertilization in the species of Ophioglossum. We investigated allozyme variation in 8 Korean populations of the homosporous fern Ophioglossum vulgatum to infer its mating system and to get some insight into the population-establishment history in South Korea. We detected homozygous genotypes for alternative alleles at several loci, which suggest the occurrence of intragametophytic self-fertilization. Populations harbor low within-population variation (% P = 7.2, A = 1.08, and H (e) = 0.026) and a high among-population differentiation (F (ST) = 0.733). This, together with the finding that alternative alleles were fixed at several loci, suggests that the number and size of populations of O. vulgatum might have been severely reduced during the last glaciation (i.e., due to its in situ persistence in small, isolated refugia). The combined effects of severe random genetic drift and high rates of intragametophytic selfing are likely responsible for the genetic structure displayed by this homosporous fern. Its low levels of genetic diversity in South Korea justify the implementation of some conservation measures to ensure its long-term preservation.

  9. 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.

  10. Nebulon: a system for the inference of functional relationships of gene products from the rearrangement of predicted operons

    PubMed Central

    Janga, Sarath Chandra; Collado-Vides, Julio; Moreno-Hagelsieb, Gabriel

    2005-01-01

    Since operons are unstable across Prokaryotes, it has been suggested that perhaps they re-combine in a conservative manner. Thus, genes belonging to a given operon in one genome might re-associate in other genomes revealing functional relationships among gene products. We developed a system to build networks of functional relationships of gene products based on their organization into operons in any available genome. The operon predictions are based on inter-genic distances. Our system can use different kinds of thresholds to accept a functional relationship, either related to the prediction of operons, or to the number of non-redundant genomes that support the associations. We also work by shells, meaning that we decide on the number of linking iterations to allow for the complementation of related gene sets. The method shows high reliability benchmarked against knowledge-bases of functional interactions. We also illustrate the use of Nebulon in finding new members of regulons, and of other functional groups of genes. Operon rearrangements produce thousands of high-quality new interactions per prokaryotic genome, and thousands of confirmations per genome to other predictions, making it another important tool for the inference of functional interactions from genomic context. PMID:15867197

  11. 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.

  12. [The System of the Suborder Zoarcoidei (Pisces, Perciformes) as Inferred from Molecular Genetic Data].

    PubMed

    Radchenko, O A

    2015-11-01

    Based on an analysis of sequence variation in mitochondrial and nuclear markers, the levels of divergence, relationships, and system of the suborder Zoarcoidei was defined. It was demonstrated that DNA lineages of the families Bathymasteridae and Cebidichthyidae were positioned at the bottom ofthe suborder phylogenetic tree. The family Zoarcidae is a monophyletic group, the youngest in the evolutionary terms. Zoarcidae, Anarhichadidae, Neozorcidae, and Eulophiidae form a group of related families. The family Stichaeidae is heterogeneous and has a polyphyletic origin; within this family, the subfamilies Chirolophinae, Alectgiinae, Xiphisterinae, and Stichaeinae are sister taxa. The subfamilies Opisthocentrinae and Lumpeninae are isolated from Stichaedae; Opisthocentrinae is closely associated with the families Pholidae and Ptilichthyidae, and Lumpeninae is closely associated with Zaproridae and Cryptacanthodidae. It is suggested that the rank of subfamilies Opisthocentrinae and Lumpeninae should be raised.

  13. 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.

  14. 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

  15. 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

  16. Using Bayesian inference for parameter estimation when the system response and experimental conditions are measured with error and some variables are considered as nuisance variables

    NASA Astrophysics Data System (ADS)

    Emery, A. F.; Valenti, E.; Bardot, D.

    2007-01-01

    Parameter estimation is generally based upon the maximum likelihood approach and often involves regularization. Typically it is desired that the results be unbiased and of minimum variance. However, it is often better to accept biased estimates that have minimum mean square error. Bayesian inference is an attractive approach that achieves this goal and incorporates regularization automatically. More importantly, it permits us to analyse experiments in which both the system response and the independent variables (time, sensor position, experimental conditions, etc) are corrupted by noise and in which the model includes nuisance variables. This paper describes the use of Bayesian inference for an apparently simple experiment which is, in fact, fundamentally difficult and is compounded by a nuisance variable. By presenting this analysis we hope that members of the inverse community will see the value of applying Bayesian inference.

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

    USGS Publications Warehouse

    Wauthier, Christelle; Cayol, Valérie; 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.

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

    PubMed

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

    2015-06-15

    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 (26)Al ((26)Al→(26)Mg; 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 (26)Al abundance [((26)Al/(27)Al)0] exists only for the oldest known solids, calcium aluminum-rich inclusions (CAIs) - the so-called canonical value. We have determined the (26)Al/(27)Al 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 ((26)Al/(27)Al)0 of [Formula: see text] 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 (54)Cr/(52)Cr ratios, most inner solar system materials likely accreted from material containing a similar (26)Al/(27)Al ratio as the APB precursor at the time of CAI formation. To satisfy the abundant evidence for widespread planetesimal differentiation, the subcanonical (26)Al budget requires that differentiated planetesimals, and hence protoplanets, accreted rapidly within 0.25 ± 0.15 Ma of the formation of canonical CAIs.

  19. 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.

  20. Physical Properties of the Saturnian Ring System Inferred from Cassini VIMS Opposition Observations

    NASA Astrophysics Data System (ADS)

    Hapke, B.; Nelson, R. M.; Brown, R. H.; Spilker, L. J.; Smythe, W. D.; Kamp, L.; Boryta, M.; Leader, F.; Matson, D. L.; Edgington, S.; Nicholson, P. D.; Filacchione, G.; Clark, R. N.; Bibring, J.; Baines, K. H.; Buratti, B. J.; Bellucci, G.; Capaccioni, F.; Cerroni, P.; Combes, M.; Coradini, A.; Cruikshank, D. P.; Drossart, P.; Formisano, V.; Jaumann, R.; Langevin, Y.; McCord, T.; Menella, V.; Sicardy, B.

    2005-12-01

    Much can be learned about the nature of Saturn's ring particles and their regoliths by studying the wavelength dependence of their reflectance as a function of phase angle. At small phase angles the reflectance of the rings exhibits the opposition effect (OE) a significant increase in reflectance as phase angle approaches zero degrees. The wavelength dependence of the width and the peak of the OE are indicators of important physical properties of the regoliths of the ring particles such as particle size, particle shape, packing density and albedo. The Cassini VIMS multi spectral imaging spectrometer obtained low phase observations of the Saturnian ring system from 0.4-5.2 microns during 2005. These data clearly show a pronounced (OE). Cassini VIMS opposition surge data indicate a wavelength dependence of the OE that relates to the size and separation of the scattering centers on the surface of the ring particles. Laboratory studies and theoretical models of the OE relate the size and shape of the reflectance increase to physical properties of the medium (Nelson et al, 2002; Spilker et al. 1995; Hapke et al., 1993)). The OE arises from two processes, shadow hiding (SH) and coherent backscattering (CB). The SHOE is observed because shadows cast by the particulate grains on one another are eliminated as phase angle approaches zero degrees. The CBOE is due to constructive interference between light rays traveling in opposite paths through the medium as the path length decreases with decreasing phase angle. The VIMS data at 1.9 microns, where the rings are highly reflective, indicate a strong CBOE effect, however, at 2.1 microns, where the rings are very absorbing, the shape of the phase curve is consistent with SHOE. Hapke et al. 1993,Science, 260, 509-511 Nelson, R. M. et al., 2002. Planetary and Space Science, 50, 849-856 Spilker aka Horn, L.J et al., 1995. IAU Colloquium #150 This work done at JPL under contract with NASA

  1. 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.

  2. 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

  3. Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

    SciTech Connect

    Marzouk, Youssef

    2016-08-31

    Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesian inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.

  4. 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.

  5. 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.

  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. 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.

  8. Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

    NASA Technical Reports Server (NTRS)

    Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

    2015-01-01

    Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

  9. 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

  10. Comparative assessment of rule-based and Bayes' theorem as inference engines in diagnosing symptoms for Islamic medication expert system

    NASA Astrophysics Data System (ADS)

    Daud, H.; Razali, R.; Low, T. J.; Sabdin, M.; Zafrul, S. Z. Mohd

    2014-06-01

    An expert system for diagnosing sickness and suggesting treatment based on Islamic Medication (IM) was constructed using Rule Based (RB) and Bayes' theorem (BT) algorithms independently as its inference engine. Comparative assessment on the quality of diagnosing based on symptoms provided by users for certain type of sickness using RB and BT reasoning that lead to the suggested treatment (based on IM) are discussed. Both approaches are found to be useful, each has its own advantages and disadvantages. Major difference of the two algorithms is the selection of symptoms during the diagnosing process. For BT, likely combinations of symptoms need to be classified for each sickness before the diagnosing process. This eliminates any irrelevant sickness based on the combination of symptoms provided by user and combination of symptoms that is unlikely. This is not the case for RB, it will diagnose the sickness as long as one the symptoms is related to the sickness regardless of unlikely combination. Few tests have been carried out using combinations of symptoms for same sickness to investigate their diagnosing accuracy in percentage. BT gives more promising diagnosing results compared to RB for each sickness that comes with common symptoms.

  11. 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.

  12. 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

  13. Hydrodynamic processes, velocity structure and stratification in natural turbidity currents: Results inferred from field data in the Var Turbidite System

    NASA Astrophysics Data System (ADS)

    Migeon, Sébastien; Mulder, Thierry; Savoye, Bruno; Sage, Françoise

    2012-03-01

    The Var Turbidite System (NW Mediterranean Sea) is fed during the present-day highstand sea level by large earthquake-induced ignitive turbidity currents, low-density turbidity currents resulting from retrogressive failures triggered on the upper continental slope, and hyperpycnal flows related to the Var River floods. Using a large dataset including bathymetric data, side-scan sonar images, seismic-reflection profiles, cores and photographs of the seafloor, this paper attempts to better constrain the hydrodynamic behaviour of debris flows and turbidity currents along the Upper and Middle Valley of the Var Turbidite System. The drastic change of the seafloor morphology between the Upper and the Middle Valley suggests that gravity flows undergo rapid transformation from cohesive to fully turbulent behaviour. This transformation is related to a hydraulic jump caused by an abrupt decrease in slope angle at the transition between the Upper and the Middle Valley and is associated with en masse deposition and elevation of the seafloor. Strong seafloor erosion prevails in the Middle Valley, suggesting that, for a low and constant slope angle, turbulent flows must regain a balance between concentration and flow thickness rapidly after they experience hydraulic jump. The internal stratification and vertical grain-size distribution within turbulent flows are inferred from the distribution of fine- to coarse-grained turbidites found in cores located along the crest of the Var Sedimentary Ridge with a decreasing elevation above the floor of the Middle Valley. The theoretical vertical velocity profile deduced from the vertical grain-size distribution exhibits a general trend and an inflection of the gradient curve different from those of the velocity profiles classically obtained using numerical modelling.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. Tracking Crust-Mantle Recycling through Superdeep Diamonds and their Mineral Inclusions

    NASA Astrophysics Data System (ADS)

    Walter, Michael; Bulanova, Galina; Smith, Chris; Thomson, Andrew; Kohn, Simon; Burnham, Antony

    2013-04-01

    Sublithospheric, or 'superdeep' diamonds, originate in the deep upper mantle, transition zone, and at least as deep as the shallow lower mantle. When diamonds crystallize in the mantle from fluids or melts they occasionally entrap coexisting mineral phases. Because of their great physical resiliency, diamonds can potentially preserve information over long distance- and time-scales, revealing important information about the petrologic, tectonic and geodynamic environment in which the diamonds grew and were transported. Superdeep diamonds and their inclusions have proven especially powerful for probing processes related to subduction of slabs into the deep mantle [1-3]. In contrast to lithospheric diamonds that are effectively frozen-in geodynamically, mineral inclusions in superdeep diamonds often record hundreds of kilometers of uplift in the convecting mantle from their original depth of origin [3-5]. The phase equilibria of unmixing of original deep mantle phases such as Ca- and Mg-perovskite, NAL-phase, CF-phase, CAS-phase, and majorite provide a means to establish amounts of uplift. The few available age constraints indicate superdeep diamond growth from the Proterozoic to the Cretaceous, and further dating can potentially lead to constraining mantle upwelling rates [4]. Here we will provide several examples showing how superdeep diamonds and their inclusions record processes of subduction and slab foundering, and ultimately recycling of slab material from the transition zone and lower mantle into the shallow upper mantle. 1. Harte, B., Mineralogical Magazine, 2010. 74: p. 189-215. 2. Tappert, R., et al., Geology, 2005. 33: p. 565-568. 3. Walter, M.J., et al., Science, 2011. 333: p. 54-57. 4. Bulanova, G.P., et al., Contributions to Mineralogy and Petrology, 2010. 160: p. 489-510. 5. Harte, B. and N. Cayzer, Physics and Chemistry of Minerals, 2007.

  19. 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.

  20. Crust-Mantle Interactions at Pico de Orizaba (Citlaltepetl) Volcano, Mexico.

    NASA Astrophysics Data System (ADS)

    Schaaf, P.; Carrasco, G.

    2006-12-01

    Pico de Orizaba (Citlaltepetl) volcano constitutes the easternmost and highest stratovolcano of the subduction- related Plio-Quaternary Trans-Mexican Volcanic Belt (TMVB). The volcano can be divided into three main constructional stages. Its activity started during the mid-Pleistocene. The present cone was built on the remnants of the ancestral buildings by eruption of amphibole-two pyroxene dacitic lava flows, the most recent of which was erupted in the seventeenth century. The volcano is surrounded to the SW by monogenetic Quaternary cindercones and maars. All representative units were sampled in this work for geochemical and isotopic purposes, including a small quartzitic xenolith found in the basaltic monogenetic suite. Volcanic products of the stratocone are quite heterogeneous and range from calc-alkaline basaltic andesites to dome rhyolites, also displayed by a wide range of SiO2 and MgO (72.6-53.2 and 7.0-0.3 wt. %, respectively). In comparison to other TMVB stratovolcanoes (e.g., Colima, Nevado de Toluca), Pico de Orizaba shows similar 87Sr/86Sr ratios (0.7037-0.7048) but considerably more evolved Nd-Pb isotopic ratios (eNd: -1.8 to + 1.4; 206Pb/204Pb: 18.61-18.78). Elevated LILE concentrations and depleted HFSE witness the importance of slab- derived aqueous fluids and metasomatic reactions between the subducting lithosphere and overlying mantle wedge. On the other hand, Pico de Orizaba volcano shows additionally high crustal contributions of a source with depleted Sr and enriched Nd and Pb isotopic signatures, best explained by considerable assimilation of the local Grenvillian basement in magma generation processes. In contrast to Popocatépetl volcano with a high-level magma reservoir emplacement (7-8 km) and obvious interaction with the carbonate-dominated shallow basement rocks (e.g. elevated 87Sr/86Sr ratios and CO2 in gas plumes), this effect cannot be observed at Pico de Orizaba volcano, although a regional Cretaceous limestone basement is also present. This, together with the different lithologies of crustal xenoliths in stratocone and cindercone magmas gives strong evidence for a deeper situated magma chamber.

  1. Copper systematics in arc magmas and implications for crust-mantle differentiation.

    PubMed

    Lee, Cin-Ty A; Luffi, Peter; Chin, Emily J; Bouchet, Romain; Dasgupta, Rajdeep; Morton, Douglas M; Le Roux, Veronique; Yin, Qing-zhu; Jin, Daphne

    2012-04-06

    Arc magmas are important building blocks of the continental crust. Because many arc lavas are oxidized, continent formation is thought to be associated with oxidizing conditions. On the basis of copper's (Cu's) affinity for reduced sulfur phases, we tracked the redox state of arc magmas from mantle source to emplacement in the crust. Primary arc and mid-ocean ridge basalts have identical Cu contents, indicating that the redox states of primitive arc magmas are indistinguishable from that of mid-ocean ridge basalts. During magmatic differentiation, the Cu content of most arc magmas decreases markedly because of sulfide segregation. Because a similar depletion in Cu characterizes global continental crust, the formation of sulfide-bearing cumulates under reducing conditions may be a critical step in continent formation.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Multiple Instance Fuzzy Inference

    DTIC Science & Technology

    2015-12-02

    INFERENCE A novel fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The...fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The framework introduces a...or learned from data. In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are

  7. Application of Transformations in Parametric Inference

    ERIC Educational Resources Information Center

    Brownstein, Naomi; Pensky, Marianna

    2008-01-01

    The objective of the present paper is to provide a simple approach to statistical inference using the method of transformations of variables. We demonstrate performance of this powerful tool on examples of constructions of various estimation procedures, hypothesis testing, Bayes analysis and statistical inference for the stress-strength systems.…

  8. Symbolic transfer entropy: inferring directionality in biosignals.

    PubMed

    Staniek, Matthäus; Lehnertz, Klaus

    2009-12-01

    Inferring directional interactions from biosignals is of crucial importance to improve understanding of dynamical interdependences underlying various physiological and pathophysiological conditions. We here present symbolic transfer entropy as a robust measure to infer the direction of interactions between multidimensional dynamical systems. We demonstrate its performance in quantifying driver-responder relationships in a network of coupled nonlinear oscillators and in the human epileptic brain.

  9. Inferring unknow boundary conditions of the Greenland Ice Sheet by assimilating ICESat-1 and IceBridge altimetry intothe Ice Sheet System Model.

    NASA Astrophysics Data System (ADS)

    Larour, E. Y.; Khazendar, A.; Seroussi, H. L.; Schlegel, N.; Csatho, B. M.; Schenk, A. F.; Rignot, E. J.; Morlighem, M.

    2014-12-01

    Altimetry signals from missions such as ICESat-1, CryoSat, EnviSat, as well as altimeters onboard Operation IceBridge provide vital insights into processes such as surface mass balance, mass transport and ice-flow dynamics. Historically however, ice-flow models have been focused on assimilating surface velocities from satellite-based radar observations, to infer properties such as basal friction or the position of the bedrock. Here, we leverage a new methodology based on automatic differentation of the Ice Sheet System Model to assimilate surface altimetry data into a reconstruction of the past decade of ice flow on the North Greenland area. We infer corrections to boundary conditions such as basal friction and surface mass balance, as well as corrections to the ice hardness, to best-match the observed altimetry record. We compare these corrections between glaciers such as Petermann Glacier, 79 North and Zacchariae Isstrom. The altimetry signals exhibit very different patterns between East and West, which translate into very different signatures for the inverted boundary conditions. This study gives us greater insights into what differentiates different basins, both in terms of mass transport and ice-flow dynamics, and what could bethe controlling mechanisms behind the very different evolutions of these basins.

  10. 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.

  11. 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

  12. Statistical inference and Aristotle's Rhetoric.

    PubMed

    Macdonald, Ranald R

    2004-11-01

    Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical.

  13. A Novel Technique for Maximum Power Point Tracking of a Photovoltaic Based on Sensing of Array Current Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

    NASA Astrophysics Data System (ADS)

    El-Zoghby, Helmy M.; Bendary, Ahmed F.

    2016-10-01

    Maximum Power Point Tracking (MPPT) is now widely used method in increasing the photovoltaic (PV) efficiency. The conventional MPPT methods have many problems concerning the accuracy, flexibility and efficiency. The MPP depends on the PV temperature and solar irradiation that randomly varied. In this paper an artificial intelligence based controller is presented through implementing of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to obtain maximum power from PV. The ANFIS inputs are the temperature and cell current, and the output is optimal voltage at maximum power. During operation the trained ANFIS senses the PV current using suitable sensor and also senses the temperature to determine the optimal operating voltage that corresponds to the current at MPP. This voltage is used to control the boost converter duty cycle. The MATLAB simulation results shows the effectiveness of the ANFIS with sensing the PV current in obtaining the MPPT from the PV.

  14. Higher order cycles, inferred chronostratigraphy, and impact of multiple lowstand systems tracts on hydrocarbon exploration, Pletmos basin, southern offshore, South Africa

    SciTech Connect

    Brink, G.J.

    1989-03-01

    Development of 67 middle Valanginian to middle Campanian cyclic depositional sequences is interpreted to be a response to the interplay of unique tectonics and higher order eustatic sea level cycles capable of imposing type 1 unconformities. Direct correlation of 16 sequences, within available paleontological age constraints, with Exxon's global third-order cycles encompasses the remaining 51 sequences, which are inferred to be fourth-order and fifth-order cycles. These unconformity bound sequences were grouped into genetic megasequences bound by major type 1 unconformities (third-order or fourth-order falls at the trough of a third-order cycle) typically displaying evidence of extensive erosion of thick highstand systems tracts.

  15. 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.

  16. On-line self-learning time forward voltage prognosis for lithium-ion batteries using adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Fleischer, Christian; Waag, Wladislaw; Bai, Ziou; Sauer, Dirk Uwe

    2013-12-01

    The battery management system (BMS) of a battery-electric road vehicle must ensure an optimal operation of the electrochemical storage system to guarantee for durability and reliability. In particular, the BMS must provide precise information about the battery's state-of-functionality, i.e. how much dis-/charging power can the battery accept at current state and condition while at the same time preventing it from operating outside its safe operating area. These critical limits have to be calculated in a predictive manner, which serve as a significant input factor for the supervising vehicle energy management (VEM). The VEM must provide enough power to the vehicle's drivetrain for certain tasks and especially in critical driving situations. Therefore, this paper describes a new approach which can be used for state-of-available-power estimation with respect to lowest/highest cell voltage prediction using an adaptive neuro-fuzzy inference system (ANFIS). The estimated voltage for a given time frame in the future is directly compared with the actual voltage, verifying the effectiveness and accuracy of a relative voltage prediction error of less than 1%. Moreover, the real-time operating capability of the proposed algorithm was verified on a battery test bench while running on a real-time system performing voltage prediction.

  17. Social Inference Through Technology

    NASA Astrophysics Data System (ADS)

    Oulasvirta, Antti

    Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.

  18. 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

  19. System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

    PubMed

    Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

    2014-01-01

    Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome

  20. 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...

  1. Temporal Development of Repeated Intrusive Events in a South Iceland Volcanic System, Inferred From InSAR Measurements

    NASA Astrophysics Data System (ADS)

    Pedersen, R.; Sigmundsson, F.

    2002-12-01

    We present measurements of volcano deformation from a series of 18 interferograms spanning the years 1993-2000. The detected deformation originates from repeated intrusions in the Eyjafjallaj”kull system, an icecap covered stratovolcano situated in, what is considered to be, a propagating rift zone in southern Iceland. The volcano erupts infrequently, with only two known eruptions in historic time (last 1100 years). The eruptive products are alkaline in composition, with only small volumes produced in recent eruptions. In spite of the apparent silence of this system two intrusive episodes have been detected within the last decade, causing major concern in the local community. In 1994, and again in 1999, seismic unrest associated with magmatic intrusions occurred in the system. Crustal deformation associated with the events was detected by dry-tilt, GPS and interferometry. During the 1994 episode, the center of deformation was situated underneath the icecap, and the area experiencing maximum uplift was therefore within the zone of decorrelation. The deformation shows an oval fringe pattern, which reaches well beyond the icecap, covering more than 300 km2 in total. Up to 15 cm of LOS ("line of sight") displacement is observed. The temporal resolution of the InSAR images during the 1999 intrusive episode is better and it is possible to follow the development of the intrusive event through time. The center of deformation does not coincide with the center from the 1994 event, but is situated just south of the icecap. The deformation during this event amounts to about 20 cm of LOS. Several of the interferograms cover the whole time-span of the 1999 intrusion, but three interferograms cover different periods of the intrusive event. The data set enables us to follow the temporal development of the crustal deformation created by the intrusion, and hence the growth of the intrusion itself through time. A previous study based on forward modeling of GPS and tilt data

  2. Stress pattern of the Shanxi rift system, North China, inferred from the inversion of new focal mechanisms

    NASA Astrophysics Data System (ADS)

    Li, Bin; Atakan, Kuvvet; Sørensen, Mathilde Bøttger; Havskov, Jens

    2015-05-01

    Earthquake focal mechanisms of the Shanxi rift system, North China, are investigated for the time period 1965-April 2014. A total of 143 focal mechanisms of ML ≥ 3.0 earthquakes were compiled. Among them, 105 solutions are newly determined in this study 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 in the Shanxi rift system exhibit normal or strike-slip faulting, and the regional stress field is transtensional and dominated by NNW-SSE extension. This correlates well with results from GPS data, geological field observations and levelling 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 along the strike of the different subzones. Based on our results and combining multidisciplinary observations from geological surveys, GPS and cross-fault monitoring, a kinematic model is proposed for the Shanxi rift system, in which the rift is situated between two opposite rotating crustal blocks, exhibiting a transtensional stress regimes. This model illustrates the present-day stress field and its correlation to 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.

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

    NASA Astrophysics Data System (ADS)

    Li, Bin; Sørensen, Mathilde; Atakan, Kuvvet; Havskov, Jens

    2015-04-01

    The Shanxi rift system is one of the most outstanding intra-plate transtensional fault zones in the North China block. Earthquake focal mechanisms of the rift system 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 transtensional and dominated by NNW-SSE extension. 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, in which the Shanxi rift system is situated between two opposite rotating blocks, exhibiting a transtensional stress regime. This model illustrates 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.

  4. Field Demonstration of a Broadband Acoustical Backscattering System Mounted on a REMUS-100 for Inferences of Zooplankton Size and Abundancy

    DTIC Science & Technology

    2011-09-30

    the Rayleigh-to-geometric scattering transition is within the frequency band of the WHOI broadband system (e.g., copepods ), and either larger fluid...that numerical abundance of zooplankton was dominated by small copepods that were relatively evenly distributed throughout the water-column...indication in either the MONESS or the VPR that the acoustic scattering layer was correlated to an increased abundance of zooplankton. Small copepods

  5. Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation

    NASA Astrophysics Data System (ADS)

    Entchev, Evgueniy; Yang, Libing

    This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5 kW el SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc. The study revealed that both ANN and ANFIS models' predictions agreed well with variety of experimental data sets representing steady-state, start-up and shut-down operations of the SOFC system. The initial data set was subjected to detailed sensitivity analysis and statistically insignificant parameters were excluded from the training set. As a result, significant reduction of computational time was achieved without affecting models' accuracy. The study showed that adaptive models can be applied with confidence during the design process and for performance optimization of existing and newly developed solid oxide fuel cell systems. It demonstrated that by using ANN and ANFIS techniques SOFC microgeneration system's performance could be modelled with minimum time demand and with a high degree of accuracy.

  6. Allogenic forcing of autogenic processes: inferences from an aggregated process-based model of fluvio-deltaic systems

    NASA Astrophysics Data System (ADS)

    Karamitopoulos, P.; Weltje, G.; Dalman, R.

    2011-12-01

    Spatial and temporal variability of sediment storage in fluvio-deltaic sedimentary systems is controlled by the interplay of allogenic and autogenic processes. In order to investigate the effects of this interplay on the resulting stratigraphy at varying spatio-temporal scales, we carried out a series of numerical experiments using an aggregated process-based model of fluvio-deltaic systems (SIMCLAST), which combines diffusive and advective transport with sub-grid channel stability algorithms in the fluvial domain. New distributary channels occur by avulsions under conditions of local superelevation or through bifurcations due to mouth bar deposition. A series of numerical experiments were performed under forcing by glacio-eustatic sealevel cycles in the order of 100 kyr. Initial conditions of all experiments are represented by classic continental-margin topography with a shelf break. In this scenario, erosional features (canyons) are developed when sea level falls below the shelf break. Sediment supply and liquid discharge remain constant throughout the experiments. In order to characterize the topographic variability during the experiments, we used a difference measure obtained by summation of local changes in net sediment accumulation rates across the entire model domain. Long-term average variability (10 kyr resolution) correlates strongly with the allogenic sea-level signal. The long-term variability reaches a maximum around the time interval corresponding to isochronous maximum flooding surfaces, when retrogradation gives way to a new episode of progradation. Long-term mean variability is lowest during periods of sea-level fall, when incision restricts sediment dispersal. Increasing the time resolution of our difference measure allows recognition of numerous small peaks which correspond to local changes in sediment accumulation rates induced by autogenic processes (avulsions and bifurcations). The amplitudes of these peaks are related to the rate of change of

  7. Statistical analysis and decoding of neural activity in the rodent geniculate ganglion using a metric-based inference system.

    PubMed

    Wu, Wei; Mast, Thomas G; Ziembko, Christopher; Breza, Joseph M; Contreras, Robert J

    2013-01-01

    We analyzed the spike discharge patterns of two types of neurons in the rodent peripheral gustatory system, Na specialists (NS) and acid generalists (AG) to lingual stimulation with NaCl, acetic acid, and mixtures of the two stimuli. Previous computational investigations found that both spike rate and spike timing contribute to taste quality coding. These studies used commonly accepted computational methods, but they do not provide a consistent statistical evaluation of spike trains. In this paper, we adopted a new computational framework that treated each spike train as an individual data point for computing summary statistics such as mean and variance in the spike train space. We found that these statistical summaries properly characterized the firing patterns (e. g. template and variability) and quantified the differences between NS and AG neurons. The same framework was also used to assess the discrimination performance of NS and AG neurons and to remove spontaneous background activity or "noise" from the spike train responses. The results indicated that the new metric system provided the desired decoding performance and noise-removal improved stimulus classification accuracy, especially of neurons with high spontaneous rates. In summary, this new method naturally conducts statistical analysis and neural decoding under one consistent framework, and the results demonstrated that individual peripheral-gustatory neurons generate a unique and reliable firing pattern during sensory stimulation and that this pattern can be reliably decoded.

  8. 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.

  9. Structure of a low-enthalpy geothermal system inferred from magnetotellurics - A case study from Sri Lanka

    NASA Astrophysics Data System (ADS)

    Nimalsiri, Thusitha Bandara; Suriyaarachchi, Nuwan Buddhika; Hobbs, Bruce; Manzella, Adele; Fonseka, Morrel; Dharmagunawardena, H. A.; Subasinghe, Nalaka Deepal

    2015-06-01

    First comprehensive geothermal exploration in Sri Lanka was conducted in 2010 encompassing seven thermal springs, of which Kapurella records the highest temperature. The study consisted of passive magnetotelluric (MT) soundings, in which static shifts were corrected using time domain electromagnetic method (TDEM). A frequency range of 12,500-0.001 Hz was used for MT acquisition and polar diagrams were employed for dimensionality determination. MT and TDEM data were jointly inverted and 2D models were created using both transverse electric and transverse magnetic modes. A conductive southeast dipping structure is revealed from both phase pseudosections and the preferred 2D inversion model. A conductive formation starting at a depth of 7.5 km shows a direct link with the dipping structure. We suggest that these conductive structures are accounted for deep circulation and accumulation of groundwater. Our results show the geothermal reservoir of Kapurella system with a lateral extension of around 2.5 km and a depth range of 3 km. It is further found that the associated dolerite dike is not the source of heat although it could be acting as an impermeable barrier to form the reservoir. The results have indicated the location of the deep reservoir and the possible fluid path of the Kapurella system, which could be utilized to direct future geothermal studies. This pioneering study makes suggestions to improve future MT data acquisition and to use boreholes and other geophysical methods to improve the investigation of structures at depth.

  10. "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

  11. A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations.

    PubMed

    Blanes-Vidal, Victoria; Cantuaria, Manuella Lech; Nadimi, Esmaeil S

    2017-04-01

    Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH3 pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered.

  12. 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.

  13. A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system.

    PubMed

    Xie, Qiuju; Ni, Ji-Qin; Su, Zhongbin

    2017-03-05

    Ammonia (NH3) is considered one of the significant pollutions contributor to indoor air quality and odor gas emission from swine house because of the negative impact on the health of pigs, the workers and local environment. Prediction models could provide a reasonable way for pig industries and environment regulatory to determine environment control strategies and give an effective method to evaluate the air quality. The adaptive neuro fuzzy inference system (ANFIS) simulates human's vague thinking manner to solve the ambiguity and nonlinear problems which are difficult to be processed by conventional mathematics. Five kinds of membership functions were used to build a well fitted ANFIS prediction model. It was shown that the prediction model with "Gbell" membership function had the best capabilities among those five kinds of membership functions, and it had the best performances compared with backpropagation (BP) neuro network model and multiple linear regression model (MLRM) both in wintertime and summertime, the smallest value of mean square error (MSE), mean absolute percentage error (MAPE) and standard deviation (SD) are 0.002 and 0.0047, 31.1599 and 23.6816, 0.0564 and 0.0802, respectively, and the largest coefficients of determination (R(2)) are 0.6351 and 0.6483, repectively. The ANFIS prediction model could be served as a beneficial strategy for the environment control system that has input parameters with highly fluctuating, complexity, and non-linear relationship.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. Model of the volcano-hydrothermal system of Tatun Volcano Group, northern Taiwan, inferred from seismicity and gas geochemistry

    NASA Astrophysics Data System (ADS)

    Konstantinou, K. I.; Rontogianni, S.; Lin, C.-H.

    2012-04-01

    The Tatun Volcano Group (TVG) is located in northern Taiwan near the capital Taipei. In this study we selected and analyzed almost four years (2004 - 2007) of its seismic activity. The seismic network established around TVG initially consisted of eight three component seismic stations with this number increasing to twelve by 2007. Local seismicity mainly involved High Frequency (HF) earthquakes occurring as isolated events or as spasmodic bursts. Mixed and Low Frequency (LF) events were observed during the same period but more rarely. During the analysis we estimated the magnitudes for the HF earthquakes and used a probabilistic non-linear method to locate all these events. We examined the temporal and spatial distribution of our data-set for each year and the monthly seismic energy distribution. In addition, complex frequencies for LF events were analyzed with the Sompi method. We juxtapose these results with gas geochemistry studies of fumaroles covering a similar period. A model for the volcano-hydrothermal system is proposed where fluids and magmatic gases ascend from a magma body that lies at around 7- 8 km depth. The movement of fluids to shallow depths increases the heat, the fracturing and also creates resonance and vibrations in cracks and conduits. This detailed analysis and previous physical volcanology observations at TVG suggest that the region is volcanically active and that measures to mitigate the risks have to be considered by the local authorities.

  2. 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.

  3. Evolution and connectivity in the world-wide migration system of the mallard: Inferences from mitochondrial DNA

    PubMed Central

    2011-01-01

    Background Main waterfowl migration systems are well understood through ringing activities. However, in mallards (Anas platyrhynchos) ringing studies suggest deviations from general migratory trends and traditions in waterfowl. Furthermore, surprisingly little is known about the population genetic structure of mallards, and studying it may yield insight into the spread of diseases such as Avian Influenza, and in management and conservation of wetlands. The study of evolution of genetic diversity and subsequent partitioning thereof during the last glaciation adds to ongoing discussions on the general evolution of waterfowl populations and flyway evolution. Hypothesised mallard flyways are tested explicitly by analysing mitochondrial mallard DNA from the whole northern hemisphere. Results Phylogenetic analyses confirm two mitochondrial mallard clades. Genetic differentiation within Eurasia and North-America is low, on a continental scale, but large differences occur between these two land masses (FST = 0.51). Half the genetic variance lies within sampling locations, and a negligible portion between currently recognised waterfowl flyways, within Eurasia and North-America. Analysis of molecular variance (AMOVA) at continent scale, incorporating sampling localities as smallest units, also shows the absence of population structure on the flyway level. Finally, demographic modelling by coalescence simulation proposes a split between Eurasia and North-America 43,000 to 74,000 years ago and strong population growth (~100fold) since then and little migration (not statistically different from zero). Conclusions Based on this first complete assessment of the mallard's world-wide population genetic structure we confirm that no more than two mtDNA clades exist. Clade A is characteristic for Eurasia, and clade B for North-America although some representatives of clade A are also found in North-America. We explain this pattern by evaluating competing hypotheses and conclude that a

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Adaptive Neuro-Fuzzy Inference system analysis on adsorption studies of Reactive Red 198 from aqueous solution by SBA-15/CTAB composite

    NASA Astrophysics Data System (ADS)

    Aghajani, Khadijeh; Tayebi, Habib-Allah

    2017-01-01

    In this study, the Mesoporous material SBA-15 were synthesized and then, the surface was modified by the surfactant Cetyltrimethylammoniumbromide (CTAB). Finally, the obtained adsorbent was used in order to remove Reactive Red 198 (RR 198) from aqueous solution. Transmission electron microscope (TEM), Fourier transform infra-red spectroscopy (FTIR), Thermogravimetric analysis (TGA), X-ray diffraction (XRD), and BET were utilized for the purpose of examining the structural characteristics of obtained adsorbent. Parameters affecting the removal of RR 198 such as pH, the amount of adsorbent, and contact time were investigated at various temperatures and were also optimized. The obtained optimized condition is as follows: pH = 2, time = 60 min and adsorbent dose = 1 g/l. Moreover, a predictive model based on ANFIS for predicting the adsorption amount according to the input variables is presented. The presented model can be used for predicting the adsorption rate based on the input variables include temperature, pH, time, dosage, concentration. The error between actual and approximated output confirm the high accuracy of the proposed model in the prediction process. This fact results in cost reduction because prediction can be done without resorting to costly experimental efforts. SBA-15, CTAB, Reactive Red 198, adsorption study, Adaptive Neuro-Fuzzy Inference systems (ANFIS).

  11. Inference as Prediction

    ERIC Educational Resources Information Center

    Watson, Jane

    2007-01-01

    Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…

  12. 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.

  13. Active inference and learning.

    PubMed

    Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O'Doherty, John; Pezzulo, Giovanni

    2016-09-01

    This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity.

  14. Network inference in the nonequilibrium steady state

    NASA Astrophysics Data System (ADS)

    Dettmer, Simon L.; Nguyen, H. Chau; Berg, Johannes

    2016-11-01

    Nonequilibrium systems lack an explicit characterization of their steady state like the Boltzmann distribution for equilibrium systems. This has drastic consequences for the inference of the parameters of a model when its dynamics lacks detailed balance. Such nonequilibrium systems occur naturally in applications like neural networks and gene regulatory networks. Here, we focus on the paradigmatic asymmetric Ising model and show that we can learn its parameters from independent samples of the nonequilibrium steady state. We present both an exact inference algorithm and a computationally more efficient, approximate algorithm for weak interactions based on a systematic expansion around mean-field theory. Obtaining expressions for magnetizations and two- and three-point spin correlations, we establish that these observables are sufficient to infer the model parameters. Further, we discuss the symmetries characterizing the different orders of the expansion around the mean field and show how different types of dynamics can be distinguished on the basis of samples from the nonequilibrium steady state.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

    2016-11-01

    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.

  4. 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…

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. Computational inference of neural information flow networks.

    PubMed

    Smith, V Anne; Yu, Jing; Smulders, Tom V; Hartemink, Alexander J; Jarvis, Erich D

    2006-11-24

    Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. The research on high speed underwater target recognition based on fuzzy logic inference

    NASA Astrophysics Data System (ADS)

    Jiang, Xiang-Dong; Yang, De-Sen; Shi, Sheng-Guo; Li, Si-Chun

    2006-06-01

    The underwater target recognition is a key technology in acoustic confrontation and underwater defence. In this article, a recognition system based of fuzzy logic inference (FLI) is set up. This system is mainly composed of three parts: the fuzzy input module, the fuzzy logic inference module with a set of inference rules and the de-fuzzy output module. The inference result shows the recognition system is effective in most conditions.

  17. Multi-attribute utility function or statistical inference models: a comparison of health state valuation models using the HUI2 health state classification system.

    PubMed

    Stevens, Katherine; McCabe, Christopher; Brazier, John; Roberts, Jennifer

    2007-09-01

    A key issue in health state valuation modelling is the choice of functional form. The two most frequently used preference based instruments adopt different approaches; one based on multi-attribute utility theory (MAUT), the other on statistical analysis. There has been no comparison of these alternative approaches in the context of health economics. We report a comparison of these approaches for the health utilities index mark 2. The statistical inference model predicts more accurately than the one based on MAUT. We discuss possible explanations for the differences in performance, the importance of the findings, and implications for future research.

  18. 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.…

  19. 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…

  20. 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…

  1. Efficient Bayesian inference for ARFIMA processes

    NASA Astrophysics Data System (ADS)

    Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.

    2015-03-01

    Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially 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 LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more 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 LRD, 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, with favorable comparison to the standard estimators.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. Bayesian Inference of Galaxy Morphology

    NASA Astrophysics Data System (ADS)

    Yoon, Ilsang; Weinberg, M.; Katz, N.

    2011-01-01

    Reliable inference on galaxy morphology from quantitative analysis of ensemble galaxy images is challenging but essential ingredient in studying galaxy formation and evolution, utilizing current and forthcoming large scale surveys. To put galaxy image decomposition problem in broader context of statistical inference problem and derive a rigorous statistical confidence levels of the inference, I developed a novel galaxy image decomposition tool, GALPHAT (GALaxy PHotometric ATtributes) that exploits recent developments in Bayesian computation to provide full posterior probability distributions and reliable confidence intervals for all parameters. I will highlight the significant improvements in galaxy image decomposition using GALPHAT, over the conventional model fitting algorithms and introduce the GALPHAT potential to infer the statistical distribution of galaxy morphological structures, using ensemble posteriors of galaxy morphological parameters from the entire galaxy population that one studies.

  7. Unified Theory of Inference for Text Understanding

    DTIC Science & Technology

    1986-11-25

    reataurant script is recognized, script application would lead to inferences such as identifying the waiter as ’ ’the waiter who is employed by the...relations between the objects. Objects have names as a convenience for the system modeler, but the names are not used for purposes other than...intent is that we can consider talking to be a frame with a talker slot which must be filled by a person. This is just a convenient notation; the

  8. A Unified Approach to Abductive Inference

    DTIC Science & Technology

    2014-09-30

    performance hacks . Alchemy Lite allows for fast, exact inference for models formulated in terms of TML, as well as the ability to update models with...Kimelfeld (bennyk@gmail.com) Molham Aref (molham.aref@logicblox.com) Charles Rivers Analytics Avi Pfeffer (apfeffer@cra.com) Facebook ...works at Yahoo; now at Facebook ) BAE systems Gregory Sullivan (gregory.sullivan@baesystems.com) Raytheon Kenric P Nelson

  9. Statistical Inference in Graphical Models

    DTIC Science & Technology

    2008-06-17

    Probabilistic Network Library ( PNL ). While not fully mature, PNL does provide the most commonly-used algorithms for inference and learning with the efficiency...of C++, and also offers interfaces for calling the library from MATLAB and R 1361. Notably, both BNT and PNL provide learning and inference algorithms...mature and has been used for research purposes for several years, it is written in MATLAB and thus is not suitable to be used in real-time settings. PNL

  10. Statistical Inference: The Big Picture.

    PubMed

    Kass, Robert E

    2011-02-01

    Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labelled here statistical pragmatism, serves as a foundation for inference. Statistical pragmatism is inclusive and emphasizes the assumptions that connect statistical models with observed data. I argue that introductory courses often mis-characterize the process of statistical inference and I propose an alternative "big picture" depiction.

  11. 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.

  12. Inferring the Why in Images

    DTIC Science & Technology

    2014-01-01

    images. To our knowledge, this challenging problem has not yet been extensively explored in computer vision. We present a novel learning based...automatically infers why people are performing actions in images by learning from visual data and written language. ∗denotes equal contribution 1 Report...explored in computer vision. We present a novel learning based framework that uses high-level visual recognition to infer why people are performing

  13. 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

  14. 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.

  15. Causal inference and developmental psychology.

    PubMed

    Foster, E Michael

    2010-11-01

    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 the risk factor actually causes outcomes. Random assignment is not possible in many instances, and for that reason, psychologists must rely on observational studies. Such studies identify associations, and causal interpretation of such associations requires additional assumptions. Research in developmental psychology generally has relied on various forms of linear regression, but this methodology has limitations for causal inference. Fortunately, methodological developments in various fields are providing new tools for causal inference-tools that rely on more plausible assumptions. This article describes the limitations of regression for causal inference and describes how new tools might offer better causal inference. This discussion highlights the importance of properly identifying covariates to include (and exclude) from the analysis. This discussion considers the directed acyclic graph for use in accomplishing this task. With the proper covariates having been chosen, many of the available methods rely on the assumption of "ignorability." The article discusses the meaning of ignorability and considers alternatives to this assumption, such as instrumental variables estimation. Finally, the article considers the use of the tools discussed in the context of a specific research question, the effect of family structure on child development.

  16. 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

  17. Scope Ambiguity and Inference

    DTIC Science & Technology

    1991-07-01

    sentence (1) is followed by sentence (4), and that the system is able to conclude that her is anaphoric to an undergrad in (1). It could immediately conclude...the system is able to conclude that her in the second sentence is anaphoric to an undergrad in the first sentence, it will also be able to conclude... anaphoric relations [Schubert and Pelletier, 1988; Groenendijk and Stokhof, 1990]. 4.2 A Relational Semantics for DRT The Syntax of DRT0 The set of

  18. 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

  19. Active inference and robot control: a case study.

    PubMed

    Pio-Lopez, Léo; Nizard, Ange; Friston, Karl; Pezzulo, Giovanni

    2016-09-01

    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.

  20. 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

  1. 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.

  2. Functional neuroanatomy of intuitive physical inference

    PubMed Central

    Mikhael, John G.; Tenenbaum, Joshua B.; Kanwisher, Nancy

    2016-01-01

    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

  3. Consistency and Plausible Inference,

    DTIC Science & Technology

    1982-10-01

    the Pros- po(:tor Consultant System for Mineral Exploration ," Expert 5,ystems in the Mi cro Electronic Age £I). Michie, ed.), Edinburgh University...Press, 11979. . Duda, R.O., P.E. Hart, K. Konolige, and R. Reboh, A Computer-Based Consultant for Mineral Exploration , SRI International, 19,9. 8. Garvey

  4. Statistical inference and string theory

    NASA Astrophysics Data System (ADS)

    Heckman, Jonathan J.

    2015-09-01

    In this paper, we expose some surprising connections between string theory and statistical inference. We consider a large collective of agents sweeping out a family of nearby statistical models for an M-dimensional manifold of statistical fitting parameters. When the agents making nearby inferences align along a d-dimensional grid, we find that the pooled probability that the collective reaches a correct inference is the partition function of a nonlinear sigma model in d dimensions. Stability under perturbations to the original inference scheme requires the agents of the collective to distribute along two dimensions. Conformal invariance of the sigma model corresponds to the condition of a stable inference scheme, directly leading to the Einstein field equations for classical gravity. By summing over all possible arrangements of the agents in the collective, we reach a string theory. We also use this perspective to quantify how much an observer can hope to learn about the internal geometry of a superstring compactification. Finally, we present some brief speculative remarks on applications to the AdS/CFT correspondence and Lorentzian signature space-times.

  5. Locative inferences in medical texts.

    PubMed

    Mayer, P S; Bailey, G H; Mayer, R J; Hillis, A; Dvoracek, J E

    1987-06-01

    Medical research relies on epidemiological studies conducted on a large set of clinical records that have been collected from physicians recording individual patient observations. These clinical records are recorded for the purpose of individual care of the patient with little consideration for their use by a biostatistician interested in studying a disease over a large population. Natural language processing of clinical records for epidemiological studies must deal with temporal, locative, and conceptual issues. This makes text understanding and data extraction of clinical records an excellent area for applied research. While much has been done in making temporal or conceptual inferences in medical texts, parallel work in locative inferences has not been done. This paper examines the locative inferences as well as the integration of temporal, locative, and conceptual issues in the clinical record understanding domain by presenting an application that utilizes two key concepts in its parsing strategy--a knowledge-based parsing strategy and a minimal lexicon.

  6. Development of Statistical Methods Using Predictive Inference and Entropy.

    DTIC Science & Technology

    1986-03-01

    Inference and Entopy APPENDIX B: Achieab Accuracy in Parametric Estimation of B-I Multivariate spectra ii LWl OF MIUMU AND TABLES FIGURES PAGE Figre1...1986e). "Achievable Accuracy in Parametric Estimation of Multivariate Spec- tra’. Draft. Larimore, WE. (1983a). ’Predictive inference, sufficiency... PARAMETRIC ESTIMATION OF MULTIVARIATE SPECTRA By Wallace E. Larimore Scientific Systems Inc., Cambridge, Massachusetts, U.SA. Research Sponsored by the

  7. 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.

  8. The state of the atmosphere as inferred from the FGGE satellite observing systems during SOP-1. [special observing period (SOP-1) of First Global Atmospheric Research Program Global Experiment (FGGE)

    NASA Technical Reports Server (NTRS)

    Halem, M.; Kalnay-Rivas, E.; Baker, W. E.; Atlas, R.

    1981-01-01

    The statistical properties, and coverage, of satellite temperature sounding data are described. Tropical regions are observed every two days, extratropics from one to four times a day. Oceans are covered two to three times a day. Asynoptic coverage is comparable to the U.S. rawinsonde network twice daily coverage. Lack of ground truth for data sparse areas makes accuracy difficult to assess. The rms differences of layer mean temperatures obtained from collocating rawinsonde observations with satellite temperature profiles in space and time differ from rms differences of layer mean satellite temperature soundings. The FGGE satellite systems can infer the three dimensional motion field and improve the representation of the large scale state of the atmosphere.

  9. The association forecasting of 13 variants within seven asthma susceptibility genes on 3 serum IgE groups in Taiwanese population by integrating of adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods.

    PubMed

    Wang, Cheng-Hang; Liu, Baw-Jhiune; Wu, Lawrence Shih-Hsin

    2012-02-01

    Asthma is one of the most common chronic diseases in children. It is caused by complicated coactions between various genetic factors and environmental allergens. The study aims to integrate the concept of implementing adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods for forecasting the association of asthma susceptibility genes on 3 serum IgE groups. The ANFIS model was trained and tested with data sets obtained from 425 asthmatic subjects and 483 non-asthma subjects from the Taiwanese population. We assessed 13 single-nucleotide polymorphisms (SNPs) in seven well-known asthma susceptibility genes; firstly, the proposed ANFIS model learned to reduce input features from the 13 SNPs. And secondly, the classification will be used to classify the serum IgE groups from the simulated SNPs results. The performance of the ANFIS model, classification accuracies and the results confirmed that the integration of ANFIS and classified analysis has potential in association discovery.

  10. Inferring Trust Based on Similarity with TILLIT

    NASA Astrophysics Data System (ADS)

    Tavakolifard, Mozhgan; Herrmann, Peter; Knapskog, Svein J.

    A network of people having established trust relations and a model for propagation of related trust scores are fundamental building blocks in many of today’s most successful e-commerce and recommendation systems. However, the web of trust is often too sparse to predict trust values between non-familiar people with high accuracy. Trust inferences are transitive associations among users in the context of an underlying social network and may provide additional information to alleviate the consequences of the sparsity and possible cold-start problems. Such approaches are helpful, provided that a complete trust path exists between the two users. An alternative approach to the problem is advocated in this paper. Based on collaborative filtering one can exploit the like-mindedness resp. similarity of individuals to infer trust to yet unknown parties which increases the trust relations in the web. For instance, if one knows that with respect to a specific property, two parties are trusted alike by a large number of different trusters, one can assume that they are similar. Thus, if one has a certain degree of trust to the one party, one can safely assume a very similar trustworthiness of the other one. In an attempt to provide high quality recommendations and proper initial trust values even when no complete trust propagation path or user profile exists, we propose TILLIT — a model based on combination of trust inferences and user similarity. The similarity is derived from the structure of the trust graph and users’ trust behavior as opposed to other collaborative-filtering based approaches which use ratings of items or user’s profile. We describe an algorithm realizing the approach based on a combination of trust inferences and user similarity, and validate the algorithm using a real large-scale data-set.

  11. 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.

  12. How Forgetting Aids Heuristic Inference

    ERIC Educational Resources Information Center

    Schooler, Lael J.; Hertwig, Ralph

    2005-01-01

    Some theorists, ranging from W. James (1890) to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2…

  13. 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…

  14. The mechanisms of temporal inference

    NASA Technical Reports Server (NTRS)

    Fox, B. R.; Green, S. R.

    1987-01-01

    The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.

  15. Word Learning as Bayesian Inference

    ERIC Educational Resources Information Center

    Xu, Fei; Tenenbaum, Joshua B.

    2007-01-01

    The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with…

  16. Starfish: Robust spectroscopic inference tools

    NASA Astrophysics Data System (ADS)

    Czekala, Ian; Andrews, Sean M.; Mandel, Kaisey S.; Hogg, David W.; Green, Gregory M.

    2015-05-01

    Starfish is a set of tools used for spectroscopic inference. It robustly determines stellar parameters using high resolution spectral models and uses Markov Chain Monte Carlo (MCMC) to explore the full posterior probability distribution of the stellar parameters. Additional potential applications include other types of spectra, such as unresolved stellar clusters or supernovae spectra.

  17. 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)…

  18. 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…

  19. 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

  20. 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

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. Towards General Algorithms for Grammatical Inference

    NASA Astrophysics Data System (ADS)

    Clark, Alexander

    Many algorithms for grammatical inference can be viewed as instances of a more general algorithm which maintains a set of primitive elements, which distributionally define sets of strings, and a set of features or tests that constrain various inference rules. Using this general framework, which we cast as a process of logical inference, we re-analyse Angluin's famous lstar algorithm and several recent algorithms for the inference of context-free grammars and multiple context-free grammars. Finally, to illustrate the advantages of this approach, we extend it to the inference of functional transductions from positive data only, and we present a new algorithm for the inference of finite state transducers.

  6. 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

  7. 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.

  8. Dynamical Inference in the Milky Way

    NASA Astrophysics Data System (ADS)

    Bovy, Jo

    Current and future surveys of the Galaxy contain a wealth of information about the structure and evolution of the Galactic disk and halo. Teasing out this information is complicated by measurement uncertainties, missing data, and sparse sampling. I develop and describe several applications of generative modeling--creating an approximate description of the probability of the data given the physical parameters of the system--to deal with these issues. I develop a method for inferring the Galactic potential from individual observations of stellar kinematics such as will be furnished by the upcoming Gaia space astrometry mission. This method takes uncertainties in our knowledge of the distribution function of stellar tracers into account through marginalization. I demonstrate the method by inferring the force law in the Solar System from observations of the positions and velocities of the eight planets at a single epoch. I apply a similar method to derive the Milky Way's circular velocity from observations of maser kinematics. I infer the velocity distribution of nearby stars from Hipparcos data, which only consist of tangential velocities, by forward modeling the underlying distribution with a flexible multi-Gaussian model. I characterize the contribution of several "moving groups"---overdensities of co-moving stars---to the full distribution. By studying the properties of stars in these moving groups, I show that they do not form a single-burst population and that they are most likely due to transient non-axisymmetric features of the disk, such as transient spiral structure. By forward modeling one such scenario, I show how the Hercules moving group can be traced around the Galaxy by future surveys, which would confirm that the Milky Way bar's outer Lindblad resonance lies near the Solar radius.

  9. 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

  10. 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

  11. Statistical learning and selective inference.

    PubMed

    Taylor, Jonathan; Tibshirani, Robert J

    2015-06-23

    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.

  12. Inferring Centrality from Network Snapshots

    NASA Astrophysics Data System (ADS)

    Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng

    2017-01-01

    The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data.

  13. 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

  14. Inferring Centrality from Network Snapshots

    PubMed Central

    Shao, Haibin; Mesbahi, Mehran; Li, Dewei; Xi, Yugeng

    2017-01-01

    The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph. Moreover, the tempo centrality also encodes the disturbance rejection of nodes in a consensus network. Our findings provide an approach to infer the performance of a consensus network from its temporal data. PMID:28098166

  15. Bayesian inference for agreement measures.

    PubMed

    Vidal, Ignacio; de Castro, Mário

    2016-08-25

    The agreement of different measurement methods is an important issue in several disciplines like, for example, Medicine, Metrology, and Engineering. In this article, some agreement measures, common in the literature, were analyzed from a Bayesian point of view. Posterior inferences for such agreement measures were obtained based on well-known Bayesian inference procedures for the bivariate normal distribution. As a consequence, a general, simple, and effective method is presented, which does not require Markov Chain Monte Carlo methods and can be applied considering a great variety of prior distributions. Illustratively, the method was exemplified using five objective priors for the bivariate normal distribution. A tool for assessing the adequacy of the model is discussed. Results from a simulation study and an application to a real dataset are also reported.

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. Inference of reversible tree languages.

    PubMed

    López, Damián; Sempere, José M; García, Pedro

    2004-08-01

    In this paper, we study the notion of k-reversibility and k-testability when regular tree languages are involved. We present an inference algorithm for learning a k-testable tree language that runs in polynomial time with respect to the size of the sample used. We also study the tree language classes in relation to other well known ones, and some properties of these languages are proven.

  1. Fast, Flexible, Rational Inductive Inference

    DTIC Science & Technology

    2013-08-23

    learning phonetic categories – the sounds that make up speech – learning the words that those sounds appear in provides sufficiently strong constraints...first to be able to infer realistic phonetic categories directly from simulated speech data. Objective 2.2: Forming feature-based representations...lexicon in phonetic category acquisition. Psychological Review. Griffiths, T. L., Austerweil, J. L., & Berthiaume, V. G. (2012). Comparing the

  2. Reputation-like inference in domestic dogs (Canis familiaris).

    PubMed

    Kundey, Shannon M A; De los Reyes, Andres; Royer, Erica; Molina, Sabrina; Monnier, Brittany; German, Rebecca; Coshun, Ariel

    2011-03-01

    Humans frequently interact with strangers absent prior direct experience with their behavior. Some conjecture that this may have favored evolution of a cognitive system within the hominoid clade or perhaps the primate order to assign reputations based on third-party exchanges. However, non-primate species' acquisition of skills from experienced individuals, attention to communicative cues, and propensity to infer social rules suggests reputation inference may be more widespread. We utilized dogs' sensitivity to humans' social and communicative cues to explore whether dogs evidenced reputation-like inference for strangers through third-party interactions. Results indicated dogs spontaneously show reputation-like inference for strangers from indirect exchanges. Further manipulations revealed that dogs continued to evidence this ability despite reduction of specific components of the observed interactions, including reduction of visual social cues (i.e., face-to-face contact between the participants in the interaction) and the nature of the recipient (i.e., living, animate agent versus living, inanimate self-propelled agent). Dogs also continued to demonstrate reputation-like inference when local enhancement was controlled and in a begging paradigm. However, dogs did not evidence reputation-like inference when the observed interaction was inadvertent.

  3. 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

  4. An introduction to causal inference.

    PubMed

    Pearl, Judea

    2010-02-26

    This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.

  5. 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

  6. Children's and adults' evaluation of the certainty of deductive inferences, inductive inferences, and guesses.

    PubMed

    Pillow, Bradford H

    2002-01-01

    Two experiments investigated kindergarten through fourth-grade children's and adults' (N = 128) ability to (1) evaluate the certainty of deductive inferences, inductive inferences, and guesses; and (2) explain the origins of inferential knowledge. When judging their own cognitive state, children in first grade and older rated deductive inferences as more certain than guesses; but when judging another person's knowledge, children did not distinguish valid inferences from invalid inferences and guesses until fourth grade. By third grade, children differentiated their own deductive inferences from inductive inferences and guesses, but only adults both differentiated deductive inferences from inductive inferences and differentiated inductive inferences from guesses. Children's recognition of their own inferences may contribute to the development of knowledge about cognitive processes, scientific reasoning, and a constructivist epistemology.

  7. Automated adaptive inference of phenomenological dynamical models

    NASA Astrophysics Data System (ADS)

    Daniels, Bryan C.; Nemenman, Ilya

    2015-08-01

    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.

  8. 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

  9. 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.

  10. Hourly to seasonal hydrochemical dynamics in lowland and upland UK river-systems: from process inference to progress in hydrochemical modelling

    NASA Astrophysics Data System (ADS)

    Wade, A. J.; Skeffington, R. A.; Halliday, S. J.; Bowes, M. J.; Palmer-Felgate, E. J.; Loewenthal, M.; Jarvie, H. P.; Neal, C.; Reynolds, B.; Norris, D.; Gozzard, E.; Newman, J.; Greenway, G.; Bell, I.; Joly, E.; Haswell, S. J.

    2012-04-01

    Model-based assessments of the impacts of environmental change on European freshwater ecosystems are needed to aid informed resource management. This talk will focus on how such model-based assessments can be improved using the latest results from in-situ, continuous sub-daily water quality monitoring in upland and lowland UK river systems. Two catchments in the lowland Thames basin, the Enborne and The Cut, have been instrumented since November 2009 to examine the water quality dynamics using laboratory instruments (Hach-Lange; Micromac) installed in the field to produce hourly measurements of nutrient dynamics. Total Phosphorus and Total Reactive Phosphorus were measured in The Cut and nitrate was measured in the Enborne. These data were supplemented at both sites by nearby flow measurements and data collected using YSI multi-parameter sondes fitted with pH, dissolved oxygen, conductivity and water temperature probes. Experiences of installing and using these in-situ technologies will be described. The observed dynamics evident in these datasets will be compared to those identified at Plynlimon, Wales, which represent the hydrochemical functioning of an upland river-system. Both the lowland and upland data will be interpreted in terms of: the gain in information by sampling at sub-daily frequencies (and what is lost by sampling at lower frequencies); new information derived in terms of hydrochemical functioning; and the implications for progressing hydrochemical models. As part of the discussion, new opportunities from 'lab-on-a-chip' technologies will be described.

  11. Inferring epigenetic dynamics from kin correlations

    PubMed Central

    Hormoz, Sahand; Desprat, Nicolas; Shraiman, Boris I.

    2015-01-01

    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

  12. Hierarchical Bayesian inference in the visual cortex

    NASA Astrophysics Data System (ADS)

    Lee, Tai Sing; Mumford, David

    2003-07-01

    Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas. 2003 Optical Society of America

  13. 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

  14. 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

  15. 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

  16. 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

  17. VARIABLE AND EXTREME IRRADIATION CONDITIONS IN THE EARLY SOLAR SYSTEM INFERRED FROM THE INITIAL ABUNDANCE OF {sup 10}Be IN ISHEYEVO CAIs

    SciTech Connect

    Gounelle, Matthieu; Chaussidon, Marc; Rollion-Bard, Claire

    2013-02-01

    A search for short-lived {sup 10}Be in 21 calcium-aluminum-rich inclusions (CAIs) from Isheyevo, a rare CB/CH chondrite, showed that only 5 CAIs had {sup 10}B/{sup 11}B ratios higher than chondritic correlating with the elemental ratio {sup 9}Be/{sup 11}B, suggestive of in situ decay of this key short-lived radionuclide. The initial ({sup 10}Be/{sup 9}Be){sub 0} ratios vary between {approx}10{sup -3} and {approx}10{sup -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 {sup 10}Be is early solar system irradiation. The low ({sup 26}Al/{sup 27}Al){sub 0} [{<=} 8.9 Multiplication-Sign 10{sup -7}] with which CAI 411 formed indicates that it was exposed to gradual flares with a proton fluence of a few 10{sup 19} protons cm{sup -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 10{sup 19} and 10{sup 20} protons cm{sup -2}. The variable and extreme value of the initial {sup 10}Be/{sup 9}Be ratios in carbonaceous chondrite CAIs is the reflection of the variable and extreme magnetic activity in young stars observed in the X-ray domain.

  18. 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.

  19. 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.

  20. A Study of Inference Problems in Distributed Databases

    DTIC Science & Technology

    2002-01-01

    524. [6] Denning, D. (1980) \\Secure Statistical Database with Random Sample Queries," ACM Transaction on Database Systems, 5(3), pp. 291-315. [7] Dobra ...1996) "Analyzing FD Inference in Relational Databases", Data and Knowledge Engineering Journal, vol. 18, pp. 167-183. [10] Heckerman, D. (1996) \\Bayesian...No. 3-4, pp. 225-241. [16] Marks, D. (1996) \\Inference in MLS Database Systems," IEEE Trans. Knowledge and Data Engineering , Vol 8, No. 1, pp. 46-55

  1. 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

  2. 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.

    2016-11-01

    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.

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. Dual-scale hydrothermal circulation inferred from detailed heat flow measurements in the Suiyo Seamount Hydrothermal System, Izu-Bonin Arc

    NASA Astrophysics Data System (ADS)

    Gomado, M.; Kinoshita, M.

    2002-12-01

    Hydrothermal activity within the caldera of Suiyo Seamount was investigated in detail using manned or remotely-operated submersibles, and by deep-tow imagery and seismic surveys. Hydrothermal regime in the Suiyo-seamount is characterized by a geochemically uniform fluid, shallow reservoir depth, very permeable seafloor, and venting without creating big chimneys. Detailed heat flow surveys were carried out through four research cruises conducted in 2001-2002. Geothermal probes, called SAHF (Stand-Alone Heat Flow) meter, are 1m in length, and five thermistors are installed at 11-12 cm intervals. Heat flow is highest (> 10 W/m2) within the active area. These values were obtained close to black smokers, thus are affected by the venting or very shallow reservoirs. To the east, heat flow is uniform around 4 W/m2. Since there were no indications of discharge, this area is dominated by thermal conduction, and its heat source would be a hydrothermal reservoir capped by some impermeable layer. To the west, we detected very low heat flow values of less than 0.3 W/m2, only several tens of meters away from the active area. A similar heat flow anomaly was detected in the TAG hyudrothermal mound of the Mid-Atlantic Ridge (Becker et al., 1996). We penetrated at 1-2 m away from two isolated active sulfide mounds. At both sites subbottom temperatures were about 40 degC at 10-20 cm depth, then they decreased to about 20 degC at 30-40cm. The temperature reversals suggest a meter-scale hydrothermal circulation, where a hot fluid discharges as a branch flow from the main vent to the mound. An impermeable structure of the mound and a permeable sediment surrounding the mound would make this very local circulation possible. We suggest a dual scale hydrothermal circulation system, one with several meters scale, and the other with few tens of meters scale. The former would be driven by a suction created by discrete venting of high temperature fluid, and the latter is a regional

  9. FRC Separatrix inference using machine-learning techniques

    NASA Astrophysics Data System (ADS)

    Romero, Jesus; Roche, Thomas; the TAE Team

    2016-10-01

    As Field Reversed Configuration (FRC) devices approach lifetimes exceeding the characteristic time of conductive structures external to the plasma, plasma stabilization cannot be achieved solely by the flux conserving effect of the external structures, and active control systems are then necessary. An essential component of such control systems is a reconstruction method for the plasma separatrix suitable for real time. We report on a method to infer the separatrix in an FRC using the information of magnetic probes located externally to the plasma. The method uses machine learning methods, namely Bayesian inference of Gaussian Processes, to obtain the most likely plasma current density distribution given the measurements of magnetic field external to the plasma. From the current sources, flux function and in particular separatrix are easily computed. The reconstruction method is non iterative and hence suitable for deterministic real time applications. Validation results with numerical simulations and application to separatrix inference of C-2U plasma discharges will be presented.

  10. Seasonal constraints on inferred planetary heat content

    NASA Astrophysics Data System (ADS)

    McKinnon, Karen A.; Huybers, Peter

    2016-10-01

    Planetary heating can be quantified using top of the atmosphere energy fluxes or through monitoring the heat content of the Earth system. It has been difficult, however, to compare the two methods with each other because of biases in satellite measurements and incomplete spatial coverage of ocean observations. Here we focus on the the seasonal cycle whose amplitude is large relative to satellite biases and observational errors. The seasonal budget can be closed through inferring contributions from high-latitude oceans and marginal seas using the covariance structure of National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM1). In contrast, if these regions are approximated as the average across well-observed regions, the amplitude of the seasonal cycle is overestimated relative to satellite constraints. Analysis of the same CESM1 simulation indicates that complete measurement of the upper ocean would increase the magnitude and precision of interannual trend estimates in ocean heating more than fully measuring the deep ocean.

  11. Statistical inference for inverse problems

    NASA Astrophysics Data System (ADS)

    Bissantz, Nicolai; Holzmann, Hajo

    2008-06-01

    In this paper we study statistical inference for certain inverse problems. We go beyond mere estimation purposes and review and develop the construction of confidence intervals and confidence bands in some inverse problems, including deconvolution and the backward heat equation. Further, we discuss the construction of certain hypothesis tests, in particular concerning the number of local maxima of the unknown function. The methods are illustrated in a case study, where we analyze the distribution of heliocentric escape velocities of galaxies in the Centaurus galaxy cluster, and provide statistical evidence for its bimodality.

  12. 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

  13. 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.

  14. Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Kim, Chan Moon; Parnichkun, Manukid

    2017-02-01

    Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system (k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

  15. 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.

  16. 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.

  17. 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.

  18. Bayesian inference for radio observations

    NASA Astrophysics Data System (ADS)

    Lochner, Michelle; Natarajan, Iniyan; Zwart, Jonathan T. L.; Smirnov, Oleg; Bassett, Bruce A.; Oozeer, Nadeem; Kunz, Martin

    2015-06-01

    New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inadequate uncertainty estimates and biased results because any correlations between parameters are ignored. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realization of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. This enables it to derive both correlations and accurate uncertainties, making use of the flexible software MEQTREES to model the sky and telescope simultaneously. We demonstrate BIRO with two simulated sets of Westerbork Synthesis Radio Telescope data sets. In the first, we perform joint estimates of 103 scientific (flux densities of sources) and instrumental (pointing errors, beamwidth and noise) parameters. In the second example, we perform source separation with BIRO. Using the Bayesian evidence, we can accurately select between a single point source, two point sources and an extended Gaussian source, allowing for `super-resolution' on scales much smaller than the synthesized beam.

  19. Dopamine, Affordance and Active Inference

    PubMed Central

    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. PMID:22241972

  20. Systematic parameter inference in stochastic mesoscopic modeling

    NASA Astrophysics Data System (ADS)

    Lei, Huan; Yang, Xiu; Li, Zhen; Karniadakis, George Em

    2017-02-01

    We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are "sparse". The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.

  1. 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)

  2. 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…

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. A Full Bayesian Approach for Boolean Genetic Network Inference

    PubMed Central

    Han, Shengtong; Wong, Raymond K. W.; Lee, Thomas C. M.; Shen, Linghao; Li, Shuo-Yen R.; Fan, Xiaodan

    2014-01-01

    Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data. PMID:25551820

  8. 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.

  9. 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.

  10. 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.

  11. Causal Inference in Multisensory Heading Estimation

    PubMed Central

    Katliar, Mikhail; Bülthoff, Heinrich H.

    2017-01-01

    A large body of research shows that the Central Nervous System (CNS) integrates multisensory information. However, this strategy should only apply to multisensory signals that have a common cause; independent signals should be segregated. Causal Inference (CI) models account for this notion. Surprisingly, previous findings suggested that visual and inertial cues on heading of self-motion are integrated regardless of discrepancy. We hypothesized that CI does occur, but that characteristics of the motion profiles affect multisensory processing. Participants estimated heading of visual-inertial motion stimuli with several different motion profiles and a range of intersensory discrepancies. The results support the hypothesis that judgments of signal causality are included in the heading estimation process. Moreover, the data suggest a decreasing tolerance for discrepancies and an increasing reliance on visual cues for longer duration motions. PMID:28060957

  12. Spontaneous Trait Inferences on Social Media

    PubMed Central

    Utz, Sonja

    2016-01-01

    The present research investigates whether spontaneous trait inferences occur under conditions characteristic of social media and networking sites: nonextreme, ostensibly self-generated content, simultaneous presentation of multiple cues, and self-paced browsing. We used an established measure of trait inferences (false recognition paradigm) and a direct assessment of impressions. Without being asked to do so, participants spontaneously formed impressions of people whose status updates they saw. Our results suggest that trait inferences occurred from nonextreme self-generated content, which is commonly found in social media updates (Experiment 1) and when nine status updates from different people were presented in parallel (Experiment 2). Although inferences did occur during free browsing, the results suggest that participants did not necessarily associate the traits with the corresponding status update authors (Experiment 3). Overall, the findings suggest that spontaneous trait inferences occur on social media. We discuss implications for online communication and research on spontaneous trait inferences. PMID:28123646

  13. Inferring echolocation in ancient bats.

    PubMed

    Simmons, Nancy B; Seymour, Kevin L; Habersetzer, Jörg; Gunnell, Gregg F

    2010-08-19

    Laryngeal echolocation, used by most living bats to form images of their surroundings and to detect and capture flying prey, is considered to be a key innovation for the evolutionary success of bats, and palaeontologists have long sought osteological correlates of echolocation that can be used to infer the behaviour of fossil bats. Veselka et al. argued that the most reliable trait indicating echolocation capabilities in bats is an articulation between the stylohyal bone (part of the hyoid apparatus that supports the throat and larynx) and the tympanic bone, which forms the floor of the middle ear. They examined the oldest and most primitive known bat, Onychonycteris finneyi (early Eocene, USA), and argued that it showed evidence of this stylohyal-tympanic articulation, from which they concluded that O. finneyi may have been capable of echolocation. We disagree with their interpretation of key fossil data and instead argue that O. finneyi was probably not an echolocating bat.

  14. Motion Inference During +Gz Acceleration

    DTIC Science & Technology

    2006-09-01

    AFRL-HW-WP-TP-2006-0091 Motion Inference During +Gz Acceleration Lloyd D . Tripp Jr. Richard A. McKinley Robert L. Esken Air Force Research Laboratory...5c. PROGRAM ELEMENT NUMBER 62202F 6. AUTHOR(S) 5d. PROJECT NUMBER Lloyd D . Tripp Jr 7184 Richard A. McKinley 5e. TASK NUMBER Robert L. Esken 03 5f...CD A Cj CL.C2 C 0~ 0. D 0 0~G)C00.E)’ca)4-100 ( 0 Eo12 E a 0 0L0mm 0a0 " C0 U) U) LUr o CLI.,a @ .- . : ) 0 " 0 C CL.. 70 E- 0 M 0.0 toE-C .- 0)c .2 0UL

  15. Inferred properties of stellar granulation

    SciTech Connect

    Gray, D.F.; Toner, C.G.

    1985-06-01

    Apparent characteristics of stellar granulation in F and G main-sequence stars are inferred directly from observed spectral-line asymmetries and from comparisons of numerical simulations with the observations: (1) the apparent granulation velocity increases with effective temperature, (2) the dispersion of granule velocities about their mean velocity of rise increases with the apparent granulation velocity, (3) the mean velocity of rise of granules must be less than the total line broadening, (4) the apparent velocity difference between granules and dark lanes corresponds to the granulation velocity deduced from stellar line bisectors, (5) the dark lanes show velocities of fall approximately twice as large as the granule rise velocities, (6) the light contributed to the stellar flux by the granules is four to ten times more than the light from the dark lanes. Stellar rotation is predicted to produce distortions in the line bisectors which may give information on the absolute velocity displacements of the line bisectors. 37 references.

  16. Synaptic Computation Underlying Probabilistic Inference

    PubMed Central

    Soltani, Alireza; Wang, Xiao-Jing

    2010-01-01

    In this paper we propose that synapses may be the workhorse of neuronal computations that underlie probabilistic reasoning. We built a neural circuit model for probabilistic inference when information provided by different sensory cues needs to be integrated, and the predictive powers of individual cues about an outcome are deduced through experience. We found that bounded synapses naturally compute, through reward-dependent plasticity, the posterior probability that a choice alternative is correct given that a cue is presented. Furthermore, a decision circuit endowed with such synapses makes choices based on the summated log posterior odds and performs near-optimal cue combination. The model is validated by reproducing salient observations of, and provide insights into, a monkey experiment using a categorization task. Our model thus suggests a biophysical instantiation of the Bayesian decision rule, while predicting important deviations from it similar to ‘base-rate neglect’ observed in human studies when alternatives have unequal priors. PMID:20010823

  17. Generic comparison of protein inference engines.

    PubMed

    Claassen, Manfred; Reiter, Lukas; Hengartner, Michael O; Buhmann, Joachim M; Aebersold, Ruedi

    2012-04-01

    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.

  18. 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.

  19. Protein inference: A protein quantification perspective.

    PubMed

    He, Zengyou; Huang, Ting; Liu, Xiaoqing; Zhu, Peijun; Teng, Ben; Deng, Shengchun

    2016-08-01

    In mass spectrometry-based shotgun proteomics, protein quantification and protein identification are two major computational problems. To quantify the protein abundance, a list of proteins must be firstly inferred from the raw data. Then the relative or absolute protein abundance is estimated with quantification methods, such as spectral counting. Until now, most researchers have been dealing with these two processes separately. In fact, the protein inference problem can be regarded as a special protein quantification problem in the sense that truly present proteins are those proteins whose abundance values are not zero. Some recent published papers have conceptually discussed this possibility. However, there is still a lack of rigorous experimental studies to test this hypothesis. In this paper, we investigate the feasibility of using protein quantification methods to solve the protein inference problem. Protein inference methods aim to determine whether each candidate protein is present in the sample or not. Protein quantification methods estimate the abundance value of each inferred protein. Naturally, the abundance value of an absent protein should be zero. Thus, we argue that the protein inference problem can be viewed as a special protein quantification problem in which one protein is considered to be present if its abundance is not zero. Based on this idea, our paper tries to use three simple protein quantification methods to solve the protein inference problem effectively. The experimental results on six data sets show that these three methods are competitive with previous protein inference algorithms. This demonstrates that it is plausible to model the protein inference problem as a special protein quantification task, which opens the door of devising more effective protein inference algorithms from a quantification perspective. The source codes of our methods are available at: http://code.google.com/p/protein-inference/.

  20. 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

  1. Synaptic and nonsynaptic plasticity approximating probabilistic inference

    PubMed Central

    Tully, Philip J.; Hennig, Matthias H.; Lansner, Anders

    2014-01-01

    Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose

  2. 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.

  3. A Comparison of Two Student Instructional Rating Forms Utilizing High-Inference Versus Moderate Inference Items.

    ERIC Educational Resources Information Center

    Wilson, Pamela W.

    Two types of items used in student evaluations of college teaching were compared: high-inference items, which require considerable inferring from what is seen or heard in the classroom to labelling of teacher behavior; and moderate-inference items, such as "teacher listens carefully." Two instruments were administered to random halves of…

  4. Forward and Backward Inference in Spatial Cognition

    PubMed Central

    Penny, Will D.; Zeidman, Peter; Burgess, Neil

    2013-01-01

    This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of ‘lower-level’ computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus. PMID:24348230

  5. Scalar Inferences in Autism Spectrum Disorders

    ERIC Educational Resources Information Center

    Chevallier, Coralie; Wilson, Deirdre; Happe, Francesca; Noveck, Ira

    2010-01-01

    On being told "John or Mary will come", one might infer that "not both" of them will come. Yet the semantics of "or" is compatible with a situation where both John and Mary come. Inferences of this type, which enrich the semantics of "or" from an "inclusive" to an "exclusive" interpretation, have been extensively studied in linguistic pragmatics.…

  6. 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…

  7. 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…

  8. Forward and backward inference in spatial cognition.

    PubMed

    Penny, Will D; Zeidman, Peter; Burgess, Neil

    2013-01-01

    This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.

  9. Inferring Learners' Knowledge from Their Actions

    ERIC Educational Resources Information Center

    Rafferty, Anna N.; LaMar, Michelle M.; Griffiths, Thomas L.

    2015-01-01

    Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we…

  10. Predictive Inferences are Represented as Hypothetical Facts

    ERIC Educational Resources Information Center

    Campion, Nicolas

    2004-01-01

    Three experiments examined the processing of predictive and deductive inferences elicited by narrative texts. In Experiment 1, lexical decision responses indicated that these inferences were activated during reading. In Experiment 2, sentences expressing that an event had ''maybe'' taken place were shown to be appropriate in verifying predictive…

  11. Causal Inferences during Text Comprehension and Production.

    ERIC Educational Resources Information Center

    Kemper, Susan

    As comprehension failure results whenever readers are unable to infer missing causal connections, recent comprehension research has focused both on assessing the inferential complexity of texts and on investigating students' developing ability to infer causal relationships. Studies have demonstrated that texts rely on four types of causal…

  12. Measuring the Inference Load of a Text.

    ERIC Educational Resources Information Center

    Kemper, Susan

    1983-01-01

    A new approach to measuring readability is proposed based on the analysis of texts as causally connected chains of actions, physical states, and mental states. Using the inference load formula reflecting the difficulty readers have in inferring causal connections, the difficulty of texts can be adjusted for readers differing in skill or knowledge.…

  13. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-05

    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. 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…

  15. 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

  16. PROBLEMS OF STATISTICAL INFERENCE FOR BIRTH AND DEATH QUEUEING MODELS

    DTIC Science & Technology

    A large sample theory is presented for birth and death queueing processes which are ergodic and metrically transitive. The theory is applied to make...inferences about how arrival and service rates vary with the number in the system. Likelihood ratio tests and maximum likelihood estimators are...derived for simple models which describe this variation. Composite hypotheses such as that the arrival rate does not vary with the number in the system are

  17. Saturn's ionosphere - Inferred electron densities

    NASA Astrophysics Data System (ADS)

    Kaiser, M. L.; Desch, M. D.; Connerney, J. E. P.

    1984-04-01

    During the two Voyager encounters with Saturn, radio bursts were detected which appear to have originated from atmospheric lightning storms. Although these bursts generally extended over frequencies from as low as 100 kHz to the upper detection limit of the instrument, 40 MHz, they often exhibited a sharp but variable low frequency cutoff below which bursts were not detected. We interpret the variable low-frequency extent of these bursts to be due to the reflection of the radio waves as they propagate through an ionosphere which varies with local time. We obtain estimates of electron densities at a variety of latitude and local time locations. These compare well with the dawn and dusk densities measured by the Pioneer 11 Voyager Radio Science investigations, and with model predictions for dayside densities. However, we infer a two-order-of-magnitude diurnal variation of electron density, which had not been anticipated by theoretical models of Saturn's ionosphere, and an equally dramatic extinction of ionospheric electron density by Saturn's rings. Previously announced in STAR as N84-17102

  18. Active Inference: A Process Theory.

    PubMed

    Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; Pezzulo, Giovanni

    2017-01-01

    This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.

  19. Reinforcement learning or active inference?

    PubMed

    Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

    2009-07-29

    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.

  20. Reinforcement Learning or Active Inference?

    PubMed Central

    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. PMID:19641614

  1. Saturn's ionosphere: Inferred electron densities

    NASA Technical Reports Server (NTRS)

    Kaiser, M. L.; Desch, M. D.; Connerney, J. E. P.

    1983-01-01

    During the two Voyager encounters with Saturn, radio bursts were detected which appear to have originated from atmospheric lightning storms. Although these bursts generally extended over frequencies from as low as 100 kHz to the upper detection limit of the instrument, 40 MHz, they often exhibited a sharp but variable low frequency cutoff below which bursts were not detected. We interpret the variable low-frequency extent of these bursts to be due to the reflection of the radio waves as they propagate through an ionosphere which varies with local time. We obtain estimates of electron densities at a variety of latitude and local time locations. These compare well with the dawn and dusk densitis measured by the Pioneer 11 Voyager Radio Science investigations, and with model predictions for dayside densities. However, we infer a two-order-of-magnitude diurnal variation of electron density, which had not been anticipated by theoretical models of Saturn's ionosphere, and an equally dramatic extinction of ionospheric electron density by Saturn's rings.

  2. Redshift data and statistical inference

    NASA Technical Reports Server (NTRS)

    Newman, William I.; Haynes, Martha P.; Terzian, Yervant

    1994-01-01

    Frequency histograms and the 'power spectrum analysis' (PSA) method, the latter developed by Yu & Peebles (1969), have been widely employed as techniques for establishing the existence of periodicities. We provide a formal analysis of these two classes of methods, including controlled numerical experiments, to better understand their proper use and application. In particular, we note that typical published applications of frequency histograms commonly employ far greater numbers of class intervals or bins than is advisable by statistical theory sometimes giving rise to the appearance of spurious patterns. The PSA method generates a sequence of random numbers from observational data which, it is claimed, is exponentially distributed with unit mean and variance, essentially independent of the distribution of the original data. We show that the derived random processes is nonstationary and produces a small but systematic bias in the usual estimate of the mean and variance. Although the derived variable may be reasonably described by an exponential distribution, the tail of the distribution is far removed from that of an exponential, thereby rendering statistical inference and confidence testing based on the tail of the distribution completely unreliable. Finally, we examine a number of astronomical examples wherein these methods have been used giving rise to widespread acceptance of statistically unconfirmed conclusions.

  3. 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

  4. Three Roads Diverged? Routes To Phylogeographic Inference

    PubMed Central

    Bloomquist, Erik W.; Lemey, Philippe

    2010-01-01

    Phylogeographic methods enable inference of the geographical history of genetic lineages. Recent examples successfully explore the patterns of human migration and the origins and spread of viral pandemics. Nevertheless, longstanding disagreement exists over the use and validity of certain phylogeographic inference methodologies. In this paper, we highlight three distinct frameworks for phylogeographic inference to give a taste of this disagreement. Each of the three approaches presents a different viewpoint on phylogeography, most fundamentally how we view the relationship between the inferred history of the sample and the history of the population the sample is embedded in. Satisfactory resolution of this relationship between history of the tree and history of the population remains a challenge for all but the most trivial models of phylogeographic processes. Intriguingly, we believe that some recent methods that entirely side-step inference about the history of the population will eventually help the field toward this goal. PMID:20863591

  5. 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.

  6. Causal inference in obesity research.

    PubMed

    Franks, P W; Atabaki-Pasdar, N

    2017-03-01

    Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.

  7. Inferring Mantle From Basalt Composition

    NASA Astrophysics Data System (ADS)

    Stracke, A.

    2014-12-01

    Isotope ratios in oceanic basalts, first reported by Gast and co-workers 50 years ago, are unique tracers of mantle composition, because they are expected to mirror the composition of their mantle sources. While the latter is certainly true for homogeneous sources, the plethora of studies over the last 50 years have shown that mantle sources are isotopically heterogeneous on different length scales. Isotopic differences exist between basalts from different ocean basins, volcanoes of individual ocean islands, lava flows of a single volcano, and even in μm sized melt inclusions in a single mineral grain. Diffusion, which acts to homogenize isotopic heterogeneity over Gyr timescales, limits the length scale of isotopic heterogeneity in the mantle to anywhere between several mm to 10s of meters. Melting regions, however, are typically several 100 km wide and up to 100 km deep. The scale of melting is thus generally orders of magnitude larger than the scale of isotopic heterogeneity. How partial melts mix during melting, melt transport, and melt storage then inevitably influences how isotopic heterogeneity is conveyed from source to melt. The isotopic composition of oceanic basalts hence provides an integrated signal of isotopically diverse melts. Recent mixing models and observed isotopic differences between source (abyssal peridotites) and melts (MORB) show that the range of isotopic heterogeneity of erupted melts need NOT directly reflect that of their source(s), nor need observed isotopic endmembers in source and melts be congruent. Many geochemical models, however, implicitly assume equivalence of source and melt composition. Especially when attempting to infer spatial patterns of isotopic heterogeneity in the mantle from those observed in erupted melts, or for linking isotopic diversity to geophysical structures in the mantle requires a more profound understanding to what extent erupted melts represent the isotopic composition of their mantle sources.

  8. 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.

  9. 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

  10. 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.

  11. Gene regulatory network inference using out of equilibrium statistical mechanics

    PubMed Central

    Benecke, Arndt

    2008-01-01

    Spatiotemporal control of gene expression is fundamental to multicellular life. Despite prodigious efforts, the encoding of gene expression regulation in eukaryotes is not understood. Gene expression analyses nourish the hope to reverse engineer effector-target gene networks using inference techniques. Inference from noisy and circumstantial data relies on using robust models with few parameters for the underlying mechanisms. However, a systematic path to gene regulatory network reverse engineering from functional genomics data is still impeded by fundamental problems. Recently, Johannes Berg from the Theoretical Physics Institute of Cologne University has made two remarkable contributions that significantly advance the gene regulatory network inference problem. Berg, who uses gene expression data from yeast, has demonstrated a nonequilibrium regime for mRNA concentration dynamics and was able to map the gene regulatory process upon simple stochastic systems driven out of equilibrium. The impact of his demonstration is twofold, affecting both the understanding of the operational constraints under which transcription occurs and the capacity to extract relevant information from highly time-resolved expression data. Berg has used his observation to predict target genes of selected transcription factors, and thereby, in principle, demonstrated applicability of his out of equilibrium statistical mechanics approach to the gene network inference problem. PMID:19404429

  12. 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.

  13. Explaining imaginal inference by operations in a propositional format.

    PubMed

    Wilton, R N

    1978-01-01

    Solving problems by imaginal inference often seems inefficient for an organism that is manipulating propositions. One explanation for the apparent inefficiency is that the problems are being solved not in propositional format by operations in an analogue format. Imaginal inference might then be the most efficient method compatible with the limitations inherent in the analogue format. In the present paper an alternative rationale is given for the use of imaginal inference by explaining how the processes involved in mental problem solving are related to those in perception: it is suggested that the mechanisms used in problem solving have evolved from a perceptual system in which hypotheses about events in the sensory field are generated from an internal representation of the world. This thesis denies that perception is passive and suggests that originally for perception. Acceptance of the thesis implies that the capabilities of a propositional format in problem solving would be limited. This limitation could account for the apparently inefficient use of that format in imaginal inference.

  14. 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.

  15. Inferring interaction partners from protein sequences

    PubMed Central

    Bitbol, Anne-Florence; Dwyer, Robert S.; Colwell, Lucy J.; Wingreen, Ned S.

    2016-01-01

    Specific protein−protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm’s performance on histidine kinases and response regulators from bacterial two-component signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data. PMID:27663738

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Late Archaean crust-mantle interactions: geochemistry of LREE-enriched mantle derived magmas. Example of the Closepet batholith, southern India

    NASA Astrophysics Data System (ADS)

    Jayananda, M.; Martin, H.; Peucat, J.-J.; Mahabaleswar, B.

    1995-03-01

    The Closepet batholith in South India is generally considered as a typical crustal granite emplaced 2.5 Ga ago and derived through partial melting of the surrounding Peninsular Gneisses (3.3 to 3.0 Ga). In the field, it appears as a composite batholith made up of at least two groups of intrusions. (a) An early SiO2-poor group (clinopyroxene quartz-monzonite and porphyritic phyritic monzogranite) is located in the central part of the batholith. These rocks display a narrow range in both initial 87Sr/86Sr (0.7017 0.7035) and ɛNd(-0.9to -4.1). (b) A later SiO2-rich group (equigranular grey and pink granites) is located along the interface between the SiO2-poor group and the Peninsular Gneisses. They progressively grade into migmatised Peninsular Gneisses, thus indicating their anatectic derivation. Their isotopic characteristics vary over a wide range (87Sr/86Sr ratios=0.7028 0.7336 and ɛNd values from-2.7 to-8.3, at 2.52 Ga). Field and geochronological evidence shows that the two groups are broadly contemporaneous (2.518 2.513 Ga) and mechanically mixed. This observation is supported by the chemical data that display well defined mixing trends in the ɛSr vs ɛNd and elemental variation diagrams. The continuous chemical variation of the two magmatic bodies is interpreted in terms of interaction and mixing of two unrelated end-members derived from different source regions (enriched peridotitic mantle and Peninsular Gneisses). It is proposed that the intrusion of mantle-derived magmas into mid-crustal levels occurred along a transcurrent shear zone; these magmas supplied additional heat and fluids that initiated anatexis of the surrounding crust. During this event, large-scale mixing occurred between mantle and crustal melts, thus generating the composite Closepet batholith. The mantle-derived magmatism is clearly associated with granulite facies metamorphism 2.51±0.01 Ga ago. Both are interpreted as resulting from a major crustal accretion event, possibly related to mantle plume activity.

  5. 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.

  6. 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

  7. 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.

  8. Experimental evidence for circular inference in schizophrenia

    NASA Astrophysics Data System (ADS)

    Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S.; Denève, Sophie

    2017-01-01

    Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to `see what we expect' (through descending loops), to `expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ.

  9. Bayesian Cosmological inference beyond statistical isotropy

    NASA Astrophysics Data System (ADS)

    Souradeep, Tarun; Das, Santanu; Wandelt, Benjamin

    2016-10-01

    With advent of rich data sets, computationally challenge of inference in cosmology has relied on stochastic sampling method. First, I review the widely used MCMC approach used to infer cosmological parameters and present a adaptive improved implementation SCoPE developed by our group. Next, I present a general method for Bayesian inference of the underlying covariance structure of random fields on a sphere. We employ the Bipolar Spherical Harmonic (BipoSH) representation of general covariance structure on the sphere. We illustrate the efficacy of the method with a principled approach to assess violation of statistical isotropy (SI) in the sky maps of Cosmic Microwave Background (CMB) fluctuations. The general, principled, approach to a Bayesian inference of the covariance structure in a random field on a sphere presented here has huge potential for application to other many aspects of cosmology and astronomy, as well as, more distant areas of research like geosciences and climate modelling.

  10. Experimental evidence for circular inference in schizophrenia

    PubMed Central

    Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S; Denève, Sophie

    2017-01-01

    Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to ‘see what we expect' (through descending loops), to ‘expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ. PMID:28139642

  11. 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

  12. 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.

  13. Functional network inference of the suprachiasmatic nucleus

    PubMed Central

    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.

    2016-01-01

    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

  14. Functional network inference of the suprachiasmatic nucleus

    SciTech Connect

    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.

    2016-04-04

    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.

  15. 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.

  16. A tutorial on time-evolving dynamical Bayesian inference

    NASA Astrophysics Data System (ADS)

    Stankovski, Tomislav; Duggento, Andrea; McClintock, Peter V. E.; Stefanovska, Aneta

    2014-12-01

    In view of the current availability and variety of measured data, there is an increasing demand for powerful signal processing tools that can cope successfully with the associated problems that often arise when data are being analysed. In practice many of the data-generating systems are not only time-variable, but also influenced by neighbouring systems and subject to random fluctuations (noise) from their environments. To encompass problems of this kind, we present a tutorial about the dynamical Bayesian inference of time-evolving coupled systems in the presence of noise. It includes the necessary theoretical description and the algorithms for its implementation. For general programming purposes, a pseudocode description is also given. Examples based on coupled phase and limit-cycle oscillators illustrate the salient features of phase dynamics inference. State domain inference is illustrated with an example of coupled chaotic oscillators. The applicability of the latter example to secure communications based on the modulation of coupling functions is outlined. MatLab codes for implementation of the method, as well as for the explicit examples, accompany the tutorial.

  17. 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.

  18. Inference of other's internal neural models from active observation.

    PubMed

    Kim, Kyung-Joong; Cho, Sung-Bae

    2015-02-01

    Recently, there have been several attempts to replicate theory of mind, which explains how humans infer the mental states of other people using multiple sensory input, with artificial systems. One example of this is a robot that observes the behavior of other artificial systems and infers their internal models, mapping sensory inputs to the actuator's control signals. In this paper, we present the internal model as an artificial neural network, similar to biological systems. During inference, an observer can use an active incremental learning algorithm to guess an actor's internal neural model. This could significantly reduce the effort needed to guess other people's internal models. We apply an algorithm to the actor-observer robot scenarios with/without prior knowledge of the internal models. To validate our approach, we use a physics-based simulator with virtual robots. A series of experiments reveal that the observer robot can construct an "other's self-model", validating the possibility that a neural-based approach can be used as a platform for learning cognitive functions.

  19. Inferring ethnicity from mitochondrial DNA sequence

    PubMed Central

    2011-01-01

    Background The assignment of DNA samples to coarse population groups can be a useful but difficult task. One such example is the inference of coarse ethnic groupings for forensic applications. Ethnicity plays an important role in forensic investigation and can be inferred with the help of genetic markers. Being maternally inherited, of high copy number, and robust persistence in degraded samples, mitochondrial DNA may be useful for inferring coarse ethnicity. In this study, we compare the performance of methods for inferring ethnicity from the sequence of the hypervariable region of the mitochondrial genome. Results We present the results of comprehensive experiments conducted on datasets extracted from the mtDNA population database, showing that ethnicity inference based on support vector machines (SVM) achieves an overall accuracy of 80-90%, consistently outperforming nearest neighbor and discriminant analysis methods previously proposed in the literature. We also evaluate methods of handling missing data and characterize the most informative segments of the hypervariable region of the mitochondrial genome. Conclusions Support vector machines can be used to infer coarse ethnicity from a small region of mitochondrial DNA sequence with surprisingly high accuracy. In the presence of missing data, utilizing only the regions common to the training sequences and a test sequence proves to be the best strategy. Given these results, SVM algorithms are likely to also be useful in other DNA sequence classification applications. PMID:21554759

  20. The deep geology of South India inferred from Moho depth and Vp/Vs ratio

    NASA Astrophysics Data System (ADS)

    Das, Ritima; Saikia, Utpal; Rai, S. S.

    2015-11-01

    We present a comprehensive study of thickness and composition of the crust; and the nature of crust-mantle boundary beneath Southern India using P-wave receiver function from 119 seismic stations. Data from distributed network of seismograph location encompass geological domains like mid to late Archean Dharwar craton, Archean and Proterozoic metamorphic terrains, Proterozoic basin, rifted margins and escarpments, and Deccan volcanics. Except for the mid to lower crust exhumed Archean terrains (of West Dharwar and Southern Granulite) all other geological domains have crustal thickness in the range 33-40 km. In the western Dharwar, crustal thickness increases from ˜40 km in the north to over 50 km in the south. The Archean domain of granulite terrain is thicker (40-45 km) and more mafic compared to its counterpart in south deformed at 550 Ma. Most of the crustal blocks have low to moderate Vp/Vs (1.72-1.76) representing a felsic to intermediate composition. Exception to the above include Archean granulite terrain with high Vp/Vs (1.76-1.81) suggestive of more mafic crust beneath them. When accounted for the paleo burial depth of 15-25 km, the study suggests a possible Himalaya-Tibet like scenario beneath the mid-late Archean in southwestern Dharwar and north granulite terrain whose deeper crust has progressively densified. This led to a gradational crust-mantle transition that is otherwise sharp elsewhere. The study suggests a more homogenized and felsic nature of the Precambrian crust beneath the terrains formed after 2.6 Ga, possibly due to delamination of the mafic lower crust. Our study does not suggest any distinction between late Archean and Proterozoic crust. The Deccan volcanism at 65 Ma does not appear to have altered the crustal character beneath it and is similar to the adjoining late Archean east Dharwar craton. The western Ghat escarpment and the coastal plain formed due to separation of India from Madagascar are underlain by mafic lower crust.

  1. 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

  2. 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

  3. MATHEMATICS OF SENSING, EXPLOITATION, AND EXECUTION (MSEE) Sensing, Exploitation, and Execution (SEE) on a Foundation for Representation, Inference, and Learning

    DTIC Science & Technology

    2016-07-01

    Representation, Inference, and Learning Song-Chun Zhu University of California Los Angeles JULY 2016 Final Report Approved for public release...EXPLOITATION, AND EXECUTION (MSEE) Sensing, Exploitation, and Execution (SEE) on a Foundation for Representation, Inference, and Learning 5a...mathematical foundation for unified representation, inference, and learning for ISR problems. The result of the project is an end-to-end system for scene and

  4. 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.

  5. 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.

  6. The NIRS Analysis Package: noise reduction and statistical inference.

    PubMed

    Fekete, Tomer; Rubin, Denis; Carlson, Joshua M; Mujica-Parodi, Lilianne R

    2011-01-01

    Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.

  7. Quantal Response: Estimation and Inference

    DTIC Science & Technology

    2014-09-01

    but this is not explicit. 5.1.1.4 Probit Design (for Normal Distributions) The probit design of test involves a number of trials at each of several...21 ; Nuremberg , Germany. Personal Armour Systems Symposium; 2012 Sep 17–21. 23. Silvapulle MJ. On likelihood ratio tests of one-sided hypotheses in

  8. 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 u