WorldWideScience

Sample records for data-driven event reconstruction

  1. Helioseismic and neutrino data-driven reconstruction of solar properties

    Science.gov (United States)

    Song, Ningqiang; Gonzalez-Garcia, M. C.; Villante, Francesco L.; Vinyoles, Nuria; Serenelli, Aldo

    2018-06-01

    In this work, we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. In particular, we use a Gaussian process to model the functional shape of the opacity uncertainty to gain flexibility and become as free as possible from prejudice in this regard. With these tools we first readdress the statistical significance of the solar composition problem. Furthermore, starting from a composition unbiased set of standard solar models (SSMs) we are able to statistically select those with solar chemical composition and other solar inputs which better describe the helioseismic and neutrino observations. In particular, we are able to reconstruct the solar opacity profile in a data-driven fashion, independently of any reference opacity tables, obtaining a 4 per cent uncertainty at the base of the convective envelope and 0.8 per cent at the solar core. When systematic uncertainties are included, results are 7.5 per cent and 2 per cent, respectively. In addition, we find that the values of most of the other inputs of the SSMs required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor S11 and the microscopic diffusion rates, for which data suggests a 1 per cent and 30 per cent reduction, respectively. As an output of the study we derive the corresponding data-driven predictions for the solar neutrino fluxes.

  2. Estimating the Probability of Wind Ramping Events: A Data-driven Approach

    OpenAIRE

    Wang, Cheng; Wei, Wei; Wang, Jianhui; Qiu, Feng

    2016-01-01

    This letter proposes a data-driven method for estimating the probability of wind ramping events without exploiting the exact probability distribution function (PDF) of wind power. Actual wind data validates the proposed method.

  3. Data-Driven Neural Network Model for Robust Reconstruction of Automobile Casting

    Science.gov (United States)

    Lin, Jinhua; Wang, Yanjie; Li, Xin; Wang, Lu

    2017-09-01

    In computer vision system, it is a challenging task to robustly reconstruct complex 3D geometries of automobile castings. However, 3D scanning data is usually interfered by noises, the scanning resolution is low, these effects normally lead to incomplete matching and drift phenomenon. In order to solve these problems, a data-driven local geometric learning model is proposed to achieve robust reconstruction of automobile casting. In order to relieve the interference of sensor noise and to be compatible with incomplete scanning data, a 3D convolution neural network is established to match the local geometric features of automobile casting. The proposed neural network combines the geometric feature representation with the correlation metric function to robustly match the local correspondence. We use the truncated distance field(TDF) around the key point to represent the 3D surface of casting geometry, so that the model can be directly embedded into the 3D space to learn the geometric feature representation; Finally, the training labels is automatically generated for depth learning based on the existing RGB-D reconstruction algorithm, which accesses to the same global key matching descriptor. The experimental results show that the matching accuracy of our network is 92.2% for automobile castings, the closed loop rate is about 74.0% when the matching tolerance threshold τ is 0.2. The matching descriptors performed well and retained 81.6% matching accuracy at 95% closed loop. For the sparse geometric castings with initial matching failure, the 3D matching object can be reconstructed robustly by training the key descriptors. Our method performs 3D reconstruction robustly for complex automobile castings.

  4. NOvA Event Building, Buffering and Data-Driven Triggering From Within the DAQ System

    Energy Technology Data Exchange (ETDEWEB)

    Fischler, M. [Fermilab; Green, C. [Fermilab; Kowalkowski, J. [Fermilab; Norman, A. [Fermilab; Paterno, M. [Fermilab; Rechenmacher, R. [Fermilab

    2012-06-22

    To make its core measurements, the NOvA experiment needs to make real-time data-driven decisions involving beam-spill time correlation and other triggering issues. NOvA-DDT is a prototype Data-Driven Triggering system, built using the Fermilab artdaq generic DAQ/Event-building tools set. This provides the advantages of sharing online software infrastructure with other Intensity Frontier experiments, and of being able to use any offline analysis module--unchanged--as a component of the online triggering decisions. The NOvA-artdaq architecture chosen has significant advantages, including graceful degradation if the triggering decision software fails or cannot be done quickly enough for some fraction of the time-slice ``events.'' We have tested and measured the performance and overhead of NOvA-DDT using an actual Hough transform based trigger decision module taken from the NOvA offline software. The results of these tests--98 ms mean time per event on only 1/16 of th e available processing power of a node, and overheads of about 2 ms per event--provide a proof of concept: NOvA-DDT is a viable strategy for data acquisition, event building, and trigger processing at the NOvA far detector.

  5. NOvA Event Building, Buffering and Data-Driven Triggering From Within the DAQ System

    International Nuclear Information System (INIS)

    Fischler, M; Rechenmacher, R; Green, C; Kowalkowski, J; Norman, A; Paterno, M

    2012-01-01

    The NOvA experiment is a long baseline neutrino experiment design to make precision probes of the structure of neutrino mixing. The experiment features a unique deadtimeless data acquisition system that is capable acquiring and building an event data stream from the continuous readout of the more than 360,000 far detector channels. In order to achieve its physics goals the experiment must be able to buffer, correlate and extract the data in this stream with the beam-spills that occur that Fermilab. In addition the NOvA experiment seeks to enhance its data collection efficiencies for rare class of event topologies that are valuable for calibration through the use of data driven triggering. The NOvA-DDT is a prototype Data-Driven Triggering system. NOvA-DDT has been developed using the Fermilab artdaq generic DAQ/Event-building toolkit. This toolkit provides the advantages of sharing online software infrastructure with other Intensity Frontier experiments, and of being able to use any offline analysis module-unchanged-as a component of the online triggering decisions. We have measured the performance and overhead of NOvA-DDT framework using a Hough transform based trigger decision module developed for the NOvA detector to identify cosmic rays. The results of these tests which were run on the NOvA prototype near detector, yielded a mean processing time of 98 ms per event, while consuming only 1/16th of the available processing capacity. These results provide a proof of concept that a NOvA-DDT based processing system is a viable strategy for data acquisition and triggering for the NOvA far detector.

  6. Data driven analysis of rain events: feature extraction, clustering, microphysical /macro physical relationship

    Science.gov (United States)

    Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

    2017-04-01

    that a rain time series can be considered by an alternation of independent rain event and no rain period. The five selected feature are used to perform a hierarchical clustering of the events. The well-known division between stratiform and convective events appears clearly. This classification into two classes is then refined in 5 fairly homogeneous subclasses. The data driven analysis performed on whole rain events instead of fixed length samples allows identifying strong relationships between macrophysics (based on rain rate) and microphysics (based on raindrops) features. We show that among the 5 identified subclasses some of them have specific microphysics characteristics. Obtaining information on microphysical characteristics of rainfall events from rain gauges measurement suggests many implications in development of the quantitative precipitation estimation (QPE), for the improvement of rain rate retrieval algorithm in remote sensing context.

  7. Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

    Science.gov (United States)

    Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

    2018-02-01

    Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model

  8. Component-Based Data-Driven Predictive Maintenance to Reduce Unscheduled Maintenance Events

    NARCIS (Netherlands)

    Verhagen, W.J.C.; Curran, R.; de Boer, L.W.M.; Chen, C.H.; Trappey, A.C.; Peruzzini, M.; Stjepandić, J.; Wognum, N.

    2017-01-01

    Costs associated with unscheduled and preventive maintenance can contribute significantly to an airline's expenditure. Reliability analysis can help to identify and plan for maintenance events. Reliability analysis in industry is often limited to statistically based

  9. An asynchronous data-driven event-building scheme based on ATM switching fabrics

    International Nuclear Information System (INIS)

    Letheren, M.; Christiansen, J.; Mandjavidze, I.; Verhille, H.; De Prycker, M.; Pauwels, B.; Petit, G.; Wright, S.; Lumley, J.

    1994-01-01

    The very high data rates expected in experiments at the next generation of high luminosity hadron colliders will be handled by pipelined front-end readout electronics and multiple levels (2 or 3) of triggering. A variety of data acquisition architectures have been proposed for use downstream of the first level trigger. Depending on the architecture, the aggregate bandwidths required for event building are expected to be of the order 10--100 Gbit/s. Here, an Asynchronous Transfer Mode (ATM) packet-switching network technology is proposed as the interconnect for building high-performance, scalable data acquisition architectures. This paper introduces the relevant characteristics of ATM and describes components for the construction of an ATM-based event builder: (1) a multi-path, self-routing, scalable ATM switching fabric, (2) an experimental high performance workstation ATM-interface, and (3) a VMEbus ATM-interface. The requirement for traffic shaping in ATM-based event-builders is discussed and an analysis of the performance of several such schemes is presented

  10. Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

    CERN Document Server

    Abler, Daniel; Davies, Jim; Dosanjh, Manjit; Jena, Raj; Kirkby, Norman; Peach, Ken

    2013-01-01

    Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of ‘general Markov models’, providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy ...

  11. Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

    Science.gov (United States)

    Abler, Daniel; Kanellopoulos, Vassiliki; Davies, Jim; Dosanjh, Manjit; Jena, Raj; Kirkby, Norman; Peach, Ken

    2013-01-01

    Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of ‘general Markov models’, providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results. PMID:23824126

  12. Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

    International Nuclear Information System (INIS)

    Abler, Daniel; Kanellopoulos, Vassiliki; Dosanjh, Manjit; Davies, Jim; Peach, Ken; Jena, Raj; Kirkby, Norman

    2013-01-01

    Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of 'general Markov models', providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results. (author)

  13. TH-EF-BRA-03: Assessment of Data-Driven Respiratory Motion-Compensation Methods for 4D-CBCT Image Registration and Reconstruction Using Clinical Datasets

    Energy Technology Data Exchange (ETDEWEB)

    Riblett, MJ; Weiss, E; Hugo, GD [Virginia Commonwealth University, Richmond, VA (United States); Christensen, GE [University of Iowa, Iowa City, IA (United States)

    2016-06-15

    method. Conclusion: Data-driven groupwise registration and motion-compensated reconstruction have the potential to improve the quality of 4D-CBCT images acquired under clinical conditions. For clinical image datasets, the addition of motion compensation after groupwise registration visibly reduced artifact impact. This work was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA166119. Hugo and Weiss hold a research agreement with Philips Healthcare and license agreement with Varian Medical Systems. Weiss receives royalties from UpToDate. Christensen receives funds from Roger Koch to support research.

  14. TH-EF-BRA-03: Assessment of Data-Driven Respiratory Motion-Compensation Methods for 4D-CBCT Image Registration and Reconstruction Using Clinical Datasets

    International Nuclear Information System (INIS)

    Riblett, MJ; Weiss, E; Hugo, GD; Christensen, GE

    2016-01-01

    method. Conclusion: Data-driven groupwise registration and motion-compensated reconstruction have the potential to improve the quality of 4D-CBCT images acquired under clinical conditions. For clinical image datasets, the addition of motion compensation after groupwise registration visibly reduced artifact impact. This work was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA166119. Hugo and Weiss hold a research agreement with Philips Healthcare and license agreement with Varian Medical Systems. Weiss receives royalties from UpToDate. Christensen receives funds from Roger Koch to support research.

  15. Development of a data-driven algorithm to determine the W+jets background in t anti t events in ATLAS

    Energy Technology Data Exchange (ETDEWEB)

    Mehlhase, Sascha

    2010-07-12

    The physics of the top quark is one of the key components in the physics programme of the ATLAS experiment at the Large Hadron Collider at CERN. In this thesis, general studies of the jet trigger performance for top quark events using fully simulated Monte Carlo samples are presented and two data-driven techniques to estimate the multi-jet trigger efficiency and the W+Jets background in top pair events are introduced to the ATLAS experiment. In a tag-and-probe based method, using a simple and common event selection and a high transverse momentum lepton as tag object, the possibility to estimate the multijet trigger efficiency from data in ATLAS is investigated and it is shown that the method is capable of estimating the efficiency without introducing any significant bias by the given tag selection. In the second data-driven analysis a new method to estimate the W+Jets background in a top-pair event selection is introduced to ATLAS. By defining signal and background dominated regions by means of the jet multiplicity and the pseudo-rapidity distribution of the lepton in the event, the W+Jets contribution is extrapolated from the background dominated into the signal dominated region. The method is found to estimate the given background contribution as a function of the jet multiplicity with an accuracy of about 25% for most of the top dominated region with an integrated luminosity of above 100 pb{sup -1} at {radical}(s) = 10 TeV. This thesis also covers a study summarising the thermal behaviour and expected performance of the Pixel Detector of ATLAS. All measurements performed during the commissioning phase of 2008/09 yield results within the specification of the system and the performance is expected to stay within those even after several years of running under LHC conditions. (orig.)

  16. Development of a data-driven algorithm to determine the W+jets background in t anti t events in ATLAS

    International Nuclear Information System (INIS)

    Mehlhase, Sascha

    2010-01-01

    The physics of the top quark is one of the key components in the physics programme of the ATLAS experiment at the Large Hadron Collider at CERN. In this thesis, general studies of the jet trigger performance for top quark events using fully simulated Monte Carlo samples are presented and two data-driven techniques to estimate the multi-jet trigger efficiency and the W+Jets background in top pair events are introduced to the ATLAS experiment. In a tag-and-probe based method, using a simple and common event selection and a high transverse momentum lepton as tag object, the possibility to estimate the multijet trigger efficiency from data in ATLAS is investigated and it is shown that the method is capable of estimating the efficiency without introducing any significant bias by the given tag selection. In the second data-driven analysis a new method to estimate the W+Jets background in a top-pair event selection is introduced to ATLAS. By defining signal and background dominated regions by means of the jet multiplicity and the pseudo-rapidity distribution of the lepton in the event, the W+Jets contribution is extrapolated from the background dominated into the signal dominated region. The method is found to estimate the given background contribution as a function of the jet multiplicity with an accuracy of about 25% for most of the top dominated region with an integrated luminosity of above 100 pb -1 at √(s) = 10 TeV. This thesis also covers a study summarising the thermal behaviour and expected performance of the Pixel Detector of ATLAS. All measurements performed during the commissioning phase of 2008/09 yield results within the specification of the system and the performance is expected to stay within those even after several years of running under LHC conditions. (orig.)

  17. Satellite data driven modeling system for predicting air quality and visibility during wildfire and prescribed burn events

    Science.gov (United States)

    Nair, U. S.; Keiser, K.; Wu, Y.; Maskey, M.; Berendes, D.; Glass, P.; Dhakal, A.; Christopher, S. A.

    2012-12-01

    The Alabama Forestry Commission (AFC) is responsible for wildfire control and also prescribed burn management in the state of Alabama. Visibility and air quality degradation resulting from smoke are two pieces of information that are crucial for this activity. Currently the tools available to AFC are the dispersion index available from the National Weather Service and also surface smoke concentrations. The former provides broad guidance for prescribed burning activities but does not provide specific information regarding smoke transport, areas affected and quantification of air quality and visibility degradation. While the NOAA operational air quality guidance includes surface smoke concentrations from existing fire events, it does not account for contributions from background aerosols, which are important for the southeastern region including Alabama. Also lacking is the quantification of visibility. The University of Alabama in Huntsville has developed a state-of-the-art integrated modeling system to address these concerns. This system based on the Community Air Quality Modeling System (CMAQ) that ingests satellite derived smoke emissions and also assimilates NASA MODIS derived aerosol optical thickness. In addition, this operational modeling system also simulates the impact of potential prescribed burn events based on location information derived from the AFC prescribed burn permit database. A lagrangian model is used to simulate smoke plumes for the prescribed burns requests. The combined air quality and visibility degradation resulting from these smoke plumes and background aerosols is computed and the information is made available through a web based decision support system utilizing open source GIS components. This system provides information regarding intersections between highways and other critical facilities such as old age homes, hospitals and schools. The system also includes satellite detected fire locations and other satellite derived datasets

  18. Reconstructing events, from electronic signals to tracks

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Reconstructing tracks in the events taken by LHC experiments is one of the most challenging and computationally expensive software tasks to be carried out in the data processing chain. A typical LHC event is composed of multiple p-p interactions, each leaving signals from many charged particles in the detector and jus building up an environment of unprecedented complexity. In the lecture I will give an overview of event reconstruction in a typical High Energy Physics experiment. After an introduction to particle tracking detectors I will discuss the concepts and techniques required to master the tracking challenge at the LHC. I will explain how track propagation in a realistic detector works, present different techniques for track fitting and track finding. At the end we will see how all of those techniques play together in the ATLAS track reconstruction application.

  19. DD4Hep based event reconstruction

    CERN Document Server

    AUTHOR|(SzGeCERN)683529; Frank, Markus; Gaede, Frank-Dieter; Hynds, Daniel; Lu, Shaojun; Nikiforou, Nikiforos; Petric, Marko; Simoniello, Rosa; Voutsinas, Georgios Gerasimos

    The DD4HEP detector description toolkit offers a flexible and easy-to-use solution for the consistent and complete description of particle physics detectors in a single system. The sub-component DDREC provides a dedicated interface to the detector geometry as needed for event reconstruction. With DDREC there is no need to define an additional, separate reconstruction geometry as is often done in HEP, but one can transparently extend the existing detailed simulation model to be also used for the reconstruction. Based on the extension mechanism of DD4HEP, DDREC allows one to attach user defined data structures to detector elements at all levels of the geometry hierarchy. These data structures define a high level view onto the detectors describing their physical properties, such as measurement layers, point resolutions, and cell sizes. For the purpose of charged particle track reconstruction, dedicated surface objects can be attached to every volume in the detector geometry. These surfaces provide the measuremen...

  20. Org.Lcsim: Event Reconstruction in Java

    International Nuclear Information System (INIS)

    Graf, Norman

    2011-01-01

    Maximizing the physics performance of detectors being designed for the International Linear Collider, while remaining sensitive to cost constraints, requires a powerful, efficient, and flexible simulation, reconstruction and analysis environment to study the capabilities of a large number of different detector designs. The preparation of Letters Of Intent for the International Linear Collider involved the detailed study of dozens of detector options, layouts and readout technologies; the final physics benchmarking studies required the reconstruction and analysis of hundreds of millions of events. We describe the Java-based software toolkit (org.lcsim) which was used for full event reconstruction and analysis. The components are fully modular and are available for tasks from digitization of tracking detector signals through to cluster finding, pattern recognition, track-fitting, calorimeter clustering, individual particle reconstruction, jet-finding, and analysis. The detector is defined by the same xml input files used for the detector response simulation, ensuring the simulation and reconstruction geometries are always commensurate by construction. We discuss the architecture as well as the performance.

  1. Data-driven storytelling

    CERN Document Server

    Hurter, Christophe; Diakopoulos, Nicholas ed.; Carpendale, Sheelagh

    2018-01-01

    This book is an accessible introduction to data-driven storytelling, resulting from discussions between data visualization researchers and data journalists. This book will be the first to define the topic, present compelling examples and existing resources, as well as identify challenges and new opportunities for research.

  2. Event Reconstruction Techniques in NOvA

    Science.gov (United States)

    Baird, M.; Bian, J.; Messier, M.; Niner, E.; Rocco, D.; Sachdev, K.

    2015-12-01

    The NOvA experiment is a long-baseline neutrino oscillation experiment utilizing the NuMI beam generated at Fermilab. The experiment will measure the oscillations within a muon neutrino beam in a 300 ton Near Detector located underground at Fermilab and a functionally-identical 14 kiloton Far Detector placed 810 km away. The detectors are liquid scintillator tracking calorimeters with a fine-grained cellular structure that provides a wealth of information for separating the different particle track and shower topologies. Each detector has its own challenges with the Near Detector seeing multiple overlapping neutrino interactions in each event and the Far Detector having a large background of cosmic rays due to being located on the surface. A series of pattern recognition techniques have been developed to go from event records, to spatially and temporally separating individual interactions, to vertexing and tracking, and particle identification. This combination of methods to achieve the full event reconstruction will be discussed.

  3. Selection of the Sample for Data-Driven $Z \\to \

    CERN Document Server

    Krauss, Martin

    2009-01-01

    The topic of this study was to improve the selection of the sample for data-driven Z → ν ν background estimation, which is a major contribution in supersymmetric searches in ̄ a no-lepton search mode. The data is based on Z → + − samples using data created with ATLAS simulation software. This method works if two leptons are reconstructed, but using cuts that are typical for SUSY searches reconstruction efficiency for electrons and muons is rather low. For this reason it was tried to enhance the data sample. Therefore events were considered, where only one electron was reconstructed. In this case the invariant mass for the electron and each jet was computed to select the jet with the best match for the Z boson mass as not reconstructed electron. This way the sample can be extended but significantly looses purity because of also reconstructed background events. To improve this method other variables have to be considered which were not available for this study. Applying a similar method to muons using ...

  4. Selected event reconstruction algorithms for the CBM experiment at FAIR

    International Nuclear Information System (INIS)

    Lebedev, Semen; Höhne, Claudia; Lebedev, Andrey; Ososkov, Gennady

    2014-01-01

    Development of fast and efficient event reconstruction algorithms is an important and challenging task in the Compressed Baryonic Matter (CBM) experiment at the future FAIR facility. The event reconstruction algorithms have to process terabytes of input data produced in particle collisions. In this contribution, several event reconstruction algorithms are presented. Optimization of the algorithms in the following CBM detectors are discussed: Ring Imaging Cherenkov (RICH) detector, Transition Radiation Detectors (TRD) and Muon Chamber (MUCH). The ring reconstruction algorithm in the RICH is discussed. In TRD and MUCH track reconstruction algorithms are based on track following and Kalman Filter methods. All algorithms were significantly optimized to achieve maximum speed up and minimum memory consumption. Obtained results showed that a significant speed up factor for all algorithms was achieved and the reconstruction efficiency stays at high level.

  5. Implementation and integration in the L3 experimentation of a level-2 trigger with event building, based on C104 data driven cross-bar switches and on T9000 transputers

    International Nuclear Information System (INIS)

    Masserot, A.

    1995-01-01

    This thesis describes the new level-2 trigger system. It has been developed to fit the L3 requirements induced by the LEP phase 2 conditions. At each beam crossing, the system memorizes the trigger data, builds-up the events selected by the level-1 hard-wired processors and finally rejects on-line the background identified by algorithms coded in Fortran. Based on T9000 Transputers and on C104 data driven cross-bar switches, the system uses prototypes designed by INMOS/SGS THOMSON for parallel processing applications. Emphasis is set on a new event building technic, on its integration in L3 and on performance. (author). 38 refs., 68 figs., 36 tabs

  6. Event reconstruction with MarlinReco at the International Linear ...

    Indian Academy of Sciences (India)

    event reconstruction system, based on the particle flow concept. Version 00-02 contains the following processors: Tracker hit digitisation: For the vertex system there are two different digitisers available. A simple digitiser translates simulated .... of showers in the calorimeter. The Fortran-based simulation and reconstruction.

  7. NEBULAS A High Performance Data-Driven Event-Building Architecture based on an Asynchronous Self-Routing Packet-Switching Network

    CERN Multimedia

    Costa, M; Letheren, M; Djidi, K; Gustafsson, L; Lazraq, T; Minerskjold, M; Tenhunen, H; Manabe, A; Nomachi, M; Watase, Y

    2002-01-01

    RD31 : The project is evaluating a new approach to event building for level-two and level-three processor farms at high rate experiments. It is based on the use of commercial switching fabrics to replace the traditional bus-based architectures used in most previous data acquisition sytems. Switching fabrics permit the construction of parallel, expandable, hardware-driven event builders that can deliver higher aggregate throughput than the bus-based architectures. A standard industrial switching fabric technology is being evaluated. It is based on Asynchronous Transfer Mode (ATM) packet-switching network technology. Commercial, expandable ATM switching fabrics and processor interfaces, now being developed for the future Broadband ISDN infrastructure, could form the basis of an implementation. The goals of the project are to demonstrate the viability of this approach, to evaluate the trade-offs involved in make versus buy options, to study the interfacing of the physics frontend data buffers to such a fabric, a...

  8. Data-driven batch schuduling

    Energy Technology Data Exchange (ETDEWEB)

    Bent, John [Los Alamos National Laboratory; Denehy, Tim [GOOGLE; Arpaci - Dusseau, Remzi [UNIV OF WISCONSIN; Livny, Miron [UNIV OF WISCONSIN; Arpaci - Dusseau, Andrea C [NON LANL

    2009-01-01

    In this paper, we develop data-driven strategies for batch computing schedulers. Current CPU-centric batch schedulers ignore the data needs within workloads and execute them by linking them transparently and directly to their needed data. When scheduled on remote computational resources, this elegant solution of direct data access can incur an order of magnitude performance penalty for data-intensive workloads. Adding data-awareness to batch schedulers allows a careful coordination of data and CPU allocation thereby reducing the cost of remote execution. We offer here new techniques by which batch schedulers can become data-driven. Such systems can use our analytical predictive models to select one of the four data-driven scheduling policies that we have created. Through simulation, we demonstrate the accuracy of our predictive models and show how they can reduce time to completion for some workloads by as much as 80%.

  9. Online Event Reconstruction in the CBM Experiment at FAIR

    Science.gov (United States)

    Akishina, Valentina; Kisel, Ivan

    2018-02-01

    Targeting for rare observables, the CBM experiment will operate at high interaction rates of up to 10 MHz, which is unprecedented in heavy-ion experiments so far. It requires a novel free-streaming readout system and a new concept of data processing. The huge data rates of the CBM experiment will be reduced online to the recordable rate before saving the data to the mass storage. Full collision reconstruction and selection will be performed online in a dedicated processor farm. In order to make an efficient event selection online a clean sample of particles has to be provided by the reconstruction package called First Level Event Selection (FLES). The FLES reconstruction and selection package consists of several modules: track finding, track fitting, event building, short-lived particles finding, and event selection. Since detector measurements contain also time information, the event building is done at all stages of the reconstruction process. The input data are distributed within the FLES farm in a form of time-slices. A time-slice is reconstructed in parallel between processor cores. After all tracks of the whole time-slice are found and fitted, they are collected into clusters of tracks originated from common primary vertices, which then are fitted, thus identifying the interaction points. Secondary tracks are associated with primary vertices according to their estimated production time. After that short-lived particles are found and the full event building process is finished. The last stage of the FLES package is a selection of events according to the requested trigger signatures. The event reconstruction procedure and the results of its application to simulated collisions in the CBM detector setup are presented and discussed in detail.

  10. Event Reconstruction Algorithms for the ATLAS Trigger

    Energy Technology Data Exchange (ETDEWEB)

    Fonseca-Martin, T.; /CERN; Abolins, M.; /Michigan State U.; Adragna, P.; /Queen Mary, U. of London; Aleksandrov, E.; /Dubna, JINR; Aleksandrov, I.; /Dubna, JINR; Amorim, A.; /Lisbon, LIFEP; Anderson, K.; /Chicago U., EFI; Anduaga, X.; /La Plata U.; Aracena, I.; /SLAC; Asquith, L.; /University Coll. London; Avolio, G.; /CERN; Backlund, S.; /CERN; Badescu, E.; /Bucharest, IFIN-HH; Baines, J.; /Rutherford; Barria, P.; /Rome U. /INFN, Rome; Bartoldus, R.; /SLAC; Batreanu, S.; /Bucharest, IFIN-HH /CERN; Beck, H.P.; /Bern U.; Bee, C.; /Marseille, CPPM; Bell, P.; /Manchester U.; Bell, W.H.; /Glasgow U. /Pavia U. /INFN, Pavia /Regina U. /CERN /Annecy, LAPP /Paris, IN2P3 /Royal Holloway, U. of London /Napoli Seconda U. /INFN, Naples /Argonne /CERN /UC, Irvine /Barcelona, IFAE /Barcelona, Autonoma U. /CERN /Montreal U. /CERN /Glasgow U. /Michigan State U. /Bucharest, IFIN-HH /Napoli Seconda U. /INFN, Naples /New York U. /Barcelona, IFAE /Barcelona, Autonoma U. /Salento U. /INFN, Lecce /Pisa U. /INFN, Pisa /Bucharest, IFIN-HH /UC, Irvine /CERN /Glasgow U. /INFN, Genoa /Genoa U. /Lisbon, LIFEP /Napoli Seconda U. /INFN, Naples /UC, Irvine /Valencia U. /Rio de Janeiro Federal U. /University Coll. London /New York U.; /more authors..

    2011-11-09

    The ATLAS experiment under construction at CERN is due to begin operation at the end of 2007. The detector will record the results of proton-proton collisions at a center-of-mass energy of 14 TeV. The trigger is a three-tier system designed to identify in real-time potentially interesting events that are then saved for detailed offline analysis. The trigger system will select approximately 200 Hz of potentially interesting events out of the 40 MHz bunch-crossing rate (with 10{sup 9} interactions per second at the nominal luminosity). Algorithms used in the trigger system to identify different event features of interest will be described, as well as their expected performance in terms of selection efficiency, background rejection and computation time per event. The talk will concentrate on recent improvements and on performance studies, using a very detailed simulation of the ATLAS detector and electronics chain that emulates the raw data as it will appear at the input to the trigger system.

  11. Event reconstruction algorithms for the ATLAS trigger

    Energy Technology Data Exchange (ETDEWEB)

    F-Martin, T; Avolio, G; Backlund, S [European Laboratory for Particle Physics (CERN), Geneva (Switzerland); Abolins, M [Michigan State University, Department of Physics and Astronomy, East Lansing, Michigan (United States); Adragna, P [Department of Physics, Queen Mary and Westfield College, University of London, London (United Kingdom); Aleksandrov, E; Aleksandrov, I [Joint Institute for Nuclear Research, Dubna (Russian Federation); Amorim, A [Laboratorio de Instrumentacao e Fisica Experimental, Lisboa (Portugal); Anderson, K [University of Chicago, Enrico Fermi Institute, Chicago, Illinois (United States); Anduaga, X [National University of La Plata, La Plata (United States); Aracena, I; Bartoldus, R [Stanford Linear Accelerator Center (SLAC), Stanford (United States); Asquith, L [Department of Physics and Astronomy, University College London, London (United Kingdom); Badescu, E [National Institute for Physics and Nuclear Engineering, Institute of Atomic Physics, Bucharest (Romania); Baines, J [Rutherford Appleton Laboratory, Chilton, Didcot (United Kingdom); Beck, H P [Laboratory for High Energy Physics, University of Bern, Bern (Switzerland); Bee, C [Centre de Physique des Particules de Marseille, IN2P3-CNRS, Marseille (France); Bell, P [Department of Physics and Astronomy, University of Manchester, Manchester (United Kingdom); Barria, P; Batreanu, S [and others

    2008-07-01

    The ATLAS experiment under construction at CERN is due to begin operation at the end of 2007. The detector will record the results of proton-proton collisions at a center-of-mass energy of 14 TeV. The trigger is a three-tier system designed to identify in real-time potentially interesting events that are then saved for detailed offline analysis. The trigger system will select approximately 200 Hz of potentially interesting events out of the 40 MHz bunch-crossing rate (with 10{sup 9} interactions per second at the nominal luminosity). Algorithms used in the trigger system to identify different event features of interest will be described, as well as their expected performance in terms of selection efficiency, background rejection and computation time per event. The talk will concentrate on recent improvements and on performance studies, using a very detailed simulation of the ATLAS detector and electronics chain that emulates the raw data as it will appear at the input to the trigger system.

  12. Event reconstruction algorithms for the ATLAS trigger

    International Nuclear Information System (INIS)

    F-Martin, T; Avolio, G; Backlund, S; Abolins, M; Adragna, P; Aleksandrov, E; Aleksandrov, I; Amorim, A; Anderson, K; Anduaga, X; Aracena, I; Bartoldus, R; Asquith, L; Badescu, E; Baines, J; Beck, H P; Bee, C; Bell, P; Barria, P; Batreanu, S

    2008-01-01

    The ATLAS experiment under construction at CERN is due to begin operation at the end of 2007. The detector will record the results of proton-proton collisions at a center-of-mass energy of 14 TeV. The trigger is a three-tier system designed to identify in real-time potentially interesting events that are then saved for detailed offline analysis. The trigger system will select approximately 200 Hz of potentially interesting events out of the 40 MHz bunch-crossing rate (with 10 9 interactions per second at the nominal luminosity). Algorithms used in the trigger system to identify different event features of interest will be described, as well as their expected performance in terms of selection efficiency, background rejection and computation time per event. The talk will concentrate on recent improvements and on performance studies, using a very detailed simulation of the ATLAS detector and electronics chain that emulates the raw data as it will appear at the input to the trigger system

  13. Dynamically adaptive data-driven simulation of extreme hydrological flows

    KAUST Repository

    Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim; Knio, Omar; Dawson, Clint

    2017-01-01

    evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses

  14. Data driven processor 'Vertex Trigger' for B experiments

    International Nuclear Information System (INIS)

    Hartouni, E.P.

    1993-01-01

    Data Driven Processors (DDP's) are specialized computation engines configured to solve specific numerical problems, such as vertex reconstruction. The architecture of the DDP which is the subject of this talk was designed and implemented by W. Sippach and B.C. Knapp at Nevis Lab. in the early 1980's. This particular implementation allows multiple parallel streams of data to provide input to a heterogenous collection of simple operators whose interconnection form an algorithm. The local data flow control allows this device to execute algorithms extremely quickly provided that care is taken in the layout of the algorithm. I/O rates of several hundred megabytes/second are routinely achieved thus making DDP's attractive candidates for complex online calculations. The original question was open-quote can a DDP reconstruct tracks in a Silicon Vertex Detector, find events with a separated vertex and do it fast enough to be used as an online trigger?close-quote Restating this inquiry as three questions and describing the answers to the questions will be the subject of this talk. The three specific questions are: (1) Can an algorithm be found which reconstructs tracks in a planar geometry and no magnetic field; (2) Can separated vertices be recognized in some way; (3) Can the algorithm be implemented in the Nevis-UMass and DDP and execute in 10-20 μs?

  15. Analytic 3D image reconstruction using all detected events

    International Nuclear Information System (INIS)

    Kinahan, P.E.; Rogers, J.G.

    1988-11-01

    We present the results of testing a previously presented algorithm for three-dimensional image reconstruction that uses all gamma-ray coincidence events detected by a PET volume-imaging scanner. By using two iterations of an analytic filter-backprojection method, the algorithm is not constrained by the requirement of a spatially invariant detector point spread function, which limits normal analytic techniques. Removing this constraint allows the incorporation of all detected events, regardless of orientation, which improves the statistical quality of the final reconstructed image

  16. Event Reconstruction in the NOvA Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Behera, Biswaranjan [Indian Inst. Tech., Hyderabad; Davies, Gavin [Indiana U.; Psihas, Fernanda [Indiana U.

    2017-10-10

    The NOvA experiment observes oscillations in two channels (electron-neutrino appearance and muon-neutrino disappearance) using a predominantly muon-neutrino NuMI beam. The Near Detector records multiple overlapping neutrino interactions in each event and the Far Detector has a large background of cosmic rays due to being located on the surface. The oscillation analyses rely on the accurate reconstruction of neutrino interactions in order to precisely measure the neutrino energy and identify the neutrino flavor and interaction mode. Similarly, measurements of neutrino cross sections using the Near Detector require accurate identification of the particle content of each interaction. A series of pattern recognition techniques have been developed to split event records into individual spatially and temporally separated interactions, to estimate the interaction vertex, and to isolate and classify individual particles within the event. This combination of methods to achieve full event reconstruction in the NOvA detectors has discussed.

  17. Online 4-dimensional event reconstruction in the CBM experiment

    Energy Technology Data Exchange (ETDEWEB)

    Akishina, Valentina [Goethe-Universitaet Frankfurt, Frankfurt am Main (Germany); GSI Helmholtzzentrum fuer Schwerionenforschung GmbH, Darmstadt (Germany); Joint Institute for Nuclear Research, Dubna (Russian Federation); Kisel, Ivan [Goethe-Universitaet Frankfurt, Frankfurt am Main (Germany); GSI Helmholtzzentrum fuer Schwerionenforschung GmbH, Darmstadt (Germany); Frankfurt Institute for Advanced Studies, Frankfurt am Main (Germany); Collaboration: CBM-Collaboration

    2015-07-01

    The heavy-ion experiment CBM will focus on the measurement of rare probes at interaction rates up to 10 MHz with data flow of up to 1 TB/s. The free-running data acquisition, delivering a stream of untriggered detector data, requires full event reconstruction and selection to be performed online not only in space, but also in time. The First-Level Event Selection package consists of several modules: track finding, track fitting, short-lived particles finding, event building and event selection. For track reconstruction the Cellular Automaton (CA) method is used, which allows to reconstruct tracks with high efficiency in a time-slice and perform event building. The time-based CA track finder allows to resolve tracks from a time-slice in event-corresponding groups. The algorithm is intrinsically local and the implementation is both vectorized and parallelized between CPU cores. The CA track finder shows a strong scalability on many-core systems. The speed-up factor of 10.6 on a CPU with 10 hyper-threaded physical cores was achieved.

  18. Data driven marketing for dummies

    CERN Document Server

    Semmelroth, David

    2013-01-01

    Embrace data and use it to sell and market your products Data is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products. Successful data analysis can help marketing professionals spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Data Driven Marketing For Dummies helps companies use all the data at their disposal to make current customers more satisfied, reach new customers, and sell to their most important customer segments more efficiently. Identifyi

  19. Event Reconstruction in the PandaRoot framework

    International Nuclear Information System (INIS)

    Spataro, Stefano

    2012-01-01

    The PANDA experiment will study the collisions of beams of anti-protons, with momenta ranging from 2-15 GeV/c, with fixed proton and nuclear targets in the charm energy range, and will be built at the FAIR facility. In preparation for the experiment, the PandaRoot software framework is under development for detector simulation, reconstruction and data analysis, running on an Alien2-based grid. The basic features are handled by the FairRoot framework, based on ROOT and Virtual Monte Carlo, while the PANDA detector specifics and reconstruction code are implemented inside PandaRoot. The realization of Technical Design Reports for the tracking detectors has pushed the finalization of the tracking reconstruction code, which is complete for the Target Spectrometer, and of the analysis tools. Particle Identification algorithms are currently implemented using Bayesian approach and compared to Multivariate Analysis methods. Moreover, the PANDA data acquisition foresees a triggerless operation in which events are not defined by a hardware 1st level trigger decision, but all the signals are stored with time stamps requiring a deconvolution by the software. This has led to a redesign of the software from an event basis to a time-ordered structure. In this contribution, the reconstruction capabilities of the Panda spectrometer will be reported, focusing on the performances of the tracking system and the results for the analysis of physics benchmark channels, as well as the new (and challenging) concept of time-based simulation and its implementation.

  20. Athena X-IFU event reconstruction software: SIRENA

    Science.gov (United States)

    Ceballos, Maria Teresa; Cobo, Beatriz; Peille, Philippe; Wilms, Joern; Brand, Thorsten; Dauser, Thomas; Bandler, Simon; Smith, Stephen

    2015-09-01

    This contribution describes the status and technical details of the SIRENA package, the software currently in development to perform the on board event energy reconstruction for the Athena calorimeter X-IFU. This on board processing will be done in the X-IFU DRE unit and it will consist in an initial triggering of event pulses followed by an analysis (with the SIRENA package) to determine the energy content of such events.The current algorithm used by SIRENA is the optimal filtering technique (also used by ASTRO-H processor) although some other algorithms are also being tested.Here we present these studies and some preliminary results about the energy resolution of the instrument based on simulations done with the SIXTE simulator (http://www.sternwarte.uni-erlangen.de/research/sixte/) in which SIRENA is integrated.

  1. Automated track recognition and event reconstruction in nuclear emulsion

    International Nuclear Information System (INIS)

    Deines-Jones, P.; Aranas, A.; Cherry, M.L.; Dugas, J.; Kudzia, D.; Nilsen, B.S.; Sengupta, K.; Waddington, C.J.; Wefel, J.P.; Wilczynska, B.; Wilczynski, H.; Wosiek, B.

    1997-01-01

    The major advantages of nuclear emulsion for detecting charged particles are its submicron position resolution and sensitivity to minimum ionizing particles. These must be balanced, however, against the difficult manual microscope measurement by skilled observers required for the analysis. We have developed an automated system to acquire and analyze the microscope images from emulsion chambers. Each emulsion plate is analyzed independently, allowing coincidence techniques to be used in order to reject background and estimate error rates. The system has been used to analyze a sample of high-multiplicity Pb-Pb interactions (charged particle multiplicities ∝ 1100) produced by the 158 GeV/c per nucleon 208 Pb beam at CERN. Automatically measured events agree with our best manual measurements on 97% of all the tracks. We describe the image analysis and track reconstruction techniques, and discuss the measurement and reconstruction uncertainties. (orig.)

  2. GPU's for event reconstruction in the FairRoot framework

    International Nuclear Information System (INIS)

    Al-Turany, M; Uhlig, F; Karabowicz, R

    2010-01-01

    FairRoot is the simulation and analysis framework used by CBM and PANDA experiments at FAIR/GSI. The use of graphics processor units (GPUs) for event reconstruction in FairRoot will be presented. The fact that CUDA (Nvidia's Compute Unified Device Architecture) development tools work alongside the conventional C/C++ compiler, makes it possible to mix GPU code with general-purpose code for the host CPU, based on this some of the reconstruction tasks can be send to the graphic cards. Moreover, tasks that run on the GPU's can also run in emulation mode on the host CPU, which has the advantage that the same code is used on both CPU and GPU.

  3. Polynomials, Riemann surfaces, and reconstructing missing-energy events

    CERN Document Server

    Gripaios, Ben; Webber, Bryan

    2011-01-01

    We consider the problem of reconstructing energies, momenta, and masses in collider events with missing energy, along with the complications introduced by combinatorial ambiguities and measurement errors. Typically, one reconstructs more than one value and we show how the wrong values may be correlated with the right ones. The problem has a natural formulation in terms of the theory of Riemann surfaces. We discuss examples including top quark decays in the Standard Model (relevant for top quark mass measurements and tests of spin correlation), cascade decays in models of new physics containing dark matter candidates, decays of third-generation leptoquarks in composite models of electroweak symmetry breaking, and Higgs boson decay into two tau leptons.

  4. SIRENA software for Athena X-IFU event reconstruction

    Science.gov (United States)

    Ceballos, M. T.; Cobo, B.; Peille, P.; Wilms, J.; Brand, T.; Dauser, T.; Bandler, S.; Smith, S.

    2017-03-01

    The X-ray Observatory Athena was proposed in April 2014 as the mission to implement the science theme "The Hot and Energetic Universe" selected by ESA for L2 (the second Large-class mission in ESA’s Cosmic Vision science programme). One of the two X-ray detectors designed to be onboard Athena is X-IFU, a cryogenic microcalorimeter based on Transition Edge Sensor (TES) technology that will provide spatially resolved high-resolution spectroscopy. X-IFU will be developed by an international consortium led by IRAP (PI), SRON (co-PI) and IAPS/INAF (co-PI) and involving ESA Member States, Japan and the United States. In Spain, IFCA (CSIC-UC) has an anticipated contribution to X-IFU through the Digital Readout Electronics (DRE) unit, in particular in the Event Processor Subsystem. For this purpose and in collaboration with the Athena end-to-end simulations team, we are currently developing the SIRENA package as part of the publicly available SIXTE end-to-end simulator. SIRENA comprises a set of processing algorithms aimed at recognizing, from a noisy signal, the intensity pulses generated by the absorption of the X-ray photons, to lately reconstruct their energy, position and arrival time. This poster describes the structure of the package and the different algorithms currently implemented as well as their comparative performance in the energy resolution achieved in the reconstruction of the instrument events.

  5. Data-driven architectural production and operation

    NARCIS (Netherlands)

    Bier, H.H.; Mostafavi, S.

    2014-01-01

    Data-driven architectural production and operation as explored within Hyperbody rely heavily on system thinking implying that all parts of a system are to be understood in relation to each other. These relations are increasingly established bi-directionally so that data-driven architecture is not

  6. Data Driven Economic Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Masoud Kheradmandi

    2018-04-01

    Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

  7. Parallel computing for event reconstruction in high-energy physics

    International Nuclear Information System (INIS)

    Wolbers, S.

    1993-01-01

    Parallel computing has been recognized as a solution to large computing problems. In High Energy Physics offline event reconstruction of detector data is a very large computing problem that has been solved with parallel computing techniques. A review of the parallel programming package CPS (Cooperative Processes Software) developed and used at Fermilab for offline reconstruction of Terabytes of data requiring the delivery of hundreds of Vax-Years per experiment is given. The Fermilab UNIX farms, consisting of 180 Silicon Graphics workstations and 144 IBM RS6000 workstations, are used to provide the computing power for the experiments. Fermilab has had a long history of providing production parallel computing starting with the ACP (Advanced Computer Project) Farms in 1986. The Fermilab UNIX Farms have been in production for over 2 years with 24 hour/day service to experimental user groups. Additional tools for management, control and monitoring these large systems will be described. Possible future directions for parallel computing in High Energy Physics will be given

  8. HEP Community White Paper on Software Trigger and Event Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Albrecht, Johannes; et al.

    2018-02-23

    Realizing the physics programs of the planned and upgraded high-energy physics (HEP) experiments over the next 10 years will require the HEP community to address a number of challenges in the area of software and computing. For this reason, the HEP software community has engaged in a planning process over the past two years, with the objective of identifying and prioritizing the research and development required to enable the next generation of HEP detectors to fulfill their full physics potential. The aim is to produce a Community White Paper which will describe the community strategy and a roadmap for software and computing research and development in HEP for the 2020s. The topics of event reconstruction and software triggers were considered by a joint working group and are summarized together in this document.

  9. Combining engineering and data-driven approaches

    DEFF Research Database (Denmark)

    Fischer, Katharina; De Sanctis, Gianluca; Kohler, Jochen

    2015-01-01

    Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many assump...... to the calibration of a generic fire risk model for single family houses to Swiss insurance data. The example demonstrates that the bias in the risk estimation can be strongly reduced by model calibration.......Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many...... assumptions that may result in a biased risk assessment. In two related papers we show how engineering and data-driven modelling can be combined by developing generic risk models that are calibrated to statistical data on observed fire events. The focus of the present paper is on the calibration procedure...

  10. Simulation and event reconstruction inside the PandaRoot framework

    International Nuclear Information System (INIS)

    Spataro, S

    2008-01-01

    The PANDA detector will be located at the future GSI accelerator FAIR. Its primary objective is the investigation of strong interaction with anti-proton beams, in the range up to 15 GeV/c as momentum of the incoming anti-proton. The PANDA offline simulation framework is called 'PandaRoot', as it is based upon the ROOT 5.14 package. It is characterized by a high versatility; it allows to perform simulation and analysis, to run different event generators (EvtGen, Pluto, UrQmd), different transport models (Geant3, Geant4, Fluka) with the same code, thus to compare the results simply by changing few macro lines without recompiling at all. Moreover auto-configuration scripts allow installing the full framework easily in different Linux distributions and with different compilers (the framework was installed and tested in more than 10 Linux platforms) without further manipulation. The final data are in a tree format, easily accessible and readable through simple clicks on the root browsers. The presentation will report on the actual status of the computing development inside the PandaRoot framework, in terms of detector implementation and event reconstruction

  11. Data-Driven Problems in Elasticity

    Science.gov (United States)

    Conti, S.; Müller, S.; Ortiz, M.

    2018-01-01

    We consider a new class of problems in elasticity, referred to as Data-Driven problems, defined on the space of strain-stress field pairs, or phase space. The problem consists of minimizing the distance between a given material data set and the subspace of compatible strain fields and stress fields in equilibrium. We find that the classical solutions are recovered in the case of linear elasticity. We identify conditions for convergence of Data-Driven solutions corresponding to sequences of approximating material data sets. Specialization to constant material data set sequences in turn establishes an appropriate notion of relaxation. We find that relaxation within this Data-Driven framework is fundamentally different from the classical relaxation of energy functions. For instance, we show that in the Data-Driven framework the relaxation of a bistable material leads to material data sets that are not graphs.

  12. Consistent data-driven computational mechanics

    Science.gov (United States)

    González, D.; Chinesta, F.; Cueto, E.

    2018-05-01

    We present a novel method, within the realm of data-driven computational mechanics, to obtain reliable and thermodynamically sound simulation from experimental data. We thus avoid the need to fit any phenomenological model in the construction of the simulation model. This kind of techniques opens unprecedented possibilities in the framework of data-driven application systems and, particularly, in the paradigm of industry 4.0.

  13. A general algorithm for the reconstruction of jet events in e+e- annihilation

    International Nuclear Information System (INIS)

    Goddard, M.C.

    1981-01-01

    A general method is described to reconstruct a predetermined number of jets. It can reconstruct the jet axes as accurately as any existing algorithm and is up to one hundred times faster. Results are shown from the reconstruction of 2-jet, 3-jet and 4-jet Monte Carlo events. (author)

  14. Data-driven sensor placement from coherent fluid structures

    Science.gov (United States)

    Manohar, Krithika; Kaiser, Eurika; Brunton, Bingni W.; Kutz, J. Nathan; Brunton, Steven L.

    2017-11-01

    Optimal sensor placement is a central challenge in the prediction, estimation and control of fluid flows. We reinterpret sensor placement as optimizing discrete samples of coherent fluid structures for full state reconstruction. This permits a drastic reduction in the number of sensors required for faithful reconstruction, since complex fluid interactions can often be described by a small number of coherent structures. Our work optimizes point sensors using the pivoted matrix QR factorization to sample coherent structures directly computed from flow data. We apply this sampling technique in conjunction with various data-driven modal identification methods, including the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). In contrast to POD-based sensors, DMD demonstrably enables the optimization of sensors for prediction in systems exhibiting multiple scales of dynamics. Finally, reconstruction accuracy from pivot sensors is shown to be competitive with sensors obtained using traditional computationally prohibitive optimization methods.

  15. Data-driven regionalization of housing markets

    NARCIS (Netherlands)

    Helbich, M.; Brunauer, W.; Hagenauer, J.; Leitner, M.

    2013-01-01

    This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The

  16. Data Driven Constraints for the SVM

    DEFF Research Database (Denmark)

    Darkner, Sune; Clemmensen, Line Katrine Harder

    2012-01-01

    We propose a generalized data driven constraint for support vector machines exemplified by classification of paired observations in general and specifically on the human ear canal. This is particularly interesting in dynamic cases such as tissue movement or pathologies developing over time. Assum...

  17. Measurement of the double-vertex reconstruction efficiency of the inclusive vertex finder with accidentally overlapping b-jets in ttbar events

    Energy Technology Data Exchange (ETDEWEB)

    Marchesini, Ivan; Nowatschin, Dominik; Ott, Jochen; Schmidt, Alexander; Tholen, Heiner [University of Hamburg (Germany)

    2015-07-01

    In LHC Run II, CMS b-tagging algorithms will employ a new core algorithm, named Inclusive Vertex Finder (IVF). The IVF is designed to perform decay vertex reconstruction of long-lived particles, such as B hadrons. Using only tracks from the silicon tracker, it does not depend on jet clustering and allows for higher reconstruction efficiency of decay vertices, which particularly applies to topologies with two or more decay vertices at low distance. Thus, the IVF will offer increased sensitivity for SM measurements (e.g. angular correlations), but also for the search of BSM physics (e.g. final states with boosted Higgs bosons decaying into b-quarks). For the first time, the dependence of the IVF reconstruction efficiency on the distance of vertices in the η-φ plane is investigated with a data-driven approach. We use a clean set of top quark pair events, selected from data recorded in 2012 in pp-collisions at 8 TeV with the CMS detector, and perform a template fit to a 2D-distribution of the masses of the vertices in an event. Correction factors are derived for the application to simulated events. We conclude that our technique will enable precise calibration of double vertexing with the IVF in the LHC Run II.

  18. Neutrino Event Reconstruction in a Liquid Argon TPC

    Energy Technology Data Exchange (ETDEWEB)

    Barker, Gary, E-mail: G.J.Barker@Warwick.ac.uk [Department of Physics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL (United Kingdom)

    2011-07-25

    We present some preliminary findings and results from activities in Europe and the USA working towards an automated, algorithmic, reconstruction of particle interactions in liquid argon time projection chambers.

  19. CMS reconstruction improvements for the tracking in large pileup events

    CERN Document Server

    Rovere, M

    2015-01-01

    The CMS tracking code is organized in several levels, known as iterative steps, each optimized to reconstruct a class of particle trajectories, as the ones of particles originating from the primary vertex or displaced tracks from particles resulting from secondary vertices. Each iterative step consists of seeding, pattern recognition and fitting by a kalman filter, and a final filtering and cleaning. Each subsequent step works on hits not yet associated to a reconstructed particle trajectory.The CMS tracking code is continuously evolving to make the reconstruction computing load compatible with the increasing instantaneous luminosity of LHC, resulting in a large number of primary vertices and tracks per bunch crossing.The major upgrade put in place during the present LHC Long Shutdown will allow the tracking code to comply with the conditions expected during RunII and the much larger pileup. In particular, new algorithms that are intrinsically more robust in high occupancy conditions were developed, iteration...

  20. Challenges of Data-driven Healthcare Management

    DEFF Research Database (Denmark)

    Bossen, Claus; Danholt, Peter; Ubbesen, Morten Bonde

    This paper describes the new kind of data-work involved in developing data-driven healthcare based on two cases from Denmark: The first case concerns a governance infrastructure based on Diagnose-Related Groups (DRG), which was introduced in Denmark in the 1990s. The DRG-system links healthcare...... activity and financing and relies of extensive data entry, reporting and calculations. This has required the development of new skills, work and work roles. The second case concerns a New Governance project aimed at developing new performance indicators for healthcare delivery as an alternative to DRG....... Here, a core challenge is select indicators and actually being able to acquire data upon them. The two cases point out that data-driven healthcare requires more and new kinds of work for which new skills, functions and work roles have to be developed....

  1. Data Driven Tuning of Inventory Controllers

    DEFF Research Database (Denmark)

    Huusom, Jakob Kjøbsted; Santacoloma, Paloma Andrade; Poulsen, Niels Kjølstad

    2007-01-01

    A systematic method for criterion based tuning of inventory controllers based on data-driven iterative feedback tuning is presented. This tuning method circumvent problems with modeling bias. The process model used for the design of the inventory control is utilized in the tuning...... as an approximation to reduce time required on experiments. The method is illustrated in an application with a multivariable inventory control implementation on a four tank system....

  2. Dynamically adaptive data-driven simulation of extreme hydrological flows

    Science.gov (United States)

    Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim; Knio, Omar; Dawson, Clint

    2018-02-01

    Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

  3. Dynamically adaptive data-driven simulation of extreme hydrological flows

    KAUST Repository

    Kumar Jain, Pushkar

    2017-12-27

    Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

  4. Event Reconstruction and Analysis in the R3BRoot Framework

    International Nuclear Information System (INIS)

    Kresan, Dmytro; Al-Turany, Mohammad; Bertini, Denis; Karabowicz, Radoslaw; Manafov, Anar; Rybalchenko, Alexey; Uhlig, Florian

    2014-01-01

    The R 3 B experiment (Reaction studies with Relativistic Radioactive Beams) will be built within the future FAIR / GSI (Facility for Antiproton and Ion Research) in Darmstadt, Germany. The international collaboration R 3 B has a scientific program devoted to the physics of stable and radioactive beams at energies between 150 MeV and 1.5 GeV per nucleon. In preparation for the experiment, the R3BRoot software framework is under development, it deliver detector simulation, reconstruction and data analysis. The basic functionalities of the framework are handled by the FairRoot framework which is used also by the other FAIR experiments (CBM, PANDA, ASYEOS, etc) while the R 3 B detector specifics and reconstruction code are implemented inside R3BRoot. In this contribution first results of data analysis from the detector prototype test in November 2012 will be reported, moreover, comparison of the tracker performance versus experimental data, will be presented

  5. Performance of CMS Muon Reconstruction in Cosmic-Ray Events

    CERN Document Server

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Osborne, I; Paul, T; Reucroft, S; Swain, J; Taylor, L; Tuura, L; Anastassov, A; Gobbi, B; Kubik, A; Ofierzynski, R A; Pozdnyakov, A; Schmitt, M; Stoynev, S; Velasco, M; Won, S; Antonelli, L; Berry, D; Hildreth, M; Jessop, C; Karmgard, D J; Kolberg, T; Lannon, K; Lynch, S; Marinelli, N; Morse, D M; Ruchti, R; Slaunwhite, J; Warchol, J; Wayne, M; Bylsma, B; Durkin, L S; Gilmore, J; Gu, J; Killewald, P; Ling, T Y; Williams, G; Adam, N; Berry, E; Elmer, P; Garmash, A; Gerbaudo, D; Halyo, V; Hunt, A; Jones, J; Laird, E; Marlow, D; Medvedeva, T; Mooney, M; Olsen, J; Piroué, P; Stickland, D; Tully, C; Werner, J S; Wildish, T; Xie, Z; Zuranski, A; Acosta, J G; Bonnett Del Alamo, M; Huang, X T; Lopez, A; Mendez, H; Oliveros, S; Ramirez Vargas, J E; Santacruz, N; Zatzerklyany, A; Alagoz, E; Antillon, E; Barnes, V E; Bolla, G; Bortoletto, D; Everett, A; Garfinkel, A F; Gecse, Z; Gutay, L; Ippolito, N; Jones, M; Koybasi, O; Laasanen, A T; Leonardo, N; Liu, C; Maroussov, V; Merkel, P; Miller, D H; Neumeister, N; Sedov, A; Shipsey, I; Yoo, H D; Zheng, Y; Jindal, P; Parashar, N; Cuplov, V; Ecklund, K M; Geurts, F J M; Liu, J H; Maronde, D; Matveev, M; Padley, B P; Redjimi, R; Roberts, J; Sabbatini, L; Tumanov, A; Betchart, B; Bodek, A; Budd, H; Chung, Y S; de Barbaro, P; Demina, R; Flacher, H; Gotra, Y; Harel, A; Korjenevski, S; Miner, D C; Orbaker, D; Petrillo, G; Vishnevskiy, D; Zielinski, M; Bhatti, A; Demortier, L; Goulianos, K; Hatakeyama, K; Lungu, G; Mesropian, C; Yan, M; Atramentov, O; Bartz, E; Gershtein, Y; Halkiadakis, E; Hits, D; Lath, A; Rose, K; Schnetzer, S; Somalwar, S; Stone, R; Thomas, S; Watts, T L; Cerizza, G; Hollingsworth, M; Spanier, S; Yang, Z C; York, A; Asaadi, J; Aurisano, A; Eusebi, R; Golyash, A; Gurrola, A; Kamon, T; Nguyen, C N; Pivarski, J; Safonov, A; Sengupta, S; Toback, D; Weinberger, M; Akchurin, N; Berntzon, L; Gumus, K; Jeong, C; Kim, H; Lee, S W; Popescu, S; Roh, Y; Sill, A; Volobouev, I; Washington, E; Wigmans, R; Yazgan, E; Engh, D; Florez, C; Johns, W; Pathak, S; Sheldon, P; Andelin, D; Arenton, M W; Balazs, M; Boutle, S; Buehler, M; Conetti, S; Cox, B; Hirosky, R; Ledovskoy, A; Neu, C; Phillips II, D; Ronquest, M; Yohay, R; Gollapinni, S; Gunthoti, K; Harr, R; Karchin, P E; Mattson, M; Sakharov, A; Anderson, M; Bachtis, M; Bellinger, J N; Carlsmith, D; Crotty, I; Dasu, S; Dutta, S; Efron, J; Feyzi, F; Flood, K; Gray, L; Grogg, K S; Grothe, M; Hall-Wilton, R; Jaworski, M; Klabbers, P; Klukas, J; Lanaro, A; Lazaridis, C; Leonard, J; Loveless, R; Magrans de Abril, M; Mohapatra, A; Ott, G; Polese, G; Reeder, D; Savin, A; Smith, W H; Sourkov, A; Swanson, J; Weinberg, M; Wenman, D; Wensveen, M; White, A

    2010-01-01

    The performance of muon reconstruction in CMS is evaluated using a large data sample of cosmic-ray muons recorded in 2008. Efficiencies of various high-level trigger, identification, and reconstruction algorithms have been measured for a broad range of muon momenta, and were found to be in good agreement with expectations from Monte Carlo simulation. The relative momentum resolution for muons crossing the barrel part of the detector is better than 1% at 10 GeV/c and is about 8% at 500 GeV/c, the latter being only a factor of two worse than expected with ideal alignment conditions. Muon charge misassignment ranges from less than 0.01% at 10 GeV/c to about 1% at 500 GeV/c.

  6. Data driven CAN node reliability assessment for manufacturing system

    Science.gov (United States)

    Zhang, Leiming; Yuan, Yong; Lei, Yong

    2017-01-01

    The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

  7. Reconstructing particle masses in events with displaced vertices

    Science.gov (United States)

    Cottin, Giovanna

    2018-03-01

    We propose a simple way to extract particle masses given a displaced vertex signature in event topologies where two long-lived mother particles decay to visible particles and an invisible daughter. The mother could be either charged or neutral and the neutral daughter could correspond to a dark matter particle in different models. The method allows to extract the parent and daughter masses by using on-shell conditions and energy-momentum conservation, in addition to the displaced decay positions of the parents, which allows to solve the kinematic equations fully on an event-by-event basis. We show the validity of the method by means of simulations including detector effects. If displaced events are seen in discovery searches at the Large Hadron Collider (LHC), this technique can be applied.

  8. Reconstruction of GeV Neutrino Events in LENA

    International Nuclear Information System (INIS)

    Moellenberg, R.; Feilitzsch, F. von; Goeger-Neff, M.; Hellgartner, D.; Lewke, T.; Meindl, Q.; Oberauer, L.; Potzel, W.; Tippmann, M.; Winter, J.; Wurm, M.; Peltoniemi, J.

    2011-01-01

    LENA (Low Energy Neutrino Astronomy) is a proposed next generation liquid-scintillator detector with about 50 kt target mass. Besides the detection of solar neutrinos, geoneutrinos, supernova neutrinos and the search for the proton decay, LENA could also be used as the far detector of a next generation neutrino beam. The present contribution outlines the status of the Monte Carlo studies towards the reconstruction of GeV neutrinos in LENA. Both the tracking capabilities at a few hundred MeV, most interesting for a beta beam, and above 1 GeV for a superbeam experiment are presented.

  9. Data-driven forward model inference for EEG brain imaging

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai

    2016-01-01

    Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain......-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models....... Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging...

  10. Data-driven discovery of Koopman eigenfunctions using deep learning

    Science.gov (United States)

    Lusch, Bethany; Brunton, Steven L.; Kutz, J. Nathan

    2017-11-01

    Koopman operator theory transforms any autonomous non-linear dynamical system into an infinite-dimensional linear system. Since linear systems are well-understood, a mapping of non-linear dynamics to linear dynamics provides a powerful approach to understanding and controlling fluid flows. However, finding the correct change of variables remains an open challenge. We present a strategy to discover an approximate mapping using deep learning. Our neural networks find this change of variables, its inverse, and a finite-dimensional linear dynamical system defined on the new variables. Our method is completely data-driven and only requires measurements of the system, i.e. it does not require derivatives or knowledge of the governing equations. We find a minimal set of approximate Koopman eigenfunctions that are sufficient to reconstruct and advance the system to future states. We demonstrate the method on several dynamical systems.

  11. Data-driven workflows for microservices

    DEFF Research Database (Denmark)

    Safina, Larisa; Mazzara, Manuel; Montesi, Fabrizio

    2016-01-01

    Microservices is an architectural style inspired by service-oriented computing that has recently started gainingpopularity. Jolie is a programming language based on the microservices paradigm: the main building block of Jolie systems are services, in contrast to, e.g., functions or objects....... The primitives offered by the Jolie language elicit many of the recurring patterns found in microservices, like load balancers and structured processes. However, Jolie still lacks some useful constructs for dealing with message types and data manipulation that are present in service-oriented computing......). We show the impact of our implementation on some of the typical scenarios found in microservice systems. This shows how computation can move from a process-driven to a data-driven approach, and leads to the preliminary identification of recurring communication patterns that can be shaped as design...

  12. Data-Driven Security-Constrained OPF

    DEFF Research Database (Denmark)

    Thams, Florian; Halilbasic, Lejla; Pinson, Pierre

    2017-01-01

    considerations, while being less conservative than current approaches. Our approach can be scalable for large systems, accounts explicitly for power system security, and enables the electricity market to identify a cost-efficient dispatch avoiding redispatching actions. We demonstrate the performance of our......In this paper we unify electricity market operations with power system security considerations. Using data-driven techniques, we address both small signal stability and steady-state security, derive tractable decision rules in the form of line flow limits, and incorporate the resulting constraints...... in market clearing algorithms. Our goal is to minimize redispatching actions, and instead allow the market to determine the most cost-efficient dispatch while considering all security constraints. To maintain tractability of our approach we perform our security assessment offline, examining large datasets...

  13. Cellular automaton and elastic net for event reconstruction in the NEMO-2 experiment

    International Nuclear Information System (INIS)

    Kisel, I.; Kovalenko, V.; Laplanche, F.

    1997-01-01

    A cellular automaton for track searching combined with an elastic net for charged particle trajectory fitting is presented. The advantages of the methods are: the simplicity of the algorithms, the fast and stable convergency to real tracks, and a good reconstruction efficiency. The combination of techniques have been used with success for event reconstruction on the data of the NEMO-2 double-beta (ββ) decay experiments. (orig.)

  14. Automated reconstruction of rainfall events responsible for shallow landslides

    Science.gov (United States)

    Vessia, G.; Parise, M.; Brunetti, M. T.; Peruccacci, S.; Rossi, M.; Vennari, C.; Guzzetti, F.

    2014-04-01

    Over the last 40 years, many contributions have been devoted to identifying the empirical rainfall thresholds (e.g. intensity vs. duration ID, cumulated rainfall vs. duration ED, cumulated rainfall vs. intensity EI) for the initiation of shallow landslides, based on local as well as worldwide inventories. Although different methods to trace the threshold curves have been proposed and discussed in literature, a systematic study to develop an automated procedure to select the rainfall event responsible for the landslide occurrence has rarely been addressed. Nonetheless, objective criteria for estimating the rainfall responsible for the landslide occurrence (effective rainfall) play a prominent role on the threshold values. In this paper, two criteria for the identification of the effective rainfall events are presented: (1) the first is based on the analysis of the time series of rainfall mean intensity values over one month preceding the landslide occurrence, and (2) the second on the analysis of the trend in the time function of the cumulated mean intensity series calculated from the rainfall records measured through rain gauges. The two criteria have been implemented in an automated procedure written in R language. A sample of 100 shallow landslides collected in Italy by the CNR-IRPI research group from 2002 to 2012 has been used to calibrate the proposed procedure. The cumulated rainfall E and duration D of rainfall events that triggered the documented landslides are calculated through the new procedure and are fitted with power law in the (D,E) diagram. The results are discussed by comparing the (D,E) pairs calculated by the automated procedure and the ones by the expert method.

  15. The thermoluminescence as tool in the reconstruction of volcanic events

    International Nuclear Information System (INIS)

    Ramirez L, A.; Schaaf, P.; Martin del Pozzo, A.L.; Gonzalez M, P.

    2000-01-01

    Within the Mexican land a great number of volcanoes are situated which a considerable part of them are still active. The relevance of dating pomex deposits, ash or lava of these poly genetic volcanoes is to determine the periodicity and magnitude of the volcanic events happened. In this work is presented the preliminary result of the dating by thermoluminescence in a pomex of a pyroclastic flux coming from a volcano in the state of Puebla with the purpose of providing elements to the knowledge which describe the eruptive history of the explosive volcanism at center of Mexico. For the sample dating the volcanic glasses of pomex were separated and it was applied the fine grain technique with a grain size between 4-11 μ m. In order to calculate the rate of annual dose it was carried out the following: in the determination of 238 U and 232 Th radioisotope concentration was used the neutron activation technique in a nuclear reactor, in the determination of the K 40 radioisotope was used a scanning electron microscope, the rate of environmental and cosmic dose was measured arranging Tl dosemeters of CaSO 4 : Dy in the sampling place. In order to calculate the paleodoses it was carried out the following: the equivalent dose (Q) was determined starting form the additive method and the supra linearity factor (I) starting from regenerative method and in both methods the irradiated process was realized with a 90 Sr beta source. With the above determinations it was calculated a paleodoses of 231 Gy and a rate of annual dose of 6.074 x 10 -3 Gy/year, estimating an age of: Age pomez = 231 Gy / 6.074 Gy x 10 -3 Gy /year = 38030 ± 4000 years. (Author)

  16. Strategies and knowledge used when reconstructing the dates of autobiographical events

    DEFF Research Database (Denmark)

    Holm, Tine; Thomsen, Dorthe Kirkegaard

    Strategies and knowledge used when reconstructing the dates of autobiographical events. Tine Holm and Dorthe Kierkegaard Thomsen Individuals rarely remember the dates of specific memories. Rather they reconstruct dates using a variety of strategies and knowledge. In this study, we examined how...... often individuals use and combine different types of strategies and knowledge. Forty-five participants were each presented with diary descriptions of approximately 42 events from their first term at university that they had written 3 ½ years prior and were asked to date remembered events while...... they thought out loud. The results revealed that participants most frequently used period knowledge as a strategy in dating events. Other frequently used strategies were temporal schemas and temporal landmarks. Participants often combined strategies. Most frequently they combined period knowledge and temporal...

  17. Data driven modelling of vertical atmospheric radiation

    International Nuclear Information System (INIS)

    Antoch, Jaromir; Hlubinka, Daniel

    2011-01-01

    In the Czech Hydrometeorological Institute (CHMI) there exists a unique set of meteorological measurements consisting of the values of vertical atmospheric levels of beta and gamma radiation. In this paper a stochastic data-driven model based on nonlinear regression and on nonhomogeneous Poisson process is suggested. In the first part of the paper, growth curves were used to establish an appropriate nonlinear regression model. For comparison we considered a nonhomogeneous Poisson process with its intensity based on growth curves. In the second part both approaches were applied to the real data and compared. Computational aspects are briefly discussed as well. The primary goal of this paper is to present an improved understanding of the distribution of environmental radiation as obtained from the measurements of the vertical radioactivity profiles by the radioactivity sonde system. - Highlights: → We model vertical atmospheric levels of beta and gamma radiation. → We suggest appropriate nonlinear regression model based on growth curves. → We compare nonlinear regression modelling with Poisson process based modeling. → We apply both models to the real data.

  18. Data driven innovations in structural health monitoring

    Science.gov (United States)

    Rosales, M. J.; Liyanapathirana, R.

    2017-05-01

    At present, substantial investments are being allocated to civil infrastructures also considered as valuable assets at a national or global scale. Structural Health Monitoring (SHM) is an indispensable tool required to ensure the performance and safety of these structures based on measured response parameters. The research to date on damage assessment has tended to focus on the utilization of wireless sensor networks (WSN) as it proves to be the best alternative over the traditional visual inspections and tethered or wired counterparts. Over the last decade, the structural health and behaviour of innumerable infrastructure has been measured and evaluated owing to several successful ventures of implementing these sensor networks. Various monitoring systems have the capability to rapidly transmit, measure, and store large capacities of data. The amount of data collected from these networks have eventually been unmanageable which paved the way to other relevant issues such as data quality, relevance, re-use, and decision support. There is an increasing need to integrate new technologies in order to automate the evaluation processes as well as to enhance the objectivity of data assessment routines. This paper aims to identify feasible methodologies towards the application of time-series analysis techniques to judiciously exploit the vast amount of readily available as well as the upcoming data resources. It continues the momentum of a greater effort to collect and archive SHM approaches that will serve as data-driven innovations for the assessment of damage through efficient algorithms and data analytics.

  19. The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

    Energy Technology Data Exchange (ETDEWEB)

    Psihas, Fernanda [Indiana U.

    2017-11-22

    In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

  20. Parallelization of an existing high energy physics event reconstruction software package

    International Nuclear Information System (INIS)

    Schiefer, R.; Francis, D.

    1996-01-01

    Software parallelization allows an efficient use of available computing power to increase the performance of applications. In a case study the authors have investigated the parallelization of high energy physics event reconstruction software in terms of costs (effort, computing resource requirements), benefits (performance increase) and the feasibility of a systematic parallelization approach. Guidelines facilitating a parallel implementation are proposed for future software development

  1. Data-driven motion correction in brain SPECT

    International Nuclear Information System (INIS)

    Kyme, A.Z.; Hutton, B.F.; Hatton, R.L.; Skerrett, D.W.

    2002-01-01

    Patient motion can cause image artifacts in SPECT despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward-projections. By optimising the orientation of the reconstruction, parameters can be obtained for each misaligned projection and applied to update this volume using a 3D reconstruction algorithm. Digital and physical phantom validation was performed to investigate this approach. Noisy projection data simulating at least one fully 3D patient head movement during acquisition were constructed by projecting the digital Huffman brain phantom at various orientations. Motion correction was applied to the reconstructed studies. The importance of including attenuation effects in the estimation of motion and the need for implementing an iterated correction were assessed in the process. Correction success was assessed visually for artifact reduction, and quantitatively using a mean square difference (MSD) measure. Physical Huffman phantom studies with deliberate movements introduced during the acquisition were also acquired and motion corrected. Effective artifact reduction in the simulated corrupt studies was achieved by motion correction. Typically the MSD ratio between the corrected and reference studies compared to the corrupted and reference studies was > 2. Motion correction could be achieved without inclusion of attenuation effects in the motion estimation stage, providing simpler implementation and greater efficiency. Moreover the additional improvement with multiple iterations of the approach was small. Improvement was also observed in the physical phantom data, though the technique appeared limited here by an object symmetry. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

  2. Dynamic Data Driven Applications Systems (DDDAS)

    Science.gov (United States)

    2013-03-06

    detected Level 1 (L1) sensors: PIR & Piezoelectric Level 2 (L2) sensor: Overhead camera (UAV) Level 1.1 sensor: LIDAR Dynamic Influence Diagram ID1...Effects of Porous Shape Memory Alloys • Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring • Fluid SLAM and the Robotic...Structural Health Monitoring – PI: Thomas Henderson, U. of Utah • Fluid SLAM and the Robotic Reconstruction of Localized Atmospheric Phenomena – PI

  3. Muon Reconstruction and Identification for the Event Filter of the ATLAS experiment

    CERN Document Server

    Ventura, A; Armstrong, S; Baines, J T M; Bee, C P; Bellomo, M; Biglietti, M; Bogaerts, J A C; Bosman, M; Carlino, G; Caron, B; Casado, M P; Cataldi, G; Cavalli, D; Comune, G; Conde, P; Conventi, F; Crone, G; Damazio, D; De Santo, A; Díaz-Gómez, M; Di Mattia, A; Ellis, Nick; Emeliyanov, D; Epp, B; Falciano, S; Garitaonandia, H; George, S; Ghete, V; Goncalo, R; Gorini, E; Grancagnolo, S; Haller, J; Kabana, S; Khomich, A; Kilvington, G; Kirk, N; Konstantinidis, N P; Kootz, A; Lankford, A J; Lowe, A; Luminari, L; Maeno, T; Masik, J; Meessen, C; Mello, A G; Moore, R; Morettini, P; Negri, A; Nikitin, N; Nisati, A; Osuna, C; Padilla, C; Panikashvili, N; Parodi, F; Pasqualucci, E; Pérez-Réale, V; Pinfold, J L; Pinto, P; Primavera, M; Qian, Z; Resconi, S; Rosati, S; Sánchez, C; Santamarina-Rios, C; Scannicchio, D A; Schiavi, C; Segura, E; De Seixas, J M; Sidoti, A; Siragusa, G; Sivoklokov, S Yu; Sobreira, A; Soluk, R A; Spagnolo, S; Stefanidis, E; Sushkov, S; Sutton, M; Tapprogge, S; Tarem, S; Thomas, E; Touchard, F; Usai, G; Venda-Pinto, B; Vercesi, V; Wengler, T; Werner, P; Wheeler, S J; Wickens, F J; Wiedenmann, W; Wielers, M; Zobernig, G; 9th ICATPP Conference on Astroparticle, Particle, Space Physics, Detectors and Medical Physics Applications

    2005-01-01

    The ATLAS Trigger requires high efficiency and selectivity in order to keep the full physics potential of the experiment and to reject uninteresting processes from the 40 MHz event production rate of the LHC. These goals are achieved with a trigger composed of three sequential levels of increasing accuracy that have to reduce the output event rate down to ~100 Hz. This work focuses on muon reconstruction and identification for the third level (Event Filter), for which specific algorithms from the off-line environment have been adapted to work in the trigger framework. Two different strategies for accessing data (wrapped and seeded modes) are described and their reconstruction potential is then shown in terms of efficiencies, resolutions and fake muon rejection power.

  4. Applying Bayesian neural networks to event reconstruction in reactor neutrino experiments

    International Nuclear Information System (INIS)

    Xu Ye; Xu Weiwei; Meng Yixiong; Zhu Kaien; Xu Wei

    2008-01-01

    A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The electron samples from the Monte-Carlo simulation of the toy detector have been reconstructed by the method of Bayesian neural networks (BNNs) and the standard algorithm, a maximum likelihood method (MLD), respectively. The result of the event reconstruction using BNN has been compared with the one using MLD. Compared to MLD, the uncertainties of the electron vertex are not improved, but the energy resolutions are significantly improved using BNN. And the improvement is more obvious for the high energy electrons than the low energy ones

  5. On-line event reconstruction using a parallel in-memory data base

    OpenAIRE

    Argante, E; Van der Stok, P D V; Willers, Ian Malcolm

    1995-01-01

    PORS is a system designed for on-line event reconstruction in high energy physics (HEP) experiments. It uses the CPREAD reconstruction program. Central to the system is a parallel in-memory database which is used as communication medium between parallel workers. A farming control structure is implemented with PORS in a natural way. The database provides structured storage of data with a short life time. PORS serves as a case study for the construction of a methodology on how to apply parallel...

  6. Methods of reconstruction of multi-particle events in the new coordinate-tracking setup

    Science.gov (United States)

    Vorobyev, V. S.; Shutenko, V. V.; Zadeba, E. A.

    2018-01-01

    At the Unique Scientific Facility NEVOD (MEPhI), a large coordinate-tracking detector based on drift chambers for investigations of muon bundles generated by ultrahigh energy primary cosmic rays is being developed. One of the main characteristics of the bundle is muon multiplicity. Three methods of reconstruction of multiple events were investigated: the sequential search method, method of finding the straight line and method of histograms. The last method determines the number of tracks with the same zenith angle in the event. It is most suitable for the determination of muon multiplicity: because of a large distance to the point of generation of muons, their trajectories are quasiparallel. The paper presents results of application of three reconstruction methods to data from the experiment, and also first results of the detector operation.

  7. Crime event 3D reconstruction based on incomplete or fragmentary evidence material--case report.

    Science.gov (United States)

    Maksymowicz, Krzysztof; Tunikowski, Wojciech; Kościuk, Jacek

    2014-09-01

    Using our own experience in 3D analysis, the authors will demonstrate the possibilities of 3D crime scene and event reconstruction in cases where originally collected material evidence is largely insufficient. The necessity to repeat forensic evaluation is often down to the emergence of new facts in the course of case proceedings. Even in cases when a crime scene and its surroundings have undergone partial or complete transformation, with regard to elements significant to the course of the case, or when the scene was not satisfactorily secured, it is still possible to reconstruct it in a 3D environment based on the originally-collected, even incomplete, material evidence. In particular cases when no image of the crime scene is available, its partial or even full reconstruction is still potentially feasible. Credibility of evidence for such reconstruction can still satisfy the evidence requirements in court. Reconstruction of the missing elements of the crime scene is still possible with the use of information obtained from current publicly available databases. In the study, we demonstrate that these can include Google Maps(®*), Google Street View(®*) and available construction and architecture archives. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  8. Data-driven architectural design to production and operation

    NARCIS (Netherlands)

    Bier, H.H.; Mostafavi, S.

    2015-01-01

    Data-driven architectural production and operation explored within Hyperbody rely heavily on system thinking implying that all parts of a system are to be understood in relation to each other. These relations are established bi-directionally so that data-driven architecture is not only produced

  9. Data-Driven Methods to Diversify Knowledge of Human Psychology

    OpenAIRE

    Jack, Rachael E.; Crivelli, Carlos; Wheatley, Thalia

    2017-01-01

    open access article Psychology aims to understand real human behavior. However, cultural biases in the scientific process can constrain knowledge. We describe here how data-driven methods can relax these constraints to reveal new insights that theories can overlook. To advance knowledge we advocate a symbiotic approach that better combines data-driven methods with theory.

  10. State-of-the-art Machine Learning in event reconstruction and object identification

    CERN Document Server

    Kagan, Michael; The ATLAS collaboration

    2017-01-01

    Recent advances in deep learning have seen great success in the realms of computer vision, natural language processing, and broadly in data science. However, these new ideas are only just beginning to be applied to the analysis of High Energy Physics data. In this talk, I will discuss developments in the application of computer vision and deep learning techniques for event reconstruction and particle identification for the LHC .

  11. Neutrino energy reconstruction from one-muon and one-proton events

    Energy Technology Data Exchange (ETDEWEB)

    Furmanski, Andrew P.; Sobczyk, Jan T.

    2017-06-01

    We propose a method of selecting a high-purity sample of charged current quasielastic neutrino interactions to obtain a precise reconstruction of the neutrino energy. The performance of the method was verified with several tests using genie, neut, and nuwro Monte Carlo event generators with both carbon and argon targets. The method can be useful in neutrino oscillation studies with beams of a few GeV.

  12. Determination of the {tau}-lepton reconstruction and identification efficiency using Z {yields} {tau}{tau} events in first data at ATLAS

    Energy Technology Data Exchange (ETDEWEB)

    Fischer, Gordon

    2011-10-15

    The Large Hadron Collider (LHC) at CERN started operation in November 2009. At the same time the ATLAS experiment started data taking. Since this time a large number of Z-bosons is produced. An important decay channel of the Z-boson is the decay into two {tau} -leptons. The large mass of the {tau}-lepton allows the decay into pions or kaons. In many models considering new physics the {tau}-lepton is an important final state. The LHC is a proton-proton collider and for that reason, the hadronic {tau}-lepton decay is difficult to distinguish from QCD multi-jet background. For the selection of hadronically decaying {tau}-leptons, reconstruction and identification algorithms were developed in order to suppress this background. In order to measure the Z-boson production cross section or possible new particles decaying into {tau}-leptons, the estimation of the {tau}-lepton reconstruction and identification efficiency is required. Furthermore, for detector calibration the Z-boson as well as the {tau}-lepton are helpful probes. In this thesis two methods are discussed which provide an estimation of {tau}-lepton reconstruction and identification efficiencies from data. The full selection of Z {yields} {tau}{tau} events including data-driven techniques for background extraction is discussed. The semi-leptonic Z {yields} {tau}{tau} channel promises a good QCD multi-jet suppression because of the selected additional lepton. For that reason also the leptonically decaying {tau}-lepton is discussed. The Z-boson production cross section can be calculated with the estimated efficiencies. (orig.)

  13. Determination of the τ-lepton reconstruction and identification efficiency using Z → ττ events in first data at ATLAS

    International Nuclear Information System (INIS)

    Fischer, Gordon

    2011-05-01

    The Large Hadron Collider (LHC) at CERN started operation in November 2009. At the same time the ATLAS experiment started data taking. Since this time a large number of Z-bosons is produced. An important decay channel of the Z-boson is the decay into two τ -leptons. The large mass of the τ-lepton allows the decay into pions or kaons. In many models considering new physics the τ-lepton is an important final state. The LHC is a proton-proton collider and for that reason, the hadronic τ-lepton decay is difficult to distinguish from QCD multi-jet background. For the selection of hadronically decaying τ-leptons, reconstruction and identification algorithms were developed in order to suppress this background. In order to measure the Z-boson production cross section or possible new particles decaying into τ-leptons, the estimation of the τ-lepton reconstruction and identification efficiency is required. Furthermore, for detector calibration the Z-boson as well as the τ-lepton are helpful probes. In this thesis two methods are discussed which provide an estimation of τ-lepton reconstruction and identification efficiencies from data. The full selection of Z → ττ events including data-driven techniques for background extraction is discussed. The semi-leptonic Z → ττ channel promises a good QCD multi-jet suppression because of the selected additional lepton. For that reason also the leptonically decaying τ-lepton is discussed. The Z-boson production cross section can be calculated with the estimated efficiencies. (orig.) environment. (orig.)

  14. Complications and Adverse Events of a Randomized Clinical Trial Comparing 3 Graft Types for ACL Reconstruction.

    Science.gov (United States)

    Mohtadi, Nicholas; Barber, Rhamona; Chan, Denise; Paolucci, Elizabeth Oddone

    2016-05-01

    Complications/adverse events of anterior cruciate ligament (ACL) surgery are underreported, despite pooled level 1 data in systematic reviews. All adverse events/complications occurring within a 2-year postoperative period after primary ACL reconstruction, as part of a large randomized clinical trial (RCT), were identified and described. Prospective, double-blind randomized clinical trial. Patients and the independent trained examiner were blinded to treatment allocation. University-based orthopedic referral practice. Three hundred thirty patients (14-50 years; 183 males) with isolated ACL deficiency were intraoperatively randomized to ACL reconstruction with 1 autograft type. Graft harvest and arthroscopic portal incisions were identical. Patients were equally distributed to patellar tendon (PT), quadruple-stranded hamstring tendon (HT), and double-bundle (DB) hamstring autograft ACL reconstruction. Adverse events/complications were patient reported, documented, and diagnoses confirmed. Two major complications occurred: pulmonary embolism and septic arthritis. Twenty-four patients (7.3%) required repeat surgery, including 25 separate operations: PT = 7 (6.4%), HT = 9 (8.2%), and DB = 8 (7.3%). Repeat surgery was performed for meniscal tears (3.6%; n = 12), intra-articular scarring (2.7%; n = 9), chondral pathology (0.6%; n = 2), and wound dehiscence (0.3%; n = 1). Other complications included wound problems, sensory nerve damage, muscle tendon injury, tibial periostitis, and suspected meniscal tears and chondral lesions. Overall, more complications occurred in the HT/DB groups (PT = 24; HT = 31; DB = 45), but more PT patients complained of moderate or severe kneeling pain (PT = 17; HT = 9; DB = 4) at 2 years. Overall, ACL reconstructive surgery is safe. Major complications were uncommon. Secondary surgery was necessary 7.3% of the time for complications/adverse events (excluding graft reinjury or revisions) within the first 2 years. Level 1 (therapeutic studies

  15. Particle-flow reconstruction and global event description with the CMS detector

    Energy Technology Data Exchange (ETDEWEB)

    Sirunyan, Albert M; et al.

    2017-06-15

    The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.

  16. Dynamic Data-Driven UAV Network for Plume Characterization

    Science.gov (United States)

    2016-05-23

    AFRL-AFOSR-VA-TR-2016-0203 Dynamic Data-Driven UAV Network for Plume Characterization Kamran Mohseni UNIVERSITY OF FLORIDA Final Report 05/23/2016...AND SUBTITLE Dynamic Data-Driven UAV Network for Plume Characterization 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-13-1-0090 5c.  PROGRAM ELEMENT...studied a dynamic data driven (DDD) approach to operation of a heterogeneous team of unmanned aerial vehicles ( UAVs ) or micro/miniature aerial

  17. Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

    Science.gov (United States)

    Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Devers, Alexandre; Graff, Benjamin

    2017-06-01

    The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

  18. Phylogenetic reconstruction of transmission events from individuals with acute HIV infection: toward more-rigorous epidemiological definitions

    NARCIS (Netherlands)

    Brown, Alison E.; Gifford, Robert J.; Clewley, Jonathan P.; Kucherer, Claudia; Masquelier, Bernard; Porter, Kholoud; Balotta, Claudia; Back, Nicole K. T.; Jorgensen, Louise Bruun; de Mendoza, Carmen; Bhaskaran, Krishnan; Gill, O. Noel; Johnson, Anne M.; Pillay, Deenan; del Amo, Julia; Meyer, Laurence; Bucher, Heiner; Chene, Genevieve; Prins, Maria; Rosinska, Magda; Sabin, Caroline; Touloumi, Giota; Lodi, Sara; Walker, Sarah; Babiker, Abdel; Darbyshire, Janet; de Luca, Andrea; Fisher, Martin; Muga, Roberto; Kaldor, John; Kelleher, Tony; Ramacciotti, Tim; Gelgor, Linda; Cooper, David; Smith, Don; Gill, John; Nielsen, Claus; Pedersen, Court; Lutsar, Irja; Dabis, Francois; Thiebaut, Rodolphe; Costagliola, Dominique; Guiguet, Marguerite; Vanhems, Philippe; Boufassa, Faroudy; Hamouda, Osamah; Pantazis, Nikos; Hatzakis, Angelos; Geskus, Ronald; Coutinho, Roel

    2009-01-01

    Phylogenetic reconstructions of transmission events from individuals with acute human immunodeficiency virus (HIV) infection are conducted to illustrate this group's heightened infectivity. Varied definitions of acute infection and assumptions about observed phylogenetic clusters may produce

  19. Data-Driven Exercises for Chemistry: A New Digital Collection

    Science.gov (United States)

    Grubbs, W. Tandy

    2007-01-01

    The analysis presents a new digital collection for various data-driven exercises that are used for teaching chemistry to the students. Such methods are expected to help the students to think in a more scientific manner.

  20. Data-Driven Model Order Reduction for Bayesian Inverse Problems

    KAUST Repository

    Cui, Tiangang; Youssef, Marzouk; Willcox, Karen

    2014-01-01

    One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce

  1. Reconstruction efficiency and precision for the events detected by the BIS-2 installation using the Perun pattern recognition program

    International Nuclear Information System (INIS)

    Burilkov, D.T.; Genchev, V.I.; Markov, P.K.; Likhachev, M.F.; Takhtamyshev, G.G.; Todorov, P.T.; Trayanov, P.K.

    1982-01-01

    Results of studying the efficiency and accuracy of the track and event reconstruction with the Perun pattern recognition program used in the experiments carried out at the BIS-2 installation are presented. The Monte Carlo method is used for simulating the processes of neutron interactions with matter. The con-- clusion is made that the Perun program correctly, with good accuracy and high efficiency reconstructs complex multiparticle events [ru

  2. CLARA: CLAS12 Reconstruction and Analysis Framework

    Energy Technology Data Exchange (ETDEWEB)

    Gyurjyan, Vardan [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Matta, Sebastian Mancilla [Santa Maria U., Valparaiso, Chile; Oyarzun, Ricardo [Santa Maria U., Valparaiso, Chile

    2016-11-01

    In this paper we present SOA based CLAS12 event Reconstruction and Analyses (CLARA) framework. CLARA design focus is on two main traits: real-time data stream processing, and service-oriented architecture (SOA) in a flow based programming (FBP) paradigm. Data driven and data centric architecture of CLARA presents an environment for developing agile, elastic, multilingual data processing applications. The CLARA framework presents solutions capable of processing large volumes of data interactively and substantially faster than batch systems.

  3. [The application of forensic medical knowledge for the reconstruction of historical events].

    Science.gov (United States)

    Kovalev, A V; Molin, Yu A; Gorshkov, A N; Smolyanitsky, A G; Mazurova, E A; Vorontsov, G A

    2015-01-01

    The objective of the present study was to summarize the results of many year investigations on the application of forensic medical methods and experience for the reconstruction of historical events including identification of the ancient Russian saints' hallows and statesmen's remains, elucidation of the genuine causes of death of the members of the Russian Imperial House of Romanovs based on the recently discovered archival materials, restoration of the character of the injuries suffered by Aleksander II, M.I. Kutuzov, P. Demidov, G. Gapon., and G. Rasputin, the attribution A.S. Pushkin's memorial belongings based on the biological traces, and the like.

  4. Live event reconstruction in an optically read out GEM-based TPC

    Science.gov (United States)

    Brunbauer, F. M.; Galgóczi, G.; Gonzalez Diaz, D.; Oliveri, E.; Resnati, F.; Ropelewski, L.; Streli, C.; Thuiner, P.; van Stenis, M.

    2018-04-01

    Combining strong signal amplification made possible by Gaseous Electron Multipliers (GEMs) with the high spatial resolution provided by optical readout, highly performing radiation detectors can be realized. An optically read out GEM-based Time Projection Chamber (TPC) is presented. The device permits 3D track reconstruction by combining the 2D projections obtained with a CCD camera with timing information from a photomultiplier tube. Owing to the intuitive 2D representation of the tracks in the images and to automated control, data acquisition and event reconstruction algorithms, the optically read out TPC permits live display of reconstructed tracks in three dimensions. An Ar/CF4 (80/20%) gas mixture was used to maximize scintillation yield in the visible wavelength region matching the quantum efficiency of the camera. The device is integrated in a UHV-grade vessel allowing for precise control of the gas composition and purity. Long term studies in sealed mode operation revealed a minor decrease in the scintillation light intensity.

  5. Triggerless Readout with Time and Amplitude Reconstruction of Event Based on Deconvolution Algorithm

    International Nuclear Information System (INIS)

    Kulis, S.; Idzik, M.

    2011-01-01

    In future linear colliders like CLIC, where the period between the bunch crossings is in a sub-nanoseconds range ( 500 ps), an appropriate detection technique with triggerless signal processing is needed. In this work we discuss a technique, based on deconvolution algorithm, suitable for time and amplitude reconstruction of an event. In the implemented method the output of a relatively slow shaper (many bunch crossing periods) is sampled and digitalised in an ADC and then the deconvolution procedure is applied to digital data. The time of an event can be found with a precision of few percent of sampling time. The signal to noise ratio is only slightly decreased after passing through the deconvolution filter. The performed theoretical and Monte Carlo studies are confirmed by the results of preliminary measurements obtained with the dedicated system comprising of radiation source, silicon sensor, front-end electronics, ADC and further digital processing implemented on a PC computer. (author)

  6. Energy Reconstruction for Events Detected in TES X-ray Detectors

    Science.gov (United States)

    Ceballos, M. T.; Cardiel, N.; Cobo, B.

    2015-09-01

    The processing of the X-ray events detected by a TES (Transition Edge Sensor) device (such as the one that will be proposed in the ESA AO call for instruments for the Athena mission (Nandra et al. 2013) as a high spectral resolution instrument, X-IFU (Barret et al. 2013)), is a several step procedure that starts with the detection of the current pulses in a noisy signal and ends up with their energy reconstruction. For this last stage, an energy calibration process is required to convert the pseudo energies measured in the detector to the real energies of the incoming photons, accounting for possible nonlinearity effects in the detector. We present the details of the energy calibration algorithm we implemented as the last part of the Event Processing software that we are developing for the X-IFU instrument, that permits the calculation of the calibration constants in an analytical way.

  7. Event reconstruction using the radio-interferometric technique in the frame of AERA

    Energy Technology Data Exchange (ETDEWEB)

    Rogozin, Dmytro [Institut fuer Experimentelle Kernphysik, Karlsruher Institut fuer Technologie (KIT) (Germany); Collaboration: Pierre-Auger-Collaboration

    2016-07-01

    It is a well-known fact that there is coherent radio emission induced by extensive air-showers. This fact is exploited in the Auger Engineering Radio Array (AERA), the radio extension of the Pierre Auger Observatory. This is a unique radio experiment due to its world-largest size of 17 km{sup 2}, and due to its precise nanosecond timing calibration. These features become crucial for detection of highly inclined air-showers with their very large foot-prints, and for the ability to apply interferometric reconstruction techniques. The standard reconstruction techniques typically treat all radio stations as separate detectors. Nevertheless there is a possibility to do an interferometric analysis. This means combining all detected signals from all antennas in a specific way. In this talk we present a beam-forming interferometric technique and its application to AERA. According to the definition of the beam-forming quantities one can expect its correlation with the shower parameters such as energy of the primary particle and distance to the shower maximum. At the first step, Monte-Carlo simulations of AERA events including the noise from measured events were used to test these dependencies. The results and the future perspectives of this method are discussed with a particular emphasis on very inclined air-showers where the aforementioned correlations are assumed to be strongest.

  8. Combining geomorphic and documentary flood evidence to reconstruct extreme events in Mediterranean basins

    Science.gov (United States)

    Thorndycraft, V. R.; Benito, G.; Barriendos, M.; Rico, M.; Sánchez-Moya, Y.; Sopeña, A.; Casas, A.

    2009-09-01

    Palaeoflood hydrology is the reconstruction of flood magnitude and frequency using geomorphological flood evidence and is particularly valuable for extending the record of extreme floods prior to the availability of instrumental data series. This paper will provide a review of recent developments in palaeoflood hydrology and will be presented in three parts: 1) an overview of the key methodological approaches used in palaeoflood hydrology and the use of historical documentary evidence for reconstructing extreme events; 2) a summary of the Llobregat River palaeoflood case study (Catalonia, NE Spain); and 3) analysis of the AD 1617 flood and its impacts across Catalonia (including the rivers Llobregat, Ter and Segre). The key findings of the Llobregat case study were that at least eight floods occurred with discharges significantly larger than events recorded in the instrumental record, for example at the Pont de Vilomara study reach the palaeodischarges of these events were 3700-4300 m3/s compared to the 1971 flood, the largest on record, of 2300 m3/s. Five of these floods were dated to the last 3000 years and the three events directly dated by radiocarbon all occurred during cold phases of global climate. Comparison of the palaeoflood record with documentary evidence indicated that one flood, radiocarbon dated to cal. AD 1540-1670, was likely to be the AD 1617 event, the largest flood of the last 700 years. Historical records indicate that this event was caused by rainfall occurring from the 2nd to 6th November and the resultant flooding caused widespread socio-economic impacts including the destruction of at least 389 houses, 22 bridges and 17 water mills. Discharges estimated from palaeoflood records and historical flood marks indicate that the Llobregat (4680 m3/s) and Ter (2700-4500 m3/s) rivers witnessed extreme discharges in comparison to observed floods in the instrumental record (2300 and 2350 m3/s, respectively); whilst further east in the Segre River

  9. Reconstructing the 2015 Flash Flood event of Salgar Colombia, The Case of a Poor Gauged Basin

    Science.gov (United States)

    Velasquez, N.; Zapata, E.; Hoyos Ortiz, C. D.; Velez, J. I.

    2017-12-01

    Flash floods events associated with severe precipitation events are highly destructive, often resulting in significant human and economic losses. Due to their nature, flash floods trend to occur in medium to small basins located within complex high mountainous regions. In the Colombian Andean region these basins are very common, with the aggravating factor that the vulnerability is considerably high as some important human settlements are located within these basins, frequently occupating flood plains and other flash-flood prone areas. During the dawn of May 18 of 2015 two severe rainfall events generated a flash flood event in the municipality ofSalgar, La Liboriana basin, locatedin the northwestern Colombian Andes, resulting in more than 100 human casualties and significant economic losses. The present work is a reconstruction of the hydrological processes that took place before and during the Liboriana flash flood event, analyzed as a case of poorly gauged basin.The event conditions where recreated based on radar retrievals and a hydrological distributed model, linked with a proposed 1D hydraulic model and simple shallow landslide model. Results suggest that the flash flood event was caused by the occurrence of two successive severe convective events over the same basin, with an important modulation associated with soil characteristics and water storage.Despite of its simplicity, the proposed hydraulic model achieves a good representation of the flooded area during the event, with limitations due to the adopted spatial scale (12.7 meters, from ALOS PALSAR images). Observed landslides were obtained from satellite images; for this case the model simulates skillfully the landslide occurrence regions with small differences in the exact locations.To understand this case, radar data shows to be key due to specific convective cores location and rainfall intensity estimation.In mountainous regions, there exists a significant number of settlements with similar

  10. The Structural Consequences of Big Data-Driven Education.

    Science.gov (United States)

    Zeide, Elana

    2017-06-01

    Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education

  11. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

    Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  12. Deep Convolutional Networks for Event Reconstruction and Particle Tagging on NOvA and DUNE

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Deep Convolutional Neural Networks (CNNs) have been widely applied in computer vision to solve complex problems in image recognition and analysis. In recent years many efforts have emerged to extend the use of this technology to HEP applications, including the Convolutional Visual Network (CVN), our implementation for identification of neutrino events. In this presentation I will describe the core concepts of CNNs, the details of our particular implementation in the Caffe framework and our application to identify NOvA events. NOvA is a long baseline neutrino experiment whose main goal is the measurement of neutrino oscillations. This relies on the accurate identification and reconstruction of the neutrino flavor in the interactions we observe. In 2016 the NOvA experiment released results for the observation of oscillations in the ν μ → ν e channel, the first HEP result employing CNNs. I will also discuss our approach at event identification on NOvA as well as recent developments in the application of CNN...

  13. External radioactive markers for PET data-driven respiratory gating in positron emission tomography.

    Science.gov (United States)

    Büther, Florian; Ernst, Iris; Hamill, James; Eich, Hans T; Schober, Otmar; Schäfers, Michael; Schäfers, Klaus P

    2013-04-01

    Respiratory gating is an established approach to overcoming respiration-induced image artefacts in PET. Of special interest in this respect are raw PET data-driven gating methods which do not require additional hardware to acquire respiratory signals during the scan. However, these methods rely heavily on the quality of the acquired PET data (statistical properties, data contrast, etc.). We therefore combined external radioactive markers with data-driven respiratory gating in PET/CT. The feasibility and accuracy of this approach was studied for [(18)F]FDG PET/CT imaging in patients with malignant liver and lung lesions. PET data from 30 patients with abdominal or thoracic [(18)F]FDG-positive lesions (primary tumours or metastases) were included in this prospective study. The patients underwent a 10-min list-mode PET scan with a single bed position following a standard clinical whole-body [(18)F]FDG PET/CT scan. During this scan, one to three radioactive point sources (either (22)Na or (18)F, 50-100 kBq) in a dedicated holder were attached the patient's abdomen. The list mode data acquired were retrospectively analysed for respiratory signals using established data-driven gating approaches and additionally by tracking the motion of the point sources in sinogram space. Gated reconstructions were examined qualitatively, in terms of the amount of respiratory displacement and in respect of changes in local image intensity in the gated images. The presence of the external markers did not affect whole-body PET/CT image quality. Tracking of the markers led to characteristic respiratory curves in all patients. Applying these curves for gated reconstructions resulted in images in which motion was well resolved. Quantitatively, the performance of the external marker-based approach was similar to that of the best intrinsic data-driven methods. Overall, the gain in measured tumour uptake from the nongated to the gated images indicating successful removal of respiratory motion

  14. Event reconstruction for the CBM-RICH prototype beamtest data in 2014

    Science.gov (United States)

    Adamczewski-Musch, J.; Akishin, P.; Becker, K.-H.; Belogurov, S.; Bendarouach, J.; Boldyreva, N.; Deveaux, C.; Dobyrn, V.; Dürr, M.; Eschke, J.; Förtsch, J.; Heep, J.; Höhne, C.; Kampert, K.-H.; Kochenda, L.; Kopfer, J.; Kravtsov, P.; Kres, I.; Lebedev, S.; Lebedeva, E.; Leonova, E.; Linev, S.; Mahmoud, T.; Michel, J.; Miftakhov, N.; Niebur, W.; Ovcharenko, E.; Patel, V.; Pauly, C.; Pfeifer, D.; Querchfeld, S.; Rautenberg, J.; Reinecke, S.; Riabov, Y.; Roshchin, E.; Samsonov, V.; Schetinin, V.; Tarasenkova, O.; Traxler, M.; Ugur, C.; Vznuzdaev, E.; Vznuzdaev, M.

    2017-12-01

    The Compressed Baryonic Matter (CBM) experiment at the future FAIR facility will investigate the QCD phase diagram at high net baryon densities and moderate temperatures in A+A collisions from 2 to 11 AGeV (SIS100). Electron identification in CBM will be performed by a Ring Imaging Cherenkov (RICH) detector and Transition Radiation Detectors (TRD). A real size prototype of the RICH detector was tested together with other CBM groups at the CERN PS/T9 beam line in 2014. For the first time the data format used the FLESnet protocol from CBM delivering free streaming data. The analysis was fully performed within the CBMROOT framework. In this contribution the data analysis and the event reconstruction methods which were used for obtained data are discussed. Rings were reconstructed using an algorithm based on the Hough Transform method and their parameters were derived with high accuracy by circle and ellipse fitting procedures. We present results of the application of the presented algorithms. In particular we compare results with and without Wavelength shifting (WLS) coating.

  15. Event reconstruction for the RICH prototype beamtest data 2012 and 2014

    Energy Technology Data Exchange (ETDEWEB)

    Lebedev, Semen [II. Physikalisches Institut, JLU Giessen (Germany); Collaboration: CBM-Collaboration

    2016-07-01

    The Compressed Baryonic Matter (CBM) experiment at the future FAIR facility will investigate the QCD phase diagram at high net baryon densities and moderate temperatures in A+A collisions from 2-11 AGeV (SIS100). Electron identification in CBM will be performed by a Ring Imaging Cherenkov (RICH) detector and Transition Radiation Detectors (TRD). A real size prototype of the RICH detector was tested together with other CBM prototypes (TRD, TOF) at the CERN PS/T9 beam line in 2012 and 2014. In 2014 for the first time the data format used the FLESnet protocol from CBM delivering free streaming data. The analysis was fully performed within the CBMROOT framework. In this contribution the event reconstruction methods which were used for obtained data are discussed. Rings were reconstructed using an algorithm based on the Hough Transform method and their parameters were derived with high accuracy by circle and ellipse fitting procedures. Results of the application of the presented algorithms are also presented.

  16. HEP Community White Paper on Software Trigger and Event Reconstruction: Executive Summary

    Energy Technology Data Exchange (ETDEWEB)

    Albrecht, Johannes; et al.

    2018-02-23

    Realizing the physics programs of the planned and upgraded high-energy physics (HEP) experiments over the next 10 years will require the HEP community to address a number of challenges in the area of software and computing. For this reason, the HEP software community has engaged in a planning process over the past two years, with the objective of identifying and prioritizing the research and development required to enable the next generation of HEP detectors to fulfill their full physics potential. The aim is to produce a Community White Paper which will describe the community strategy and a roadmap for software and computing research and development in HEP for the 2020s. The topics of event reconstruction and software triggers were considered by a joint working group and are summarized together in this document.

  17. Extension of a data-driven gating technique to 3D, whole body PET studies

    International Nuclear Information System (INIS)

    Schleyer, Paul J; O'Doherty, Michael J; Marsden, Paul K

    2011-01-01

    Respiratory gating can be used to separate a PET acquisition into a series of near motion-free bins. This is typically done using additional gating hardware; however, software-based methods can derive the respiratory signal from the acquired data itself. The aim of this work was to extend a data-driven respiratory gating method to acquire gated, 3D, whole body PET images of clinical patients. The existing method, previously demonstrated with 2D, single bed-position data, uses a spectral analysis to find regions in raw PET data which are subject to respiratory motion. The change in counts over time within these regions is then used to estimate the respiratory signal of the patient. In this work, the gating method was adapted to only accept lines of response from a reduced set of axial angles, and the respiratory frequency derived from the lung bed position was used to help identify the respiratory frequency in all other bed positions. As the respiratory signal does not identify the direction of motion, a registration-based technique was developed to align the direction for all bed positions. Data from 11 clinical FDG PET patients were acquired, and an optical respiratory monitor was used to provide a hardware-based signal for comparison. All data were gated using both the data-driven and hardware methods, and reconstructed. The centre of mass of manually defined regions on gated images was calculated, and the overall displacement was defined as the change in the centre of mass between the first and last gates. The mean displacement was 10.3 mm for the data-driven gated images and 9.1 mm for the hardware gated images. No significant difference was found between the two gating methods when comparing the displacement values. The adapted data-driven gating method was demonstrated to successfully produce respiratory gated, 3D, whole body, clinical PET acquisitions.

  18. Event reconstruction in the RICH detector of the CBM experiment at FAIR

    International Nuclear Information System (INIS)

    Adamczewski, J.; Becker, K.-H.; Belogurov, S.; Boldyreva, N.; Chernogorov, A.; Deveaux, C.; Dobyrn, V.; Dürr, M.; Eom, J.; Eschke, J.; Höhne, C.; Kampert, K.-H.; Kleipa, V.; Kochenda, L.; Kolb, B.; Kopfer, J.; Kravtsov, P.; Lebedev, S.; Lebedeva, E.; Leonova, E.

    2014-01-01

    The Compressed Baryonic Matter (CBM) experiment at the future FAIR facility will investigate the QCD phase diagram at high net-baryon densities and moderate temperatures. One of the key signatures will be di-leptons emitted from the hot and dense phase in heavy-ion collisions. Measuring di-electrons, a high purity of identified electrons is required in order to suppress the background. Electron identification in CBM will be performed by a Ring Imaging Cherenkov (RICH) detector and Transition Radiation Detectors (TRD). In order to access the foreseen rare probes, the detector and the data acquisition have to handle interaction rates up to 10 MHz. Therefore, the development of fast and efficient event reconstruction algorithms is an important and challenging task in CBM. In this contribution event reconstruction and electron identification algorithms in the RICH detector are presented. So far they have been developed on simulated data but could already be tested on real data from a RICH prototype testbeam experiment at the CERN-PS. Efficient and fast ring recognition algorithms in the CBM-RICH are based on the Hough Transform method. Due to optical distortions of the rings, an ellipse fitting algorithm was elaborated to improve the ring radius resolution. An efficient algorithm based on the Artificial Neural Network was implemented for electron identification in RICH. All algorithms were significantly optimized to achieve maximum speed and minimum memory consumption. - Highlights: • Ring Imaging Cherenkov detector will serve for electron identification in CBM. • We present efficient ring recognition algorithm based on the Hough Transform method. • Developed algorithms were significantly optimized to achieve maximum speed up. • Electron identification algorithm in RICH based on the Artificial Neural Network. • Developed algorithms were successfully tested on real data from the RICH prototype

  19. Event reconstruction in the RICH detector of the CBM experiment at FAIR

    Energy Technology Data Exchange (ETDEWEB)

    Adamczewski, J. [GSI Darmstadt (Germany); Becker, K.-H. [University Wuppertal (Germany); Belogurov, S. [ITEP Moscow (Russian Federation); Boldyreva, N. [PNPI Gatchina (Russian Federation); Chernogorov, A. [ITEP Moscow (Russian Federation); Deveaux, C. [University Gießen (Germany); Dobyrn, V. [PNPI Gatchina (Russian Federation); Dürr, M. [University Gießen (Germany); Eom, J. [Pusan National University (Korea, Republic of); Eschke, J. [GSI Darmstadt (Germany); Höhne, C. [University Gießen (Germany); Kampert, K.-H. [University Wuppertal (Germany); Kleipa, V. [GSI Darmstadt (Germany); Kochenda, L. [PNPI Gatchina (Russian Federation); Kolb, B. [GSI Darmstadt (Germany); Kopfer, J. [University Wuppertal (Germany); Kravtsov, P. [PNPI Gatchina (Russian Federation); Lebedev, S., E-mail: s.lebedev@gsi.de [University Gießen (Germany); Lebedeva, E. [University Gießen (Germany); Leonova, E. [PNPI Gatchina (Russian Federation); and others

    2014-12-01

    The Compressed Baryonic Matter (CBM) experiment at the future FAIR facility will investigate the QCD phase diagram at high net-baryon densities and moderate temperatures. One of the key signatures will be di-leptons emitted from the hot and dense phase in heavy-ion collisions. Measuring di-electrons, a high purity of identified electrons is required in order to suppress the background. Electron identification in CBM will be performed by a Ring Imaging Cherenkov (RICH) detector and Transition Radiation Detectors (TRD). In order to access the foreseen rare probes, the detector and the data acquisition have to handle interaction rates up to 10 MHz. Therefore, the development of fast and efficient event reconstruction algorithms is an important and challenging task in CBM. In this contribution event reconstruction and electron identification algorithms in the RICH detector are presented. So far they have been developed on simulated data but could already be tested on real data from a RICH prototype testbeam experiment at the CERN-PS. Efficient and fast ring recognition algorithms in the CBM-RICH are based on the Hough Transform method. Due to optical distortions of the rings, an ellipse fitting algorithm was elaborated to improve the ring radius resolution. An efficient algorithm based on the Artificial Neural Network was implemented for electron identification in RICH. All algorithms were significantly optimized to achieve maximum speed and minimum memory consumption. - Highlights: • Ring Imaging Cherenkov detector will serve for electron identification in CBM. • We present efficient ring recognition algorithm based on the Hough Transform method. • Developed algorithms were significantly optimized to achieve maximum speed up. • Electron identification algorithm in RICH based on the Artificial Neural Network. • Developed algorithms were successfully tested on real data from the RICH prototype.

  20. Direct reconstruction of parametric images for brain PET with event-by-event motion correction: evaluation in two tracers across count levels

    Science.gov (United States)

    Germino, Mary; Gallezot, Jean-Dominque; Yan, Jianhua; Carson, Richard E.

    2017-07-01

    Parametric images for dynamic positron emission tomography (PET) are typically generated by an indirect method, i.e. reconstructing a time series of emission images, then fitting a kinetic model to each voxel time activity curve. Alternatively, ‘direct reconstruction’, incorporates the kinetic model into the reconstruction algorithm itself, directly producing parametric images from projection data. Direct reconstruction has been shown to achieve parametric images with lower standard error than the indirect method. Here, we present direct reconstruction for brain PET using event-by-event motion correction of list-mode data, applied to two tracers. Event-by-event motion correction was implemented for direct reconstruction in the Parametric Motion-compensation OSEM List-mode Algorithm for Resolution-recovery reconstruction. The direct implementation was tested on simulated and human datasets with tracers [11C]AFM (serotonin transporter) and [11C]UCB-J (synaptic density), which follow the 1-tissue compartment model. Rigid head motion was tracked with the Vicra system. Parametric images of K 1 and distribution volume (V T  =  K 1/k 2) were compared to those generated by the indirect method by regional coefficient of variation (CoV). Performance across count levels was assessed using sub-sampled datasets. For simulated and real datasets at high counts, the two methods estimated K 1 and V T with comparable accuracy. At lower count levels, the direct method was substantially more robust to outliers than the indirect method. Compared to the indirect method, direct reconstruction reduced regional K 1 CoV by 35-48% (simulated dataset), 39-43% ([11C]AFM dataset) and 30-36% ([11C]UCB-J dataset) across count levels (averaged over regions at matched iteration); V T CoV was reduced by 51-58%, 54-60% and 30-46%, respectively. Motion correction played an important role in the dataset with larger motion: correction increased regional V T by 51% on average in the [11C

  1. Data-Driven Learning: Reasonable Fears and Rational Reassurance

    Science.gov (United States)

    Boulton, Alex

    2009-01-01

    Computer corpora have many potential applications in teaching and learning languages, the most direct of which--when the learners explore a corpus themselves--has become known as data-driven learning (DDL). Despite considerable enthusiasm in the research community and interest in higher education, the approach has not made major inroads to…

  2. Data-driven Regulation and Governance in Smart Cities

    NARCIS (Netherlands)

    Ranchordás, Sofia; Klop, Abram; Mak, Vanessa; Berlee, Anna; Tjong Tjin Tai, Eric

    2018-01-01

    This chapter discusses the concept of data-driven regulation and governance in the context of smart cities by describing how these urban centres harness these technologies to collect and process information about citizens, traffic, urban planning or waste production. It describes how several smart

  3. Data-Driven Planning: Using Assessment in Strategic Planning

    Science.gov (United States)

    Bresciani, Marilee J.

    2010-01-01

    Data-driven planning or evidence-based decision making represents nothing new in its concept. For years, business leaders have claimed they have implemented planning informed by data that have been strategically and systematically gathered. Within higher education and student affairs, there may be less evidence of the actual practice of…

  4. Data-Driven Model Order Reduction for Bayesian Inverse Problems

    KAUST Repository

    Cui, Tiangang

    2014-01-06

    One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.

  5. Data mining, knowledge discovery and data-driven modelling

    NARCIS (Netherlands)

    Solomatine, D.P.; Velickov, S.; Bhattacharya, B.; Van der Wal, B.

    2003-01-01

    The project was aimed at exploring the possibilities of a new paradigm in modelling - data-driven modelling, often referred as "data mining". Several application areas were considered: sedimentation problems in the Port of Rotterdam, automatic soil classification on the basis of cone penetration

  6. Scalable data-driven short-term traffic prediction

    NARCIS (Netherlands)

    Friso, K.; Wismans, L. J.J.; Tijink, M. B.

    2017-01-01

    Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models-which do not scale very well to large networks, computationally-or on data-driven methods for freeways, leaving out urban arterials completely. Urban

  7. Data-driven analysis of blood glucose management effectiveness

    NARCIS (Netherlands)

    Nannings, B.; Abu-Hanna, A.; Bosman, R. J.

    2005-01-01

    The blood-glucose-level (BGL) of Intensive Care (IC) patients requires close monitoring and control. In this paper we describe a general data-driven analytical method for studying the effectiveness of BGL management. The method is based on developing and studying a clinical outcome reflecting the

  8. Data-Driven Learning of Q-Matrix

    Science.gov (United States)

    Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

    2012-01-01

    The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

  9. Knowledge-Driven Versus Data-Driven Logics

    Czech Academy of Sciences Publication Activity Database

    Dubois, D.; Hájek, Petr; Prade, H.

    2000-01-01

    Roč. 9, č. 1 (2000), s. 65-89 ISSN 0925-8531 R&D Projects: GA AV ČR IAA1030601 Grant - others:CNRS(FR) 4008 Institutional research plan: AV0Z1030915 Keywords : epistemic logic * possibility theory * data-driven reasoning * deontic logic Subject RIV: BA - General Mathematics

  10. Developing Annotation Solutions for Online Data Driven Learning

    Science.gov (United States)

    Perez-Paredes, Pascual; Alcaraz-Calero, Jose M.

    2009-01-01

    Although "annotation" is a widely-researched topic in Corpus Linguistics (CL), its potential role in Data Driven Learning (DDL) has not been addressed in depth by Foreign Language Teaching (FLT) practitioners. Furthermore, most of the research in the use of DDL methods pays little attention to annotation in the design and implementation…

  11. Data-driven modelling of LTI systems using symbolic regression

    NARCIS (Netherlands)

    Khandelwal, D.; Toth, R.; Van den Hof, P.M.J.

    2017-01-01

    The aim of this project is to automate the task of data-driven identification of dynamical systems. The underlying goal is to develop an identification tool that models a physical system without distinguishing between classes of systems such as linear, nonlinear or possibly even hybrid systems. Such

  12. R and D on a Fast LXe TPC with real-time event reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Dussoni, S., E-mail: simeone.dussoni@pi.infn.it [INFN Sezione di Pisa, Largo B. Pontecorvo 3, 56127 Pisa (Italy); Baldini, A. [INFN Sezione di Pisa, Largo B. Pontecorvo 3, 56127 Pisa (Italy); Galli, L. [INFN Sezione di Pisa, Largo B. Pontecorvo 3, 56127 Pisa (Italy); Paul Scherrer Institute PSI, CH-5232 Villigen (Switzerland); Cerri, C.; Grassi, M. [INFN Sezione di Pisa, Largo B. Pontecorvo 3, 56127 Pisa (Italy); Papa, A. [Paul Scherrer Institute PSI, CH-5232 Villigen (Switzerland); Signorelli, G. [INFN Sezione di Pisa, Largo B. Pontecorvo 3, 56127 Pisa (Italy)

    2013-12-21

    The FOXFIRE project (Feasibility Of a Xenon detector with Front-end for Ionization Real-time Extraction) aims at the realization of a Liquid Xenon TPC optimized for high rate particle physics experiments, in particular in the field of rare event searches, with particles in the 10–100 MeV energy range. Liquid Xenon has several attractive properties to be exploited resulting in superior time and energy resolution, by using the scintillation light readout with suitable photo-detectors. A novel approach with a complementary TPC readout scheme can improve the space resolution to a level of a few hundred microns. We are studying both the feasibility of a light readout with higher granularity by means of Silicon PhotoMultipliers optimized for the Xenon emission spectrum as well as on an innovative micro-fabricated device capable of charge multiplication in liquid phase. The detector will be equipped with a readout electronics capable of online reconstruction of events, allowing the detector to sustain a high rate of interactions.

  13. Atmospheric pCO2 reconstructed across five early Eocene global warming events

    Science.gov (United States)

    Cui, Ying; Schubert, Brian A.

    2017-11-01

    Multiple short-lived global warming events, known as hyperthermals, occurred during the early Eocene (56-52 Ma). Five of these events - the Paleocene-Eocene Thermal Maximum (PETM or ETM1), H1 (or ETM2), H2, I1, and I2 - are marked by a carbon isotope excursion (CIE) within both marine and terrestrial sediments. The magnitude of CIE, which is a function of the amount and isotopic composition of carbon added to the ocean-atmosphere system, varies significantly between marine versus terrestrial substrates. Here we use the increase in carbon isotope fractionation by C3 land plants in response to increased pCO2 to reconcile this difference and reconstruct a range of background pCO2 and peak pCO2 for each CIE, provided two potential carbon sources: methane hydrate destabilization and permafrost-thawing/organic matter oxidation. Although the uncertainty on each pCO2 estimate using this approach is low (e.g., median uncertainty = + 23% / - 18%), this work highlights the potential for significant systematic bias in the pCO2 estimate resulting from sampling resolution, substrate type, diagenesis, and environmental change. Careful consideration of each of these factors is required especially when applying this approach to a single marine-terrestrial CIE pair. Given these limitations, we provide an upper estimate for background early Eocene pCO2 of 463 +248/-131 ppmv (methane hydrate scenario) to 806 +127/-104 ppmv (permafrost-thawing/organic matter oxidation scenario). These results, which represent the first pCO2 proxy estimates directly tied to the Eocene hyperthermals, demonstrate that early Eocene warmth was supported by background pCO2 less than ∼3.5× preindustrial levels and that pCO2 > 1000 ppmv may have occurred only briefly, during hyperthermal events.

  14. Telling and measuring urban floods: event reconstruction by means of public-domain media

    Science.gov (United States)

    Macchia, S.; Gallo, E.; Claps, P.

    2012-04-01

    In the last decade, the diffusion of mobile telephones and ond of low-cost digital cameras have changed the public approach to catastrophes. As regards floods, it has become widespread the availability of images and videos taken in urban areas. Searching into Youtube or Youreporter, for example, one can understand how often citizen are considering to report even scary events. Nowadays these amateurs videos are often used in news world reports, which often increase or dampen the public perception of flood risk. More importantly, these amateur videos can play a crucial role in a didactic and technical representation of media flooding problems. The question so arise: why don't use the amateur videos for civil protection purposes? This work shows a new way to use flood images and videos to obtain technical data and spread safety information. Specifically, we show how to determine the height and speed of water flow, which have been achieved in some places during Genoa flood - 4th November 2011 - For this event we have downloaded more than 50 videos from different websites, where the authors have provided information about the time of recording, the geographical coordinates and the height above ground of the point of recording. The support by Google tools, such as Google maps and StreetWiew © has allowed us to geographically locate the recording points, so to put together shots and slides necessary to put together a whole reconstruction of the event. Future research will be in the direction of using these videos to generate a tool for the Google platforms, in order to address an easily achievable, yet accurate, information to the public, so to warn people on how to behave in front of imminent floods.

  15. Reconstructing extreme AMOC events through nudging of the ocean surface: a perfect model approach

    Science.gov (United States)

    Ortega, Pablo; Guilyardi, Eric; Swingedouw, Didier; Mignot, Juliette; Nguyen, Sébastien

    2017-11-01

    While the Atlantic Meridional Overturning Circulation (AMOC) is thought to be a crucial component of the North Atlantic climate, past changes in its strength are challenging to quantify, and only limited information is available. In this study, we use a perfect model approach with the IPSL-CM5A-LR model to assess the performance of several surface nudging techniques in reconstructing the variability of the AMOC. Special attention is given to the reproducibility of an extreme positive AMOC peak from a preindustrial control simulation. Nudging includes standard relaxation techniques towards the sea surface temperature and salinity anomalies of this target control simulation, and/or the prescription of the wind-stress fields. Surface nudging approaches using standard fixed restoring terms succeed in reproducing most of the target AMOC variability, including the timing of the extreme event, but systematically underestimate its amplitude. A detailed analysis of the AMOC variability mechanisms reveals that the underestimation of the extreme AMOC maximum comes from a deficit in the formation of the dense water masses in the main convection region, located south of Iceland in the model. This issue is largely corrected after introducing a novel surface nudging approach, which uses a varying restoring coefficient that is proportional to the simulated mixed layer depth, which, in essence, keeps the restoring time scale constant. This new technique substantially improves water mass transformation in the regions of convection, and in particular, the formation of the densest waters, which are key for the representation of the AMOC extreme. It is therefore a promising strategy that may help to better constrain the AMOC variability and other ocean features in the models. As this restoring technique only uses surface data, for which better and longer observations are available, it opens up opportunities for improved reconstructions of the AMOC over the last few decades.

  16. Investigating the Use of the Intel Xeon Phi for Event Reconstruction

    Science.gov (United States)

    Sherman, Keegan; Gilfoyle, Gerard

    2014-09-01

    The physics goal of Jefferson Lab is to understand how quarks and gluons form nuclei and it is being upgraded to a higher, 12-GeV beam energy. The new CLAS12 detector in Hall B will collect 5-10 terabytes of data per day and will require considerable computing resources. We are investigating tools, such as the Intel Xeon Phi, to speed up the event reconstruction. The Kalman Filter is one of the methods being studied. It is a linear algebra algorithm that estimates the state of a system by combining existing data and predictions of those measurements. The tools required to apply this technique (i.e. matrix multiplication, matrix inversion) are being written using C++ intrinsics for Intel's Xeon Phi Coprocessor, which uses the Many Integrated Cores (MIC) architecture. The Intel MIC is a new high-performance chip that connects to a host machine through the PCIe bus and is built to run highly vectorized and parallelized code making it a well-suited device for applications such as the Kalman Filter. Our tests of the MIC optimized algorithms needed for the filter show significant increases in speed. For example, matrix multiplication of 5x5 matrices on the MIC was able to run up to 69 times faster than the host core. The physics goal of Jefferson Lab is to understand how quarks and gluons form nuclei and it is being upgraded to a higher, 12-GeV beam energy. The new CLAS12 detector in Hall B will collect 5-10 terabytes of data per day and will require considerable computing resources. We are investigating tools, such as the Intel Xeon Phi, to speed up the event reconstruction. The Kalman Filter is one of the methods being studied. It is a linear algebra algorithm that estimates the state of a system by combining existing data and predictions of those measurements. The tools required to apply this technique (i.e. matrix multiplication, matrix inversion) are being written using C++ intrinsics for Intel's Xeon Phi Coprocessor, which uses the Many Integrated Cores (MIC

  17. Data-Driven Controller Design The H2 Approach

    CERN Document Server

    Sanfelice Bazanella, Alexandre; Eckhard, Diego

    2012-01-01

    Data-driven methodologies have recently emerged as an important paradigm alternative to model-based controller design and several such methodologies are formulated as an H2 performance optimization. This book presents a comprehensive theoretical treatment of the H2 approach to data-driven control design. The fundamental properties implied by the H2 problem formulation are analyzed in detail, so that common features to all solutions are identified. Direct methods (VRFT) and iterative methods (IFT, DFT, CbT) are put under a common theoretical framework. The choice of the reference model, the experimental conditions, the optimization method to be used, and several other designer’s choices are crucial to the quality of the final outcome, and firm guidelines for all these choices are derived from the theoretical analysis presented. The practical application of the concepts in the book is illustrated with a large number of practical designs performed for different classes of processes: thermal, fluid processing a...

  18. Data-driven importance distributions for articulated tracking

    DEFF Research Database (Denmark)

    Hauberg, Søren; Pedersen, Kim Steenstrup

    2011-01-01

    We present two data-driven importance distributions for particle filterbased articulated tracking; one based on background subtraction, another on depth information. In order to keep the algorithms efficient, we represent human poses in terms of spatial joint positions. To ensure constant bone le...... filter, where they improve both accuracy and efficiency of the tracker. In fact, they triple the effective number of samples compared to the most commonly used importance distribution at little extra computational cost....

  19. A data-driven framework for investigating customer retention

    OpenAIRE

    Mgbemena, Chidozie Simon

    2016-01-01

    This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London. This study presents a data-driven simulation framework in order to understand customer behaviour and therefore improve customer retention. The overarching system design methodology used for this study is aligned with the design science paradigm. The Social Media Domain Analysis (SoMeDoA) approach is adopted and evaluated to build a model on the determinants of customer satisfaction ...

  20. Retrospective data-driven respiratory gating for PET/CT

    International Nuclear Information System (INIS)

    Schleyer, Paul J; O'Doherty, Michael J; Barrington, Sally F; Marsden, Paul K

    2009-01-01

    Respiratory motion can adversely affect both PET and CT acquisitions. Respiratory gating allows an acquisition to be divided into a series of motion-reduced bins according to the respiratory signal, which is typically hardware acquired. In order that the effects of motion can potentially be corrected for, we have developed a novel, automatic, data-driven gating method which retrospectively derives the respiratory signal from the acquired PET and CT data. PET data are acquired in listmode and analysed in sinogram space, and CT data are acquired in cine mode and analysed in image space. Spectral analysis is used to identify regions within the CT and PET data which are subject to respiratory motion, and the variation of counts within these regions is used to estimate the respiratory signal. Amplitude binning is then used to create motion-reduced PET and CT frames. The method was demonstrated with four patient datasets acquired on a 4-slice PET/CT system. To assess the accuracy of the data-derived respiratory signal, a hardware-based signal was acquired for comparison. Data-driven gating was successfully performed on PET and CT datasets for all four patients. Gated images demonstrated respiratory motion throughout the bin sequences for all PET and CT series, and image analysis and direct comparison of the traces derived from the data-driven method with the hardware-acquired traces indicated accurate recovery of the respiratory signal.

  1. Authoring Data-Driven Videos with DataClips.

    Science.gov (United States)

    Amini, Fereshteh; Riche, Nathalie Henry; Lee, Bongshin; Monroy-Hernandez, Andres; Irani, Pourang

    2017-01-01

    Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven "clips" together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.

  2. Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

    Science.gov (United States)

    Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

    2015-11-01

    The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

  3. Combination of various data analysis techniques for efficient track reconstruction in very high multiplicity events

    Science.gov (United States)

    Siklér, Ferenc

    2017-08-01

    A novel combination of established data analysis techniques for reconstructing charged-particles in high energy collisions is proposed. It uses all information available in a collision event while keeping competing choices open as long as possible. Suitable track candidates are selected by transforming measured hits to a binned, three- or four-dimensional, track parameter space. It is accomplished by the use of templates taking advantage of the translational and rotational symmetries of the detectors. Track candidates and their corresponding hits, the nodes, form a usually highly connected network, a bipartite graph, where we allow for multiple hit to track assignments, edges. In order to get a manageable problem, the graph is cut into very many minigraphs by removing a few of its vulnerable components, edges and nodes. Finally the hits are distributed among the track candidates by exploring a deterministic decision tree. A depth-limited search is performed maximizing the number of hits on tracks, and minimizing the sum of track-fit χ2. Simplified but realistic models of LHC silicon trackers including the relevant physics processes are used to test and study the performance (efficiency, purity, timing) of the proposed method in the case of single or many simultaneous proton-proton collisions (high pileup), and for single heavy-ion collisions at the highest available energies.

  4. Practical aspects of data-driven motion correction approach for brain SPECT

    International Nuclear Information System (INIS)

    Kyme, A.Z.; Hutton, B.F.; Hatton, R.L.; Skerrett, D.; Barnden, L.

    2002-01-01

    Full text: Patient motion can cause image artifacts in SPECT despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward-projections. By optimising the orientation of a partial reconstruction, parameters can be obtained for each misaligned projection and applied to update this volume using a 3D reconstruction algorithm. Phantom validation was performed to explore practical aspects of this approach. Noisy projection datasets simulating a patient undergoing at least one fully 3D movement during acquisition were compiled from various projections of the digital Hoffman brain phantom. Motion correction was then applied to the reconstructed studies. Correction success was assessed visually and quantitatively. Resilience with respect to subset order and missing data in the reconstruction and updating stages, detector geometry considerations, and the need for implementing an iterated correction were assessed in the process. Effective correction of the corrupted studies was achieved. Visually, artifactual regions in the reconstructed slices were suppressed and/or removed. Typically the ratio of mean square difference between the corrected and reference studies compared to that between the corrupted and reference studies was > 2. Although components of the motions are missed using a single-head implementation, improvement was still evident in the correction. The need for multiple iterations in the approach was small due to the bulk of misalignment errors being corrected in the first pass. Dispersion of subsets for reconstructing and updating the partial reconstruction appears to give optimal correction. Further validation is underway using triple-head physical phantom data. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

  5. Reconstruction and numerical modelling of a flash flood event: Atrani 2010

    Science.gov (United States)

    Ciervo, F.; Papa, M. N.; Medina, V.; Bateman, A.

    2012-04-01

    The work intends to reproduce the flash-flood event that occurred in Atrani (Amalfi Coast - Southern Italy) on the 9 September 2010. In the days leading up to the event, intense low pressure system affected the North Europe attracting hot humid air masses from the Mediterranean areas and pushing them to the southern regions of Italy. These conditions contributed to the development of strong convective storm systems, Mesoscale Convective Systems (MCS) type. The development of intense convective rain cells, over an extremely confined areas, leaded to a cumulative daily rainfall of 129.2 mm; the maximum precipitation in 1hr was 19.4mm. The Dragone river is artificially forced to flow underneath the urban estate of Atrani through a culvert until it finally flows out into the sea. In correspondence of the culvert inlet a minor fraction of the water discharge (5.9m^3/s), skimming over the channel cover, flowed on the street and invaded the village. The channelized flow generated overpressure involving the breaking of the cover of culvert slab and caused a new discharge inlet (20 m^3/s) on the street modifying the downstream flood dynamics. Information acquired, soon after the event, through the local people interviews and the field measurements significantly contributed to the rainfall event reconstruction and to the characterization of the induced effects. In absence of hydrometric data, the support of the amateur videos was of crucial importance for the hydraulic model development and calibration. A geomorphology based rainfall-runoff model, WFIUH type (Instantaneous Unit Hydrograph Width Function), is implemented to extract the hydrograph of the hydrological event. All analysis are performed with GIS support basing on a Digital Terrain System (DTM) 5x5m. Two parameters have been used to calibrate the model: the average watershed velocity (Vmean = 0.08m/s) and hydrodynamic diffusivity (D=10E^-6 m^2/s). The model is calibrated basing on the peak discharge assessed value

  6. Effect of PYTHIA8 tunes on event shapes and top-quark reconstruction in e$^+$e$^-$ annihilation at CLIC

    CERN Document Server

    Chekanov, Sergei; Fischer, Andrew; Zhang, Jinlong

    2017-01-01

    This paper describes the effect of PYTHIA8 tunes on event simulation of e$^+$e$^-$ collisions with center-of-mass (CM) energies of 380 GeV and 3 TeV at the proposed CLIC collider. Event shapes, such as thrust, thrust major, thrust minor, oblateness, as well as particle multiplicities have been analyzed and relative differences with respect to the default PYTHIA8 tune were determined. The effect of tunes on top-mass reconstruction in the resolved and boosted regimes was analyzed. No statistically significant variation for reconstructed top masses using invariant masses of three jets was found for events with a CM energy of 380 GeV. For the fully boosted top reconstruction at a CM energy of 3 TeV, a significant shift in reconstructed top mass of about 700 MeV for the "Montull" tune was observed. This shift correlates with an increase in particle multiplicity compared to all other PYTHIA8 tunes.

  7. PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

    DEFF Research Database (Denmark)

    Churchill, Nathan W.; Yourganov, Grigori; Spring, Robyn

    2012-01-01

    , autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality...... with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al., 2000) physiological denoising algorithm, which uses externally...

  8. Vertex Reconstruction and Performance in ATLAS

    CERN Document Server

    Whitmore, Ben William; The ATLAS collaboration

    2017-01-01

    Efficient and precise reconstruction of the primary vertices in LHC collisions is essential in both the reconstruction of the full kinematic properties of a hard-scatter event and of soft interactions as a measure of the amount of pile-up. The reconstruction of the primary vertices in the busy, high pile up environment of the LHC is a challenging task. The challenges and novel methods developed by the ATLAS experiment to reconstruct vertices in such environments will be presented. The performance of the current vertexing algorithms using Run-2 data will be presented and compared to results from simulation. Additionally, data-driven methods to evaluate vertex resolution, and details of upgrades to the ATLAS inner detector will be presented.

  9. NEW FERMI-LAT EVENT RECONSTRUCTION REVEALS MORE HIGH-ENERGY GAMMA RAYS FROM GAMMA-RAY BURSTS

    Energy Technology Data Exchange (ETDEWEB)

    Atwood, W. B. [Santa Cruz Institute for Particle Physics, Department of Physics and Department of Astronomy and Astrophysics, University of California at Santa Cruz, Santa Cruz, CA 95064 (United States); Baldini, L. [Universita di Pisa and Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa (Italy); Bregeon, J.; Pesce-Rollins, M.; Sgro, C.; Tinivella, M. [Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa (Italy); Bruel, P. [Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Palaiseau (France); Chekhtman, A. [Center for Earth Observing and Space Research, College of Science, George Mason University, Fairfax, VA 22030 (United States); Cohen-Tanugi, J. [Laboratoire Univers et Particules de Montpellier, Universite Montpellier 2, CNRS/IN2P3, F-34095 Montpellier (France); Drlica-Wagner, A.; Omodei, N.; Rochester, L. S.; Usher, T. L. [W. W. Hansen Experimental Physics Laboratory, Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics and SLAC National Accelerator Laboratory, Stanford University, Stanford, CA 94305 (United States); Granot, J. [Department of Natural Sciences, The Open University of Israel, 1 University Road, P.O. Box 808, Ra' anana 43537 (Israel); Longo, F. [Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, I-34127 Trieste (Italy); Razzaque, S. [Department of Physics, University of Johannesburg, Auckland Park 2006 (South Africa); Zimmer, S., E-mail: melissa.pesce.rollins@pi.infn.it, E-mail: nicola.omodei@stanford.edu, E-mail: granot@openu.ac.il [Department of Physics, Stockholm University, AlbaNova, SE-106 91 Stockholm (Sweden)

    2013-09-01

    Based on the experience gained during the four and a half years of the mission, the Fermi-LAT Collaboration has undertaken a comprehensive revision of the event-level analysis going under the name of Pass 8. Although it is not yet finalized, we can test the improvements in the new event reconstruction with the special case of the prompt phase of bright gamma-ray bursts (GRBs), where the signal-to-noise ratio is large enough that loose selection cuts are sufficient to identify gamma rays associated with the source. Using the new event reconstruction, we have re-analyzed 10 GRBs previously detected by the Large Area Telescope (LAT) for which an X-ray/optical follow-up was possible and found four new gamma rays with energies greater than 10 GeV in addition to the seven previously known. Among these four is a 27.4 GeV gamma ray from GRB 080916C, which has a redshift of 4.35, thus making it the gamma ray with the highest intrinsic energy ({approx}147 GeV) detected from a GRB. We present here the salient aspects of the new event reconstruction and discuss the scientific implications of these new high-energy gamma rays, such as constraining extragalactic background light models, Lorentz invariance violation tests, the prompt emission mechanism, and the bulk Lorentz factor of the emitting region.

  10. General Purpose Data-Driven Monitoring for Space Operations

    Science.gov (United States)

    Iverson, David L.; Martin, Rodney A.; Schwabacher, Mark A.; Spirkovska, Liljana; Taylor, William McCaa; Castle, Joseph P.; Mackey, Ryan M.

    2009-01-01

    As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault

  11. Data-driven algorithm to estimate friction in automobile engine

    DEFF Research Database (Denmark)

    Stotsky, Alexander A.

    2010-01-01

    Algorithms based on the oscillations of the engine angular rotational speed under fuel cutoff and no-load were proposed for estimation of the engine friction torque. The recursive algorithm to restore the periodic signal is used to calculate the amplitude of the engine speed signal at fuel cutoff....... The values of the friction torque in the corresponding table entries are updated at acquiring new measurements of the friction moment. A new, data-driven algorithm for table adaptation on the basis of stepwise regression was developed and verified using the six-cylinder Volvo engine....

  12. Data driven information system for supervision of judicial open

    Directory of Open Access Journals (Sweden)

    Ming LI

    2016-08-01

    Full Text Available Aiming at the four outstanding problems of informationized supervision for judicial publicity, the judicial public data is classified based on data driven to form the finally valuable data. Then, the functional structure, technical structure and business structure of the data processing system are put forward, including data collection module, data reduction module, data analysis module, data application module and data security module, etc. The development of the data processing system based on these structures can effectively reduce work intensity of judicial open iformation management, summarize the work state, find the problems, and promote the level of judicial publicity.

  13. Product design pattern based on big data-driven scenario

    OpenAIRE

    Conggang Yu; Lusha Zhu

    2016-01-01

    This article discusses about new product design patterns in the big data era, gives designer a new rational thinking way, and is a new way to understand the design of the product. Based on the key criteria of the product design process, category, element, and product are used to input the data, which comprises concrete data and abstract data as an enlargement of the criteria of product design process for the establishment of a big data-driven product design pattern’s model. Moreover, an exper...

  14. Controller synthesis for negative imaginary systems: a data driven approach

    KAUST Repository

    Mabrok, Mohamed

    2016-02-17

    The negative imaginary (NI) property occurs in many important applications. For instance, flexible structure systems with collocated force actuators and position sensors can be modelled as negative imaginary systems. In this study, a data-driven controller synthesis methodology for NI systems is presented. In this approach, measured frequency response data of the plant is used to construct the controller frequency response at every frequency by minimising a cost function. Then, this controller response is used to identify the controller transfer function using system identification methods. © The Institution of Engineering and Technology 2016.

  15. Data-Driven Model Reduction and Transfer Operator Approximation

    Science.gov (United States)

    Klus, Stefan; Nüske, Feliks; Koltai, Péter; Wu, Hao; Kevrekidis, Ioannis; Schütte, Christof; Noé, Frank

    2018-06-01

    In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis, dynamic mode decomposition, and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods.

  16. Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization.

    Science.gov (United States)

    Gotz, David; Borland, David

    2016-01-01

    The healthcare industry's widespread digitization efforts are reshaping one of the largest sectors of the world's economy. This transformation is enabling systems that promise to use ever-improving data-driven evidence to help doctors make more precise diagnoses, institutions identify at risk patients for intervention, clinicians develop more personalized treatment plans, and researchers better understand medical outcomes within complex patient populations. Given the scale and complexity of the data required to achieve these goals, advanced data visualization tools have the potential to play a critical role. This article reviews a number of visualization challenges unique to the healthcare discipline.

  17. Microenvironment temperature prediction between body and seat interface using autoregressive data-driven model.

    Science.gov (United States)

    Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W

    2015-11-01

    There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of body insensitivity and disability requiring them to be immobile in seats for prolonged periods. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

  18. Reconstructing Heat Fluxes Over Lake Erie During the Lake Effect Snow Event of November 2014

    Science.gov (United States)

    Fitzpatrick, L.; Fujisaki-Manome, A.; Gronewold, A.; Anderson, E. J.; Spence, C.; Chen, J.; Shao, C.; Posselt, D. J.; Wright, D. M.; Lofgren, B. M.; Schwab, D. J.

    2017-12-01

    The extreme North American winter storm of November 2014 triggered a record lake effect snowfall (LES) event in southwest New York. This study examined the evaporation from Lake Erie during the record lake effect snowfall event, November 17th-20th, 2014, by reconstructing heat fluxes and evaporation rates over Lake Erie using the unstructured grid, Finite-Volume Community Ocean Model (FVCOM). Nine different model runs were conducted using combinations of three different flux algorithms: the Met Flux Algorithm (COARE), a method routinely used at NOAA's Great Lakes Environmental Research Laboratory (SOLAR), and the Los Alamos Sea Ice Model (CICE); and three different meteorological forcings: the Climate Forecast System version 2 Operational Analysis (CFSv2), Interpolated observations (Interp), and the High Resolution Rapid Refresh (HRRR). A few non-FVCOM model outputs were also included in the evaporation analysis from an atmospheric reanalysis (CFSv2) and the large lake thermodynamic model (LLTM). Model-simulated water temperature and meteorological forcing data (wind direction and air temperature) were validated with buoy data at three locations in Lake Erie. The simulated sensible and latent heat fluxes were validated with the eddy covariance measurements at two offshore sites; Long Point Lighthouse in north central Lake Erie and Toledo water crib intake in western Lake Erie. The evaluation showed a significant increase in heat fluxes over three days, with the peak on the 18th of November. Snow water equivalent data from the National Snow Analyses at the National Operational Hydrologic Remote Sensing Center showed a spike in water content on the 20th of November, two days after the peak heat fluxes. The ensemble runs presented a variation in spatial pattern of evaporation, lake-wide average evaporation, and resulting cooling of the lake. Overall, the evaporation tended to be larger in deep water than shallow water near the shore. The lake-wide average evaporations

  19. Data Driven Performance Evaluation of Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Antonio A. F. Loureiro

    2010-03-01

    Full Text Available Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i signal granularity, (ii spatial distribution of sensors, (iii sensors clustering, and (iv signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown that all these factors have significant impact on the quality of the reconstructed signal. The extent of such impact is quantitatively assessed.

  20. Data-driven execution of fast multipole methods

    KAUST Repository

    Ltaief, Hatem

    2013-09-17

    Fast multipole methods (FMMs) have O (N) complexity, are compute bound, and require very little synchronization, which makes them a favorable algorithm on next-generation supercomputers. Their most common application is to accelerate N-body problems, but they can also be used to solve boundary integral equations. When the particle distribution is irregular and the tree structure is adaptive, load balancing becomes a non-trivial question. A common strategy for load balancing FMMs is to use the work load from the previous step as weights to statically repartition the next step. The authors discuss in the paper another approach based on data-driven execution to efficiently tackle this challenging load balancing problem. The core idea consists of breaking the most time-consuming stages of the FMMs into smaller tasks. The algorithm can then be represented as a directed acyclic graph where nodes represent tasks and edges represent dependencies among them. The execution of the algorithm is performed by asynchronously scheduling the tasks using the queueing and runtime for kernels runtime environment, in a way such that data dependencies are not violated for numerical correctness purposes. This asynchronous scheduling results in an out-of-order execution. The performance results of the data-driven FMM execution outperform the previous strategy and show linear speedup on a quad-socket quad-core Intel Xeon system.Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.

  1. A data driven nonlinear stochastic model for blood glucose dynamics.

    Science.gov (United States)

    Zhang, Yan; Holt, Tim A; Khovanova, Natalia

    2016-03-01

    The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  2. Locative media and data-driven computing experiments

    Directory of Open Access Journals (Sweden)

    Sung-Yueh Perng

    2016-06-01

    Full Text Available Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insight from them. These projects often start from the data, rather than from a question or theory, and try to imagine and identify their potential utility. In this paper, we explore the desires and mechanics of data-driven computing experiments. We demonstrate how both locative media data and computing experiments are ‘staged’ to create new values and computing techniques, which in turn are used to try and derive possible futures that are ridden with unintended consequences. We argue that using computing experiments to imagine potential urban futures produces effects that often have little to do with creating new urban practices. Instead, these experiments promote Big Data science and the prospect that data produced for one purpose can be recast for another and act as alternative mechanisms of envisioning urban futures.

  3. Product design pattern based on big data-driven scenario

    Directory of Open Access Journals (Sweden)

    Conggang Yu

    2016-07-01

    Full Text Available This article discusses about new product design patterns in the big data era, gives designer a new rational thinking way, and is a new way to understand the design of the product. Based on the key criteria of the product design process, category, element, and product are used to input the data, which comprises concrete data and abstract data as an enlargement of the criteria of product design process for the establishment of a big data-driven product design pattern’s model. Moreover, an experiment and a product design case are conducted to verify the feasibility of the new pattern. Ultimately, we will conclude that the data-driven product design has two patterns: one is the concrete data supporting the product design, namely “product–data–product” pattern, and the second is based on the value of the abstract data for product design, namely “data–product–data” pattern. Through the data, users are involving themselves in the design development process. Data and product form a huge network, and data plays a role of connection or node. So the essence of the design is to find a new connection based on element, and to find a new node based on category.

  4. Reconstruction of t anti tH (H → bb) events using deep neural networks with the CMS detector

    Energy Technology Data Exchange (ETDEWEB)

    Rieger, Marcel; Erdmann, Martin; Fischer, Benjamin; Fischer, Robert; Heidemann, Fabian; Quast, Thorben; Rath, Yannik [III. Physikalisches Institut A, RWTH Aachen University (Germany)

    2016-07-01

    The measurement of Higgs boson production in association with top-quark pairs (t anti tH) is an important goal of Run 2 of the LHC as it allows for a direct measurement of the underlying Yukawa coupling. Due to the complex final state, however, the analysis of semi-leptonic t anti tH events with the Higgs boson decaying into a pair of bottom-quarks is challenging. A promising method for tackling jet parton associations are Deep Neural Networks (DNN). While being a widely spread machine learning algorithm in modern industry, DNNs are on the way to becoming established in high energy physics. We present a study on the reconstruction of the final state using DNNs, comparing to Boosted Decision Trees (BDT) as benchmark scenario. This is accomplished by generating permutations of simulated events and comparing them with truth information to extract reconstruction efficiencies.

  5. Extreme flood event reconstruction spanning the last century in the El Bibane Lagoon (southeastern Tunisia: a multi-proxy approach

    Directory of Open Access Journals (Sweden)

    A. Affouri

    2017-06-01

    Full Text Available Climate models project that rising atmospheric carbon dioxide concentrations will increase the frequency and the severity of some extreme weather events. The flood events represent a major risk for populations and infrastructures settled on coastal lowlands. Recent studies of lagoon sediments have enhanced our knowledge on extreme hydrological events such as palaeo-storms and on their relation with climate change over the last millennium. However, few studies have been undertaken to reconstruct past flood events from lagoon sediments. Here, the past flood activity was investigated using a multi-proxy approach combining sedimentological and geochemical analysis of surfaces sediments from a southeastern Tunisian catchment in order to trace the origin of sediment deposits in the El Bibane Lagoon. Three sediment sources were identified: marine, fluvial and aeolian. When applying this multi-proxy approach on core BL12-10, recovered from the El Bibane Lagoon, we can see that finer material, a high content of the clay and silt, and a high content of the elemental ratios (Fe ∕ Ca and Ti ∕ Ca characterise the sedimentological signature of the palaeo-flood levels identified in the lagoonal sequence. For the last century, which is the period covered by the BL12-10 short core, three palaeo-flood events were identified. The age of these flood events have been determined by 210Pb and 137Cs chronology and give ages of AD 1995 ± 6, 1970 ± 9 and 1945 ± 9. These results show a good temporal correlation with historical flood events recorded in southern Tunisia in the last century (AD 1932, 1969, 1979 and 1995. Our finding suggests that reconstruction of the history of the hydrological extreme events during the upper Holocene is possible in this location through the use of the sedimentary archives.

  6. Reconstructing Methane Emission Events in the Arctic Ocean: Observations from the Past to Present

    Science.gov (United States)

    Panieri, G.; Mienert, J.; Fornari, D. J.; Torres, M. E.; Lepland, A.

    2015-12-01

    Methane hydrates are ice-like crystals that are present along continental margins, occurring in the pore space of deep sediments or as massive blocks near the seafloor. They form in high pressure and low temperature environments constrained by thermodynamic stability, and supply of methane. In the Arctic, gas hydrates are abundant, and the methane released by their destabilization can affect local to global carbon budgets and cycles, ocean acidification, and benthic community survival. With the aim to locate in space and time the periodicity of methane venting, CAGE is engaged in a vast research program in the Arctic, a component of which comprises the analyses of numerous sediment cores and correlative geophysical and geochemical data from different areas. Here we present results from combined analyses of biogenic carbonate archives along the western Svalbard Margin, which reveal past methane venting events in this region. The reconstruction of paleo-methane discharge is complicated by precipitation of secondary carbonate on foraminifera shells, driven by an increase in alkalinity during anaerobic oxidation of methane (AOM). The biogeochemical processes involved in methane cycling and processes that drive methane migration affect the depth where AOM occurs, with relevance to secondary carbonate formation. Our results show the value and complexity of separating primary vs. secondary signals in bioarchives with relevance to understanding fluid-burial history in methane seep provinces. Results from our core analyses are integrated with observations made during the CAGE15-2 cruise in May 2015, when we deployed a towed vehicle equipped with camera, multicore and water sampling capabilities. The instrument design was based on the Woods Hole Oceanographic Institution (WHOI) MISO TowCam sled equipped with a deep-sea digital camera and CTD real-time system. Sediment sampling was visually-guided using this system. In one of the pockmarks along the Vestnesa Ridge where high

  7. Data-driven simulation methodology using DES 4-layer architecture

    Directory of Open Access Journals (Sweden)

    Aida Saez

    2016-05-01

    Full Text Available In this study, we present a methodology to build data-driven simulation models of manufacturing plants. We go further than other research proposals and we suggest focusing simulation model development under a 4-layer architecture (network, logic, database and visual reality. The Network layer includes system infrastructure. The Logic layer covers operations planning and control system, and material handling equipment system. The Database holds all the information needed to perform the simulation, the results used to analyze and the values that the Logic layer is using to manage the Plant. Finally, the Visual Reality displays an augmented reality system including not only the machinery and the movement but also blackboards and other Andon elements. This architecture provides numerous advantages as helps to build a simulation model that consistently considers the internal logistics, in a very flexible way.

  8. Data driven approaches for diagnostics and optimization of NPP operation

    International Nuclear Information System (INIS)

    Pliska, J.; Machat, Z.

    2014-01-01

    The efficiency and heat rate is an important indicator of both the health of the power plant equipment and the quality of power plant operation. To achieve this challenges powerful tool is a statistical data processing of large data sets which are stored in data historians. These large data sets contain useful information about process quality and equipment and sensor health. The paper discusses data-driven approaches for model building of main power plant equipment such as condenser, cooling tower and the overall thermal cycle as well using multivariate regression techniques based on so called a regression triplet - data, model and method. Regression models comprise a base for diagnostics and optimization tasks. Diagnostics and optimization tasks are demonstrated on practical cases - diagnostics of main power plant equipment to early identify equipment fault, and optimization task of cooling circuit by cooling water flow control to achieve for a given boundary conditions the highest power output. (authors)

  9. submitter Data-driven RBE parameterization for helium ion beams

    CERN Document Server

    Mairani, A; Dokic, I; Valle, S M; Tessonnier, T; Galm, R; Ciocca, M; Parodi, K; Ferrari, A; Jäkel, O; Haberer, T; Pedroni, P; Böhlen, T T

    2016-01-01

    Helium ion beams are expected to be available again in the near future for clinical use. A suitable formalism to obtain relative biological effectiveness (RBE) values for treatment planning (TP) studies is needed. In this work we developed a data-driven RBE parameterization based on published in vitro experimental values. The RBE parameterization has been developed within the framework of the linear-quadratic (LQ) model as a function of the helium linear energy transfer (LET), dose and the tissue specific parameter ${{(\\alpha /\\beta )}_{\\text{ph}}}$ of the LQ model for the reference radiation. Analytic expressions are provided, derived from the collected database, describing the $\\text{RB}{{\\text{E}}_{\\alpha}}={{\\alpha}_{\\text{He}}}/{{\\alpha}_{\\text{ph}}}$ and ${{\\text{R}}_{\\beta}}={{\\beta}_{\\text{He}}}/{{\\beta}_{\\text{ph}}}$ ratios as a function of LET. Calculated RBE values at 2 Gy photon dose and at 10% survival ($\\text{RB}{{\\text{E}}_{10}}$ ) are compared with the experimental ones. Pearson's correlati...

  10. Data-Driven Predictive Direct Load Control of Refrigeration Systems

    DEFF Research Database (Denmark)

    Shafiei, Seyed Ehsan; Knudsen, Torben; Wisniewski, Rafal

    2015-01-01

    A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control...... is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model...... against real data. The performance improvement results in a 22% reduction in the energy consumption. A comparative simulation is accomplished showing the superiority of the method over the existing approaches in terms of the load following performance....

  11. Data-Driven Assistance Functions for Industrial Automation Systems

    International Nuclear Information System (INIS)

    Windmann, Stefan; Niggemann, Oliver

    2015-01-01

    The increasing amount of data in industrial automation systems overburdens the user in process control and diagnosis tasks. One possibility to cope with these challenges consists of using smart assistance systems that automatically monitor and optimize processes. This article deals with aspects of data-driven assistance systems such as assistance functions, process models and data acquisition. The paper describes novel approaches for self-diagnosis and self-optimization, and shows how these assistance functions can be integrated in different industrial environments. The considered assistance functions are based on process models that are automatically learned from process data. Fault detection and isolation is based on the comparison of observations of the real system with predictions obtained by application of the process models. The process models are further employed for energy efficiency optimization of industrial processes. Experimental results are presented for fault detection and energy efficiency optimization of a drive system. (paper)

  12. Data-driven identification of potential Zika virus vectors

    Science.gov (United States)

    Evans, Michelle V; Dallas, Tad A; Han, Barbara A; Murdock, Courtney C; Drake, John M

    2017-01-01

    Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States. DOI: http://dx.doi.org/10.7554/eLife.22053.001 PMID:28244371

  13. Objective, Quantitative, Data-Driven Assessment of Chemical Probes.

    Science.gov (United States)

    Antolin, Albert A; Tym, Joseph E; Komianou, Angeliki; Collins, Ian; Workman, Paul; Al-Lazikani, Bissan

    2018-02-15

    Chemical probes are essential tools for understanding biological systems and for target validation, yet selecting probes for biomedical research is rarely based on objective assessment of all potential compounds. Here, we describe the Probe Miner: Chemical Probes Objective Assessment resource, capitalizing on the plethora of public medicinal chemistry data to empower quantitative, objective, data-driven evaluation of chemical probes. We assess >1.8 million compounds for their suitability as chemical tools against 2,220 human targets and dissect the biases and limitations encountered. Probe Miner represents a valuable resource to aid the identification of potential chemical probes, particularly when used alongside expert curation. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Data-driven system to predict academic grades and dropout

    Science.gov (United States)

    Rovira, Sergi; Puertas, Eloi

    2017-01-01

    Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona. PMID:28196078

  15. Using Shape Memory Alloys: A Dynamic Data Driven Approach

    KAUST Repository

    Douglas, Craig C.

    2013-06-01

    Shape Memory Alloys (SMAs) are capable of changing their crystallographic structure due to changes of either stress or temperature. SMAs are used in a number of aerospace devices and are required in some devices in exotic environments. We are developing dynamic data driven application system (DDDAS) tools to monitor and change SMAs in real time for delivering payloads by aerospace vehicles. We must be able to turn on and off the sensors and heating units, change the stress on the SMA, monitor on-line data streams, change scales based on incoming data, and control what type of data is generated. The application must have the capability to be run and steered remotely as an unmanned feedback control loop.

  16. Facilitating Data Driven Business Model Innovation - A Case study

    DEFF Research Database (Denmark)

    Bjerrum, Torben Cæsar Bisgaard; Andersen, Troels Christian; Aagaard, Annabeth

    2016-01-01

    . The businesses interdisciplinary capabilities come into play in the BMI process, where knowledge from the facilitation strategy and knowledge from phases of the BMI process needs to be present to create new knowledge, hence new BMs and innovations. Depending on the environment and shareholders, this also exposes......This paper aims to understand the barriers that businesses meet in understanding their current business models (BM) and in their attempt at innovating new data driven business models (DDBM) using data. The interdisciplinary challenge of knowledge exchange occurring outside and/or inside businesses......, that gathers knowledge is of great importance. The SMEs have little, if no experience, within data handling, data analytics, and working with structured Business Model Innovation (BMI), that relates to both new and conventional products, processes and services. This new frontier of data and BMI will have...

  17. Econophysics and Data Driven Modelling of Market Dynamics

    CERN Document Server

    Aoyama, Hideaki; Chakrabarti, Bikas; Chakraborti, Anirban; Ghosh, Asim; Econophysics and Data Driven Modelling of Market Dynamics

    2015-01-01

    This book presents the works and research findings of physicists, economists, mathematicians, statisticians, and financial engineers who have undertaken data-driven modelling of market dynamics and other empirical studies in the field of Econophysics. During recent decades, the financial market landscape has changed dramatically with the deregulation of markets and the growing complexity of products. The ever-increasing speed and decreasing costs of computational power and networks have led to the emergence of huge databases. The availability of these data should permit the development of models that are better founded empirically, and econophysicists have accordingly been advocating that one should rely primarily on the empirical observations in order to construct models and validate them. The recent turmoil in financial markets and the 2008 crash appear to offer a strong rationale for new models and approaches. The Econophysics community accordingly has an important future role to play in market modelling....

  18. A Transition Towards a Data-Driven Business Model (DDBM)

    DEFF Research Database (Denmark)

    Zaki, Mohamed; Bøe-Lillegraven, Tor; Neely, Andy

    2016-01-01

    Nettavisen is a Norwegian online start-up that experienced a boost after the financial crisis of 2009. Since then, the firm has been able to increase its market share and profitability through the use of highly disruptive business models, allowing the relatively small staff to outcompete powerhouse...... legacy-publishing companies and new media players such as Facebook and Google. These disruptive business models have been successful, as Nettavisen captured a large market share in Norway early on, and was consistently one of the top-three online news sites in Norway. Capitalising on media data explosion...... and the recent acquisition of blogger network ‘Blog.no’, Nettavisen is moving towards a data-driven business model (DDBM). In particular, the firm aims to analyse huge volumes of user Web browsing and purchasing habits....

  19. Late Holocene environmental reconstructions and the implications on flood events, typhoon patterns, and agriculture activities in NE Taiwan

    Science.gov (United States)

    Wang, L.-C.; Behling, H.; Lee, T.-Q.; Li, H.-C.; Huh, C.-A.; Shiau, L.-J.; Chang, Y.-P.

    2014-05-01

    In this study, we reconstructed the paleoenvironmental changes from a sediment archive of the floodplain lake in Ilan Plain of NE Taiwan on multi-decadal resolution for the last ca. 1900 years. On the basis of pollen and diatom records, we evaluated the record of past vegetation, floods, typhoons and agriculture activities of this area, which is sensitive to the hydrological conditions of the West Pacific. High sedimentation rates with low microfossil preservations reflected multiple flood events and humid climatic conditions during 100-1400 AD. A shortly interrupted dry phase can be found during 940-1010 AD. The driest phase corresponds to the Little Ice Age phase 1 (LIA1, 1400-1620 AD) with less disturbance by flood events, which enhanced the occurrence of wetlands (Cyperaceae) and diatom depositions. Humid phases with frequent typhoons are inferred by high percentages of Lagerstroemia and high ratios of planktonic/benthic diatoms, respectively, during 500-700 AD and Little Ice Age phase 2 (LIA2, 1630-1850 AD). The occurrences of cultivated Poaceae (Oryza) during 1250-1300 AD and the last ~400 years, reflect agriculture activities, which seems to implicate strongly with the environmental stability. Finally, we found flood events which dominated during the El Niño-like stage, but dry events as well as frequent typhoon events happened during the La Niña-like stage. After comparing our results with the reconstructed proxy for tropical hydrological conditions, we suggested that the local hydrology in coastal East Asia were strongly affected by the typhoon-triggered heavy rainfalls which were influenced by the variation of global temperature, expansion of the Pacific warm pool and intensification of ENSO events.

  20. Cellular automaton and elastic net for event reconstruction in the NEMO-2 experiment

    International Nuclear Information System (INIS)

    Kovalenko, V.

    1997-01-01

    A cellular automaton for track searching and an elastic net for charged particle trajectory fitting are presented. The advantages of the methods are: simplicity of the algorithms, fast and stable convergence to real tracks, and a reconstruction efficiency close to 100%. Demonstration programs are available at http://nuweb.jinr.dubna.su/LNP/NEMO using a Java enabled browser. (orig.)

  1. Data-driven non-Markovian closure models

    Science.gov (United States)

    Kondrashov, Dmitri; Chekroun, Mickaël D.; Ghil, Michael

    2015-03-01

    This paper has two interrelated foci: (i) obtaining stable and efficient data-driven closure models by using a multivariate time series of partial observations from a large-dimensional system; and (ii) comparing these closure models with the optimal closures predicted by the Mori-Zwanzig (MZ) formalism of statistical physics. Multilayer stochastic models (MSMs) are introduced as both a generalization and a time-continuous limit of existing multilevel, regression-based approaches to closure in a data-driven setting; these approaches include empirical model reduction (EMR), as well as more recent multi-layer modeling. It is shown that the multilayer structure of MSMs can provide a natural Markov approximation to the generalized Langevin equation (GLE) of the MZ formalism. A simple correlation-based stopping criterion for an EMR-MSM model is derived to assess how well it approximates the GLE solution. Sufficient conditions are derived on the structure of the nonlinear cross-interactions between the constitutive layers of a given MSM to guarantee the existence of a global random attractor. This existence ensures that no blow-up can occur for a broad class of MSM applications, a class that includes non-polynomial predictors and nonlinearities that do not necessarily preserve quadratic energy invariants. The EMR-MSM methodology is first applied to a conceptual, nonlinear, stochastic climate model of coupled slow and fast variables, in which only slow variables are observed. It is shown that the resulting closure model with energy-conserving nonlinearities efficiently captures the main statistical features of the slow variables, even when there is no formal scale separation and the fast variables are quite energetic. Second, an MSM is shown to successfully reproduce the statistics of a partially observed, generalized Lotka-Volterra model of population dynamics in its chaotic regime. The challenges here include the rarity of strange attractors in the model's parameter

  2. Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision.

    Science.gov (United States)

    Tu, Junchao; Zhang, Liyan

    2018-01-12

    A new solution to the problem of galvanometric laser scanning (GLS) system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN)to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM). By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

  3. Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision

    Directory of Open Access Journals (Sweden)

    Junchao Tu

    2018-01-01

    Full Text Available A new solution to the problem of galvanometric laser scanning (GLS system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN)to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM. By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

  4. Late Holocene environmental reconstructions and their implications on flood events, typhoon, and agricultural activities in NE Taiwan

    Science.gov (United States)

    Wang, L.-C.; Behling, H.; Lee, T.-Q.; Li, H.-C.; Huh, C.-A.; Shiau, L.-J.; Chang, Y.-P.

    2014-10-01

    We reconstructed paleoenvironmental changes from a sediment archive of a lake in the floodplain of the Ilan Plain of NE Taiwan on multi-decadal resolution for the last ca. 1900 years. On the basis of pollen and diatom records, we evaluated past floods, typhoons, and agricultural activities in this area which are sensitive to the hydrological conditions in the western Pacific. Considering the high sedimentation rates with low microfossil preservations in our sedimentary record, multiple flood events were. identified during the period AD 100-1400. During the Little Ice Age phase 1 (LIA 1 - AD 1400-1620), the abundant occurrences of wetland plant (Cyperaceae) and diatom frustules imply less flood events under stable climate conditions in this period. Between AD 500 and 700 and the Little Ice Age phase 2 (LIA 2 - AD 1630-1850), the frequent typhoons were inferred by coarse sediments and planktonic diatoms, which represented more dynamical climate conditions than in the LIA 1. By comparing our results with the reconstructed changes in tropical hydrological conditions, we suggested that the local hydrology in NE Taiwan is strongly influenced by typhoon-triggered heavy rainfalls, which could be influenced by the variation of global temperature, the expansion of the Pacific warm pool, and the intensification of El Niño-Southern Oscillation (ENSO) events.

  5. A Data-Driven Approach to Realistic Shape Morphing

    KAUST Repository

    Gao, Lin; Lai, Yu-Kun; Huang, Qi-Xing; Hu, Shi-Min

    2013-01-01

    Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper. © 2013 The Eurographics Association and Blackwell Publishing Ltd.

  6. Data driven parallelism in experimental high energy physics applications

    International Nuclear Information System (INIS)

    Pohl, M.

    1987-01-01

    I present global design principles for the implementation of high energy physics data analysis code on sequential and parallel processors with mixed shared and local memory. Potential parallelism in the structure of high energy physics tasks is identified with granularity varying from a few times 10 8 instructions all the way down to a few times 10 4 instructions. It follows the hierarchical structure of detector and data acquisition systems. To take advantage of this - yet preserving the necessary portability of the code - I propose a computational model with purely data driven concurrency in Single Program Multiple Data (SPMD) mode. The task granularity is defined by varying the granularity of the central data structure manipulated. Concurrent processes coordiate themselves asynchroneously using simple lock constructs on parts of the data structure. Load balancing among processes occurs naturally. The scheme allows to map the internal layout of the data structure closely onto the layout of local and shared memory in a parallel architecture. It thus allows to optimize the application with respect to synchronization as well as data transport overheads. I present a coarse top level design for a portable implementation of this scheme on sequential machines, multiprocessor mainframes (e.g. IBM 3090), tightly coupled multiprocessors (e.g. RP-3) and loosely coupled processor arrays (e.g. LCAP, Emulating Processor Farms). (orig.)

  7. A data-driven approach to quality risk management.

    Science.gov (United States)

    Alemayehu, Demissie; Alvir, Jose; Levenstein, Marcia; Nickerson, David

    2013-10-01

    An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. ONLY A SUBSET OF THE RISK FACTORS HAD A SIGNIFICANT ASSOCIATION WITH QUALITY ISSUES, AND INCLUDED: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.

  8. A data-driven approach to quality risk management

    Directory of Open Access Journals (Sweden)

    Demissie Alemayehu

    2013-01-01

    Full Text Available Aim: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Materials and Methods: Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. Results: Only a subset of the risk factors had a significant association with quality issues, and included: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Conclusion: Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.

  9. ATLAS job transforms: a data driven workflow engine

    International Nuclear Information System (INIS)

    Stewart, G A; Breaden-Madden, W B; Maddocks, H J; Harenberg, T; Sandhoff, M; Sarrazin, B

    2014-01-01

    The need to run complex workflows for a high energy physics experiment such as ATLAS has always been present. However, as computing resources have become even more constrained, compared to the wealth of data generated by the LHC, the need to use resources efficiently and manage complex workflows within a single grid job have increased. In ATLAS, a new Job Transform framework has been developed that we describe in this paper. This framework manages the multiple execution steps needed to 'transform' one data type into another (e.g., RAW data to ESD to AOD to final ntuple) and also provides a consistent interface for the ATLAS production system. The new framework uses a data driven workflow definition which is both easy to manage and powerful. After a transform is defined, jobs are expressed simply by specifying the input data and the desired output data. The transform infrastructure then executes only the necessary substeps to produce the final data products. The global execution cost of running the job is minimised and the transform can adapt to scenarios where data can be produced along different execution paths. Transforms for specific physics tasks which support up to 60 individual substeps have been successfully run. As the new transforms infrastructure has been deployed in production many features have been added to the framework which improve reliability, quality of error reporting and also provide support for multi-process jobs.

  10. Human body segmentation via data-driven graph cut.

    Science.gov (United States)

    Li, Shifeng; Lu, Huchuan; Shao, Xingqing

    2014-11-01

    Human body segmentation is a challenging and important problem in computer vision. Existing methods usually entail a time-consuming training phase for prior knowledge learning with complex shape matching for body segmentation. In this paper, we propose a data-driven method that integrates top-down body pose information and bottom-up low-level visual cues for segmenting humans in static images within the graph cut framework. The key idea of our approach is first to exploit human kinematics to search for body part candidates via dynamic programming for high-level evidence. Then, by using the body parts classifiers, obtaining bottom-up cues of human body distribution for low-level evidence. All the evidence collected from top-down and bottom-up procedures are integrated in a graph cut framework for human body segmentation. Qualitative and quantitative experiment results demonstrate the merits of the proposed method in segmenting human bodies with arbitrary poses from cluttered backgrounds.

  11. Data-driven classification of patients with primary progressive aphasia.

    Science.gov (United States)

    Hoffman, Paul; Sajjadi, Seyed Ahmad; Patterson, Karalyn; Nestor, Peter J

    2017-11-01

    Current diagnostic criteria classify primary progressive aphasia into three variants-semantic (sv), nonfluent (nfv) and logopenic (lv) PPA-though the adequacy of this scheme is debated. This study took a data-driven approach, applying k-means clustering to data from 43 PPA patients. The algorithm grouped patients based on similarities in language, semantic and non-linguistic cognitive scores. The optimum solution consisted of three groups. One group, almost exclusively those diagnosed as svPPA, displayed a selective semantic impairment. A second cluster, with impairments to speech production, repetition and syntactic processing, contained a majority of patients with nfvPPA but also some lvPPA patients. The final group exhibited more severe deficits to speech, repetition and syntax as well as semantic and other cognitive deficits. These results suggest that, amongst cases of non-semantic PPA, differentiation mainly reflects overall degree of language/cognitive impairment. The observed patterns were scarcely affected by inclusion/exclusion of non-linguistic cognitive scores. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  12. A Data-Driven Approach to Realistic Shape Morphing

    KAUST Repository

    Gao, Lin

    2013-05-01

    Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper. © 2013 The Eurographics Association and Blackwell Publishing Ltd.

  13. Data driven parallelism in experimental high energy physics applications

    Science.gov (United States)

    Pohl, Martin

    1987-08-01

    I present global design principles for the implementation of High Energy Physics data analysis code on sequential and parallel processors with mixed shared and local memory. Potential parallelism in the structure of High Energy Physics tasks is identified with granularity varying from a few times 10 8 instructions all the way down to a few times 10 4 instructions. It follows the hierarchical structure of detector and data acquisition systems. To take advantage of this - yet preserving the necessary portability of the code - I propose a computational model with purely data driven concurrency in Single Program Multiple Data (SPMD) mode. The Task granularity is defined by varying the granularity of the central data structure manipulated. Concurrent processes coordinate themselves asynchroneously using simple lock constructs on parts of the data structure. Load balancing among processes occurs naturally. The scheme allows to map the internal layout of the data structure closely onto the layout of local and shared memory in a parallel architecture. It thus allows to optimize the application with respect to synchronization as well as data transport overheads. I present a coarse top level design for a portable implementation of this scheme on sequential machines, multiprocessor mainframes (e.g. IBM 3090), tightly coupled multiprocessors (e.g. RP-3) and loosely coupled processor arrays (e.g. LCAP, Emulating Processor Farms).

  14. Data driven profiting from your most important business asset

    CERN Document Server

    Redman, Thomas C

    2008-01-01

    Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professi...

  15. EXPLORING DATA-DRIVEN SPECTRAL MODELS FOR APOGEE M DWARFS

    Science.gov (United States)

    Lua Birky, Jessica; Hogg, David; Burgasser, Adam J.; Jessica Birky

    2018-01-01

    The Cannon (Ness et al. 2015; Casey et al. 2016) is a flexible, data-driven spectral modeling and parameter inference framework, demonstrated on high-resolution Apache Point Galactic Evolution Experiment (APOGEE; λ/Δλ~22,500, 1.5-1.7µm) spectra of giant stars to estimate stellar labels (Teff, logg, [Fe/H], and chemical abundances) to precisions higher than the model-grid pipeline. The lack of reliable stellar parameters reported by the APOGEE pipeline for temperatures less than ~3550K, motivates extension of this approach to M dwarf stars. Using a training set of 51 M dwarfs with spectral types ranging M0-M9 obtained from SDSS optical spectra, we demonstrate that the Cannon can infer spectral types to a precision of +/-0.6 types, making it an effective tool for classifying high-resolution near-infrared spectra. We discuss the potential for extending this work to determine the physical stellar labels Teff, logg, and [Fe/H].This work is supported by the SDSS Faculty and Student (FAST) initiative.

  16. Data driven fault detection and isolation: a wind turbine scenario

    Directory of Open Access Journals (Sweden)

    Rubén Francisco Manrique Piramanrique

    2015-04-01

    Full Text Available One of the greatest drawbacks in wind energy generation is the high maintenance cost associated to mechanical faults. This problem becomes more evident in utility scale wind turbines, where the increased size and nominal capacity comes with additional problems associated with structural vibrations and aeroelastic effects in the blades. Due to the increased operation capability, it is imperative to detect system degradation and faults in an efficient manner, maintaining system integrity, reliability and reducing operation costs. This paper presents a comprehensive comparison of four different Fault Detection and Isolation (FDI filters based on “Data Driven” (DD techniques. In order to enhance FDI performance, a multi-level strategy is used where:  the first level detects the occurrence of any given fault (detection, while  the second identifies the source of the fault (isolation. Four different DD classification techniques (namely Support Vector Machines, Artificial Neural Networks, K Nearest Neighbors and Gaussian Mixture Models were studied and compared for each of the proposed classification levels. The best strategy at each level could be selected to build the final data driven FDI system. The performance of the proposed scheme is evaluated on a benchmark model of a commercial wind turbine. 

  17. An Operational Implementation of a CBRN Sensor-Driven Modeling Paradigm for Stochastic Event Reconstruction

    Science.gov (United States)

    2010-05-01

    operationnelle pour la reconstruction de sources, au systeme de model- isation urbaine multi echelle integre mis en oeuvre dans !’infrastructure informatique...or urbanLS, respectively. However, for the Bayesian inversion of concentration data to be practical , fast and efficient techniques are required for...sensitivity and/or uncertainty analysis methods that have been used to quantify and reduce them. In this paper , all the various error contributions to

  18. Multiscale vision model for event detection and reconstruction in two-photon imaging data

    DEFF Research Database (Denmark)

    Brazhe, Alexey; Mathiesen, Claus; Lind, Barbara Lykke

    2014-01-01

    on a modified multiscale vision model, an object detection framework based on the thresholding of wavelet coefficients and hierarchical trees of significant coefficients followed by nonlinear iterative partial object reconstruction, for the analysis of two-photon calcium imaging data. The framework is discussed...... of the multiscale vision model is similar in the denoising, but provides a better segmenation of the image into meaningful objects, whereas other methods need to be combined with dedicated thresholding and segmentation utilities....

  19. Data-driven outbreak forecasting with a simple nonlinear growth model.

    Science.gov (United States)

    Lega, Joceline; Brown, Heidi E

    2016-12-01

    Recent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  20. Data-driven approach for creating synthetic electronic medical records.

    Science.gov (United States)

    Buczak, Anna L; Babin, Steven; Moniz, Linda

    2010-10-14

    New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed. This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population. We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified. A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4

  1. Evidence-based and data-driven road safety management

    Directory of Open Access Journals (Sweden)

    Fred Wegman

    2015-07-01

    Full Text Available Over the past decades, road safety in highly-motorised countries has made significant progress. Although we have a fair understanding of the reasons for this progress, we don't have conclusive evidence for this. A new generation of road safety management approaches has entered road safety, starting when countries decided to guide themselves by setting quantitative targets (e.g. 50% less casualties in ten years' time. Setting realistic targets, designing strategies and action plans to achieve these targets and monitoring progress have resulted in more scientific research to support decision-making on these topics. Three subjects are key in this new approach of evidence-based and data-driven road safety management: ex-post and ex-ante evaluation of both individual interventions and intervention packages in road safety strategies, and transferability (external validity of the research results. In this article, we explore these subjects based on recent experiences in four jurisdictions (Western Australia, the Netherlands, Sweden and Switzerland. All four apply similar approaches and tools; differences are considered marginal. It is concluded that policy-making and political decisions were influenced to a great extent by the results of analysis and research. Nevertheless, to compensate for a relatively weak theoretical basis and to improve the power of this new approach, a number of issues will need further research. This includes ex-post and ex-ante evaluation, a better understanding of extrapolation of historical trends and the transferability of research results. This new approach cannot be realized without high-quality road safety data. Good data and knowledge are indispensable for this new and very promising approach.

  2. Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

    Science.gov (United States)

    Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.

    2018-02-01

    The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.

  3. Architectural Strategies for Enabling Data-Driven Science at Scale

    Science.gov (United States)

    Crichton, D. J.; Law, E. S.; Doyle, R. J.; Little, M. M.

    2017-12-01

    architectural strategies, including a 2015-2016 NASA AIST Study on Big Data, for evolving scientific research towards massively distributed data-driven discovery. It will include example use cases across earth science, planetary science, and other disciplines.

  4. SIDEKICK: Genomic data driven analysis and decision-making framework

    Directory of Open Access Journals (Sweden)

    Yoon Kihoon

    2010-12-01

    Full Text Available Abstract Background Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation. Results Sidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick. Conclusions Sidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to

  5. Tales from the Paleoclimate Underground: Lessons Learned from Reconstructing Extreme Events

    Science.gov (United States)

    Frappier, A. E.

    2017-12-01

    Tracing patterns of paleoclimate extremes over the past two millennia is becoming ever more important in the effort to understand and predict costly weather hazards and their varied societal impacts. I present three paleoclimate vignettes from the past ten years of different paleotempestology projects I have worked on closely, illustrating our collective challenges and productive pathways in reconstructing rainfall extremes: temporal, spatial, and combining information from disparate proxies. Finally, I aim to share new results from modeling multiple extremes and hazards in Yucatan, a climate change hotspot.

  6. Data-driven approach for creating synthetic electronic medical records

    Directory of Open Access Journals (Sweden)

    Moniz Linda

    2010-10-01

    Full Text Available Abstract Background New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed. Methods This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia and for background records. The method developed has three major steps: 1 synthetic patient identity and basic information generation; 2 identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3 adaptation of these care patterns to the synthetic patient population. Results We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified. Conclusions A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders. The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious

  7. Data-driven modeling of nano-nose gas sensor arrays

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård

    2010-01-01

    We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence...... the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state...

  8. Bayesian Estimator for Angle Recovery: Event Classification and Reconstruction in Positron Emission Tomography

    International Nuclear Information System (INIS)

    Foudray, Angela M K; Levin, Craig S

    2007-01-01

    PET at the highest level is an inverse problem: reconstruct the location of the emission (which localize biological function) from detected photons. Ideally, one would like to directly measure an annihilation photon's incident direction on the detector. In the developed algorithm, Bayesian Estimation for Angle Recovery (BEAR), we utilized the increased information gathered from localizing photon interactions in the detector and developed a Bayesian estimator for a photon's incident direction. Probability distribution functions (PDFs) were filled using an interaction energy weighted mean or center of mass (COM) reference space, which had the following computational advantages: (1) a significant reduction in the size of the data in measurement space, making further manipulation and searches faster (2) the construction of COM space does not depend on measurement location, it takes advantage of measurement symmetries, and data can be added to the training set without knowledge and recalculation of prior training data, (3) calculation of posterior probability map is fully parallelizable, it can scale to any number of processors. These PDFs were used to estimate the point spread function (PSF) in incident angle space for (i) algorithm assessment and (ii) to provide probability selection criteria for classification. The algorithm calculates both the incident θ and φ angle, with ∼16 degrees RMS in both angles, limiting the incoming direction to a narrow cone. Feature size did not improve using the BEAR algorithm as an angle filter, but the contrast ratio improved 40% on average

  9. Reconstructing the deadly eruptive events of 1790 CE at Kīlauea Volcano, Hawai‘i

    Science.gov (United States)

    Swanson, Don; Weaver, Samantha J; Houghton, Bruce F.

    2014-01-01

    A large number of people died during an explosive eruption of Kīlauea Volcano in 1790 CE. Detailed study of the upper part of the Keanakāko‘i Tephra has identified the deposits that may have been responsible for the deaths. Three successive units record shifts in eruption style that agree well with accounts of the eruption based on survivor interviews 46 yr later. First, a wet fall of very fine, accretionary-lapilli–bearing ash created a “cloud of darkness.” People walked across the soft deposit, leaving footprints as evidence. While the ash was still unconsolidated, lithic lapilli fell into it from a high eruption column that was seen from 90 km away. Either just after this tephra fall or during its latest stage, pulsing dilute pyroclastic density currents, probably products of a phreatic eruption, swept across the western flank of Kīlauea, embedding lapilli in the muddy ash and crossing the trail along which the footprints occur. The pyroclastic density currents were most likely responsible for the fatalities, as judged from the reported condition and probable location of the bodies. This reconstruction is relevant today, as similar eruptions will probably occur in the future at Kīlauea and represent its most dangerous and least predictable hazard.

  10. The processor farm for online triggering and full event reconstruction of the HERA-B experiment at HERA

    International Nuclear Information System (INIS)

    Gellrich, A.; Dippel, R.; Gensch, U.; Kowallik, R.; Legrand, I.C.; Leich, H.; Sun, F.; Wegner, P.

    1996-01-01

    The main goal of the HERA-B experiment which start taking data in 1988 is to study CP violation in B decays. This article describes the concept and the planned implementation of a multi-processor system, called processor farm,as the last part of the data acquisition and trigger system of the HERA B experiment. The third level trigger task and a full online event reconstruction will be performed on this processor farm, consisting of more then 100 powerful RISC processors which are based on commercial hardware boards. The controlling will be done by a real-time operating system which provides a software development environment, including FORTRAN and C compilers. (author)

  11. Beam-helicity asymmetry arising from deeply virtual Compton scattering measured with kinematically complete event reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Airapetian, A. [Giessen Univ. (Germany). Physikalisches Inst.; Michigan Univ., Ann Arbor, MI (United States). Randall Lab. of Physics; Akopov, N. [Yerevan Physics Inst. (Armenia); Akopov, Z. [DESY, Hamburg (DE)] (and others)

    2012-06-15

    The beam-helicity asymmetry in exclusive electroproduction of real photons by the longitudinally polarized HERA positron beam scattering off an unpolarized hydrogen target is measured at HERMES. The asymmetry arises from deeply virtual Compton scattering and its interference with the Bethe-Heitler process. Azimuthal amplitudes of the beam-helicity asymmetry are extracted from a data sample consisting of ep{yields}ep{gamma} events with detection of all particles in the final state including the recoiling proton. The installation of a recoil detector, while reducing the acceptance of the experiment, allows the elimination of resonant background that was estimated to contribute an average of about 12% to the signal in previous HERMES publications. The removal of the resonant background from the present data sample is shown to increase the magnitude of the leading asymmetry amplitude by 0.054{+-}0.016 to -0.328{+-}0.027(stat.){+-}0.045(syst.).

  12. Beam-helicity asymmetry arising from deeply virtual Compton scattering measured with kinematically complete event reconstruction

    International Nuclear Information System (INIS)

    Airapetian, A.; Akopov, Z.

    2012-06-01

    The beam-helicity asymmetry in exclusive electroproduction of real photons by the longitudinally polarized HERA positron beam scattering off an unpolarized hydrogen target is measured at HERMES. The asymmetry arises from deeply virtual Compton scattering and its interference with the Bethe-Heitler process. Azimuthal amplitudes of the beam-helicity asymmetry are extracted from a data sample consisting of ep→epγ events with detection of all particles in the final state including the recoiling proton. The installation of a recoil detector, while reducing the acceptance of the experiment, allows the elimination of resonant background that was estimated to contribute an average of about 12% to the signal in previous HERMES publications. The removal of the resonant background from the present data sample is shown to increase the magnitude of the leading asymmetry amplitude by 0.054±0.016 to -0.328±0.027(stat.)±0.045(syst.).

  13. Measurement of Event Background Fluctuations for Charged Particle Jet Reconstruction in Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV

    OpenAIRE

    Abelev, Betty; Adam, Jaroslav; Adamova, Dagmar; Adare, Andrew Marshall; Aggarwal, Madan; Aglieri Rinella, Gianluca; Agocs, Andras Gabor; Agostinelli, Andrea; Aguilar Salazar, Saul; Ahammed, Zubayer; Ahmad, Arshad; Ahmad, Nazeer; Ahn, Sang Un; Akindinov, Alexander; Aleksandrov, Dmitry

    2012-01-01

    The effect of event background fluctuations on charged particle jet reconstruction in Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV has been measured with the ALICE experiment. The main sources of non-statistical fluctuations are characterized based purely on experimental data with an unbiased method, as well as by using single high p_t particles and simulated jets embedded into real Pb-Pb events and reconstructed with the anti-kt jet finder. The influence of a low transverse momentum cut-of...

  14. Data driven model generation based on computational intelligence

    Science.gov (United States)

    Gemmar, Peter; Gronz, Oliver; Faust, Christophe; Casper, Markus

    2010-05-01

    The simulation of discharges at a local gauge or the modeling of large scale river catchments are effectively involved in estimation and decision tasks of hydrological research and practical applications like flood prediction or water resource management. However, modeling such processes using analytical or conceptual approaches is made difficult by both complexity of process relations and heterogeneity of processes. It was shown manifold that unknown or assumed process relations can principally be described by computational methods, and that system models can automatically be derived from observed behavior or measured process data. This study describes the development of hydrological process models using computational methods including Fuzzy logic and artificial neural networks (ANN) in a comprehensive and automated manner. Methods We consider a closed concept for data driven development of hydrological models based on measured (experimental) data. The concept is centered on a Fuzzy system using rules of Takagi-Sugeno-Kang type which formulate the input-output relation in a generic structure like Ri : IFq(t) = lowAND...THENq(t+Δt) = ai0 +ai1q(t)+ai2p(t-Δti1)+ai3p(t+Δti2)+.... The rule's premise part (IF) describes process states involving available process information, e.g. actual outlet q(t) is low where low is one of several Fuzzy sets defined over variable q(t). The rule's conclusion (THEN) estimates expected outlet q(t + Δt) by a linear function over selected system variables, e.g. actual outlet q(t), previous and/or forecasted precipitation p(t ?Δtik). In case of river catchment modeling we use head gauges, tributary and upriver gauges in the conclusion part as well. In addition, we consider temperature and temporal (season) information in the premise part. By creating a set of rules R = {Ri|(i = 1,...,N)} the space of process states can be covered as concise as necessary. Model adaptation is achieved by finding on optimal set A = (aij) of conclusion

  15. Measurement of Forward-Backward Asymmetry of Simulated and Reconstructed $Z' \\to \\mu^{+}\\mu^{-}$ Events in CMS

    CERN Document Server

    Cousins, Robert; Valuev, Vyacheslav

    2005-01-01

    This note describes a fitting technique for measuring the forward-backward asymmetry A_FB of Z' --> mu+ mu- events. We extract A_FB of fully simulated and reconstructed events by using an unbinned maximum likelihood fit based on a probability density function for six observables. We illustrate the potential for the measured on-peak A_FB to be used to distinguish among various couplings of a Z', such as those associated with Zssm, Zpsi, Zeta, Zchi, ZLRM, or ZALRM models. With 400 fb^-1 of integrated luminosity at CMS, one can distinguish between either a Zchi or ZALRM and one of the other four models with significance level alpha >= 3 sigma up to a Z' mass between 2.0 and 2.7 TeV. One can distinguish among the other four models with the same level of significance only up to Z' mass equal to 1 - 1.5 TeV, whereas ZALRM and Zchi are indistinguishable in the mass range of Z' mass >= 1 TeV.

  16. WIFIRE: A Scalable Data-Driven Monitoring, Dynamic Prediction and Resilience Cyberinfrastructure for Wildfires

    Science.gov (United States)

    Altintas, I.; Block, J.; Braun, H.; de Callafon, R. A.; Gollner, M. J.; Smarr, L.; Trouve, A.

    2013-12-01

    Recent studies confirm that climate change will cause wildfires to increase in frequency and severity in the coming decades especially for California and in much of the North American West. The most critical sustainability issue in the midst of these ever-changing dynamics is how to achieve a new social-ecological equilibrium of this fire ecology. Wildfire wind speeds and directions change in an instant, and first responders can only be effective when they take action as quickly as the conditions change. To deliver information needed for sustainable policy and management in this dynamically changing fire regime, we must capture these details to understand the environmental processes. We are building an end-to-end cyberinfrastructure (CI), called WIFIRE, for real-time and data-driven simulation, prediction and visualization of wildfire behavior. The WIFIRE integrated CI system supports social-ecological resilience to the changing fire ecology regime in the face of urban dynamics and climate change. Networked observations, e.g., heterogeneous satellite data and real-time remote sensor data is integrated with computational techniques in signal processing, visualization, modeling and data assimilation to provide a scalable, technological, and educational solution to monitor weather patterns to predict a wildfire's Rate of Spread. Our collaborative WIFIRE team of scientists, engineers, technologists, government policy managers, private industry, and firefighters architects implement CI pathways that enable joint innovation for wildfire management. Scientific workflows are used as an integrative distributed programming model and simplify the implementation of engineering modules for data-driven simulation, prediction and visualization while allowing integration with large-scale computing facilities. WIFIRE will be scalable to users with different skill-levels via specialized web interfaces and user-specified alerts for environmental events broadcasted to receivers before

  17. Flexible event reconstruction software chains with the ALICE High-Level Trigger

    International Nuclear Information System (INIS)

    Ram, D; Breitner, T; Szostak, A

    2012-01-01

    The ALICE High-Level Trigger (HLT) has a large high-performance computing cluster at CERN whose main objective is to perform real-time analysis on the data generated by the ALICE experiment and scale it down to at-most 4GB/sec - which is the current maximum mass-storage bandwidth available. Data-flow in this cluster is controlled by a custom designed software framework. It consists of a set of components which can communicate with each other via a common control interface. The software framework also supports the creation of different configurations based on the detectors participating in the HLT. These configurations define a logical data processing “chain” of detector data-analysis components. Data flows through this software chain in a pipelined fashion so that several events can be processed at the same time. An instance of such a chain can run and manage a few thousand physics analysis and data-flow components. The HLT software and the configuration scheme used in the 2011 heavy-ion runs of ALICE, has been discussed in this contribution.

  18. A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

    KAUST Repository

    Zerrouki, Nabil

    2016-07-26

    Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.

  19. A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

    KAUST Repository

    Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

    2016-01-01

    Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures

  20. The Future of Data-Driven Wound Care.

    Science.gov (United States)

    Woods, Jon S; Saxena, Mayur; Nagamine, Tasha; Howell, Raelina S; Criscitelli, Theresa; Gorenstein, Scott; M Gillette, Brian

    2018-04-01

    Care for patients with chronic wounds can be complex, and the chances of poor outcomes are high if wound care is not optimized through evidence-based protocols. Tracking and managing every variable and comorbidity in patients with wounds is difficult despite the increasing use of wound-specific electronic medical records. Harnessing the power of big data analytics to help nurses and physicians provide optimized care based on the care provided to millions of patients can result in better outcomes. Numerous applications of machine learning toward workflow improvements, inpatient monitoring, outpatient communication, and hospital operations can improve overall efficiency and efficacy of care delivery in and out of the hospital, while reducing adverse events and complications. This article provides an overview of the application of big data analytics and machine learning in health care, highlights important recent advances, and discusses how these technologies may revolutionize advanced wound care. © AORN, Inc, 2018.

  1. An evaluation of data-driven motion estimation in comparison to the usage of external-surrogates in cardiac SPECT imaging

    International Nuclear Information System (INIS)

    Mukherjee, Joyeeta Mitra; Johnson, Karen L; Pretorius, P Hendrik; King, Michael A; Hutton, Brian F

    2013-01-01

    Motion estimation methods in single photon emission computed tomography (SPECT) can be classified into methods which depend on just the emission data (data-driven), or those that use some other source of information such as an external surrogate. The surrogate-based methods estimate the motion exhibited externally which may not correlate exactly with the movement of organs inside the body. The accuracy of data-driven strategies on the other hand is affected by the type and timing of motion occurrence during acquisition, the source distribution, and various degrading factors such as attenuation, scatter, and system spatial resolution. The goal of this paper is to investigate the performance of two data-driven motion estimation schemes based on the rigid-body registration of projections of motion-transformed source distributions to the acquired projection data for cardiac SPECT studies. Comparison is also made of six intensity based registration metrics to an external surrogate-based method. In the data-driven schemes, a partially reconstructed heart is used as the initial source distribution. The partially-reconstructed heart has inaccuracies due to limited angle artifacts resulting from using only a part of the SPECT projections acquired while the patient maintained the same pose. The performance of different cost functions in quantifying consistency with the SPECT projection data in the data-driven schemes was compared for clinically realistic patient motion occurring as discrete pose changes, one or two times during acquisition. The six intensity-based metrics studied were mean-squared difference, mutual information, normalized mutual information (NMI), pattern intensity (PI), normalized cross-correlation and entropy of the difference. Quantitative and qualitative analysis of the performance is reported using Monte-Carlo simulations of a realistic heart phantom including degradation factors such as attenuation, scatter and system spatial resolution. Further the

  2. Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

    Directory of Open Access Journals (Sweden)

    Jimyung Kang

    2017-10-01

    Full Text Available Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider’s perspective.

  3. Data-Driven Baseline Estimation of Residential Buildings for Demand Response

    Directory of Open Access Journals (Sweden)

    Saehong Park

    2015-09-01

    Full Text Available The advent of advanced metering infrastructure (AMI generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL estimation in demand response (DR management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities’ residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate.

  4. A Data-Driven Air Transportation Delay Propagation Model Using Epidemic Process Models

    Directory of Open Access Journals (Sweden)

    B. Baspinar

    2016-01-01

    Full Text Available In air transport network management, in addition to defining the performance behavior of the system’s components, identification of their interaction dynamics is a delicate issue in both strategic and tactical decision-making process so as to decide which elements of the system are “controlled” and how. This paper introduces a novel delay propagation model utilizing epidemic spreading process, which enables the definition of novel performance indicators and interaction rates of the elements of the air transportation network. In order to understand the behavior of the delay propagation over the network at different levels, we have constructed two different data-driven epidemic models approximating the dynamics of the system: (a flight-based epidemic model and (b airport-based epidemic model. The flight-based epidemic model utilizing SIS epidemic model focuses on the individual flights where each flight can be in susceptible or infected states. The airport-centric epidemic model, in addition to the flight-to-flight interactions, allows us to define the collective behavior of the airports, which are modeled as metapopulations. In network model construction, we have utilized historical flight-track data of Europe and performed analysis for certain days involving certain disturbances. Through this effort, we have validated the proposed delay propagation models under disruptive events.

  5. CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

    Science.gov (United States)

    Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

    2016-09-01

    Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD

  6. Data-driven models of dominantly-inherited Alzheimer's disease progression.

    Science.gov (United States)

    Oxtoby, Neil P; Young, Alexandra L; Cash, David M; Benzinger, Tammie L S; Fagan, Anne M; Morris, John C; Bateman, Randall J; Fox, Nick C; Schott, Jonathan M; Alexander, Daniel C

    2018-03-22

    Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1

  7. Primary Vertex Reconstruction at the ATLAS Experiment

    CERN Document Server

    Grimm, Kathryn; The ATLAS collaboration

    2016-01-01

    Efficient and precise reconstruction of the primary vertex in an LHC collision is essential in both the reconstruction of the full kinematic properties of a hard-scatter event and of soft interactions as a measure of the amount of pile-up. The reconstruction of primary vertices in the busy, high pile-up environment of Run-2 of the LHC is a challenging task. New methods have been developed by the ATLAS experiment to reconstruct vertices in such environments. Advances in vertex seeding include methods taken from medical imaging, which allow for reconstruction of multiple vertices with small spatial separation. The adoption of this new seeding algorithm within the ATLAS adaptive vertex finding and fitting procedure will be discussed, and the first results of the new techniques from Run-2 data will be presented. Additionally, data-driven methods to evaluate vertex resolution will be presented with special focus on correct methods to evaluate the effect of the beam spot constraint; results from these methods in Ru...

  8. The Orion GN and C Data-Driven Flight Software Architecture for Automated Sequencing and Fault Recovery

    Science.gov (United States)

    King, Ellis; Hart, Jeremy; Odegard, Ryan

    2010-01-01

    The Orion Crew Exploration Vehicle (CET) is being designed to include significantly more automation capability than either the Space Shuttle or the International Space Station (ISS). In particular, the vehicle flight software has requirements to accommodate increasingly automated missions throughout all phases of flight. A data-driven flight software architecture will provide an evolvable automation capability to sequence through Guidance, Navigation & Control (GN&C) flight software modes and configurations while maintaining the required flexibility and human control over the automation. This flexibility is a key aspect needed to address the maturation of operational concepts, to permit ground and crew operators to gain trust in the system and mitigate unpredictability in human spaceflight. To allow for mission flexibility and reconfrgurability, a data driven approach is being taken to load the mission event plan as well cis the flight software artifacts associated with the GN&C subsystem. A database of GN&C level sequencing data is presented which manages and tracks the mission specific and algorithm parameters to provide a capability to schedule GN&C events within mission segments. The flight software data schema for performing automated mission sequencing is presented with a concept of operations for interactions with ground and onboard crew members. A prototype architecture for fault identification, isolation and recovery interactions with the automation software is presented and discussed as a forward work item.

  9. Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

    NARCIS (Netherlands)

    Caat, Michael ten; Maurits, Natasha M.; Roerdink, Jos B.T.M.

    2008-01-01

    A typical data- driven visualization of electroencephalography ( EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter,

  10. Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection

    Science.gov (United States)

    Raimalwala, K.; Faragalli, M.; Reid, E.

    2018-04-01

    The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.

  11. Designing Data-Driven Battery Prognostic Approaches for Variable Loading Profiles: Some Lessons Learned

    Data.gov (United States)

    National Aeronautics and Space Administration — Among various approaches for implementing prognostic algorithms data-driven algorithms are popular in the industry due to their intuitive nature and relatively fast...

  12. Short-term stream flow forecasting at Australian river sites using data-driven regression techniques

    CSIR Research Space (South Africa)

    Steyn, Melise

    2017-09-01

    Full Text Available This study proposes a computationally efficient solution to stream flow forecasting for river basins where historical time series data are available. Two data-driven modeling techniques are investigated, namely support vector regression...

  13. Measurement of Event Background Fluctuations for Charged Particle Jet Reconstruction in Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV

    CERN Document Server

    Abelev, Betty; Adamova, Dagmar; Adare, Andrew Marshall; Aggarwal, Madan; Aglieri Rinella, Gianluca; Agocs, Andras Gabor; Agostinelli, Andrea; Aguilar Salazar, Saul; Ahammed, Zubayer; Ahmad, Arshad; Ahmad, Nazeer; Ahn, Sang Un; Akindinov, Alexander; Aleksandrov, Dmitry; Alessandro, Bruno; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Almaraz Avina, Erick Jonathan; Alt, Torsten; Altini, Valerio; Altinpinar, Sedat; Altsybeev, Igor; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anielski, Jonas; Anson, Christopher Daniel; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshauser, Harald; Arbor, Nicolas; Arcelli, Silvia; Arend, Andreas; Armesto, Nestor; Arnaldi, Roberta; Aronsson, Tomas Robert; Arsene, Ionut Cristian; Arslandok, Mesut; Asryan, Andzhey; Augustinus, Andre; Averbeck, Ralf Peter; Awes, Terry; Aysto, Juha Heikki; Azmi, Mohd Danish; Bach, Matthias Jakob; Badala, Angela; Baek, Yong Wook; Bailhache, Raphaelle Marie; Bala, Renu; Baldini Ferroli, Rinaldo; Baldisseri, Alberto; Baldit, Alain; Baltasar Dos Santos Pedrosa, Fernando; Ban, Jaroslav; Baral, Rama Chandra; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Barret, Valerie; Bartke, Jerzy Gustaw; Basile, Maurizio; Bastid, Nicole; Bathen, Bastian; Batigne, Guillaume; Batyunya, Boris; Baumann, Christoph Heinrich; Bearden, Ian Gardner; Beck, Hans; Belikov, Iouri; Bellini, Francesca; Bellwied, Rene; Belmont-Moreno, Ernesto; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bergmann, Cyrano; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhati, Ashok Kumar; Bianchi, Nicola; Bianchi, Livio; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Blanco, Francesco; Blanco, F.; Blau, Dmitry; Blume, Christoph; Boccioli, Marco; Bock, Nicolas; Bogdanov, Alexey; Boggild, Hans; Bogolyubsky, Mikhail; Boldizsar, Laszlo; Bombara, Marek; Book, Julian; Borel, Herve; Borissov, Alexander; Bose, Suvendu Nath; Bossu, Francesco; Botje, Michiel; Bottger, Stefan; Boyer, Bruno Alexandre; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broz, Michal; Brun, Rene; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Bugaiev, Kyrylo; Busch, Oliver; Buthelezi, Edith Zinhle; Caballero Orduna, Diego; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calvo Villar, Ernesto; Camerini, Paolo; Canoa Roman, Veronica; Cara Romeo, Giovanni; Carena, Francesco; Carena, Wisla; Carlin Filho, Nelson; Carminati, Federico; Carrillo Montoya, Camilo Andres; Casanova Diaz, Amaya Ofelia; Caselle, Michele; Castillo Castellanos, Javier Ernesto; Castillo Hernandez, Juan Francisco; Casula, Ester Anna Rita; Catanescu, Vasile; Cavicchioli, Costanza; Cepila, Jan; Cerello, Piergiorgio; Chang, Beomsu; Chapeland, Sylvain; Charvet, Jean-Luc Fernand; Chattopadhyay, Sukalyan; Chattopadhyay, Subhasis; Cherney, Michael Gerard; Cheshkov, Cvetan; Cheynis, Brigitte; Chiavassa, Emilio; Chibante Barroso, Vasco Miguel; Chinellato, David; Chochula, Peter; Chojnacki, Marek; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Coccetti, Fabrizio; Colamaria, Fabio; Coffin, Jean-Pierre Michel; Conesa Balbastre, Gustavo; Conesa del Valle, Zaida; Constantin, Paul; Contin, Giacomo; Contreras, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortese, Pietro; Cortes Maldonado, Ismael; Cosentino, Mauro Rogerio; Costa, Filippo; Cotallo, Manuel Enrique; Crescio, Elisabetta; Crochet, Philippe; Cruz Alaniz, Emilia; Cuautle, Eleazar; Cunqueiro, Leticia; Dainese, Andrea; Dalsgaard, Hans Hjersing; Danu, Andrea; Das, Indranil; Das, Kushal; Das, Debasish; Dash, Sadhana; Dash, Ajay Kumar; De, Sudipan; De Azevedo Moregula, Andrea; de Barros, Gabriel; De Caro, Annalisa; de Cataldo, Giacinto; de Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; Delagrange, Hugues; Del Castillo Sanchez, Eduardo; Deloff, Andrzej; Demanov, Vyacheslav; De Marco, Nora; Denes, Ervin; De Pasquale, Salvatore; Deppman, Airton; D'Erasmo, Ginevra; de Rooij, Raoul Stefan; Di Bari, Domenico; Dietel, Thomas; Di Giglio, Carmelo; Di Liberto, Sergio; Di Mauro, Antonio; Di Nezza, Pasquale; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Dobrowolski, Tadeusz Antoni; Dominguez, Isabel; Donigus, Benjamin; Dordic, Olja; Driga, Olga; Dubey, Anand Kumar; Ducroux, Laurent; Dupieux, Pascal; Dutta Majumdar, A.K.; Dutta Majumdar, Mihir Ranjan; Elia, Domenico; Emschermann, David Philip; Engel, Heiko; Erdal, Hege Austrheim; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Evans, David; Eyyubova, Gyulnara; Fabris, Daniela; Faivre, Julien; Falchieri, Davide; Fantoni, Alessandra; Fasel, Markus; Fearick, Roger Worsley; Fedunov, Anatoly; Fehlker, Dominik; Feldkamp, Linus; Felea, Daniel; Feofilov, Grigory; Fernandez Tellez, Arturo; Ferretti, Roberta; Ferretti, Alessandro; Figiel, Jan; Figueredo, Marcel; Filchagin, Sergey; Fini, Rosa Ana; Fionda, Fiorella; Fiore, Enrichetta Maria; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Fragkiadakis, Michail; Frankenfeld, Ulrich Michael; Fuchs, Ulrich; Furget, Christophe; Fusco Girard, Mario; Gaardhoje, Jens Joergen; Gagliardi, Martino; Gago, Alberto; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Garabatos, Jose; Garcia-Solis, Edmundo; Garishvili, Irakli; Gerhard, Jochen; Germain, Marie; Geuna, Claudio; Gheata, Andrei George; Gheata, Mihaela; Ghidini, Bruno; Ghosh, Premomoy; Gianotti, Paola; Girard, Martin Robert; Giubellino, Paolo; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez, Ramon; Gonzalez Ferreiro, Elena; Gonzalez-Trueba, Laura Helena; Gonzalez-Zamora, Pedro; Gorbunov, Sergey; Goswami, Ankita; Gotovac, Sven; Grabski, Varlen; Graczykowski, Lukasz Kamil; Grajcarek, Robert; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoriev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grinyov, Boris; Grion, Nevio; Gros, Philippe; Grosse-Oetringhaus, Jan Fiete; Grossiord, Jean-Yves; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerra Gutierrez, Cesar; Guerzoni, Barbara; Guilbaud, Maxime Rene Joseph; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Gutbrod, Hans; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Han, Byounghee; Hanratty, Luke David; Hansen, Alexander; Harmanova, Zuzana; Harris, John William; Hartig, Matthias; Hasegan, Dumitru; Hatzifotiadou, Despoina; Hayrapetyan, Arsen; Heckel, Stefan Thomas; Heide, Markus Ansgar; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Herrmann, Norbert; Hetland, Kristin Fanebust; Hicks, Bernard; Hille, Per Thomas; Hippolyte, Boris; Horaguchi, Takuma; Hori, Yasuto; Hristov, Peter Zahariev; Hrivnacova, Ivana; Huang, Meidana; Huber, Sebastian Bernd; Humanic, Thomas; Hwang, Dae Sung; Ichou, Raphaelle; Ilkaev, Radiy; Ilkiv, Iryna; Inaba, Motoi; Incani, Elisa; Innocenti, Gian Michele; Innocenti, Pier Giorgio; Ippolitov, Mikhail; Irfan, Muhammad; Ivan, Cristian George; Ivanov, Marian; Ivanov, Andrey; Ivanov, Vladimir; Ivanytskyi, Oleksii; Jacholkowski, Adam Wlodzimierz; Jacobs, Peter; Jancurova, Lucia; Jang, Haeng Jin; Jangal, Swensy Gwladys; Janik, Malgorzata Anna; Janik, Rudolf; Jayarathna, Sandun; Jena, Satyajit; Jimenez Bustamante, Raul Tonatiuh; Jirden, Lennart; Jones, Peter Graham; Jung, Hyung Taik; Jung, Won Woong; Jusko, Anton; Kaidalov, Alexei; Kakoyan, Vanik; Kalcher, Sebastian; Kalinak, Peter; Kalisky, Matus; Kalliokoski, Tuomo Esa Aukusti; Kalweit, Alexander Philipp; Kanaki, Kalliopi; Kang, Ju Hwan; Kaplin, Vladimir; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karpechev, Evgeny; Kazantsev, Andrey; Kebschull, Udo Wolfgang; Keidel, Ralf; Khan, Shuaib Ahmad; Khan, Mohisin Mohammed; Khan, Palash; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Minwoo; Kim, Taesoo; Kim, Se Yong; Kim, Dong Jo; Kim, Jonghyun; Kim, Jin Sook; Kim, Seon Hee; Kim, Do Won; Kim, Beomkyu; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Klay, Jennifer Lynn; Klein, Jochen; Klein-Bosing, Christian; Kliemant, Michael; Kluge, Alexander; Knichel, Michael Linus; Koch, Kathrin; Kohler, Markus; Kolojvari, Anatoly; Kondratiev, Valery; Kondratyeva, Natalia; Konevskih, Artem; Korneev, Andrey; Kottachchi Kankanamge Don, Chamath; Kour, Ravjeet; Kowalski, Marek; Kox, Serge; Koyithatta Meethaleveedu, Greeshma; Kral, Jiri; Kralik, Ivan; Kramer, Frederick; Kraus, Ingrid Christine; Krawutschke, Tobias; Krelina, Michal; Kretz, Matthias; Krivda, Marian; Krizek, Filip; Krus, Miroslav; Kryshen, Evgeny; Krzewicki, Mikolaj; Kucheriaev, Yury; Kuhn, Christian Claude; Kuijer, Paul; Kurashvili, Podist; Kurepin, A.B.; Kurepin, A.; Kuryakin, Alexey; Kushpil, Vasily; Kushpil, Svetlana; Kvaerno, Henning; Kweon, Min Jung; Kwon, Youngil; Ladron de Guevara, Pedro; Lakomov, Igor; Langoy, Rune; Lara, Camilo Ernesto; Lardeux, Antoine Xavier; La Rocca, Paola; Lazzeroni, Cristina; Lea, Ramona; Le Bornec, Yves; Lee, Ki Sang; Lee, Sung Chul; Lefevre, Frederic; Lehnert, Joerg Walter; Leistam, Lars; Lenhardt, Matthieu Laurent; Lenti, Vito; Leon, Hermes; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Levai, Peter; Lien, Jorgen; Li, Xiaomei; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Liu, Lijiao; Loenne, Per-Ivar; Loggins, Vera; Loginov, Vitaly; Lohn, Stefan Bernhard; Lohner, Daniel; Loizides, Constantinos; Loo, Kai Krister; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lovhoiden, Gunnar; Lu, Xianguo; Luettig, Philipp; Lunardon, Marcello; Luo, Jiebin; Luparello, Grazia; Luquin, Lionel; Luzzi, Cinzia; Ma, Ke; Ma, Rongrong; Madagodahettige-Don, Dilan Minthaka; Maevskaya, Alla; Mager, Magnus; Mahapatra, Durga Prasad; Maire, Antonin; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Ludmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manceau, Loic Henri Antoine; Mangotra, Lalit Kumar; Manko, Vladislav; Manso, Franck; Manzari, Vito; Mao, Yaxian; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Marin, Ana Maria; Markert, Christina; Martashvili, Irakli; Martinengo, Paolo; Martinez, Mario Ivan; Martinez Davalos, Arnulfo; Martinez Garcia, Gines; Martynov, Yevgen; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Massacrier, Laure Marie; Mastromarco, Mario; Mastroserio, Annalisa; Matthews, Zoe Louise; Matyja, Adam Tomasz; Mayani, Daniel; Mayer, Christoph; Mazer, Joel; Mazzoni, Alessandra Maria; Meddi, Franco; Menchaca-Rocha, Arturo Alejandro; Mercado Perez, Jorge; Meres, Michal; Miake, Yasuo; Michalon, Alain; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz; Mitu, Ciprian Mihai; Mlynarz, Jocelyn; Mohanty, Bedangadas; Mohanty, Ajit Kumar; Molnar, Levente; Montano Zetina, Luis Manuel; Monteno, Marco; Montes, Esther; Moon, Taebong; Morando, Maurizio; Moreira De Godoy, Denise Aparecida; Moretto, Sandra; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhuri, Sanjib; Muller, Hans; Munhoz, Marcelo; Musa, Luciano; Musso, Alfredo; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Nattrass, Christine; Naumov, Nikolay; Navin, Sparsh; Nayak, Tapan Kumar; Nazarenko, Sergey; Nazarov, Gleb; Nedosekin, Alexander; Nicassio, Maria; Nielsen, Borge Svane; Niida, Takafumi; Nikolaev, Sergey; Nikolic, Vedran; Nikulin, Sergey; Nikulin, Vladimir; Nilsen, Bjorn Steven; Nilsson, Mads Stormo; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Novitzky, Norbert; Nyanin, Alexandre; Nyatha, Anitha; Nygaard, Casper; Nystrand, Joakim Ingemar; Ochirov, Alexander; Oeschler, Helmut Oskar; Oh, Sun Kun; Oh, Saehanseul; Oleniacz, Janusz; Oppedisano, Chiara; Ortiz Velasquez, Antonio; Ortona, Giacomo; Oskarsson, Anders Nils Erik; Ostrowski, Piotr Krystian; Otterlund, Ingvar; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozawa, Kyoichiro; Pachmayer, Yvonne Chiara; Pachr, Milos; Padilla, Fatima; Pagano, Paola; Paic, Guy; Painke, Florian; Pajares, Carlos; Pal, Susanta Kumar; Pal, S.; Palaha, Arvinder Singh; Palmeri, Armando; Papikyan, Vardanush; Pappalardo, Giuseppe; Park, Woo Jin; Passfeld, Annika; Pastircak, Blahoslav; Patalakha, Dmitri Ivanovich; Paticchio, Vincenzo; Pavlinov, Alexei; Pawlak, Tomasz Jan; Peitzmann, Thomas; Perales, Marianela; Pereira De Oliveira Filho, Elienos; Peresunko, Dmitri; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Perini, Diego; Perrino, Davide; Peryt, Wiktor Stanislaw; Pesci, Alessandro; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petran, Michal; Petris, Mariana; Petrov, Plamen Rumenov; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Piccotti, Anna; Pikna, Miroslav; Pillot, Philippe; Pinazza, Ombretta; Pinsky, Lawrence; Pitz, Nora; Piuz, Francois; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Pluta, Jan Marian; Pocheptsov, Timur; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polak, Karel; Polichtchouk, Boris; Pop, Amalia; Porteboeuf-Houssais, Sarah; Pospisil, Vladimir; Potukuchi, Baba; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puchagin, Sergey; Puddu, Giovanna; Pulvirenti, Alberto; Punin, Valery; Putis, Marian; Putschke, Jorn Henning; Quercigh, Emanuele; Qvigstad, Henrik; Rachevski, Alexandre; Rademakers, Alphonse; Radomski, Sylwester; Raiha, Tomi Samuli; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Ramirez Reyes, Abdiel; Raniwala, Sudhir; Raniwala, Rashmi; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Real, Jean-Sebastien; Redlich, Krzysztof; Reichelt, Patrick; Reicher, Martijn; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Rettig, Felix Vincenz; Revol, Jean-Pierre; Reygers, Klaus Johannes; Riccati, Lodovico; Ricci, Renato Angelo; Richert, Tuva; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Rodriguez Cahuantzi, Mario; Roed, Ketil; Rohr, David; Rohrich, Dieter; Romita, Rosa; Ronchetti, Federico; Rosnet, Philippe; Rossegger, Stefan; Rossi, Andrea; Roukoutakis, Filimon; Roy, Pradip Kumar; Roy, Christelle Sophie; Rubio Montero, Antonio Juan; Rui, Rinaldo; Ryabinkin, Evgeny; Rybicki, Andrzej; Sadovsky, Sergey; Safarik, Karel; Sahu, Pradip Kumar; Saini, Jogender; Sakaguchi, Hiroaki; Sakai, Shingo; Sakata, Dosatsu; Salgado, Carlos Albert; Salzwedel, Jai; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sanchez Castro, Xitzel; Sandor, Ladislav; Sandoval, Andres; Sano, Satoshi; Sano, Masato; Santo, Rainer; Santoro, Romualdo; Sarkamo, Juho Jaako; Scapparone, Eugenio; Scarlassara, Fernando; Scharenberg, Rolf Paul; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schreiner, Steffen; Schuchmann, Simone; Schukraft, Jurgen; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Patrick Aaron; Scott, Rebecca; Segato, Gianfranco; Selyuzhenkov, Ilya; Senyukov, Serhiy; Seo, Jeewon; Serci, Sergio; Serradilla, Eulogio; Sevcenco, Adrian; Sgura, Irene; Shabetai, Alexandre; Shabratova, Galina; Shahoyan, Ruben; Sharma, Natasha; Sharma, Satish; Shigaki, Kenta; Shimomura, Maya; Shtejer, Katherin; Sibiriak, Yury; Siciliano, Melinda; Sicking, Eva; Siddhanta, Sabyasachi; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Sinha, Bikash; Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Skjerdal, Kyrre; Smakal, Radek; Smirnov, Nikolai; Snellings, Raimond; Sogaard, Carsten; Soltz, Ron Ariel; Son, Hyungsuk; Song, Jihye; Song, Myunggeun; Soos, Csaba; Soramel, Francesca; Sputowska, Iwona; Spyropoulou-Stassinaki, Martha; Srivastava, Brijesh Kumar; Stachel, Johanna; Stan, Ionel; Stefanek, Grzegorz; Stefanini, Giorgio; Steinbeck, Timm Morten; Steinpreis, Matthew; Stenlund, Evert Anders; Steyn, Gideon Francois; Stocco, Diego; Stolpovskiy, Mikhail; Strabykin, Kirill; Strmen, Peter; Suaide, Alexandre Alarcon do Passo; Subieta Vasquez, Martin Alfonso; Sugitate, Toru; Suire, Christophe Pierre; Sukhorukov, Mikhail; Sultanov, Rishat; Sumbera, Michal; Susa, Tatjana; Szanto de Toledo, Alejandro; Szarka, Imrich; Szostak, Artur Krzysztof; Tagridis, Christos; Takahashi, Jun; Tapia Takaki, Daniel Jesus; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terrevoli, Cristina; Thader, Jochen Mathias; Thomas, Deepa; Thomas, Jim; Tieulent, Raphael Noel; Timmins, Anthony; Tlusty, David; Toia, Alberica; Torii, Hisayuki; Toscano, Luca; Tosello, Flavio; Traczyk, Tomasz; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ulery, Jason Glyndwr; Ullaland, Kjetil; Ulrich, Jochen; Uras, Antonio; Urban, Jozef; Urciuoli, Guido Marie; Usai, Gianluca; Vajzer, Michal; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; van der Kolk, Naomi; Vande Vyvre, Pierre; van Leeuwen, Marco; Vannucci, Luigi; Vargas, Aurora Diozcora; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vechernin, Vladimir; Veldhoen, Misha; Venaruzzo, Massimo; Vercellin, Ermanno; Vergara, Sergio; Vernekohl, Don Constantin; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Vikhlyantsev, Oleg; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Viyogi, Yogendra; Vodopianov, Alexander; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; von Haller, Barthelemy; Vranic, Danilo; Øvrebekk, Gaute; Vrlakova, Janka; Vulpescu, Bogdan; Vyushin, Alexey; Wagner, Boris; Wagner, Vladimir; Wan, Renzhuo; Wang, Dong; Wang, Mengliang; Wang, Yifei; Wang, Yaping; Watanabe, Kengo; Wessels, Johannes; Westerhoff, Uwe; Wiechula, Jens; Wikne, Jon; Wilde, Martin Rudolf; Wilk, Grzegorz Andrzej; Wilk, Alexander; Williams, Crispin; Windelband, Bernd Stefan; Xaplanteris Karampatsos, Leonidas; Yang, Hongyan; Yang, Shiming; Yasnopolsky, Stanislav; Yi, JunGyu; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yoon, Jongik; Yu, Weilin; Yuan, Xianbao; Yushmanov, Igor; Zach, Cenek; Zampolli, Chiara; Zaporozhets, Sergey; Zarochentsev, Andrey; Zavada, Petr; Zaviyalov, Nikolai; Zbroszczyk, Hanna Paulina; Zelnicek, Pierre; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Xiaoming; Zhou, You; Zhou, Daicui; Zhou, Fengchu; Zhu, Xiangrong; Zichichi, Antonino; Zimmermann, Alice; Zinovjev, Gennady; Zoccarato, Yannick Denis; Zynovyev, Mykhaylo

    2012-01-01

    The effect of event background fluctuations on charged particle jet reconstruction in Pb-Pb collisions at $\\sqrt{s_{NN}}$ = 2.76 TeV has been measured with the ALICE experiment. The main sources of non-statistical fluctuations are characterized based purely on experimental data with an unbiased method, as well as by using single high p_t particles and simulated jets embedded into real Pb-Pb events and reconstructed with the anti-kt jet finder. The influence of a low transverse momentum cut-off on particles used in the jet reconstruction is quantified by varying the minimum track p_t between 0.15 GeV/c and 2 GeV/c. For embedded jets reconstructed from charged particles with $p_t$ > 0.15 GeV/c, the uncertainty in the reconstructed jet transverse momentum due to the heavy-ion background is measured to be 11.3 GeV/c (standard deviation) for the 10% most central Pb-Pb collisions, slightly larger than the value of 11.0 GeV/c measured using the unbiased method. For a higher particle transverse momentum threshold of ...

  14. Identification of the high pt jet events produced by a resolved photon at HERA and reconstruction of the initial state parton kinematics

    International Nuclear Information System (INIS)

    D'Agostini, G.; Monaldi, D.

    1992-01-01

    We have studied the possibility offered by the HERA detectors to identify the events where a proton interacts with a parton of the (quasi) real photon. We find that the presence of hadronic fragments of the photon outside of the beam pipe allows the identification of the two jet events produced by a resolved photon, with good efficiency and low background from the direct photon events. We show that it is also possible to reconstruct the fractional momenta of the two incoming partons. (orig.)

  15. Service and Data Driven Multi Business Model Platform in a World of Persuasive Technologies

    DEFF Research Database (Denmark)

    Andersen, Troels Christian; Bjerrum, Torben Cæsar Bisgaard

    2016-01-01

    companies in establishing a service organization that delivers, creates and captures value through service and data driven business models by utilizing their network, resources and customers and/or users. Furthermore, based on literature and collaboration with the case company, the suggestion of a new...... framework provides the necessary construction of how the manufac- turing companies can evolve their current business to provide multi service and data driven business models, using the same resources, networks and customers....

  16. Data-Driven Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning

    OpenAIRE

    Akkaya, Ilge

    2016-01-01

    Emerging distributed cyber-physical systems (CPSs) integrate a wide range of heterogeneous components that need to be orchestrated in a dynamic environment. While model-based techniques are commonly used in CPS design, they be- come inadequate in capturing the complexity as systems become larger and extremely dynamic. The adaptive nature of the systems makes data-driven approaches highly desirable, if not necessary.Traditionally, data-driven systems utilize large volumes of static data sets t...

  17. Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

    Directory of Open Access Journals (Sweden)

    Roerdink Jos BTM

    2008-04-01

    Full Text Available Abstract Background We present a simple, data-driven method to extract haemodynamic response functions (HRF from functional magnetic resonance imaging (fMRI time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD technique. HRF data are required for many fMRI applications, such as defining region-specific HRFs, effciently representing a general HRF, or comparing subject-specific HRFs. Results ForWaRD is applied to fMRI time signals, after removing low-frequency trends by a wavelet-based method, and the output of ForWaRD is a time series of volumes, containing the HRF in each voxel. Compared to more complex methods, this extraction algorithm requires few assumptions (separability of signal and noise in the frequency and wavelet domains and the general linear model and it is fast (HRF extraction from a single fMRI data set takes about the same time as spatial resampling. The extraction method is tested on simulated event-related activation signals, contaminated with noise from a time series of real MRI images. An application for HRF data is demonstrated in a simple event-related experiment: data are extracted from a region with significant effects of interest in a first time series. A continuous-time HRF is obtained by fitting a nonlinear function to the discrete HRF coeffcients, and is then used to analyse a later time series. Conclusion With the parameters used in this paper, the extraction method presented here is very robust to changes in signal properties. Comparison of analyses with fitted HRFs and with a canonical HRF shows that a subject-specific, regional HRF significantly improves detection power. Sensitivity and specificity increase not only in the region from which the HRFs are extracted, but also in other regions of interest.

  18. Fast parallel event reconstruction

    CERN Multimedia

    CERN. Geneva

    2010-01-01

    On-line processing of large data volumes produced in modern HEP experiments requires using maximum capabilities of modern and future many-core CPU and GPU architectures.One of such powerful feature is a SIMD instruction set, which allows packing several data items in one register and to operate on all of them, thus achievingmore operations per clock cycle. Motivated by the idea of using the SIMD unit ofmodern processors, the KF based track fit has been adapted for parallelism, including memory optimization, numerical analysis, vectorization with inline operator overloading, and optimization using SDKs. The speed of the algorithm has been increased in 120000 times with 0.1 ms/track, running in parallel on 16 SPEs of a Cell Blade computer.  Running on a Nehalem CPU with 8 cores it shows the processing speed of 52 ns/track using the Intel Threading Building Blocks. The same KF algorithm running on an Nvidia GTX 280 in the CUDA frameworkprovi...

  19. Simulating the energy deposits of particles in the KASCADE-grande detector stations as a preliminary step for EAS event reconstruction

    International Nuclear Information System (INIS)

    Toma, G.; Brancus, I.M.; Mitrica, B.; Sima, O.; Rebel, H.; Haungs, A.

    2005-01-01

    The study of primary cosmic rays with energies higher than 10 14 eV is done mostly by indirect observation techniques such as the study of Extensive Air Showers (EAS). In the much larger framework effort of inferring data on the mass and energy of the primaries from EAS observables, the present study aims at developing a versatile method and software tool that will be used to reconstruct lateral particle densities from the energy deposits of particles in the KASCADE-Grande detector stations. The study has been performed on simulated events, by taking into account the interaction of the EAS components with the detector array (energy deposits). The energy deposits have been simulated using the GEANT code and then the energy deposits have been parametrized for different incident energies and angles of EAS particles. Thus the results obtained for simulated events have the same level of consistency as the experimental data. This technique will allow an increased speed of lateral particle density reconstruction when studying real events detected by the KASCADE-Grande array. The particle densities in detectors have been reconstructed from the energy deposits. A correlation between lateral particle density and primary mass and primary energy (at ∼600 m from shower core) has been established. The study puts great emphasis on the quality of reconstruction and also on the speed of the technique. The data obtained from the study on simulated events creates the basis for the next stage of the study, the study of real events detected by the KASCADE-Grande array. (authors)

  20. Weather models as virtual sensors to data-driven rainfall predictions in urban watersheds

    Science.gov (United States)

    Cozzi, Lorenzo; Galelli, Stefano; Pascal, Samuel Jolivet De Marc; Castelletti, Andrea

    2013-04-01

    Weather and climate predictions are a key element of urban hydrology where they are used to inform water management and assist in flood warning delivering. Indeed, the modelling of the very fast dynamics of urbanized catchments can be substantially improved by the use of weather/rainfall predictions. For example, in Singapore Marina Reservoir catchment runoff processes have a very short time of concentration (roughly one hour) and observational data are thus nearly useless for runoff predictions and weather prediction are required. Unfortunately, radar nowcasting methods do not allow to carrying out long - term weather predictions, whereas numerical models are limited by their coarse spatial scale. Moreover, numerical models are usually poorly reliable because of the fast motion and limited spatial extension of rainfall events. In this study we investigate the combined use of data-driven modelling techniques and weather variables observed/simulated with a numerical model as a way to improve rainfall prediction accuracy and lead time in the Singapore metropolitan area. To explore the feasibility of the approach, we use a Weather Research and Forecast (WRF) model as a virtual sensor network for the input variables (the states of the WRF model) to a machine learning rainfall prediction model. More precisely, we combine an input variable selection method and a non-parametric tree-based model to characterize the empirical relation between the rainfall measured at the catchment level and all possible weather input variables provided by WRF model. We explore different lead time to evaluate the model reliability for different long - term predictions, as well as different time lags to see how past information could improve results. Results show that the proposed approach allow a significant improvement of the prediction accuracy of the WRF model on the Singapore urban area.

  1. Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator.

    Science.gov (United States)

    Li, Qianxiao; Dietrich, Felix; Bollt, Erik M; Kevrekidis, Ioannis G

    2017-10-01

    Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD) 51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.

  2. Data-driven design of fault diagnosis and fault-tolerant control systems

    CERN Document Server

    Ding, Steven X

    2014-01-01

    Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods, and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and...

  3. Observer and data-driven model based fault detection in Power Plant Coal Mills

    DEFF Research Database (Denmark)

    Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

    2008-01-01

    model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault......This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles...

  4. Data-driven remaining useful life prognosis techniques stochastic models, methods and applications

    CERN Document Server

    Si, Xiao-Sheng; Hu, Chang-Hua

    2017-01-01

    This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based pro...

  5. Events

    Directory of Open Access Journals (Sweden)

    Igor V. Karyakin

    2016-02-01

    Full Text Available The 9th ARRCN Symposium 2015 was held during 21st–25th October 2015 at the Novotel Hotel, Chumphon, Thailand, one of the most favored travel destinations in Asia. The 10th ARRCN Symposium 2017 will be held during October 2017 in the Davao, Philippines. International Symposium on the Montagu's Harrier (Circus pygargus «The Montagu's Harrier in Europe. Status. Threats. Protection», organized by the environmental organization «Landesbund für Vogelschutz in Bayern e.V.» (LBV was held on November 20-22, 2015 in Germany. The location of this event was the city of Wurzburg in Bavaria.

  6. An Interactive Platform to Visualize Data-Driven Clinical Pathways for the Management of Multiple Chronic Conditions.

    Science.gov (United States)

    Zhang, Yiye; Padman, Rema

    2017-01-01

    Patients with multiple chronic conditions (MCC) pose an increasingly complex health management challenge worldwide, particularly due to the significant gap in our understanding of how to provide coordinated care. Drawing on our prior research on learning data-driven clinical pathways from actual practice data, this paper describes a prototype, interactive platform for visualizing the pathways of MCC to support shared decision making. Created using Python web framework, JavaScript library and our clinical pathway learning algorithm, the visualization platform allows clinicians and patients to learn the dominant patterns of co-progression of multiple clinical events from their own data, and interactively explore and interpret the pathways. We demonstrate functionalities of the platform using a cluster of 36 patients, identified from a dataset of 1,084 patients, who are diagnosed with at least chronic kidney disease, hypertension, and diabetes. Future evaluation studies will explore the use of this platform to better understand and manage MCC.

  7. Tree-ring based reconstruction of the seasonal timing, major events and origin of rockfall on a case-study slope in the Swiss Alps

    Directory of Open Access Journals (Sweden)

    D. M. Schneuwly

    2008-03-01

    Full Text Available Tree-ring analysis has been used to reconstruct 22 years of rockfall behavior on an active rockfall slope near Saas Balen (Swiss Alps. We analyzed 32 severely injured trees (L. decidua, P. abies and P. cembra and investigated cross-sections of 154 wounds.

    The intra-annual position of callus tissue and of tangential rows of traumatic resin ducts was determined in order to reconstruct the seasonality of past rockfall events. Results indicate strong intra- and inter-annual variations of rockfall activity, with a peak (76% observed in the dormant season (early October – end of May. Within the growth season, rockfall regularly occurs between the end of May and mid July (21.4%, whereas events later in the season appear to be quite rare (2.6%. Findings suggest that rockfall activity at the study site is driven by annual thawing processes and the circulation of melt water in preexisting fissures. Data also indicate that 43% of all rockfall events occurred in 1995, when two major precipitation events are recorded in nearby meteorological stations. Finally, data on impact angles are in very good agreement with the geomorphic situation in the field.

  8. Neutrino interaction event reconstruction and analysis in the Opera emulsion targets and charmed background rejection in the τ → 3h channel

    International Nuclear Information System (INIS)

    Besnier, M.

    2008-07-01

    OPERA (Gran-Sasso, Italy) is a long-baseline neutrino experiment dedicated to the tau neutrino detection in a pure muon neutrino beam, produced at CERN (730 km away). The main goal is to observe the ν μ → ν τ oscillation. The experiment uses a hybrid technology with electronic detectors and target blocks made of lead plates interleaved with nuclear emulsion sheets, in order to sign efficiently the ν τ interactions. The fundamental features of OPERA are its particle reconstruction performances achieved in spatial and angular resolutions. The first studies done in this thesis concern the development of several analysis tools like the particle momentum determination using the multiple coulomb scattering in target, and a method of neutrino interaction reconstruction for multi-vertex events. Then it has been possible to develop a multivariable analysis in order to separate τ and charmed events in the τ decay channel into three charged hadrons. These tools have been tested with neutrino interactions observed first with the OPERA test-beam called PEANUT, then with the OPERA events accumulated during the first CNGS (CERN Neutrino to Gran Sasso) run in 2007. The combined analysis of these events has shown that both the analysis method and the OPERA detector behaviour are well understood. (author)

  9. A novel String Banana Template Method for Tracks Reconstruction in High Multiplicity Events with significant Multiple Scattering and its Firmware Implementation

    CERN Document Server

    Kulinich, P; Krylov, V

    2004-01-01

    Novel String Banana Template Method (SBTM) for track reconstruction in difficult conditions is proposed and implemented for off-line analysis of relativistic heavy ion collision events. The main idea of the method is in use of features of ensembles of tracks selected by 3-fold coincidence. Two steps model of track is used: the first one - averaged over selected ensemble and the second - per event dependent. It takes into account Multiple Scattering (MS) for this particular track. SBTM relies on use of stored templates generated by precise Monte Carlo simulation, so it's more time efficient for the case of 2D spectrometer. All data required for track reconstruction in such difficult conditions could be prepared in convenient format for fast use. Its template based nature and the fact that the SBTM track model is actually very close to the hits implies that it can be implemented in a firmware processor. In this report a block diagram of firmware based pre-processor for track reconstruction in CMS-like Si tracke...

  10. Robust Data-Driven Inference for Density-Weighted Average Derivatives

    DEFF Research Database (Denmark)

    Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael

    This paper presents a new data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density- weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error...

  11. Ability Grouping and Differentiated Instruction in an Era of Data-Driven Decision Making

    Science.gov (United States)

    Park, Vicki; Datnow, Amanda

    2017-01-01

    Despite data-driven decision making being a ubiquitous part of policy and school reform efforts, little is known about how teachers use data for instructional decision making. Drawing on data from a qualitative case study of four elementary schools, we examine the logic and patterns of teacher decision making about differentiation and ability…

  12. Data-driven diagnostics of terrestrial carbon dynamics over North America

    Science.gov (United States)

    Jingfeng Xiao; Scott V. Ollinger; Steve Frolking; George C. Hurtt; David Y. Hollinger; Kenneth J. Davis; Yude Pan; Xiaoyang Zhang; Feng Deng; Jiquan Chen; Dennis D. Baldocchi; Bevery E. Law; M. Altaf Arain; Ankur R. Desai; Andrew D. Richardson; Ge Sun; Brian Amiro; Hank Margolis; Lianhong Gu; Russell L. Scott; Peter D. Blanken; Andrew E. Suyker

    2014-01-01

    The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale...

  13. Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

    NARCIS (Netherlands)

    Wink, Alle Meije; Hoogduin, Hans; Roerdink, Jos B.T.M.

    2008-01-01

    Background: We present a simple, data-driven method to extract haemodynamic response functions (HRF) from functional magnetic resonance imaging (fMRI) time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD) technique. HRF data are required for many fMRI applications, such as

  14. Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

    NARCIS (Netherlands)

    Wink, Alle Meije; Hoogduin, Hans; Roerdink, Jos B.T.M.

    2010-01-01

    Background: We present a simple, data-driven method to extract haemodynamic response functions (HRF) from functional magnetic resonance imaging (fMRI) time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD) technique. HRF data are required for many fMRI applications, such as

  15. Perspectives of data-driven LPV modeling of high-purity distillation columns

    NARCIS (Netherlands)

    Bachnas, A.A.; Toth, R.; Mesbah, A.; Ludlage, J.H.A.

    2013-01-01

    Abstract—This paper investigates data-driven, Linear- Parameter-Varying (LPV) modeling of a high-purity distillation column. Two LPV modeling approaches are studied: a local approach, corresponding to the interpolation of Linear Time- Invariant (LTI) models identified at steady-state purity levels,

  16. The Role of Guided Induction in Paper-Based Data-Driven Learning

    Science.gov (United States)

    Smart, Jonathan

    2014-01-01

    This study examines the role of guided induction as an instructional approach in paper-based data-driven learning (DDL) in the context of an ESL grammar course during an intensive English program at an American public university. Specifically, it examines whether corpus-informed grammar instruction is more effective through inductive, data-driven…

  17. Design and evaluation of a data-driven scenario generation framework for game-based training

    NARCIS (Netherlands)

    Luo, L.; Yin, H.; Cai, W.; Zhong, J.; Lees, M.

    Generating suitable game scenarios that can cater for individual players has become an emerging challenge in procedural content generation. In this paper, we propose a data-driven scenario generation framework for game-based training. An evolutionary scenario generation process is designed with a

  18. Data-driven Development of ROTEM and TEG Algorithms for the Management of Trauma Hemorrhage

    DEFF Research Database (Denmark)

    Baksaas-Aasen, Kjersti; Van Dieren, Susan; Balvers, Kirsten

    2018-01-01

    for ROTEM, TEG, and CCTs to be used in addition to ratio driven transfusion and tranexamic acid. CONCLUSIONS: We describe a systematic approach to define threshold parameters for ROTEM and TEG. These parameters were incorporated into algorithms to support data-driven adjustments of resuscitation...

  19. Teacher Talk about Student Ability and Achievement in the Era of Data-Driven Decision Making

    Science.gov (United States)

    Datnow, Amanda; Choi, Bailey; Park, Vicki; St. John, Elise

    2018-01-01

    Background: Data-driven decision making continues to be a common feature of educational reform agendas across the globe. In many U.S. schools, the teacher team meeting is a key setting in which data use is intended to take place, with the aim of planning instruction to address students' needs. However, most prior research has not examined how the…

  20. Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities.

    Science.gov (United States)

    Del Sol, Antonio; Thiesen, Hans J; Imitola, Jaime; Carazo Salas, Rafael E

    2017-02-02

    Achieving the promises of stem cell science to generate precise disease models and designer cell samples for personalized therapeutics will require harnessing pheno-genotypic cell-level data quantitatively and predictively in the lab and clinic. Those requirements could be met by developing a Big-Data-driven stem cell science strategy and community. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Exploring Techniques of Developing Writing Skill in IELTS Preparatory Courses: A Data-Driven Study

    Science.gov (United States)

    Ostovar-Namaghi, Seyyed Ali; Safaee, Seyyed Esmail

    2017-01-01

    Being driven by the hypothetico-deductive mode of inquiry, previous studies have tested the effectiveness of theory-driven interventions under controlled experimental conditions to come up with universally applicable generalizations. To make a case in the opposite direction, this data-driven study aims at uncovering techniques and strategies…

  2. A framework for the automated data-driven constitutive characterization of composites

    Science.gov (United States)

    J.G. Michopoulos; John Hermanson; T. Furukawa; A. Iliopoulos

    2010-01-01

    We present advances on the development of a mechatronically and algorithmically automated framework for the data-driven identification of constitutive material models based on energy density considerations. These models can capture both the linear and nonlinear constitutive response of multiaxially loaded composite materials in a manner that accounts for progressive...

  3. Writing through Big Data: New Challenges and Possibilities for Data-Driven Arguments

    Science.gov (United States)

    Beveridge, Aaron

    2017-01-01

    As multimodal writing continues to shift and expand in the era of Big Data, writing studies must confront the new challenges and possibilities emerging from data mining, data visualization, and data-driven arguments. Often collected under the broad banner of "data literacy," students' experiences of data visualization and data-driven…

  4. Data-driven directions for effective footwear provision for the high-risk diabetic foot

    NARCIS (Netherlands)

    Arts, M. L. J.; de Haart, M.; Waaijman, R.; Dahmen, R.; Berendsen, H.; Nollet, F.; Bus, S. A.

    2015-01-01

    Custom-made footwear is used to offload the diabetic foot to prevent plantar foot ulcers. This prospective study evaluates the offloading effects of modifying custom-made footwear and aims to provide data-driven directions for the provision of effectively offloading footwear in clinical practice.

  5. Toward Data-Driven Design of Educational Courses: A Feasibility Study

    Science.gov (United States)

    Agrawal, Rakesh; Golshan, Behzad; Papalexakis, Evangelos

    2016-01-01

    A study plan is the choice of concepts and the organization and sequencing of the concepts to be covered in an educational course. While a good study plan is essential for the success of any course offering, the design of study plans currently remains largely a manual task. We present a novel data-driven method, which given a list of concepts can…

  6. Retesting the Limits of Data-Driven Learning: Feedback and Error Correction

    Science.gov (United States)

    Crosthwaite, Peter

    2017-01-01

    An increasing number of studies have looked at the value of corpus-based data-driven learning (DDL) for second language (L2) written error correction, with generally positive results. However, a potential conundrum for language teachers involved in the process is how to provide feedback on students' written production for DDL. The study looks at…

  7. Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach

    Science.gov (United States)

    Wang, Jun; Green, Jordan R.; Samal, Ashok; Yunusova, Yana

    2013-01-01

    Purpose: To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach. Method: Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were

  8. Data-Driven Hint Generation in Vast Solution Spaces: A Self-Improving Python Programming Tutor

    Science.gov (United States)

    Rivers, Kelly; Koedinger, Kenneth R.

    2017-01-01

    To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not…

  9. A Data-Driven Noise Reduction Method and Its Application for the Enhancement of Stress Wave Signals

    Directory of Open Access Journals (Sweden)

    Hai-Lin Feng

    2012-01-01

    Full Text Available Ensemble empirical mode decomposition (EEMD has been recently used to recover a signal from observed noisy data. Typically this is performed by partial reconstruction or thresholding operation. In this paper we describe an efficient noise reduction method. EEMD is used to decompose a signal into several intrinsic mode functions (IMFs. The time intervals between two adjacent zero-crossings within the IMF, called instantaneous half period (IHP, are used as a criterion to detect and classify the noise oscillations. The undesirable waveforms with a larger IHP are set to zero. Furthermore, the optimum threshold in this approach can be derived from the signal itself using the consecutive mean square error (CMSE. The method is fully data driven, and it requires no prior knowledge of the target signals. This method can be verified with the simulative program by using Matlab. The denoising results are proper. In comparison with other EEMD based methods, it is concluded that the means adopted in this paper is suitable to preprocess the stress wave signals in the wood nondestructive testing.

  10. An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development

    KAUST Repository

    Douglas, Craig

    2014-01-01

    In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. A DDDAS is an application that has data assimilation that can change the models and/or scales of the computation and that the application controls the data collection based on the computational results. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

  11. Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

    Directory of Open Access Journals (Sweden)

    Grzegorz Żak

    2017-01-01

    Full Text Available The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, α-stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.

  12. Data Driven Modelling of the Dynamic Wake Between Two Wind Turbines

    DEFF Research Database (Denmark)

    Knudsen, Torben; Bak, Thomas

    2012-01-01

    turbine. This paper establishes flow models relating the wind speeds at turbines in a farm. So far, research in this area has been mainly based on first principles static models and the data driven modelling done has not included the loading of the upwind turbine and its impact on the wind speed downwind......Wind turbines in a wind farm, influence each other through the wind flow. Downwind turbines are in the wake of upwind turbines and the wind speed experienced at downwind turbines is hence a function of the wind speeds at upwind turbines but also the momentum extracted from the wind by the upwind....... This paper is the first where modern commercial mega watt turbines are used for data driven modelling including the upwind turbine loading by changing power reference. Obtaining the necessary data is difficult and data is therefore limited. A simple dynamic extension to the Jensen wake model is tested...

  13. Pipe break prediction based on evolutionary data-driven methods with brief recorded data

    International Nuclear Information System (INIS)

    Xu Qiang; Chen Qiuwen; Li Weifeng; Ma Jinfeng

    2011-01-01

    Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe's probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency.

  14. Data-driven modeling and real-time distributed control for energy efficient manufacturing systems

    International Nuclear Information System (INIS)

    Zou, Jing; Chang, Qing; Arinez, Jorge; Xiao, Guoxian

    2017-01-01

    As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. In this paper, a novel data-driven stochastic manufacturing system modeling method is proposed to identify and predict energy saving opportunities and their impact on production. A real-time distributed feedback production control policy, which integrates the current and predicted system performance, is established to improve the overall profit and energy efficiency. A case study is presented to demonstrate the effectiveness of the proposed control policy. - Highlights: • A data-driven stochastic manufacturing system model is proposed. • Real-time system performance and energy saving opportunity identification method is developed. • Prediction method for future potential system performance and energy saving opportunity is developed. • A real-time distributed feedback control policy is established to improve energy efficiency and overall system profit.

  15. An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development

    KAUST Repository

    Douglas, Craig

    2014-06-06

    In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. A DDDAS is an application that has data assimilation that can change the models and/or scales of the computation and that the application controls the data collection based on the computational results. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

  16. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

    OpenAIRE

    Rui Sun; Qi Cheng; Guanyu Wang; Washington Yotto Ochieng

    2017-01-01

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in ...

  17. Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains

    OpenAIRE

    Sethi, Tegjyot Singh; Kantardzic, Mehmed

    2017-01-01

    While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication of attacks, has prompted the need for a data driven solution, with machine learning forming the core of many cybersecurity systems. Machine learning was not designed with security in mind, and the essential assumption of stationarity, requiri...

  18. Data Driven Marketing in Apple and Back to School Campaign 2011

    OpenAIRE

    Bernátek, Martin

    2011-01-01

    Out of the campaign analysis the most important contribution is that Data-Driven Marketing makes sense only once it is already part of the marketing plan. So the team preparing the marketing plan defines the goals and sets the proper measurement matrix according to those goals. It enables to adjust the marketing plan to extract more value, watch the execution and do adjustments if necessary and evaluate at the end of the campaign.

  19. Data-driven automatic parking constrained control for four-wheeled mobile vehicles

    OpenAIRE

    Wenxu Yan; Jing Deng; Dezhi Xu

    2016-01-01

    In this article, a novel data-driven constrained control scheme is proposed for automatic parking systems. The design of the proposed scheme only depends on the steering angle and the orientation angle of the car, and it does not involve any model information of the car. Therefore, the proposed scheme-based automatic parking system is applicable to different kinds of cars. In order to further reduce the desired trajectory coordinate tracking errors, a coordinates compensation algorithm is als...

  20. A data-driven approach for retrieving temperatures and abundances in brown dwarf atmospheres

    OpenAIRE

    Line, MR; Fortney, JJ; Marley, MS; Sorahana, S

    2014-01-01

    © 2014. The American Astronomical Society. All rights reserved. Brown dwarf spectra contain a wealth of information about their molecular abundances, temperature structure, and gravity. We present a new data driven retrieval approach, previously used in planetary atmosphere studies, to extract the molecular abundances and temperature structure from brown dwarf spectra. The approach makes few a priori physical assumptions about the state of the atmosphere. The feasibility of the approach is fi...

  1. Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis

    Directory of Open Access Journals (Sweden)

    Ágatha Nogueira Previdelli

    2016-09-01

    Full Text Available The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis. Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information; thus, we aimed to use both approaches to gain knowledge of adolescents’ dietary patterns. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR. In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits, while, in data-driven analysis, fruits and whole grains were not presented in any pattern. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations.

  2. Data-Driven and Expectation-Driven Discovery of Empirical Laws.

    Science.gov (United States)

    1982-10-10

    occurred in small integer proportions to each other. In 1809, Joseph Gay- Lussac found evidence for his law of combining volumes, which stated that a...of Empirical Laws Patrick W. Langley Gary L. Bradshaw Herbert A. Simon T1he Robotics Institute Carnegie-Mellon University Pittsburgh, Pennsylvania...Subtitle) S. TYPE OF REPORT & PERIOD COVERED Data-Driven and Expectation-Driven Discovery Interim Report 2/82-10/82 of Empirical Laws S. PERFORMING ORG

  3. Multivariate modeling of complications with data driven variable selection: Guarding against overfitting and effects of data set size

    International Nuclear Information System (INIS)

    Schaaf, Arjen van der; Xu Chengjian; Luijk, Peter van; Veld, Aart A. van’t; Langendijk, Johannes A.; Schilstra, Cornelis

    2012-01-01

    Purpose: Multivariate modeling of complications after radiotherapy is frequently used in conjunction with data driven variable selection. This study quantifies the risk of overfitting in a data driven modeling method using bootstrapping for data with typical clinical characteristics, and estimates the minimum amount of data needed to obtain models with relatively high predictive power. Materials and methods: To facilitate repeated modeling and cross-validation with independent datasets for the assessment of true predictive power, a method was developed to generate simulated data with statistical properties similar to real clinical data sets. Characteristics of three clinical data sets from radiotherapy treatment of head and neck cancer patients were used to simulate data with set sizes between 50 and 1000 patients. A logistic regression method using bootstrapping and forward variable selection was used for complication modeling, resulting for each simulated data set in a selected number of variables and an estimated predictive power. The true optimal number of variables and true predictive power were calculated using cross-validation with very large independent data sets. Results: For all simulated data set sizes the number of variables selected by the bootstrapping method was on average close to the true optimal number of variables, but showed considerable spread. Bootstrapping is more accurate in selecting the optimal number of variables than the AIC and BIC alternatives, but this did not translate into a significant difference of the true predictive power. The true predictive power asymptotically converged toward a maximum predictive power for large data sets, and the estimated predictive power converged toward the true predictive power. More than half of the potential predictive power is gained after approximately 200 samples. Our simulations demonstrated severe overfitting (a predicative power lower than that of predicting 50% probability) in a number of small

  4. Data-driven non-linear elasticity: constitutive manifold construction and problem discretization

    Science.gov (United States)

    Ibañez, Ruben; Borzacchiello, Domenico; Aguado, Jose Vicente; Abisset-Chavanne, Emmanuelle; Cueto, Elias; Ladeveze, Pierre; Chinesta, Francisco

    2017-11-01

    The use of constitutive equations calibrated from data has been implemented into standard numerical solvers for successfully addressing a variety problems encountered in simulation-based engineering sciences (SBES). However, the complexity remains constantly increasing due to the need of increasingly detailed models as well as the use of engineered materials. Data-Driven simulation constitutes a potential change of paradigm in SBES. Standard simulation in computational mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,\\ldots ), whereas the second one consists of models that scientists have extracted from collected, either natural or synthetic, data. Data-driven (or data-intensive) simulation consists of directly linking experimental data to computers in order to perform numerical simulations. These simulations will employ laws, universally recognized as epistemic, while minimizing the need of explicit, often phenomenological, models. The main drawback of such an approach is the large amount of required data, some of them inaccessible from the nowadays testing facilities. Such difficulty can be circumvented in many cases, and in any case alleviated, by considering complex tests, collecting as many data as possible and then using a data-driven inverse approach in order to generate the whole constitutive manifold from few complex experimental tests, as discussed in the present work.

  5. Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype

    Science.gov (United States)

    Martin, Rodney A.; Schwabacher, Mark A.; Matthews, Bryan L.

    2010-01-01

    In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.

  6. Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features

    Directory of Open Access Journals (Sweden)

    Chang-Hee Han

    2016-01-01

    Full Text Available It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

  7. Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

    Science.gov (United States)

    Kamesh, Reddi; Rani, K Yamuna

    2016-09-01

    A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  8. KNMI DataLab experiences in serving data-driven innovations

    Science.gov (United States)

    Noteboom, Jan Willem; Sluiter, Raymond

    2016-04-01

    Climate change research and innovations in weather forecasting rely more and more on (Big) data. Besides increasing data from traditional sources (such as observation networks, radars and satellites), the use of open data, crowd sourced data and the Internet of Things (IoT) is emerging. To deploy these sources of data optimally in our services and products, KNMI has established a DataLab to serve data-driven innovations in collaboration with public and private sector partners. Big data management, data integration, data analytics including machine learning and data visualization techniques are playing an important role in the DataLab. Cross-domain data-driven innovations that arise from public-private collaborative projects and research programmes can be explored, experimented and/or piloted by the KNMI DataLab. Furthermore, advice can be requested on (Big) data techniques and data sources. In support of collaborative (Big) data science activities, scalable environments are offered with facilities for data integration, data analysis and visualization. In addition, Data Science expertise is provided directly or from a pool of internal and external experts. At the EGU conference, gained experiences and best practices are presented in operating the KNMI DataLab to serve data-driven innovations for weather and climate applications optimally.

  9. Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.

    Science.gov (United States)

    Han, Chang-Hee; Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan; Im, Chang-Hwan

    2016-01-01

    It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

  10. A novel paleo-bleaching proxy using boron isotopes and high-resolution laser ablation to reconstruct coral bleaching events

    OpenAIRE

    Dishon, G.; Fisch, J.; Horn, Ingo; Kaczmarek, Karina; Bijma, Jelle; Gruber, D.F.; Nir, O.; Popovich, Y.; Tchernov, D.

    2015-01-01

    Coral reefs occupy only ~ 0.1 percent of the ocean's habitat, but are the most biologically diverse marine ecosystem. In recent decades, coral reefs have experienced a significant global decline due to a variety of causes, one of the major causes being widespread coral bleaching events. During bleaching, the coral expels its symbiotic algae, thereby losing its main source of nutrition generally obtained through photosynthesis. While recent coral bleaching events have been ex...

  11. Performance of CMS muon reconstruction in pp collision events at $\\sqrt{s}$ = 7 TeV

    CERN Document Server

    Chatrchyan, Serguei; Sirunyan, Albert M; Tumasyan, Armen; Adam, Wolfgang; Bergauer, Thomas; Dragicevic, Marko; Erö, Janos; Fabjan, Christian; Friedl, Markus; Fruehwirth, Rudolf; Ghete, Vasile Mihai; Hammer, Josef; Hoch, Michael; Hörmann, Natascha; Hrubec, Josef; Jeitler, Manfred; Kiesenhofer, Wolfgang; Krammer, Manfred; Liko, Dietrich; Mikulec, Ivan; Pernicka, Manfred; Rahbaran, Babak; Rohringer, Christine; Rohringer, Herbert; Schöfbeck, Robert; Strauss, Josef; Taurok, Anton; Teischinger, Florian; Wagner, Philipp; Waltenberger, Wolfgang; Walzel, Gerhard; Widl, Edmund; Wulz, Claudia-Elisabeth; Mossolov, Vladimir; Shumeiko, Nikolai; Suarez Gonzalez, Juan; Bansal, Sunil; Benucci, Leonardo; Cornelis, Tom; De Wolf, Eddi A; Janssen, Xavier; Luyckx, Sten; Maes, Thomas; Mucibello, Luca; Ochesanu, Silvia; Roland, Benoit; Rougny, Romain; Selvaggi, Michele; Van Haevermaet, Hans; Van Mechelen, Pierre; Van Remortel, Nick; Van Spilbeeck, Alex; Blekman, Freya; Blyweert, Stijn; D'Hondt, Jorgen; Gonzalez Suarez, Rebeca; Kalogeropoulos, Alexis; Maes, Michael; Olbrechts, Annik; Van Doninck, Walter; Van Mulders, Petra; Van Onsem, Gerrit Patrick; Villella, Ilaria; Charaf, Otman; Clerbaux, Barbara; De Lentdecker, Gilles; Dero, Vincent; Gay, Arnaud; Hammad, Gregory Habib; Hreus, Tomas; Léonard, Alexandre; Marage, Pierre Edouard; Thomas, Laurent; Vander Velde, Catherine; Vanlaer, Pascal; Wickens, John; Adler, Volker; Beernaert, Kelly; Cimmino, Anna; Costantini, Silvia; Garcia, Guillaume; Grunewald, Martin; Klein, Benjamin; Lellouch, Jérémie; Marinov, Andrey; Mccartin, Joseph; Ocampo Rios, Alberto Andres; Ryckbosch, Dirk; Strobbe, Nadja; Thyssen, Filip; Tytgat, Michael; Vanelderen, Lukas; Verwilligen, Piet; Walsh, Sinead; Yazgan, Efe; Zaganidis, Nicolas; Basegmez, Suzan; Bruno, Giacomo; Ceard, Ludivine; De Favereau De Jeneret, Jerome; Delaere, Christophe; Du Pree, Tristan; Favart, Denis; Forthomme, Laurent; Giammanco, Andrea; Grégoire, Ghislain; Hollar, Jonathan; Lemaitre, Vincent; Liao, Junhui; Militaru, Otilia; Nuttens, Claude; Pagano, Davide; Pin, Arnaud; Piotrzkowski, Krzysztof; Schul, Nicolas; Beliy, Nikita; Caebergs, Thierry; Daubie, Evelyne; Alves, Gilvan; Correa Martins Junior, Marcos; De Jesus Damiao, Dilson; Martins, Thiago; Pol, Maria Elena; Henrique Gomes E Souza, Moacyr; Aldá Júnior, Walter Luiz; Carvalho, Wagner; Custódio, Analu; Melo Da Costa, Eliza; De Oliveira Martins, Carley; Fonseca De Souza, Sandro; Matos Figueiredo, Diego; Mundim, Luiz; Nogima, Helio; Oguri, Vitor; Prado Da Silva, Wanda Lucia; Santoro, Alberto; Silva Do Amaral, Sheila Mara; Soares Jorge, Luana; Sznajder, Andre; Souza Dos Anjos, Tiago; Bernardes, Cesar Augusto; De Almeida Dias, Flavia; Tomei, Thiago; De Moraes Gregores, Eduardo; Lagana, Caio; Da Cunha Marinho, Franciole; Mercadante, Pedro G; Novaes, Sergio F; Padula, Sandra; Genchev, Vladimir; Iaydjiev, Plamen; Piperov, Stefan; Rodozov, Mircho; Stoykova, Stefka; Sultanov, Georgi; Tcholakov, Vanio; Trayanov, Rumen; Vutova, Mariana; Dimitrov, Anton; Hadjiiska, Roumyana; Karadzhinova, Aneliya; Kozhuharov, Venelin; Litov, Leander; Pavlov, Borislav; Petkov, Peicho; Bian, Jian-Guo; Chen, Guo-Ming; Chen, He-Sheng; Jiang, Chun-Hua; Liang, Dong; Liang, Song; Meng, Xiangwei; Tao, Junquan; Wang, Jian; Wang, Jian; Wang, Xianyou; Wang, Zheng; Xiao, Hong; Xu, Ming; Zang, Jingjing; Zhang, Zhen; Asawatangtrakuldee, Chayanit; Ban, Yong; Guo, Shuang; Guo, Yifei; Li, Wenbo; Liu, Shuai; Mao, Yajun; Qian, Si-Jin; Teng, Haiyun; Wang, Siguang; Zhu, Bo; Zou, Wei; Cabrera, Andrés; Gomez Moreno, Bernardo; Osorio Oliveros, Andres Felipe; Sanabria, Juan Carlos; Godinovic, Nikola; Lelas, Damir; Plestina, Roko; Polic, Dunja; Puljak, Ivica; Antunovic, Zeljko; Dzelalija, Mile; Kovac, Marko; Brigljevic, Vuko; Duric, Senka; Kadija, Kreso; Luetic, Jelena; Morovic, Srecko; Attikis, Alexandros; Galanti, Mario; Mousa, Jehad; Nicolaou, Charalambos; Ptochos, Fotios; Razis, Panos A; Finger, Miroslav; Finger Jr, Michael; Assran, Yasser; Ellithi Kamel, Ali; Khalil, Shaaban; Mahmoud, Mohammed; Radi, Amr; Hektor, Andi; Kadastik, Mario; Müntel, Mait; Raidal, Martti; Rebane, Liis; Tiko, Andres; Azzolini, Virginia; Eerola, Paula; Fedi, Giacomo; Voutilainen, Mikko; Czellar, Sandor; Härkönen, Jaakko; Heikkinen, Mika Aatos; Karimäki, Veikko; Kinnunen, Ritva; Kortelainen, Matti J; Lampén, Tapio; Lassila-Perini, Kati; Lehti, Sami; Lindén, Tomas; Luukka, Panja-Riina; Mäenpää, Teppo; Peltola, Timo; Tuominen, Eija; Tuominiemi, Jorma; Tuovinen, Esa; Ungaro, Donatella; Wendland, Lauri; Banzuzi, Kukka; Korpela, Arja; Tuuva, Tuure; Sillou, Daniel; Besancon, Marc; Choudhury, Somnath; Dejardin, Marc; Denegri, Daniel; Fabbro, Bernard; Faure, Jean-Louis; Ferri, Federico; Ganjour, Serguei; Givernaud, Alain; Gras, Philippe; Hamel de Monchenault, Gautier; Jarry, Patrick; Locci, Elizabeth; Malcles, Julie; Millischer, Laurent; Rander, John; Rosowsky, André; Shreyber, Irina; Titov, Maksym; Baffioni, Stephanie; Beaudette, Florian; Benhabib, Lamia; Bianchini, Lorenzo; Bluj, Michal; Broutin, Clementine; Busson, Philippe; Charlot, Claude; Daci, Nadir; Dahms, Torsten; Dobrzynski, Ludwik; Elgammal, Sherif; Granier de Cassagnac, Raphael; Haguenauer, Maurice; Miné, Philippe; Mironov, Camelia; Ochando, Christophe; Paganini, Pascal; Sabes, David; Salerno, Roberto; Sirois, Yves; Thiebaux, Christophe; Veelken, Christian; Zabi, Alexandre; Agram, Jean-Laurent; Andrea, Jeremy; Bloch, Daniel; Bodin, David; Brom, Jean-Marie; Cardaci, Marco; Chabert, Eric Christian; Collard, Caroline; Conte, Eric; Drouhin, Frédéric; Ferro, Cristina; Fontaine, Jean-Charles; Gelé, Denis; Goerlach, Ulrich; Juillot, Pierre; Karim, Mehdi; Le Bihan, Anne-Catherine; Van Hove, Pierre; Fassi, Farida; Mercier, Damien; Baty, Clement; Beauceron, Stephanie; Beaupere, Nicolas; Bedjidian, Marc; Bondu, Olivier; Boudoul, Gaelle; Boumediene, Djamel; Brun, Hugues; Chasserat, Julien; Chierici, Roberto; Contardo, Didier; Depasse, Pierre; El Mamouni, Houmani; Falkiewicz, Anna; Fay, Jean; Gascon, Susan; Gouzevitch, Maxime; Ille, Bernard; Kurca, Tibor; Le Grand, Thomas; Lethuillier, Morgan; Mirabito, Laurent; Perries, Stephane; Sordini, Viola; Tosi, Silvano; Tschudi, Yohann; Verdier, Patrice; Viret, Sébastien; Lomidze, David; Anagnostou, Georgios; Beranek, Sarah; Edelhoff, Matthias; Feld, Lutz; Heracleous, Natalie; Hindrichs, Otto; Jussen, Ruediger; Klein, Katja; Merz, Jennifer; Ostapchuk, Andrey; Perieanu, Adrian; Raupach, Frank; Sammet, Jan; Schael, Stefan; Sprenger, Daniel; Weber, Hendrik; Wittmer, Bruno; Zhukov, Valery; Ata, Metin; Caudron, Julien; Dietz-Laursonn, Erik; Erdmann, Martin; Güth, Andreas; Hebbeker, Thomas; Heidemann, Carsten; Hoepfner, Kerstin; Klimkovich, Tatsiana; Klingebiel, Dennis; Kreuzer, Peter; Lanske, Dankfried; Lingemann, Joschka; Magass, Carsten; Merschmeyer, Markus; Meyer, Arnd; Olschewski, Mark; Papacz, Paul; Pieta, Holger; Reithler, Hans; Schmitz, Stefan Antonius; Sonnenschein, Lars; Steggemann, Jan; Teyssier, Daniel; Weber, Martin; Bontenackels, Michael; Cherepanov, Vladimir; Davids, Martina; Flügge, Günter; Geenen, Heiko; Geisler, Matthias; Haj Ahmad, Wael; Hoehle, Felix; Kargoll, Bastian; Kress, Thomas; Kuessel, Yvonne; Linn, Alexander; Nowack, Andreas; Perchalla, Lars; Pooth, Oliver; Rennefeld, Jörg; Sauerland, Philip; Stahl, Achim; Zoeller, Marc Henning; Aldaya Martin, Maria; Behrenhoff, Wolf; Behrens, Ulf; Bergholz, Matthias; Bethani, Agni; Borras, Kerstin; Burgmeier, Armin; Cakir, Altan; Calligaris, Luigi; Campbell, Alan; Castro, Elena; Dammann, Dirk; Eckerlin, Guenter; Eckstein, Doris; Flossdorf, Alexander; Flucke, Gero; Geiser, Achim; Hauk, Johannes; Jung, Hannes; Kasemann, Matthias; Katsas, Panagiotis; Kleinwort, Claus; Kluge, Hannelies; Knutsson, Albert; Krämer, Mira; Krücker, Dirk; Kuznetsova, Ekaterina; Lange, Wolfgang; Lohmann, Wolfgang; Lutz, Benjamin; Mankel, Rainer; Marfin, Ihar; Marienfeld, Markus; Melzer-Pellmann, Isabell-Alissandra; Meyer, Andreas Bernhard; Mnich, Joachim; Mussgiller, Andreas; Naumann-Emme, Sebastian; Olzem, Jan; Petrukhin, Alexey; Pitzl, Daniel; Raspereza, Alexei; Ribeiro Cipriano, Pedro M; Rosin, Michele; Salfeld-Nebgen, Jakob; Schmidt, Ringo; Schoerner-Sadenius, Thomas; Sen, Niladri; Spiridonov, Alexander; Stein, Matthias; Tomaszewska, Justyna; Walsh, Roberval; Wissing, Christoph; Autermann, Christian; Blobel, Volker; Bobrovskyi, Sergei; Draeger, Jula; Enderle, Holger; Erfle, Joachim; Gebbert, Ulla; Görner, Martin; Hermanns, Thomas; Höing, Rebekka Sophie; Kaschube, Kolja; Kaussen, Gordon; Kirschenmann, Henning; Klanner, Robert; Lange, Jörn; Mura, Benedikt; Nowak, Friederike; Pietsch, Niklas; Sander, Christian; Schettler, Hannes; Schleper, Peter; Schlieckau, Eike; Schmidt, Alexander; Schröder, Matthias; Schum, Torben; Stadie, Hartmut; Steinbrück, Georg; Thomsen, Jan; Barth, Christian; Berger, Joram; Chwalek, Thorsten; De Boer, Wim; Dierlamm, Alexander; Dirkes, Guido; Feindt, Michael; Gruschke, Jasmin; Guthoff, Moritz; Hackstein, Christoph; Hartmann, Frank; Heinrich, Michael; Held, Hauke; Hoffmann, Karl-Heinz; Honc, Simon; Katkov, Igor; Komaragiri, Jyothsna Rani; Kuhr, Thomas; Martschei, Daniel; Mueller, Steffen; Müller, Thomas; Niegel, Martin; Nürnberg, Andreas; Oberst, Oliver; Oehler, Andreas; Ott, Jochen; Peiffer, Thomas; Quast, Gunter; Rabbertz, Klaus; Ratnikov, Fedor; Ratnikova, Natalia; Renz, Manuel; Röcker, Steffen; Saout, Christophe; Scheurer, Armin; Schieferdecker, Philipp; Schilling, Frank-Peter; Schmanau, Mike; Schott, Gregory; Simonis, Hans-Jürgen; Stober, Fred-Markus Helmut; Troendle, Daniel; Wagner-Kuhr, Jeannine; Weiler, Thomas; Zeise, Manuel; Ziebarth, Eva Barbara; Daskalakis, Georgios; Geralis, Theodoros; Kesisoglou, Stilianos; Kyriakis, Aristotelis; Loukas, Demetrios; Manolakos, Ioannis; Markou, Athanasios; Markou, Christos; Mavrommatis, Charalampos; Ntomari, Eleni; Gouskos, Loukas; Mertzimekis, Theodoros; Panagiotou, Apostolos; Saoulidou, Niki; Stiliaris, Efstathios; Evangelou, Ioannis; Foudas, Costas; Kokkas, Panagiotis; Manthos, Nikolaos; Papadopoulos, Ioannis; Patras, Vaios; Triantis, Frixos A; Aranyi, Attila; Bencze, Gyorgy; Boldizsar, Laszlo; Hajdu, Csaba; Hidas, Pàl; Horvath, Dezso; Kapusi, Anita; Krajczar, Krisztian; Sikler, Ferenc; Veszpremi, Viktor; Vesztergombi, Gyorgy; Beni, Noemi; Molnar, Jozsef; Palinkas, Jozsef; Szillasi, Zoltan; Karancsi, János; Raics, Peter; Trocsanyi, Zoltan Laszlo; Ujvari, Balazs; Beri, Suman Bala; Bhatnagar, Vipin; Dhingra, Nitish; Gupta, Ruchi; Bansal, Monika; Kaur, Manjit; Kohli, Jatinder Mohan; Mehta, Manuk Zubin; Nishu, Nishu; Saini, Lovedeep Kaur; Sharma, Archana; Singh, Anil; Singh, Jasbir; Singh, Supreet Pal; Kumar, Ashok; Kumar, Arun; Ahuja, Sudha; Choudhary, Brajesh C; Malhotra, Shivali; Naimuddin, Md; Ranjan, Kirti; Sharma, Varun; Shivpuri, Ram Krishen; Banerjee, Sunanda; Bhattacharya, Satyaki; Dutta, Suchandra; Gomber, Bhawna; Jain, Sandhya; Jain, Shilpi; Khurana, Raman; Sarkar, Subir; Choudhury, Rajani Kant; Dutta, Dipanwita; Kailas, Swaminathan; Kumar, Vineet; Mohanty, Ajit Kumar; Pant, Lalit Mohan; Shukla, Prashant; Aziz, Tariq; Ganguly, Sanmay; Guchait, Monoranjan; Gurtu, Atul; Maity, Manas; Majumder, Gobinda; Mazumdar, Kajari; Mohanty, Gagan Bihari; Parida, Bibhuti; Saha, Anirban; Sudhakar, Katta; Wickramage, Nadeesha; Banerjee, Sudeshna; Dugad, Shashikant; Mondal, Naba Kumar; Arfaei, Hessamaddin; Bakhshiansohi, Hamed; Etesami, Seyed Mohsen; Fahim, Ali; Hashemi, Majid; Hesari, Hoda; Jafari, Abideh; Khakzad, Mohsen; Mohammadi, Abdollah; Mohammadi Najafabadi, Mojtaba; Paktinat Mehdiabadi, Saeid; Safarzadeh, Batool; Zeinali, Maryam; Abbrescia, Marcello; Barbone, Lucia; Calabria, Cesare; Chhibra, Simranjit Singh; Colaleo, Anna; Creanza, Donato; De Filippis, Nicola; De Palma, Mauro; Fiore, Luigi; Iaselli, Giuseppe; Lusito, Letizia; Maggi, Giorgio; Maggi, Marcello; Manna, Norman; Marangelli, Bartolomeo; My, Salvatore; Nuzzo, Salvatore; Pacifico, Nicola; Pompili, Alexis; Pugliese, Gabriella; Romano, Francesco; Selvaggi, Giovanna; Silvestris, Lucia; Singh, Gurpreet; Tupputi, Salvatore; Zito, Giuseppe; Abbiendi, Giovanni; Benvenuti, Alberto; Bonacorsi, Daniele; Braibant-Giacomelli, Sylvie; Brigliadori, Luca; Capiluppi, Paolo; Castro, Andrea; Cavallo, Francesca Romana; Cuffiani, Marco; Dallavalle, Gaetano-Marco; Fabbri, Fabrizio; Fanfani, Alessandra; Fasanella, Daniele; Giacomelli, Paolo; Grandi, Claudio; Marcellini, Stefano; Masetti, Gianni; Meneghelli, Marco; Montanari, Alessandro; Navarria, Francesco; Odorici, Fabrizio; Perrotta, Andrea; Primavera, Federica; Rossi, Antonio; Rovelli, Tiziano; Siroli, Gianni; Travaglini, Riccardo; Albergo, Sebastiano; Cappello, Gigi; Chiorboli, Massimiliano; Costa, Salvatore; Potenza, Renato; Tricomi, Alessia; Tuve, Cristina; Barbagli, Giuseppe; Ciulli, Vitaliano; Civinini, Carlo; D'Alessandro, Raffaello; Focardi, Ettore; Frosali, Simone; Gallo, Elisabetta; Gonzi, Sandro; Meschini, Marco; Paoletti, Simone; Sguazzoni, Giacomo; Tropiano, Antonio; Benussi, Luigi; Bianco, Stefano; Colafranceschi, Stefano; Fabbri, Franco; Piccolo, Davide; Fabbricatore, Pasquale; Musenich, Riccardo; Benaglia, Andrea; De Guio, Federico; Di Matteo, Leonardo; Fiorendi, Sara; Gennai, Simone; Ghezzi, Alessio; Malvezzi, Sandra; Manzoni, Riccardo Andrea; Martelli, Arabella; Massironi, Andrea; Menasce, Dario; Moroni, Luigi; Paganoni, Marco; Pedrini, Daniele; Ragazzi, Stefano; Redaelli, Nicola; Sala, Silvano; Tabarelli de Fatis, Tommaso; Buontempo, Salvatore; Carrillo Montoya, Camilo Andres; Cavallo, Nicola; De Cosa, Annapaola; Dogangun, Oktay; Fabozzi, Francesco; Iorio, Alberto Orso Maria; Lista, Luca; Merola, Mario; Paolucci, Pierluigi; Azzi, Patrizia; Bacchetta, Nicola; Bellan, Paolo; Bellato, Marco; Bisello, Dario; Branca, Antonio; Carlin, Roberto; Checchia, Paolo; Dorigo, Tommaso; Gasparini, Fabrizio; Gozzelino, Andrea; Kanishchev, Konstantin; Lacaprara, Stefano; Lazzizzera, Ignazio; Margoni, Martino; Maron, Gaetano; Meneguzzo, Anna Teresa; Nespolo, Massimo; Passaseo, Marina; Perrozzi, Luca; Pozzobon, Nicola; Ronchese, Paolo; Simonetto, Franco; Torassa, Ezio; Tosi, Mia; Vanini, Sara; Ventura, Sandro; Zotto, Pierluigi; Zumerle, Gianni; Baesso, Paolo; Berzano, Umberto; Gabusi, Michele; Ratti, Sergio P; Riccardi, Cristina; Torre, Paola; Vitulo, Paolo; Viviani, Claudio; Biasini, Maurizio; Bilei, Gian Mario; Caponeri, Benedetta; Fanò, Livio; Lariccia, Paolo; Lucaroni, Andrea; Mantovani, Giancarlo; Menichelli, Mauro; Nappi, Aniello; Romeo, Francesco; Santocchia, Attilio; Taroni, Silvia; Valdata, Marisa; Azzurri, Paolo; Bagliesi, Giuseppe; Boccali, Tommaso; Broccolo, Giuseppe; Castaldi, Rino; D'Agnolo, Raffaele Tito; Dell'Orso, Roberto; Fiori, Francesco; Foà, Lorenzo; Giassi, Alessandro; Kraan, Aafke; Ligabue, Franco; Lomtadze, Teimuraz; Martini, Luca; Messineo, Alberto; Palla, Fabrizio; Palmonari, Francesco; Rizzi, Andrea; Serban, Alin Titus; Spagnolo, Paolo; Tenchini, Roberto; Tonelli, Guido; Venturi, Andrea; Verdini, Piero Giorgio; Barone, Luciano; Cavallari, Francesca; Del Re, Daniele; Diemoz, Marcella; Fanelli, Cristiano; Franci, Daniele; Grassi, Marco; Longo, Egidio; Meridiani, Paolo; Micheli, Francesco; Nourbakhsh, Shervin; Organtini, Giovanni; Pandolfi, Francesco; Paramatti, Riccardo; Rahatlou, Shahram; Sigamani, Michael; Soffi, Livia; Amapane, Nicola; Arcidiacono, Roberta; Argiro, Stefano; Arneodo, Michele; Biino, Cristina; Botta, Cristina; Cartiglia, Nicolo; Castello, Roberto; Costa, Marco; Demaria, Natale; Graziano, Alberto; Mariotti, Chiara; Maselli, Silvia; Migliore, Ernesto; Monaco, Vincenzo; Musich, Marco; Obertino, Maria Margherita; Pastrone, Nadia; Pelliccioni, Mario; Potenza, Alberto; Romero, Alessandra; Ruspa, Marta; Sacchi, Roberto; Sola, Valentina; Solano, Ada; Staiano, Amedeo; Vilela Pereira, Antonio; Belforte, Stefano; Cossutti, Fabio; Della Ricca, Giuseppe; Gobbo, Benigno; Marone, Matteo; Montanino, Damiana; Penzo, Aldo; Heo, Seong Gu; Nam, Soon-Kwon; Chang, Sunghyun; Chung, Jin Hyuk; Kim, Dong Hee; Kim, Gui Nyun; Kim, Ji Eun; Kong, Dae Jung; Park, Hyangkyu; Ro, Sang-Ryul; Son, Dong-Chul; Kim, Jae Yool; Kim, Zero Jaeho; Song, Sanghyeon; Jo, Hyun Yong; Choi, Suyong; Gyun, Dooyeon; Hong, Byung-Sik; Jo, Mihee; Kim, Hyunchul; Kim, Tae Jeong; Lee, Kyong Sei; Moon, Dong Ho; Park, Sung Keun; Seo, Eunsung; Sim, Kwang Souk; Choi, Minkyoo; Kang, Seokon; Kim, Hyunyong; Kim, Ji Hyun; Park, Chawon; Park, Inkyu; Park, Sangnam; Ryu, Geonmo; Cho, Yongjin; Choi, Young-Il; Choi, Young Kyu; Goh, Junghwan; Kim, Min Suk; Lee, Byounghoon; Lee, Jongseok; Lee, Sungeun; Seo, Hyunkwan; Yu, Intae; Bilinskas, Mykolas Jurgis; Grigelionis, Ignas; Janulis, Mindaugas; Castilla-Valdez, Heriberto; De La Cruz-Burelo, Eduard; Heredia-de La Cruz, Ivan; Lopez-Fernandez, Ricardo; Magaña Villalba, Ricardo; Martínez-Ortega, Jorge; Sánchez-Hernández, Alberto; Villasenor-Cendejas, Luis Manuel; Carrillo Moreno, Salvador; Vazquez Valencia, Fabiola; Salazar Ibarguen, Humberto Antonio; Casimiro Linares, Edgar; Morelos Pineda, Antonio; Reyes-Santos, Marco A; Krofcheck, David; Bell, Alan James; Butler, Philip H; Doesburg, Robert; Reucroft, Steve; Silverwood, Hamish; Ahmad, Muhammad; Asghar, Muhammad Irfan; Hoorani, Hafeez R; Khalid, Shoaib; Khan, Wajid Ali; Khurshid, Taimoor; Qazi, Shamona; Shah, Mehar Ali; Shoaib, Muhammad; Brona, Grzegorz; Cwiok, Mikolaj; Dominik, Wojciech; Doroba, Krzysztof; Kalinowski, Artur; Konecki, Marcin; Krolikowski, Jan; Bialkowska, Helena; Boimska, Bozena; Frueboes, Tomasz; Gokieli, Ryszard; Górski, Maciej; Kazana, Malgorzata; Nawrocki, Krzysztof; Romanowska-Rybinska, Katarzyna; Szleper, Michal; Wrochna, Grzegorz; Zalewski, Piotr; Almeida, Nuno; Bargassa, Pedrame; David Tinoco Mendes, Andre; Faccioli, Pietro; Ferreira Parracho, Pedro Guilherme; Gallinaro, Michele; Musella, Pasquale; Nayak, Aruna; Pela, Joao; Ribeiro, Pedro Quinaz; Seixas, Joao; Varela, Joao; Vischia, Pietro; Belotelov, Ivan; Golunov, Alexander; Golutvin, Igor; Gorbounov, Nikolai; Gramenitski, Igor; Kamenev, Alexey; Karjavin, Vladimir; Kurenkov, Alexander; Lanev, Alexander; Makankin, Alexander; Moisenz, Petr; Palichik, Vladimir; Perelygin, Victor; Shmatov, Sergey; Smolin, Dmitry; Vasilyev, Sergey; Zarubin, Anatoli; Evstyukhin, Sergey; Golovtsov, Victor; Ivanov, Yury; Kim, Victor; Levchenko, Petr; Murzin, Victor; Oreshkin, Vadim; Smirnov, Igor; Sulimov, Valentin; Uvarov, Lev; Vavilov, Sergey; Vorobyev, Alexey; Vorobyev, Andrey; Andreev, Yuri; Dermenev, Alexander; Gninenko, Sergei; Golubev, Nikolai; Kirsanov, Mikhail; Krasnikov, Nikolai; Matveev, Viktor; Pashenkov, Anatoli; Toropin, Alexander; Troitsky, Sergey; Epshteyn, Vladimir; Erofeeva, Maria; Gavrilov, Vladimir; Kossov, Mikhail; Krokhotin, Andrey; Lychkovskaya, Natalia; Popov, Vladimir; Safronov, Grigory; Semenov, Sergey; Stolin, Viatcheslav; Vlasov, Evgueni; Zhokin, Alexander; Belyaev, Andrey; Boos, Edouard; Dubinin, Mikhail; Dudko, Lev; Ershov, Alexander; Gribushin, Andrey; Klyukhin, Vyacheslav; Kodolova, Olga; Markina, Anastasia; Obraztsov, Stepan; Perfilov, Maxim; Petrushanko, Sergey; Sarycheva, Ludmila; Savrin, Viktor; Snigirev, Alexander; Andreev, Vladimir; Azarkin, Maksim; Dremin, Igor; Kirakosyan, Martin; Leonidov, Andrey; Mesyats, Gennady; Rusakov, Sergey V; Vinogradov, Alexey; Azhgirey, Igor; Bayshev, Igor; Bitioukov, Sergei; Grishin, Viatcheslav; Kachanov, Vassili; Konstantinov, Dmitri; Korablev, Andrey; Krychkine, Victor; Petrov, Vladimir; Ryutin, Roman; Sobol, Andrei; Tourtchanovitch, Leonid; Troshin, Sergey; Tyurin, Nikolay; Uzunian, Andrey; Volkov, Alexey; Adzic, Petar; Djordjevic, Milos; Ekmedzic, Marko; Krpic, Dragomir; Milosevic, Jovan; Aguilar-Benitez, Manuel; Alcaraz Maestre, Juan; Arce, Pedro; Battilana, Carlo; Calvo, Enrique; Cerrada, Marcos; Chamizo Llatas, Maria; Colino, Nicanor; De La Cruz, Begona; Delgado Peris, Antonio; Diez Pardos, Carmen; Domínguez Vázquez, Daniel; Fernandez Bedoya, Cristina; Fernández Ramos, Juan Pablo; Ferrando, Antonio; Flix, Jose; Fouz, Maria Cruz; Garcia-Abia, Pablo; Gonzalez Lopez, Oscar; Goy Lopez, Silvia; Hernandez, Jose M; Josa, Maria Isabel; Merino, Gonzalo; Puerta Pelayo, Jesus; Redondo, Ignacio; Romero, Luciano; Santaolalla, Javier; Senghi Soares, Mara; Willmott, Carlos; Albajar, Carmen; Codispoti, Giuseppe; de Trocóniz, Jorge F; Cuevas, Javier; Fernandez Menendez, Javier; Folgueras, Santiago; Gonzalez Caballero, Isidro; Lloret Iglesias, Lara; Piedra Gomez, Jonatan; Vizan Garcia, Jesus Manuel; Brochero Cifuentes, Javier Andres; Cabrillo, Iban Jose; Calderon, Alicia; Chuang, Shan-Huei; Duarte Campderros, Jordi; Felcini, Marta; Fernandez, Marcos; Gomez, Gervasio; Gonzalez Sanchez, Javier; Jorda, Clara; Lobelle Pardo, Patricia; Lopez Virto, Amparo; Marco, Jesus; Marco, Rafael; Martinez Rivero, Celso; Matorras, Francisco; Munoz Sanchez, Francisca Javiela; Rodrigo, Teresa; Rodríguez-Marrero, Ana Yaiza; Ruiz-Jimeno, Alberto; Scodellaro, Luca; Sobron Sanudo, Mar; Vila, Ivan; Vilar Cortabitarte, Rocio; Abbaneo, Duccio; Auffray, Etiennette; Auzinger, Georg; Baillon, Paul; Ball, Austin; Barney, David; Bernet, Colin; Bialas, Wojciech; Bianchi, Giovanni; Bloch, Philippe; Bocci, Andrea; Breuker, Horst; Bunkowski, Karol; Camporesi, Tiziano; Cerminara, Gianluca; Christiansen, Tim; Coarasa Perez, Jose Antonio; Curé, Benoît; D'Enterria, David; De Roeck, Albert; Di Guida, Salvatore; Dobson, Marc; Dupont-Sagorin, Niels; Elliott-Peisert, Anna; Frisch, Benjamin; Funk, Wolfgang; Gaddi, Andrea; Georgiou, Georgios; Gerwig, Hubert; Giffels, Manuel; Gigi, Dominique; Gill, Karl; Giordano, Domenico; Giunta, Marina; Glege, Frank; Gomez-Reino Garrido, Robert; Govoni, Pietro; Gowdy, Stephen; Guida, Roberto; Guiducci, Luigi; Hansen, Magnus; Harris, Philip; Hartl, Christian; Harvey, John; Hegner, Benedikt; Hinzmann, Andreas; Hoffmann, Hans Falk; Innocente, Vincenzo; Janot, Patrick; Kaadze, Ketino; Karavakis, Edward; Kousouris, Konstantinos; Lecoq, Paul; Lenzi, Piergiulio; Lourenco, Carlos; Maki, Tuula; Malberti, Martina; Malgeri, Luca; Mannelli, Marcello; Masetti, Lorenzo; Mavromanolakis, Georgios; Meijers, Frans; Mersi, Stefano; Meschi, Emilio; Moser, Roland; Mozer, Matthias Ulrich; Mulders, Martijn; Nesvold, Erik; Nguyen, Matthew; Orimoto, Toyoko; Orsini, Luciano; Palencia Cortezon, Enrique; Perez, Emmanuelle; Petrilli, Achille; Pfeiffer, Andreas; Pierini, Maurizio; Pimiä, Martti; Piparo, Danilo; Polese, Giovanni; Quertenmont, Loic; Racz, Attila; Reece, William; Rodrigues Antunes, Joao; Rolandi, Gigi; Rommerskirchen, Tanja; Rovelli, Chiara; Rovere, Marco; Sakulin, Hannes; Santanastasio, Francesco; Schäfer, Christoph; Schwick, Christoph; Segoni, Ilaria; Sharma, Archana; Siegrist, Patrice; Silva, Pedro; Simon, Michal; Sphicas, Paraskevas; Spiga, Daniele; Spiropulu, Maria; Stoye, Markus; Tsirou, Andromachi; Veres, Gabor Istvan; Vichoudis, Paschalis; Wöhri, Hermine Katharina; Worm, Steven; Zeuner, Wolfram Dietrich; Bertl, Willi; Deiters, Konrad; Erdmann, Wolfram; Gabathuler, Kurt; Horisberger, Roland; Ingram, Quentin; Kaestli, Hans-Christian; König, Stefan; Kotlinski, Danek; Langenegger, Urs; Meier, Frank; Renker, Dieter; Rohe, Tilman; Sibille, Jennifer; Bäni, Lukas; Bortignon, Pierluigi; Buchmann, Marco-Andrea; Casal, Bruno; Chanon, Nicolas; Chen, Zhiling; Deisher, Amanda; Dissertori, Günther; Dittmar, Michael; Dünser, Marc; Eugster, Jürg; Freudenreich, Klaus; Grab, Christoph; Lecomte, Pierre; Lustermann, Werner; Martinez Ruiz del Arbol, Pablo; Mohr, Niklas; Moortgat, Filip; Nägeli, Christoph; Nef, Pascal; Nessi-Tedaldi, Francesca; Pape, Luc; Pauss, Felicitas; Peruzzi, Marco; Ronga, Frederic Jean; Rossini, Marco; Sala, Leonardo; Sanchez, Ann - Karin; Sawley, Marie-Christine; Starodumov, Andrei; Stieger, Benjamin; Takahashi, Maiko; Tauscher, Ludwig; Thea, Alessandro; Theofilatos, Konstantinos; Treille, Daniel; Urscheler, Christina; Wallny, Rainer; Weber, Hannsjoerg Artur; Wehrli, Lukas; Weng, Joanna; Aguilo, Ernest; Amsler, Claude; Chiochia, Vincenzo; De Visscher, Simon; Favaro, Carlotta; Ivova Rikova, Mirena; Millan Mejias, Barbara; Otiougova, Polina; Robmann, Peter; Snoek, Hella; Verzetti, Mauro; Chang, Yuan-Hann; Chen, Kuan-Hsin; Kuo, Chia-Ming; Li, Syue-Wei; Lin, Willis; Liu, Zong-Kai; Lu, Yun-Ju; Mekterovic, Darko; Volpe, Roberta; Yu, Shin-Shan; Bartalini, Paolo; Chang, Paoti; Chang, You-Hao; Chang, Yu-Wei; Chao, Yuan; Chen, Kai-Feng; Dietz, Charles; Grundler, Ulysses; Hou, George Wei-Shu; Hsiung, Yee; Kao, Kai-Yi; Lei, Yeong-Jyi; Lu, Rong-Shyang; Majumder, Devdatta; Petrakou, Eleni; Shi, Xin; Shiu, Jing-Ge; Tzeng, Yeng-Ming; Wang, Minzu; Adiguzel, Aytul; Bakirci, Mustafa Numan; Cerci, Salim; Dozen, Candan; Dumanoglu, Isa; Eskut, Eda; Girgis, Semiray; Gokbulut, Gul; Hos, Ilknur; Kangal, Evrim Ersin; Karapinar, Guler; Kayis Topaksu, Aysel; Onengut, Gulsen; Ozdemir, Kadri; Ozturk, Sertac; Polatoz, Ayse; Sogut, Kenan; Sunar Cerci, Deniz; Tali, Bayram; Topakli, Huseyin; Uzun, Dilber; Vergili, Latife Nukhet; Vergili, Mehmet; Akin, Ilina Vasileva; Aliev, Takhmasib; Bilin, Bugra; Bilmis, Selcuk; Deniz, Muhammed; Gamsizkan, Halil; Guler, Ali Murat; Ocalan, Kadir; Ozpineci, Altug; Serin, Meltem; Sever, Ramazan; Surat, Ugur Emrah; Yalvac, Metin; Yildirim, Eda; Zeyrek, Mehmet; Deliomeroglu, Mehmet; Gülmez, Erhan; Isildak, Bora; Kaya, Mithat; Kaya, Ozlem; Ozkorucuklu, Suat; Sonmez, Nasuf; Levchuk, Leonid; Bostock, Francis; Brooke, James John; Clement, Emyr; Cussans, David; Flacher, Henning; Frazier, Robert; Goldstein, Joel; Grimes, Mark; Heath, Greg P; Heath, Helen F; Kreczko, Lukasz; Metson, Simon; Newbold, Dave M; Nirunpong, Kachanon; Poll, Anthony; Senkin, Sergey; Smith, Vincent J; Williams, Thomas; Basso, Lorenzo; Bell, Ken W; Belyaev, Alexander; Brew, Christopher; Brown, Robert M; Cockerill, David JA; Coughlan, John A; Harder, Kristian; Harper, Sam; Jackson, James; Kennedy, Bruce W; Olaiya, Emmanuel; Petyt, David; Radburn-Smith, Benjamin Charles; Shepherd-Themistocleous, Claire; Tomalin, Ian R; Womersley, William John; Bainbridge, Robert; Ball, Gordon; Beuselinck, Raymond; Buchmuller, Oliver; Colling, David; Cripps, Nicholas; Cutajar, Michael; Dauncey, Paul; Davies, Gavin; Della Negra, Michel; Ferguson, William; Fulcher, Jonathan; Futyan, David; Gilbert, Andrew; Guneratne Bryer, Arlo; Hall, Geoffrey; Hatherell, Zoe; Hays, Jonathan; Iles, Gregory; Jarvis, Martyn; Karapostoli, Georgia; Lyons, Louis; Magnan, Anne-Marie; Marrouche, Jad; Mathias, Bryn; Nandi, Robin; Nash, Jordan; Nikitenko, Alexander; Papageorgiou, Anastasios; Pesaresi, Mark; Petridis, Konstantinos; Pioppi, Michele; Raymond, David Mark; Rogerson, Samuel; Rompotis, Nikolaos; Rose, Andrew; Ryan, Matthew John; Seez, Christopher; Sharp, Peter; Sparrow, Alex; Tapper, Alexander; Tourneur, Stephane; Vazquez Acosta, Monica; Virdee, Tejinder; Wakefield, Stuart; Wardle, Nicholas; Wardrope, David; Whyntie, Tom; Barrett, Matthew; Chadwick, Matthew; Cole, Joanne; Hobson, Peter R; Khan, Akram; Kyberd, Paul; Leslie, Dawn; Martin, William; Reid, Ivan; Symonds, Philip; Teodorescu, Liliana; Turner, Mark; Hatakeyama, Kenichi; Liu, Hongxuan; Scarborough, Tara; Henderson, Conor; Avetisyan, Aram; Bose, Tulika; Carrera Jarrin, Edgar; Fantasia, Cory; Heister, Arno; St John, Jason; Lawson, Philip; Lazic, Dragoslav; Rohlf, James; Sperka, David; Sulak, Lawrence; Bhattacharya, Saptaparna; Cutts, David; Ferapontov, Alexey; Heintz, Ulrich; Jabeen, Shabnam; Kukartsev, Gennadiy; Landsberg, Greg; Luk, Michael; Narain, Meenakshi; Nguyen, Duong; Segala, Michael; Sinthuprasith, Tutanon; Speer, Thomas; Tsang, Ka Vang; Breedon, Richard; Breto, Guillermo; Calderon De La Barca Sanchez, Manuel; Caulfield, Matthew; Chauhan, Sushil; Chertok, Maxwell; Conway, John; Conway, Rylan; Cox, Peter Timothy; Dolen, James; Erbacher, Robin; Gardner, Michael; Houtz, Rachel; Ko, Winston; Kopecky, Alexandra; Lander, Richard; Mall, Orpheus; Miceli, Tia; Nelson, Randy; Pellett, Dave; Robles, Jorge; Rutherford, Britney; Searle, Matthew; Smith, John; Squires, Michael; Tripathi, Mani; Vasquez Sierra, Ricardo; Andreev, Valeri; Arisaka, Katsushi; Cline, David; Cousins, Robert; Duris, Joseph; Erhan, Samim; Everaerts, Pieter; Farrell, Chris; Hauser, Jay; Ignatenko, Mikhail; Jarvis, Chad; Plager, Charles; Rakness, Gregory; Schlein, Peter; Tucker, Jordan; Valuev, Vyacheslav; Weber, Matthias; Babb, John; Clare, Robert; Ellison, John Anthony; Gary, J William; Giordano, Ferdinando; Hanson, Gail; Jeng, Geng-Yuan; Liu, Hongliang; Long, Owen Rosser; Luthra, Arun; Nguyen, Harold; Paramesvaran, Sudarshan; Sturdy, Jared; Sumowidagdo, Suharyo; Wilken, Rachel; Wimpenny, Stephen; Andrews, Warren; Branson, James G; Cerati, Giuseppe Benedetto; Cittolin, Sergio; Evans, David; Golf, Frank; Holzner, André; Kelley, Ryan; Lebourgeois, Matthew; Letts, James; Macneill, Ian; Mangano, Boris; Padhi, Sanjay; Palmer, Christopher; Petrucciani, Giovanni; Pi, Haifeng; Pieri, Marco; Ranieri, Riccardo; Sani, Matteo; Sfiligoi, Igor; Sharma, Vivek; Simon, Sean; Sudano, Elizabeth; Tadel, Matevz; Tu, Yanjun; Vartak, Adish; Wasserbaech, Steven; Würthwein, Frank; Yagil, Avraham; Yoo, Jaehyeok; Barge, Derek; Bellan, Riccardo; Campagnari, Claudio; D'Alfonso, Mariarosaria; Danielson, Thomas; Flowers, Kristen; Geffert, Paul; Incandela, Joe; Justus, Christopher; Kalavase, Puneeth; Koay, Sue Ann; Kovalskyi, Dmytro; Krutelyov, Vyacheslav; Lowette, Steven; Mccoll, Nickolas; Pavlunin, Viktor; Rebassoo, Finn; Ribnik, Jacob; Richman, Jeffrey; Rossin, Roberto; Stuart, David; To, Wing; Vlimant, Jean-Roch; West, Christopher; Apresyan, Artur; Bornheim, Adolf; Bunn, Julian; Chen, Yi; Di Marco, Emanuele; Duarte, Javier; Gataullin, Marat; Ma, Yousi; Mott, Alexander; Newman, Harvey B; Rogan, Christopher; Timciuc, Vladlen; Traczyk, Piotr; Veverka, Jan; Wilkinson, Richard; Yang, Yong; Zhu, Ren-Yuan; Akgun, Bora; Carroll, Ryan; Ferguson, Thomas; Iiyama, Yutaro; Jang, Dong Wook; Jun, Soon Yung; Liu, Yueh-Feng; Paulini, Manfred; Russ, James; Vogel, Helmut; Vorobiev, Igor; Cumalat, John Perry; Dinardo, Mauro Emanuele; Drell, Brian Robert; Edelmaier, Christopher; Ford, William T; Gaz, Alessandro; Heyburn, Bernadette; Luiggi Lopez, Eduardo; Nauenberg, Uriel; Smith, James; Stenson, Kevin; Ulmer, Keith; Wagner, Stephen Robert; Zang, Shi-Lei; Agostino, Lorenzo; Alexander, James; Chatterjee, Avishek; Eggert, Nicholas; Gibbons, Lawrence Kent; Heltsley, Brian; Hopkins, Walter; Khukhunaishvili, Aleko; Kreis, Benjamin; Mirman, Nathan; Nicolas Kaufman, Gala; Patterson, Juliet Ritchie; Ryd, Anders; Salvati, Emmanuele; Sun, Werner; Teo, Wee Don; Thom, Julia; Thompson, Joshua; Vaughan, Jennifer; Weng, Yao; Winstrom, Lucas; Wittich, Peter; Biselli, Angela; Cirino, Guy; Winn, Dave; Abdullin, Salavat; Albrow, Michael; Anderson, Jacob; Apollinari, Giorgio; Atac, Muzaffer; Bakken, Jon Alan; Bauerdick, Lothar AT; Beretvas, Andrew; Berryhill, Jeffrey; Bhat, Pushpalatha C; Bloch, Ingo; Burkett, Kevin; Butler, Joel Nathan; Chetluru, Vasundhara; Cheung, Harry; Chlebana, Frank; Cihangir, Selcuk; Cooper, William; Eartly, David P; Elvira, Victor Daniel; Esen, Selda; Fisk, Ian; Freeman, Jim; Gao, Yanyan; Gottschalk, Erik; Green, Dan; Gutsche, Oliver; Hanlon, Jim; Harris, Robert M; Hirschauer, James; Hooberman, Benjamin; Jensen, Hans; Jindariani, Sergo; Johnson, Marvin; Joshi, Umesh; Klima, Boaz; Kunori, Shuichi; Kwan, Simon; Leonidopoulos, Christos; Lincoln, Don; Lipton, Ron; Lykken, Joseph; Maeshima, Kaori; Marraffino, John Michael; Maruyama, Sho; Mason, David; McBride, Patricia; Miao, Ting; Mishra, Kalanand; Mrenna, Stephen; Musienko, Yuri; Newman-Holmes, Catherine; O'Dell, Vivian; Pivarski, James; Pordes, Ruth; Prokofyev, Oleg; Schwarz, Thomas; Sexton-Kennedy, Elizabeth; Sharma, Seema; Spalding, William J; Spiegel, Leonard; Tan, Ping; Taylor, Lucas; Tkaczyk, Slawek; Uplegger, Lorenzo; Vaandering, Eric Wayne; Vidal, Richard; Whitmore, Juliana; Wu, Weimin; Yang, Fan; Yumiceva, Francisco; Yun, Jae Chul; Acosta, Darin; Avery, Paul; Bourilkov, Dimitri; Chen, Mingshui; Das, Souvik; De Gruttola, Michele; Di Giovanni, Gian Piero; Dobur, Didar; Drozdetskiy, Alexey; Field, Richard D; Fisher, Matthew; Fu, Yu; Furic, Ivan-Kresimir; Gartner, Joseph; Goldberg, Sean; Hugon, Justin; Kim, Bockjoo; Konigsberg, Jacobo; Korytov, Andrey; Kropivnitskaya, Anna; Kypreos, Theodore; Low, Jia Fu; Matchev, Konstantin; Milenovic, Predrag; Mitselmakher, Guenakh; Muniz, Lana; Remington, Ronald; Rinkevicius, Aurelijus; Schmitt, Michael Houston; Scurlock, Bobby; Sellers, Paul; Skhirtladze, Nikoloz; Snowball, Matthew; Wang, Dayong; Yelton, John; Zakaria, Mohammed; Gaultney, Vanessa; Lebolo, Luis Miguel; Linn, Stephan; Markowitz, Pete; Martinez, German; Rodriguez, Jorge Luis; Adams, Todd; Askew, Andrew; Bochenek, Joseph; Chen, Jie; Diamond, Brendan; Gleyzer, Sergei V; Haas, Jeff; Hagopian, Sharon; Hagopian, Vasken; Jenkins, Merrill; Johnson, Kurtis F; Prosper, Harrison; Sekmen, Sezen; Veeraraghavan, Venkatesh; Weinberg, Marc; Baarmand, Marc M; Dorney, Brian; Hohlmann, Marcus; Kalakhety, Himali; Vodopiyanov, Igor; Adams, Mark Raymond; Anghel, Ioana Maria; Apanasevich, Leonard; Bai, Yuting; Bazterra, Victor Eduardo; Betts, Russell Richard; Callner, Jeremy; Cavanaugh, Richard; Dragoiu, Cosmin; Gauthier, Lucie; Gerber, Cecilia Elena; Hofman, David Jonathan; Khalatyan, Samvel; Kunde, Gerd J; Lacroix, Florent; Malek, Magdalena; O'Brien, Christine; Silkworth, Christopher; Silvestre, Catherine; Strom, Derek; Varelas, Nikos; Akgun, Ugur; Albayrak, Elif Asli; Bilki, Burak; Clarida, Warren; Duru, Firdevs; Griffiths, Scott; Lae, Chung Khim; McCliment, Edward; Merlo, Jean-Pierre; Mermerkaya, Hamit; Mestvirishvili, Alexi; Moeller, Anthony; Nachtman, Jane; Newsom, Charles Ray; Norbeck, Edwin; Olson, Jonathan; Onel, Yasar; Ozok, Ferhat; Sen, Sercan; Tiras, Emrah; Wetzel, James; Yetkin, Taylan; Yi, Kai; Barnett, Bruce Arnold; Blumenfeld, Barry; Bolognesi, Sara; Bonato, Alessio; Fehling, David; Giurgiu, Gavril; Gritsan, Andrei; Guo, Zijin; Hu, Guofan; Maksimovic, Petar; Rappoccio, Salvatore; Swartz, Morris; Tran, Nhan Viet; Whitbeck, Andrew; Baringer, Philip; Bean, Alice; Benelli, Gabriele; Grachov, Oleg; Kenny Iii, Raymond Patrick; Murray, Michael; Noonan, Daniel; Sanders, Stephen; Stringer, Robert; Tinti, Gemma; Wood, Jeffrey Scott; Zhukova, Victoria; Barfuss, Anne-Fleur; Bolton, Tim; Chakaberia, Irakli; Ivanov, Andrew; Khalil, Sadia; Makouski, Mikhail; Maravin, Yurii; Shrestha, Shruti; Svintradze, Irakli; Gronberg, Jeffrey; Lange, David; Wright, Douglas; Baden, Drew; Boutemeur, Madjid; Calvert, Brian; Eno, Sarah Catherine; Gomez, Jaime; Hadley, Nicholas John; Kellogg, Richard G; Kirn, Malina; Kolberg, Ted; Lu, Ying; Marionneau, Matthieu; Mignerey, Alice; Peterman, Alison; Rossato, Kenneth; Rumerio, Paolo; Skuja, Andris; Temple, Jeffrey; Tonjes, Marguerite; Tonwar, Suresh C; Twedt, Elizabeth; Alver, Burak; Bauer, Gerry; Bendavid, Joshua; Busza, Wit; Butz, Erik; Cali, Ivan Amos; Chan, Matthew; Dutta, Valentina; Gomez Ceballos, Guillelmo; Goncharov, Maxim; Hahn, Kristan Allan; Kim, Yongsun; Klute, Markus; Lee, Yen-Jie; Li, Wei; Luckey, Paul David; Ma, Teng; Nahn, Steve; Paus, Christoph; Ralph, Duncan; Roland, Christof; Roland, Gunther; Rudolph, Matthew; Stephans, George; Stöckli, Fabian; Sumorok, Konstanty; Sung, Kevin; Velicanu, Dragos; Wenger, Edward Allen; Wolf, Roger; Wyslouch, Bolek; Xie, Si; Yang, Mingming; Yilmaz, Yetkin; Yoon, Sungho; Zanetti, Marco; Cooper, Seth; Cushman, Priscilla; Dahmes, Bryan; De Benedetti, Abraham; Franzoni, Giovanni; Gude, Alexander; Haupt, Jason; Kao, Shih-Chuan; Klapoetke, Kevin; Kubota, Yuichi; Mans, Jeremy; Pastika, Nathaniel; Rekovic, Vladimir; Rusack, Roger; Sasseville, Michael; Singovsky, Alexander; Tambe, Norbert; Turkewitz, Jared; Cremaldi, Lucien Marcus; Godang, Romulus; Kroeger, Rob; Perera, Lalith; Rahmat, Rahmat; Sanders, David A; Summers, Don; Avdeeva, Ekaterina; Bloom, Kenneth; Bose, Suvadeep; Butt, Jamila; Claes, Daniel R; Dominguez, Aaron; Eads, Michael; Jindal, Pratima; Keller, Jason; Kravchenko, Ilya; Lazo-Flores, Jose; Malbouisson, Helena; Malik, Sudhir; Snow, Gregory R; Baur, Ulrich; Godshalk, Andrew; Iashvili, Ia; Jain, Supriya; Kharchilava, Avto; Kumar, Ashish; Shipkowski, Simon Peter; Smith, Kenneth; Wan, Zongru; Alverson, George; Barberis, Emanuela; Baumgartel, Darin; Chasco, Matthew; Trocino, Daniele; Wood, Darien; Zhang, Jinzhong; Anastassov, Anton; Kubik, Andrew; Mucia, Nicholas; Odell, Nathaniel; Ofierzynski, Radoslaw Adrian; Pollack, Brian; Pozdnyakov, Andrey; Schmitt, Michael Henry; Stoynev, Stoyan; Velasco, Mayda; Won, Steven; Antonelli, Louis; Berry, Douglas; Brinkerhoff, Andrew; Hildreth, Michael; Jessop, Colin; Karmgard, Daniel John; Kolb, Jeff; Lannon, Kevin; Luo, Wuming; Lynch, Sean; Marinelli, Nancy; Morse, David Michael; Pearson, Tessa; Ruchti, Randy; Slaunwhite, Jason; Valls, Nil; Wayne, Mitchell; Wolf, Matthias; Ziegler, Jill; Bylsma, Ben; Durkin, Lloyd Stanley; Hill, Christopher; Killewald, Phillip; Kotov, Khristian; Ling, Ta-Yung; Puigh, Darren; Rodenburg, Marissa; Vuosalo, Carl; Williams, Grayson; Adam, Nadia; Berry, Edmund; Elmer, Peter; Gerbaudo, Davide; Halyo, Valerie; Hebda, Philip; Hegeman, Jeroen; Hunt, Adam; Laird, Edward; Lopes Pegna, David; Lujan, Paul; Marlow, Daniel; Medvedeva, Tatiana; Mooney, Michael; Olsen, James; Piroué, Pierre; Quan, Xiaohang; Raval, Amita; Saka, Halil; Stickland, David; Tully, Christopher; Werner, Jeremy Scott; Zuranski, Andrzej; Acosta, Jhon Gabriel; Huang, Xing Tao; Lopez, Angel; Mendez, Hector; Oliveros, Sandra; Ramirez Vargas, Juan Eduardo; Zatserklyaniy, Andriy; Alagoz, Enver; Barnes, Virgil E; Benedetti, Daniele; Bolla, Gino; Bortoletto, Daniela; De Mattia, Marco; Everett, Adam; Gutay, Laszlo; Hu, Zhen; Jones, Matthew; Koybasi, Ozhan; Kress, Matthew; Laasanen, Alvin T; Leonardo, Nuno; Maroussov, Vassili; Merkel, Petra; Miller, David Harry; Neumeister, Norbert; Shipsey, Ian; Silvers, David; Svyatkovskiy, Alexey; Vidal Marono, Miguel; Yoo, Hwi Dong; Zablocki, Jakub; Zheng, Yu; Guragain, Samir; Parashar, Neeti; Adair, Antony; Boulahouache, Chaouki; Cuplov, Vesna; Ecklund, Karl Matthew; Geurts, Frank JM; Padley, Brian Paul; Redjimi, Radia; Roberts, Jay; Zabel, James; Betchart, Burton; Bodek, Arie; Chung, Yeon Sei; Covarelli, Roberto; de Barbaro, Pawel; Demina, Regina; Eshaq, Yossof; Garcia-Bellido, Aran; Goldenzweig, Pablo; Gotra, Yury; Han, Jiyeon; Harel, Amnon; Miner, Daniel Carl; Petrillo, Gianluca; Sakumoto, Willis; Vishnevskiy, Dmitry; Zielinski, Marek; Bhatti, Anwar; Ciesielski, Robert; Demortier, Luc; Goulianos, Konstantin; Lungu, Gheorghe; Malik, Sarah; Mesropian, Christina; Arora, Sanjay; Atramentov, Oleksiy; Barker, Anthony; Chou, John Paul; Contreras-Campana, Christian; Contreras-Campana, Emmanuel; Duggan, Daniel; Ferencek, Dinko; Gershtein, Yuri; Gray, Richard; Halkiadakis, Eva; Hidas, Dean; Hits, Dmitry; Lath, Amitabh; Panwalkar, Shruti; Park, Michael; Patel, Rishi; Richards, Alan; Rose, Keith; Salur, Sevil; Schnetzer, Steve; Seitz, Claudia; Somalwar, Sunil; Stone, Robert; Thomas, Scott; Cerizza, Giordano; Hollingsworth, Matthew; Spanier, Stefan; Yang, Zong-Chang; York, Andrew; Eusebi, Ricardo; Flanagan, Will; Gilmore, Jason; Kamon, Teruki; Khotilovich, Vadim; Montalvo, Roy; Osipenkov, Ilya; Pakhotin, Yuriy; Perloff, Alexx; Roe, Jeffrey; Safonov, Alexei; Sakuma, Tai; Sengupta, Sinjini; Suarez, Indara; Tatarinov, Aysen; Toback, David; Akchurin, Nural; Bardak, Cemile; Damgov, Jordan; Dudero, Phillip Russell; Jeong, Chiyoung; Kovitanggoon, Kittikul; Lee, Sung Won; Libeiro, Terence; Mane, Poonam; Roh, Youn; Sill, Alan; Volobouev, Igor; Wigmans, Richard; Appelt, Eric; Brownson, Eric; Engh, Daniel; Florez, Carlos; Gabella, William; Gurrola, Alfredo; Issah, Michael; Johns, Willard; Kurt, Pelin; Maguire, Charles; Melo, Andrew; Sheldon, Paul; Snook, Benjamin; Tuo, Shengquan; Velkovska, Julia; Arenton, Michael Wayne; Balazs, Michael; Boutle, Sarah; Conetti, Sergio; Cox, Bradley; Francis, Brian; Goadhouse, Stephen; Goodell, Joseph; Hirosky, Robert; Ledovskoy, Alexander; Lin, Chuanzhe; Neu, Christopher; Wood, John; Yohay, Rachel; Gollapinni, Sowjanya; Harr, Robert; Karchin, Paul Edmund; Kottachchi Kankanamge Don, Chamath; Lamichhane, Pramod; Mattson, Mark; Milstène, Caroline; Sakharov, Alexandre; Anderson, Michael; Bachtis, Michail; Belknap, Donald; Bellinger, James Nugent; Bernardini, Jacopo; Borrello, Laura; Carlsmith, Duncan; Cepeda, Maria; Dasu, Sridhara; Efron, Jonathan; Friis, Evan; Gray, Lindsey; Grogg, Kira Suzanne; Grothe, Monika; Hall-Wilton, Richard; Herndon, Matthew; Hervé, Alain; Klabbers, Pamela; Klukas, Jeffrey; Lanaro, Armando; Lazaridis, Christos; Leonard, Jessica; Loveless, Richard; Mohapatra, Ajit; Ojalvo, Isabel; Pierro, Giuseppe Antonio; Ross, Ian; Savin, Alexander; Smith, Wesley H; Swanson, Joshua

    2012-01-01

    The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at $\\sqrt{s}$ = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum $p_T$ larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta)<2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with $p_T$ above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with $p_T$ below 100 GeV and, using cosmic rays, it is shown to be bett...

  12. Performance of CMS muon reconstruction in pp collision events at √s = 7 TeV

    Energy Technology Data Exchange (ETDEWEB)

    collaboration, The CMS

    2012-10-01

    The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at $ \\sqrt{s}=7 $ TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.

  13. Data-driven gating in PET: Influence of respiratory signal noise on motion resolution.

    Science.gov (United States)

    Büther, Florian; Ernst, Iris; Frohwein, Lynn Johann; Pouw, Joost; Schäfers, Klaus Peter; Stegger, Lars

    2018-05-21

    Data-driven gating (DDG) approaches for positron emission tomography (PET) are interesting alternatives to conventional hardware-based gating methods. In DDG, the measured PET data themselves are utilized to calculate a respiratory signal, that is, subsequently used for gating purposes. The success of gating is then highly dependent on the statistical quality of the PET data. In this study, we investigate how this quality determines signal noise and thus motion resolution in clinical PET scans using a center-of-mass-based (COM) DDG approach, specifically with regard to motion management of target structures in future radiotherapy planning applications. PET list mode datasets acquired in one bed position of 19 different radiotherapy patients undergoing pretreatment [ 18 F]FDG PET/CT or [ 18 F]FDG PET/MRI were included into this retrospective study. All scans were performed over a region with organs (myocardium, kidneys) or tumor lesions of high tracer uptake and under free breathing. Aside from the original list mode data, datasets with progressively decreasing PET statistics were generated. From these, COM DDG signals were derived for subsequent amplitude-based gating of the original list mode file. The apparent respiratory shift d from end-expiration to end-inspiration was determined from the gated images and expressed as a function of signal-to-noise ratio SNR of the determined gating signals. This relation was tested against additional 25 [ 18 F]FDG PET/MRI list mode datasets where high-precision MR navigator-like respiratory signals were available as reference signal for respiratory gating of PET data, and data from a dedicated thorax phantom scan. All original 19 high-quality list mode datasets demonstrated the same behavior in terms of motion resolution when reducing the amount of list mode events for DDG signal generation. Ratios and directions of respiratory shifts between end-respiratory gates and the respective nongated image were constant over all

  14. A Data-Driven, Integrated Flare Model Based on Self-Organized Criticality

    Science.gov (United States)

    Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M.

    2013-09-01

    We interpret solar flares as events originating in solar active regions having reached the self-organized critical state, by alternatively using two versions of an "integrated flare model" - one static and one dynamic. In both versions the initial conditions are derived from observations aiming to investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. In the static model, we first apply a nonlinear force-free extrapolation that reconstructs the three-dimensional magnetic fields from two-dimensional vector magnetograms. We then locate magnetic discontinuities exceeding a threshold in the Laplacian of the magnetic field. These discontinuities are relaxed in local diffusion events, implemented in the form of cellular-automaton evolution rules. Subsequent loading and relaxation steps lead the system to self-organized criticality, after which the statistical properties of the simulated events are examined. In the dynamic version we deploy an enhanced driving mechanism, which utilizes the observed evolution of active regions, making use of sequential vector magnetograms. We first apply the static cellular automaton model to consecutive solar vector magnetograms until the self-organized critical state is reached. We then evolve the magnetic field inbetween these processed snapshots through spline interpolation, acting as a natural driver in the dynamic model. The identification of magnetically unstable sites as well as their relaxation follow the same rules as in the static model after each interpolation step. Subsequent interpolation/driving and relaxation steps cover all transitions until the end of the sequence. Physical requirements, such as the divergence-free condition for the magnetic field vector, are approximately satisfied in both versions of the model. We obtain robust power laws in the distribution functions of the modelled

  15. Analysis and reconstructed modelling of the debris flow event of the 21st of July 2012 of St. Lorenzen (Styria, Austira)

    Science.gov (United States)

    Janu, Stefan; Mehlhorn, Susanne; Moser, Markus

    2013-04-01

    Analysis and reconstructed modelling of the debris flow event of the 21st of July 2012 of St. Lorenzen (Styria, Austria) Authors: Stefan Janu, Susanne Mehlhorn, Markus Moser The village of St. Lorenzen, in the Styrian Palten valley is situated on the banks of the Lorenz torrent, in which a debris flow event occurred in the early morning hours of the 21st of July 2012, causing catastrophic damage to residential buildings and other infrastructural facilities. In the ministry-approved hazard zone map of 2009, the flood water discharge and bedload volume associated with a 150-year event was estimated at 34 m³/s and 25,000 m³ respectively for the 5.84 km² catchment area. The bedload transport capacity of the torrent was classified as ranging from 'heavy' to 'capable of producing debris flows'. The dominant process type of the mass movement event may be described as a fine-grained debris flow. The damage in the residential area of St.Lorenzen was caused by a debris flow pulse in the lower reach of the Lorenz torrent. This debris flow pulse was in turn caused by numerous landslides along the middle reaches of the torrent, some of which caused blockages, ultimately leading to an outburst event in the main torrent. Discharge cross-sections ranging from 65 - 90 m², and over 100 m² in a few instances, were measured upstream of the St. Lorenzen residential area. Back-calculations of velocities yielded an average debris flow velocity along the middle reaches of the torrent between 11 and 16 m/s. An average velocity of 9 m/s was calculated for the debris flow at the neck of the alluvial fan directly behind the center of the village. Due to both the high discharge values as well as to the height of the mass movement deposits, the natural hazard event of 21 July 2012 in St. Lorenzen is clearly to be described as having had an extreme intensity. A total of 67 buildings were damaged along the Lorenz torrent, 7 of were completely destroyed. According to the Austrian Service for

  16. Development and Evaluation of Vectorised and Multi-Core Event Reconstruction Algorithms within the CMS Software Framework

    CERN Multimedia

    CERN. Geneva

    2012-01-01

    The processing of data acquired by the CMS detector at LHC is carried out with an object-oriented C++ software framework: CMSSW. With the increasing luminosity delivered by the LHC, the treatment of recorded data requires extraordinary large computing resources, also in terms of CPU usage. A possible solution to cope with this task is the exploitation of the features offered by the latest microprocessor architectures. Modern CPUs present several vector units, the capacity of which is growing steadily with the introduction of new processor generations. Moreover, an increasing number of cores per die is offered by the main vendors, even on consumer hardware. Most recent C++ compilers provide facilities to take advantage of such innovations, either by explicit statements in the programs’ sources or automatically adapting the generated machine instructions to the available hardware, without the need of modifying the existing code base. Programming techniques to implement reconstruction algorithms and optimised ...

  17. A data-driven reconstruction of historic land cover/use change of Europe for the period 1900 to 2010

    NARCIS (Netherlands)

    Fuchs, R.

    2015-01-01

    Summary

    The population in Europe almost has doubled within just a little more than 100 years. The related need for food, fibre, water, and shelter led to a tremendous reorganization of the European landscape and its use. These land cover/use changes have far-reaching consequences

  18. Data-driven Inference and Investigation of Thermosphere Dynamics and Variations

    Science.gov (United States)

    Mehta, P. M.; Linares, R.

    2017-12-01

    This paper presents a methodology for data-driven inference and investigation of thermosphere dynamics and variations. The approach uses data-driven modal analysis to extract the most energetic modes of variations for neutral thermospheric species using proper orthogonal decomposition, where the time-independent modes or basis represent the dynamics and the time-depedent coefficients or amplitudes represent the model parameters. The data-driven modal analysis approach combined with sparse, discrete observations is used to infer amplitues for the dynamic modes and to calibrate the energy content of the system. In this work, two different data-types, namely the number density measurements from TIMED/GUVI and the mass density measurements from CHAMP/GRACE are simultaneously ingested for an accurate and self-consistent specification of the thermosphere. The assimilation process is achieved with a non-linear least squares solver and allows estimation/tuning of the model parameters or amplitudes rather than the driver. In this work, we use the Naval Research Lab's MSIS model to derive the most energetic modes for six different species, He, O, N2, O2, H, and N. We examine the dominant drivers of variations for helium in MSIS and observe that seasonal latitudinal variation accounts for about 80% of the dynamic energy with a strong preference of helium for the winter hemisphere. We also observe enhanced helium presence near the poles at GRACE altitudes during periods of low solar activity (Feb 2007) as previously deduced. We will also examine the storm-time response of helium derived from observations. The results are expected to be useful in tuning/calibration of the physics-based models.

  19. Data-driven criteria to assess fear remission and phenotypic variability of extinction in rats.

    Science.gov (United States)

    Shumake, Jason; Jones, Carolyn; Auchter, Allison; Monfils, Marie-Hélène

    2018-03-19

    Fear conditioning is widely employed to examine the mechanisms that underlie dysregulations of the fear system. Various manipulations are often used following fear acquisition to attenuate fear memories. In rodent studies, freezing is often the main output measure to quantify 'fear'. Here, we developed data-driven criteria for defining a standard benchmark that indicates remission from conditioned fear and for identifying subgroups with differential treatment responses. These analyses will enable a better understanding of individual differences in treatment responding.This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'. © 2018 The Author(s).

  20. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

    OpenAIRE

    Kim, Eun-Kyung; Kim, Hyo-Eun; Han, Kyunghwa; Kang, Bong Joo; Sohn, Yu-Mee; Woo, Ok Hee; Lee, Chan Wha

    2018-01-01

    We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remai...

  1. Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer

    Science.gov (United States)

    Clark, Jeremy; Cooper, Colin S; Mills, Robert; Rayward-Smith, Victor J; de la Iglesia, Beatriz

    2015-01-01

    Background Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the

  2. Classification Systems, their Digitization and Consequences for Data-Driven Decision Making

    DEFF Research Database (Denmark)

    Stein, Mari-Klara; Newell, Sue; Galliers, Robert D.

    2013-01-01

    Classification systems are foundational in many standardized software tools. This digitization of classification systems gives them a new ‘materiality’ that, jointly with the social practices of information producers/consumers, has significant consequences on the representational quality of such ...... and the foundational role of representational quality in understanding the success and consequences of data-driven decision-making.......-narration and meta-narration), and three different information production/consumption situations. We contribute to the relational theorization of representational quality and extend classification systems research by drawing explicit attention to the importance of ‘materialization’ of classification systems...

  3. Data-driven Discovery: A New Era of Exploiting the Literature and Data

    Directory of Open Access Journals (Sweden)

    Ying Ding

    2016-11-01

    Full Text Available In the current data-intensive era, the traditional hands-on method of conducting scientific research by exploring related publications to generate a testable hypothesis is well on its way of becoming obsolete within just a year or two. Analyzing the literature and data to automatically generate a hypothesis might become the de facto approach to inform the core research efforts of those trying to master the exponentially rapid expansion of publications and datasets. Here, viewpoints are provided and discussed to help the understanding of challenges of data-driven discovery.

  4. A data driven method to measure electron charge mis-identification rate

    CERN Document Server

    Bakhshiansohi, Hamed

    2009-01-01

    Electron charge mis-measurement is an important challenge in analyses which depend on the charge of electron. To estimate the probability of {\\it electron charge mis-measurement} a data driven method is introduced and a good agreement with MC based methods is achieved.\\\\ The third moment of $\\phi$ distribution of hits in electron SuperCluster is studied. The correlation between this variable and the electron charge is also investigated. Using this `new' variable and some other variables the electron charge measurement is improved by two different approaches.

  5. Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

    Science.gov (United States)

    Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

    2016-02-01

    This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

  6. Development and Evaluation of Vectorised and Multi-Core Event Reconstruction Algorithms within the CMS Software Framework

    Science.gov (United States)

    Hauth, T.; Innocente and, V.; Piparo, D.

    2012-12-01

    The processing of data acquired by the CMS detector at LHC is carried out with an object-oriented C++ software framework: CMSSW. With the increasing luminosity delivered by the LHC, the treatment of recorded data requires extraordinary large computing resources, also in terms of CPU usage. A possible solution to cope with this task is the exploitation of the features offered by the latest microprocessor architectures. Modern CPUs present several vector units, the capacity of which is growing steadily with the introduction of new processor generations. Moreover, an increasing number of cores per die is offered by the main vendors, even on consumer hardware. Most recent C++ compilers provide facilities to take advantage of such innovations, either by explicit statements in the programs sources or automatically adapting the generated machine instructions to the available hardware, without the need of modifying the existing code base. Programming techniques to implement reconstruction algorithms and optimised data structures are presented, that aim to scalable vectorization and parallelization of the calculations. One of their features is the usage of new language features of the C++11 standard. Portions of the CMSSW framework are illustrated which have been found to be especially profitable for the application of vectorization and multi-threading techniques. Specific utility components have been developed to help vectorization and parallelization. They can easily become part of a larger common library. To conclude, careful measurements are described, which show the execution speedups achieved via vectorised and multi-threaded code in the context of CMSSW.

  7. Characterization of a high resolution and high sensitivity pre-clinical PET scanner with 3D event reconstruction

    CERN Document Server

    Rissi, M; Bolle, E; Dorholt, O; Hines, K E; Rohne, O; Skretting, A; Stapnes, S; Volgyes, D

    2012-01-01

    COMPET is a preclinical PET scanner aiming towards a high sensitivity, a high resolution and MRI compatibility by implementing a novel detector geometry. In this approach, long scintillating LYSO crystals are used to absorb the gamma-rays. To determine the point of interaction (P01) between gamma-ray and crystal, the light exiting the crystals on one of the long sides is collected with wavelength shifters (WLS) perpendicularly arranged to the crystals. This concept has two main advantages: (1) The parallax error is reduced to a minimum and is equal for the whole field of view (FOV). (2) The P01 and its energy deposit is known in all three dimension with a high resolution, allowing for the reconstruction of Compton scattered gamma-rays. Point (1) leads to a uniform point source resolution (PSR) distribution over the whole FOV, and also allows to place the detector close to the object being imaged. Both points (1) and (2) lead to an increased sensitivity and allow for both high resolution and sensitivity at the...

  8. NA49 event display of the reconstructed tracks emanating from the "little bang" created in a central collision of Lead projectile with a Lead nucleus

    CERN Document Server

    1996-01-01

    When a 33 TeV Lead nucleous projectile hits head on a Lead target it creates for a brief instant of time a system of quarks and gluons that more than 100'000 times hotter than the sun. As this fireball expands it reconstitutes normal matter creating thousands of particles (pions, kaons, protons, ....). The NA49 Time Projection Chambers extending over 13 m can record a large majority of the produced charged particles. The result of a complex analysis is shown here in a event display of the reconstructed tracks. The blue lines represent the boundaries of the detectors in a perspective view from the far end of the experiment towards the interaction point, where all the tracks originate.

  9. Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications.

    Science.gov (United States)

    Kalantari, Zahra; Cavalli, Marco; Cantone, Carolina; Crema, Stefano; Destouni, Georgia

    2017-03-01

    Climate-driven increase in the frequency of extreme hydrological events is expected to impose greater strain on the built environment and major transport infrastructure, such as roads and railways. This study develops a data-driven spatial-statistical approach to quantifying and mapping the probability of flooding at critical road-stream intersection locations, where water flow and sediment transport may accumulate and cause serious road damage. The approach is based on novel integration of key watershed and road characteristics, including also measures of sediment connectivity. The approach is concretely applied to and quantified for two specific study case examples in southwest Sweden, with documented road flooding effects of recorded extreme rainfall. The novel contributions of this study in combining a sediment connectivity account with that of soil type, land use, spatial precipitation-runoff variability and road drainage in catchments, and in extending the connectivity measure use for different types of catchments, improve the accuracy of model results for road flood probability. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

    Directory of Open Access Journals (Sweden)

    Natalie Jane de Vries

    Full Text Available Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

  11. A Data-driven Concept Schema for Defining Clinical Research Data Needs

    Science.gov (United States)

    Hruby, Gregory W.; Hoxha, Julia; Ravichandran, Praveen Chandar; Mendonça, Eneida A.; Hanauer, David A; Weng, Chunhua

    2016-01-01

    OBJECTIVES The Patient, Intervention, Control/Comparison, and Outcome (PICO) framework is an effective technique for framing a clinical question. We aim to develop the counterpart of PICO to structure clinical research data needs. METHODS We use a data-driven approach to abstracting key concepts representing clinical research data needs by adapting and extending an expert-derived framework originally developed for defining cancer research data needs. We annotated clinical trial eligibility criteria, EHR data request logs, and data queries to electronic health records (EHR), to extract and harmonize concept classes representing clinical research data needs. We evaluated the class coverage, class preservation from the original framework, schema generalizability, schema understandability, and schema structural correctness through a semi-structured interview with eight multidisciplinary domain experts. We iteratively refined the schema based on the evaluations. RESULTS Our data-driven schema preserved 68% of the 63 classes from the original framework and covered 88% (73/82) of the classes proposed by evaluators. Class coverage for participants of different backgrounds ranged from 60% to 100% with a median value of 95% agreement among the individual evaluators. The schema was found understandable and structurally sound. CONCLUSIONS Our proposed schema may serve as the counterpart to PICO for improving the research data needs communication between researchers and informaticians. PMID:27185504

  12. A copula-based sampling method for data-driven prognostics

    International Nuclear Information System (INIS)

    Xi, Zhimin; Jing, Rong; Wang, Pingfeng; Hu, Chao

    2014-01-01

    This paper develops a Copula-based sampling method for data-driven prognostics. The method essentially consists of an offline training process and an online prediction process: (i) the offline training process builds a statistical relationship between the failure time and the time realizations at specified degradation levels on the basis of off-line training data sets; and (ii) the online prediction process identifies probable failure times for online testing units based on the statistical model constructed in the offline process and the online testing data. Our contributions in this paper are three-fold, namely the definition of a generic health index system to quantify the health degradation of an engineering system, the construction of a Copula-based statistical model to learn the statistical relationship between the failure time and the time realizations at specified degradation levels, and the development of a simulation-based approach for the prediction of remaining useful life (RUL). Two engineering case studies, namely the electric cooling fan health prognostics and the 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology. - Highlights: • We develop a novel mechanism for data-driven prognostics. • A generic health index system quantifies health degradation of engineering systems. • Off-line training model is constructed based on the Bayesian Copula model. • Remaining useful life is predicted from a simulation-based approach

  13. Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

    Science.gov (United States)

    Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

    2016-01-01

    In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.

  14. General Purpose Data-Driven Online System Health Monitoring with Applications to Space Operations

    Science.gov (United States)

    Iverson, David L.; Spirkovska, Lilly; Schwabacher, Mark

    2010-01-01

    Modern space transportation and ground support system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional parameter limit checking, or model-based or rule-based methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults, failures, or precursors of significant failures. The Inductive Monitoring System (IMS) is a general purpose, data-driven system health monitoring software tool that has been successfully applied to several aerospace applications and is under evaluation for anomaly detection in vehicle and ground equipment for next generation launch systems. After an introduction to IMS application development, we discuss these NASA online monitoring applications, including the integration of IMS with complementary model-based and rule-based methods. Although the examples presented in this paper are from space operations applications, IMS is a general-purpose health-monitoring tool that is also applicable to power generation and transmission system monitoring.

  15. A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

    Science.gov (United States)

    de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo

    2014-01-01

    Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

  16. Data-driven risk identification in phase III clinical trials using central statistical monitoring.

    Science.gov (United States)

    Timmermans, Catherine; Venet, David; Burzykowski, Tomasz

    2016-02-01

    Our interest lies in quality control for clinical trials, in the context of risk-based monitoring (RBM). We specifically study the use of central statistical monitoring (CSM) to support RBM. Under an RBM paradigm, we claim that CSM has a key role to play in identifying the "risks to the most critical data elements and processes" that will drive targeted oversight. In order to support this claim, we first see how to characterize the risks that may affect clinical trials. We then discuss how CSM can be understood as a tool for providing a set of data-driven key risk indicators (KRIs), which help to organize adaptive targeted monitoring. Several case studies are provided where issues in a clinical trial have been identified thanks to targeted investigation after the identification of a risk using CSM. Using CSM to build data-driven KRIs helps to identify different kinds of issues in clinical trials. This ability is directly linked with the exhaustiveness of the CSM approach and its flexibility in the definition of the risks that are searched for when identifying the KRIs. In practice, a CSM assessment of the clinical database seems essential to ensure data quality. The atypical data patterns found in some centers and variables are seen as KRIs under a RBM approach. Targeted monitoring or data management queries can be used to confirm whether the KRIs point to an actual issue or not.

  17. Data-driven integration of genome-scale regulatory and metabolic network models

    Science.gov (United States)

    Imam, Saheed; Schäuble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

    2015-01-01

    Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert—a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system. PMID:25999934

  18. Data-driven HR how to use analytics and metrics to drive performance

    CERN Document Server

    Marr, Bernard

    2018-01-01

    Traditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-driven HR is a practical guide which enables HR practitioners to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, how to collect information in a transparent way that is in line with data protection requirements and how to turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR profession...

  19. Data-driven integration of genome-scale regulatory and metabolic network models

    Directory of Open Access Journals (Sweden)

    Saheed eImam

    2015-05-01

    Full Text Available Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription and signaling have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert – a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

  20. Data-driven CT protocol review and management—experience from a large academic hospital.

    Science.gov (United States)

    Zhang, Da; Savage, Cristy A; Li, Xinhua; Liu, Bob

    2015-03-01

    Protocol review plays a critical role in CT quality assurance, but large numbers of protocols and inconsistent protocol names on scanners and in exam records make thorough protocol review formidable. In this investigation, we report on a data-driven cataloging process that can be used to assist in the reviewing and management of CT protocols. We collected lists of scanner protocols, as well as 18 months of recent exam records, for 10 clinical scanners. We developed computer algorithms to automatically deconstruct the protocol names on the scanner and in the exam records into core names and descriptive components. Based on the core names, we were able to group the scanner protocols into a much smaller set of "core protocols," and to easily link exam records with the scanner protocols. We calculated the percentage of usage for each core protocol, from which the most heavily used protocols were identified. From the percentage-of-usage data, we found that, on average, 18, 33, and 49 core protocols per scanner covered 80%, 90%, and 95%, respectively, of all exams. These numbers are one order of magnitude smaller than the typical numbers of protocols that are loaded on a scanner (200-300, as reported in the literature). Duplicated, outdated, and rarely used protocols on the scanners were easily pinpointed in the cataloging process. The data-driven cataloging process can facilitate the task of protocol review. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  1. Data-driven approach for assessing utility of medical tests using electronic medical records.

    Science.gov (United States)

    Skrøvseth, Stein Olav; Augestad, Knut Magne; Ebadollahi, Shahram

    2015-02-01

    To precisely define the utility of tests in a clinical pathway through data-driven analysis of the electronic medical record (EMR). The information content was defined in terms of the entropy of the expected value of the test related to a given outcome. A kernel density classifier was used to estimate the necessary distributions. To validate the method, we used data from the EMR of the gastrointestinal department at a university hospital. Blood tests from patients undergoing surgery for gastrointestinal surgery were analyzed with respect to second surgery within 30 days of the index surgery. The information content is clearly reflected in the patient pathway for certain combinations of tests and outcomes. C-reactive protein tests coupled to anastomosis leakage, a severe complication show a clear pattern of information gain through the patient trajectory, where the greatest gain from the test is 3-4 days post index surgery. We have defined the information content in a data-driven and information theoretic way such that the utility of a test can be precisely defined. The results reflect clinical knowledge. In the case we used the tests carry little negative impact. The general approach can be expanded to cases that carry a substantial negative impact, such as in certain radiological techniques. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  2. Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

    Directory of Open Access Journals (Sweden)

    Huan-Kai Peng

    Full Text Available In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.

  3. Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization

    Science.gov (United States)

    Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

    2016-01-01

    In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications. PMID:26771830

  4. Data-driven directions for effective footwear provision for the high-risk diabetic foot.

    Science.gov (United States)

    Arts, M L J; de Haart, M; Waaijman, R; Dahmen, R; Berendsen, H; Nollet, F; Bus, S A

    2015-06-01

    Custom-made footwear is used to offload the diabetic foot to prevent plantar foot ulcers. This prospective study evaluates the offloading effects of modifying custom-made footwear and aims to provide data-driven directions for the provision of effectively offloading footwear in clinical practice. Eighty-five people with diabetic neuropathy and a recently healed plantar foot ulcer, who participated in a clinical trial on footwear effectiveness, had their custom-made footwear evaluated with in-shoe plantar pressure measurements at three-monthly intervals. Footwear was modified when peak pressure was ≥ 200 kPa. The effect of single and combined footwear modifications on in-shoe peak pressure at these high-pressure target locations was assessed. All footwear modifications significantly reduced peak pressure at the target locations compared with pre-modification levels (range -6.7% to -24.0%, P diabetic neuropathy and a recently healed plantar foot ulcer, significant offloading can be achieved at high-risk foot regions by modifying custom-made footwear. These results provide data-driven directions for the design and evaluation of custom-made footwear for high-risk people with diabetes, and essentially mean that each shoe prescribed should incorporate those design features that effectively offload the foot. © 2015 The Authors. Diabetic Medicine © 2015 Diabetes UK.

  5. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

    International Nuclear Information System (INIS)

    Hu Chao; Youn, Byeng D.; Wang Pingfeng; Taek Yoon, Joung

    2012-01-01

    Prognostics aims at determining whether a failure of an engineered system (e.g., a nuclear power plant) is impending and estimating the remaining useful life (RUL) before the failure occurs. The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust; (ii) it wastes the resources for constructing the algorithms that are discarded; (iii) it requires the testing data in addition to the training data. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.

  6. Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

    International Nuclear Information System (INIS)

    An, Dawn; Kim, Nam H.; Choi, Joo-Ho

    2015-01-01

    This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available. - Highlights: • Practical review of data-driven and physics-based prognostics are provided. • As common prognostics algorithms, NN, GP, PF and BM are introduced. • Algorithms’ attributes, pros and cons, and applicable conditions are discussed. • This will be helpful to choose the best algorithm for different applications

  7. The effects of data-driven learning activities on EFL learners' writing development.

    Science.gov (United States)

    Luo, Qinqin

    2016-01-01

    Data-driven learning has been proved as an effective approach in helping learners solve various writing problems such as correcting lexical or grammatical errors, improving the use of collocations and generating ideas in writing, etc. This article reports on an empirical study in which data-driven learning was accomplished with the assistance of the user-friendly BNCweb, and presents the evaluation of the outcome by comparing the effectiveness of BNCweb and a search engine Baidu which is most commonly used as reference resource by Chinese learners of English as a foreign language. The quantitative results about 48 Chinese college students revealed that the experimental group which used BNCweb performed significantly better in the post-test in terms of writing fluency and accuracy, as compared with the control group which used the search engine Baidu. However, no significant difference was found between the two groups in terms of writing complexity. The qualitative results about the interview revealed that learners generally showed a positive attitude toward the use of BNCweb but there were still some problems of using corpora in the writing process, thus the combined use of corpora and other types of reference resource was suggested as a possible way to counter the potential barriers for Chinese learners of English.

  8. Real-time digital filtering, event triggering, and tomographic reconstruction of JET soft x-ray data (abstract)

    Science.gov (United States)

    Edwards, A. W.; Blackler, K.; Gill, R. D.; van der Goot, E.; Holm, J.

    1990-10-01

    Based upon the experience gained with the present soft x-ray data acquisition system, new techniques are being developed which make extensive use of digital signal processors (DSPs). Digital filters make 13 further frequencies available in real time from the input sampling frequency of 200 kHz. In parallel, various algorithms running on further DSPs generate triggers in response to a range of events in the plasma. The sawtooth crash can be detected, for example, with a delay of only 50 μs from the onset of the collapse. The trigger processor interacts with the digital filter boards to ensure data of the appropriate frequency is recorded throughout a plasma discharge. An independent link is used to pass 780 and 24 Hz filtered data to a network of transputers. A full tomographic inversion and display of the 24 Hz data is carried out in real time using this 15 transputer array. The 780 Hz data are stored for immediate detailed playback following the pulse. Such a system could considerably improve the quality of present plasma diagnostic data which is, in general, sampled at one fixed frequency throughout a discharge. Further, it should provide valuable information towards designing diagnostic data acquisition systems for future long pulse operation machines when a high degree of real-time processing will be required, while retaining the ability to detect, record, and analyze events of interest within such long plasma discharges.

  9. Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

    Science.gov (United States)

    Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

    2017-11-01

    Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

  10. A tephra lattice for Greenland and a reconstruction of volcanic events spanning 25-45 ka b2k

    Science.gov (United States)

    Bourne, A. J.; Cook, E.; Abbott, P. M.; Seierstad, I. K.; Steffensen, J. P.; Svensson, A.; Fischer, H.; Schüpbach, S.; Davies, S. M.

    2015-06-01

    Tephra layers preserved within the Greenland ice-cores are crucial for the independent synchronisation of these high-resolution records to other palaeoclimatic archives. Here we present a new and detailed tephrochronological framework for the time period 25,000-45,000 a b2k that brings together results from 4 deep Greenland ice-cores. In total, 99 tephra deposits, the majority of which are preserved as cryptotephra, are described from the NGRIP, NEEM, GRIP and DYE-3 records. The major element signatures of single glass shards within these deposits indicate that 93 are basaltic in composition all originating from Iceland. Specifically, 43 originate from Grimsvötn, 20 are thought to be sourced from the Katla volcanic system and 17 show affinity to the Kverkfjöll system. Robust geochemical characterisations, independent ages derived from the GICC05 ice-core chronology, and the stratigraphic positions of these deposits relative to the Dansgaard-Oeschger climate events represent a key framework that provides new information on the frequency and nature of volcanic events in the North Atlantic region between GS-3 and GI-12. Of particular importance are 19 tephra deposits that lie on the rapid climatic transitions that punctuate the last glacial period. This framework of well-constrained, time-synchronous tie-lines represents an important step towards the independent synchronisation of marine, terrestrial and ice-core records from the North Atlantic region, in order to assess the phasing of rapid climatic changes during the last glacial period.

  11. Global retrieval of soil moisture and vegetation properties using data-driven methods

    Science.gov (United States)

    Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

    2017-04-01

    Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre

  12. Automatic translation of MPI source into a latency-tolerant, data-driven form

    International Nuclear Information System (INIS)

    Nguyen, Tan; Cicotti, Pietro; Bylaska, Eric; Quinlan, Dan; Baden, Scott

    2017-01-01

    Hiding communication behind useful computation is an important performance programming technique but remains an inscrutable programming exercise even for the expert. We present Bamboo, a code transformation framework that can realize communication overlap in applications written in MPI without the need to intrusively modify the source code. We reformulate MPI source into a task dependency graph representation, which partially orders the tasks, enabling the program to execute in a data-driven fashion under the control of an external runtime system. Experimental results demonstrate that Bamboo significantly reduces communication delays while requiring only modest amounts of programmer annotation for a variety of applications and platforms, including those employing co-processors and accelerators. Moreover, Bamboo’s performance meets or exceeds that of labor-intensive hand coding. As a result, the translator is more than a means of hiding communication costs automatically; it demonstrates the utility of semantic level optimization against a well-known library.

  13. The test of data driven TDC application in high energy physics experiment

    International Nuclear Information System (INIS)

    Liu Shubin; Guo Jianhua; Zhang Yanli; Zhao Long; An Qi

    2006-01-01

    In the high energy physics domain there is a trend to use integrated, high resolution, multi-hit time-digital-converter for time measurement, of which the data driven TDC is an important direction. Study on the method of how to test high performance TDC's characters and how to improve these characters will help us to select the proper TDC. The authors have studied the testing of a new high resolution TDC, which is planned to use in the third modification project of Beijing Spectrometer (BESIII). This paper introduces the test platform we built for the TDC, and the method by which we tested for nonlinearity, resolution, double pulse resolution characters, etc. The paper also gives the test results and introduces the compensation way to achieve a very high resolution (24.4 ps). (authors)

  14. Data-driven techniques to estimate parameters in a rate-dependent ferromagnetic hysteresis model

    International Nuclear Information System (INIS)

    Hu Zhengzheng; Smith, Ralph C.; Ernstberger, Jon M.

    2012-01-01

    The quantification of rate-dependent ferromagnetic hysteresis is important in a range of applications including high speed milling using Terfenol-D actuators. There exist a variety of frameworks for characterizing rate-dependent hysteresis including the magnetic model in Ref. , the homogenized energy framework, Preisach formulations that accommodate after-effects, and Prandtl-Ishlinskii models. A critical issue when using any of these models to characterize physical devices concerns the efficient estimation of model parameters through least squares data fits. A crux of this issue is the determination of initial parameter estimates based on easily measured attributes of the data. In this paper, we present data-driven techniques to efficiently and robustly estimate parameters in the homogenized energy model. This framework was chosen due to its physical basis and its applicability to ferroelectric, ferromagnetic and ferroelastic materials.

  15. Data-driven fault detection for industrial processes canonical correlation analysis and projection based methods

    CERN Document Server

    Chen, Zhiwen

    2017-01-01

    Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Contents A New Index for Performance Evaluation of FD Methods CCA-based FD Method for the Monitoring of Stationary Processes Projection-based FD Method for the Monitoring of Dynamic Processes Benchmark Study and Real-Time Implementat...

  16. Combining engineering and data-driven approaches: Development of a generic fire risk model facilitating calibration

    DEFF Research Database (Denmark)

    De Sanctis, G.; Fischer, K.; Kohler, J.

    2014-01-01

    Fire risk models support decision making for engineering problems under the consistent consideration of the associated uncertainties. Empirical approaches can be used for cost-benefit studies when enough data about the decision problem are available. But often the empirical approaches...... a generic risk model that is calibrated to observed fire loss data. Generic risk models assess the risk of buildings based on specific risk indicators and support risk assessment at a portfolio level. After an introduction to the principles of generic risk assessment, the focus of the present paper...... are not detailed enough. Engineering risk models, on the other hand, may be detailed but typically involve assumptions that may result in a biased risk assessment and make a cost-benefit study problematic. In two related papers it is shown how engineering and data-driven modeling can be combined by developing...

  17. Sensor fault analysis using decision theory and data-driven modeling of pressurized water reactor subsystems

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Skorska, M.

    1984-01-01

    Instrument fault detection and estimation is important for process surveillance, control, and safety functions of a power plant. The method incorporates the dual-hypotheses decision procedure and system characterization using data-driven time-domain models of signals representing the system. The multivariate models can be developed on-line and can be adapted to changing system conditions. For the method to be effective, specific subsystems of pressurized water reactors were considered, and signal selection was made such that a strong causal relationship exists among the measured variables. The technique is applied to the reactor core subsystem of the loss-of-fluid test reactor using in-core neutron detector and core-exit thermocouple signals. Thermocouple anomalies such as bias error, noise error, and slow drift in the sensor are detected and estimated using appropriate measurement models

  18. Data-driven process decomposition and robust online distributed modelling for large-scale processes

    Science.gov (United States)

    Shu, Zhang; Lijuan, Li; Lijuan, Yao; Shipin, Yang; Tao, Zou

    2018-02-01

    With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.

  19. A new data-driven controllability measure with application in intelligent buildings

    DEFF Research Database (Denmark)

    Shaker, Hamid Reza; Lazarova-Molnar, Sanja

    2017-01-01

    and instrumentation within today's intelligent buildings enable collecting high quality data which could be used directly in data-based analysis and control methods. The area of data-based systems analysis and control is concentrating on developing analysis and control methods that rely on data collected from meters...... and sensors, and information obtained by data processing. This differs from the traditional model-based approaches that are based on mathematical models of systems. We propose and describe a data-driven controllability measure for discrete-time linear systems. The concept is developed within a data......-based system analysis and control framework. Therefore, only measured data is used to obtain the proposed controllability measure. The proposed controllability measure not only shows if the system is controllable or not, but also reveals the level of controllability, which is the information its previous...

  20. Beyond Crowd Judgments: Data-driven Estimation of Market Value in Association Football

    DEFF Research Database (Denmark)

    Müller, Oliver; Simons, Alexander; Weinmann, Markus

    2017-01-01

    concern. Market values can be understood as estimates of transfer fees—that is, prices that could be paid for a player on the football market—so they play an important role in transfer negotiations. These values have traditionally been estimated by football experts, but crowdsourcing has emerged......Association football is a popular sport, but it is also a big business. From a managerial perspective, the most important decisions that team managers make concern player transfers, so issues related to player valuation, especially the determination of transfer fees and market values, are of major......’ market values using multilevel regression analysis. The regression results suggest that data-driven estimates of market value can overcome several of the crowd's practical limitations while producing comparably accurate numbers. Our results have important implications for football managers and scouts...

  1. Data-driven design of fault diagnosis systems nonlinear multimode processes

    CERN Document Server

    Haghani Abandan Sari, Adel

    2014-01-01

    In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target...

  2. Data-driven modeling, control and tools for cyber-physical energy systems

    Science.gov (United States)

    Behl, Madhur

    Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about

  3. Automatic sleep classification using a data-driven topic model reveals latent sleep states

    DEFF Research Database (Denmark)

    Koch, Henriette; Christensen, Julie Anja Engelhard; Frandsen, Rune

    2014-01-01

    Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group......Background: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. New method: To meet the criticism and reveal the latent...... sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1 s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model...

  4. A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

    CERN Document Server

    AUTHOR|(CDS)2092751; Martino, Michele

    The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. Albeit a model may be available; however, such a model may be too complex to consider for an appropriate controller design. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs...

  5. Data-Driven Based Asynchronous Motor Control for Printing Servo Systems

    Science.gov (United States)

    Bian, Min; Guo, Qingyun

    Modern digital printing equipment aims to the environmental-friendly industry with high dynamic performances and control precision and low vibration and abrasion. High performance motion control system of printing servo systems was required. Control system of asynchronous motor based on data acquisition was proposed. Iterative learning control (ILC) algorithm was studied. PID control was widely used in the motion control. However, it was sensitive to the disturbances and model parameters variation. The ILC applied the history error data and present control signals to approximate the control signal directly in order to fully track the expect trajectory without the system models and structures. The motor control algorithm based on the ILC and PID was constructed and simulation results were given. The results show that data-driven control method is effective dealing with bounded disturbances for the motion control of printing servo systems.

  6. DOE High Performance Computing Operational Review (HPCOR): Enabling Data-Driven Scientific Discovery at HPC Facilities

    Energy Technology Data Exchange (ETDEWEB)

    Gerber, Richard; Allcock, William; Beggio, Chris; Campbell, Stuart; Cherry, Andrew; Cholia, Shreyas; Dart, Eli; England, Clay; Fahey, Tim; Foertter, Fernanda; Goldstone, Robin; Hick, Jason; Karelitz, David; Kelly, Kaki; Monroe, Laura; Prabhat,; Skinner, David; White, Julia

    2014-10-17

    U.S. Department of Energy (DOE) High Performance Computing (HPC) facilities are on the verge of a paradigm shift in the way they deliver systems and services to science and engineering teams. Research projects are producing a wide variety of data at unprecedented scale and level of complexity, with community-specific services that are part of the data collection and analysis workflow. On June 18-19, 2014 representatives from six DOE HPC centers met in Oakland, CA at the DOE High Performance Operational Review (HPCOR) to discuss how they can best provide facilities and services to enable large-scale data-driven scientific discovery at the DOE national laboratories. The report contains findings from that review.

  7. A data-driven fault-tolerant control design of linear multivariable systems with performance optimization.

    Science.gov (United States)

    Li, Zhe; Yang, Guang-Hong

    2017-09-01

    In this paper, an integrated data-driven fault-tolerant control (FTC) design scheme is proposed under the configuration of the Youla parameterization for multiple-input multiple-output (MIMO) systems. With unknown system model parameters, the canonical form identification technique is first applied to design the residual observer in fault-free case. In faulty case, with online tuning of the Youla parameters based on the system data via the gradient-based algorithm, the fault influence is attenuated with system performance optimization. In addition, to improve the robustness of the residual generator to a class of system deviations, a novel adaptive scheme is proposed for the residual generator to prevent its over-activation. Simulation results of a two-tank flow system demonstrate the optimized performance and effect of the proposed FTC scheme. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics

    Directory of Open Access Journals (Sweden)

    Richard Mark Leggett

    2013-12-01

    Full Text Available The processes of quality assessment and control are an active area of research at The Genome Analysis Centre (TGAC. Unlike other sequencing centres that often concentrate on a certain species or technology, TGAC applies expertise in genomics and bioinformatics to a wide range of projects, often requiring bespoke wet lab and in silico workflows. TGAC is fortunate to have access to a diverse range of sequencing and analysis platforms, and we are at the forefront of investigations into library quality and sequence data assessment. We have developed and implemented a number of algorithms, tools, pipelines and packages to ascertain, store, and expose quality metrics across a number of next-generation sequencing platforms, allowing rapid and in-depth cross-platform QC bioinformatics. In this review, we describe these tools as a vehicle for data-driven informatics, offering the potential to provide richer context for downstream analysis and to inform experimental design.

  9. A data-driven multiplicative fault diagnosis approach for automation processes.

    Science.gov (United States)

    Hao, Haiyang; Zhang, Kai; Ding, Steven X; Chen, Zhiwen; Lei, Yaguo

    2014-09-01

    This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented. Copyright © 2013. Published by Elsevier Ltd.

  10. Data-driven gradient algorithm for high-precision quantum control

    Science.gov (United States)

    Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

    2018-04-01

    In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

  11. USACM Thematic Workshop On Uncertainty Quantification And Data-Driven Modeling.

    Energy Technology Data Exchange (ETDEWEB)

    Stewart, James R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    The USACM Thematic Workshop on Uncertainty Quantification and Data-Driven Modeling was held on March 23-24, 2017, in Austin, TX. The organizers of the technical program were James R. Stewart of Sandia National Laboratories and Krishna Garikipati of University of Michigan. The administrative organizer was Ruth Hengst, who serves as Program Coordinator for the USACM. The organization of this workshop was coordinated through the USACM Technical Thrust Area on Uncertainty Quantification and Probabilistic Analysis. The workshop website (http://uqpm2017.usacm.org) includes the presentation agenda as well as links to several of the presentation slides (permission to access the presentations was granted by each of those speakers, respectively). Herein, this final report contains the complete workshop program that includes the presentation agenda, the presentation abstracts, and the list of posters.

  12. Linear dynamical modes as new variables for data-driven ENSO forecast

    Science.gov (United States)

    Gavrilov, Andrey; Seleznev, Aleksei; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander; Kurths, Juergen

    2018-05-01

    A new data-driven model for analysis and prediction of spatially distributed time series is proposed. The model is based on a linear dynamical mode (LDM) decomposition of the observed data which is derived from a recently developed nonlinear dimensionality reduction approach. The key point of this approach is its ability to take into account simple dynamical properties of the observed system by means of revealing the system's dominant time scales. The LDMs are used as new variables for empirical construction of a nonlinear stochastic evolution operator. The method is applied to the sea surface temperature anomaly field in the tropical belt where the El Nino Southern Oscillation (ENSO) is the main mode of variability. The advantage of LDMs versus traditionally used empirical orthogonal function decomposition is demonstrated for this data. Specifically, it is shown that the new model has a competitive ENSO forecast skill in comparison with the other existing ENSO models.

  13. The thermoluminescence as tool in the reconstruction of volcanic events; La termoluminiscencia como herramienta en la reconstruccion de eventos volcanicos

    Energy Technology Data Exchange (ETDEWEB)

    Ramirez L, A.; Schaaf, P.; Martin del Pozzo, A.L.; Gonzalez M, P. [Instituto de Geofisica, UNAM, C.P. 04500, Mexico D.F. (Mexico)

    2000-07-01

    Within the Mexican land a great number of volcanoes are situated which a considerable part of them are still active. The relevance of dating pomex deposits, ash or lava of these poly genetic volcanoes is to determine the periodicity and magnitude of the volcanic events happened. In this work is presented the preliminary result of the dating by thermoluminescence in a pomex of a pyroclastic flux coming from a volcano in the state of Puebla with the purpose of providing elements to the knowledge which describe the eruptive history of the explosive volcanism at center of Mexico. For the sample dating the volcanic glasses of pomex were separated and it was applied the fine grain technique with a grain size between 4-11 {mu} m. In order to calculate the rate of annual dose it was carried out the following: in the determination of {sup 238} U and {sup 232} Th radioisotope concentration was used the neutron activation technique in a nuclear reactor, in the determination of the K 40 radioisotope was used a scanning electron microscope, the rate of environmental and cosmic dose was measured arranging Tl dosemeters of CaSO{sub 4}: Dy in the sampling place. In order to calculate the paleodoses it was carried out the following: the equivalent dose (Q) was determined starting form the additive method and the supra linearity factor (I) starting from regenerative method and in both methods the irradiated process was realized with a {sup 90} Sr beta source. With the above determinations it was calculated a paleodoses of 231 Gy and a rate of annual dose of 6.074 x 10{sup -3} Gy/year, estimating an age of: Age{sub pomez} = 231 Gy / 6.074 Gy x 10{sup -3} Gy /year = 38030 {+-} 4000 years. (Author)

  14. Testing the Accuracy of Data-driven MHD Simulations of Active Region Evolution

    Energy Technology Data Exchange (ETDEWEB)

    Leake, James E.; Linton, Mark G. [U.S. Naval Research Laboratory, 4555 Overlook Avenue, SW, Washington, DC 20375 (United States); Schuck, Peter W., E-mail: james.e.leake@nasa.gov [NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771 (United States)

    2017-04-01

    Models for the evolution of the solar coronal magnetic field are vital for understanding solar activity, yet the best measurements of the magnetic field lie at the photosphere, necessitating the development of coronal models which are “data-driven” at the photosphere. We present an investigation to determine the feasibility and accuracy of such methods. Our validation framework uses a simulation of active region (AR) formation, modeling the emergence of magnetic flux from the convection zone to the corona, as a ground-truth data set, to supply both the photospheric information and to perform the validation of the data-driven method. We focus our investigation on how the accuracy of the data-driven model depends on the temporal frequency of the driving data. The Helioseismic and Magnetic Imager on NASA’s Solar Dynamics Observatory produces full-disk vector magnetic field measurements at a 12-minute cadence. Using our framework we show that ARs that emerge over 25 hr can be modeled by the data-driving method with only ∼1% error in the free magnetic energy, assuming the photospheric information is specified every 12 minutes. However, for rapidly evolving features, under-sampling of the dynamics at this cadence leads to a strobe effect, generating large electric currents and incorrect coronal morphology and energies. We derive a sampling condition for the driving cadence based on the evolution of these small-scale features, and show that higher-cadence driving can lead to acceptable errors. Future work will investigate the source of errors associated with deriving plasma variables from the photospheric magnetograms as well as other sources of errors, such as reduced resolution, instrument bias, and noise.

  15. Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

    Directory of Open Access Journals (Sweden)

    Merima Kulin

    2016-06-01

    Full Text Available Data science or “data-driven research” is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i clarifies when, why and how to use data science in wireless network research; (ii provides a generic framework for applying data science in wireless networks; (iii gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

  16. First-principles data-driven discovery of transition metal oxides for artificial photosynthesis

    Science.gov (United States)

    Yan, Qimin

    We develop a first-principles data-driven approach for rapid identification of transition metal oxide (TMO) light absorbers and photocatalysts for artificial photosynthesis using the Materials Project. Initially focusing on Cr, V, and Mn-based ternary TMOs in the database, we design a broadly-applicable multiple-layer screening workflow automating density functional theory (DFT) and hybrid functional calculations of bulk and surface electronic and magnetic structures. We further assess the electrochemical stability of TMOs in aqueous environments from computed Pourbaix diagrams. Several promising earth-abundant low band-gap TMO compounds with desirable band edge energies and electrochemical stability are identified by our computational efforts and then synergistically evaluated using high-throughput synthesis and photoelectrochemical screening techniques by our experimental collaborators at Caltech. Our joint theory-experiment effort has successfully identified new earth-abundant copper and manganese vanadate complex oxides that meet highly demanding requirements for photoanodes, substantially expanding the known space of such materials. By integrating theory and experiment, we validate our approach and develop important new insights into structure-property relationships for TMOs for oxygen evolution photocatalysts, paving the way for use of first-principles data-driven techniques in future applications. This work is supported by the Materials Project Predictive Modeling Center and the Joint Center for Artificial Photosynthesis through the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-05CH11231. Computational resources also provided by the Department of Energy through the National Energy Supercomputing Center.

  17. Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem

    Directory of Open Access Journals (Sweden)

    Xianming Dou

    2017-12-01

    Full Text Available Accurate estimation of carbon and water fluxes of forest ecosystems is of particular importance for addressing the problems originating from global environmental change, and providing helpful information about carbon and water content for analyzing and diagnosing past and future climate change. The main focus of the current work was to investigate the feasibility of four comparatively new methods, including generalized regression neural network, group method of data handling (GMDH, extreme learning machine and adaptive neuro-fuzzy inference system (ANFIS, for elucidating the carbon and water fluxes in a forest ecosystem. A comparison was made between these models and two widely used data-driven models, artificial neural network (ANN and support vector machine (SVM. All the models were evaluated based on the following statistical indices: coefficient of determination, Nash-Sutcliffe efficiency, root mean square error and mean absolute error. Results indicated that the data-driven models are capable of accounting for most variance in each flux with the limited meteorological variables. The ANN model provided the best estimates for gross primary productivity (GPP and net ecosystem exchange (NEE, while the ANFIS model achieved the best for ecosystem respiration (R, indicating that no single model was consistently superior to others for the carbon flux prediction. In addition, the GMDH model consistently produced somewhat worse results for all the carbon flux and evapotranspiration (ET estimations. On the whole, among the carbon and water fluxes, all the models produced similar highly satisfactory accuracy for GPP, R and ET fluxes, and did a reasonable job of reproducing the eddy covariance NEE. Based on these findings, it was concluded that these advanced models are promising alternatives to ANN and SVM for estimating the terrestrial carbon and water fluxes.

  18. Preface [HD3-2015: International meeting on high-dimensional data-driven science

    International Nuclear Information System (INIS)

    2016-01-01

    A never-ending series of innovations in measurement technology and evolutions in information and communication technologies have led to the ongoing generation and accumulation of large quantities of high-dimensional data every day. While detailed data-centric approaches have been pursued in respective research fields, situations have been encountered where the same mathematical framework of high-dimensional data analysis can be found in a wide variety of seemingly unrelated research fields, such as estimation on the basis of undersampled Fourier transform in nuclear magnetic resonance spectroscopy in chemistry, in magnetic resonance imaging in medicine, and in astronomical interferometry in astronomy. In such situations, bringing diverse viewpoints together therefore becomes a driving force for the creation of innovative developments in various different research fields. This meeting focuses on “Sparse Modeling” (SpM) as a methodology for creation of innovative developments through the incorporation of a wide variety of viewpoints in various research fields. The objective of this meeting is to offer a forum where researchers with interest in SpM can assemble and exchange information on the latest results and newly established methodologies, and discuss future directions of the interdisciplinary studies for High-Dimensional Data-Driven science (HD 3 ). The meeting was held in Kyoto from 14-17 December 2015. We are pleased to publish 22 papers contributed by invited speakers in this volume of Journal of Physics: Conference Series. We hope that this volume will promote further development of High-Dimensional Data-Driven science. (paper)

  19. A data-driven weighting scheme for multivariate phenotypic endpoints recapitulates zebrafish developmental cascades

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Guozhu, E-mail: gzhang6@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Roell, Kyle R., E-mail: krroell@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Truong, Lisa, E-mail: lisa.truong@oregonstate.edu [Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR (United States); Tanguay, Robert L., E-mail: robert.tanguay@oregonstate.edu [Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR (United States); Reif, David M., E-mail: dmreif@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC (United States)

    2017-01-01

    Zebrafish have become a key alternative model for studying health effects of environmental stressors, partly due to their genetic similarity to humans, fast generation time, and the efficiency of generating high-dimensional systematic data. Studies aiming to characterize adverse health effects in zebrafish typically include several phenotypic measurements (endpoints). While there is a solid biomedical basis for capturing a comprehensive set of endpoints, making summary judgments regarding health effects requires thoughtful integration across endpoints. Here, we introduce a Bayesian method to quantify the informativeness of 17 distinct zebrafish endpoints as a data-driven weighting scheme for a multi-endpoint summary measure, called weighted Aggregate Entropy (wAggE). We implement wAggE using high-throughput screening (HTS) data from zebrafish exposed to five concentrations of all 1060 ToxCast chemicals. Our results show that our empirical weighting scheme provides better performance in terms of the Receiver Operating Characteristic (ROC) curve for identifying significant morphological effects and improves robustness over traditional curve-fitting approaches. From a biological perspective, our results suggest that developmental cascade effects triggered by chemical exposure can be recapitulated by analyzing the relationships among endpoints. Thus, wAggE offers a powerful approach for analysis of multivariate phenotypes that can reveal underlying etiological processes. - Highlights: • Introduced a data-driven weighting scheme for multiple phenotypic endpoints. • Weighted Aggregate Entropy (wAggE) implies differential importance of endpoints. • Endpoint relationships reveal developmental cascade effects triggered by exposure. • wAggE is generalizable to multi-endpoint data of different shapes and scales.

  20. A data-driven prediction method for fast-slow systems

    Science.gov (United States)

    Groth, Andreas; Chekroun, Mickael; Kondrashov, Dmitri; Ghil, Michael

    2016-04-01

    In this work, we present a prediction method for processes that exhibit a mixture of variability on low and fast scales. The method relies on combining empirical model reduction (EMR) with singular spectrum analysis (SSA). EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity and serial correlation in the estimated noise, while SSA provides a decomposition of the complex dynamics into low-order components that capture spatio-temporal behavior on different time scales. Our study focuses on the data-driven modeling of partial observations from dynamical systems that exhibit power spectra with broad peaks. The main result in this talk is that the combination of SSA pre-filtering with EMR modeling improves, under certain circumstances, the modeling and prediction skill of such a system, as compared to a standard EMR prediction based on raw data. Specifically, it is the separation into "fast" and "slow" temporal scales by the SSA pre-filtering that achieves the improvement. We show, in particular that the resulting EMR-SSA emulators help predict intermittent behavior such as rapid transitions between specific regions of the system's phase space. This capability of the EMR-SSA prediction will be demonstrated on two low-dimensional models: the Rössler system and a Lotka-Volterra model for interspecies competition. In either case, the chaotic dynamics is produced through a Shilnikov-type mechanism and we argue that the latter seems to be an important ingredient for the good prediction skills of EMR-SSA emulators. Shilnikov-type behavior has been shown to arise in various complex geophysical fluid models, such as baroclinic quasi-geostrophic flows in the mid-latitude atmosphere and wind-driven double-gyre ocean circulation models. This pervasiveness of the Shilnikow mechanism of fast-slow transition opens interesting perspectives for the extension of the proposed EMR-SSA approach to more realistic situations.

  1. Dust Deposition Events on Mt. Elbrus, Caucasus Mountains in the 21st Century Reconstructed from the Shallow Firn and Ice Cores (Invited)

    Science.gov (United States)

    Shahgedanova, M.; Kutuzov, S.; Mikhalenko, V.; Ginot, P.; Lavrentiev, I.

    2013-12-01

    This paper presents and discusses a record of dust deposition events reconstructed from the shallow firn and ice cores extracted on the Western Plateau, Mt. Elbrus, Caucasus Mountains, Russia. A combination of SEVIRI imagery, HYSPLIT trajectory model, meteorological and atmospheric optical depth data were used to establish timing of deposition events and source regions of dust with very high temporal (hours) and spatial (c. 50-100 km) resolution. The source regions of the desert dust transported to Mt. Elbrus were primarily located in the Middle East, in particular in eastern Syria and in the Syrian Desert at the border between Saudi Arabia, Iraq and Jordan. Northern Sahara, the foothills of the Djebel Akhdar Mountains in eastern Libya and the border region between Libya and Algeria were other important sources of desert dust. Dust sources in the Sahara were natural (e.g. palaeolakes and alluvial deposits in the foothills) while in the Middle East, dust entrainment occurred from both natural (e.g. dry river beds) and anthropogenic (e.g. agricultural fields) sources. The overall majority of dust deposition events occurred between March and June and, less frequently, dust deposition events occurred in February and October. In all cases, dust deposition was associated with depressions causing strong surface wind and dust uplift in the source areas, transportation of dust to the Caucasus with a strong south-westerly flow from the Sahara or southerly flow from the Middle East, merging of the dust clouds with precipitation-bearing weather fronts and precipitation over the Caucasus region. The Saharan depressions were vigorous and associated with stronger daily wind speeds of 20-30 m/s at the 700 hPa level; depressions forming over the Middle East and the associated wind speeds were weaker at 12-15 m/s. The Saharan depressions were less frequent than those carrying dust from the Middle East but higher dust loads were associated with the Saharan depressions. A higher

  2. RWater - A Novel Cyber-enabled Data-driven Educational Tool for Interpreting and Modeling Hydrologic Processes

    Science.gov (United States)

    Rajib, M. A.; Merwade, V.; Zhao, L.; Song, C.

    2014-12-01

    Explaining the complex cause-and-effect relationships in hydrologic cycle can often be challenging in a classroom with the use of traditional teaching approaches. With the availability of observed rainfall, streamflow and other hydrology data on the internet, it is possible to provide the necessary tools to students to explore these relationships and enhance their learning experience. From this perspective, a new online educational tool, called RWater, is developed using Purdue University's HUBzero technology. RWater's unique features include: (i) its accessibility including the R software from any java supported web browser; (ii) no installation of any software on user's computer; (iii) all the work and resulting data are stored in user's working directory on RWater server; and (iv) no prior programming experience with R software is necessary. In its current version, RWater can dynamically extract streamflow data from any USGS gaging station without any need for post-processing for use in the educational modules. By following data-driven modules, students can write small scripts in R and thereby create visualizations to identify the effect of rainfall distribution and watershed characteristics on runoff generation, investigate the impacts of landuse and climate change on streamflow, and explore the changes in extreme hydrologic events in actual locations. Each module contains relevant definitions, instructions on data extraction and coding, as well as conceptual questions based on the possible analyses which the students would perform. In order to assess its suitability in classroom implementation, and to evaluate users' perception over its utility, the current version of RWater has been tested with three different groups: (i) high school students, (ii) middle and high school teachers; and (iii) upper undergraduate/graduate students. The survey results from these trials suggest that the RWater has potential to improve students' understanding on various

  3. Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM

    KAUST Repository

    Amer, Abdelhalim

    2013-01-01

    Extracting maximum performance of multi-core architectures is a difficult task primarily due to bandwidth limitations of the memory subsystem and its complex hierarchy. In this work, we study the implications of fork-join and data-driven execution models on this type of architecture at the level of task parallelism. For this purpose, we use a highly optimized fork-join based implementation of the FMM and extend it to a data-driven implementation using a distributed task scheduling approach. This study exposes some limitations of the conventional fork-join implementation in terms of synchronization overheads. We find that these are not negligible and their elimination by the data-driven method, with a careful data locality strategy, was beneficial. Experimental evaluation of both methods on state-of-the-art multi-socket multi-core architectures showed up to 22% speed-ups of the data-driven approach compared to the original method. We demonstrate that a data-driven execution of FMM not only improves performance by avoiding global synchronization overheads but also reduces the memory-bandwidth pressure caused by memory-intensive computations. © 2013 Springer-Verlag.

  4. Tracking Invasive Alien Species (TrIAS: Building a data-driven framework to inform policy

    Directory of Open Access Journals (Sweden)

    Sonia Vanderhoeven

    2017-05-01

    Full Text Available Imagine a future where dynamically, from year to year, we can track the progression of alien species (AS, identify emerging problem species, assess their current and future risk and timely inform policy in a seamless data-driven workflow. One that is built on open science and open data infrastructures. By using international biodiversity standards and facilities, we would ensure interoperability, repeatability and sustainability. This would make the process adaptable to future requirements in an evolving AS policy landscape both locally and internationally. In recent years, Belgium has developed decision support tools to inform invasive alien species (IAS policy, including information systems, early warning initiatives and risk assessment protocols. However, the current workflows from biodiversity observations to IAS science and policy are slow, not easily repeatable, and their scope is often taxonomically, spatially and temporally limited. This is mainly caused by the diversity of actors involved and the closed, fragmented nature of the sources of these biodiversity data, which leads to considerable knowledge gaps for IAS research and policy. We will leverage expertise and knowledge from nine former and current BELSPO projects and initiatives: Alien Alert, Invaxen, Diars, INPLANBEL, Alien Impact, Ensis, CORDEX.be, Speedy and the Belgian Biodiversity Platform. The project will be built on two components: 1 The establishment of a data mobilization framework for AS data from diverse data sources and 2 the development of data-driven procedures for risk evaluation based on risk modelling, risk mapping and risk assessment. We will use facilities from the Global Biodiversity Information Facility (GBIF, standards from the Biodiversity Information Standards organization (TDWG and expertise from Lifewatch to create and facilitate a systematic workflow. Alien species data will be gathered from a large set of regional, national and international

  5. Measurement of the helicity fractions of W bosons from top quark decays using fully reconstructed t anti-t events with CDF II

    Energy Technology Data Exchange (ETDEWEB)

    Abulencia, A.; Adelman, J.; Affolder, T.; Akimoto, T.; Albrow, M.G.; Ambrose, D.; Amerio, S.; Amidei, D.; Anastassov, A.; Anikeev, K.; Annovi, A.; /Taiwan, Inst. Phys.

    2006-12-01

    The authors present a measurement of the fractions F{sub 0} and F{sub +} of longitudinally polarized and right-handed W bosons in top quark decays using data collected with the CDF II detector. The data set used in the analysis corresponds to an integrated luminosity of approximately 318 pb{sup -1}. They select t{bar t} candidate events with one lepton, at least four jets, and missing transverse energy. The helicity measurement uses the decay angle {theta}*, which is defined as the angle between the momentum of the charged lepton in the W boson rest frame and the W momentum in the top quark rest frame. The cos {theta}* distribution in the data is determined by full kinematic reconstruction of the t{bar t} candidates. They find F{sub 0} = 0.85{sub -0.22}{sup +0.15}(stat){+-}0.06(syst) and F{sub +} = 0.05{sub -0.05}{sup +0.11}(stat) {+-} 0.03(syst), which is consistent with the standard model prediction. They set an upper limit on the fraction of right-handed W bosons of F{sub +} < 0.26 at the 95% confidence level.

  6. Towards Data-Driven Simulations of Wildfire Spread using Ensemble-based Data Assimilation

    Science.gov (United States)

    Rochoux, M. C.; Bart, J.; Ricci, S. M.; Cuenot, B.; Trouvé, A.; Duchaine, F.; Morel, T.

    2012-12-01

    Real-time predictions of a propagating wildfire remain a challenging task because the problem involves both multi-physics and multi-scales. The propagation speed of wildfires, also called the rate of spread (ROS), is indeed determined by complex interactions between pyrolysis, combustion and flow dynamics, atmospheric dynamics occurring at vegetation, topographical and meteorological scales. Current operational fire spread models are mainly based on a semi-empirical parameterization of the ROS in terms of vegetation, topographical and meteorological properties. For the fire spread simulation to be predictive and compatible with operational applications, the uncertainty on the ROS model should be reduced. As recent progress made in remote sensing technology provides new ways to monitor the fire front position, a promising approach to overcome the difficulties found in wildfire spread simulations is to integrate fire modeling and fire sensing technologies using data assimilation (DA). For this purpose we have developed a prototype data-driven wildfire spread simulator in order to provide optimal estimates of poorly known model parameters [*]. The data-driven simulation capability is adapted for more realistic wildfire spread : it considers a regional-scale fire spread model that is informed by observations of the fire front location. An Ensemble Kalman Filter algorithm (EnKF) based on a parallel computing platform (OpenPALM) was implemented in order to perform a multi-parameter sequential estimation where wind magnitude and direction are in addition to vegetation properties (see attached figure). The EnKF algorithm shows its good ability to track a small-scale grassland fire experiment and ensures a good accounting for the sensitivity of the simulation outcomes to the control parameters. As a conclusion, it was shown that data assimilation is a promising approach to more accurately forecast time-varying wildfire spread conditions as new airborne-like observations of

  7. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    Science.gov (United States)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-06-01

    Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space Rn. An isometric mapping F from M to a low-dimensional, compact, connected set A⊂Rd(d≪n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructing low

  8. Developing a Data Driven Process-Based Model for Remote Sensing of Ecosystem Production

    Science.gov (United States)

    Elmasri, B.; Rahman, A. F.

    2010-12-01

    Estimating ecosystem carbon fluxes at various spatial and temporal scales is essential for quantifying the global carbon cycle. Numerous models have been developed for this purpose using several environmental variables as well as vegetation indices derived from remotely sensed data. Here we present a data driven modeling approach for gross primary production (GPP) that is based on a process based model BIOME-BGC. The proposed model was run using available remote sensing data and it does not depend on look-up tables. Furthermore, this approach combines the merits of both empirical and process models, and empirical models were used to estimate certain input variables such as light use efficiency (LUE). This was achieved by using remotely sensed data to the mathematical equations that represent biophysical photosynthesis processes in the BIOME-BGC model. Moreover, a new spectral index for estimating maximum photosynthetic activity, maximum photosynthetic rate index (MPRI), is also developed and presented here. This new index is based on the ratio between the near infrared and the green bands (ρ858.5/ρ555). The model was tested and validated against MODIS GPP product and flux measurements from two eddy covariance flux towers located at Morgan Monroe State Forest (MMSF) in Indiana and Harvard Forest in Massachusetts. Satellite data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) and MODIS were used. The data driven model showed a strong correlation between the predicted and measured GPP at the two eddy covariance flux towers sites. This methodology produced better predictions of GPP than did the MODIS GPP product. Moreover, the proportion of error in the predicted GPP for MMSF and Harvard forest was dominated by unsystematic errors suggesting that the results are unbiased. The analysis indicated that maintenance respiration is one of the main factors that dominate the overall model outcome errors and improvement in maintenance respiration estimation

  9. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    International Nuclear Information System (INIS)

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-01-01

    Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R n . An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R d (d<< n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology

  10. Enabling Data-Driven Methodologies Across the Data Lifecycle and Ecosystem

    Science.gov (United States)

    Doyle, R. J.; Crichton, D.

    2017-12-01

    NASA has unlocked unprecedented scientific knowledge through exploration of the Earth, our solar system, and the larger universe. NASA is generating enormous amounts of data that are challenging traditional approaches to capturing, managing, analyzing and ultimately gaining scientific understanding from science data. New architectures, capabilities and methodologies are needed to span the entire observing system, from spacecraft to archive, while integrating data-driven discovery and analytic capabilities. NASA data have a definable lifecycle, from remote collection point to validated accessibility in multiple archives. Data challenges must be addressed across this lifecycle, to capture opportunities and avoid decisions that may limit or compromise what is achievable once data arrives at the archive. Data triage may be necessary when the collection capacity of the sensor or instrument overwhelms data transport or storage capacity. By migrating computational and analytic capability to the point of data collection, informed decisions can be made about which data to keep; in some cases, to close observational decision loops onboard, to enable attending to unexpected or transient phenomena. Along a different dimension than the data lifecycle, scientists and other end-users must work across an increasingly complex data ecosystem, where the range of relevant data is rarely owned by a single institution. To operate effectively, scalable data architectures and community-owned information models become essential. NASA's Planetary Data System is having success with this approach. Finally, there is the difficult challenge of reproducibility and trust. While data provenance techniques will be part of the solution, future interactive analytics environments must support an ability to provide a basis for a result: relevant data source and algorithms, uncertainty tracking, etc., to assure scientific integrity and to enable confident decision making. Advances in data science offer

  11. A Data-Driven Reliability Estimation Approach for Phased-Mission Systems

    Directory of Open Access Journals (Sweden)

    Hua-Feng He

    2014-01-01

    Full Text Available We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example.

  12. Data-driven classification of bipolar I disorder from longitudinal course of mood.

    Science.gov (United States)

    Cochran, A L; McInnis, M G; Forger, D B

    2016-10-11

    The Diagnostic and Statistical Manual of Mental Disorder (DSM) classification of bipolar disorder defines categories to reflect common understanding of mood symptoms rather than scientific evidence. This work aimed to determine whether bipolar I can be objectively classified from longitudinal mood data and whether resulting classes have clinical associations. Bayesian nonparametric hierarchical models with latent classes and patient-specific models of mood are fit to data from Longitudinal Interval Follow-up Evaluations (LIFE) of bipolar I patients (N=209). Classes are tested for clinical associations. No classes are justified using the time course of DSM-IV mood states. Three classes are justified using the course of subsyndromal mood symptoms. Classes differed in attempted suicides (P=0.017), disability status (P=0.012) and chronicity of affective symptoms (P=0.009). Thus, bipolar I disorder can be objectively classified from mood course, and individuals in the resulting classes share clinical features. Data-driven classification from mood course could be used to enrich sample populations for pharmacological and etiological studies.

  13. Data-driven automatic parking constrained control for four-wheeled mobile vehicles

    Directory of Open Access Journals (Sweden)

    Wenxu Yan

    2016-11-01

    Full Text Available In this article, a novel data-driven constrained control scheme is proposed for automatic parking systems. The design of the proposed scheme only depends on the steering angle and the orientation angle of the car, and it does not involve any model information of the car. Therefore, the proposed scheme-based automatic parking system is applicable to different kinds of cars. In order to further reduce the desired trajectory coordinate tracking errors, a coordinates compensation algorithm is also proposed. In the design procedure of the controller, a novel dynamic anti-windup compensator is used to deal with the change magnitude and rate saturations of automatic parking control input. It is theoretically proven that all the signals in the closed-loop system are uniformly ultimately bounded based on Lyapunov stability analysis method. Finally, a simulation comparison among the proposed scheme with coordinates compensation and Proportion Integration Differentiation (PID control algorithm is given. It is shown that the proposed scheme with coordinates compensation has smaller tracking errors and more rapid responses than PID scheme.

  14. The Facilitation of a Sustainable Power System: A Practice from Data-Driven Enhanced Boiler Control

    Directory of Open Access Journals (Sweden)

    Zhenlong Wu

    2018-04-01

    Full Text Available An increasing penetration of renewable energy may bring significant challenges to a power system due to its inherent intermittency. To achieve a sustainable future for renewable energy, a conventional power plant is required to be able to change its power output rapidly for a grid balance purpose. However, the rapid power change may result in the boiler operating in a dangerous manner. To this end, this paper aims to improve boiler control performance via a data-driven control strategy, namely Active Disturbance Rejection Control (ADRC. For practical implementation, a tuning method is developed for ADRC controller parameters to maximize its potential in controlling a boiler operating in different conditions. Based on a Monte Carlo simulation, a Probabilistic Robustness (PR index is subsequently formulated to represent the controller’s sensitivity to the varying conditions. The stability region of the ADRC controller is depicted to provide the search space in which the optimal group of parameters is searched for based on the PR index. Illustrative simulations are performed to verify the efficacy of the proposed method. Finally, the proposed method is experimentally applied to a boiler’s secondary air control system successfully. The results of the field application show that the proposed ADRC based on PR can ensure the expected control performance even though it works in a wider range of operating conditions. The field application depicts a promising future for the ADRC controller as an alternative solution in the power industry to integrate more renewable energy into the power grid.

  15. Data-driven analysis of functional brain interactions during free listening to music and speech.

    Science.gov (United States)

    Fang, Jun; Hu, Xintao; Han, Junwei; Jiang, Xi; Zhu, Dajiang; Guo, Lei; Liu, Tianming

    2015-06-01

    Natural stimulus functional magnetic resonance imaging (N-fMRI) such as fMRI acquired when participants were watching video streams or listening to audio streams has been increasingly used to investigate functional mechanisms of the human brain in recent years. One of the fundamental challenges in functional brain mapping based on N-fMRI is to model the brain's functional responses to continuous, naturalistic and dynamic natural stimuli. To address this challenge, in this paper we present a data-driven approach to exploring functional interactions in the human brain during free listening to music and speech streams. Specifically, we model the brain responses using N-fMRI by measuring the functional interactions on large-scale brain networks with intrinsically established structural correspondence, and perform music and speech classification tasks to guide the systematic identification of consistent and discriminative functional interactions when multiple subjects were listening music and speech in multiple categories. The underlying premise is that the functional interactions derived from N-fMRI data of multiple subjects should exhibit both consistency and discriminability. Our experimental results show that a variety of brain systems including attention, memory, auditory/language, emotion, and action networks are among the most relevant brain systems involved in classic music, pop music and speech differentiation. Our study provides an alternative approach to investigating the human brain's mechanism in comprehension of complex natural music and speech.

  16. Forecasting success via early adoptions analysis: A data-driven study.

    Directory of Open Access Journals (Sweden)

    Giulio Rossetti

    Full Text Available Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

  17. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

    Science.gov (United States)

    Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

    2017-01-01

    Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

  18. Optimizing preventive maintenance policy: A data-driven application for a light rail braking system.

    Science.gov (United States)

    Corman, Francesco; Kraijema, Sander; Godjevac, Milinko; Lodewijks, Gabriel

    2017-10-01

    This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.

  19. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study.

    Science.gov (United States)

    Kim, Eun-Kyung; Kim, Hyo-Eun; Han, Kyunghwa; Kang, Bong Joo; Sohn, Yu-Mee; Woo, Ok Hee; Lee, Chan Wha

    2018-02-09

    We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients' age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remainder were used for training. The core algorithm of DIB-MG is a deep convolutional neural network; a deep learning algorithm specialized for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth. At the operating point (threshold) of 0.5, sensitivity was 75.6% and 76.1% when specificity was 90.2% and 88.5%, and AUC was 0.903 and 0.906 for the validation and test sets, respectively. This research showed the potential of DIB-MG as a screening tool for breast cancer.

  20. Using data-driven agent-based models for forecasting emerging infectious diseases

    Directory of Open Access Journals (Sweden)

    Srinivasan Venkatramanan

    2018-03-01

    Full Text Available Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models provide a comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper, we describe one such agent-based model framework developed for forecasting the 2014–2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refined and adapted for future epidemics, and share the lessons learned over the course of the challenge. Keywords: Emerging infectious diseases, Agent-based models, Simulation optimization, Bayesian calibration, Ebola

  1. A priori data-driven multi-clustered reservoir generation algorithm for echo state network.

    Directory of Open Access Journals (Sweden)

    Xiumin Li

    Full Text Available Echo state networks (ESNs with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.

  2. Data-free and data-driven spectral perturbations for RANS UQ

    Science.gov (United States)

    Edeling, Wouter; Mishra, Aashwin; Iaccarino, Gianluca

    2017-11-01

    Despite recent developments in high-fidelity turbulent flow simulations, RANS modeling is still vastly used by industry, due to its inherent low cost. Since accuracy is a concern in RANS modeling, model-form UQ is an essential tool for assessing the impacts of this uncertainty on quantities of interest. Applying the spectral decomposition to the modeled Reynolds-Stress Tensor (RST) allows for the introduction of decoupled perturbations into the baseline intensity (kinetic energy), shape (eigenvalues), and orientation (eigenvectors). This constitutes a natural methodology to evaluate the model form uncertainty associated to different aspects of RST modeling. In a predictive setting, one frequently encounters an absence of any relevant reference data. To make data-free predictions with quantified uncertainty we employ physical bounds to a-priori define maximum spectral perturbations. When propagated, these perturbations yield intervals of engineering utility. High-fidelity data opens up the possibility of inferring a distribution of uncertainty, by means of various data-driven machine-learning techniques. We will demonstrate our framework on a number of flow problems where RANS models are prone to failure. This research was partially supported by the Defense Advanced Research Projects Agency under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo), and the DOE PSAAP-II program.

  3. BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

    Science.gov (United States)

    Zhao, Ming; Rattanatamrong, Prapaporn; DiGiovanna, Jack; Mahmoudi, Babak; Figueiredo, Renato J; Sanchez, Justin C; Príncipe, José C; Fortes, José A B

    2008-01-01

    Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

  4. Data-driven modelling of structured populations a practical guide to the integral projection model

    CERN Document Server

    Ellner, Stephen P; Rees, Mark

    2016-01-01

    This book is a “How To” guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includes code in the R language (in the text and online) to carry out all computations. The intended audience are ecologists, evolutionary biologists, and mathematical biologists interested in developing data-driven models for animal and plant populations. IPMs may seem hard as they involve integrals. The aim of this book is to demystify IPMs, so they become the model of choice for populations structured by size or other continuously varying traits. The book uses real examples of increasing complexity to show how the life-cycle of the study organism naturally leads to the appropriate statistical analysis, which leads directly to the IPM itself. A wide range of model types and analyses are presented, including model construction, computational methods, and the underlying theory, with the more technical material in B...

  5. Lessons learned from a data-driven college access program: The National College Advising Corps.

    Science.gov (United States)

    Horng, Eileen L; Evans, Brent J; Antonio, Anthony L; Foster, Jesse D; Kalamkarian, Hoori S; Hurd, Nicole F; Bettinger, Eric P

    2013-01-01

    This chapter discusses the collaboration between a national college access program, the National College Advising Corps (NCAC), and its research and evaluation team at Stanford University. NCAC is currently active in almost four hundred high schools and through the placement of a recent college graduate to serve as a college adviser provides necessary information and support for students who may find it difficult to navigate the complex college admission process. The advisers also conduct outreach to underclassmen in an effort to improve the school-wide college-going culture. Analyses include examination of both quantitative and qualitative data from numerous sources and partners with every level of the organization from the national office to individual high schools. The authors discuss balancing the pursuit of evaluation goals with academic scholarship. In an effort to benefit other programs seeking to form successful data-driven interventions, the authors provide explicit examples of the partnership and present several examples of how the program has benefited from the data gathered by the evaluation team. © WILEY PERIODICALS, INC.

  6. A data-driven approach for evaluating multi-modal therapy in traumatic brain injury.

    Science.gov (United States)

    Haefeli, Jenny; Ferguson, Adam R; Bingham, Deborah; Orr, Adrienne; Won, Seok Joon; Lam, Tina I; Shi, Jian; Hawley, Sarah; Liu, Jialing; Swanson, Raymond A; Massa, Stephen M

    2017-02-16

    Combination therapies targeting multiple recovery mechanisms have the potential for additive or synergistic effects, but experimental design and analyses of multimodal therapeutic trials are challenging. To address this problem, we developed a data-driven approach to integrate and analyze raw source data from separate pre-clinical studies and evaluated interactions between four treatments following traumatic brain injury. Histologic and behavioral outcomes were measured in 202 rats treated with combinations of an anti-inflammatory agent (minocycline), a neurotrophic agent (LM11A-31), and physical therapy consisting of assisted exercise with or without botulinum toxin-induced limb constraint. Data was curated and analyzed in a linked workflow involving non-linear principal component analysis followed by hypothesis testing with a linear mixed model. Results revealed significant benefits of the neurotrophic agent LM11A-31 on learning and memory outcomes after traumatic brain injury. In addition, modulations of LM11A-31 effects by co-administration of minocycline and by the type of physical therapy applied reached statistical significance. These results suggest a combinatorial effect of drug and physical therapy interventions that was not evident by univariate analysis. The study designs and analytic techniques applied here form a structured, unbiased, internally validated workflow that may be applied to other combinatorial studies, both in animals and humans.

  7. Forecasting success via early adoptions analysis: A data-driven study.

    Science.gov (United States)

    Rossetti, Giulio; Milli, Letizia; Giannotti, Fosca; Pedreschi, Dino

    2017-01-01

    Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

  8. Data-Driven Astrochemistry: One Step Further within the Origin of Life Puzzle.

    Science.gov (United States)

    Ruf, Alexander; d'Hendecourt, Louis L S; Schmitt-Kopplin, Philippe

    2018-06-01

    Astrochemistry, meteoritics and chemical analytics represent a manifold scientific field, including various disciplines. In this review, clarifications on astrochemistry, comet chemistry, laboratory astrophysics and meteoritic research with respect to organic and metalorganic chemistry will be given. The seemingly large number of observed astrochemical molecules necessarily requires explanations on molecular complexity and chemical evolution, which will be discussed. Special emphasis should be placed on data-driven analytical methods including ultrahigh-resolving instruments and their interplay with quantum chemical computations. These methods enable remarkable insights into the complex chemical spaces that exist in meteorites and maximize the level of information on the huge astrochemical molecular diversity. In addition, they allow one to study even yet undescribed chemistry as the one involving organomagnesium compounds in meteorites. Both targeted and non-targeted analytical strategies will be explained and may touch upon epistemological problems. In addition, implications of (metal)organic matter toward prebiotic chemistry leading to the emergence of life will be discussed. The precise description of astrochemical organic and metalorganic matter as seeds for life and their interactions within various astrophysical environments may appear essential to further study questions regarding the emergence of life on a most fundamental level that is within the molecular world and its self-organization properties.

  9. Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

    Directory of Open Access Journals (Sweden)

    Hua-pu Lu

    2015-01-01

    Full Text Available With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzy c-means based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

  10. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  11. Data-driven model-independent searches for long-lived particles at the LHC

    Science.gov (United States)

    Coccaro, Andrea; Curtin, David; Lubatti, H. J.; Russell, Heather; Shelton, Jessie

    2016-12-01

    Neutral long-lived particles (LLPs) are highly motivated by many beyond the Standard Model scenarios, such as theories of supersymmetry, baryogenesis, and neutral naturalness, and present both tremendous discovery opportunities and experimental challenges for the LHC. A major bottleneck for current LLP searches is the prediction of Standard Model backgrounds, which are often impossible to simulate accurately. In this paper, we propose a general strategy for obtaining differential, data-driven background estimates in LLP searches, thereby notably extending the range of LLP masses and lifetimes that can be discovered at the LHC. We focus on LLPs decaying in the ATLAS muon system, where triggers providing both signal and control samples are available at LHC run 2. While many existing searches require two displaced decays, a detailed knowledge of backgrounds will allow for very inclusive searches that require just one detected LLP decay. As we demonstrate for the h →X X signal model of LLP pair production in exotic Higgs decays, this results in dramatic sensitivity improvements for proper lifetimes ≳10 m . In theories of neutral naturalness, this extends reach to glueball masses far below the b ¯b threshold. Our strategy readily generalizes to other signal models and other detector subsystems. This framework therefore lends itself to the development of a systematic, model-independent LLP search program, in analogy to the highly successful simplified-model framework of prompt searches.

  12. Clinical review: optimizing enteral nutrition for critically ill patients - a simple data-driven formula

    Science.gov (United States)

    2011-01-01

    In modern critical care, the paradigm of 'therapeutic nutrition' is replacing traditional 'supportive nutrition'. Standard enteral formulas meet basic macro- and micronutrient needs; therapeutic enteral formulas meet these basic needs and also contain specific pharmaconutrients that may attenuate hyperinflammatory responses, enhance the immune responses to infection, or improve gastrointestinal tolerance. Choosing the right enteral feeding formula may positively affect a patient's outcome; targeted use of therapeutic formulas can reduce the incidence of infectious complications, shorten lengths of stay in the ICU and in the hospital, and lower risk for mortality. In this paper, we review principles of how to feed (enteral, parenteral, or both) and when to feed (early versus delayed start) patients who are critically ill. We discuss what to feed these patients in the context of specific pharmaconutrients in specialized feeding formulations, that is, arginine, glutamine, antioxidants, certain ω-3 and ω-6 fatty acids, hydrolyzed proteins, and medium-chain triglycerides. We summarize current expert guidelines for nutrition in patients with critical illness, and we present specific clinical evidence on the use of enteral formulas supplemented with anti-inflammatory or immune-modulating nutrients, and gastrointestinal tolerance-promoting nutritional formulas. Finally, we introduce an algorithm to help bedside clinicians make data-driven feeding decisions for patients with critical illness. PMID:22136305

  13. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

    Science.gov (United States)

    Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

    2017-09-29

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  14. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

    Science.gov (United States)

    Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

    2017-12-12

    Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

  15. Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

    Directory of Open Access Journals (Sweden)

    Po-Chiang Lin

    2016-01-01

    Full Text Available Network densification is regarded as one of the important ingredients to increase capacity for next generation mobile communication networks. However, it also leads to mobility problems since users are more likely to hand over to another cell in dense or even ultradense mobile communication networks. Therefore, supporting seamless and robust connectivity through such networks becomes a very important issue. In this paper, we investigate handover (HO optimization in next generation mobile communication networks. We propose a data-driven handover optimization (DHO approach, which aims to mitigate mobility problems including too-late HO, too-early HO, HO to wrong cell, ping-pong HO, and unnecessary HO. The key performance indicator (KPI is defined as the weighted average of the ratios of these mobility problems. The DHO approach collects data from the mobile communication measurement results and provides a model to estimate the relationship between the KPI and features from the collected dataset. Based on the model, the handover parameters, including the handover margin and time-to-trigger, are optimized to minimize the KPI. Simulation results show that the proposed DHO approach could effectively mitigate mobility problems.

  16. Design of a data-driven predictive controller for start-up process of AMT vehicles.

    Science.gov (United States)

    Lu, Xiaohui; Chen, Hong; Wang, Ping; Gao, Bingzhao

    2011-12-01

    In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.

  17. Data-driven quantification of the robustness and sensitivity of cell signaling networks

    International Nuclear Information System (INIS)

    Mukherjee, Sayak; Seok, Sang-Cheol; Vieland, Veronica J; Das, Jayajit

    2013-01-01

    Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems. (paper)

  18. Cloudweaver: Adaptive and Data-Driven Workload Manager for Generic Clouds

    Science.gov (United States)

    Li, Rui; Chen, Lei; Li, Wen-Syan

    Cloud computing denotes the latest trend in application development for parallel computing on massive data volumes. It relies on clouds of servers to handle tasks that used to be managed by an individual server. With cloud computing, software vendors can provide business intelligence and data analytic services for internet scale data sets. Many open source projects, such as Hadoop, offer various software components that are essential for building a cloud infrastructure. Current Hadoop (and many others) requires users to configure cloud infrastructures via programs and APIs and such configuration is fixed during the runtime. In this chapter, we propose a workload manager (WLM), called CloudWeaver, which provides automated configuration of a cloud infrastructure for runtime execution. The workload management is data-driven and can adapt to dynamic nature of operator throughput during different execution phases. CloudWeaver works for a single job and a workload consisting of multiple jobs running concurrently, which aims at maximum throughput using a minimum set of processors.

  19. VLAM-G: Interactive Data Driven Workflow Engine for Grid-Enabled Resources

    Directory of Open Access Journals (Sweden)

    Vladimir Korkhov

    2007-01-01

    Full Text Available Grid brings the power of many computers to scientists. However, the development of Grid-enabled applications requires knowledge about Grid infrastructure and low-level API to Grid services. In turn, workflow management systems provide a high-level environment for rapid prototyping of experimental computing systems. Coupling Grid and workflow paradigms is important for the scientific community: it makes the power of the Grid easily available to the end user. The paradigm of data driven workflow execution is one of the ways to enable distributed workflow on the Grid. The work presented in this paper is carried out in the context of the Virtual Laboratory for e-Science project. We present the VLAM-G workflow management system and its core component: the Run-Time System (RTS. The RTS is a dataflow driven workflow engine which utilizes Grid resources, hiding the complexity of the Grid from a scientist. Special attention is paid to the concept of dataflow and direct data streaming between distributed workflow components. We present the architecture and components of the RTS, describe the features of VLAM-G workflow execution, and evaluate the system by performance measurements and a real life use case.

  20. An asynchronous data-driven readout prototype for CEPC vertex detector

    Science.gov (United States)

    Yang, Ping; Sun, Xiangming; Huang, Guangming; Xiao, Le; Gao, Chaosong; Huang, Xing; Zhou, Wei; Ren, Weiping; Li, Yashu; Liu, Jianchao; You, Bihui; Zhang, Li

    2017-12-01

    The Circular Electron Positron Collider (CEPC) is proposed as a Higgs boson and/or Z boson factory for high-precision measurements on the Higgs boson. The precision of secondary vertex impact parameter plays an important role in such measurements which typically rely on flavor-tagging. Thus silicon CMOS Pixel Sensors (CPS) are the most promising technology candidate for a CEPC vertex detector, which can most likely feature a high position resolution, a low power consumption and a fast readout simultaneously. For the R&D of the CEPC vertex detector, we have developed a prototype MIC4 in the Towerjazz 180 nm CMOS Image Sensor (CIS) process. We have proposed and implemented a new architecture of asynchronous zero-suppression data-driven readout inside the matrix combined with a binary front-end inside the pixel. The matrix contains 128 rows and 64 columns with a small pixel pitch of 25 μm. The readout architecture has implemented the traditional OR-gate chain inside a super pixel combined with a priority arbiter tree between the super pixels, only reading out relevant pixels. The MIC4 architecture will be introduced in more detail in this paper. It will be taped out in May and will be characterized when the chip comes back.

  1. Dynamic model reduction using data-driven Loewner-framework applied to thermally morphing structures

    Science.gov (United States)

    Phoenix, Austin A.; Tarazaga, Pablo A.

    2017-05-01

    The work herein proposes the use of the data-driven Loewner-framework for reduced order modeling as applied to dynamic Finite Element Models (FEM) of thermally morphing structures. The Loewner-based modeling approach is computationally efficient and accurately constructs reduced models using analytical output data from a FEM. This paper details the two-step process proposed in the Loewner approach. First, a random vibration FEM simulation is used as the input for the development of a Single Input Single Output (SISO) data-based dynamic Loewner state space model. Second, an SVD-based truncation is used on the Loewner state space model, such that the minimal, dynamically representative, state space model is achieved. For this second part, varying levels of reduction are generated and compared. The work herein can be extended to model generation using experimental measurements by replacing the FEM output data in the first step and following the same procedure. This method will be demonstrated on two thermally morphing structures, a rigidly fixed hexapod in multiple geometric configurations and a low mass anisotropic morphing boom. This paper is working to detail the method and identify the benefits of the reduced model methodology.

  2. Outcomes from the GLEON fellowship program. Training graduate students in data driven network science.

    Science.gov (United States)

    Dugan, H.; Hanson, P. C.; Weathers, K. C.

    2016-12-01

    In the water sciences there is a massive need for graduate students who possess the analytical and technical skills to deal with large datasets and function in the new paradigm of open, collaborative -science. The Global Lake Ecological Observatory Network (GLEON) graduate fellowship program (GFP) was developed as an interdisciplinary training program to supplement the intensive disciplinary training of traditional graduate education. The primary goal of the GFP was to train a diverse cohort of graduate students in network science, open-web technologies, collaboration, and data analytics, and importantly to provide the opportunity to use these skills to conduct collaborative research resulting in publishable scientific products. The GFP is run as a series of three week-long workshops over two years that brings together a cohort of twelve students. In addition, fellows are expected to attend and contribute to at least one international GLEON all-hands' meeting. Here, we provide examples of training modules in the GFP (model building, data QA/QC, information management, bayesian modeling, open coding/version control, national data programs), as well as scientific outputs (manuscripts, software products, and new global datasets) produced by the fellows, as well as the process by which this team science was catalyzed. Data driven education that lets students apply learned skills to real research projects reinforces concepts, provides motivation, and can benefit their publication record. This program design is extendable to other institutions and networks.

  3. Automatic data-driven real-time segmentation and recognition of surgical workflow.

    Science.gov (United States)

    Dergachyova, Olga; Bouget, David; Huaulmé, Arnaud; Morandi, Xavier; Jannin, Pierre

    2016-06-01

    With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection. The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision. On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases. Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.

  4. A transparent and data-driven global tectonic regionalization model for seismic hazard assessment

    Science.gov (United States)

    Chen, Yen-Shin; Weatherill, Graeme; Pagani, Marco; Cotton, Fabrice

    2018-05-01

    A key concept that is common to many assumptions inherent within seismic hazard assessment is that of tectonic similarity. This recognizes that certain regions of the globe may display similar geophysical characteristics, such as in the attenuation of seismic waves, the magnitude scaling properties of seismogenic sources or the seismic coupling of the lithosphere. Previous attempts at tectonic regionalization, particularly within a seismic hazard assessment context, have often been based on expert judgements; in most of these cases, the process for delineating tectonic regions is neither reproducible nor consistent from location to location. In this work, the regionalization process is implemented in a scheme that is reproducible, comprehensible from a geophysical rationale, and revisable when new relevant data are published. A spatial classification-scheme is developed based on fuzzy logic, enabling the quantification of concepts that are approximate rather than precise. Using the proposed methodology, we obtain a transparent and data-driven global tectonic regionalization model for seismic hazard applications as well as the subjective probabilities (e.g. degree of being active/degree of being cratonic) that indicate the degree to which a site belongs in a tectonic category.

  5. Modern data-driven decision support systems: the role of computing with words and computational linguistics

    Science.gov (United States)

    Kacprzyk, Janusz; Zadrożny, Sławomir

    2010-05-01

    We present how the conceptually and numerically simple concept of a fuzzy linguistic database summary can be a very powerful tool for gaining much insight into the very essence of data. The use of linguistic summaries provides tools for the verbalisation of data analysis (mining) results which, in addition to the more commonly used visualisation, e.g. via a graphical user interface, can contribute to an increased human consistency and ease of use, notably for supporting decision makers via the data-driven decision support system paradigm. Two new relevant aspects of the analysis are also outlined which were first initiated by the authors. First, following Kacprzyk and Zadrożny, it is further considered how linguistic data summarisation is closely related to some types of solutions used in natural language generation (NLG). This can make it possible to use more and more effective and efficient tools and techniques developed in NLG. Second, similar remarks are given on relations to systemic functional linguistics. Moreover, following Kacprzyk and Zadrożny, comments are given on an extremely relevant aspect of scalability of linguistic summarisation of data, using a new concept of a conceptual scalability.

  6. Data-driven classification of ventilated lung tissues using electrical impedance tomography

    International Nuclear Information System (INIS)

    Gómez-Laberge, Camille; Hogan, Matthew J; Elke, Gunnar; Weiler, Norbert; Frerichs, Inéz; Adler, Andy

    2011-01-01

    Current methods for identifying ventilated lung regions utilizing electrical impedance tomography images rely on dividing the image into arbitrary regions of interest (ROI), manually delineating ROI, or forming ROI with pixels whose signal properties surpass an arbitrary threshold. In this paper, we propose a novel application of a data-driven classification method to identify ventilated lung ROI based on forming k clusters from pixels with correlated signals. A standard first-order model for lung mechanics is then applied to determine which ROI correspond to ventilated lung tissue. We applied the method in an experimental study of 16 mechanically ventilated swine in the supine position, which underwent changes in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (F I O 2 ). In each stage of the experimental protocol, the method performed best with k = 4 and consistently identified 3 lung tissue ROI and 1 boundary tissue ROI in 15 of the 16 subjects. When testing for changes from baseline in lung position, tidal volume, and respiratory system compliance, we found that PEEP displaced the ventilated lung region dorsally by 2 cm, decreased tidal volume by 1.3%, and increased the respiratory system compliance time constant by 0.3 s. F I O 2 decreased tidal volume by 0.7%. All effects were tested at p < 0.05 with n = 16. These findings suggest that the proposed ROI detection method is robust and sensitive to ventilation dynamics in the experimental setting

  7. On the selection of user-defined parameters in data-driven stochastic subspace identification

    Science.gov (United States)

    Priori, C.; De Angelis, M.; Betti, R.

    2018-02-01

    The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI. Then, the case of non-stationary seismic excitations with a reduced number of samples is considered. In this paper, partitions of the data matrix different from the one proposed in the SSI literature are investigated, together with the influence of different choices of the weighting matrices. The study is carried out considering two different applications: (1) data obtained from vibration tests on a scaled structure and (2) in-situ tests on a reinforced concrete building. Referring to the former, the identification of a steel frame structure tested on a shaking table is performed using its responses in terms of absolute accelerations to a stationary (white noise) base excitation and to non-stationary seismic excitations of low intensity. Black-box and modal models are identified in both cases and the results are compared with those from an input-output subspace technique. With regards to the latter, the identification of a complex hospital building is conducted using data obtained from ambient vibration tests.

  8. Data-driven asthma endotypes defined from blood biomarker and gene expression data.

    Directory of Open Access Journals (Sweden)

    Barbara Jane George

    Full Text Available The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.

  9. Data Driven - Android based displays on data acquisition and system status

    CERN Document Server

    Canilho, Paulo

    2014-01-01

    For years, both hardware and software engineers have struggled with the acquisition of device information in a flexible and fast perspective, numerous devices cannot have their status quickly tested due to time limitation associated with the travelling to a computer terminal. For instance, in order to test a scintillator status, one has to inject beam into the device and quickly return to a terminal to see the results, this is not only time demanding but extremely inconvenient for the person responsible, it consumes time that would be used in more pressing matters. In this train of thoughts, the proposal of creating an interface to bring a stable, flexible, user friendly and data driven solution to this problem was created. Being the most common operative system for mobile display, the Android API proved to have the best efficient in financing, since it is based on an open source software, and in implementation difficulty since it’s backend development resides in JAVA calls and XML for visual representation...

  10. NERI PROJECT 99-119. TASK 2. DATA-DRIVEN PREDICTION OF PROCESS VARIABLES. FINAL REPORT

    Energy Technology Data Exchange (ETDEWEB)

    Upadhyaya, B.R.

    2003-04-10

    This report describes the detailed results for task 2 of DOE-NERI project number 99-119 entitled ''Automatic Development of Highly Reliable Control Architecture for Future Nuclear Power Plants''. This project is a collaboration effort between the Oak Ridge National Laboratory (ORNL,) The University of Tennessee, Knoxville (UTK) and the North Carolina State University (NCSU). UTK is the lead organization for Task 2 under contract number DE-FG03-99SF21906. Under task 2 we completed the development of data-driven models for the characterization of sub-system dynamics for predicting state variables, control functions, and expected control actions. We have also developed the ''Principal Component Analysis (PCA)'' approach for mapping system measurements, and a nonlinear system modeling approach called the ''Group Method of Data Handling (GMDH)'' with rational functions, and includes temporal data information for transient characterization. The majority of the results are presented in detailed reports for Phases 1 through 3 of our research, which are attached to this report.

  11. Data-driven fault detection, isolation and estimation of aircraft gas turbine engine actuator and sensors

    Science.gov (United States)

    Naderi, E.; Khorasani, K.

    2018-02-01

    In this work, a data-driven fault detection, isolation, and estimation (FDI&E) methodology is proposed and developed specifically for monitoring the aircraft gas turbine engine actuator and sensors. The proposed FDI&E filters are directly constructed by using only the available system I/O data at each operating point of the engine. The healthy gas turbine engine is stimulated by a sinusoidal input containing a limited number of frequencies. First, the associated system Markov parameters are estimated by using the FFT of the input and output signals to obtain the frequency response of the gas turbine engine. These data are then used for direct design and realization of the fault detection, isolation and estimation filters. Our proposed scheme therefore does not require any a priori knowledge of the system linear model or its number of poles and zeros at each operating point. We have investigated the effects of the size of the frequency response data on the performance of our proposed schemes. We have shown through comprehensive case studies simulations that desirable fault detection, isolation and estimation performance metrics defined in terms of the confusion matrix criterion can be achieved by having access to only the frequency response of the system at only a limited number of frequencies.

  12. A data-driven, mathematical model of mammalian cell cycle regulation.

    Directory of Open Access Journals (Sweden)

    Michael C Weis

    Full Text Available Few of >150 published cell cycle modeling efforts use significant levels of data for tuning and validation. This reflects the difficultly to generate correlated quantitative data, and it points out a critical uncertainty in modeling efforts. To develop a data-driven model of cell cycle regulation, we used contiguous, dynamic measurements over two time scales (minutes and hours calculated from static multiparametric cytometry data. The approach provided expression profiles of cyclin A2, cyclin B1, and phospho-S10-histone H3. The model was built by integrating and modifying two previously published models such that the model outputs for cyclins A and B fit cyclin expression measurements and the activation of B cyclin/Cdk1 coincided with phosphorylation of histone H3. The model depends on Cdh1-regulated cyclin degradation during G1, regulation of B cyclin/Cdk1 activity by cyclin A/Cdk via Wee1, and transcriptional control of the mitotic cyclins that reflects some of the current literature. We introduced autocatalytic transcription of E2F, E2F regulated transcription of cyclin B, Cdc20/Cdh1 mediated E2F degradation, enhanced transcription of mitotic cyclins during late S/early G2 phase, and the sustained synthesis of cyclin B during mitosis. These features produced a model with good correlation between state variable output and real measurements. Since the method of data generation is extensible, this model can be continually modified based on new correlated, quantitative data.

  13. A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea

    Directory of Open Access Journals (Sweden)

    Jining Yan

    2015-05-01

    Full Text Available In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. Based on the DDDAS, combing a remote sensor system that detects oil spills with a numerical simulation, an integrated data processing, analysis, forecasting and emergency response system was established. Once an oil spill accident occurs, the DDDAS-based oil spill model receives information about the oil slick extracted from the dynamic remote sensor data in the simulation. Through comparison, information fusion and feedback updates, continuous and more precise oil spill simulation results can be obtained. Then, the simulation results can provide help for disaster control and clean-up. The Penglai, Xingang and Suizhong oil spill results showed our simulation model could increase the prediction accuracy and reduce the error caused by empirical parameters in existing simulation systems. Therefore, the DDDAS-based detection and simulation system can effectively improve oil spill simulation and diffusion forecasting, as well as provide decision-making information and technical support for emergency responses to oil spills.

  14. A data-driven decomposition approach to model aerodynamic forces on flapping airfoils

    Science.gov (United States)

    Raiola, Marco; Discetti, Stefano; Ianiro, Andrea

    2017-11-01

    In this work, we exploit a data-driven decomposition of experimental data from a flapping airfoil experiment with the aim of isolating the main contributions to the aerodynamic force and obtaining a phenomenological model. Experiments are carried out on a NACA 0012 airfoil in forward flight with both heaving and pitching motion. Velocity measurements of the near field are carried out with Planar PIV while force measurements are performed with a load cell. The phase-averaged velocity fields are transformed into the wing-fixed reference frame, allowing for a description of the field in a domain with fixed boundaries. The decomposition of the flow field is performed by means of the POD applied on the velocity fluctuations and then extended to the phase-averaged force data by means of the Extended POD approach. This choice is justified by the simple consideration that aerodynamic forces determine the largest contributions to the energetic balance in the flow field. Only the first 6 modes have a relevant contribution to the force. A clear relationship can be drawn between the force and the flow field modes. Moreover, the force modes are closely related (yet slightly different) to the contributions of the classic potential models in literature, allowing for their correction. This work has been supported by the Spanish MINECO under Grant TRA2013-41103-P.

  15. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

    Directory of Open Access Journals (Sweden)

    Rui Sun

    2017-09-01

    Full Text Available The use of Unmanned Aerial Vehicles (UAVs has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  16. Data-driven strategies for robust forecast of continuous glucose monitoring time-series.

    Science.gov (United States)

    Fiorini, Samuele; Martini, Chiara; Malpassi, Davide; Cordera, Renzo; Maggi, Davide; Verri, Alessandro; Barla, Annalisa

    2017-07-01

    Over the past decade, continuous glucose monitoring (CGM) has proven to be a very resourceful tool for diabetes management. To date, CGM devices are employed for both retrospective and online applications. Their use allows to better describe the patients' pathology as well as to achieve a better control of patients' level of glycemia. The analysis of CGM sensor data makes possible to observe a wide range of metrics, such as the glycemic variability during the day or the amount of time spent below or above certain glycemic thresholds. However, due to the high variability of the glycemic signals among sensors and individuals, CGM data analysis is a non-trivial task. Standard signal filtering solutions fall short when an appropriate model personalization is not applied. State-of-the-art data-driven strategies for online CGM forecasting rely upon the use of recursive filters. Each time a new sample is collected, such models need to adjust their parameters in order to predict the next glycemic level. In this paper we aim at demonstrating that the problem of online CGM forecasting can be successfully tackled by personalized machine learning models, that do not need to recursively update their parameters.

  17. Pengembangan Data Warehouse Menggunakan Pendekatan Data-Driven untuk Membantu Pengelolaan SDM

    Directory of Open Access Journals (Sweden)

    Mujiono Mujiono

    2016-01-01

    Full Text Available The basis of bureaucratic reform is the reform of human resources management. One supporting factor is the development of an employee database. To support the management of human resources required including data warehouse and business intelligent tools. The data warehouse is an integrated concept of reliable data storage to provide support to all the needs of the data analysis. In this study developed a data warehouse using the data-driven approach to the source data comes from SIMPEG, SAPK and electronic presence. Data warehouses are designed using the nine steps methodology and unified modeling language (UML notation. Extract transform load (ETL is done by using Pentaho Data Integration by applying transformation maps. Furthermore, to help human resource management, the system is built to perform online analytical processing (OLAP to facilitate web-based information. In this study generated BI application development framework with Model-View-Controller (MVC architecture and OLAP operations are built using the dynamic query generation, PivotTable, and HighChart to present information about PNS, CPNS, Retirement, Kenpa and Presence

  18. Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

    Directory of Open Access Journals (Sweden)

    Lifeng Wu

    2016-05-01

    Full Text Available Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

  19. Evaluating the Applicability of Data-Driven Dietary Patterns to Independent Samples with a Focus on Measurement Tools for Pattern Similarity.

    Science.gov (United States)

    Castelló, Adela; Buijsse, Brian; Martín, Miguel; Ruiz, Amparo; Casas, Ana M; Baena-Cañada, Jose M; Pastor-Barriuso, Roberto; Antolín, Silvia; Ramos, Manuel; Muñoz, Monserrat; Lluch, Ana; de Juan-Ferré, Ana; Jara, Carlos; Lope, Virginia; Jimeno, María A; Arriola-Arellano, Esperanza; Díaz, Elena; Guillem, Vicente; Carrasco, Eva; Pérez-Gómez, Beatriz; Vioque, Jesús; Pollán, Marina

    2016-12-01

    Diet is a key modifiable risk for many chronic diseases, but it remains unclear whether dietary patterns from one study sample are generalizable to other independent populations. The primary objective of this study was to assess whether data-driven dietary patterns from one study sample are applicable to other populations. The secondary objective was to assess the validity of two criteria of pattern similarity. Six dietary patterns-Western (n=3), Mediterranean, Prudent, and Healthy- from three published studies on breast cancer were reconstructed in a case-control study of 973 breast cancer patients and 973 controls. Three more internal patterns (Western, Prudent, and Mediterranean) were derived from this case-control study's own data. Applicability was assessed by comparing the six reconstructed patterns with the three internal dietary patterns, using the congruence coefficient (CC) between pattern loadings. In cases where any pair met either of two commonly used criteria for declaring patterns similar (CC ≥0.85 or a statistically significant [Pdietary patterns was double-checked by comparing their associations to risk for breast cancer, to assess whether those two criteria of similarity are actually reliable. Five of the six reconstructed dietary patterns showed high congruence (CC >0.9) to their corresponding dietary pattern derived from the case-control study's data. Similar associations with risk for breast cancer were found in all pairs of dietary patterns that had high CC but not in all pairs of dietary patterns with statistically significant correlations. Similar dietary patterns can be found in independent samples. The P value of a correlation coefficient is less reliable than the CC as a criterion for declaring two dietary patterns similar. This study shows that diet scores based on a particular study are generalizable to other populations. Copyright © 2016 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

  20. Study of B¯→Xuℓν¯ decays in BB¯ events tagged by a fully reconstructed B-meson decay and determination of |Vub|

    Science.gov (United States)

    Lees, J. P.; Poireau, V.; Tisserand, V.; Garra Tico, J.; Grauges, E.; Martinelli, M.; Milanes, D. A.; Palano, A.; Pappagallo, M.; Eigen, G.; Stugu, B.; Brown, D. N.; Kerth, L. T.; Kolomensky, Yu. G.; Lynch, G.; Tackmann, K.; Koch, H.; Schroeder, T.; Asgeirsson, D. J.; Hearty, C.; Mattison, T. S.; McKenna, J. A.; Khan, A.; Blinov, V. E.; Buzykaev, A. R.; Druzhinin, V. P.; Golubev, V. B.; Kravchenko, E. A.; Onuchin, A. P.; Serednyakov, S. I.; Skovpen, Yu. I.; Solodov, E. P.; Todyshev, K. Yu.; Yushkov, A. N.; Bondioli, M.; Kirkby, D.; Lankford, A. J.; Mandelkern, M.; Stoker, D. P.; Atmacan, H.; Gary, J. W.; Liu, F.; Long, O.; Vitug, G. M.; Campagnari, C.; Hong, T. M.; Kovalskyi, D.; Richman, J. D.; West, C. A.; Eisner, A. M.; Kroseberg, J.; Lockman, W. S.; Martinez, A. J.; Schalk, T.; Schumm, B. A.; Seiden, A.; Cheng, C. H.; Doll, D. A.; Echenard, B.; Flood, K. T.; Hitlin, D. G.; Ongmongkolkul, P.; Porter, F. C.; Rakitin, A. Y.; Andreassen, R.; Dubrovin, M. S.; Huard, Z.; Meadows, B. T.; Sokoloff, M. D.; Sun, L.; Bloom, P. C.; Ford, W. T.; Gaz, A.; Nagel, M.; Nauenberg, U.; Smith, J. G.; Wagner, S. R.; Ayad, R.; Toki, W. H.; Spaan, B.; Kobel, M. J.; Schubert, K. R.; Schwierz, R.; Bernard, D.; Verderi, M.; Clark, P. J.; Playfer, S.; Bettoni, D.; Bozzi, C.; Calabrese, R.; Cibinetto, G.; Fioravanti, E.; Garzia, I.; Luppi, E.; Munerato, M.; Negrini, M.; Petrella, A.; Piemontese, L.; Santoro, V.; Baldini-Ferroli, R.; Calcaterra, A.; de Sangro, R.; Finocchiaro, G.; Nicolaci, M.; Patteri, P.; Peruzzi, I. M.; Piccolo, M.; Rama, M.; Zallo, A.; Contri, R.; Guido, E.; Lo Vetere, M.; Monge, M. R.; Passaggio, S.; Patrignani, C.; Robutti, E.; Bhuyan, B.; Prasad, V.; Lee, C. L.; Morii, M.; Edwards, A. J.; Adametz, A.; Marks, J.; Uwer, U.; Bernlochner, F. U.; Ebert, M.; Lacker, H. M.; Lueck, T.; Dauncey, P. D.; Tibbetts, M.; Behera, P. K.; Mallik, U.; Chen, C.; Cochran, J.; Meyer, W. T.; Prell, S.; Rosenberg, E. I.; Rubin, A. E.; Gritsan, A. V.; Guo, Z. J.; Arnaud, N.; Davier, M.; Grosdidier, G.; Le Diberder, F.; Lutz, A. M.; Malaescu, B.; Roudeau, P.; Schune, M. H.; Stocchi, A.; Wormser, G.; Lange, D. J.; Wright, D. M.; Bingham, I.; Chavez, C. A.; Coleman, J. P.; Fry, J. R.; Gabathuler, E.; Hutchcroft, D. E.; Payne, D. J.; Touramanis, C.; Bevan, A. J.; Di Lodovico, F.; Sacco, R.; Sigamani, M.; Cowan, G.; Brown, D. N.; Davis, C. L.; Denig, A. G.; Fritsch, M.; Gradl, W.; Hafner, A.; Prencipe, E.; Alwyn, K. E.; Bailey, D.; Barlow, R. J.; Jackson, G.; Lafferty, G. D.; Cenci, R.; Hamilton, B.; Jawahery, A.; Roberts, D. A.; Simi, G.; Dallapiccola, C.; Cowan, R.; Dujmic, D.; Sciolla, G.; Lindemann, D.; Patel, P. M.; Robertson, S. H.; Schram, M.; Biassoni, P.; Lazzaro, A.; Lombardo, V.; Neri, N.; Palombo, F.; Stracka, S.; Cremaldi, L.; Godang, R.; Kroeger, R.; Sonnek, P.; Summers, D. J.; Nguyen, X.; Taras, P.; De Nardo, G.; Monorchio, D.; Onorato, G.; Sciacca, C.; Raven, G.; Snoek, H. L.; Jessop, C. P.; Knoepfel, K. J.; LoSecco, J. M.; Wang, W. F.; Honscheid, K.; Kass, R.; Brau, J.; Frey, R.; Sinev, N. B.; Strom, D.; Torrence, E.; Feltresi, E.; Gagliardi, N.; Margoni, M.; Morandin, M.; Posocco, M.; Rotondo, M.; Simonetto, F.; Stroili, R.; Ben-Haim, E.; Bomben, M.; Bonneaud, G. R.; Briand, H.; Calderini, G.; Chauveau, J.; Hamon, O.; Leruste, Ph.; Marchiori, G.; Ocariz, J.; Sitt, S.; Biasini, M.; Manoni, E.; Pacetti, S.; Rossi, A.; Angelini, C.; Batignani, G.; Bettarini, S.; Carpinelli, M.; Casarosa, G.; Cervelli, A.; Forti, F.; Giorgi, M. A.; Lusiani, A.; Oberhof, B.; Paoloni, E.; Perez, A.; Rizzo, G.; Walsh, J. J.; Lopes Pegna, D.; Lu, C.; Olsen, J.; Smith, A. J. S.; Telnov, A. V.; Anulli, F.; Cavoto, G.; Faccini, R.; Ferrarotto, F.; Ferroni, F.; Gaspero, M.; Li Gioi, L.; Mazzoni, M. A.; Piredda, G.; Bünger, C.; Grünberg, O.; Hartmann, T.; Leddig, T.; Schröder, H.; Waldi, R.; Adye, T.; Olaiya, E. O.; Wilson, F. F.; Emery, S.; Hamel de Monchenault, G.; Vasseur, G.; Yèche, Ch.; Aston, D.; Bard, D. J.; Bartoldus, R.; Cartaro, C.; Convery, M. R.; Dorfan, J.; Dubois-Felsmann, G. P.; Dunwoodie, W.; Field, R. C.; Franco Sevilla, M.; Fulsom, B. G.; Gabareen, A. M.; Graham, M. T.; Grenier, P.; Hast, C.; Innes, W. R.; Kelsey, M. H.; Kim, H.; Kim, P.; Kocian, M. L.; Leith, D. W. G. S.; Lewis, P.; Li, S.; Lindquist, B.; Luitz, S.; Luth, V.; Lynch, H. L.; MacFarlane, D. B.; Muller, D. R.; Neal, H.; Nelson, S.; Ofte, I.; Perl, M.; Pulliam, T.; Ratcliff, B. N.; Roodman, A.; Salnikov, A. A.; Schindler, R. H.; Snyder, A.; Su, D.; Sullivan, M. K.; Va'vra, J.; Wagner, A. P.; Weaver, M.; Wisniewski, W. J.; Wittgen, M.; Wright, D. H.; Wulsin, H. W.; Yarritu, A. K.; Young, C. C.; Ziegler, V.; Park, W.; Purohit, M. V.; White, R. M.; Wilson, J. R.; Randle-Conde, A.; Sekula, S. J.; Bellis, M.; Benitez, J. F.; Burchat, P. R.; Miyashita, T. S.; Alam, M. S.; Ernst, J. A.; Gorodeisky, R.; Guttman, N.; Peimer, D. R.; Soffer, A.; Lund, P.; Spanier, S. M.; Eckmann, R.; Ritchie, J. L.; Ruland, A. M.; Schilling, C. J.; Schwitters, R. F.; Wray, B. C.; Izen, J. M.; Lou, X. C.; Bianchi, F.; Gamba, D.; Lanceri, L.; Vitale, L.; Azzolini, V.; Martinez-Vidal, F.; Oyanguren, A.; Ahmed, H.; Albert, J.; Banerjee, Sw.; Choi, H. H. F.; King, G. J.; Kowalewski, R.; Lewczuk, M. J.; Lindsay, C.; Nugent, I. M.; Roney, J. M.; Sobie, R. J.; Tasneem, N.; Gershon, T. J.; Harrison, P. F.; Latham, T. E.; Puccio, E. M. T.; Band, H. R.; Dasu, S.; Pan, Y.; Prepost, R.; Wu, S. L.

    2012-08-01

    We report measurements of partial branching fractions for inclusive charmless semileptonic B decays B¯→Xuℓν¯ and the determination of the Cabibbo-Kobayashi-Maskawa (CKM) matrix element |Vub|. The analysis is based on a sample of 467×106 Υ(4S)→BB¯ decays recorded with the BABAR detector at the PEP-II e+e- storage rings. We select events in which the decay of one of the B mesons is fully reconstructed and an electron or a muon signals the semileptonic decay of the other B meson. We measure partial branching fractions ΔB in several restricted regions of phase space and determine the CKM element |Vub| based on different QCD predictions. For decays with a charged lepton momentum pℓ*>1.0GeV in the B meson rest frame, we obtain ΔB=(1.80±0.13stat±0.15sys±0.02theo)×10-3 from a fit to the two-dimensional MX-q2 distribution. Here, MX refers to the invariant mass of the final state hadron X and q2 is the invariant mass squared of the charged lepton and neutrino. From this measurement we extract |Vub|=(4.33±0.24exp⁡±0.15theo)×10-3 as the arithmetic average of four results obtained from four different QCD predictions of the partial rate. We separately determine partial branching fractions for B¯0 and B- decays and derive a limit on the isospin breaking in B¯→Xuℓν¯ decays.

  1. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

    Science.gov (United States)

    Hao, Ming; Bryant, Stephen H; Wang, Yanli

    2018-02-06

    While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

  2. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Directory of Open Access Journals (Sweden)

    A. Elshorbagy

    2010-10-01

    Full Text Available In this second part of the two-part paper, the data driven modeling (DDM experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs, genetic programming (GP, evolutionary polynomial regression (EPR, Support vector machines (SVM, M5 model trees (M5, K-nearest neighbors (K-nn, and multiple linear regression (MLR techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it

  3. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Science.gov (United States)

    Elshorbagy, A.; Corzo, G.; Srinivasulu, S.; Solomatine, D. P.

    2010-10-01

    In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it should

  4. Data-driven analysis of simultaneous EEG/fMRI reveals neurophysiological phenotypes of impulse control.

    Science.gov (United States)

    Schmüser, Lena; Sebastian, Alexandra; Mobascher, Arian; Lieb, Klaus; Feige, Bernd; Tüscher, Oliver

    2016-09-01

    Response inhibition is the ability to suppress inadequate but prepotent or ongoing response tendencies. A fronto-striatal network is involved in these processes. Between-subject differences in the intra-individual variability have been suggested to constitute a key to pathological processes underlying impulse control disorders. Single-trial EEG/fMRI analysis allows to increase sensitivity for inter-individual differences by incorporating intra-individual variability. Thirty-eight healthy subjects performed a visual Go/Nogo task during simultaneous EEG/fMRI. Of 38 healthy subjects, 21 subjects reliably showed Nogo-related ICs (Nogo-IC-positive) while 17 subjects (Nogo-IC-negative) did not. Comparing both groups revealed differences on various levels: On trait level, Nogo-IC-negative subjects scored higher on questionnaires regarding attention deficit/hyperactivity disorder; on a behavioral level, they displayed slower response times (RT) and higher intra-individual RT variability while both groups did not differ in their inhibitory performance. On the neurophysiological level, Nogo-IC-negative subjects showed a hyperactivation of left inferior frontal cortex/insula and left putamen as well as significantly reduced P3 amplitudes. Thus, a data-driven approach for IC classification and the resulting presence or absence of early Nogo-specific ICs as criterion for group selection revealed group differences at behavioral and neurophysiological levels. This may indicate electrophysiological phenotypes characterized by inter-individual variations of neural and behavioral correlates of impulse control. We demonstrated that the inter-individual difference in an electrophysiological correlate of response inhibition is correlated with distinct, potentially compensatory neural activity. This may suggest the existence of electrophysiologically dissociable phenotypes of behavioral and neural motor response inhibition with the Nogo-IC-positive phenotype possibly providing

  5. Access Control with Delegated Authorization Policy Evaluation for Data-Driven Microservice Workflows

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    Davy Preuveneers

    2017-09-01

    Full Text Available Microservices offer a compelling competitive advantage for building data flow systems as a choreography of self-contained data endpoints that each implement a specific data processing functionality. Such a ‘single responsibility principle’ design makes them well suited for constructing scalable and flexible data integration and real-time data flow applications. In this paper, we investigate microservice based data processing workflows from a security point of view, i.e., (1 how to constrain data processing workflows with respect to dynamic authorization policies granting or denying access to certain microservice results depending on the flow of the data; (2 how to let multiple microservices contribute to a collective data-driven authorization decision and (3 how to put adequate measures in place such that the data within each individual microservice is protected against illegitimate access from unauthorized users or other microservices. Due to this multifold objective, enforcing access control on the data endpoints to prevent information leakage or preserve one’s privacy becomes far more challenging, as authorization policies can have dependencies and decision outcomes cross-cutting data in multiple microservices. To address this challenge, we present and evaluate a workflow-oriented authorization framework that enforces authorization policies in a decentralized manner and where the delegated policy evaluation leverages feature toggles that are managed at runtime by software circuit breakers to secure the distributed data processing workflows. The benefit of our solution is that, on the one hand, authorization policies restrict access to the data endpoints of the microservices, and on the other hand, microservices can safely rely on other data endpoints to collectively evaluate cross-cutting access control decisions without having to rely on a shared storage backend holding all the necessary information for the policy evaluation.

  6. A data-driven approach for denoising GNSS position time series

    Science.gov (United States)

    Li, Yanyan; Xu, Caijun; Yi, Lei; Fang, Rongxin

    2017-12-01

    Global navigation satellite system (GNSS) datasets suffer from common mode error (CME) and other unmodeled errors. To decrease the noise level in GNSS positioning, we propose a new data-driven adaptive multiscale denoising method in this paper. Both synthetic and real-world long-term GNSS datasets were employed to assess the performance of the proposed method, and its results were compared with those of stacking filtering, principal component analysis (PCA) and the recently developed multiscale multiway PCA. It is found that the proposed method can significantly eliminate the high-frequency white noise and remove the low-frequency CME. Furthermore, the proposed method is more precise for denoising GNSS signals than the other denoising methods. For example, in the real-world example, our method reduces the mean standard deviation of the north, east and vertical components from 1.54 to 0.26, 1.64 to 0.21 and 4.80 to 0.72 mm, respectively. Noise analysis indicates that for the original signals, a combination of power-law plus white noise model can be identified as the best noise model. For the filtered time series using our method, the generalized Gauss-Markov model is the best noise model with the spectral indices close to - 3, indicating that flicker walk noise can be identified. Moreover, the common mode error in the unfiltered time series is significantly reduced by the proposed method. After filtering with our method, a combination of power-law plus white noise model is the best noise model for the CMEs in the study region.

  7. Dynamic Data-Driven Prediction of Lean Blowout in a Swirl-Stabilized Combustor

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    Soumalya Sarkar

    2015-09-01

    Full Text Available This paper addresses dynamic data-driven prediction of lean blowout (LBO phenomena in confined combustion processes, which are prevalent in many physical applications (e.g., land-based and aircraft gas-turbine engines. The underlying concept is built upon pattern classification and is validated for LBO prediction with time series of chemiluminescence sensor data from a laboratory-scale swirl-stabilized dump combustor. The proposed method of LBO prediction makes use of the theory of symbolic dynamics, where (finite-length time series data are partitioned to produce symbol strings that, in turn, generate a special class of probabilistic finite state automata (PFSA. These PFSA, called D-Markov machines, have a deterministic algebraic structure and their states are represented by symbol blocks of length D or less, where D is a positive integer. The D-Markov machines are constructed in two steps: (i state splitting, i.e., the states are split based on their information contents, and (ii state merging, i.e., two or more states (of possibly different lengths are merged together to form a new state without any significant loss of the embedded information. The modeling complexity (e.g., number of states of a D-Markov machine model is observed to be drastically reduced as the combustor approaches LBO. An anomaly measure, based on Kullback-Leibler divergence, is constructed to predict the proximity of LBO. The problem of LBO prediction is posed in a pattern classification setting and the underlying algorithms have been tested on experimental data at different extents of fuel-air premixing and fuel/air ratio. It is shown that, over a wide range of fuel-air premixing, D-Markov machines with D > 1 perform better as predictors of LBO than those with D = 1.

  8. Full field reservoir modeling of shale assets using advanced data-driven analytics

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    Soodabeh Esmaili

    2016-01-01

    Full Text Available Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems - induced or natural leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called “hard data” directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The “hard data” refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of “soft data” (non-measured, interpretive data such as frac length, width, height and conductivity in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.

  9. Assigning clinical codes with data-driven concept representation on Dutch clinical free text.

    Science.gov (United States)

    Scheurwegs, Elyne; Luyckx, Kim; Luyten, Léon; Goethals, Bart; Daelemans, Walter

    2017-05-01

    Clinical codes are used for public reporting purposes, are fundamental to determining public financing for hospitals, and form the basis for reimbursement claims to insurance providers. They are assigned to a patient stay to reflect the diagnosis and performed procedures during that stay. This paper aims to enrich algorithms for automated clinical coding by taking a data-driven approach and by using unsupervised and semi-supervised techniques for the extraction of multi-word expressions that convey a generalisable medical meaning (referred to as concepts). Several methods for extracting concepts from text are compared, two of which are constructed from a large unannotated corpus of clinical free text. A distributional semantic model (i.c. the word2vec skip-gram model) is used to generalize over concepts and retrieve relations between them. These methods are validated on three sets of patient stay data, in the disease areas of urology, cardiology, and gastroenterology. The datasets are in Dutch, which introduces a limitation on available concept definitions from expert-based ontologies (e.g. UMLS). The results show that when expert-based knowledge in ontologies is unavailable, concepts derived from raw clinical texts are a reliable alternative. Both concepts derived from raw clinical texts perform and concepts derived from expert-created dictionaries outperform a bag-of-words approach in clinical code assignment. Adding features based on tokens that appear in a semantically similar context has a positive influence for predicting diagnostic codes. Furthermore, the experiments indicate that a distributional semantics model can find relations between semantically related concepts in texts but also introduces erroneous and redundant relations, which can undermine clinical coding performance. Copyright © 2017. Published by Elsevier Inc.

  10. WaveSeq: a novel data-driven method of detecting histone modification enrichments using wavelets.

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    Apratim Mitra

    Full Text Available BACKGROUND: Chromatin immunoprecipitation followed by next-generation sequencing is a genome-wide analysis technique that can be used to detect various epigenetic phenomena such as, transcription factor binding sites and histone modifications. Histone modification profiles can be either punctate or diffuse which makes it difficult to distinguish regions of enrichment from background noise. With the discovery of histone marks having a wide variety of enrichment patterns, there is an urgent need for analysis methods that are robust to various data characteristics and capable of detecting a broad range of enrichment patterns. RESULTS: To address these challenges we propose WaveSeq, a novel data-driven method of detecting regions of significant enrichment in ChIP-Seq data. Our approach utilizes the wavelet transform, is free of distributional assumptions and is robust to diverse data characteristics such as low signal-to-noise ratios and broad enrichment patterns. Using publicly available datasets we showed that WaveSeq compares favorably with other published methods, exhibiting high sensitivity and precision for both punctate and diffuse enrichment regions even in the absence of a control data set. The application of our algorithm to a complex histone modification data set helped make novel functional discoveries which further underlined its utility in such an experimental setup. CONCLUSIONS: WaveSeq is a highly sensitive method capable of accurate identification of enriched regions in a broad range of data sets. WaveSeq can detect both narrow and broad peaks with a high degree of accuracy even in low signal-to-noise ratio data sets. WaveSeq is also suited for application in complex experimental scenarios, helping make biologically relevant functional discoveries.

  11. Data Science and its Relationship to Big Data and Data-Driven Decision Making.

    Science.gov (United States)

    Provost, Foster; Fawcett, Tom

    2013-03-01

    Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

  12. Management and Nonlinear Analysis of Disinfection System of Water Distribution Networks Using Data Driven Methods

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    Mohammad Zounemat-Kermani

    2018-03-01

    Full Text Available Chlorination unit is widely used to supply safe drinking water and removal of pathogens from water distribution networks. Data-driven approach is one appropriate method for analyzing performance of chlorine in water supply network. In this study, multi-layer perceptron neural network (MLP with three training algorithms (gradient descent, conjugate gradient and BFGS and support vector machine (SVM with RBF kernel function were used to predict the concentration of residual chlorine in water supply networks of Ahmadabad Dafeh and Ahruiyeh villages in Kerman Province. Daily data including discharge (flow, chlorine consumption and residual chlorine were employed from the beginning of 1391 Hijri until the end of 1393 Hijri (for 3 years. To assess the performance of studied models, the criteria such as Nash-Sutcliffe efficiency (NS, root mean square error (RMSE, mean absolute percentage error (MAPE and correlation coefficient (CORR were used that in best modeling situation were 0.9484, 0.0255, 1.081, and 0.974 respectively which resulted from BFGS algorithm. The criteria indicated that MLP model with BFGS and conjugate gradient algorithms were better than all other models in 90 and 10 percent of cases respectively; while the MLP model based on gradient descent algorithm and the SVM model were better in none of the cases. According to the results of this study, proper management of chlorine concentration can be implemented by predicted values of residual chlorine in water supply network. Thus, decreased performance of perceptron network and support vector machine in water supply network of Ahruiyeh in comparison to Ahmadabad Dafeh can be inferred from improper management of chlorination.

  13. Simulation of shallow groundwater levels: Comparison of a data-driven and a conceptual model

    Science.gov (United States)

    Fahle, Marcus; Dietrich, Ottfried; Lischeid, Gunnar

    2015-04-01

    Despite an abundance of models aimed at simulating shallow groundwater levels, application of such models is often hampered by a lack of appropriate input data. Difficulties especially arise with regard to soil data, which are typically hard to obtain and prone to spatial variability, eventually leading to uncertainties in the model results. Modelling approaches relying entirely on easily measured quantities are therefore an alternative to encourage the applicability of models. We present and compare two models for calculating 1-day-ahead predictions of the groundwater level that are only based on measurements of potential evapotranspiration, precipitation and groundwater levels. The first model is a newly developed conceptual model that is parametrized using the White method (which estimates the actual evapotranspiration on basis of diurnal groundwater fluctuations) and a rainfall-response ratio. Inverted versions of the two latter approaches are then used to calculate the predictions of the groundwater level. Furthermore, as a completely data-driven alternative, a simple feed-forward multilayer perceptron neural network was trained based on the same inputs and outputs. Data of 4 growing periods (April to October) from a study site situated in the Spreewald wetland in North-east Germany were taken to set-up the models and compare their performance. In addition, response surfaces that relate model outputs to combinations of different input variables are used to reveal those aspects in which the two approaches coincide and those in which they differ. Finally, it will be evaluated whether the conceptual approach can be enhanced by extracting knowledge of the neural network. This is done by replacing in the conceptual model the default function that relates groundwater recharge and groundwater level, which is assumed to be linear, by the non-linear function extracted from the neural network.

  14. Alaska/Yukon Geoid Improvement by a Data-Driven Stokes's Kernel Modification Approach

    Science.gov (United States)

    Li, Xiaopeng; Roman, Daniel R.

    2015-04-01

    Geoid modeling over Alaska of USA and Yukon Canada being a trans-national issue faces a great challenge primarily due to the inhomogeneous surface gravity data (Saleh et al, 2013) and the dynamic geology (Freymueller et al, 2008) as well as its complex geological rheology. Previous study (Roman and Li 2014) used updated satellite models (Bruinsma et al 2013) and newly acquired aerogravity data from the GRAV-D project (Smith 2007) to capture the gravity field changes in the targeting areas primarily in the middle-to-long wavelength. In CONUS, the geoid model was largely improved. However, the precision of the resulted geoid model in Alaska was still in the decimeter level, 19cm at the 32 tide bench marks and 24cm on the 202 GPS/Leveling bench marks that gives a total of 23.8cm at all of these calibrated surface control points, where the datum bias was removed. Conventional kernel modification methods in this area (Li and Wang 2011) had limited effects on improving the precision of the geoid models. To compensate the geoid miss fits, a new Stokes's kernel modification method based on a data-driven technique is presented in this study. First, the method was tested on simulated data sets (Fig. 1), where the geoid errors have been reduced by 2 orders of magnitude (Fig 2). For the real data sets, some iteration steps are required to overcome the rank deficiency problem caused by the limited control data that are irregularly distributed in the target area. For instance, after 3 iterations, the standard deviation dropped about 2.7cm (Fig 3). Modification at other critical degrees can further minimize the geoid model miss fits caused either by the gravity error or the remaining datum error in the control points.

  15. Data Driven Trigger Design and Analysis for the NOvA Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Kurbanov, Serdar [Univ. of Virginia, Charlottesville, VA (United States)

    2016-01-01

    This thesis primarily describes analysis related to studying the Moon shadow with cosmic rays, an analysis using upward-going muons trigger data, and other work done as part of MSc thesis work conducted at Fermi National Laboratory. While at Fermilab I made hardware and software contributions to two experiments - NOvA and Mu2e. NOvA is a neutrino experiment with the primary goal of measuring parameters related to neutrino oscillation. This is a running experiment, so it's possible to provide analysis of real beam and cosmic data. Most of this work was related to the Data-Driven Trigger (DDT) system of NOvA. The results of the Upward-Going muon analysis was presented at ICHEP in August 2016. The analysis demonstrates the proof of principle for a low-mass dark matter search. Mu2e is an experiment currently being built at Fermilab. Its primary goal is to detect the hypothetical neutrinoless conversion from a muon into an electron. I contributed to the production and tests of Cathode Strip Chambers (CSCs) which are required for testing the Cosmic Ray Veto (CRV) system for the experiment. This contribution is described in the last chapter along with a short description of the technical work provided for the DDT system of the NOvA experiment. All of the work described in this thesis will be extended by the next generation of UVA graduate students and postdocs as new data is collected by the experiment. I hope my eorts of have helped lay the foundation for many years of beautiful results from Mu2e and NOvA.

  16. Data-driven nutrient analysis and reality check: Human inputs, catchment delivery and management effects

    Science.gov (United States)

    Destouni, G.

    2017-12-01

    Measures for mitigating nutrient loads to aquatic ecosystems should have observable effects, e.g, in the Baltic region after joint first periods of nutrient management actions under the Baltic Sea Action Plan (BASP; since 2007) and the EU Water Framework Directive (WFD; since 2009). Looking for such observable effects, all openly available water and nutrient monitoring data since 2003 are compiled and analyzed for Sweden as a case study. Results show that hydro-climatically driven water discharge dominates the determination of waterborne loads of both phosphorus and nitrogen. Furthermore, the nutrient loads and water discharge are all similarly well correlated with the ecosystem status classification of Swedish water bodies according to the WFD. Nutrient concentrations, which are hydro-climatically correlated and should thus reflect human effects better than loads, have changed only slightly over the study period (2003-2013) and even increased in moderate-to-bad status waters, where the WFD and BSAP jointly target nutrient decreases. These results indicate insufficient distinction and mitigation of human-driven nutrient components by the internationally harmonized applications of both the WFD and the BSAP. Aiming for better general identification of such components, nutrient data for the large transboundary catchments of the Baltic Sea and the Sava River are compared. The comparison shows cross-regional consistency in nutrient relationships to driving hydro-climatic conditions (water discharge) for nutrient loads, and socio-economic conditions (population density and farmland share) for nutrient concentrations. A data-driven screening methodology is further developed for estimating nutrient input and retention-delivery in catchments. Its first application to nested Sava River catchments identifies characteristic regional values of nutrient input per area and relative delivery, and hotspots of much larger inputs, related to urban high-population areas.

  17. A review on data-driven fault severity assessment in rolling bearings

    Science.gov (United States)

    Cerrada, Mariela; Sánchez, René-Vinicio; Li, Chuan; Pacheco, Fannia; Cabrera, Diego; Valente de Oliveira, José; Vásquez, Rafael E.

    2018-01-01

    Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field.

  18. Data-Driven Modeling of Complex Systems by means of a Dynamical ANN

    Science.gov (United States)

    Seleznev, A.; Mukhin, D.; Gavrilov, A.; Loskutov, E.; Feigin, A.

    2017-12-01

    The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: (i) determining the phase subspace of the system, or embedding, from available time series and (ii) constructing an evolution operator acting in this reduced subspace. In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network (ANN) with special topology. The proposed ANN-based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold. Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on. The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed. The study was supported by the Russian Science Foundation (grant #16-12-10198).

  19. Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

    Energy Technology Data Exchange (ETDEWEB)

    Leistedt, Boris; Hogg, David W., E-mail: boris.leistedt@nyu.edu, E-mail: david.hogg@nyu.edu [Center for Cosmology and Particle Physics, Department of Physics, New York University, New York, NY 10003 (United States)

    2017-03-20

    We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

  20. Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

    International Nuclear Information System (INIS)

    Leistedt, Boris; Hogg, David W.

    2017-01-01

    We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

  1. Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection

    Directory of Open Access Journals (Sweden)

    Yan Pei

    2018-03-01

    Full Text Available Wind turbine yaw control plays an important role in increasing the wind turbine production and also in protecting the wind turbine. Accurate measurement of yaw angle is the basis of an effective wind turbine yaw controller. The accuracy of yaw angle measurement is affected significantly by the problem of zero-point shifting. Hence, it is essential to evaluate the zero-point shifting error on wind turbines on-line in order to improve the reliability of yaw angle measurement in real time. Particularly, qualitative evaluation of the zero-point shifting error could be useful for wind farm operators to realize prompt and cost-effective maintenance on yaw angle sensors. In the aim of qualitatively evaluating the zero-point shifting error, the yaw angle sensor zero-point shifting fault is firstly defined in this paper. A data-driven method is then proposed to detect the zero-point shifting fault based on Supervisory Control and Data Acquisition (SCADA data. The zero-point shifting fault is detected in the proposed method by analyzing the power performance under different yaw angles. The SCADA data are partitioned into different bins according to both wind speed and yaw angle in order to deeply evaluate the power performance. An indicator is proposed in this method for power performance evaluation under each yaw angle. The yaw angle with the largest indicator is considered as the yaw angle measurement error in our work. A zero-point shifting fault would trigger an alarm if the error is larger than a predefined threshold. Case studies from several actual wind farms proved the effectiveness of the proposed method in detecting zero-point shifting fault and also in improving the wind turbine performance. Results of the proposed method could be useful for wind farm operators to realize prompt adjustment if there exists a large error of yaw angle measurement.

  2. Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

    Science.gov (United States)

    Chen, Daizhuo; Fraiberger, Samuel P; Moakler, Robert; Provost, Foster

    2017-09-01

    Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from "Likes" on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the "cloaking device"-a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users.

  3. Disruption of functional networks in dyslexia: A whole-brain, data-driven analysis of connectivity

    Science.gov (United States)

    Finn, Emily S.; Shen, Xilin; Holahan, John M.; Scheinost, Dustin; Lacadie, Cheryl; Papademetris, Xenophon; Shaywitz, Sally E.; Shaywitz, Bennett A.; Constable, R. Todd

    2013-01-01

    Background Functional connectivity analyses of fMRI data are a powerful tool for characterizing brain networks and how they are disrupted in neural disorders. However, many such analyses examine only one or a small number of a priori seed regions. Studies that consider the whole brain frequently rely on anatomic atlases to define network nodes, which may result in mixing distinct activation timecourses within a single node. Here, we improve upon previous methods by using a data-driven brain parcellation to compare connectivity profiles of dyslexic (DYS) versus non-impaired (NI) readers in the first whole-brain functional connectivity analysis of dyslexia. Methods Whole-brain connectivity was assessed in children (n = 75; 43 NI, 32 DYS) and adult (n = 104; 64 NI, 40 DYS) readers. Results Compared to NI readers, DYS readers showed divergent connectivity within the visual pathway and between visual association areas and prefrontal attention areas; increased right-hemisphere connectivity; reduced connectivity in the visual word-form area (part of the left fusiform gyrus specialized for printed words); and persistent connectivity to anterior language regions around the inferior frontal gyrus. Conclusions Together, findings suggest that NI readers are better able to integrate visual information and modulate their attention to visual stimuli, allowing them to recognize words based on their visual properties, while DYS readers recruit altered reading circuits and rely on laborious phonology-based “sounding out” strategies into adulthood. These results deepen our understanding of the neural basis of dyslexia and highlight the importance of synchrony between diverse brain regions for successful reading. PMID:24124929

  4. Improving Spoken Language Outcomes for Children With Hearing Loss: Data-driven Instruction.

    Science.gov (United States)

    Douglas, Michael

    2016-02-01

    To assess the effects of data-driven instruction (DDI) on spoken language outcomes of children with cochlear implants and hearing aids. Retrospective, matched-pairs comparison of post-treatment speech/language data of children who did and did not receive DDI. Private, spoken-language preschool for children with hearing loss. Eleven matched pairs of children with cochlear implants who attended the same spoken language preschool. Groups were matched for age of hearing device fitting, time in the program, degree of predevice fitting hearing loss, sex, and age at testing. Daily informal language samples were collected and analyzed over a 2-year period, per preschool protocol. Annual informal and formal spoken language assessments in articulation, vocabulary, and omnibus language were administered at the end of three time intervals: baseline, end of year one, and end of year two. The primary outcome measures were total raw score performance of spontaneous utterance sentence types and syntax element use as measured by the Teacher Assessment of Spoken Language (TASL). In addition, standardized assessments (the Clinical Evaluation of Language Fundamentals--Preschool Version 2 (CELF-P2), the Expressive One-Word Picture Vocabulary Test (EOWPVT), the Receptive One-Word Picture Vocabulary Test (ROWPVT), and the Goldman-Fristoe Test of Articulation 2 (GFTA2)) were also administered and compared with the control group. The DDI group demonstrated significantly higher raw scores on the TASL each year of the study. The DDI group also achieved statistically significant higher scores for total language on the CELF-P and expressive vocabulary on the EOWPVT, but not for articulation nor receptive vocabulary. Post-hoc assessment revealed that 78% of the students in the DDI group achieved scores in the average range compared with 59% in the control group. The preliminary results of this study support further investigation regarding DDI to investigate whether this method can consistently

  5. STUDY OF THE POYNTING FLUX IN ACTIVE REGION 10930 USING DATA-DRIVEN MAGNETOHYDRODYNAMIC SIMULATION

    International Nuclear Information System (INIS)

    Fan, Y. L.; Wang, H. N.; He, H.; Zhu, X. S.

    2011-01-01

    Powerful solar flares are closely related to the evolution of magnetic field configuration on the photosphere. We choose the Poynting flux as a parameter in the study of magnetic field changes. We use time-dependent multidimensional MHD simulations around a flare occurrence to generate the results, with the temporal variation of the bottom boundary conditions being deduced from the projected normal characteristic method. By this method, the photospheric magnetogram could be incorporated self-consistently as the bottom condition of data-driven simulations. The model is first applied to a simulation datum produced by an emerging magnetic flux rope as a test case. Then, the model is used to study NOAA AR 10930, which has an X3.4 flare, the data of which has been obtained by the Hinode/Solar Optical Telescope on 2006 December 13. We compute the magnitude of Poynting flux (S total ), radial Poynting flux (S z ), a proxy for ideal radial Poynting flux (S proxy ), Poynting flux due to plasma surface motion (S sur ), and Poynting flux due to plasma emergence (S emg ) and analyze their extensive properties in four selected areas: the whole sunspot, the positive sunspot, the negative sunspot, and the strong-field polarity inversion line (SPIL) area. It is found that (1) the S total , S z , and S proxy parameters show similar behaviors in the whole sunspot area and in the negative sunspot area. The evolutions of these three parameters in the positive area and the SPIL area are more volatile because of the effect of sunspot rotation and flux emergence. (2) The evolution of S sur is largely influenced by the process of sunspot rotation, especially in the positive sunspot. The evolution of S emg is greatly affected by flux emergence, especially in the SPIL area.

  6. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.

    Science.gov (United States)

    Wang, Shuo; Zhou, Mu; Liu, Zaiyi; Liu, Zhenyu; Gu, Dongsheng; Zang, Yali; Dong, Di; Gevaert, Olivier; Tian, Jie

    2017-08-01

    Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%. Copyright © 2017. Published by Elsevier B.V.

  7. Telling Anthropocene Tales: Localizing the impacts of global change using data-driven story maps

    Science.gov (United States)

    Mychajliw, A.; Hadly, E. A.

    2016-12-01

    Navigating the Anthropocene requires innovative approaches for generating scientific knowledge and for its communication outside academia. The global, synergistic nature of the environmental challenges we face - climate change, human population growth, biodiversity loss, pollution, invasive species and diseases - highlight the need for public outreach strategies that incorporate multiple scales and perspectives in an easily understandable and rapidly accessible format. Data-driven story-telling maps are optimal in that they can display variable geographic scales and their intersections with the environmental challenges relevant to both scientists and non-scientists. Maps are a powerful way to present complex data to all stakeholders. We present an overview of best practices in community-engaged scientific story-telling and data translation for policy-makers by reviewing three Story Map projects that map the geographic impacts of global change across multiple spatial and policy scales: the entire United States, the state of California, and the town of Pescadero, California. We document a chain of translation from a primary scientific manscript to a policy document (Scientific Consensus Statement on Maintaining Humanity's Life Support Systems in the 21st Century) to a set of interactive ArcGIS Story Maps. We discuss the widening breadth of participants (students, community members) and audiences (White House, Governor's Office of California, California Congressional Offices, general public) involved. We highlight how scientists, through careful curation of popular news media articles and stakeholder interviews, can co-produce these communication modules with community partners such as non-governmental organizations and government agencies. The placement of scientific and citizen's everyday knowledge of global change into an appropriate geographic context allows for effective dissemination by political units such as congressional districts and agency management units

  8. The Application of Cyber Physical System for Thermal Power Plants: Data-Driven Modeling

    Directory of Open Access Journals (Sweden)

    Yongping Yang

    2018-03-01

    Full Text Available Optimal operation of energy systems plays an important role to enhance their lifetime security and efficiency. The determination of optimal operating strategies requires intelligent utilization of massive data accumulated during operation or prediction. The investigation of these data solely without combining physical models may run the risk that the established relationships between inputs and outputs, the models which reproduce the behavior of the considered system/component in a wide range of boundary conditions, are invalid for certain boundary conditions, which never occur in the database employed. Therefore, combining big data with physical models via cyber physical systems (CPS is of great importance to derive highly-reliable and -accurate models and becomes more and more popular in practical applications. In this paper, we focus on the description of a systematic method to apply CPS to the performance analysis and decision making of thermal power plants. We proposed a general procedure of CPS with both offline and online phases for its application to thermal power plants and discussed the corresponding methods employed to support each sub-procedure. As an example, a data-driven model of turbine island of an existing air-cooling based thermal power plant is established with the proposed procedure and demonstrates its practicality, validity and flexibility. To establish such model, the historical operating data are employed in the cyber layer for modeling and linking each physical component. The decision-making procedure of optimal frequency of air-cooling condenser is also illustrated to show its applicability of online use. It is concluded that the cyber physical system with the data mining technique is effective and promising to facilitate the real-time analysis and control of thermal power plants.

  9. Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology

    Science.gov (United States)

    Moosavi, V.; Aschwanden, G.; Velasco, E.

    2015-09-01

    Finding the number and best locations of fixed air quality monitoring stations at street level is challenging because of the complexity of the urban environment and the large number of factors affecting the pollutants concentration. Data sets of such urban parameters as land use, building morphology and street geometry in high-resolution grid cells in combination with direct measurements of airborne pollutants at high frequency (1-10 s) along a reasonable number of streets can be used to interpolate concentration of pollutants in a whole gridded domain and determine the optimum number of monitoring sites and best locations for a network of fixed monitors at ground level. In this context, a data-driven modeling methodology is developed based on the application of Self-Organizing Map (SOM) to approximate the nonlinear relations between urban parameters (80 in this work) and aerosol pollution data, such as mass and number concentrations measured along streets of a commercial/residential neighborhood of Singapore. Cross-validations between measured and predicted aerosol concentrations based on the urban parameters at each individual grid cell showed satisfying results. This proof of concept study showed that the selected urban parameters proved to be an appropriate indirect measure of aerosol concentrations within the studied area. The potential locations for fixed air quality monitors are identified through clustering of areas (i.e., group of cells) with similar urban patterns. The typological center of each cluster corresponds to the most representative cell for all other cells in the cluster. In the studied neighborhood four different clusters were identified and for each cluster potential sites for air quality monitoring at ground level are identified.

  10. Probing the dynamics of identified neurons with a data-driven modeling approach.

    Directory of Open Access Journals (Sweden)

    Thomas Nowotny

    2008-07-01

    Full Text Available In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach.

  11. Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

    International Nuclear Information System (INIS)

    Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

    2017-01-01

    Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction. (paper)

  12. Employment relations: A data driven analysis of job markets using online job boards and online professional networks

    CSIR Research Space (South Africa)

    Marivate, Vukosi N

    2017-08-01

    Full Text Available Data from online job boards and online professional networks present an opportunity to understand job markets as well as how professionals transition from one job/career to another. We propose a data driven approach to begin to understand a slice...

  13. Keys to success for data-driven decision making: Lessons from participatory monitoring and collaborative adaptive management

    Science.gov (United States)

    Recent years have witnessed a call for evidence-based decisions in conservation and natural resource management, including data-driven decision-making. Adaptive management (AM) is one prevalent model for integrating scientific data into decision-making, yet AM has faced numerous challenges and limit...

  14. The Effects of Data-Driven Learning upon Vocabulary Acquisition for Secondary International School Students in Vietnam

    Science.gov (United States)

    Karras, Jacob Nolen

    2016-01-01

    Within the field of computer assisted language learning (CALL), scant literature exists regarding the effectiveness and practicality for secondary students to utilize data-driven learning (DDL) for vocabulary acquisition. In this study, there were 100 participants, who had a mean age of thirteen years, and were attending an international school in…

  15. Data-driven drug safety signal detection methods in pharmacovigilance using electronic primary care records: A population based study

    Directory of Open Access Journals (Sweden)

    Shang-Ming Zhou

    2017-04-01

    Data-driven analytic methods are a valuable aid to signal detection of ADEs from large electronic health records for drug safety monitoring. This study finds the methods can detect known ADE and so could potentially be used to detect unknown ADE.

  16. How Instructional Coaches Support Data-Driven Decision Making: Policy Implementation and Effects in Florida Middle Schools

    Science.gov (United States)

    Marsh, Julie A.; McCombs, Jennifer Sloan; Martorell, Francisco

    2010-01-01

    This article examines the convergence of two popular school improvement policies: instructional coaching and data-driven decision making (DDDM). Drawing on a mixed methods study of a statewide reading coach program in Florida middle schools, the article examines how coaches support DDDM and how this support relates to student and teacher outcomes.…

  17. Analyzing the Discourse of Chais Conferences for the Study of Innovation and Learning Technologies via a Data-Driven Approach

    Science.gov (United States)

    Silber-Varod, Vered; Eshet-Alkalai, Yoram; Geri, Nitza

    2016-01-01

    The current rapid technological changes confront researchers of learning technologies with the challenge of evaluating them, predicting trends, and improving their adoption and diffusion. This study utilizes a data-driven discourse analysis approach, namely culturomics, to investigate changes over time in the research of learning technologies. The…

  18. The Use of Linking Adverbials in Academic Essays by Non-Native Writers: How Data-Driven Learning Can Help

    Science.gov (United States)

    Garner, James Robert

    2013-01-01

    Over the past several decades, the TESOL community has seen an increased interest in the use of data-driven learning (DDL) approaches. Most studies of DDL have focused on the acquisition of vocabulary items, including a wide range of information necessary for their correct usage. One type of vocabulary that has yet to be properly investigated has…

  19. Examining Data Driven Decision Making via Formative Assessment: A Confluence of Technology, Data Interpretation Heuristics and Curricular Policy

    Science.gov (United States)

    Swan, Gerry; Mazur, Joan

    2011-01-01

    Although the term data-driven decision making (DDDM) is relatively new (Moss, 2007), the underlying concept of DDDM is not. For example, the practices of formative assessment and computer-managed instruction have historically involved the use of student performance data to guide what happens next in the instructional sequence (Morrison, Kemp, &…

  20. Strength in Numbers: Data-Driven Collaboration May Not Sound Sexy, But it Could Save Your Job

    Science.gov (United States)

    Buzzeo, Toni

    2010-01-01

    This article describes a practical, sure-fire way for media specialists to boost student achievement. The method is called data-driven collaboration, and it's a practical, easy-to-use technique in which media specialists and teachers work together to pinpoint kids' instructional needs and improve their essential skills. The author discusses the…

  1. Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM

    KAUST Repository

    Amer, Abdelhalim; Maruyama, Naoya; Pericà s, Miquel; Taura, Kenjiro; Yokota, Rio; Matsuoka, Satoshi

    2013-01-01

    Extracting maximum performance of multi-core architectures is a difficult task primarily due to bandwidth limitations of the memory subsystem and its complex hierarchy. In this work, we study the implications of fork-join and data-driven execution

  2. Data-driven sampling method for building 3D anatomical models from serial histology

    Science.gov (United States)

    Salunke, Snehal Ulhas; Ablove, Tova; Danforth, Theresa; Tomaszewski, John; Doyle, Scott

    2017-03-01

    In this work, we investigate the effect of slice sampling on 3D models of tissue architecture using serial histopathology. We present a method for using a single fully-sectioned tissue block as pilot data, whereby we build a fully-realized 3D model and then determine the optimal set of slices needed to reconstruct the salient features of the model objects under biological investigation. In our work, we are interested in the 3D reconstruction of microvessel architecture in the trigone region between the vagina and the bladder. This region serves as a potential avenue for drug delivery to treat bladder infection. We collect and co-register 23 serial sections of CD31-stained tissue images (6 μm thick sections), from which four microvessels are selected for analysis. To build each model, we perform semi-automatic segmentation of the microvessels. Subsampled meshes are then created by removing slices from the stack, interpolating the missing data, and re-constructing the mesh. We calculate the Hausdorff distance between the full and subsampled meshes to determine the optimal sampling rate for the modeled structures. In our application, we found that a sampling rate of 50% (corresponding to just 12 slices) was sufficient to recreate the structure of the microvessels without significant deviation from the fullyrendered mesh. This pipeline effectively minimizes the number of histopathology slides required for 3D model reconstruction, and can be utilized to either (1) reduce the overall costs of a project, or (2) enable additional analysis on the intermediate slides.

  3. Unravelling abiotic and biotic controls on the seasonal water balance using data-driven dimensionless diagnostics

    Directory of Open Access Journals (Sweden)

    S. P. Seibert

    2017-06-01

    Full Text Available The baffling diversity of runoff generation processes, alongside our sketchy understanding of how physiographic characteristics control fundamental hydrological functions of water collection, storage, and release, continue to pose major research challenges in catchment hydrology. Here, we propose innovative data-driven diagnostic signatures for overcoming the prevailing status quo in catchment inter-comparison. More specifically, we present dimensionless double mass curves (dDMC which allow inference of information on runoff generation and the water balance at the seasonal and annual timescales. By separating the vegetation and winter periods, dDMC furthermore provide information on the role of biotic and abiotic controls in seasonal runoff formation. A key aspect we address in this paper is the derivation of dimensionless expressions of fluxes which ensure the comparability of the signatures in space and time. We achieve this by using the limiting factors of a hydrological process as a scaling reference. We show that different references result in different diagnostics. As such we define two kinds of dDMC which allow us to derive seasonal runoff coefficients and to characterize dimensionless streamflow release as a function of the potential renewal rate of the soil storage. We expect these signatures for storage controlled seasonal runoff formation to remain invariant, as long as the ratios of release over supply and supply over storage capacity develop similarly in different catchments. We test the proposed methods by applying them to an operational data set comprising 22 catchments (12–166 km2 from different environments in southern Germany and hydrometeorological data from 4 hydrological years. The diagnostics are used to compare the sites and to reveal the dominant controls on runoff formation. The key findings are that dDMC are meaningful signatures for catchment runoff formation at the seasonal to annual scale and that the type of

  4. qPortal: A platform for data-driven biomedical research.

    Science.gov (United States)

    Mohr, Christopher; Friedrich, Andreas; Wojnar, David; Kenar, Erhan; Polatkan, Aydin Can; Codrea, Marius Cosmin; Czemmel, Stefan; Kohlbacher, Oliver; Nahnsen, Sven

    2018-01-01

    Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis. We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software's strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy). qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on

  5. Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.

    Directory of Open Access Journals (Sweden)

    David Henriques

    2017-02-01

    Full Text Available Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models, which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks: it builds dynamic (based on ordinary differential equation models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training. For this task, SELDOM's ensemble prediction is not only consistently better

  6. Current Trends in the Detection of Sociocultural Signatures: Data-Driven Models

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Bell, Eric B.; Corley, Courtney D.

    2014-09-15

    available that are shaping social computing as a strongly data-driven experimental discipline with an increasingly stronger impact on the decision-making process of groups and individuals alike. In this chapter, we review current advances and trends in the detection of sociocultural signatures. Specific embodiments of the issues discussed are provided with respect to the assessment of violent intent and sociopolitical contention. We begin by reviewing current approaches to the detection of sociocultural signatures in these domains. Next, we turn to the review of novel data harvesting methods for social media content. Finally, we discuss the application of sociocultural models to social media content, and conclude by commenting on current challenges and future developments.

  7. Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

    Science.gov (United States)

    Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

    2018-05-01

    We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

  8. Cognitive load privileges memory-based over data-driven processing, not group-level over person-level processing.

    Science.gov (United States)

    Skorich, Daniel P; Mavor, Kenneth I

    2013-09-01

    In the current paper, we argue that categorization and individuation, as traditionally discussed and as experimentally operationalized, are defined in terms of two confounded underlying dimensions: a person/group dimension and a memory-based/data-driven dimension. In a series of three experiments, we unconfound these dimensions and impose a cognitive load. Across the three experiments, two with laboratory-created targets and one with participants' friends as the target, we demonstrate that cognitive load privileges memory-based over data-driven processing, not group- over person-level processing. We discuss the results in terms of their implications for conceptualizations of the categorization/individuation distinction, for the equivalence of person and group processes, for the ultimate 'purpose' and meaningfulness of group-based perception and, fundamentally, for the process of categorization, broadly defined. © 2012 The British Psychological Society.

  9. Data-driven technology for engineering systems health management design approach, feature construction, fault diagnosis, prognosis, fusion and decisions

    CERN Document Server

    Niu, Gang

    2017-01-01

    This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

  10. A Data-Driven Control Design Approach for Freeway Traffic Ramp Metering with Virtual Reference Feedback Tuning

    Directory of Open Access Journals (Sweden)

    Shangtai Jin

    2014-01-01

    Full Text Available ALINEA is a simple, efficient, and easily implemented ramp metering strategy. Virtual reference feedback tuning (VRFT is most suitable for many practical systems since it is a “one-shot” data-driven control design methodology. This paper presents an application of VRFT to a ramp metering problem of freeway traffic system. When there is not enough prior knowledge of the controlled system to select a proper parameter of ALINEA, the VRFT approach is used to optimize the ALINEA's parameter by only using a batch of input and output data collected from the freeway traffic system. The extensive simulations are built on both the macroscopic MATLAB platform and the microscopic PARAMICS platform to show the effectiveness and applicability of the proposed data-driven controller tuning approach.

  11. A Data-Driven Stochastic Reactive Power Optimization Considering Uncertainties in Active Distribution Networks and Decomposition Method

    DEFF Research Database (Denmark)

    Ding, Tao; Yang, Qingrun; Yang, Yongheng

    2018-01-01

    To address the uncertain output of distributed generators (DGs) for reactive power optimization in active distribution networks, the stochastic programming model is widely used. The model is employed to find an optimal control strategy with minimum expected network loss while satisfying all......, in this paper, a data-driven modeling approach is introduced to assume that the probability distribution from the historical data is uncertain within a confidence set. Furthermore, a data-driven stochastic programming model is formulated as a two-stage problem, where the first-stage variables find the optimal...... control for discrete reactive power compensation equipment under the worst probability distribution of the second stage recourse. The second-stage variables are adjusted to uncertain probability distribution. In particular, this two-stage problem has a special structure so that the second-stage problem...

  12. Knowledge Based Cloud FE simulation - data-driven material characterization guidelines for the hot stamping of aluminium alloys

    Science.gov (United States)

    Wang, Ailing; Zheng, Yang; Liu, Jun; El Fakir, Omer; Masen, Marc; Wang, Liliang

    2016-08-01

    The Knowledge Based Cloud FEA (KBC-FEA) simulation technique allows multiobjective FE simulations to be conducted on a cloud-computing environment, which effectively reduces computation time and expands the capability of FE simulation software. In this paper, a novel functional module was developed for the data mining of experimentally verified FE simulation results for metal forming processes obtained from KBC-FE. Through this functional module, the thermo-mechanical characteristics of a metal forming process were deduced, enabling a systematic and data-driven guideline for mechanical property characterization to be developed, which will directly guide the material tests for a metal forming process towards the most efficient and effective scheme. Successful application of this data-driven guideline would reduce the efforts for material characterization, leading to the development of more accurate material models, which in turn enhance the accuracy of FE simulations.

  13. Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition

    OpenAIRE

    Bettadapura, Vinay; Schindler, Grant; Plotz, Thomaz; Essa, Irfan

    2015-01-01

    We present data-driven techniques to augment Bag of Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are not known a priori. Our approach specifically addresses the limitations of standard BoW approaches, which fail to represent the underlying temporal and causal information that is inherent in activity streams. In addition, we also propose the use of randomly sampled regular ...

  14. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology

    Directory of Open Access Journals (Sweden)

    A. Elshorbagy

    2010-10-01

    Full Text Available A comprehensive data driven modeling experiment is presented in a two-part paper. In this first part, an extensive data-driven modeling experiment is proposed. The most important concerns regarding the way data driven modeling (DDM techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions were drawn are discussed. A concise review of key articles that presented comparisons among various DDM techniques is presented. Six DDM techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbors are proposed and explained. Multiple linear regression and naïve models are also suggested as baseline for comparison with the various techniques. Five datasets from Canada and Europe representing evapotranspiration, upper and lower layer soil moisture content, and rainfall-runoff process are described and proposed, in the second paper, for the modeling experiment. Twelve different realizations (groups from each dataset are created by a procedure involving random sampling. Each group contains three subsets; training, cross-validation, and testing. Each modeling technique is proposed to be applied to each of the 12 groups of each dataset. This way, both prediction accuracy and uncertainty of the modeling techniques can be evaluated. The description of the datasets, the implementation of the modeling techniques, results and analysis, and the findings of the modeling experiment are deferred to the second part of this paper.

  15. Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method

    International Nuclear Information System (INIS)

    Kusiak, Andrew; Xu, Guanglin; Zhang, Zijun

    2014-01-01

    Highlights: • We study the energy saving of HVAC systems with a data-driven approach. • We conduct an in-depth analysis of the topology of developed Neural Network based HVAC model. • We apply interior-point method to solving a Neural Network based HVAC optimization model. • The uncertain building occupancy is incorporated in the minimization of HVAC energy consumption. • A significant potential of saving HVAC energy is discovered. - Abstract: In this paper, a data-driven approach is applied to minimize energy consumption of a heating, ventilating, and air conditioning (HVAC) system while maintaining the thermal comfort of a building with uncertain occupancy level. The uncertainty of arrival and departure rate of occupants is modeled by the Poisson and uniform distributions, respectively. The internal heating gain is calculated from the stochastic process of the building occupancy. Based on the observed and simulated data, a multilayer perceptron algorithm is employed to model and simulate the HVAC system. The data-driven models accurately predict future performance of the HVAC system based on the control settings and the observed historical information. An optimization model is formulated and solved with the interior-point method. The optimization results are compared with the results produced by the simulation models

  16. A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty

    Science.gov (United States)

    Friedel, Michael J.

    2011-01-01

    This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.

  17. Towards a data-driven analysis of hadronic light-by-light scattering

    Science.gov (United States)

    Colangelo, Gilberto; Hoferichter, Martin; Kubis, Bastian; Procura, Massimiliano; Stoffer, Peter

    2014-11-01

    The hadronic light-by-light contribution to the anomalous magnetic moment of the muon was recently analyzed in the framework of dispersion theory, providing a systematic formalism where all input quantities are expressed in terms of on-shell form factors and scattering amplitudes that are in principle accessible in experiment. We briefly review the main ideas behind this framework and discuss the various experimental ingredients needed for the evaluation of one- and two-pion intermediate states. In particular, we identify processes that in the absence of data for doubly-virtual pion-photon interactions can help constrain parameters in the dispersive reconstruction of the relevant input quantities, the pion transition form factor and the helicity partial waves for γ*γ* → ππ.

  18. Towards a data-driven analysis of hadronic light-by-light scattering

    International Nuclear Information System (INIS)

    Colangelo, Gilberto; Hoferichter, Martin; Kubis, Bastian; Procura, Massimiliano; Stoffer, Peter

    2014-01-01

    The hadronic light-by-light contribution to the anomalous magnetic moment of the muon was recently analyzed in the framework of dispersion theory, providing a systematic formalism where all input quantities are expressed in terms of on-shell form factors and scattering amplitudes that are in principle accessible in experiment. We briefly review the main ideas behind this framework and discuss the various experimental ingredients needed for the evaluation of one- and two-pion intermediate states. In particular, we identify processes that in the absence of data for doubly-virtual pion–photon interactions can help constrain parameters in the dispersive reconstruction of the relevant input quantities, the pion transition form factor and the helicity partial waves for γ * γ * →ππ

  19. Towards a data-driven analysis of hadronic light-by-light scattering

    Energy Technology Data Exchange (ETDEWEB)

    Colangelo, Gilberto; Hoferichter, Martin [Albert Einstein Center for Fundamental Physics, Institute for Theoretical Physics, Universität Bern, Sidlerstrasse 5, CH-3012 Bern (Switzerland); Kubis, Bastian [Helmholtz-Institut für Strahlen- und Kernphysik (Theorie) and Bethe Center for Theoretical Physics, Universität Bonn, D-53115 Bonn (Germany); Procura, Massimiliano; Stoffer, Peter [Albert Einstein Center for Fundamental Physics, Institute for Theoretical Physics, Universität Bern, Sidlerstrasse 5, CH-3012 Bern (Switzerland)

    2014-11-10

    The hadronic light-by-light contribution to the anomalous magnetic moment of the muon was recently analyzed in the framework of dispersion theory, providing a systematic formalism where all input quantities are expressed in terms of on-shell form factors and scattering amplitudes that are in principle accessible in experiment. We briefly review the main ideas behind this framework and discuss the various experimental ingredients needed for the evaluation of one- and two-pion intermediate states. In particular, we identify processes that in the absence of data for doubly-virtual pion–photon interactions can help constrain parameters in the dispersive reconstruction of the relevant input quantities, the pion transition form factor and the helicity partial waves for γ{sup *}γ{sup *}→ππ.

  20. Residential Mobility and Lung Cancer Risk: Data-Driven Exploration Using Internet Sources

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, Hong-Jun [ORNL; Tourassi, Georgia [ORNL; Xu, Songhua [New Jersey Insitute of Technology

    2015-01-01

    Frequent relocation has been linked to health decline, particularly with respect to emotional and psychological wellbeing. In this paper we investigate whether there is an association between frequent relocation and lung cancer risk. For the initial investigation we leverage two online data sources to collect cancer and control subjects using web crawling and tailored text mining. The two data sources share different strengths and weaknesses in terms of the amount of detail, population representation, and sample size. One data source includes online obituaries. The second data source includes augmented LinkedIn profiles. For each data source, the subjects spatiotemporal history is reconstructed from the available information provided in the obituaries and from the education and work experience provided in the LinkedIn profiles. The study shows that lung cancer subjects have higher mobility frequency than the control group. This trend is consistent for both data sources.