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Sample records for crust-mantle system inferred

  1. 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

  2. Petrology, geochemistry and modelling of the granulitic-ultramafic rocks in Beni Bousera (Rif, Morocco): implications for direct crust-mantle interactions and melt-extraction systems

    NASA Astrophysics Data System (ADS)

    Manthei, C. D.; Álvarez-Valero, A.; Jagoutz, O. E.

    2011-12-01

    The Beni Bousera (N. Morocco) and Ronda (S. Spain) ultramafic massifs of the Betic-Rif orogenic belt are two of the most pristine exposures of upper-mantle/lower crustal material on Earth's surface. Unlike other samples of the mantle, they are relatively unaltered and preserve a record of ultra-high pressure conditions, within the diamond-stability field (e.g. Slodkevich, 1980; Pearson et al., 1989). The process of removing of the massifs from the diamond-stability field, and the ensuing emplacement into the continental crust, is an ongoing area of research in regional tectonics. Here, we focus specifically on Beni Bousera, and note that the up-risen material is of higher density than its host, prompting the development of models that use melt-induced buoyancy forces as the primary driver of exhumation (Jagoutz et al., 2006; Gerya and Burg, 2007). We find evidence for discrete reaction zones in the ultramafic rocks that were formed by pervasive infiltration of melt, which may have channelized, lowered the integrated bulk density of the massif (e.g., Jagoutz et al., 2006), and driven exhumation. Since key questions concerning the emplacement mechanisms are still unanswered, complementary studies of the surrounding crustal material -granulitic rocks, which are mostly metapelitic with local intercalation of mafic composition-, assist in deepening our understanding crust-mantle processes. We will discuss our ongoing research at Beni Bousera, focusing on: (1) the petrological, structural, geochronological and physical relationships between mantle and crust by combining field petrology, petrography and phase diagram modeling, geochemistry, zircons/monazite dating, and numerical modeling; (2) the emplacement mechanisms of ultramafic and granulitic rocks by proposing a new hypothesis of very rapid exhumation of the mantle material. This rapid ascent is currently being constrained/tested by combining geobarometric calculations and high precision U-Pb zircon geochronology on

  3. Moho vs crust-mantle boundary: Evolution of an idea

    NASA Astrophysics Data System (ADS)

    O'Reilly, Suzanne Y.; Griffin, W. L.

    2013-12-01

    The concept that the Mohorovicic Discontinuity (Moho) does not necessarily coincide with the base of the continental crust as defined by rock-type compositions was introduced in the early 1980s. This had an important impact on understanding the nature of the crust-mantle boundary using information from seismology and from deep-seated samples brought to the surface as xenoliths in magmas, or as tectonic terranes. The use of empirically-constrained P-T estimates to plot the locus of temperature vs depth for xenoliths defined a variety of geotherms depending on tectonic environment. The xenolith geotherms provided a framework for constructing lithological sections through the deep lithosphere, and revealed that the crust-mantle boundary in off-craton regions commonly is transitional over a depth range of about 5-20 km. Early seismic-reflection data showed common layering near the Moho, correlating with the petrological observation of multiple episodes of basaltic intrusion around the crust-mantle boundary. Developments in seismology, petrophysics and experimental petrology have refined interpretation of lithospheric domains. The expansion of in situ geochronology (especially zircon U-Pb ages and Hf-isotopes; Os isotopes of mantle sulfides) has defined tectonic events that affected whole crust-mantle sections, and revealed that the crust-mantle boundary can change in depth through time. However, the nature of the crust-mantle boundary in cratonic regions remains enigmatic, mainly due to lack of key xenoliths or exposed sections. The observation that the Moho may lie significantly deeper than the crust-mantle boundary has important implications for modeling the volume of the crust. Mapping the crust using seismic techniques alone, without consideration of the petrological problems, may lead to an overestimation of crustal thickness by 15-30%. This will propagate to large uncertainties in the calculation of elemental mass balances relevant to crust-formation processes

  4. Hydrothermal experiments on serpentinization at crust/mantle boundary

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    PubMed 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. PMID:22592788

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

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

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

  8. 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.

  9. 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.

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

    PubMed

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

    2014-07-17

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

  11. 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

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

    PubMed

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. Crust-mantle density distribution in the eastern Qinghai-Tibet Plateau revealed by satellite-derived gravity gradients

    NASA Astrophysics Data System (ADS)

    LI, Honglei; Fang, Jian; Braitenberg, Carla; Wang, Xinsheng

    2015-04-01

    As the highest, largest and most active plateau on Earth, the Qinghai-Tibet Plateau has a complex crust-mantle structure, especially in its eastern part. In response to the subduction of the lithospheric mantle of the Indian plate, large-scale crustal motion occurs in this area. Despite the many previous studies, geodynamic processes at depth remain unclear. Knowledge of crust and upper mantle density distribution allows a better definition of the deeper geological structure and thus provides critically needed information for understanding of the underlying geodynamic processes. With an unprecedented precision of 1-2 mGal and a spatial resolution better than 100 km, GOCE (Gravity field and steady-state Ocean Circulation Explorer) mission products can be used to constrain the crust-mantle density distribution. Here we used GOCE gravitational gradients at an altitude of 10km after reducing the effects of terrain, sediment thickness variations, and Moho undulations to image the density structures of eastern Tibet up to 200 km depths. We inverted the residual satellite gravitational gradients using a least square approach. The initial density model for the inversion is based on seismic velocities from the tomography. The model is composed of rectangular blocks, having a uniform density, with widths of about 100 km and variable thickness and depths. The thickness of the rectangular cells changes from10 to 60km in accordance with the seismic model. Our results reveal some large-scale, structurally controlled density variations at depths. The lithospheric root defined by higher-density contrast features from southwest to northeast, with shallowing in the central part: base of lithosphere reaches a depth of180 km, less than 100km, and 200 km underneath the Lhasa, Songpan-Ganzi, and Ordos crustal blocks, respectively. However, these depth values only represent a first-order parameterization because they depend on model discretization inherited from the original seismic

  19. 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).

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

    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.

  2. 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

  3. Coupling, decoupling and metasomatism: Evolution of crust-mantle relationships beneath NW Spitsbergen

    NASA Astrophysics Data System (ADS)

    Griffin, W. L.; Nikolic, N.; O'Reilly, Suzanne Y.; Pearson, N. J.

    2012-09-01

    The Bockfjord area of NW Spitsbergen (Norwegian Arctic) exposes a long history of crustal evolution, culminating in the Caledonian (400-500 Ma) orogeny; prior to the opening of the N. Atlantic Ocean, this area was part of the Laurentian (Greenland) side of the orogen. The N-striking Breibogen-Bockfjorden (BB) fault marks the western margin of a large graben filled with Devonian redbeds. West of the fault the basement consists of gneisses, schists and granites of the Hekla Hoek formation, inferred to represent a Caldeonian thrust sheet. U-Pb and Hf-isotope data for detrital zircons from this area show that the Hekla Hoek protoliths formed at ca 1.8 Ga, but were heavily reworked ca 800-1000 Ma ago, and again during the Caledonian orogeny. Quaternary alkali-basalt volcanism has provided abundant xenoliths of mantle and crustal rocks from both sides of the BB fault. Lower-crustal xenoliths are mainly mafic to intermediate granulites. Whole-rock Sr-, Nd- and Hf-isotope data for the granulites from both sides of the BB fault show significant disequilibrium, implying the removal of melts late in the evolution of the lower crust. Most zircons from eight xenoliths have Neoarchean/Paleoproterozoic and Paleozoic U-Pb ages; several also contain zircons with ages and/or Hf model ages > 3.2 Ga. The peridotite xenoliths are dominantly spinel lherzolites, metasomatized to varying extents. Xenoliths from basalts east of the BB fault commonly contain metasomatic amphibole, phlogopite and apatite; peridotites from west of the fault rarely display these phases. West of the fault, there is no clear correlation between cpx REE patterns and whole-rock Al contents; east of the fault there is a clear negative correlation between LREE enrichment and whole-rock Al2O3. In-situ Re-Os isotope analysis of sulfides in the peridotites shows another dichotomy. Xenoliths from west of the fault contain sulfides with Re depletion (TRD) model ages extending back to 3.3 Ga, with major populations at 2

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

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2004-09-01

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

  6. Clockwise Rotation of Upper-Mantle Strain and Crust-Mantle Coupling Beneath the Eastern Syntaxis Tibet

    NASA Astrophysics Data System (ADS)

    Sol, S.; Meltzer, A.; Zurek, B.; Zeitler, P.; Zhang, X.; Zhang, J.

    2005-12-01

    a potential contribution from the crust. This argues for the presence of an effective crust-mantle coupling beneath the eastern syntaxis, in contrast with the presence of a low strength (weak) decoupling lower crust relative to the upper mantle that has been suggested by data from the central plateau and some geodynamic modelling of the whole orogen. Our results indicate that although the lithosphere in the syntaxis appears to deform internally, fault block rotation via strike-slip tectonics plays an important role in the southeastward extrusion of the plateau.

  7. 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.

  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. 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.

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

    NASA Astrophysics Data System (ADS)

    Li, Yuan; Audétat, Andreas

    2013-10-01

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

  11. Inference System Integration Via Logic Morphisms

    NASA Technical Reports Server (NTRS)

    Bjorner, Nikolaj S.; Espinosa, David

    2000-01-01

    This is a final report on the accomplishments during the period of the NASA grant. The work on inference servers accomplished the integration of the SLANG logic (Specware's default specification logic) with a number of inference servers in order to make their complementary strengths available. These inverence servers are (1) SNARK. (2) Gandalf, Setheo, and Spass, (3) the Prototype Verification System (PVS) from SRI. (4) HOL98. We designed and implemented MetaSlang, an ML-like language, which we are using to specify and implement all our logic morphisms.

  12. Magma mixing and crust-mantle interaction in Southeast China during the Early Cretaceous: Evidence from the Furongshan granite porphyry and mafic microgranular enclaves

    NASA Astrophysics Data System (ADS)

    Wang, Hong-Zuo; Chen, Pei-Rong; Sun, Li-Qiang; Ling, Hong-Fei; Zhao, You-Dong; Lan, Hong-Feng

    2015-11-01

    The petrogenesis and tectonic setting of Early Cretaceous granitoids and their enclaves emplaced in the Gan-Hang Tectonic Belt are still controversial. Here, we investigate mafic microgranular enclaves (MMEs) and their host granite porphyry from the Furongshan caldera to elucidate magma mixing and crust-mantle interaction in the Gan-Hang Tectonic Belt. The Furongshan granite porphyry is characterized by enrichments of alkalis, REE, Zr + Nb + Ce + Y contents (averaging 377 ppm), and high zircon saturation temperatures (793-843 °C), suggesting A-type granitic affinities. The granite porphyry can be further classified as an A2 subtype granite based on high Y/Nb ratios (averaging 1.37). Zircon cores from the Furongshan MMEs exhibit the same εHf(t) values (-10.0 to -3.0) and U-Pb ages (127-129 Ma) as zircons form the granite porphyry, implying that they were captured from the felsic magma as xenocrysts. Petrological and mineralogical characteristics (such as needle-like apatite and disequilibrium feldspar xenocryst) suggest that the Furongshan MMEs and host granite porphyry were formed by magma mixing rather than restite, xenolith or fractional crystallization of mafic magma. The Furongshan granite porphyry samples have initial 87Sr/86Sr ratios of 0.7073-0.7099 and εNd(t) values of -3.7 to -3.3, which are similar to those of the MMEs (0.7068-0.7077 and -3.2 to -2.9, respectively). Similar trace element and Sr-Nd isotopic compositions imply a high degree of geochemical equilibration between the granite porphyry and its MMEs, and hence intense magma mixing, although some element contents and zircons εHf(t) values differ due to high zircon closure temperature and rapid cooling of commingled magmas. A binary mixing model based on Sr-Nd isotopes indicates a contribution of ∼50% basaltic melt to the hybrid magma of the Furongshan granite porphyry. A compilation of Sr-Nd-Hf isotopic data of the granitoids and MMEs from the Xiangshan, Furongshan and Muchen areas suggest

  13. Crust-mantle interaction beneath the Luxi Block, eastern North China Craton: Evidence from coexisting mantle- and crust-derived enclaves in a quartz monzonite pluton

    NASA Astrophysics Data System (ADS)

    Lan, Ting-Guang; Fan, Hong-Rui; Santosh, M.; Hu, Fang-Fang; Yang, Kui-Feng; Yang, Yue-Heng; Liu, Yongsheng

    2013-09-01

    The Laiwu quartz monzonite in the Luxi Block of eastern North China Craton (NCC) is characterized by the presence of abundant plagioclase amphibolite and gabbro-diorite enclaves. Here we present LA-ICPMS zircon U-Pb ages which show that the host quartz monzonite was emplaced at 129.8 ± 1.0 Ma, whereas the protolith of the plagioclase amphibolite enclaves formed during early Paleoproterozoic. The gabbro-diorite enclaves were produced simultaneously with or slightly earlier than the formation of the host quartz monzonite. Combined with the Archean and Paleoproterozoic zircons as well as the low εNd(0) values (- 18.4 to - 18.0) in the plagioclase amphibolite enclaves, the equilibrium temperature and pressure conditions (645-670 °C and 4.8-6.5 Kb) suggest that the plagioclase amphibolite enclaves are fragments of the middle crust. The gabbro-diorite enclaves mainly originated from an enriched lithospheric mantle metasomatized by melts/fluids derived from the continental crust, as indicated by their low SiO2 (54.4-54.7 wt.%) and high MgO (10.9-11.1 wt.%) contents as well as the negative εNd(t) values (- 13.5 to - 10.7) and enrichment of LILEs (e.g., Ba and Sr) and depletion of HFSEs (e.g., Nb, Ta, P and Ti). Compared with the ancient crustal rocks and the mafic plutons considered to have been derived from lithospheric mantle in the Luxi Block, the moderate εNd(t) (- 15.7 to - 15.1) and εHf(t) (- 20.7 to - 13.0) values of the quartz monzonite in our study suggest that both mantle- and crust-derived melts were involved in the magma generation. Thus we propose a model involving magma mixing between mantle- and crust-derived melts for the formation of the quartz monzonite. Since significant crust-mantle interaction is recorded not only in the quartz monzonite and its enclaves in the Luxi Block but also in the other granitoids widespread in the NCC, it is considered that large-scale crust-mantle interaction and magmatic underplating were associated with the Mesozoic

  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. 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.

  16. Inference by replication in densely connected systems.

    PubMed

    Neirotti, Juan P; Saad, David

    2007-10-01

    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. PMID:17995074

  17. 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.

  18. 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…

  19. Crust/mantle interaction during the construction of an extensional magmatic dome: Middle to Late Jurassic plutonic complex from western Liaoning, North China Craton

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaohui; Yuan, Lingling; Wilde, Simon A.

    2014-09-01

    differentiated monzogranites with low Sr/Y and highly evolved isotopes (87Sr/86Sri = 0.70496 to 0.70605, εNd(t) = - 16.0 to - 18.7, zircon εHf(t) = - 14.3 to - 21.5). Apart from distinguishing Middle-Late Jurassic extensional magmatic doming from Early Cretaceous detachment faulting, this complex mafic-felsic magma association encapsulates a multi-level crust/mantle interaction leading to lithospheric thinning and concomitant crustal architectural reorganization in the Yanshan belt during the Late Mesozoic. Near-synchronization of a two-stage extensional pattern in the Yanshan belt and even across NE continental Asia accords well with gravitational collapse and convective removal of lithospheric mantle within an evolved post-collisional to within-plate extensional regime.

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

    NASA Astrophysics Data System (ADS)

    Freymuth, Heye; Brandmeier, Melanie; Wörner, Gerhard

    2015-06-01

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

  1. 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.

  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. Magma reservoir systems inferred from tilt patterns

    NASA Astrophysics Data System (ADS)

    Schimozuru, D.

    1981-09-01

    Inflation patterns based on water-tube tiltmeter and levelling observation show different features for Krafla Volcano in Iceland and Kilauea Volcano in Hawaii. Monotonous sawtooth shape inflation is observed at Krafla, while inflation curves at Kileauea are more or less complicated. The difference was attributed to differences in the system of magma reservoir for the two volcanoes. By using the electrical equivalent of a magma reservoir and volcanic conduit as a capacitor and a resistor, an electrical oseillator was considered to be a possible model for a magma reservoir system. In the case of Krafla, the magma reservoir system is replaced with one electric oscillator called «Single system» or «Icelandic type» system. The complicated inflation pattern of Kilauea was interpreted as the assembly of a main magma reservoir and the group of surrounding small reservoirs. The equivalent electric analogue is the composite parallel and serial connection of a single oscillator which generates irregular output voltage during a charging process. The proposed magma reservoir system of Kilauea is called «Multi-coupled system» or «Hawaiian type system» which also help in interpreting the wondering of the uplift center and tidal phenomena of the Halemaumau lava lake.

  5. Fuzzy exemplar-based inference system for flood forecasting

    NASA Astrophysics Data System (ADS)

    Chang, Li-Chiu; Chang, Fi-John; Tsai, Ya-Hsin

    2005-02-01

    Fuzzy inference systems have been successfully applied in numerous fields since they can effectively model human knowledge and adaptively make decision processes. In this paper we present an innovative fuzzy exemplar-based inference system (FEIS) for flood forecasting. The FEIS is based on a fuzzy inference system, with its clustering ability enhanced through the Exemplar-Aided Constructor of Hyper-rectangles algorithm, which can effectively simulate human intelligence by learning from experience. The FEIS exhibits three important properties: knowledge extraction from numerical data, knowledge (rule) modeling, and fuzzy reasoning processes. The proposed model is employed to predict streamflow 1 hour ahead during flood events in the Lan-Yang River, Taiwan. For the purpose of comparison the back propagation neural network (BPNN) is also performed. The results show that the FEIS model performs better than the BPNN. The FEIS provides a great learning ability, robustness, and high predictive accuracy for flood forecasting.

  6. Diagnosis of arthritis through fuzzy inference system.

    PubMed

    Singh, Sachidanand; Kumar, Atul; Panneerselvam, K; Vennila, J Jannet

    2012-06-01

    Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process. PMID:20927572

  7. A knowledge-based expert system for inferring vegetation characteristics

    NASA Technical Reports Server (NTRS)

    Kimes, Daniel S.; Harrison, Patrick R.; Ratcliffe, P. A.

    1991-01-01

    A prototype knowledge-based expert system VEG is presented that focuses on extracting spectral hemispherical reflectance using any combination of nadir and/or directional reflectance data as input. The system is designed to facilitate expansion to handle other inferences regarding vegetation properties such as total hemispherical reflectance, leaf area index, percent ground cover, phosynthetic capacity, and biomass. This approach is more robust and accurate than conventional extraction techniques previously developed.

  8. 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.

  9. Nb/Ta, Zr/Hf and REE in the depleted mantle: implications for the differentiation history of the crust-mantle system

    NASA Astrophysics Data System (ADS)

    Weyer, Stefan; Münker, Carsten; Mezger, Klaus

    2003-01-01

    High-precision Nb, Ta, Zr, Hf, Sm, Nd and Lu concentration data of depleted mantle rocks from the Balmuccia peridotite complex (Ivrea Zone, Italian Alps) were determined by isotope dilution using multiple collector inductively coupled plasma mass spectrometry (MC-ICPMS) and thermal ionisation mass spectrometry (TIMS). The Zr/Hf ratios of all investigated samples from the Balmuccia peridotite complex are significantly lower than the chondritic value of 34.2, and the most depleted samples have Zr/Hf ratios as low as 10. Correlated Zr/Hf ratios and Zr abundances of the lherzolites preserve the trend of a mantle residue that has been depleted by fractional melting. This trend confirms experimental studies that predict Hf to behave more compatibly than Zr during mantle melting. Experimentally determined partition coefficients imply that the major Zr and Hf depletion most likely occurred in the spinel stability field, with ( DZr/ DHf) cpx≈0.5, and not in the garnet stability field, where ( DZr/ DHf) grt is probably close to one. However, minor amounts of melting must have also occurred in a garnet facies mantle, as indicated by low Sm/Lu ratios in the Balmuccia peridotites. The Nb/Ta ratios of most lherzolites are subchondritic and vary only from 7 to 10, with the exception of three samples that have higher Nb/Ta ratios (18-24). The overall low Nb/Ta ratios of most depleted mantle rocks confirm a higher compatibility of Ta in the mantle. The uniform Nb/Ta ratios in most samples imply that even in 'depleted' mantle domains the budget of the highly incompatible Nb and Ta is controlled by enrichment processes. Such a model is supported by the positive correlation of Zr/Nb with the Zr concentration. However, the overall enrichment was weak and did barely affect the moderately incompatible elements Zr and Hf. The new constraints from the partitioning behaviour of Zr-Hf and Nb-Ta provide important insights into processes that formed the Earth's major silicate reservoirs. The correlation of Zr/Hf and Sm/Nd in depleted MORB can be assigned to previous melting events in the MORB source. However, such trends were unlikely produced during continental crust formation processes, where Sm/Nd and Zr/Hf are decoupled. The different fractionation behaviour of Zr/Hf and Sm/Nd in the depleted mantle (correlated) and the crust (decoupled) indicates that crustal growth by a simple partial melting process in the mantle has little effect on the mass budget of LREE and HFSE between crust and mantle. A more complex source composition, similar to that of modern subduction rocks, is needed to fractionate the LREE, but not Zr/Hf and the HREE.

  10. Inference and learning in sparse systems with multiple states

    SciTech Connect

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

    2011-05-15

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

  11. 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.

  12. 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.

  13. 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.

  14. 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. PMID:22970879

  15. 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

  16. 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

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

    PubMed

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

    2013-01-01

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

  18. Automatic generation of fuzzy inference systems via unsupervised learning.

    PubMed

    Er, Meng Joo; Zhou, Yi

    2008-12-01

    In this paper, a novel approach termed Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automatically generating Fuzzy Inference Systems (FISs) is presented. In the EDSGFQL approach, structure identification and parameter estimations of FISs are achieved via Unsupervised Learning (UL) (including Reinforcement Learning (RL)). Instead of using Supervised Learning (SL), UL clustering methods are adopted for input space clustering when generating FISs. At the same time, structure and preconditioning parts of a FIS are generated in a RL manner in that fuzzy rules are adjusted and deleted according to reinforcement signals. The proposed EDSGFQL methodologies can automatically create, delete and adjust fuzzy rules dynamically. Simulation studies on wall-following and obstacle avoidance tasks by a mobile robot show that the proposed approach is superior in generating efficient FISs. PMID:18653313

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

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

  20. Metainference: A Bayesian inference method for heterogeneous systems

    PubMed Central

    Bonomi, Massimiliano; Camilloni, Carlo; Cavalli, Andrea; Vendruscolo, Michele

    2016-01-01

    Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called “metainference,” that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors. PMID:26844300

  1. Metainference: A Bayesian inference method for heterogeneous systems.

    PubMed

    Bonomi, Massimiliano; Camilloni, Carlo; Cavalli, Andrea; Vendruscolo, Michele

    2016-01-01

    Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called "metainference," that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors. PMID:26844300

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

    NASA Astrophysics Data System (ADS)

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

    2008-09-01

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

  3. 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.

  4. 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.

  5. Streamflow Forecasting Using Nuero-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

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

  6. Classification of Microarray Data Using Kernel Fuzzy Inference System

    PubMed Central

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

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

    NASA Technical Reports Server (NTRS)

    Hancock, Thomas M., III

    1994-01-01

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

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

    NASA Technical Reports Server (NTRS)

    1993-01-01

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

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

    PubMed

    Zhou, Yi; Er, Meng Joo

    2008-08-01

    An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods. PMID:18632385

  13. 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.

  14. 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.

  15. Dynamical inference for transitions in stochastic systems with α-stable Lévy noise

    NASA Astrophysics Data System (ADS)

    Gao, Ting; Duan, Jinqiao; Kan, Xingye; Cheng, Zhuan

    2016-07-01

    A goal of data assimilation is to infer stochastic dynamical behaviors with available observations. We consider transition phenomena between metastable states for a stochastic system with (non-Gaussian) α -stable Lévy noise. With either discrete time or continuous time observations, we infer such transitions between metastable states by computing the corresponding non-local Zakai equation (and its discrete time counterpart) and examining the most probable orbits for the state system. Examples are presented to demonstrate this approach. This work was partly supported by the NSF Grant 1025422.

  16. 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.

  17. 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

  18. 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.

  19. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses.

    PubMed

    Bucci, Vanni; Tzen, Belinda; Li, Ning; Simmons, Matt; Tanoue, Takeshi; Bogart, Elijah; Deng, Luxue; Yeliseyev, Vladimir; Delaney, Mary L; Liu, Qing; Olle, Bernat; Stein, Richard R; Honda, Kenya; Bry, Lynn; Gerber, Georg K

    2016-01-01

    Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE's utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations. PMID:27259475

  20. 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

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

    NASA Astrophysics Data System (ADS)

    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.

  2. 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.

  3. 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

  4. 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. PMID:26450589

  5. 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. PMID:25976632

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

    SciTech Connect

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

    2005-06-15

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

  7. 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.

  8. On the functional equivalence of fuzzy inference systems and spline-based networks.

    PubMed

    Hunt, K J; Haas, R; Brown, M

    1995-06-01

    The conditions under which spline-based networks are functionally equivalent to the Takagi-Sugeno-model of fuzzy inference are formally established. We consider a generalized form of basis function network whose basis functions are splines. The result admits a wide range of fuzzy membership functions which are commonly encountered in fuzzy systems design. We use the theoretical background of functional equivalence to develop a hybrid fuzzy-spline net for inverse dynamic modeling of a hydraulically driven robot manipulator. PMID:7496588

  9. 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. PMID:22479819

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

    PubMed Central

    Huynh-Thu, Vân Anh; Sanguinetti, Guido

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

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

    PubMed Central

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

    2008-01-01

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

  13. Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning.

    PubMed

    Er, Meng Joo; Deng, Chang

    2004-06-01

    This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior. PMID:15484918

  14. Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey

    NASA Astrophysics Data System (ADS)

    Yurdusev, Mehmet Ali; Firat, Mahmut

    2009-02-01

    SummaryIn this study, an adaptive neuro fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors including average monthly water bill, population, number of households, gross national product, monthly average temperature observed, monthly total rainfall, monthly average humidity observed and inflation rate. Water consumption modeling in this way will be more consistent than doing it using a single variable as more effective parameter could be incorporated. The ANFIS system is applied to modeling monthly water consumptions of Izmir, Turkey. The results indicated that ANFIS can be successfully applied for monthly water consumption modeling.

  15. 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.

  16. 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

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

    PubMed

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

    2015-01-01

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

  18. 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

  19. Early Jurassic high-K calc-alkaline and shoshonitic rocks from the Tongshi intrusive complex, eastern North China Craton: Implication for crust-mantle interaction and post-collisional magmatism

    NASA Astrophysics Data System (ADS)

    Lan, Ting-Guang; Fan, Hong-Rui; Santosh, M.; Hu, Fang-Fang; Yang, Kui-Feng; Yang, Yue-Heng; Liu, Yongsheng

    2012-05-01

    The Tongshi intrusive complex, located within the western Shandong Province (Luxi Block) in the eastern North China Craton, comprises high-K calc-alkaline series (fine-grained quartz monzonite and porphyritic quartz monzonite) and shoshonitic series (coarse- to fine-grained porphyritic syenites). Here we report comprehensive data on petrology, geochemistry, Sr-Nd-Pb isotopes and zircon U-Pb and Hf isotopic compositions from the intrusive complex. LA-ICPMS zircon U-Pb ages show that this complex was emplaced at 180.1-184.7 Ma. The fine-grained quartz monzonite and porphyritic quartz monzonite have similar major and trace elements features, implying a similar petrogenetic history. Coupled with the widespread Neoarchean inherited zircons in these rocks, the high SiO2 and Na2O as well as the low MgO contents and low Pb isotopic ratios ((206Pb/204Pb)i = 15.850-16.881, (207Pb/204Pb)i = 14.932-15.261, (208Pb/204Pb)i = 35.564-36.562) of the quartz monzonites suggest an origin from ancient tonalite-trondhjemite-granodiorite (TTG) crust. However, their higher Nd and Hf isotopic ratios (ɛNd (t) = - 11.7 to - 7.0, ɛHf (t) = - 25.0 to - 10.3) as compared to the basement rocks indicate input of enriched lithospheric mantle-derived materials. The coarse- to fine-grained porphyritic syenites were derived from similar sources as inferred from their comparable major and trace elements contents as well as the Nd, Hf and Pb isotopic compositions. The Neoarchean inherited zircons and depletion of Nb, Ta, P and Ti in these rocks indicate the involvement of ancient crust. However, the high Nd and Hf isotopic ratios (ɛNd (t) = - 0.8 to 1.5, ɛHf (t) = - 4.4 to 4.8) coupled with high Pb isotopic compositions ((206Pb/204Pb)i = 18.082-19.560, (207Pb/204Pb)i = 15.510-15.730, (208Pb/204Pb)i = 37.748-39.498) suggest that the porphyritic syenites were mainly derived from an asthenospheric mantle. Based on the geochemical and isotopic features, a magmatic process similar to MASH (melting

  20. Multiplex genotyping system for efficient inference of matrilineal genetic ancestry with continental resolution

    PubMed Central

    2011-01-01

    Background In recent years, phylogeographic studies have produced detailed knowledge on the worldwide distribution of mitochondrial DNA (mtDNA) variants, linking specific clades of the mtDNA phylogeny with certain geographic areas. However, a multiplex genotyping system for the detection of the mtDNA haplogroups of major continental distribution that would be desirable for efficient DNA-based bio-geographic ancestry testing in various applications is still missing. Results Three multiplex genotyping assays, based on single-base primer extension technology, were developed targeting a total of 36 coding-region mtDNA variants that together differentiate 43 matrilineal haplo-/paragroups. These include the major diagnostic haplogroups for Africa, Western Eurasia, Eastern Eurasia and Native America. The assays show high sensitivity with respect to the amount of template DNA: successful amplification could still be obtained when using as little as 4 pg of genomic DNA and the technology is suitable for medium-throughput analyses. Conclusions We introduce an efficient and sensitive multiplex genotyping system for bio-geographic ancestry inference from mtDNA that provides resolution on the continental level. The method can be applied in forensics, to aid tracing unknown suspects, as well as in population studies, genealogy and personal ancestry testing. For more complete inferences of overall bio-geographic ancestry from DNA, the mtDNA system provided here can be combined with multiplex systems for suitable autosomal and, in the case of males, Y-chromosomal ancestry-sensitive DNA markers. PMID:21429198

  1. 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.

  2. 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.

  3. 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

  4. A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment

    NASA Astrophysics Data System (ADS)

    Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.

    2015-11-01

    This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

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

    PubMed

    Chiang, Tzu-Chiang; Liang, Wen-Hua

    2015-09-01

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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Pumping system fault detection and diagnosis utilizing pattern recognition and fuzzy inference techniques

    SciTech Connect

    Singer, R.M.; Gross, K.C. ); Humenik, K.E. . Dept. of Computer Science)

    1991-01-01

    An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and thus has the potential of providing incipient fault detection information to operators sufficiently early to avoid forced process shutdowns. This system also provides a diagnosis of the cause of the initiating fault(s) by a physical-model-derived rule-based expert system in which system and subsystem state uncertainties are handled using fuzzy inference techniques. This system has been initially applied to the monitoring of the operational state of the primary coolant pumping system on the EBR-II nuclear reactor. Early validation studies have shown that a rapidly developing incipient fault on centrifugal pumps can be detected well in advance of any changes in the nominal process signals. 17 refs., 6 figs.

  11. 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.

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

    PubMed

    Ubeyli, Elif Derya

    2009-10-01

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

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

    PubMed

    Ubeyli, Elif Derya

    2006-01-01

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

  14. 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.

  15. 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

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

    PubMed

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

    2016-01-01

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

  17. Inferring thresholds in karst aquifers from spring responses: the example of the Lurbach karst system (Austria)

    NASA Astrophysics Data System (ADS)

    Birk, Steffen; Wagner, Thomas; Mayaud, Cyril

    2014-05-01

    Threshold behavior in hydrological systems generally involves a qualitative change of a single process, the system response or the functioning of the system. Different types of thresholds and their underlying controls are examined using the example of the Lurbach karst system (Austria). This karst system receives concentrated allogenic recharge from the sinking stream Lurbach, which under low-flow conditions only resurges at the Hammerbach spring. Under medium- to high-flow conditions, however, an overflow toward another spring, the Schmelzbach outlet occurs. The overflow probably is activated when a conduit pathway connecting the two sub-catchments is flooded at a given threshold water level. Unfortunately, the value of this threshold cannot be determined, as information about water levels within this karst system are scarce due to the lack of observation wells and the inaccessibility of relevant cave sections. Yet a corresponding threshold discharge of the Hammerbach spring can be inferred from tracer test results. Interestingly, a tracer test conducted in 2008 suggests that the overflow is activated at a discharge lower than that inferred from tracer tests reported earlier (Wagner et al., EGU2011-7962). In order to better understand this suspected change in the discharge threshold, the physicochemical responses of the Hammerbach spring were analyzed. Applying the concept of process time scales (Birk and Wagner, EGU2013-11365) to the Hammerbach spring suggests that the threshold travel time controlling the response of the spring water temperature was changed in the time period from 2006 to 2009 relative to the years before. At the same time, the Hammerbach spring hydrograph appears to have changed. For instance, the flow duration curve and the master recession curves for the time period from 2006 to 2009 are found to be markedly different from those of earlier time periods. All of these observations can be consistently explained by a reduction of the conduit

  18. Grain Size Estimation of Superalloy Inconel 718 After Upset Forging by a Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Toro, Luis; Cavazos, Alberto; Colás, Rafael

    2009-12-01

    A fuzzy logic inference system was designed to predict the grain size of Inconel 718 alloy after upset forging. The system takes as input the original grain size, temperature, and reduction rate at forging and predicts the final grain size at room temperature. It is assumed that the system takes into account the effects that the heterogeneity of deformation and grain growth exerts in this particular material. Experimental trials were conducted in a factory that relies on upset forging to produce preforms for ring rolling. The grain size was reported as ASTM number, as this value is used on site. A first attempt was carried out using a series of 15 empirically based set of rules; the estimation error with these was above two ASTM numbers; which is considered to be very high. The system was modified and expanded to take into account 28 rules; the estimation error of this new system resulted to be close to one ASTM number, which is considered to be adequate for the prediction.

  19. 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

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

    PubMed

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

    2014-01-01

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

  1. Development of an on-line diagnosis system for rotor vibration via model-based intelligent inference

    PubMed

    Bai; Hsiao; Tsai; Lin

    2000-01-01

    An on-line fault detection and isolation technique is proposed for the diagnosis of rotating machinery. The architecture of the system consists of a feature generation module and a fault inference module. Lateral vibration data are used for calculating the system features. Both continuous-time and discrete-time parameter estimation algorithms are employed for generating the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed method is implemented on a digital signal processor. Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance. PMID:10641641

  2. 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.

  3. 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…

  4. 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.

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

    PubMed Central

    Pullen, Nick; Morris, Richard J.

    2014-01-01

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

  6. Human action recognition using meta-cognitive neuro-fuzzy inference system.

    PubMed

    Subramanian, K; Suresh, S

    2012-12-01

    We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort. PMID:23186277

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

    PubMed

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

    2015-01-01

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

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

    PubMed

    Pullen, Nick; Morris, Richard J

    2014-01-01

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

  9. Bayesian inference of interaction properties of noisy dynamical systems with time-varying coupling: capabilities and limitations

    NASA Astrophysics Data System (ADS)

    Wilting, Jens; Lehnertz, Klaus

    2015-08-01

    We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.

  10. 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.

  11. 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

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

    PubMed

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

    2016-07-01

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

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

    PubMed

    Azar, Ahmad Taher

    2013-09-01

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

  14. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2005-10-01

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

  16. 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.

  17. 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.

  18. Children's Inferential Responses to a Wordless Picture Book: Development and Use of a Classification System for Verbalized Inference.

    ERIC Educational Resources Information Center

    Jett-Simpson, Mary

    High, middle, and low readers in kindergarten, second, and fourth grades participated in a study of inferential comprehension. The Jett-Simpson Classification System for Verbalized Inference was developed, its reliability was established, and children's open responses to a wordless picture book, "Frog Goes to Dinner," were analyzed using it.…

  19. 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. PMID:26385550

  20. 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).

  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. PMID:25295291

  2. 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

  3. Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Yi, J.; Choi, C.

    2014-12-01

    Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

  4. Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir

    NASA Astrophysics Data System (ADS)

    Zoveidavianpoor, Mansoor; Samsuri, Ariffin; Shadizadeh, Seyed Reza

    2013-02-01

    Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedures. Since Vp is affected by several factors such as lithology, porosity, density, and etc., it is difficult to model their non-linear relationships using conventional approaches. In addition, currently available techniques are not efficient for Vp prediction, especially in carbonates. There is a growing interest in incorporating advanced technologies for an accurate prediction of lacking data in wells. The objectives of this study, therefore, are to analyze and predict Vp as a function of some conventional well logs by two approaches; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). Also, the significant impact of selected input parameters on response variable will be investigated. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from conventional well logs and Dipole Sonic Imager (DSI) log were utilized in this study. The quality of the prediction was quantified in terms of the mean squared error (MSE), correlation coefficient (R-square), and prediction efficiency error (PEE). Results show that the ANFIS outperforms MLR with MSE of 0.0552, R-square of 0.964, and PEE of 2%. It is posited that porosity has a significant impact in predicting Vp in the investigated carbonate reservoir.

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

    PubMed

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

    2012-01-01

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

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

    PubMed

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

    2016-05-01

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

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

    PubMed

    Ubeyli, Elif Derya

    2009-03-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

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

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

    PubMed

    Ling, Steve S H; Nguyen, Hung T

    2011-03-01

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

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

    PubMed Central

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

    2013-01-01

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

  11. 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

  12. Estimating daily pan evaporation using adaptive neural-based fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Keskin, M. Erol; Terzi, Özlem; Taylan, Dilek

    2009-09-01

    Estimation of evaporation is important for water planning, management, and hydrological practices. There are many available methods to estimate evaporation from a water surface, comprising both direct and indirect methods. All the evaporation models are based on crisp conceptions with no uncertainty element coupled into the model structure although in daily evaporation variations there are uncontrollable effects to a certain extent. The probabilistic, statistical, and stochastic approaches require large amounts of data for the modeling purposes and therefore are not practical in local evaporation studies. It is therefore necessary to adopt a better approach for evaporation modeling, which is the fuzzy sets and adaptive neural-based fuzzy inference system (ANFIS) as used in this paper. ANFIS and fuzzy sets have been evaluated for its applicability to estimate evaporation from meteorological data which is including air and water temperatures, solar radiation, and air pressure obtained from Automated GroWheather meteorological station located near Lake Eğirdir and daily pan evaporation values measured by XVIII. District Directorate of State Hydraulic Works. Results of ANFIS and fuzzy logic approaches were analyzed and compared with measured daily pan evaporation values. ANFIS approach could be employed more successfully in modeling the evaporation process than fuzzy sets.

  13. Fracture density estimation from petrophysical log data using the adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Ja'fari, Ahmad; Kadkhodaie-Ilkhchi, Ali; Sharghi, Yoosef; Ghanavati, Kiarash

    2012-02-01

    Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density.

  14. 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.

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Valyrakis, Manousos; Zhang, Hanqing

    2014-05-01

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

  17. 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.

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

    PubMed

    Heddam, Salim

    2014-01-01

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

  19. 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. PMID:22155075

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

  1. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

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

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

    NASA Astrophysics Data System (ADS)

    Knuth, K. H.

    2001-05-01

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

  4. Entropic Inference

    NASA Astrophysics Data System (ADS)

    Caticha, Ariel

    2011-03-01

    In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.

  5. 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

  6. Information warfare-worthy jamming attack detection mechanism for wireless sensor networks using a fuzzy inference system.

    PubMed

    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

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

    NASA Astrophysics Data System (ADS)

    Neapolitan, Richard E.

    1986-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

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

    PubMed

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

    2016-07-01

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

  10. 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

  11. 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

  12. Discrimination of Human Forearm Motions on the Basis of Myoelectric Signals by Using Adaptive Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Kiso, Atsushi; Seki, Hirokazu

    This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

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

    NASA Astrophysics Data System (ADS)

    Huang, Jian-Jing

    2014-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

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

    PubMed

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

    2012-01-01

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

  16. 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. PMID:22752811

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  18. 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

  19. Application of Adaptive Neuro Fuzzy Inference System (ANFIS) In Implementing of New CMOS Fuzzy Logic Controller (FLC) Chip

    NASA Astrophysics Data System (ADS)

    Aminifar, S.; Yosefi, Gh.

    2007-09-01

    In this paper, we present away of using Anfis architecture to implement a new fuzzy logic controller chip. Anfis which tunes the fuzzy inference system with a backpropagation algorithm based on collection of input-output data makes fuzzy system to learn. This training is given from a standard response of the system and membership functions are suitably modified. For adaptive Anfis based fuzzy controller and its circuit design, we propose new circuits for implementing each controller block, and illustrate the test results and control surface of Anfis controller along with CMOS fuzzy logic controller using Matlab and Hspice software respectively. For implementing controller according to the Anfis training, we proposed new and improved integrated circuits which consist of Fuzzifier, Min operator and Multiplier/Divider. The control surfaces of controller are obtained by using Anfis training and simulation results of integrated circuits in less than 0.075 mm2 area in 0.35 μm CMOS standard technology.

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

    PubMed

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

    2016-05-01

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

  1. Application of Artificial Neuro-Fuzzy Logic Inference System for Predicting the Microbiological Pollution in Fresh Water

    NASA Astrophysics Data System (ADS)

    Bouharati, S.; Benmahammed, K.; Harzallah, D.; El-Assaf, Y. M.

    The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh water. The proposed system is a hierarchical integration of an Artificial Neuro-Fuzzy Inference System (ANFIS). This method is based on the variations of the physical and chemical parameters occurred during bacteria growth. The instantaneous result obtained by the measurements of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors.

  2. 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

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

    PubMed Central

    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

    2010-01-01

    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. PMID:20542910

  4. 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.

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

    PubMed

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

    2013-01-01

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

  6. 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.

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

    NASA Technical Reports Server (NTRS)

    Sigwarth, John B.; Bekerat, Hamed A.

    2008-01-01

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

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

    PubMed Central

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

    1999-01-01

    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. PMID:9927470

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

    PubMed

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

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

  10. 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.

  11. 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

  12. 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. PMID:27058285

  13. A hybrid conceptual-fuzzy inference streamflow modelling for the Letaba River system in South Africa

    NASA Astrophysics Data System (ADS)

    Katambara, Zacharia; Ndiritu, John G.

    There has been considerable water resources developments in South Africa and other regions in the world in order to meet the ever-increasing water demands. These developments have not been matched with a similar development of hydrological monitoring systems and hence there is inadequate data for managing the developed water resources systems. The Letaba River system ( Fig. 1) is a typical case of such a system in South Africa. The available water on this river is over-allocated and reliable daily streamflow modelling of the Letaba River that adequately incorporates the main components and processes would be an invaluable aid to optimal operation of the system. This study describes the development of a calibrated hybrid conceptual-fuzzy-logic model and explores its capability in reproducing the natural processes and human effects on the daily stream flow in the Letaba River. The model performance is considered satisfactory in view of the complexity of the system and inadequacy of relevant data. Performance in modelling streamflow improves towards the downstream and matches that of a stand-alone fuzzy-logic model. The hybrid model obtains realistic estimates of the major system components and processes including the capacities of the farm dams and storage weirs and their trajectories. This suggests that for complex data-scarce River systems, hybrid conceptual-fuzzy-logic modelling may be used for more detailed and dependable operational and planning analysis than stand-alone fuzzy modelling. Further work will include developing and testing other hybrid model configurations.

  14. 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…

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  16. Seeding-inspired chemotaxis genetic algorithm for the inference of biological systems.

    PubMed

    Wu, Shinq-Jen; Wu, Cheng-Tao

    2014-09-18

    A large challenge in the post-genomic era is to obtain the quantitatively dynamic interactive information of the important constitutes of underlying systems. The S-system is a dynamic and structurally rich model that determines the net strength of interactions between genes and/or proteins. Good generation characteristics without the need for prior information have allowed S-systems to become one of the most promising canonical models. Various evolutionary computation technologies have recently been developed for the identification of system parameters and skeletal-network structures. However, the gaps between the truncated and preserved terms remain too small. Additionally, current research methods fail to identify the structures of high dimensional systems (e.g., 30 genes with 1800 connections). Optimization technologies should converge fast and have the ability to adaptively adjust the search. In this study, we propose a seeding-inspired chemotaxis genetic algorithm (SCGA) that can force evolution to adjust the population movement to identify a favorable location. The seeding-inspired training strategy is a method to achieve optimal results with limited resources. SCGA introduces seeding-inspired genetic operations to allow a population to possess competitive power (exploitation and exploration) and a winner-chemotaxis-induced population migration to force a population to repeatedly tumble away from an attractor and swim toward another attractor. SCGA was tested on several canonical biological systems. SCGA not only learned the correct structure within only one to three pruning steps but also ensures pruning safety. The values of the truncated terms were all smaller than 10(-14), even for a thirty-gene system. PMID:25462336

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

    NASA Astrophysics Data System (ADS)

    Christensen, Claire Petra

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

  18. Magnetospheric current systems as inferred from SYM and ASY mid-latitude indices

    NASA Astrophysics Data System (ADS)

    Ganushkina, Natalia; Dubyagin, Stepan

    2015-04-01

    Separating the contributions from different current systems from point magnetic field measurements and interpreting them as belonging to one system or another is very difficult, and caution must be used when deciphering near-Earth currents from either data or modeling results. At the same time, there are other continuously measured quantities, which can provide, though indirectly, information about the dynamics of the magnetospheric current systems. The SYM-H and ASY-H indices, computed from the observations of magnetic field at low latitude ground-based stations, contain contributions from major magnetospheric current systems, such as the symmetric and asymmetric ring current, tail current, magnetopause currents and field-aligned currents. Highly distorted magnetospheric magnetic field during storm times due to disturbances in the current systems is reflected in the SYM-H and ASY-H observed variations. Using empirical magnetospheric models we study the relative contribution from different current systems to the SYM and ASY mid-latitude indices. It was found that the models can reproduce ground based mid-latitude indices rather well. The good agreement between the indices computed using magnetospheric models and real ones indicates that purely ionospheric current systems, on average, give modest contribution to these indices. The superposed epoch analysis of the indices computed using the models shows that the cross-tail current gives dominant contribution to SYM-H index during the main phase though this contribution can not be separated from FAC region 2 and partial ring current contributions since these systems are overlapped. The relative contribution from symmetric ring current to SYM-H starts to increase a bit prior or just after SYM-H minimum and attains its maximum during recovery phase. The ASY-H and ASY-D indices are controlled by interplay between three current systems which close via the ionosphere. The region 1 FAC gives the largest contribution to ASY

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

    PubMed

    Frasso, Gianluca; Jaeger, Jonathan; Lambert, Philippe

    2016-05-01

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

  20. Hydrothermal system of Central Tenerife Volcanic Complex, Canary Islands (Spain), inferred from self-potential measurements

    NASA Astrophysics Data System (ADS)

    Villasante-Marcos, Víctor; Finizola, Anthony; Abella, Rafael; Barde-Cabusson, Stéphanie; Blanco, María José; Brenes, Beatriz; Cabrera, Víctor; Casas, Benito; De Agustín, Pablo; Di Gangi, Fabio; Domínguez, Itahiza; García, Olaya; Gomis, Almudena; Guzmán, Juan; Iribarren, Ilazkiñe; Levieux, Guillaume; López, Carmen; Luengo-Oroz, Natividad; Martín, Isidoro; Moreno, Manuel; Meletlidis, Stavros; Morin, Julie; Moure, David; Pereda, Jorge; Ricci, Tullio; Romero, Enrique; Schütze, Claudia; Suski-Ricci, Barbara; Torres, Pedro; Trigo, Patricia

    2014-02-01

    An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~ 237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the

  1. Hydrothermal system of Central Tenerife Volcanic Complex, Canary Islands (Spain), inferred from self-potential measurements

    NASA Astrophysics Data System (ADS)

    Villasante-Marcos, Víctor; Finizola, Anthony; Barde-Cabusson, Stéphanie; López, Carmen; Di Gangi, Fabio; Levieux, Guillaume; Morin, Julie; Ricci, Tullio; Schütze, Claudia; Suski-Ricci, Barbara

    2014-05-01

    An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the

  2. RULE-BASED INFERENCE SYSTEM FOR PREDICTING LINER/WASTE COMPATIBILITY

    EPA Science Inventory

    Determining the chemical compatibility of a liner material for containment of wastes rests mainly on the application of expert opinion to interpret the results of short-term immersion tests. A methodology known as a production system is employed to encode such expert opinion into...

  3. 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. PMID:24953199

  4. Methodology development of an engineering design expert system utilizing a modular knowledge-base inference process

    NASA Astrophysics Data System (ADS)

    Winter, Steven John

    Methodology development was conducted to incorporate a modular knowledge-base representation into an expert system engineering design application. The objective for using multidisciplinary methodologies in defining a design system was to develop a system framework that would be applicable to a wide range of engineering applications. The technique of "knowledge clustering" was used to construct a general decision tree for all factual information relating to the design application. This construction combined the design process surface knowledge and specific application depth knowledge. Utilization of both levels of knowledge created a system capable of processing multiple controlling tasks including; organizing factual information relative to the cognitive levels of the design process, building finite element models for depth knowledge analysis, developing a standardized finite element code for parallel processing, and determining a best solution generated by design optimization procedures. Proof of concept for the methodology developed here is shown in the implementation of an application defining the analysis and optimization of a composite aircraft canard subjected to a general compound loading condition. This application contained a wide range of factual information and heuristic rules. The analysis tools used included a finite element (FE) processor and numerical optimizer. An advisory knowledge-base was also developed to provide a standard for conversion of serial FE code for parallel processing. All knowledge-bases developed operated as either an advisory, selection, or classification systems. Laminate properties are limited to even-numbered, quasi-isotropic ply stacking sequences. This retained full influence of the coupled in-plane and bending effects of the structures behavior. The canard is modeled as a constant thickness plate and discretized into a varying number of four or nine-noded, quadrilateral, shear-deformable plate elements. The benefit gained by

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

    NASA Astrophysics Data System (ADS)

    d'Onofrio, Alberto

    2005-09-01

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

  6. Inferring complex networks from time series of dynamical systems: Pitfalls, misinterpretations, and possible solutions

    NASA Astrophysics Data System (ADS)

    Bearden, Kathryn

    Interpenetrating polymer networks (IPNs), where polymer chains mechanically entangle during network formation, are of interest for their unique properties. The reaction sequence of a DGEBF epoxy/polybutadiene-dimethacrylate simultaneous IPN system was varied with differing catalysts to observe the correlation between reaction steps and physical properties. When the acrylate components were reacted first an IPN with two glass transitions and discrete phase separation was observed via scanning electron microscopy (SEM). When all components were reacted in parallel, two glass transitions were also observed but the morphology presented a single phase or a visible macro phase seperation. The IPN showed an increase in fracture toughness but a decrease in tensile strength compared to the single phase system and an epoxy control. Varying the amounts of polybutadiene-dimethacrylate in relation to the epoxy also showed a limit to the toughening effect.

  7. Taal volcanic hydrothermal system (Philippines) inferred by electromagnetic and other geophysical methods

    NASA Astrophysics Data System (ADS)

    Zlotnicki, Jacques; Toutain, Jean Paul; Sasai, Yoichi; Villacorte, Egardo; Bernard, Alain; Fauquet, Frederic; Nagao, Toshiyatsu

    2010-05-01

    On volcanoes which display hydrothermal/magmatic unrests, Electromagnetic (EM) methods can be combined with geochemical (GC) and thermal methods. The integration of these methods allows to image in detail hydrothermal systems, to find out possible scenarios of volcanic unrest, and to monitor the on-going activity with knowledge on the sources of heat, gas and fluid transfers. Since the 1990's the volcano shows recurrent periods of seismic activity, ground deformation, hydrothermal activity, and surface activity (geysers). Combined EM and GC methods noticeably contribute to map in detail the hydrothermal system and to analyse the sources of the activity: - Total magnetic field mapping evidences demagnetised zones over the two main areas forming the hydrothermal system (in the northern part of Main crater (MC)). These low magnetized areas are ascribed to thermal sources located at some hundreds metres of depth, - Self-potential surveys, delineate the contours of the fluids-heat transfer, and the northern and southern structural discontinuities enclosing the hydrothermal system, - Ground temperature gradient measurements evidence the distinctive heat transfer modes, from low fluxes related to soil temperature dominated by solar input to extremely high temperature gradients of 1200 °C m-1 or to more related to magmatic fluids. - Ground temperature and surface temperature of central acidic lake calculated by Thermal Aster imaging highlight the location of the most active ground fissures, outcrops and diffuse areas. Higher and larger anomalies are observed in the northern part of MC. A rough estimation of the thermal discharge in the northern part of the volcano gives 17 MW. - CO2 concentrations and fluxes from soil supply inform on fluids origin and on local processes operating along active fractures. Much higher carbon dioxide fluxes at MC sites confirm that the source of Taal activity is presently located in the northern part of the crater. - Heat and fluids release

  8. 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.

  9. 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.

  10. 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

  11. 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. PMID:26636071

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

    PubMed Central

    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. PMID:26636071

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

    PubMed

    Davies, Andrew J; Hope, Max J

    2015-07-15

    Contingency plans are essential in guiding the response to marine oil spills. However, they are written before the pollution event occurs so must contain some degree of assumption and prediction and hence may be unsuitable for a real incident when it occurs. The use of Bayesian networks in ecology, environmental management, oil spill contingency planning and post-incident analysis is reviewed and analysed to establish their suitability for use as real-time environmental decision support systems during an oil spill response. It is demonstrated that Bayesian networks are appropriate for facilitating the re-assessment and re-validation of contingency plans following pollutant release, thus helping ensure that the optimum response strategy is adopted. This can minimise the possibility of sub-optimal response strategies causing additional environmental and socioeconomic damage beyond the original pollution event. PMID:26006775

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  15. 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

  16. 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

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

    USGS Publications Warehouse

    Wauthier, Christelle; Cayol, Valerie; Poland, Michael; Kervyn, François; D'Oreye, Nicolas; Hooper, Andrew; Samsonov, Sergei; Tiampo, Kristy; Smets, Benoit

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2014-11-01

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

  20. 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.

  1. 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

  2. Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron

    NASA Astrophysics Data System (ADS)

    Tabari, Hossein; Hosseinzadeh Talaee, P.; Abghari, Hirad

    2012-05-01

    Estimation of pan evaporation ( E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient ( r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions ( r = 0.97, RMSE = 0.81 mm day-1, MAE = 0.63 mm day-1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.

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

    PubMed

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

    2013-01-01

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

  4. 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

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

    PubMed

    Jhin, Changho; Hwang, Keum Taek

    2015-01-01

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

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

    PubMed

    Jhin, Changho; Hwang, Keum Taek

    2014-01-01

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

  7. 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

  8. 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.

  9. 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.

  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. 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.

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

    PubMed

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  14. 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

    2015-06-01

    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

  15. 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

  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. Eruptive styles and inferences about plumbing systems at Hidden Cone and Little Black Peak scoria cone volcanoes (Nevada, U.S.A.)

    NASA Astrophysics Data System (ADS)

    Valentine, Greg A.; Keating, Gordon N.

    2007-09-01

    We describe two small scoria cone volcanoes, Hidden Cone and Little Black Peak (ages between ~320-390 ka), in the Southwestern Nevada Volcanic Field and discuss their eruption mechanisms and inferences about their plumbing systems. Cone-forming pyroclastic deposits are consistent with eruptive styles ranging from Strombolian to violent Strombolian, and lavas emanated from near the bases of the cones. The volcanoes are monogenetic (rather than polycyclic, as allowed by previous geomorphic interpretations). Vents at each volcano appear to coincide with pre-existing normal faults, consistent with observations at older, deeply eroded volcanoes in the region. The existence of these two volcanoes on a topographically high area (particularly Hidden Cone) provides evidence for short feeder dike lengths (~500 m at the surface). We infer that this short length reflects the small length scale of the mantle source region that was tapped to feed each volcano.

  18. Inference or Observation?

    ERIC Educational Resources Information Center

    Finson, Kevin D.

    2010-01-01

    Learning about what inferences are, and what a good inference is, will help students become more scientifically literate and better understand the nature of science in inquiry. Students in K-4 should be able to give explanations about what they investigate (NSTA 1997) and that includes doing so through inferring. This article provides some tips…

  19. 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. PMID:27000282

  20. Physical limits of inference

    NASA Astrophysics Data System (ADS)

    Wolpert, David H.

    2008-07-01

    We show that physical devices that perform observation, prediction, or recollection share an underlying mathematical structure. We call devices with that structure “inference devices”. We present a set of existence and impossibility results concerning inference devices. These results hold independent of the precise physical laws governing our universe. In a limited sense, the impossibility results establish that Laplace was wrong to claim that even in a classical, non-chaotic universe the future can be unerringly predicted, given sufficient knowledge of the present. Alternatively, these impossibility results can be viewed as a non-quantum-mechanical “uncertainty principle”. The mathematics of inference devices has close connections to the mathematics of Turing Machines (TMs). In particular, the impossibility results for inference devices are similar to the Halting theorem for TMs. Furthermore, one can define an analog of Universal TMs (UTMs) for inference devices. We call those analogs “strong inference devices”. We use strong inference devices to define the “inference complexity” of an inference task, which is the analog of the Kolmogorov complexity of computing a string. A task-independent bound is derived on how much the inference complexity of an inference task can differ for two different inference devices. This is analogous to the “encoding” bound governing how much the Kolmogorov complexity of a string can differ between two UTMs used to compute that string. However no universe can contain more than one strong inference device. So whereas the Kolmogorov complexity of a string is arbitrary up to specification of the UTM, there is no such arbitrariness in the inference complexity of an inference task. We informally discuss the philosophical implications of these results, e.g., for whether the universe “is” a computer. We also derive some graph-theoretic properties governing any set of multiple inference devices. We also present an

  1. Eight challenges in phylodynamic inference

    PubMed Central

    Frost, Simon D.W.; Pybus, Oliver G.; Gog, Julia R.; Viboud, Cecile; Bonhoeffer, Sebastian; Bedford, Trevor

    2015-01-01

    The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data. PMID:25843391

  2. Inferring biotic interactions from proxies.

    PubMed

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

    2015-06-01

    Inferring biotic interactions from functional, phylogenetic and geographical proxies remains one great challenge in ecology. We propose a conceptual framework to infer the backbone of biotic interaction networks within regional species pools. First, interacting groups are identified to order links and remove forbidden interactions between species. Second, additional links are removed by examination of the geographical context in which species co-occur. Third, hypotheses are proposed to establish interaction probabilities between species. We illustrate the framework using published food-webs in terrestrial and marine systems. We conclude that preliminary descriptions of the web of life can be made by careful integration of data with theory. PMID:25922148

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

    NASA Astrophysics Data System (ADS)

    Woldetinsae, G.; Götze, H. J.

    2004-12-01

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

  4. Crustal stress field in Yunnan: implication for crust-mantle coupling

    NASA Astrophysics Data System (ADS)

    Xu, Zhigang; Huang, Zhouchuan; Wang, Liangshu; Xu, Mingjie; Ding, Zhifeng; Wang, Pan; Mi, Ning; Yu, Dayong; Li, Hua

    2016-04-01

    We applied the gCAP algorithm to determine 239 focal mechanism solutions ( {3.0 ≤ M_{W} ≤ 6.0} ) with records of dense ChinArray stations deployed in Yunnan, and then inverted 686 focal mechanisms (including 447 previous results) for the regional crustal stress field with a damped linear inversion. The results indicate dominantly strike-slip environment in Yunnan as both the maximum (σ 1) and minimum (σ 3) principal stress axes are sub-horizontal. We further calculated the horizontal stress orientations (i.e., maximum and minimum horizontal compressive stress axes: S H and S h, respectively) accordingly and found an abrupt change near 26°N. To the north, S H aligns NW-SE to nearly E-W while S h aligns nearly N-S. In contrast, to the south, both S H and S h rotate laterally and show dominantly fan-shaped patterns. The minimum horizontal stress (i.e., maximum strain axis) S h rotates from NW-SE to the west of Tengchong volcano gradually to nearly E-W in west Yunnan, and further to NE-SW in the South China block in the east. The crustal strain field is consistent with the upper mantle strain field indicated by shear-wave splitting observations in Yunnan but not in other regions. Therefore, the crust and upper mantle in Yunnan are coupled and suffering vertically coherent pure-shear deformation in the lithosphere.

  5. 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.

  6. Magnetic mapping of (carbonated) oceanic crust-mantle boundary: New insights from Linnajavri, northern Norway

    NASA Astrophysics Data System (ADS)

    Tominaga, M.; Beinlich, A.; Tivey, M.; Andrade Lima, E.; Weiss, B. P.

    2012-12-01

    The contribution of lower oceanic crust and upper mantle to marine magnetic anomalies has long been recognized, but the detailed magnetic character of this non-volcanic source layer remains to be fully defined. Here, we report preliminary results of a magnetic survey and source characterization of a "carbonated" oceanic Moho (petrological "Mohorovicic discontinuity") sequence observed at the Linnajavri Serpentinite Complex (LSC), northern Norway. The LSC is located at 67° 36'N and 16° 24'E within the upper Allochthon of the Norwegian Caledonides and represents a dismembered ophiolite. Particularly in the southern ("Ridoalggicohkka") area of the LSC, gabbro, serpentinite and its talc-carbonate (soapstone) and quartz-carbonate (listvenite) altered equivalents are extraordinarily well-exposed [1]. An intact oceanic Moho is exposed here, despite its complex tectonic setting. The small degree of arctic rock weathering (≤ 2 mm weathering surface) allowed for detailed regional-scale surface magnetic mapping across alteration fronts (serpentinite-soapstone; soapstone-listvenite) and lithological contacts (soapstone-gabbro). Magnetic mapping was conducted using a handheld 3-axis magnetometer, surface-towed resistivity meter and Teka surface magnetic susceptometer with sample spacing of 1 m. Geophysical field mapping was combined with petrological observations and scanning SQUID microscopy (SM) mapping conducted on thin sections from rock samples that were drilled along the survey lines. Regional scale magnetic mapping indicates that the total magnetic field across both the "carbonated" Moho and the soapstone-serpentinite interfaces show higher frequency changes in their magnetic anomaly character and amplitudes than the surface-towed resistivity data. SQUID microscopy mapping of both natural remanence magnetization (NRM) and anhysteretic remanence magnetization (ARM) on gabbro, serpentinite, soapstone, and listvenite samples, with a sensor-sample separation of ˜190 μm, show that the distribution of microscopically measurable ferromagnetic and possibly sulfide minerals produces a different bulk intensity for each of the rock types. SM vector magnetic field maps of these samples also reveal that the magnetization associated with these grains (observed as dipole-like fields in SM maps) is variable in direction from grain to grain, which may result from different alteration histories for each grain. These complex magnetization patterns acquired through thermal and chemical alteration history may explain the short wavelength magnetic anomalies observed along our traverse lines. [1] Beinlich, A., Plümper, O., Hövelmann, J., Austrheim, H. and Jamtveit, B. (2012), Terra Nova, in press.

  7. Crustal stress field in Yunnan: implication for crust-mantle coupling

    NASA Astrophysics Data System (ADS)

    Xu, Zhigang; Huang, Zhouchuan; Wang, Liangshu; Xu, Mingjie; Ding, Zhifeng; Wang, Pan; Mi, Ning; Yu, Dayong; Li, Hua

    2016-04-01

    We applied the gCAP algorithm to determine 239 focal mechanism solutions ( {3.0 ≤ M_{{W}} ≤ 6.0} ) with records of dense ChinArray stations deployed in Yunnan, and then inverted 686 focal mechanisms (including 447 previous results) for the regional crustal stress field with a damped linear inversion. The results indicate dominantly strike-slip environment in Yunnan as both the maximum ( σ 1) and minimum ( σ 3) principal stress axes are sub-horizontal. We further calculated the horizontal stress orientations (i.e., maximum and minimum horizontal compressive stress axes: S H and S h, respectively) accordingly and found an abrupt change near 26°N. To the north, S H aligns NW-SE to nearly E-W while S h aligns nearly N-S. In contrast, to the south, both S H and S h rotate laterally and show dominantly fan-shaped patterns. The minimum horizontal stress (i.e., maximum strain axis) S h rotates from NW-SE to the west of Tengchong volcano gradually to nearly E-W in west Yunnan, and further to NE-SW in the South China block in the east. The crustal strain field is consistent with the upper mantle strain field indicated by shear-wave splitting observations in Yunnan but not in other regions. Therefore, the crust and upper mantle in Yunnan are coupled and suffering vertically coherent pure-shear deformation in the lithosphere.

  8. Fault friction, regional stress, and crust-mantle coupling in southern California from finite element models

    NASA Technical Reports Server (NTRS)

    Bird, P.; Baumgardner, J.

    1984-01-01

    To determine the correct fault rheology of the Transverse Ranges area of California, a new finite element to represent faults and a mangle drag element are introduced into a set of 63 simulation models of anelastic crustal strain. It is shown that a slip rate weakening rheology for faults is not valid in California. Assuming that mantle drag effects on the crust's base are minimal, the optimal coefficient of friction in the seismogenic portion of the fault zones is 0.4-0.6 (less than Byerly's law assumed to apply elsewhere). Depending on how the southern California upper mantle seismic velocity anomaly is interpreted, model results are improved or degraded. It is found that the location of the mantle plate boundary is the most important secondary parameter, and that the best model is either a low-stress model (fault friction = 0.3) or a high-stress model (fault friction = 0.85), each of which has strong mantel drag. It is concluded that at least the fastest moving faults in southern California have a low friction coefficient (approximtely 0.3) because they contain low strength hydrated clay gouges throughout the low-temperature seismogenic zone.

  9. 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.

  10. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-02-01

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

  12. 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

  13. Bayesian inference on proportional elections.

    PubMed

    Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio

    2015-01-01

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

  14. 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.

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

    PubMed Central

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

    2016-01-01

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

  16. Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

    NASA Astrophysics Data System (ADS)

    Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

    2013-09-01

    This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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...

  2. A unique sex chromosome system in the knifefish Gymnotus bahianus with inferences about chromosomal evolution of Gymnotidae.

    PubMed

    Almeida, Josivanda S; Migues, Vitor H; Diniz, Débora; Affonso, Paulo Roberto A M

    2015-01-01

    Cytogenetic studies in Neotropical electric knifefish of genus Gymnotus have shown a remarkable interspecific variability, including distinct sex chromosome systems. In this study, we present the first chromosomal data in Gymnotus bahianus from Contas River basin, northeastern South America. Based on extensive analyses, the modal diploid values were 2n = 36 (30m/sm + 6st) for females and 2n = 37 (32m/sm + 5st) for males. Therefore, a novel XX/XY1Y2 sex chromosome system is described for the genus. Single nucleolar organizer regions (NORs) interspersed to GC-rich sites were detected on a subtelocentric pair (7th) for both sexes and confirmed by fluorescent in situ hybridization with 18S rDNA probes. Heterochromatin was detected at pericentromeric regions of all chromosomes and interspersed to NORs on pair 7 and 5S rDNA cistrons on pair 9. The highly differentiated karyotype of Gymnoytus bahianus, with low diploid numbers and a unique XX/XY1Y2 system, reinforces the independent origin of sex chromosomes in Gymnotiformes and seems to reflect the particular evolutionary history of this species in a small and isolated drainage system. Moreover, in spite of morphological similarities, the present results indicate a remarkable chromosomal divergence in relation to closely related species such as G. sylvius and G. carapo. PMID:25596613

  3. The evolution of reproductive systems and sex-determining mechanisms within rumex (polygonaceae) inferred from nuclear and chloroplastidial sequence data.

    PubMed

    Navajas-Pérez, Rafael; de la Herrán, Roberto; López González, Ginés; Jamilena, Manuel; Lozano, Rafael; Ruiz Rejón, Carmelo; Ruiz Rejón, Manuel; Garrido-Ramos, Manuel A

    2005-09-01

    The genus Rumex includes hermaphroditic, polygamous, gynodioecious, monoecious, and dioecious species, with the dioecious species being represented by different sex-determining mechanisms and sex-chromosome systems. Therefore, this genus represents an exceptional case study to test several hypotheses concerning the evolution of both mating systems and the genetic control of sex determination in plants. Here, we compare nuclear intergenic transcribed spacers and chloroplast intergenic sequences of 31 species of Rumex. Our phylogenetic analysis supports a systematic classification of the genus, which differs from that currently accepted. In contrast to the current view, this new phylogeny suggests a common origin for all Eurasian and American dioecious species of Rumex, with gynodioecy as an intermediate state on the way to dioecy. Our results support the contention that sex determination based on the balance between the number of X chromosomes and the number of autosomes (X/A balance) has evolved secondarily from male-determining Y mechanisms and that multiple sex-chromosome systems, XX/XY1Y2, were derived twice from an XX/XY system. The resulting phylogeny is consistent with a classification of Rumex species according to their basic chromosome number, implying that the evolution of Rumex species might have followed a process of chromosomal reduction from x = 10 toward x = 7 through intermediate stages (x = 9 and x = 8). PMID:15944442

  4. The CO2 system in the Mediterranean Sea inferred from a 3D coupled physical-biogeochemical model

    NASA Astrophysics Data System (ADS)

    Ulses, Caroline; Kessouri, Fayçal; Estournel, Claude; Marsaleix, Patrick; Beuvier, Jonathan; Somot, Samuel; Touratier, Frank; Goyet, Catherine; Coppola, Laurent; Diamond, Emilie; Metzl, Nicolas

    2015-04-01

    The semi-enclosed Mediterranean Sea characterized by short residence times is considered as a region particularly sensitive to natural and anthropogenic forcing. Due to scarce CO2 measurements in the whole basin, the CO2 system, for instance the air-sea CO2 exchanges and the effects of the increase of atmospheric CO2, are poorly characterized. 3D physical-biogeochemical coupled models are unique tools that can provide integrated view and gain understanding in the temporal and spatial variation of the CO2 system variables (dissolved inorganic carbon, total alkalinity, partial pressure of CO2 and pH). An extended version of the biogeochemical model Eco3m-S (Auger et al., 2014), that describes the cycles of carbon, nitrogen, phosphorus and silica, was forced by a regional circulation model (Beuvier et al., 2012) to investigate the CO2 system in the Mediterranean Sea over a 13-years period (2001-2013). First, the quality of the modelling was evaluated through comparisons with satellite and in situ observations collected in the whole basin over the study period (Touratier and Goyet, 2009; 2011 ; Rivaro et al., 2010 ; Pujo-Pay et al., 2011 ; Alvarez et al, 2014). The model reasonably reproduced the various biological regimes (north-western phytoplanctonic bloom regime, oligotrophic eastern regime, etc.) as well as the recorded spatial distribution and temporal variations of the carbonate system variables. The coupled model was then used to estimate the air-sea pCO2 exchanges and the transport of DIC and TA towards the Atlantic Ocean at the Strait of Gibraltar.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    PubMed

    Zimmer, Christoph; Sahle, Sven

    2015-10-01

    Estimating model parameters from experimental data is a crucial technique for working with computational models in systems biology. Since stochastic models are increasingly important, parameter estimation methods for stochastic modelling are also of increasing interest. This study presents an extension to the 'multiple shooting for stochastic systems (MSS)' method for parameter estimation. The transition probabilities of the likelihood function are approximated with normal distributions. Means and variances are calculated with a linear noise approximation on the interval between succeeding measurements. The fact that the system is only approximated on intervals which are short in comparison with the total observation horizon allows to deal with effects of the intrinsic stochasticity. The study presents scenarios in which the extension is essential for successfully estimating the parameters and scenarios in which the extension is of modest benefit. Furthermore, it compares the estimation results with reversible jump techniques showing that the approximation does not lead to a loss of accuracy. Since the method is not based on stochastic simulations or approximative sampling of distributions, its computational speed is comparable with conventional least-squares parameter estimation methods. PMID:26405142

  7. Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system.

    PubMed

    Aqil, M; Kita, I; Yano, A; Nishiyama, S

    2006-01-01

    It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other

  8. "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

  9. 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.

  10. 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.

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

    USGS Publications Warehouse

    Griscom, A.; Jachens, R.C.

    1989-01-01

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

  12. 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.

  13. 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. PMID:25833811

  14. Probabilistic inferences related to the measurement process

    NASA Astrophysics Data System (ADS)

    Rossi, G. B.

    2010-07-01

    In measurement indications from a measuring system are acquired and, on the basis of them, some inference about the measurand is made. The final result may be the assignment of a probability distribution for the possible values of the measurand. We discuss the logical structure of such an inference and some of its epistemological consequences. In particular, we propose a new solution to the problem of systematic effects in measurement.

  15. 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. PMID:19938889

  16. 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

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

    PubMed

    Torshabi, Ahmad Esmaili

    2014-12-01

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

  18. 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.

  19. 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.

  20. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  2. 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

  3. 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

  4. System-Level Insights into the Cellular Interactome of a Non-Model Organism: Inferring, Modelling and Analysing Functional Gene Network of Soybean (Glycine max)

    PubMed Central

    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

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

    PubMed

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

    2015-09-01

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

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

    PubMed

    Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin

    2014-06-01

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

  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. PMID:24566279

  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. 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.

  11. On the criticality of inferred models

    NASA Astrophysics Data System (ADS)

    Mastromatteo, Iacopo; Marsili, Matteo

    2011-10-01

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

  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. PMID:27130319

  13. INFERENCES FROM ROSSI TRACES

    SciTech Connect

    KENNETH M. HANSON; JANE M. BOOKER

    2000-09-08

    The authors an uncertainty analysis of data taken using the Rossi technique, in which the horizontal oscilloscope sweep is driven sinusoidally in time ,while the vertical axis follows the signal amplitude. The analysis is done within a Bayesian framework. Complete inferences are obtained by tilting the Markov chain Monte Carlo technique, which produces random samples from the posterior probability distribution expressed in terms of the parameters.

  14. 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. PMID:27375276

  15. Bayesian inference in physics

    NASA Astrophysics Data System (ADS)

    von Toussaint, Udo

    2011-07-01

    Bayesian inference provides a consistent method for the extraction of information from physics experiments even in ill-conditioned circumstances. The approach provides a unified rationale for data analysis, which both justifies many of the commonly used analysis procedures and reveals some of the implicit underlying assumptions. This review summarizes the general ideas of the Bayesian probability theory with emphasis on the application to the evaluation of experimental data. As case studies for Bayesian parameter estimation techniques examples ranging from extra-solar planet detection to the deconvolution of the apparatus functions for improving the energy resolution and change point estimation in time series are discussed. Special attention is paid to the numerical techniques suited for Bayesian analysis, with a focus on recent developments of Markov chain Monte Carlo algorithms for high-dimensional integration problems. Bayesian model comparison, the quantitative ranking of models for the explanation of a given data set, is illustrated with examples collected from cosmology, mass spectroscopy, and surface physics, covering problems such as background subtraction and automated outlier detection. Additionally the Bayesian inference techniques for the design and optimization of future experiments are introduced. Experiments, instead of being merely passive recording devices, can now be designed to adapt to measured data and to change the measurement strategy on the fly to maximize the information of an experiment. The applied key concepts and necessary numerical tools which provide the means of designing such inference chains and the crucial aspects of data fusion are summarized and some of the expected implications are highlighted.

  16. Towards Context Sensitive Information Inference.

    ERIC Educational Resources Information Center

    Song, D.; Bruza, P. D.

    2003-01-01

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

  17. Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Iphar, Melih; Yavuz, Mahmut; Ak, Hakan

    2008-11-01

    The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.

  18. 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.

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

    PubMed

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

    2014-10-15

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

  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. PMID:23266912

  1. 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.

  2. 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.

  3. 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.

  4. 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.

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

    NASA Astrophysics Data System (ADS)

    Al-Abadi, Alaa M.

    2014-12-01

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

  6. 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…

  7. Infering Networks From Collective Dynamics

    NASA Astrophysics Data System (ADS)

    Timme, Marc

    How can we infer direct physical interactions between pairs of units from only knowing the units' time series? Here we present a dynamical systems' view on collective network dynamics, and propose the concept of a dynamics' space to reveal interaction networks from time series. We present two examples: one, where the time series stem from standard ordinary differential equations, and a second, more abstract, where the time series exhibits only partial information about the units' states. We apply the latter to neural circuit dynamics where the observables are spike timing data, i.e. only a discrete, state-dependent outputs of the neurons. These results may help revealing network structure for systems where direct access to dynamics is simpler than to connectivity, cf.. This is work with Jose Casadiego, Srinivas Gorur Shandilya, Mor Nitzan, Hauke Haehne and Dimitra Maoutsa. Supported by Grants of the BMBF (Future Compliant Power Grids - CoNDyNet) and by the Max Planck Society to MT.

  8. 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.

  9. Circular inferences in schizophrenia.

    PubMed

    Jardri, Renaud; Denève, Sophie

    2013-11-01

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

  10. 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.

  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. An evaluation of explanations of probabilistic inference.

    PubMed Central

    Suermondt, H. J.; Cooper, G. F.

    1992-01-01

    Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results, to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear. PMID:1482939

  16. Dynamical inference of hidden biological populations

    NASA Astrophysics Data System (ADS)

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

    2008-10-01

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

  17. Structural inference for uncertain networks

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

  18. Bayes factors and multimodel inference

    USGS Publications Warehouse

    Link, W.A.; Barker, R.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.

  19. 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…

  20. 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.…

  1. 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…

  2. Learning to Observe "and" Infer

    ERIC Educational Resources Information Center

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

    2008-01-01

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

  3. INFERRING THE ECCENTRICITY DISTRIBUTION

    SciTech Connect

    Hogg, David W.; Bovy, Jo; Myers, Adam D.

    2010-12-20

    Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here, we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision-other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, distances, or photometric redshifts-so long as the measurements have been communicated as a likelihood function or a posterior sampling.

  4. Inferring the Eccentricity Distribution

    NASA Astrophysics Data System (ADS)

    Hogg, David W.; Myers, Adam D.; Bovy, Jo

    2010-12-01

    Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here, we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision—other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, distances, or photometric redshifts—so long as the measurements have been communicated as a likelihood function or a posterior sampling.

  5. Inference from aging information.

    PubMed

    de Oliveira, Evaldo Araujo; Caticha, Nestor

    2010-06-01

    For many learning tasks the duration of the data collection can be greater than the time scale for changes of the underlying data distribution. The question we ask is how to include the information that data are aging. Ad hoc methods to achieve this include the use of validity windows that prevent the learning machine from making inferences based on old data. This introduces the problem of how to define the size of validity windows. In this brief, a new adaptive Bayesian inspired algorithm is presented for learning drifting concepts. It uses the analogy of validity windows in an adaptive Bayesian way to incorporate changes in the data distribution over time. We apply a theoretical approach based on information geometry to the classification problem and measure its performance in simulations. The uncertainty about the appropriate size of the memory windows is dealt with in a Bayesian manner by integrating over the distribution of the adaptive window size. Thus, the posterior distribution of the weights may develop algebraic tails. The learning algorithm results from tracking the mean and variance of the posterior distribution of the weights. It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps. PMID:20421181

  6. A Comparison of High-Inference and Low-Inference Measures of Teacher Behaviors as Predictors of Pupil Attitudes and Achievements.

    ERIC Educational Resources Information Center

    McConnell, John W.; Bowers, Norman D.

    Data derived from a year-long study of 43 algebra classes in 13 high schools were collected to assess the differences between high-inference and low-inference measures of teacher behavior as predictors of pupil achievement and attitude change. The high-inference systems rated characteristics such as enthusiasm and clarity while low-inference…

  7. 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.

  8. 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.

  9. 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

  10. Ensemble Inference and Inferability of Gene Regulatory Networks

    PubMed Central

    Ud-Dean, S. M. Minhaz; Gunawan, Rudiyanto

    2014-01-01

    The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of E. coli and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 in silico network inference challenge. PMID:25093509

  11. High and Ultrahigh pressure peridotites: fossil reservoirs of subduction zone processes and deep crust-mantle wedge interaction

    NASA Astrophysics Data System (ADS)

    Scambelluri, Marco

    2010-05-01

    The large-scale mass transfer allied with subduction recycles surface volatiles and crustal materials into the mantle, to affect its composition and rheology. Most geological processes related to subduction thus originate from an interplay between subducting plates and overlying lithospheric and asthenospheric mantle. Much information on phase relations during subduction has been provided by experiments and by studies of natural high- (HP) and ultrahigh-pressure (UHP) rocks and fluids. In contrast, knowledge on supra-subduction mantle wedges is much less. Here, the interaction between slab fluids and mantle rocks at variable subduction depths is discussed considering two case-studies: the UHP garnet websterites from Bardane (Western Gneiss Region, Norway) and the HP garnet peridotites from the Ulten Zone (Eastern Alps). The Bardane websterites derive from cold Archean subcontinental mantle involved in Scandian subduction to UHP. Subduction metamorphism was promoted by slab fluid infiltration in the overlying mantle up to P of 6.5 - 7 GPa (c.a. 200 km depth), as witnessed by micro-diamond-bearing inclusions and by crystallization of majoritic garnet in veins. The Ulten peridotites are slices of Variscan mantle wedge which experienced infiltration of metasomatic subduction fluids. These favoured transformation of spinel-peridotites into garnet + amphibole + dolomite peridotites at P < 3GPa. Formation of metasomatized garnet peridotite mylonites suggest channelled influx of subduction fluids. The high XMg and the incompatible element-enriched composition of subduction minerals in Bardane indicate that previously depleted websterites were refertilized by COH subduction fluids. Comparison with the Ulten garnet + amphibole + dolomite peridotites outlines relevant similarity in the metasomatic fingerprints and in the COH fluid phase involved. This calls for concomitant subduction of the continental crust, to provide carbon and incompatible element-enriched fluids. For Bardane this implies crustal subduction to 200 km depth. Mantle refertilization by crust-derived COH subduction fluids thus operates over a large depth range during subduction. Textures of the Bardane and Ulten rocks indicate that mantle recrystallization and refertilization are concomitant with fluid input along channelways, outside which long-lasting pre-subduction stories and pristine compositions are preserved. Comparably with uprising magmas, the subducted continental crust efficiently carries to the surface mantle tectonic ‘xenoliths', representing major observatories on the Earth's mantle at variable depths.

  12. 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.

  13. 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.

  14. 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.

  15. Active inference, communication and hermeneutics.

    PubMed

    Friston, Karl J; Frith, Christopher D

    2015-07-01

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

  16. 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. PMID:20677855

  17. 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

  18. Inference in {open_quotes}poor{close_quotes} languages

    SciTech Connect

    Petrov, S.

    1996-12-31

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

  19. 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

  20. 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

  1. Maximum caliber inference of nonequilibrium processes.

    PubMed

    Otten, Moritz; Stock, Gerhard

    2010-07-21

    Thirty years ago, Jaynes suggested a general theoretical approach to nonequilibrium statistical mechanics, called maximum caliber (MaxCal) [Annu. Rev. Phys. Chem. 31, 579 (1980)]. MaxCal is a variational principle for dynamics in the same spirit that maximum entropy is a variational principle for equilibrium statistical mechanics. Motivated by the success of maximum entropy inference methods for equilibrium problems, in this work the MaxCal formulation is applied to the inference of nonequilibrium processes. That is, given some time-dependent observables of a dynamical process, one constructs a model that reproduces these input data and moreover, predicts the underlying dynamics of the system. For example, the observables could be some time-resolved measurements of the folding of a protein, which are described by a few-state model of the free energy landscape of the system. MaxCal then calculates the probabilities of an ensemble of trajectories such that on average the data are reproduced. From this probability distribution, any dynamical quantity of the system can be calculated, including population probabilities, fluxes, or waiting time distributions. After briefly reviewing the formalism, the practical numerical implementation of MaxCal in the case of an inference problem is discussed. Adopting various few-state models of increasing complexity, it is demonstrated that the MaxCal principle indeed works as a practical method of inference: The scheme is fairly robust and yields correct results as long as the input data are sufficient. As the method is unbiased and general, it can deal with any kind of time dependency such as oscillatory transients and multitime decays. PMID:20649320

  2. Physics of Inference

    NASA Astrophysics Data System (ADS)

    Toroczkai, Zoltan

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

  3. Functional neuroanatomy of intuitive physical inference.

    PubMed

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

    2016-08-23

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

  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. Thermodynamics of cellular statistical inference

    NASA Astrophysics Data System (ADS)

    Lang, Alex; Fisher, Charles; Mehta, Pankaj

    2014-03-01

    Successful organisms must be capable of accurately sensing the surrounding environment in order to locate nutrients and evade toxins or predators. However, single cell organisms face a multitude of limitations on their accuracy of sensing. Berg and Purcell first examined the canonical example of statistical limitations to cellular learning of a diffusing chemical and established a fundamental limit to statistical accuracy. Recent work has shown that the Berg and Purcell learning limit can be exceeded using Maximum Likelihood Estimation. Here, we recast the cellular sensing problem as a statistical inference problem and discuss the relationship between the efficiency of an estimator and its thermodynamic properties. We explicitly model a single non-equilibrium receptor and examine the constraints on statistical inference imposed by noisy biochemical networks. Our work shows that cells must balance sample number, specificity, and energy consumption when performing statistical inference. These tradeoffs place significant constraints on the practical implementation of statistical estimators in a cell.

  6. Causal inference from observational data.

    PubMed

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

    2016-10-01

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

  7. We infer light in space.

    PubMed

    Schirillo, James A

    2013-10-01

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

  8. Inferring Epidemic Network Topology from Surveillance Data

    PubMed Central

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

    2014-01-01

    The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases. PMID:24979215

  9. CAUSAL INFERENCE IN BIOLOGY NETWORKS WITH INTEGRATED BELIEF PROPAGATION

    PubMed Central

    CHANG, RUI; KARR, JONATHAN R; SCHADT, ERIC E

    2014-01-01

    Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the ‘fitness’ of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot. PMID:25592596

  10. 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.

  11. 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…

  12. Sample Size and Correlational Inference

    ERIC Educational Resources Information Center

    Anderson, Richard B.; Doherty, Michael E.; Friedrich, Jeff C.

    2008-01-01

    In 4 studies, the authors examined the hypothesis that the structure of the informational environment makes small samples more informative than large ones for drawing inferences about population correlations. The specific purpose of the studies was to test predictions arising from the signal detection simulations of R. B. Anderson, M. E. Doherty,…

  13. 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…

  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. 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…

  16. 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)…

  17. 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

  18. The effects of liquid composition, temperature, and pressure on the equilibrium dihedral angles of binary solid-liquid systems inferred from a lattice-like model

    NASA Astrophysics Data System (ADS)

    Takei, Yasuko; Shimizu, Ichiko

    2003-10-01

    Dihedral angles of binary eutectic systems, such as silicate+melt systems, silicate+H 2O systems, binary alloys, and binary organic systems, tend to decrease with increasing concentration of the solid component in the liquid phase. This empirical law is useful to estimate dihedral angles in the Earth's interior from phase diagrams of solid-liquid systems. In this paper, we investigate the mechanism underlying this empirical law. By employing a lattice-like model in which the liquid phase is treated as a regular solution, we clarify the liquid composition, temperature, and pressure effects on the solid-liquid interfacial tension. It is shown that the non-ideality in chemical bonding causes a strong compositional dependence of the solid-liquid interfacial tension; due to the non-ideality in chemical bonding, the solid surface preferentially adsorbs the solid component, which results in the decrease of the interfacial tension with increasing concentration of this component in the bulk liquid phase. With this effect, the significant decrease of the dihedral angle with T observed in the SiO 2-H 2O system near the monotectic temperature, and the decrease with P observed in the forsterite-H 2O system, can be explained semi-quantitatively.

  19. 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

  20. 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.

  1. Active inference, eye movements and oculomotor delays.

    PubMed

    Perrinet, Laurent U; Adams, Rick A; Friston, Karl J

    2014-12-01

    This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference. Active inference uses a generalisation of Kalman filtering to provide Bayes optimal estimates of hidden states and action in generalised coordinates of motion. Representing hidden states in generalised coordinates provides a simple way of compensating for both sensory and oculomotor delays. The efficacy of this scheme is illustrated using neuronal simulations of pursuit initiation responses, with and without compensation. We then consider an extension of the generative model to simulate smooth pursuit eye movements-in which the visuo-oculomotor system believes both the target and its centre of gaze are attracted to a (hidden) point moving in the visual field. Finally, the generative model is equipped with a hierarchical structure, so that it can recognise and remember unseen (occluded) trajectories and emit anticipatory responses. These simulations speak to a straightforward and neurobiologically plausible solution to the generic problem of integrating information from different sources with different temporal delays and the particular difficulties encountered when a system-like the oculomotor system-tries to control its environment with delayed signals. PMID:25128318

  2. 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

  3. 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

  4. 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

  5. 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

  6. Bayesian Estimation and Inference Using Stochastic Electronics.

    PubMed

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

  7. Inference for interacting linear waves in ordered and random media

    NASA Astrophysics Data System (ADS)

    Tyagi, P.; Pagnani, A.; Antenucci, F.; Ibánez Berganza, M.; Leuzzi, L.

    2015-05-01

    A statistical inference method is developed and tested for pairwise interacting systems whose degrees of freedom are continuous angular variables, such as planar spins in magnetic systems or wave phases in optics and acoustics. We investigate systems with both deterministic and quenched disordered couplings on two extreme topologies: complete and sparse graphs. To match further applications in optics also complex couplings and external fields are considered and general inference formulas are derived for real and imaginary parts of Hermitian coupling matrices from real and imaginary parts of complex correlation functions. The whole procedure is, eventually, tested on numerically generated correlation functions and local magnetizations by means of Monte Carlo simulations.

  8. Crystallization Processes in Mercury's Core Inferred from In-situ High-Pressure Melting Experiments in the Fe-S-Si-C System

    NASA Astrophysics Data System (ADS)

    Martin, A. M.; Van Orman, J. A.; Hauck, S. A., II; Sun, N.; Yu, T.; Wang, Y.

    2014-12-01

    Based upon the high pressure melting temperatures in the Fe-FeS system, an iron "snow" process has been suggested to occur in Mercury's core. However, recent results from the MESSENGER mission indicate very reducing conditions in Mercury, under which a substantial amount of silicon should also dissolve into the core. The presence of Si can significantly modify the chemical and physical properties of Mercury's core (e.g., phase relations, crystallization, density). Moreover, up to 4 wt% C could have been incorporated into the core during the planet formation. In order to test the iron snow hypothesis in a system that is likely to be closer to the actual core composition, we performed in situ high-pressure, high-temperature experiments in the Fe-FeS-Fe2Si-Fe3C system using a multi-anvil press on a synchrotron (Advanced Photon Source, Argonne). To observe low degree eutectic melting, we separated the samples in two parts: (1) an iron rod presaturated with Si and C and (2) a mixture of FeS, Fe2Si and Fe3C. Eutectic melting temperature and phase relations were determined at various pressures between 4.5 and 15.5 GPa using energy dispersive X-ray diffraction and imaging. Temperature was quenched soon after melting in order to preserve the eutectic melt composition. The X-ray images, diffraction spectra and back-scattered electron images of the recovered samples show that eutectic melting occurs in the range of 800 - 900°C in all our experiments. These temperatures are close to the eutectic temperatures in the Fe-FeS-Fe3C system, indicating that Si does not change the eutectic temperatures significantly. Melting therefore occurs at much lower temperature than suggested for the Fe-S-Si system at similar pressures. This difference may be explained by the presence of C and by the higher silicon content in our starting composition. Our experimental setup may also be more suitable for detecting the low degrees of melting in metallic systems. Such low eutectic melting

  9. Self-enforcing Private Inference Control

    NASA Astrophysics Data System (ADS)

    Yang, Yanjiang; Li, Yingjiu; Weng, Jian; Zhou, Jianying; Bao, Feng

    Private inference control enables simultaneous enforcement of inference control and protection of users' query privacy. Private inference control is a useful tool for database applications, especially when users are increasingly concerned about individual privacy nowadays. However, protection of query privacy on top of inference control is a double-edged sword: without letting the database server know the content of user queries, users can easily launch DoS attacks. To assuage DoS attacks in private inference control, we propose the concept of self-enforcing private inference control, whose intuition is to force users to only make inference-free queries by enforcing inference control themselves; otherwise, penalty will inflict upon the violating users.

  10. 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

  11. Origin of the p-process radionuclides 92Nb and 146Sm in the early solar system and inferences on the birth of the Sun

    NASA Astrophysics Data System (ADS)

    Lugaro, Maria; Pignatari, Marco; Ott, Ulrich; Zuber, Kai; Travaglio, Claudia; Gyürky, György; Fülöp, Zsolt

    2016-01-01

    The abundances of 92Nb and 146Sm in the early solar system are determined from meteoritic analysis, and their stellar production is attributed to the p process. We investigate if their origin from thermonuclear supernovae deriving from the explosion of white dwarfs with mass above the Chandrasekhar limit is in agreement with the abundance of 53Mn, another radionuclide present in the early solar system and produced in the same events. A consistent solution for 92Nb and 53Mn cannot be found within the current uncertainties and requires the 92Nb/92Mo ratio in the early solar system to be at least 50% lower than the current nominal value, which is outside its present error bars. A different solution is to invoke another production site for 92Nb, which we find in the α-rich freezeout during core-collapse supernovae from massive stars. Whichever scenario we consider, we find that a relatively long time interval of at least ˜10 My must have elapsed from when the star-forming region where the Sun was born was isolated from the interstellar medium and the birth of the Sun. This is in agreement with results obtained from radionuclides heavier than iron produced by neutron captures and lends further support to the idea that the Sun was born in a massive star-forming region together with many thousands of stellar siblings.

  12. Origin of the p-process radionuclides 92Nb and 146Sm in the early solar system and inferences on the birth of the Sun.

    PubMed

    Lugaro, Maria; Pignatari, Marco; Ott, Ulrich; Zuber, Kai; Travaglio, Claudia; Gyürky, György; Fülöp, Zsolt

    2016-01-26

    The abundances of (92)Nb and (146)Sm in the early solar system are determined from meteoritic analysis, and their stellar production is attributed to the p process. We investigate if their origin from thermonuclear supernovae deriving from the explosion of white dwarfs with mass above the Chandrasekhar limit is in agreement with the abundance of (53)Mn, another radionuclide present in the early solar system and produced in the same events. A consistent solution for (92)Nb and (53)Mn cannot be found within the current uncertainties and requires the (92)Nb/(92)Mo ratio in the early solar system to be at least 50% lower than the current nominal value, which is outside its present error bars. A different solution is to invoke another production site for (92)Nb, which we find in the α-rich freezeout during core-collapse supernovae from massive stars. Whichever scenario we consider, we find that a relatively long time interval of at least ∼ 10 My must have elapsed from when the star-forming region where the Sun was born was isolated from the interstellar medium and the birth of the Sun. This is in agreement with results obtained from radionuclides heavier than iron produced by neutron captures and lends further support to the idea that the Sun was born in a massive star-forming region together with many thousands of stellar siblings. PMID:26755600

  13. 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.

  14. 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

  15. The ventral pallidum and orbitofrontal cortex support food pleasantness inferences.

    PubMed

    Simmons, W Kyle; Rapuano, Kristina M; Ingeholm, John E; Avery, Jason; Kallman, Seth; Hall, Kevin D; Martin, Alex

    2014-03-01

    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

  16. 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

  17. 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

  18. 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

  19. 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.

  20. Inferring epigenetic dynamics from kin correlations.

    PubMed

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

    2015-05-01

    Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity that arises from intrinsic stochastic dynamics of cells. The phenotypic state of a cell can be transmitted epigenetically in cell division, leading to correlations in the states of cells related by descent. The extent of these correlations is determined by the rates of transitions between the phenotypic states. Therefore, a snapshot of the phenotypes of a collection of cells with known genealogical structure contains information on phenotypic dynamics. Here, we use a model of phenotypic dynamics on a genealogical tree to define an inference method that allows extraction of an approximate probabilistic description of the dynamics from observed phenotype correlations as a function of the degree of kinship. The approach is tested and validated on the example of Pyoverdine dynamics in Pseudomonas aeruginosa colonies. Interestingly, we find that correlations among pairs and triples of distant relatives have a simple but nontrivial structure indicating that observed phenotypic dynamics on the genealogical tree is approximately conformal--a symmetry characteristic of critical behavior in physical systems. The proposed inference method is sufficiently general to be applied in any system where lineage information is available. PMID:25902540

  1. The Anatomy of a Fumarole inferred from a 3-D High-Resolution Electrical Resistivity Image of Solfatara Hydrothermal System (Phlegrean Fields, Italy)

    NASA Astrophysics Data System (ADS)

    Gresse, M.; Vandemeulebrouck, J.; Chiodini, G.; Byrdina, S.; Lebourg, T.; Johnson, T. C.

    2015-12-01

    Solfatara, the most active crater in the Phlegrean Fields volcanic complex, shows since ten years a remarkable renewal of activity characterized by an increase of CO2 total degassing from 1500 up to 3000 tons/day, associated with a large ground uplift (Chiodini et al., 2015). In order to precisely image the structure of the shallow hydrothermal system, we performed an extended electrical DC resistivity survey at Solfatara, with about 40 2-D profiles of length up to 1 km, as well as soil temperature and CO2 flux measurements over the area. We then realized a 3-D inversion from the ~40 000 resistivity data points, using E4D code (Johnson et al., 2010). At large scale, results clearly delineate two contrasted structures: - A very conductive body (resistivity < 5 Ohm.m) located beneath the Fangaia mud pools, and likely associated to a mineralized liquid rich plume. - An elongated more resistive body (20-30 Ohm.m) connected to the main fumarolic area and interpreted as the gas reservoir feeding the fumaroles. At smaller scale, our resistivity model originally highlights the 3-D anatomy of a fumarole and the interactions between condensate layers and gas chimneys. This high-resolution image of the shallow hydrothermal structure is a new step for the modeling of this system.

  2. 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.

  3. Transient processes in Stromboli's shallow basaltic system inferred from dolerite and magmatic breccia blocks erupted during the 5 April 2003 paroxysm

    NASA Astrophysics Data System (ADS)

    Renzulli, Alberto; Del Moro, Stefano; Menna, Michele; Landi, Patrizia; Piermattei, Marco

    2009-09-01

    shallow basaltic system during the late evening of 28 December 2002 coupled with the short break in the summit persistent explosions between December 2002 and March 2003 permitted the CR magma pockets to solidify as dolerites, which were confined to the uppermost portion of the system and thus not involved in the ongoing flank effusive activity. Crystal size distribution of the basaltic blocks and crystallization of the finer-grained (<0.1 mm) mafic minerals of the dolerites over a time interval of ˜100 days closely agrees with the above interpretation. Vesicle filling (miarolitic cavities) locally found in some dolerites, with minerals deposited as vapor-phase crystallization is a result of continuous gas percolation through the rocks of the uppermost portion of the volcanic system. Poorly welded magmatic breccias formed during syn-eruptive processes of 5 April 2003, when the paroxysm strongly shattered the shallow subvolcanic system and many dolerite fragments were entrapped in the CR magma. In contrast, the high degree of welding between the dolerite clasts and the CR basaltic matrix in the strongly welded magmatic breccias provides a snapshot of subvolcanic intrusions of the CR basalt into the dolerite when, after a 2-month break in activity, CR magmas started to rise again to the summit craters. Blocks similar to these subvolcanic ejecta of 5 April 2003 were also erupted during previous paroxysms (e.g., 1930) suggesting that changes in the usual Strombolian activity (e.g., short breaks in the persistent mild explosions and/or flank effusive activity) lead to transient crystallization of dolerites in the shallow plumbing system.

  4. 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.

  5. 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

  6. 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

  7. Investigating the usefulness of satellite-derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system

    NASA Astrophysics Data System (ADS)

    Koffi, E. N.; Rayner, P. J.; Norton, A. J.; Frankenberg, C.; Scholze, M.

    2015-07-01

    Simulations of carbon fluxes with terrestrial biosphere models still exhibit significant uncertainties, in part due to the uncertainty in model parameter values. With the advent of satellite measurements of solar induced chlorophyll fluorescence (SIF), there exists a novel pathway for constraining simulated carbon fluxes and parameter values. We investigate the utility of SIF in constraining gross primary productivity (GPP). As a first test we assess whether SIF simulations are sensitive to important parameters in a biosphere model. SIF measurements at the wavelength of 755 nm are simulated by the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. Idealized sensitivity tests of the SCOPE model stand-alone indicate strong sensitivity of GPP to the carboxylation capacity (Vcmax) and of SIF to the chlorophyll AB content (Cab) and incoming short wave radiation. Low sensitivity is found for SIF to Vcmax, however the relationship is subtle, with increased sensitivity under high radiation conditions and lower Vcmax ranges. CCDAS simulates well the patterns of satellite-measured SIF suggesting the combined model is capable of ingesting the data. CCDAS supports the idealized sensitivity tests of SCOPE, with SIF exhibiting sensitivity to Cab and incoming radiation, both of which are treated as perfectly known in previous CCDAS versions. These results demonstrate the need for careful consideration of Cab and incoming radiation when interpreting SIF and the limitations of utilizing SIF to constrain Vcmax in the present set-up in the CCDAS system.

  8. 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

  9. Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province, Zambia.

    PubMed

    Alderton, Simon; Noble, Jason; Schaten, Kathrin; Welburn, Susan C; Atkinson, Peter M

    2015-01-01

    In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra's algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread. PMID:26421926

  10. 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

  11. Evolution of an Intermontane Basin Along the Northern San Andreas System: Evidence from Basin Structure of Little Lake Valley (Willits), Northern California Inferred from Gravity and Geologic Data

    NASA Astrophysics Data System (ADS)

    Erickson, G.; Kelsey, H.; Langenheim, V.; Furlong, K.

    2007-12-01

    Associated with the northern strands of the San Andreas fault system in California is a series of small intermontane basins. While it is tempting to ascribe their formation to simple pull-apart tectonics along the dominantly strike-slip fault strands, direct evidence for basin genesis is lacking. In this study, a detailed gravity survey throughout the Little Lake Valley region (Willits, California) provides constraints on mechanisms of basin formation along this young segment of the San Andreas fault system. Interpretation of isostatic gravity anomaly data provides insight into fault geometry, basin structure, and thickness of Quaternary fill in Little Lake Valley, California. Although the active strike-slip Maacama fault zone diagonally trends through the southwest part of the valley, gravity and geologic interpretations indicate the valley conceals an earlier basin and faulting history. The isostatic gravity anomaly of the basin is negative (up to 13 mGals) and rhombic in shape. Modeling indicates two splays, less than a km apart, of an up-to-the-east East Valley fault; the basinward fault is buried by fill and the more easterly fault defines the eastern margin of the basin. Cumulative up-to-the-east vertical fault displacement along the East Valley fault increases southward up to 610 m in the southern portion of the valley. Gravity gradients also suggest approximately east-west trending faults bound the northern and southern sides of the valley and offset Quaternary fill. From gravity and geologic data combined, the basin floor dips approximately 7 degrees to the south in the north part of the valley and both the Quaternary sediment and basin floor dip approximately 13 degrees to the north in the south part of the valley, implying an approximately east-west axis of dip reversal of the basin floor at the northern stretch of East Hill Road (latitude 39.39 degrees N). Faults and basin fill structure are not consistent with any one simple structural model of basin

  12. 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.

  13. 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

  14. 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

  15. 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. PMID:23242675

  16. 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.

  17. 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.

  18. 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

  19. Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

    NASA Astrophysics Data System (ADS)

    Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

    2016-02-01

    All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

  20. The Laws of Natural Deduction in Inference by DNA Computer

    PubMed Central

    Sosík, Petr

    2014-01-01

    We present a DNA-based implementation of reaction system with molecules encoding elements of the propositional logic, that is, propositions and formulas. The protocol can perform inference steps using, for example, modus ponens and modus tollens rules and de Morgan's laws. The set of the implemented operations allows for inference of formulas using the laws of natural deduction. The system can also detect whether a certain proposition a can be deduced from the basic facts and given rules. The whole protocol is fully autonomous; that is, after introducing the initial set of molecules, no human assistance is needed. Only one restriction enzyme is used throughout the inference process. Unlike some other similar implementations, our improved design allows representing simultaneously a fact a and its negation ~a, including special reactions to detect the inconsistency, that is, a simultaneous occurrence of a fact and its negation. An analysis of correctness, completeness, and complexity is included. PMID:25133261

  1. Information Theory, Inference and Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Mackay, David J. C.

    2003-10-01

    Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

  2. Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

    NASA Astrophysics Data System (ADS)

    Khoshbin, Fatemeh; Bonakdari, Hossein; Hamed Ashraf Talesh, Seyed; Ebtehaj, Isa; Zaji, Amir Hossein; Azimi, Hamed

    2016-06-01

    In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron-artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.

  3. Comparison of an adaptive neuro-fuzzy inference system and an artificial neural network in the cross-talk correction of simultaneous 99 m Tc / 201Tl SPECT imaging using a GATE Monte-Carlo simulation

    NASA Astrophysics Data System (ADS)

    Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad

    2014-09-01

    The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99 m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman's rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.

  4. 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…

  5. 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)

  6. Bayesian Inference of Tumor Hypoxia

    NASA Astrophysics Data System (ADS)

    Gunawan, R.; Tenti, G.; Sivaloganathan, S.

    2009-12-01

    Tumor hypoxia is a state of oxygen deprivation in tumors. It has been associated with aggressive tumor phenotypes and with increased resistance to conventional cancer therapies. In this study, we report on the application of Bayesian sequential analysis in estimating the most probable value of tumor hypoxia quantification based on immunohistochemical assays of a biomarker. The `gold standard' of tumor hypoxia assessment is a direct measurement of pO2 in vivo by the Eppendorf polarographic electrode, which is an invasive technique restricted to accessible sites and living tissues. An attractive alternative is immunohistochemical staining to detect proteins expressed by cells during hypoxia. Carbonic anhydrase IX (CAIX) is an enzyme expressed on the cell membrane during hypoxia to balance the immediate extracellular microenvironment. CAIX is widely regarded as a surrogate marker of chronic hypoxia in various cancers. The study was conducted with two different experimental procedures. The first data set was a group of three patients with invasive cervical carcinomas, from which five biopsies were obtained. Each of the biopsies was fully sectioned and from each section, the proportion of CAIX-positive cells was estimated. Measurements were made by image analysis of multiple deep sections cut through these biopsies, labeled for CAIX using both immunofluorescence and immunohistochemical techniques [1]. The second data set was a group of 24 patients, also with invasive cervical carcinomas, from which two biopsies were obtained. Bayesian parameter estimation was applied to obtain a reliable inference about the proportion of CAIX-positive cells within the carcinomas, based on the available biopsies. From the first data set, two to three biopsies were found to be sufficient to infer the overall CAIX percentage in the simple form: best estimate±uncertainty. The second data-set led to a similar result in 70% of the cases. In the remaining cases Bayes' theorem warned us

  7. 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.

  8. Universal Darwinism As a Process of Bayesian Inference.

    PubMed

    Campbell, John O

    2016-01-01

    Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. PMID:27375438

  9. Entropic Biological Score: a cell cycle investigation for GRNs inference.

    PubMed

    Lopes, Fabrício M; Ray, Shubhra Sankar; Hashimoto, Ronaldo F; Cesar, Roberto M

    2014-05-15

    Inference of gene regulatory networks (GRNs) is one of the most challenging research problems of Systems Biology. In this investigation, a new GRNs inference methodology, called Entropic Biological Score (EBS), which linearly combines the mean conditional entropy (MCE) from expression levels and a Biological Score (BS), obtained by integrating different biological data sources, is proposed. The EBS is validated with the Cell Cycle related functional annotation information, available from Munich Information Center for Protein Sequences (MIPS), and compared with some existing methods like MRNET, ARACNE, CLR and MCE for GRNs inference. For real networks, the performance of EBS, which uses the concept of integrating different data sources, is found to be superior to the aforementioned inference methods. The best results for EBS are obtained by considering the weights w1=0.2 and w2=0.8 for MCE and BS values, respectively, where approximately 40% of the inferred connections are found to be correct and significantly better than related methods. The results also indicate that expression profile is able to recover some true connections, that are not present in biological annotations, thus leading to the possibility of discovering new relations between its genes. PMID:24631265

  10. An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, M.

    2013-09-01

    Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.

  11. 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.

  12. Transdimensional inference in the geosciences.

    PubMed

    Sambridge, M; Bodin, T; Gallagher, K; Tkalcic, H

    2013-02-13

    Seismologists construct images of the Earth's interior structure using observations, derived from seismograms, collected at the surface. A common approach to such inverse problems is to build a single 'best' Earth model, in some sense. This is despite the fact that the observations by themselves often do not require, or even allow, a single best-fit Earth model to exist. Interpretation of optimal models can be fraught with difficulties, particularly when formal uncertainty estimates become heavily dependent on the regularization imposed. Similar issues occur across the physical sciences with model construction in ill-posed problems. An alternative approach is to embrace the non-uniqueness directly and employ an inference process based on parameter space sampling. Instead of seeking a best model within an optimization framework, one seeks an ensemble of solutions and derives properties of that ensemble for inspection. While this idea has itself been employed for more than 30 years, it is now receiving increasing attention in the geosciences. Recently, it has been shown that transdimensional and hierarchical sampling methods have some considerable benefits for problems involving multiple parameter types, uncertain data errors and/or uncertain model parametrizations, as are common in seismology. Rather than being forced to make decisions on parametrization, the level of data noise and the weights between data types in advance, as is often the case in an optimization framework, the choice can be informed by the data themselves. Despite the relatively high computational burden involved, the number of areas where sampling methods are now feasible is growing rapidly. The intention of this article is to introduce concepts of transdimensional inference to a general readership and illustrate with particular seismological examples. A growing body of references provide necessary detail. PMID:23277604

  13. 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.

  14. Methods for causal inference from gene perturbation experiments and validation.

    PubMed

    Meinshausen, Nicolai; Hauser, Alain; Mooij, Joris M; Peters, Jonas; Versteeg, Philip; Bühlmann, Peter

    2016-07-01

    Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques. PMID:27382150

  15. Methods for causal inference from gene perturbation experiments and validation

    PubMed Central

    Meinshausen, Nicolai; Hauser, Alain; Mooij, Joris M.; Peters, Jonas; Versteeg, Philip; Bühlmann, Peter

    2016-01-01

    Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae. The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques. PMID:27382150

  16. 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.

  17. 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.

  18. Neural representation of probabilities for Bayesian inference.

    PubMed

    Rich, Dylan; Cazettes, Fanny; Wang, Yunyan; Peña, José Luis; Fischer, Brian J

    2015-04-01

    Bayesian models are often successful in describing perception and behavior, but the neural representation of probabilities remains in question. There are several distinct proposals for the neural representation of probabilities, but they have not been directly compared in an example system. Here we consider three models: a non-uniform population code where the stimulus-driven activity and distribution of preferred stimuli in the population represent a likelihood function and a prior, respectively; the sampling hypothesis which proposes that the stimulus-driven activity over time represents a posterior probability and that the spontaneous activity represents a prior; and the class of models which propose that a population of neurons represents a posterior probability in a distributed code. It has been shown that the non-uniform population code model matches the representation of auditory space generated in the owl's external nucleus of the inferior colliculus (ICx). However, the alternative models have not been tested, nor have the three models been directly compared in any system. Here we tested the three models in the owl's ICx. We found that spontaneous firing rate and the average stimulus-driven response of these neurons were not consistent with predictions of the sampling hypothesis. We also found that neural activity in ICx under varying levels of sensory noise did not reflect a posterior probability. On the other hand, the responses of ICx neurons were consistent with the non-uniform population code model. We further show that Bayesian inference can be implemented in the non-uniform population code model using one spike per neuron when the population is large and is thus able to support the rapid inference that is necessary for sound localization. PMID:25561333

  19. Bayesian Nonparametric Inference – Why and How

    PubMed Central

    Müller, Peter; Mitra, Riten

    2013-01-01

    We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP. PMID:24368932

  20. Inference engine using optical array logic

    NASA Astrophysics Data System (ADS)

    Iwata, Masaya; Tanida, Jun; Ichioka, Yoshiki

    1990-07-01

    An implementation method for an inference engine using optical array logic is presented. Optical array logic is a technique for parallel neighborhood operation using spatial coding and 2-D correlation. For efficient execution of inference in artificial intelligence problems, a large number of data must be searched effectively. To achieve this demand, a template matching technique is applied to the inference operation. By introducing a new function of data conversion, the inference operation can be implemented with optical array logic, which utilizes parallelism in optical techniques.

  1. Likelihood free inference for Markov processes: a comparison.

    PubMed

    Owen, Jamie; Wilkinson, Darren J; Gillespie, Colin S

    2015-04-01

    Approaches to Bayesian inference for problems with intractable likelihoods have become increasingly important in recent years. Approximate Bayesian computation (ABC) and "likelihood free" Markov chain Monte Carlo techniques are popular methods for tackling inference in these scenarios but such techniques are computationally expensive. In this paper we compare the two approaches to inference, with a particular focus on parameter inference for stochastic kinetic models, widely used in systems biology. Discrete time transition kernels for models of this type are intractable for all but the most trivial systems yet forward simulation is usually straightforward. We discuss the relative merits and drawbacks of each approach whilst considering the computational cost implications and efficiency of these techniques. In order to explore the properties of each approach we examine a range of observation regimes using two example models. We use a Lotka-Volterra predator-prey model to explore the impact of full or partial species observations using various time course observations under the assumption of known and unknown measurement error. Further investigation into the impact of observation error is then made using a Schlögl system, a test case which exhibits bi-modal state stability in some regions of parameter space. PMID:25720092

  2. Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Models for Predicting the Weld Bead Width and Depth of Penetration from the Infrared Thermal Image of the Weld Pool

    NASA Astrophysics Data System (ADS)

    Subashini, L.; Vasudevan, M.

    2012-02-01

    Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld bead width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, width of the hot spot, thermal area determined from the Gaussian fit, and thermal bead width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld bead width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld bead width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld bead width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models.

  3. 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/. PMID:26935399

  4. 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.

  5. Network geometry inference using common neighbors.

    PubMed

    Papadopoulos, Fragkiskos; Aldecoa, Rodrigo; Krioukov, Dmitri

    2015-08-01

    We introduce and explore a method for inferring hidden geometric coordinates of nodes in complex networks based on the number of common neighbors between the nodes. We compare this approach to the HyperMap method, which is based only on the connections (and disconnections) between the nodes, i.e., on the links that the nodes have (or do not have). We find that for high degree nodes, the common-neighbors approach yields a more accurate inference than the link-based method, unless heuristic periodic adjustments (or "correction steps") are used in the latter. The common-neighbors approach is computationally intensive, requiring O(t4) running time to map a network of t nodes, versus O(t3) in the link-based method. But we also develop a hybrid method with O(t3) running time, which combines the common-neighbors and link-based approaches, and we explore a heuristic that reduces its running time further to O(t2), without significant reduction in the mapping accuracy. We apply this method to the autonomous systems (ASs) Internet, and we reveal how soft communities of ASs evolve over time in the similarity space. We further demonstrate the method's predictive power by forecasting future links between ASs. Taken altogether, our results advance our understanding of how to efficiently and accurately map real networks to their latent geometric spaces, which is an important necessary step toward understanding the laws that govern the dynamics of nodes in these spaces, and the fine-grained dynamics of network connections. PMID:26382454

  6. 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

  7. 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

  8. 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…

  9. 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…

  10. 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…

  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. 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.…

  13. Genetic Network Inference Using Hierarchical Structure.

    PubMed

    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

  14. 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…

  15. Reinforcement learning or active inference?

    PubMed

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

    2009-01-01

    This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain. PMID:19641614

  16. 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

  17. 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

  18. Active inference and epistemic value.

    PubMed

    Friston, Karl; Rigoli, Francesco; Ognibene, Dimitri; Mathys, Christoph; Fitzgerald, Thomas; Pezzulo, Giovanni

    2015-01-01

    We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms. PMID:25689102

  19. 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.

  20. Classical methods for interpreting objective function minimization as intelligent inference

    SciTech Connect

    Golden, R.M.

    1996-12-31

    Most recognition algorithms and neural networks can be formally viewed as seeking a minimum value of an appropriate objective function during either classification or learning phases. The goal of this paper is to argue that in order to show a recognition algorithm is making intelligent inferences, it is not sufficient to show that the recognition algorithm is computing (or trying to compute) the global minimum of some objective function. One must explicitly define a {open_quotes}relational system{close_quotes} for the recognition algorithm or neural network which identifies the: (i) sample space, (ii) the relevant sigmafield of events generated by the sample space, and (iii) the {open_quotes}relation{close_quotes} for that relational system. Only when such a {open_quotes}relational system{close_quotes} is properly defined, is it possible to formally establish the sense in which computing the global minimum of an objective function is an intelligent, inference.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. Bayesian inference for Markov jump processes with informative observations.

    PubMed

    Golightly, Andrew; Wilkinson, Darren J

    2015-04-01

    In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC. Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process. When observations are highly informative, use of the forward simulator is likely to be inefficient and may even preclude an exact (simulation based) analysis. We therefore propose three methods for improving the efficiency of simulating conditioned jump processes. A conditioned hazard is derived based on an approximation to the jump process, and used to generate end-point conditioned trajectories for use inside an importance sampling algorithm. We also adapt a recently proposed sequential Monte Carlo scheme to our problem. Essentially, trajectories are reweighted at a set of intermediate time points, with more weight assigned to trajectories that are consistent with the next observation. We consider two implementations of this approach, based on two continuous approximations of the MJP. We compare these constructs for a simple tractable jump process before using them to perform inference for a Lotka-Volterra system. The best performing construct is used to infer the parameters governing a simple model of motility regulation in Bacillus subtilis. PMID:25720091

  8. 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.

  9. Metamodel-Driven Evolution with Grammar Inference

    NASA Astrophysics Data System (ADS)

    Bryant, Barrett R.; Liu, Qichao; Mernik, Marjan

    2010-10-01

    Domain-specific modeling (DSM) has become one of the most popular techniques for incorporating model-driven engineering (MDE) into software engineering. In DSM, domain experts define metamodels to describe the essential problems in a domain. A model conforms to a schema definition represented by a metamodel in a similar manner to a programming language conforms to a grammar. Metamodel-driven evolution is when a metamodel undergoes evolutions to incorporate new concerns in the domain. However, this results in losing the ability to use existing model instances. Grammar inference is the problem of inferring a grammar from sample strings which the grammar should generate. This paper describes our work in solving the problem of metamodel-driven evolution with grammar inference, by inferring the metamodel from model instances.

  10. Critical Thinking: Distinguishing between Inferences and Assumptions.

    ERIC Educational Resources Information Center

    Elder, Linda; Paul, Richard

    2002-01-01

    Outlines the differences between inferences and assumptions in critical thinking processes. Explains that as students develop critical intuitions, they increasingly notice how their point of view shapes their experiences. (AUTH/NB)

  11. Are Evaluations Inferred Directly From Overt Actions?

    ERIC Educational Resources Information Center

    Brown, Donald; And Others

    1975-01-01

    The operation of a covert information processing mechanism was investigated in two experiments of the self-persuasion phenomena; i. e., making an inference about a stimulus on the basis of one's past behavior. (Editor)

  12. Multisensory causal inference in the brain.

    PubMed

    Kayser, Christoph; Shams, Ladan

    2015-02-01

    At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic inference, known as Bayesian causal inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory causal inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the causal inference process with specific visual and parietal regions. PMID:25710476

  13. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-01

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

  14. 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

  15. 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.

  16. 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

  17. How difficult is inference of mammalian causal gene regulatory networks?

    PubMed

    Djordjevic, Djordje; Yang, Andrian; Zadoorian, Armella; Rungrugeecharoen, Kevin; Ho, Joshua W K

    2014-01-01

    Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for

  18. Biological Network Inference and Analysis using SEBINI and CABIN

    SciTech Connect

    Taylor, Ronald C.; Singhal, Mudita

    2008-01-01

    Attaining a detailed understanding of the various biological networks in an organism lies at the core of the emerging discipline of systems biology. A precise description of the relationships formed between genes, mRNA molecules, and proteins is a necessary step toward a complete description of the dynamic behavior of an organism at the cellular level; and towards intelligent, efficient and directed modification of an organism. The importance of understanding such regulatory, signaling, and interaction networks has fueled the development of numerous in silico inference algorithms, as well as new experimental techniques and a growing collection of public databases. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, evaluation, and improvement of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to analyze high-throughput gene expression, protein expression, or protein activation data via a suite of state-of-the-art network inference algorithms. It also allows algorithm developers to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. SEBINI can therefore be used by software developers wishing to evaluate, refine, or combine inference techniques, as well as by bioinformaticians analyzing experimental data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, which is exploratory data analysis software that enables integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. The collection of edges in public databases, along with the confidence held in each edge (if available), can be fed into CABIN as one “evidence network”, using the Cytoscape SIF file format. Using CABIN, one may

  19. Role of fluids in faulting inferred from stress field signatures

    PubMed

    Hardebeck; Hauksson

    1999-07-01

    The stress orientation signature of weak faults containing high-pressure fluids has been observed for segments of the San Andreas fault system in southern California. The inferred lithostatic fluid pressures extend into the surrounding relatively intact rock in a zone scaling with the width of the interseismic strain accumulation. Repeated strain-related fracturing and crack sealing may have created low-permeability barriers that seal fluids into the network of currently active fractures. PMID:10398596

  20. Bayesian inference with adaptive fuzzy priors and likelihoods.

    PubMed

    Osoba, Osonde; Mitaim, Sanya; Kosko, Bart

    2011-10-01

    Fuzzy rule-based systems can approximate prior and likelihood probabilities in Bayesian inference and thereby approximate posterior probabilities. This fuzzy approximation technique allows users to apply a much wider and more flexible range of prior and likelihood probability density functions than found in most Bayesian inference schemes. The technique does not restrict the user to the few known closed-form conjugacy relations between the prior and likelihood. It allows the user in many cases to describe the densities with words and just two rules can absorb any bounded closed-form probability density directly into the rulebase. Learning algorithms can tune the expert rules as well as grow them from sample data. The learning laws and fuzzy approximators have a tractable form because of the convex-sum structure of additive fuzzy systems. This convex-sum structure carries over to the fuzzy posterior approximator. We prove a uniform approximation theorem for Bayesian posteriors: An additive fuzzy posterior uniformly approximates the posterior probability density if the prior or likelihood densities are continuous and bounded and if separate additive fuzzy systems approximate the prior and likelihood densities. Simulations demonstrate this fuzzy approximation of priors and posteriors for the three most common conjugate priors (as when a beta prior combines with a binomial likelihood to give a beta posterior). Adaptive fuzzy systems can also approximate non-conjugate priors and likelihoods as well as approximate hyperpriors in hierarchical Bayesian inference. The number of fuzzy rules can grow exponentially in iterative Bayesian inference if the previous posterior approximator becomes the new prior approximator. PMID:21478078

  1. 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.

  2. A network inference workflow applied to virulence-related processes in Salmonella typhimurium.

    PubMed

    Taylor, Ronald C; Singhal, Mudita; Weller, Jennifer; Khoshnevis, Saeed; Shi, Liang; McDermott, Jason

    2009-03-01

    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 Interaction Networks (CABIN) tool for further postanalysis 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. PMID:19348639

  3. Active Inference and Learning in the Cerebellum.

    PubMed

    Friston, Karl; Herreros, Ivan

    2016-09-01

    This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry-and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception. PMID:27391681

  4. The renormalization group via statistical inference

    NASA Astrophysics Data System (ADS)

    Bény, Cédric; Osborne, Tobias J.

    2015-08-01

    In physics, one attempts to infer the rules governing a system given only the results of imperfect measurements. Hence, microscopic theories may be effectively indistinguishable experimentally. We develop an operationally motivated procedure to identify the corresponding equivalence classes of states, and argue that the renormalization group (RG) arises from the inherent ambiguities associated with the classes: one encounters flow parameters as, e.g., a regulator, a scale, or a measure of precision, which specify representatives in a given equivalence class. This provides a unifying framework and reveals the role played by information in renormalization. We validate this idea by showing that it justifies the use of low-momenta n-point functions as statistically relevant observables around a Gaussian hypothesis. These results enable the calculation of distinguishability in quantum field theory. Our methods also provide a way to extend renormalization techniques to effective models which are not based on the usual quantum-field formalism, and elucidates the relationships between various type of RG.

  5. Functional network inference of the suprachiasmatic nucleus.

    PubMed

    Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel; St John, Peter C; Wang, Thomas J; Bales, Benjamin B; Doyle, Francis J; Herzog, Erik D; Petzold, Linda R

    2016-04-19

    In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure. PMID:27044085

  6. 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.

  7. 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.

  8. 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. PMID:25617791

  9. Improving the extraction of complex regulatory events from scientific text by using ontology-based inference

    PubMed Central

    2011-01-01

    Background The extraction of complex events from biomedical text is a challenging task and requires in-depth semantic analysis. Previous approaches associate lexical and syntactic resources with ontologies for the semantic analysis, but fall short in testing the benefits from the use of domain knowledge. Results We developed a system that deduces implicit events from explicitly expressed events by using inference rules that encode domain knowledge. We evaluated the system with the inference module on three tasks: First, when tested against a corpus with manually annotated events, the inference module of our system contributes 53.2% of correct extractions, but does not cause any incorrect results. Second, the system overall reproduces 33.1% of the transcription regulatory events contained in RegulonDB (up to 85.0% precision) and the inference module is required for 93.8% of the reproduced events. Third, we applied the system with minimum adaptations to the identification of cell activity regulation events, confirming that the inference improves the performance of the system also on this task. Conclusions Our research shows that the inference based on domain knowledge plays a significant role in extracting complex events from text. This approach has great potential in recognizing the complex concepts of such biomedical ontologies as Gene Ontology in the literature. PMID:22166672

  10. 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

  11. 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.

  12. Mental state inference using visual control parameters.

    PubMed

    Oztop, Erhan; Wolpert, Daniel; Kawato, Mitsuo

    2005-02-01

    Although we can often infer the mental states of others by observing their actions, there are currently no computational models of this remarkable ability. Here we develop a computational model of mental state inference that builds upon a generic visuomanual feedback controller, and implements mental simulation and mental state inference functions using circuitry that subserves sensorimotor control. Our goal is (1) to show that control mechanisms developed for manual manipulation are readily endowed with visual and predictive processing capabilities and thus allows a natural extension to the understanding of movements performed by others; and (2) to give an explanation on how cortical regions, in particular the parietal and premotor cortices, may be involved in such dual mechanism. To analyze the model, we simulate tasks in which an observer watches an actor performing either a reaching or a grasping movement. The observer's goal is to estimate the 'mental state' of the actor: the goal of the reaching movement or the intention of the agent performing the grasping movement. We show that the motor modules of the observer can be used in a 'simulation mode' to infer the mental state of the actor. The simulations with different grasping and non-straight line reaching strategies show that the mental state inference model is applicable to complex movements. Moreover, we simulate deceptive reaching, where an actor imposes false beliefs about his own mental state on an observer. The simulations show that computational elements developed for sensorimotor control are effective in inferring the mental states of others. The parallels between the model and cortical organization of movement suggest that primates might have developed a similar resource utilization strategy for action understanding, and thus lead to testable predictions about the brain mechanisms of mental state inference. PMID:15653289

  13. 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.

  14. 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.

  15. Estimating uncertainty of inference for validation

    SciTech Connect

    Booker, Jane M; Langenbrunner, James R; Hemez, Francois M; Ross, Timothy J

    2010-09-30

    We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the

  16. Deep Learning for Population Genetic Inference

    PubMed Central

    Sheehan, Sara; Song, Yun S.

    2016-01-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

  17. Scene Construction, Visual Foraging, and Active Inference

    PubMed Central

    Mirza, M. Berk; Adams, Rick A.; Mathys, Christoph D.; Friston, Karl J.

    2016-01-01

    This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia). PMID:27378899

  18. Scene Construction, Visual Foraging, and Active Inference.

    PubMed

    Mirza, M Berk; Adams, Rick A; Mathys, Christoph D; Friston, Karl J

    2016-01-01

    This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia). PMID:27378899

  19. Computationally efficient Bayesian inference for inverse problems.

    SciTech Connect

    Marzouk, Youssef M.; Najm, Habib N.; Rahn, Larry A.

    2007-10-01

    Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced.

  20. Deep Learning for Population Genetic Inference.

    PubMed

    Sheehan, Sara; Song, Yun S

    2016-03-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

  1. Hierarchical cosmic shear power spectrum inference

    NASA Astrophysics Data System (ADS)

    Alsing, Justin; Heavens, Alan; Jaffe, Andrew H.; Kiessling, Alina; Wandelt, Benjamin; Hoffmann, Till

    2016-02-01

    We develop a Bayesian hierarchical modelling approach for cosmic shear power spectrum inference, jointly sampling from the posterior distribution of the cosmic shear field and its (tomographic) power spectra. Inference of the shear power spectrum is a powerful intermediate product for a cosmic shear analysis, since it requires very few model assumptions and can be used to perform inference on a wide range of cosmological models a posteriori without loss of information. We show that joint posterior for the shear map and power spectrum can be sampled effectively by Gibbs sampling, iteratively drawing samples from the map and power spectrum, each conditional on the other. This approach neatly circumvents difficulties associated with complicated survey geometry and masks that plague frequentist power spectrum estimators, since the power spectrum inference provides prior information about the field in masked regions at every sampling step. We demonstrate this approach for inference of tomographic shear E-mode, B-mode and EB-cross power spectra from a simulated galaxy shear catalogue with a number of important features; galaxies distributed on the sky and in redshift with photometric redshift uncertainties, realistic random ellipticity noise for every galaxy and a complicated survey mask. The obtained posterior distributions for the tomographic power spectrum coefficients recover the underlying simulated power spectra for both E- and B-modes.

  2. Inferring learners' knowledge from their actions.

    PubMed

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

    2015-04-01

    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 develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments. Our approach relies on modeling action planning: We formalize the problem as a Markov decision process in which one must choose what actions to take to complete a goal, where choices will be dependent on one's beliefs about how actions affect the environment. We use a variation of inverse reinforcement learning to infer these beliefs. Through two lab experiments, we show that this model can recover people's beliefs in a simple environment, with accuracy comparable to that of human observers. We then demonstrate that the model can be used to provide real-time feedback and to model data from an existing educational game. PMID:25155381

  3. Cluster Mass Inference via Random Field Theory

    PubMed Central

    Zhang, Hui; Nichols, Thomas E.; Johnson, Timothy D.

    2009-01-01

    Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference method available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single-subject and a group fMRI dataset demonstrate better power than traditional cluster extent inference, and good accuracy relative to a gold-standard permutation test. PMID:18805493

  4. Inferring Mathematical Equations Using Crowdsourcing

    PubMed Central

    Wasik, Szymon

    2015-01-01

    Crowdsourcing, understood as outsourcing work to a large network of people in the form of an open call, has been utilized successfully many times, including a very interesting concept involving the implementation of computer games with the objective of solving a scientific problem by employing users to play a game—so-called crowdsourced serious games. Our main objective was to verify whether such an approach could be successfully applied to the discovery of mathematical equations that explain experimental data gathered during the observation of a given dynamic system. Moreover, we wanted to compare it with an approach based on artificial intelligence that uses symbolic regression to find such formulae automatically. To achieve this, we designed and implemented an Internet game in which players attempt to design a spaceship representing an equation that models the observed system. The game was designed while considering that it should be easy to use for people without strong mathematical backgrounds. Moreover, we tried to make use of the collective intelligence observed in crowdsourced systems by enabling many players to collaborate on a single solution. The idea was tested on several hundred players playing almost 10,000 games and conducting a user opinion survey. The results prove that the proposed solution has very high potential. The function generated during weeklong tests was almost as precise as the analytical solution of the model of the system and, up to a certain complexity level of the formulae, it explained data better than the solution generated automatically by Eureqa, the leading software application for the implementation of symbolic regression. Moreover, we observed benefits of using crowdsourcing; the chain of consecutive solutions that led to the best solution was obtained by the continuous collaboration of several players. PMID:26713846

  5. Inferring Mathematical Equations Using Crowdsourcing.

    PubMed

    Wasik, Szymon; Fratczak, Filip; Krzyskow, Jakub; Wulnikowski, Jaroslaw

    2015-01-01

    Crowdsourcing, understood as outsourcing work to a large network of people in the form of an open call, has been utilized successfully many times, including a very interesting concept involving the implementation of computer games with the objective of solving a scientific problem by employing users to play a game-so-called crowdsourced serious games. Our main objective was to verify whether such an approach could be successfully applied to the discovery of mathematical equations that explain experimental data gathered during the observation of a given dynamic system. Moreover, we wanted to compare it with an approach based on artificial intelligence that uses symbolic regression to find such formulae automatically. To achieve this, we designed and implemented an Internet game in which players attempt to design a spaceship representing an equation that models the observed system. The game was designed while considering that it should be easy to use for people without strong mathematical backgrounds. Moreover, we tried to make use of the collective intelligence observed in crowdsourced systems by enabling many players to collaborate on a single solution. The idea was tested on several hundred players playing almost 10,000 games and conducting a user opinion survey. The results prove that the proposed solution has very high potential. The function generated during weeklong tests was almost as precise as the analytical solution of the model of the system and, up to a certain complexity level of the formulae, it explained data better than the solution generated automatically by Eureqa, the leading software application for the implementation of symbolic regression. Moreover, we observed benefits of using crowdsourcing; the chain of consecutive solutions that led to the best solution was obtained by the continuous collaboration of several players. PMID:26713846

  6. Children's and Adults' Judgments of the Certainty of Deductive Inferences, Inductive Inferences, and Guesses

    ERIC Educational Resources Information Center

    Pillow, Bradford H.; Pearson, RaeAnne M.; Hecht, Mary; Bremer, Amanda

    2010-01-01

    Children and adults rated their own certainty following inductive inferences, deductive inferences, and guesses. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Deductions were rated as more certain than strong inductions beginning in Grade 3, and fourth-grade children and adults…

  7. The Observation/Inference Chart: Improving Students' Abilities to Make Inferences while Reading Nontraditional Texts

    ERIC Educational Resources Information Center

    Nokes, Jeffery D.

    2008-01-01

    The Observation/Inference (OI) Chart is a strategy that can help students learn to make observations and inferences when reading nontraditional texts such as artifacts, paintings or movies. Nontraditional texts can be highly engaging and provide authentic thinking experiences for students, but they can also be difficult to comprehend. Teachers can…

  8. Early Paleozoic crust-mantle interaction and lithosphere delamination in South China Block: Evidence from geochronology, geochemistry, and Sr-Nd-Hf isotopes of granites

    NASA Astrophysics Data System (ADS)

    Xia, Yan; Xu, Xisheng; Zou, Haibo; Liu, Lei

    2014-01-01

    The early Paleozoic orogen in South China Block is an intracontinental orogen, and synchronous magmatism (440-390 Ma) is mainly acidic with minor intermediate-mafic magmatism. Previous studies suggest that most of the early Paleozoic granites in South China belong to peraluminous S-type genesis while amphibole-bearing I-type granites are subordinate. However, our results indicate that considerable amounts of these early Paleozoic granites have characteristics of both S- and I-type granites. Thus, we propose to divide these granites into two groups: fewer of them are Group A with relatively high ɛHf(t) values (clustering within - 3.0 to + 9.0) and ɛNd(t) values (- 5.2 to + 1.3) as well as higher initial temperatures at 810-850 °C, and most of them are Group B with relatively low ɛHf(t) values (clustering within - 16.0 to - 1.0) and ɛNd(t) values (- 13.2 to - 4.1) as well as relatively low initial temperatures at 700-830 °C. The Xiawan monzogranite and Duntou granodiorite are typical Group A granitoids and yield zircon U-Pb ages of ca. 410 Ma. These two granites are characterized by high SiO2 (between 67.59 and 74.87 wt.%), metaluminous to peraluminous (A/CNK = 0.96-1.48) compositions, and a negative correlation between P2O5 and SiO2. Their biotites belong to magnesium biotites, indicating that they have partial features of either I- or S-type granites. Duntou granodiorites exhibit higher ɛHf(t) values (clustering within + 1 to + 8) and ɛNd(t) values (- 3.0 to + 1.1) while Xiawan monzogranites show relatively low ɛHf(t) values (clustering within - 1 to + 5) and ɛNd(t) values (- 5.0 to - 3.7). Group B granitoids are represented by the Miao'ershan-Yuechengling batholith, which are characterized by high SiO2 (between 64.57 and 77.37 wt.%), metaluminous compositions (A/CNK = 0.90-1.24), and a negative correlation between P2O5 and SiO2. Yuechengling porphyritic amphibole-bearing biotite granites in this batholith contain abundant amphibole, indicating that they are I-type granites. Miao'ershan-Yuechengling batholith also exhibits relatively low ɛHf(t) values (- 12.7 to - 1.8) and ɛNd(t) values (- 8.9 to - 6.7).

  9. 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.

  10. 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.

  11. Hydrogen and oxygen isotope evidence for magma degassing and crust-mantle interaction : example from clinopyroxene megacrysts of Nushan volcano, eastern China

    NASA Astrophysics Data System (ADS)

    Deloule, E.; Xia, K. Q.; Dallai, L.

    2003-04-01

    D/H ratio measurements on hydroxylated minerals has been a key point to study the water-rock interactions and the behaviour of water in the solid Earth. Since more than 10 years, ion microprobe allows in situ measurements on the scale of single crystal (Deloule et al, 1991), and during the last years, the detection limit for water measurement by ion probe moves from several hundred ppm (Deloule et al, 1996), to less than 10 ppm (Deloule, 2002, Hauri, 2002). This high sensitivity allow for D/H ratios measurements of nominally anhydrous minerals (NAMs). In this study, hydrogen and oxygen isotope ratios were measured on a suite of 13 clinopyroxene megacrysts from Nushan volcano by ion microprobe and laser fluorination, respectively, in order to constrain the water behaviour during the volcanic process. The Nushan volcanic crater is an isolated eruption center at the southern part of the Tancheng-Lujiang fault, Eastern China. The lava is an alkali basanite dated at 0.63 Ma (Chen and Peng, 1988). Clinopyroxene were collected associated with peridotite and pyroxenite xenoliths and other mineral megacrysts. Among 13 xenocrysts , dD values display large variations from -45 to -112, while each xenocryst displays a constant value in the analytical error (±10). The d18O values vary from +4.8 to +6.5, with both higher and lower values than the published values of mantle clinopyroxenes (+5.1 to +6.2). A positive correlation is observed between dD values and H2O , in agreement with a normal evolution during magma degassing, following a Rayleigh model from dD value of -45 and H2O content of 0.19% down to -110 and 0.12%, respectively. For oxygen isotopes, the samples may be divided in two groups. Group 1 displays higher d18O values (5.33 - 6.48) than ‘normal’ mantle clinopyroxene and negative correlation between d18O and Mg# values [82.5 - 75.9]. Group 2 displays lower d18O values (4.8 - 5.5) and also a negative correlation between d18O and Mg# values [74.5 - 71.6]. The large ranges of variation for oxygen isotope for each group imply cristallisation-assimilation processes, with Group 1 and Group 2 megacrysts having mixed by magmatic stirring. The comparison of hydrogen and oxygen isotope behavior points out that during the cristallization-assimilation process, the overall water content of a basaltic magma is mainly affected by the degassing process, and is decreasing rather than increasing due to the assimilation of material.

  12. Pb, Sr, and Nd isotopic compositions of a suite of Late Archean, igneous rocks, eastern Beartooth Mountains: implications for crust-mantle evolution

    USGS Publications Warehouse

    Wooden, J.L.; Mueller, P.A.

    1988-01-01

    A series of compositionally diverse, Late Archean rocks (2.74-2.79 Ga old) from the eastern Beartooth Mountains, Montana and Wyoming, U.S.A., have the same initial Pb, Sr, and Nd isotopic ratios. Lead and Sr initial ratios are higher and Nd initial ratios lower than would be expected for rocks derived from model mantle sources and strongly indicate the involvement of an older crustal reservoir in the genesis of these rocks. Crustal contamination during emplacement can be ruled out for a variety of reasons. Instead a model involving subduction of continental detritus and contamination of the overlying mantle as is often proposed for modern subduction environments is preferred. This contaminated mantle would have all the isotopic characteristics of mantle enriched by internal mantle metasomatism but would require no long-term growth or changes in parent to daughter element ratios. This contaminated mantle would make a good source for some of the Cenozoic mafic volcanics of the Columbia River, Snake River Plain, and Yellowstone volcanic fields that are proposed to come from ancient, enriched lithospheric mantle. The isotopic characteristics of the 2.70 Ga old Stillwater Complex are a perfect match for the proposed contaminated mantle which provides an alternative to crustal contamination during emplacement. The Pb isotopic characteristics of the Late Archean rocks of the eastern Beartooth Mountains are similar to those of other Late Archean rocks of the Wyoming Province and suggest that Early Archean, upper crustal rocks were common in this terrane. The isotopic signatures of Late Archean rocks in the Wyoming Province are distinctive from those of other Archean cratons in North America which are dominated by a MORB-like, Archean mantle source (Superior Province) and/or a long-term depleted crustal source (Greenland). ?? 1988.

  13. Petrogenesis of the early Cretaceous volcanic rocks in the North Huaiyang tectono-magmatic unit of the Dabie Orogen, eastern China: Implications for crust-mantle interaction

    NASA Astrophysics Data System (ADS)

    Gao, Xin-Yu; Zhao, Tai-Ping; Zhao, Jun-Hong

    2016-03-01

    New elemental and isotopic data are presented for the early Cretaceous felsic to mafic volcanic rocks in the North Huaiyang tectono-magmatic unit (NHY) of the Dabie Orogen, in order to investigate their petrogenesis and provide insights into the nature of the late Mesozoic lithosphere mantle beneath the region and its tectonic relationship with neighboring blocks. LA-ICP-MS zircon U-Pb dating reveals that volcanic rocks of the Jingangtai Formation erupted in a quite short interval about 5 Mys during the Early Cretaceous (128-123 Ma). The rocks have wide ranges of SiO2 (48-68 wt.%) and MgO (0.6-5.6 wt.%) contents. They are enriched in large-ion-lithophile-elements (LILE) (e.g. Rb, Ba) and light rare-earth-elements (LREE), and depleted in high field strength elements (e.g. Nb, Ta and Ti) with weak negative Eu anomalies (Eu/Eu∗ = 0.71-0.94). Meanwhile, the rocks show relatively high whole-rock initial 87Sr/86Sr ratios (0.7074-0.7094), strong negative εNd(t) (-19.1 to -15.8) and zircon εHf values (-20.7 to -14.1). Such typical "continental" geochemical characteristics did not result from crustal contamination during magma ascent, but from an enriched mantle source modified by materials from the subducted Yangtze Craton during the Triassic continental collision. We propose that the petrogenesis of the large-scale contemporaneous magmatism of Dabie Orogen including felsic to mafic volcanic rocks in the NHY reflects an intensive lithospheric thinning and extension during the early Cretaceous as a tectonic response to the change of plate motion of westward subducted Pacific Plate beneath the Eurasian continent.

  14. Using Alien Coins to Test Whether Simple Inference Is Bayesian

    ERIC Educational Resources Information Center

    Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.

    2016-01-01

    Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…