WorldWideScience

Sample records for learning probability trees

  1. Maximum parsimony, substitution model, and probability phylogenetic trees.

    Science.gov (United States)

    Weng, J F; Thomas, D A; Mareels, I

    2011-01-01

    The problem of inferring phylogenies (phylogenetic trees) is one of the main problems in computational biology. There are three main methods for inferring phylogenies-Maximum Parsimony (MP), Distance Matrix (DM) and Maximum Likelihood (ML), of which the MP method is the most well-studied and popular method. In the MP method the optimization criterion is the number of substitutions of the nucleotides computed by the differences in the investigated nucleotide sequences. However, the MP method is often criticized as it only counts the substitutions observable at the current time and all the unobservable substitutions that really occur in the evolutionary history are omitted. In order to take into account the unobservable substitutions, some substitution models have been established and they are now widely used in the DM and ML methods but these substitution models cannot be used within the classical MP method. Recently the authors proposed a probability representation model for phylogenetic trees and the reconstructed trees in this model are called probability phylogenetic trees. One of the advantages of the probability representation model is that it can include a substitution model to infer phylogenetic trees based on the MP principle. In this paper we explain how to use a substitution model in the reconstruction of probability phylogenetic trees and show the advantage of this approach with examples.

  2. Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty

    International Nuclear Information System (INIS)

    Purba, Julwan Hendry; Sony Tjahyani, D.T.; Ekariansyah, Andi Sofrany; Tjahjono, Hendro

    2015-01-01

    Highlights: • Fuzzy probability based fault tree analysis is to evaluate epistemic uncertainty in fuzzy fault tree analysis. • Fuzzy probabilities represent likelihood occurrences of all events in a fault tree. • A fuzzy multiplication rule quantifies epistemic uncertainty of minimal cut sets. • A fuzzy complement rule estimate epistemic uncertainty of the top event. • The proposed FPFTA has successfully evaluated the U.S. Combustion Engineering RPS. - Abstract: A number of fuzzy fault tree analysis approaches, which integrate fuzzy concepts into the quantitative phase of conventional fault tree analysis, have been proposed to study reliabilities of engineering systems. Those new approaches apply expert judgments to overcome the limitation of the conventional fault tree analysis when basic events do not have probability distributions. Since expert judgments might come with epistemic uncertainty, it is important to quantify the overall uncertainties of the fuzzy fault tree analysis. Monte Carlo simulation is commonly used to quantify the overall uncertainties of conventional fault tree analysis. However, since Monte Carlo simulation is based on probability distribution, this technique is not appropriate for fuzzy fault tree analysis, which is based on fuzzy probabilities. The objective of this study is to develop a fuzzy probability based fault tree analysis to overcome the limitation of fuzzy fault tree analysis. To demonstrate the applicability of the proposed approach, a case study is performed and its results are then compared to the results analyzed by a conventional fault tree analysis. The results confirm that the proposed fuzzy probability based fault tree analysis is feasible to propagate and quantify epistemic uncertainties in fault tree analysis

  3. Analytical and numerical studies of creation probabilities of hierarchical trees

    Directory of Open Access Journals (Sweden)

    S.S. Borysov

    2011-03-01

    Full Text Available We consider the creation conditions of diverse hierarchical trees both analytically and numerically. A connection between the probabilities to create hierarchical levels and the probability to associate these levels into a united structure is studied. We argue that a consistent probabilistic picture requires the use of deformed algebra. Our consideration is based on the study of the main types of hierarchical trees, among which both regular and degenerate ones are studied analytically, while the creation probabilities of Fibonacci, scale-free and arbitrary trees are determined numerically.

  4. Meta-learning in decision tree induction

    CERN Document Server

    Grąbczewski, Krzysztof

    2014-01-01

    The book focuses on different variants of decision tree induction but also describes  the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimen...

  5. Blind Students' Learning of Probability through the Use of a Tactile Model

    Science.gov (United States)

    Vita, Aida Carvalho; Kataoka, Verônica Yumi

    2014-01-01

    The objective of this paper is to discuss how blind students learn basic concepts of probability using the tactile model proposed by Vita (2012). Among the activities were part of the teaching sequence "Jefferson's Random Walk", in which students built a tree diagram (using plastic trays, foam cards, and toys), and pictograms in 3D…

  6. Probability distribution of long-run indiscriminate felling of trees in ...

    African Journals Online (AJOL)

    The study was undertaken to determine the probability distribution of Long-run indiscriminate felling of trees in northern senatorial district of Adamawa State. Specifically, the study focused on examining the future direction of indiscriminate felling of trees as well as its equilibrium distribution. A multi-stage and simple random ...

  7. Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

    Science.gov (United States)

    Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.

    2011-01-01

    Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433

  8. USING RASCH ANALYSIS TO EXPLORE WHAT STUDENTS LEARN ABOUT PROBABILITY CONCEPTS

    Directory of Open Access Journals (Sweden)

    Zamalia Mahmud

    2015-01-01

    Full Text Available Students’ understanding of probability concepts have been investigated from various different perspectives. This study was set out to investigate perceived understanding of probability concepts of forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW. Rasch measurement which is based on a probabilistic model was used to identify concepts that students find easy, moderate and difficult to understand. Data were captured from the e-learning Moodle platform where students provided their responses through an on-line quiz. As illustrated in the Rasch map, 96% of the students could understand about sample space, simple events, mutually exclusive events and tree diagram while 67% of the students found concepts of conditional and independent events rather easy to understand

  9. Using Rasch Analysis To Explore What Students Learn About Probability Concepts

    Directory of Open Access Journals (Sweden)

    Zamalia Mahmud

    2015-01-01

    Full Text Available Students’ understanding of probability concepts have been investigated from various different perspectives. This study was set out to investigate perceived understanding of probability concepts of forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW. Rasch measurement which is based on a probabilistic model was used to identify concepts that students find easy, moderate and difficult to understand. Data were captured from the e-learning Moodle platform where students provided their responses through an on-line quiz. As illustrated in the Rasch map, 96% of the students could understand about sample space, simple events, mutually exclusive events and tree diagram while 67% of the students found concepts of conditional and independent events rather easy to understand.

  10. USING RASCH ANALYSIS TO EXPLORE WHAT STUDENTS LEARN ABOUT PROBABILITY CONCEPTS

    Directory of Open Access Journals (Sweden)

    Zamalia Mahmud

    2015-01-01

    Full Text Available Students’ understanding of probability concepts have been investigated from various different perspectives. This study was set out to investigate perceived understanding of probability concepts of forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW. Rasch measurement which is based on a probabilistic model was used to identify concepts that students find easy, moderate and difficult to understand.  Data were captured from the e-learning Moodle platform where students provided their responses through an on-line quiz. As illustrated in the Rasch map, 96% of the students could understand about sample space, simple events, mutually exclusive events and tree diagram while 67% of the students found concepts of conditional and independent events rather easy to understand.Keywords: Perceived Understanding, Probability Concepts, Rasch Measurement Model DOI: dx.doi.org/10.22342/jme.61.1

  11. Predicting the probability of mortality of gastric cancer patients using decision tree.

    Science.gov (United States)

    Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

    2015-06-01

    Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

  12. Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

    Science.gov (United States)

    Doubravsky, Karel; Dohnal, Mirko

    2015-01-01

    Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

  13. Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

    Directory of Open Access Journals (Sweden)

    Karel Doubravsky

    Full Text Available Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (rechecked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

  14. Python for probability, statistics, and machine learning

    CERN Document Server

    Unpingco, José

    2016-01-01

    This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowl...

  15. Probability intervals for the top event unavailability of fault trees

    International Nuclear Information System (INIS)

    Lee, Y.T.; Apostolakis, G.E.

    1976-06-01

    The evaluation of probabilities of rare events is of major importance in the quantitative assessment of the risk from large technological systems. In particular, for nuclear power plants the complexity of the systems, their high reliability and the lack of significant statistical records have led to the extensive use of logic diagrams in the estimation of low probabilities. The estimation of probability intervals for the probability of existence of the top event of a fault tree is examined. Given the uncertainties of the primary input data, a method is described for the evaluation of the first four moments of the top event occurrence probability. These moments are then used to estimate confidence bounds by several approaches which are based on standard inequalities (e.g., Tchebycheff, Cantelli, etc.) or on empirical distributions (the Johnson family). Several examples indicate that the Johnson family of distributions yields results which are in good agreement with those produced by Monte Carlo simulation

  16. New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

    Science.gov (United States)

    L.R. Iverson; A.M. Prasad; A. Liaw

    2004-01-01

    More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

  17. Extensions and applications of ensemble-of-trees methods in machine learning

    Science.gov (United States)

    Bleich, Justin

    Ensemble-of-trees algorithms have emerged to the forefront of machine learning due to their ability to generate high forecasting accuracy for a wide array of regression and classification problems. Classic ensemble methodologies such as random forests (RF) and stochastic gradient boosting (SGB) rely on algorithmic procedures to generate fits to data. In contrast, more recent ensemble techniques such as Bayesian Additive Regression Trees (BART) and Dynamic Trees (DT) focus on an underlying Bayesian probability model to generate the fits. These new probability model-based approaches show much promise versus their algorithmic counterparts, but also offer substantial room for improvement. The first part of this thesis focuses on methodological advances for ensemble-of-trees techniques with an emphasis on the more recent Bayesian approaches. In particular, we focus on extensions of BART in four distinct ways. First, we develop a more robust implementation of BART for both research and application. We then develop a principled approach to variable selection for BART as well as the ability to naturally incorporate prior information on important covariates into the algorithm. Next, we propose a method for handling missing data that relies on the recursive structure of decision trees and does not require imputation. Last, we relax the assumption of homoskedasticity in the BART model to allow for parametric modeling of heteroskedasticity. The second part of this thesis returns to the classic algorithmic approaches in the context of classification problems with asymmetric costs of forecasting errors. First we consider the performance of RF and SGB more broadly and demonstrate its superiority to logistic regression for applications in criminology with asymmetric costs. Next, we use RF to forecast unplanned hospital readmissions upon patient discharge with asymmetric costs taken into account. Finally, we explore the construction of stable decision trees for forecasts of

  18. Recognizing human actions by learning and matching shape-motion prototype trees.

    Science.gov (United States)

    Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

    2012-03-01

    A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.

  19. Mathematical analysis and modeling of epidemics of rubber tree root diseases: Probability of infection of an individual tree

    Energy Technology Data Exchange (ETDEWEB)

    Chadoeuf, J.; Joannes, H.; Nandris, D.; Pierrat, J.C.

    1988-12-01

    The spread of root diseases in rubber tree (Hevea brasiliensis) due to Rigidoporus lignosus and Phellinus noxius was investigated epidemiologically using data collected every 6 month during a 6-year survey in a plantation. The aim of the present study is to see what factors could predict whether a given tree would be infested at the following inspection. Using a qualitative regression method we expressed the probability of pathogenic attack on a tree in terms of three factors: the state of health of the surrounding trees, the method used to clear the forest prior to planting, and evolution with time. The effects of each factor were ranked, and the roles of the various classes of neighbors were established and quantified. Variability between successive inspections was small, and the method of forest clearing was important only while primary inocula in the soil were still infectious. The state of health of the immediate neighbors was most significant; more distant neighbors in the same row had some effect; interrow spread was extremely rare. This investigation dealt only with trees as individuals, and further study of the interrelationships of groups of trees is needed.

  20. Rooting phylogenetic trees under the coalescent model using site pattern probabilities.

    Science.gov (United States)

    Tian, Yuan; Kubatko, Laura

    2017-12-19

    Phylogenetic tree inference is a fundamental tool to estimate ancestor-descendant relationships among different species. In phylogenetic studies, identification of the root - the most recent common ancestor of all sampled organisms - is essential for complete understanding of the evolutionary relationships. Rooted trees benefit most downstream application of phylogenies such as species classification or study of adaptation. Often, trees can be rooted by using outgroups, which are species that are known to be more distantly related to the sampled organisms than any other species in the phylogeny. However, outgroups are not always available in evolutionary research. In this study, we develop a new method for rooting species tree under the coalescent model, by developing a series of hypothesis tests for rooting quartet phylogenies using site pattern probabilities. The power of this method is examined by simulation studies and by application to an empirical North American rattlesnake data set. The method shows high accuracy across the simulation conditions considered, and performs well for the rattlesnake data. Thus, it provides a computationally efficient way to accurately root species-level phylogenies that incorporates the coalescent process. The method is robust to variation in substitution model, but is sensitive to the assumption of a molecular clock. Our study establishes a computationally practical method for rooting species trees that is more efficient than traditional methods. The method will benefit numerous evolutionary studies that require rooting a phylogenetic tree without having to specify outgroups.

  1. Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification.

    Science.gov (United States)

    Fan, Jianping; Zhou, Ning; Peng, Jinye; Gao, Ling

    2015-11-01

    In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.

  2. Statistical learning of action: the role of conditional probability.

    Science.gov (United States)

    Meyer, Meredith; Baldwin, Dare

    2011-12-01

    Identification of distinct units within a continuous flow of human action is fundamental to action processing. Such segmentation may rest in part on statistical learning. In a series of four experiments, we examined what types of statistics people can use to segment a continuous stream involving many brief, goal-directed action elements. The results of Experiment 1 showed no evidence for sensitivity to conditional probability, whereas Experiment 2 displayed learning based on joint probability. In Experiment 3, we demonstrated that additional exposure to the input failed to engender sensitivity to conditional probability. However, the results of Experiment 4 showed that a subset of adults-namely, those more successful at identifying actions that had been seen more frequently than comparison sequences-were also successful at learning conditional-probability statistics. These experiments help to clarify the mechanisms subserving processing of intentional action, and they highlight important differences from, as well as similarities to, prior studies of statistical learning in other domains, including language.

  3. α-Cut method based importance measure for criticality analysis in fuzzy probability – Based fault tree analysis

    International Nuclear Information System (INIS)

    Purba, Julwan Hendry; Sony Tjahyani, D.T.; Widodo, Surip; Tjahjono, Hendro

    2017-01-01

    Highlights: •FPFTA deals with epistemic uncertainty using fuzzy probability. •Criticality analysis is important for reliability improvement. •An α-cut method based importance measure is proposed for criticality analysis in FPFTA. •The α-cut method based importance measure utilises α-cut multiplication, α-cut subtraction, and area defuzzification technique. •Benchmarking confirm that the proposed method is feasible for criticality analysis in FPFTA. -- Abstract: Fuzzy probability – based fault tree analysis (FPFTA) has been recently developed and proposed to deal with the limitations of conventional fault tree analysis. In FPFTA, reliabilities of basic events, intermediate events and top event are characterized by fuzzy probabilities. Furthermore, the quantification of the FPFTA is based on fuzzy multiplication rule and fuzzy complementation rule to propagate uncertainties from basic event to the top event. Since the objective of the fault tree analysis is to improve the reliability of the system being evaluated, it is necessary to find the weakest path in the system. For this purpose, criticality analysis can be implemented. Various importance measures, which are based on conventional probabilities, have been developed and proposed for criticality analysis in fault tree analysis. However, not one of those importance measures can be applied for criticality analysis in FPFTA, which is based on fuzzy probability. To be fully applied in nuclear power plant probabilistic safety assessment, FPFTA needs to have its corresponding importance measure. The objective of this study is to develop an α-cut method based importance measure to evaluate and rank the importance of basic events for criticality analysis in FPFTA. To demonstrate the applicability of the proposed measure, a case study is performed and its results are then benchmarked to the results generated by the four well known importance measures in conventional fault tree analysis. The results

  4. Metabolite identification through multiple kernel learning on fragmentation trees.

    Science.gov (United States)

    Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

    2014-06-15

    Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

  5. Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

    Science.gov (United States)

    Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.

    2017-12-01

    Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. Since sub-daily streamflow information is unavailable for most small basins in China, one of the main challenges is finding appropriate parameter values for simulating flash floods in ungauged catchments. In this study, we use decision tree learning to explore parameter set transferability between different catchments. For this purpose, the physically-based, semi-distributed rainfall-runoff model PRMS-OMS is set up for 35 catchments in ten Chinese provinces. Hourly data from more than 800 storm runoff events are used to calibrate the model and evaluate the performance of parameter set transfers between catchments. For each catchment, 58 catchment attributes are extracted from several data sets available for whole China. We then use a data mining technique (decision tree learning) to identify catchment similarities that can be related to good transfer performance. Finally, we use the splitting rules of decision trees for finding suitable donor catchments for ungauged target catchments. We show that decision tree learning allows to optimally utilize the information content of available catchment descriptors and outperforms regionalization based on a conventional measure of physiographic-climatic similarity by 15%-20%. Similar performance can be achieved with a regionalization method based on spatial proximity, but decision trees offer flexible rules for selecting suitable donor catchments, not relying on the vicinity of gauged catchments. This flexibility makes the method particularly suitable for implementation in sparsely gauged environments. We evaluate the probability to detect flood events exceeding a given return period, considering measured discharge and PRMS-OMS simulated flows with regionalized parameters

  6. Mutual learning in a tree parity machine and its application to cryptography

    International Nuclear Information System (INIS)

    Rosen-Zvi, Michal; Klein, Einat; Kanter, Ido; Kinzel, Wolfgang

    2002-01-01

    Mutual learning of a pair of tree parity machines with continuous and discrete weight vectors is studied analytically. The analysis is based on a mapping procedure that maps the mutual learning in tree parity machines onto mutual learning in noisy perceptrons. The stationary solution of the mutual learning in the case of continuous tree parity machines depends on the learning rate where a phase transition from partial to full synchronization is observed. In the discrete case the learning process is based on a finite increment and a full synchronized state is achieved in a finite number of steps. The synchronization of discrete parity machines is introduced in order to construct an ephemeral key-exchange protocol. The dynamic learning of a third tree parity machine (an attacker) that tries to imitate one of the two machines while the two still update their weight vectors is also analyzed. In particular, the synchronization times of the naive attacker and the flipping attacker recently introduced in Ref. 9 are analyzed. All analytical results are found to be in good agreement with simulation results

  7. Some Limit Properties of Random Transition Probability for Second-Order Nonhomogeneous Markov Chains Indexed by a Tree

    Directory of Open Access Journals (Sweden)

    Shi Zhiyan

    2009-01-01

    Full Text Available We study some limit properties of the harmonic mean of random transition probability for a second-order nonhomogeneous Markov chain and a nonhomogeneous Markov chain indexed by a tree. As corollary, we obtain the property of the harmonic mean of random transition probability for a nonhomogeneous Markov chain.

  8. Learning Type Extension Trees for Metal Bonding State Prediction

    DEFF Research Database (Denmark)

    Frasconi, Paolo; Jaeger, Manfred; Passerini, Andrea

    2008-01-01

    Type Extension Trees (TET) have been recently introduced as an expressive representation language allowing to encode complex combinatorial features of relational entities. They can be efficiently learned with a greedy search strategy driven by a generalized relational information gain and a discr......Type Extension Trees (TET) have been recently introduced as an expressive representation language allowing to encode complex combinatorial features of relational entities. They can be efficiently learned with a greedy search strategy driven by a generalized relational information gain...

  9. Unequal Probability Marking Approach to Enhance Security of Traceback Scheme in Tree-Based WSNs.

    Science.gov (United States)

    Huang, Changqin; Ma, Ming; Liu, Xiao; Liu, Anfeng; Zuo, Zhengbang

    2017-06-17

    Fog (from core to edge) computing is a newly emerging computing platform, which utilizes a large number of network devices at the edge of a network to provide ubiquitous computing, thus having great development potential. However, the issue of security poses an important challenge for fog computing. In particular, the Internet of Things (IoT) that constitutes the fog computing platform is crucial for preserving the security of a huge number of wireless sensors, which are vulnerable to attack. In this paper, a new unequal probability marking approach is proposed to enhance the security performance of logging and migration traceback (LM) schemes in tree-based wireless sensor networks (WSNs). The main contribution of this paper is to overcome the deficiency of the LM scheme that has a higher network lifetime and large storage space. In the unequal probability marking logging and migration (UPLM) scheme of this paper, different marking probabilities are adopted for different nodes according to their distances to the sink. A large marking probability is assigned to nodes in remote areas (areas at a long distance from the sink), while a small marking probability is applied to nodes in nearby area (areas at a short distance from the sink). This reduces the consumption of storage and energy in addition to enhancing the security performance, lifetime, and storage capacity. Marking information will be migrated to nodes at a longer distance from the sink for increasing the amount of stored marking information, thus enhancing the security performance in the process of migration. The experimental simulation shows that for general tree-based WSNs, the UPLM scheme proposed in this paper can store 1.12-1.28 times the amount of stored marking information that the equal probability marking approach achieves, and has 1.15-1.26 times the storage utilization efficiency compared with other schemes.

  10. Semantic and associative factors in probability learning with words.

    Science.gov (United States)

    Schipper, L M; Hanson, B L; Taylor, G; Thorpe, J A

    1973-09-01

    Using a probability-learning technique with a single word as the cue and with the probability of a given event following this word fixed at .80, it was found (1) that neither high nor low associates to the original word and (2) that neither synonyms nor antonyms showed differential learning curves subsequent to original learning when the probability for the following event was shifted to .20. In a second study when feedback, in the form of knowledge of results, was withheld, there was a clear-cut similarity of predictions to the originally trained word and the synonyms of both high and low association value and a dissimilarity of these words to a set of antonyms of both high and low association value. Two additional studies confirmed the importance of the semantic dimension as compared with association value as traditionally measured.

  11. Learning difficulties of senior high school students based on probability understanding levels

    Science.gov (United States)

    Anggara, B.; Priatna, N.; Juandi, D.

    2018-05-01

    Identifying students' difficulties in learning concept of probability is important for teachers to prepare the appropriate learning processes and can overcome obstacles that may arise in the next learning processes. This study revealed the level of students' understanding of the concept of probability and identified their difficulties as a part of the epistemological obstacles identification of the concept of probability. This study employed a qualitative approach that tends to be the character of descriptive research involving 55 students of class XII. In this case, the writer used the diagnostic test of probability concept learning difficulty, observation, and interview as the techniques to collect the data needed. The data was used to determine levels of understanding and the learning difficulties experienced by the students. From the result of students' test result and learning observation, it was found that the mean cognitive level was at level 2. The findings indicated that students had appropriate quantitative information of probability concept but it might be incomplete or incorrectly used. The difficulties found are the ones in arranging sample space, events, and mathematical models related to probability problems. Besides, students had difficulties in understanding the principles of events and prerequisite concept.

  12. Learning decision trees with flexible constraints and objectives using integer optimization

    NARCIS (Netherlands)

    Verwer, S.; Zhang, Y.

    2017-01-01

    We encode the problem of learning the optimal decision tree of a given depth as an integer optimization problem. We show experimentally that our method (DTIP) can be used to learn good trees up to depth 5 from data sets of size up to 1000. In addition to being efficient, our new formulation allows

  13. PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION

    Data.gov (United States)

    National Aeronautics and Space Administration — PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION GUICHONG LI, NATHALIE JAPKOWICZ, IAN HOFFMAN,...

  14. A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Sho Fukuda

    2014-12-01

    Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

  15. Difficulties of learning probability concepts, the reasons why these concepts cannot be learned and suggestions for solution

    Directory of Open Access Journals (Sweden)

    Dilek Sezgin MEMNUN

    2008-06-01

    Full Text Available Probability holds the first place among the subjects that both teachers and students have difficulty in handling. Although probability has an important role in many professions and a great many decisions we make for our daily lives, the understanding of the probability concepts is not an easy ability to gain for many students. Most of the students develop perception about lots of probability concepts and they have difficulty finding a reason for probability events. Thus, in the present study, the difficulties faced while learning probability concepts and the reasons why these concepts cannot be learned well are investigated, these reasons are tried to be put forward, and some suggestions for solutions regarding these concepts are presented. In this study, cross-hatching model was used. National and international studies on the subject of probability are investigated, the reasons why these concepts cannot be learned were categorized in the light of findings obtained, and the reasons why these concepts cannot be learned and taught are tried to be discovered. The categorization was displayed with Ishikawa diagram. In the diagram, the reasons why these concepts cannot be learned were noted as six categories. These categories were age, the insufficiency of advanced information, the deficiency of argumentation ability, teacher, error in concept, and students’ negative attitudes.

  16. STRIP: stream learning of influence probabilities

    DEFF Research Database (Denmark)

    Kutzkov, Konstantin

    2013-01-01

    cascades, and developing applications such as viral marketing. Motivated by modern microblogging platforms, such as twitter, in this paper we study the problem of learning influence probabilities in a data-stream scenario, in which the network topology is relatively stable and the challenge of a learning...... algorithm is to keep up with a continuous stream of tweets using a small amount of time and memory. Our contribution is a number of randomized approximation algorithms, categorized according to the available space (superlinear, linear, and sublinear in the number of nodes n) and according to dierent models...

  17. Learning a constrained conditional random field for enhanced segmentation of fallen trees in ALS point clouds

    Science.gov (United States)

    Polewski, Przemyslaw; Yao, Wei; Heurich, Marco; Krzystek, Peter; Stilla, Uwe

    2018-06-01

    In this study, we present a method for improving the quality of automatic single fallen tree stem segmentation in ALS data by applying a specialized constrained conditional random field (CRF). The entire processing pipeline is composed of two steps. First, short stem segments of equal length are detected and a subset of them is selected for further processing, while in the second step the chosen segments are merged to form entire trees. The first step is accomplished using the specialized CRF defined on the space of segment labelings, capable of finding segment candidates which are easier to merge subsequently. To achieve this, the CRF considers not only the features of every candidate individually, but incorporates pairwise spatial interactions between adjacent segments into the model. In particular, pairwise interactions include a collinearity/angular deviation probability which is learned from training data as well as the ratio of spatial overlap, whereas unary potentials encode a learned probabilistic model of the laser point distribution around each segment. Each of these components enters the CRF energy with its own balance factor. To process previously unseen data, we first calculate the subset of segments for merging on a grid of balance factors by minimizing the CRF energy. Then, we perform the merging and rank the balance configurations according to the quality of their resulting merged trees, obtained from a learned tree appearance model. The final result is derived from the top-ranked configuration. We tested our approach on 5 plots from the Bavarian Forest National Park using reference data acquired in a field inventory. Compared to our previous segment selection method without pairwise interactions, an increase in detection correctness and completeness of up to 7 and 9 percentage points, respectively, was observed.

  18. Automated Sleep Stage Scoring by Decision Tree Learning

    National Research Council Canada - National Science Library

    Hanaoka, Masaaki

    2001-01-01

    In this paper we describe a waveform recognition method that extracts characteristic parameters from wave- forms and a method of automated sleep stage scoring using decision tree learning that is in...

  19. Feedback Valence Affects Auditory Perceptual Learning Independently of Feedback Probability

    Science.gov (United States)

    Amitay, Sygal; Moore, David R.; Molloy, Katharine; Halliday, Lorna F.

    2015-01-01

    Previous studies have suggested that negative feedback is more effective in driving learning than positive feedback. We investigated the effect on learning of providing varying amounts of negative and positive feedback while listeners attempted to discriminate between three identical tones; an impossible task that nevertheless produces robust learning. Four feedback conditions were compared during training: 90% positive feedback or 10% negative feedback informed the participants that they were doing equally well, while 10% positive or 90% negative feedback informed them they were doing equally badly. In all conditions the feedback was random in relation to the listeners’ responses (because the task was to discriminate three identical tones), yet both the valence (negative vs. positive) and the probability of feedback (10% vs. 90%) affected learning. Feedback that informed listeners they were doing badly resulted in better post-training performance than feedback that informed them they were doing well, independent of valence. In addition, positive feedback during training resulted in better post-training performance than negative feedback, but only positive feedback indicating listeners were doing badly on the task resulted in learning. As we have previously speculated, feedback that better reflected the difficulty of the task was more effective in driving learning than feedback that suggested performance was better than it should have been given perceived task difficulty. But contrary to expectations, positive feedback was more effective than negative feedback in driving learning. Feedback thus had two separable effects on learning: feedback valence affected motivation on a subjectively difficult task, and learning occurred only when feedback probability reflected the subjective difficulty. To optimize learning, training programs need to take into consideration both feedback valence and probability. PMID:25946173

  20. Imitation learning of car driving skills with decision trees and random forests

    Directory of Open Access Journals (Sweden)

    Cichosz Paweł

    2014-09-01

    Full Text Available Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision tree knowledge representation is adopted, which combines potentially high accuracy with human readability, an advantage that can be important in many applications. Decision trees are demonstrated to be capable of representing high quality control models, reaching the performance level of sophisticated pre-designed algorithms. This is achieved by enhancing the basic imitation learning scenario to include active retraining, automatically triggered on control failures. It is also demonstrated how better stability and generalization can be achieved by sacrificing human-readability and using decision tree model ensembles. The methodology for learning control models contributed by this article can be hopefully applied to solve real-world control tasks, as well as to develop video game bots

  1. Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities.

    Science.gov (United States)

    Remillard, Gilbert

    2011-07-01

    There is evidence that sequence learning in the traditional serial reaction time task (SRTT), where target location is the response dimension, and sequence learning in the perceptual SRTT, where target location is not the response dimension, are handled by different mechanisms. The ability of the latter mechanism to learn sequential contingencies that can be learned by the former mechanism was examined. Prior research has established that people can learn second-, third-, and fourth-order probabilities in the traditional SRTT. The present study reveals that people can learn such probabilities in the perceptual SRTT. This suggests that the two mechanisms may have similar architectures. A possible neural basis of the two mechanisms is discussed.

  2. Fostering Positive Attitude in Probability Learning Using Graphing Calculator

    Science.gov (United States)

    Tan, Choo-Kim; Harji, Madhubala Bava; Lau, Siong-Hoe

    2011-01-01

    Although a plethora of research evidence highlights positive and significant outcomes of the incorporation of the Graphing Calculator (GC) in mathematics education, its use in the teaching and learning process appears to be limited. The obvious need to revisit the teaching and learning of Probability has resulted in this study, i.e. to incorporate…

  3. Link importance incorporated failure probability measuring solution for multicast light-trees in elastic optical networks

    Science.gov (United States)

    Li, Xin; Zhang, Lu; Tang, Ying; Huang, Shanguo

    2018-03-01

    The light-tree-based optical multicasting (LT-OM) scheme provides a spectrum- and energy-efficient method to accommodate emerging multicast services. Some studies focus on the survivability technologies for LTs against a fixed number of link failures, such as single-link failure. However, a few studies involve failure probability constraints when building LTs. It is worth noting that each link of an LT plays different important roles under failure scenarios. When calculating the failure probability of an LT, the importance of its every link should be considered. We design a link importance incorporated failure probability measuring solution (LIFPMS) for multicast LTs under independent failure model and shared risk link group failure model. Based on the LIFPMS, we put forward the minimum failure probability (MFP) problem for the LT-OM scheme. Heuristic approaches are developed to address the MFP problem in elastic optical networks. Numerical results show that the LIFPMS provides an accurate metric for calculating the failure probability of multicast LTs and enhances the reliability of the LT-OM scheme while accommodating multicast services.

  4. Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

    Science.gov (United States)

    Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas

    2014-07-01

    Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Probability of bystander effect induced by alpha-particles emitted by radon progeny using the analytical model of tracheobronchial tree

    International Nuclear Information System (INIS)

    Jovanovic, B.; Nikezic, D.

    2010-01-01

    Radiation-induced biological bystander effects have become a phenomenon associated with the interaction of radiation with cells. There is a need to include the influence of biological effects in the dosimetry of the human lung. With this aim, the purpose of this work is to calculate the probability of bystander effect induced by alpha-particle radiation on sensitive cells of the human lung. Probability was calculated by applying the analytical model cylinder bifurcation, which was created to simulate the geometry of the human lung with the geometric distribution of cell nuclei in the airway wall of the tracheobronchial tree. This analytical model of the human tracheobronchial tree represents the extension of the ICRP 66 model, and follows it as much as possible. Reported probabilities are calculated for various targets and alpha-particle energies. Probability of bystander effect has been calculated for alpha particles with 6 and 7.69 MeV energies, which are emitted in the 222 Rn chain. The application of these results may enhance current dose risk estimation approaches in the sense of the inclusion of the influence of the biological effects. (authors)

  6. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

    N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

    2014-01-01

    htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

  7. Deep Multi-Task Learning for Tree Genera Classification

    Science.gov (United States)

    Ko, C.; Kang, J.; Sohn, G.

    2018-05-01

    The goal for our paper is to classify tree genera using airborne Light Detection and Ranging (LiDAR) data with Convolution Neural Network (CNN) - Multi-task Network (MTN) implementation. Unlike Single-task Network (STN) where only one task is assigned to the learning outcome, MTN is a deep learning architect for learning a main task (classification of tree genera) with other tasks (in our study, classification of coniferous and deciduous) simultaneously, with shared classification features. The main contribution of this paper is to improve classification accuracy from CNN-STN to CNN-MTN. This is achieved by introducing a concurrence loss (Lcd) to the designed MTN. This term regulates the overall network performance by minimizing the inconsistencies between the two tasks. Results show that we can increase the classification accuracy from 88.7 % to 91.0 % (from STN to MTN). The second goal of this paper is to solve the problem of small training sample size by multiple-view data generation. The motivation of this goal is to address one of the most common problems in implementing deep learning architecture, the insufficient number of training data. We address this problem by simulating training dataset with multiple-view approach. The promising results from this paper are providing a basis for classifying a larger number of dataset and number of classes in the future.

  8. Utilising Tree-Based Ensemble Learning for Speaker Segmentation

    DEFF Research Database (Denmark)

    Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

    2014-01-01

    In audio and speech processing, accurate detection of the changing points between multiple speakers in speech segments is an important stage for several applications such as speaker identification and tracking. Bayesian Information Criteria (BIC)-based approaches are the most traditionally used...... for a certain condition, the model becomes biased to the data used for training limiting the model’s generalisation ability. In this paper, we propose a BIC-based tuning-free approach for speaker segmentation through the use of ensemble-based learning. A forest of segmentation trees is constructed in which each...... tree is trained using a sampled version of the speech segment. During the tree construction process, a set of randomly selected points in the input sequence is examined as potential segmentation points. The point that yields the highest ΔBIC is chosen and the same process is repeated for the resultant...

  9. Probability Modeling and Thinking: What Can We Learn from Practice?

    Science.gov (United States)

    Pfannkuch, Maxine; Budgett, Stephanie; Fewster, Rachel; Fitch, Marie; Pattenwise, Simeon; Wild, Chris; Ziedins, Ilze

    2016-01-01

    Because new learning technologies are enabling students to build and explore probability models, we believe that there is a need to determine the big enduring ideas that underpin probabilistic thinking and modeling. By uncovering the elements of the thinking modes of expert users of probability models we aim to provide a base for the setting of…

  10. Detecting Structural Metadata with Decision Trees and Transformation-Based Learning

    National Research Council Canada - National Science Library

    Kim, Joungbum; Schwarm, Sarah E; Ostendorf, Mari

    2004-01-01

    .... Specifically, combinations of decision trees and language models are used to predict sentence ends and interruption points and given these events transformation based learning is used to detect edit...

  11. Living Classrooms: Learning Guide for Famous & Historic Trees.

    Science.gov (United States)

    American Forest Foundation, Washington, DC.

    This guide provides information to create and care for a Famous and Historic Trees Living Classroom in which students learn American history and culture in the context of environmental change. The booklet contains 10 hands-on activities that emphasize observation, critical thinking, and teamwork. Worksheets and illustrations provide students with…

  12. Prostate Cancer Probability Prediction By Machine Learning Technique.

    Science.gov (United States)

    Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

    2017-11-26

    The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

  13. Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees.

    Science.gov (United States)

    Yang, Ziheng; Zhu, Tianqi

    2018-02-20

    The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increases, but the method may become overconfident before it becomes reliable. We suggest that this extreme behavior may be a major factor for the spuriously high posterior probabilities for evolutionary trees. The philosophical implications of our results to the application of Bayesian model selection to evaluate opposing scientific hypotheses are yet to be explored, as are the behaviors of non-Bayesian methods in similar situations.

  14. Practical secure decision tree learning in a teletreatment application

    NARCIS (Netherlands)

    de Hoogh, Sebastiaan; Schoenmakers, Berry; Chen, Ping; op den Akker, Harm

    In this paper we develop a range of practical cryptographic protocols for secure decision tree learning, a primary problem in privacy preserving data mining. We focus on particular variants of the well-known ID3 algorithm allowing a high level of security and performance at the same time. Our

  15. Practical secure decision tree learning in a teletreatment application

    NARCIS (Netherlands)

    Hoogh, de S.J.A.; Schoenmakers, B.; Chen, Ping; Op den Akker, H.; Christin, N.; Safavi-Naini, R.

    2014-01-01

    In this paper we develop a range of practical cryptographic protocols for secure decision tree learning, a primary problem in privacy preserving data mining. We focus on particular variants of the well-known ID3 algorithm allowing a high level of security and performance at the same time. Our

  16. The effect of incremental changes in phonotactic probability and neighborhood density on word learning by preschool children

    Science.gov (United States)

    Storkel, Holly L.; Bontempo, Daniel E.; Aschenbrenner, Andrew J.; Maekawa, Junko; Lee, Su-Yeon

    2013-01-01

    Purpose Phonotactic probability or neighborhood density have predominately been defined using gross distinctions (i.e., low vs. high). The current studies examined the influence of finer changes in probability (Experiment 1) and density (Experiment 2) on word learning. Method The full range of probability or density was examined by sampling five nonwords from each of four quartiles. Three- and 5-year-old children received training on nonword-nonobject pairs. Learning was measured in a picture-naming task immediately following training and 1-week after training. Results were analyzed using multi-level modeling. Results A linear spline model best captured nonlinearities in phonotactic probability. Specifically word learning improved as probability increased in the lowest quartile, worsened as probability increased in the midlow quartile, and then remained stable and poor in the two highest quartiles. An ordinary linear model sufficiently described neighborhood density. Here, word learning improved as density increased across all quartiles. Conclusion Given these different patterns, phonotactic probability and neighborhood density appear to influence different word learning processes. Specifically, phonotactic probability may affect recognition that a sound sequence is an acceptable word in the language and is a novel word for the child, whereas neighborhood density may influence creation of a new representation in long-term memory. PMID:23882005

  17. Estimating the Probability of Vegetation to Be Groundwater Dependent Based on the Evaluation of Tree Models

    Directory of Open Access Journals (Sweden)

    Isabel C. Pérez Hoyos

    2016-04-01

    Full Text Available Groundwater Dependent Ecosystems (GDEs are increasingly threatened by humans’ rising demand for water resources. Consequently, it is imperative to identify the location of GDEs to protect them. This paper develops a methodology to identify the probability of an ecosystem to be groundwater dependent. Probabilities are obtained by modeling the relationship between the known locations of GDEs and factors influencing groundwater dependence, namely water table depth and climatic aridity index. Probabilities are derived for the state of Nevada, USA, using modeled water table depth and aridity index values obtained from the Global Aridity database. The model selected results from the performance comparison of classification trees (CT and random forests (RF. Based on a threshold-independent accuracy measure, RF has a better ability to generate probability estimates. Considering a threshold that minimizes the misclassification rate for each model, RF also proves to be more accurate. Regarding training accuracy, performance measures such as accuracy, sensitivity, and specificity are higher for RF. For the test set, higher values of accuracy and kappa for CT highlight the fact that these measures are greatly affected by low prevalence. As shown for RF, the choice of the cutoff probability value has important consequences on model accuracy and the overall proportion of locations where GDEs are found.

  18. Tree Nut Allergies

    Science.gov (United States)

    ... Blog Vision Awards Common Allergens Tree Nut Allergy Tree Nut Allergy Learn about tree nut allergy, how ... a Tree Nut Label card . Allergic Reactions to Tree Nuts Tree nuts can cause a severe and ...

  19. Adapting and Evaluating a Tree of Life Group for Women with Learning Disabilities

    Science.gov (United States)

    Randle-Phillips, Cathy; Farquhar, Sarah; Thomas, Sally

    2016-01-01

    Background: This study describes how a specific narrative therapy approach called 'the tree of life' was adapted to run a group for women with learning disabilities. The group consisted of four participants and ran for five consecutive weeks. Materials and Methods: Participants each constructed a tree to represent their lives and presented their…

  20. Probability Learning: Changes in Behavior across Time and Development

    Science.gov (United States)

    Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

    2018-01-01

    Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…

  1. Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station

    Science.gov (United States)

    Lee, Charles; Alena, Richard L.; Robinson, Peter

    2004-01-01

    We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

  2. Development of probabilistic thinking-oriented learning tools for probability materials at junior high school students

    Science.gov (United States)

    Sari, Dwi Ivayana; Hermanto, Didik

    2017-08-01

    This research is a developmental research of probabilistic thinking-oriented learning tools for probability materials at ninth grade students. This study is aimed to produce a good probabilistic thinking-oriented learning tools. The subjects were IX-A students of MTs Model Bangkalan. The stages of this development research used 4-D development model which has been modified into define, design and develop. Teaching learning tools consist of lesson plan, students' worksheet, learning teaching media and students' achievement test. The research instrument used was a sheet of learning tools validation, a sheet of teachers' activities, a sheet of students' activities, students' response questionnaire and students' achievement test. The result of those instruments were analyzed descriptively to answer research objectives. The result was teaching learning tools in which oriented to probabilistic thinking of probability at ninth grade students which has been valid. Since teaching and learning tools have been revised based on validation, and after experiment in class produced that teachers' ability in managing class was effective, students' activities were good, students' responses to the learning tools were positive and the validity, sensitivity and reliability category toward achievement test. In summary, this teaching learning tools can be used by teacher to teach probability for develop students' probabilistic thinking.

  3. More than words: Adults learn probabilities over categories and relationships between them.

    Science.gov (United States)

    Hudson Kam, Carla L

    2009-04-01

    This study examines whether human learners can acquire statistics over abstract categories and their relationships to each other. Adult learners were exposed to miniature artificial languages containing variation in the ordering of the Subject, Object, and Verb constituents. Different orders (e.g. SOV, VSO) occurred in the input with different frequencies, but the occurrence of one order versus another was not predictable. Importantly, the language was constructed such that participants could only match the overall input probabilities if they were tracking statistics over abstract categories, not over individual words. At test, participants reproduced the probabilities present in the input with a high degree of accuracy. Closer examination revealed that learner's were matching the probabilities associated with individual verbs rather than the category as a whole. However, individual nouns had no impact on word orders produced. Thus, participants learned the probabilities of a particular ordering of the abstract grammatical categories Subject and Object associated with each verb. Results suggest that statistical learning mechanisms are capable of tracking relationships between abstract linguistic categories in addition to individual items.

  4. Calculating the probability of multitaxon evolutionary trees: bootstrappers Gambit.

    OpenAIRE

    Lake, J A

    1995-01-01

    The reconstruction of multitaxon trees from molecular sequences is confounded by the variety of algorithms and criteria used to evaluate trees, making it difficult to compare the results of different analyses. A global method of multitaxon phylogenetic reconstruction described here, Bootstrappers Gambit, can be used with any four-taxon algorithm, including distance, maximum likelihood, and parsimony methods. It incorporates a Bayesian-Jeffreys'-bootstrap analysis to provide a uniform probabil...

  5. Visualizing Biological Data in Museums: Visitor Learning with an Interactive Tree of Life Exhibit

    Science.gov (United States)

    Horn, Michael S.; Phillips, Brenda C.; Evans, Evelyn Margaret; Block, Florian; Diamond, Judy; Shen, Chia

    2016-01-01

    In this study, we investigate museum visitor learning and engagement at an interactive visualization of an evolutionary tree of life consisting of over 70,000 species. The study was conducted at two natural history museums where visitors collaboratively explored the tree of life using direct touch gestures on a multi-touch tabletop display. In the…

  6. Learning about Posterior Probability: Do Diagrams and Elaborative Interrogation Help?

    Science.gov (United States)

    Clinton, Virginia; Alibali, Martha W.; Nathan, Mitchell J.

    2016-01-01

    To learn from a text, students must make meaningful connections among related ideas in that text. This study examined the effectiveness of two methods of improving connections--elaborative interrogation and diagrams--in written lessons about posterior probability. Undergraduate students (N = 198) read a lesson in one of three questioning…

  7. Incidental learning of probability information is differentially affected by the type of visual working memory representation.

    Science.gov (United States)

    van Lamsweerde, Amanda E; Beck, Melissa R

    2015-12-01

    In this study, we investigated whether the ability to learn probability information is affected by the type of representation held in visual working memory. Across 4 experiments, participants detected changes to displays of coloured shapes. While participants detected changes in 1 dimension (e.g., colour), a feature from a second, nonchanging dimension (e.g., shape) predicted which object was most likely to change. In Experiments 1 and 3, items could be grouped by similarity in the changing dimension across items (e.g., colours and shapes were repeated in the display), while in Experiments 2 and 4 items could not be grouped by similarity (all features were unique). Probability information from the predictive dimension was learned and used to increase performance, but only when all of the features within a display were unique (Experiments 2 and 4). When it was possible to group by feature similarity in the changing dimension (e.g., 2 blue objects appeared within an array), participants were unable to learn probability information and use it to improve performance (Experiments 1 and 3). The results suggest that probability information can be learned in a dimension that is not explicitly task-relevant, but only when the probability information is represented with the changing dimension in visual working memory. (c) 2015 APA, all rights reserved).

  8. Estimating the probability of survival of individual shortleaf pine (Pinus echinata mill.) trees

    Science.gov (United States)

    Sudip Shrestha; Thomas B. Lynch; Difei Zhang; James M. Guldin

    2012-01-01

    A survival model is needed in a forest growth system which predicts the survival of trees on individual basis or on a stand basis (Gertner, 1989). An individual-tree modeling approach is one of the better methods available for predicting growth and yield as it provides essential information about particular tree species; tree size, tree quality and tree present status...

  9. Quantum probability and cognitive modeling: some cautions and a promising direction in modeling physics learning.

    Science.gov (United States)

    Franceschetti, Donald R; Gire, Elizabeth

    2013-06-01

    Quantum probability theory offers a viable alternative to classical probability, although there are some ambiguities inherent in transferring the quantum formalism to a less determined realm. A number of physicists are now looking at the applicability of quantum ideas to the assessment of physics learning, an area particularly suited to quantum probability ideas.

  10. Supervised learning of probability distributions by neural networks

    Science.gov (United States)

    Baum, Eric B.; Wilczek, Frank

    1988-01-01

    Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

  11. Fault tree handbook

    International Nuclear Information System (INIS)

    Haasl, D.F.; Roberts, N.H.; Vesely, W.E.; Goldberg, F.F.

    1981-01-01

    This handbook describes a methodology for reliability analysis of complex systems such as those which comprise the engineered safety features of nuclear power generating stations. After an initial overview of the available system analysis approaches, the handbook focuses on a description of the deductive method known as fault tree analysis. The following aspects of fault tree analysis are covered: basic concepts for fault tree analysis; basic elements of a fault tree; fault tree construction; probability, statistics, and Boolean algebra for the fault tree analyst; qualitative and quantitative fault tree evaluation techniques; and computer codes for fault tree evaluation. Also discussed are several example problems illustrating the basic concepts of fault tree construction and evaluation

  12. Decision-Tree Program

    Science.gov (United States)

    Buntine, Wray

    1994-01-01

    IND computer program introduces Bayesian and Markov/maximum-likelihood (MML) methods and more-sophisticated methods of searching in growing trees. Produces more-accurate class-probability estimates important in applications like diagnosis. Provides range of features and styles with convenience for casual user, fine-tuning for advanced user or for those interested in research. Consists of four basic kinds of routines: data-manipulation, tree-generation, tree-testing, and tree-display. Written in C language.

  13. Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

    Directory of Open Access Journals (Sweden)

    Hu Yuh-Jyh

    2012-11-01

    Full Text Available Abstract Background Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA, which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results The prediction accuracies of total analgesic consumption (continuous dose and PCA dose and PCA analgesic requirement (PCA dose only by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Conclusion This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA

  14. The Effect of Simulation-Based Learning on Prospective Teachers' Inference Skills in Teaching Probability

    Science.gov (United States)

    Koparan, Timur; Yilmaz, Gül Kaleli

    2015-01-01

    The effect of simulation-based probability teaching on the prospective teachers' inference skills has been examined with this research. In line with this purpose, it has been aimed to examine the design, implementation and efficiency of a learning environment for experimental probability. Activities were built on modeling, simulation and the…

  15. Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

    Science.gov (United States)

    Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.; Guo, L.

    2017-12-01

    Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. One of the main challenges of setting up such a system is finding appropriate model parameter values for ungauged catchments. Previous studies have shown that the transfer of parameter sets from hydrologically similar gauged catchments is one of the best performing regionalization methods. However, a remaining key issue is the identification of suitable descriptors of similarity. In this study, we use decision tree learning to explore parameter set transferability in the full space of catchment descriptors. For this purpose, a semi-distributed rainfall-runoff model is set up for 35 catchments in ten Chinese provinces. Hourly runoff data from in total 858 storm events are used to calibrate the model and to evaluate the performance of parameter set transfers between catchments. We then present a novel technique that uses the splitting rules of classification and regression trees (CART) for finding suitable donor catchments for ungauged target catchments. The ability of the model to detect flood events in assumed ungauged catchments is evaluated in series of leave-one-out tests. We show that CART analysis increases the probability of detection of 10-year flood events in comparison to a conventional measure of physiographic-climatic similarity by up to 20%. Decision tree learning can outperform other regionalization approaches because it generates rules that optimally consider spatial proximity and physical similarity. Spatial proximity can be used as a selection criteria but is skipped in the case where no similar gauged catchments are in the vicinity. We conclude that the CART regionalization concept is particularly suitable for implementation in sparsely gauged and topographically complex environments where a proximity

  16. The effect of the fragmentation problem in decision tree learning applied to the search for single top quark production

    International Nuclear Information System (INIS)

    Vilalta, R; Ocegueda-Hernandez, F; Valerio, R; Watts, G

    2010-01-01

    Decision tree learning constitutes a suitable approach to classification due to its ability to partition the variable space into regions of class-uniform events, while providing a structure amenable to interpretation, in contrast to other methods such as neural networks. But an inherent limitation of decision tree learning is the progressive lessening of the statistical support of the final classifier as clusters of single-class events are split on every partition, a problem known as the fragmentation problem. We describe a software system called DTFE, for Decision Tree Fragmentation Evaluator, that measures the degree of fragmentation caused by a decision tree learner on every event cluster. Clusters are found through a decomposition of the data using a technique known as Spectral Clustering. Each cluster is analyzed in terms of the number and type of partitions induced by the decision tree. Our domain of application lies on the search for single top quark production, a challenging problem due to large and similar backgrounds, low energetic signals, and low number of jets. The output of the machine-learning software tool consists of a series of statistics describing the degree of data fragmentation.

  17. Cosmic String Detection with Tree-Based Machine Learning

    Science.gov (United States)

    Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

    2018-05-01

    We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9΄-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

  18. Identifying the rooted species tree from the distribution of unrooted gene trees under the coalescent.

    Science.gov (United States)

    Allman, Elizabeth S; Degnan, James H; Rhodes, John A

    2011-06-01

    Gene trees are evolutionary trees representing the ancestry of genes sampled from multiple populations. Species trees represent populations of individuals-each with many genes-splitting into new populations or species. The coalescent process, which models ancestry of gene copies within populations, is often used to model the probability distribution of gene trees given a fixed species tree. This multispecies coalescent model provides a framework for phylogeneticists to infer species trees from gene trees using maximum likelihood or Bayesian approaches. Because the coalescent models a branching process over time, all trees are typically assumed to be rooted in this setting. Often, however, gene trees inferred by traditional phylogenetic methods are unrooted. We investigate probabilities of unrooted gene trees under the multispecies coalescent model. We show that when there are four species with one gene sampled per species, the distribution of unrooted gene tree topologies identifies the unrooted species tree topology and some, but not all, information in the species tree edges (branch lengths). The location of the root on the species tree is not identifiable in this situation. However, for 5 or more species with one gene sampled per species, we show that the distribution of unrooted gene tree topologies identifies the rooted species tree topology and all its internal branch lengths. The length of any pendant branch leading to a leaf of the species tree is also identifiable for any species from which more than one gene is sampled.

  19. Activity in inferior parietal and medial prefrontal cortex signals the accumulation of evidence in a probability learning task.

    Directory of Open Access Journals (Sweden)

    Mathieu d'Acremont

    Full Text Available In an uncertain environment, probabilities are key to predicting future events and making adaptive choices. However, little is known about how humans learn such probabilities and where and how they are encoded in the brain, especially when they concern more than two outcomes. During functional magnetic resonance imaging (fMRI, young adults learned the probabilities of uncertain stimuli through repetitive sampling. Stimuli represented payoffs and participants had to predict their occurrence to maximize their earnings. Choices indicated loss and risk aversion but unbiased estimation of probabilities. BOLD response in medial prefrontal cortex and angular gyri increased linearly with the probability of the currently observed stimulus, untainted by its value. Connectivity analyses during rest and task revealed that these regions belonged to the default mode network. The activation of past outcomes in memory is evoked as a possible mechanism to explain the engagement of the default mode network in probability learning. A BOLD response relating to value was detected only at decision time, mainly in striatum. It is concluded that activity in inferior parietal and medial prefrontal cortex reflects the amount of evidence accumulated in favor of competing and uncertain outcomes.

  20. A framework for sensitivity analysis of decision trees.

    Science.gov (United States)

    Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

    2018-01-01

    In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

  1. Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

    Directory of Open Access Journals (Sweden)

    Tan Zhou

    2017-12-01

    Full Text Available A plethora of information contained in full-waveform (FW Light Detection and Ranging (LiDAR data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with machine learning methods and Bayesian inference. Specifically, we first conducted automatic tree segmentation based on the waveform-based canopy height model (CHM using three approaches including TreeVaW, watershed algorithms and the combination of TreeVaW and watershed (TW algorithms. Subsequently, the Random forests (RF and Conditional inference forests (CF models were employed to identify important tree-level waveform metrics derived from three distinct sources, such as raw waveforms, composite waveforms, the waveform-based point cloud and the combined variables from these three sources. Further, we discriminated tree (gray pine, blue oak, interior live oak and shrub species through the RF, CF and Bayesian multinomial logistic regression (BMLR using important waveform metrics identified in this study. Results of the tree segmentation demonstrated that the TW algorithms outperformed other algorithms for delineating individual tree crowns. The CF model overcomes waveform metrics selection bias caused by the RF model which favors correlated metrics and enhances the accuracy of subsequent classification. We also found that composite waveforms are more informative than raw waveforms and waveform-based point cloud for characterizing tree species in our study area. Both classical machine learning methods (the RF and CF and the BMLR generated satisfactory average overall accuracy (74% for the RF, 77% for the CF and 81% for the BMLR and the BMLR slightly outperformed the other two methods. However, these three methods suffered from low individual classification accuracy for the blue oak which is prone to being misclassified as the interior live oak due

  2. Web-based experiments controlled by JavaScript: an example from probability learning.

    Science.gov (United States)

    Birnbaum, Michael H; Wakcher, Sandra V

    2002-05-01

    JavaScript programs can be used to control Web experiments. This technique is illustrated by an experiment that tested the effects of advice on performance in the classic probability-learning paradigm. Previous research reported that people tested via the Web or in the lab tended to match the probabilities of their responses to the probabilities that those responses would be reinforced. The optimal strategy, however, is to consistently choose the more frequent event; probability matching produces suboptimal performance. We investigated manipulations we reasoned should improve performance. A horse race scenario in which participants predicted the winner in each of a series of races between two horses was compared with an abstract scenario used previously. Ten groups of learners received different amounts of advice, including all combinations of (1) explicit instructions concerning the optimal strategy, (2) explicit instructions concerning a monetary sum to maximize, and (3) accurate information concerning the probabilities of events. The results showed minimal effects of horse race versus abstract scenario. Both advice concerning the optimal strategy and probability information contributed significantly to performance in the task. This paper includes a brief tutorial on JavaScript, explaining with simple examples how to assemble a browser-based experiment.

  3. A Decision-Tree-Oriented Guidance Mechanism for Conducting Nature Science Observation Activities in a Context-Aware Ubiquitous Learning

    Science.gov (United States)

    Hwang, Gwo-Jen; Chu, Hui-Chun; Shih, Ju-Ling; Huang, Shu-Hsien; Tsai, Chin-Chung

    2010-01-01

    A context-aware ubiquitous learning environment is an authentic learning environment with personalized digital supports. While showing the potential of applying such a learning environment, researchers have also indicated the challenges of providing adaptive and dynamic support to individual students. In this paper, a decision-tree-oriented…

  4. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  5. The Reliability and Stability of an Inferred Phylogenetic Tree from Empirical Data.

    Science.gov (United States)

    Katsura, Yukako; Stanley, Craig E; Kumar, Sudhir; Nei, Masatoshi

    2017-03-01

    The reliability of a phylogenetic tree obtained from empirical data is usually measured by the bootstrap probability (Pb) of interior branches of the tree. If the bootstrap probability is high for most branches, the tree is considered to be reliable. If some interior branches show relatively low bootstrap probabilities, we are not sure that the inferred tree is really reliable. Here, we propose another quantity measuring the reliability of the tree called the stability of a subtree. This quantity refers to the probability of obtaining a subtree (Ps) of an inferred tree obtained. We then show that if the tree is to be reliable, both Pb and Ps must be high. We also show that Ps is given by a bootstrap probability of the subtree with the closest outgroup sequence, and computer program RESTA for computing the Pb and Ps values will be presented. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  6. A practical method for accurate quantification of large fault trees

    International Nuclear Information System (INIS)

    Choi, Jong Soo; Cho, Nam Zin

    2007-01-01

    This paper describes a practical method to accurately quantify top event probability and importance measures from incomplete minimal cut sets (MCS) of a large fault tree. The MCS-based fault tree method is extensively used in probabilistic safety assessments. Several sources of uncertainties exist in MCS-based fault tree analysis. The paper is focused on quantification of the following two sources of uncertainties: (1) the truncation neglecting low-probability cut sets and (2) the approximation in quantifying MCSs. The method proposed in this paper is based on a Monte Carlo simulation technique to estimate probability of the discarded MCSs and the sum of disjoint products (SDP) approach complemented by the correction factor approach (CFA). The method provides capability to accurately quantify the two uncertainties and estimate the top event probability and importance measures of large coherent fault trees. The proposed fault tree quantification method has been implemented in the CUTREE code package and is tested on the two example fault trees

  7. Probability cueing of distractor locations: both intertrial facilitation and statistical learning mediate interference reduction.

    Science.gov (United States)

    Goschy, Harriet; Bakos, Sarolta; Müller, Hermann J; Zehetleitner, Michael

    2014-01-01

    Targets in a visual search task are detected faster if they appear in a probable target region as compared to a less probable target region, an effect which has been termed "probability cueing." The present study investigated whether probability cueing cannot only speed up target detection, but also minimize distraction by distractors in probable distractor regions as compared to distractors in less probable distractor regions. To this end, three visual search experiments with a salient, but task-irrelevant, distractor ("additional singleton") were conducted. Experiment 1 demonstrated that observers can utilize uneven spatial distractor distributions to selectively reduce interference by distractors in frequent distractor regions as compared to distractors in rare distractor regions. Experiments 2 and 3 showed that intertrial facilitation, i.e., distractor position repetitions, and statistical learning (independent of distractor position repetitions) both contribute to the probability cueing effect for distractor locations. Taken together, the present results demonstrate that probability cueing of distractor locations has the potential to serve as a strong attentional cue for the shielding of likely distractor locations.

  8. The influence of phonotactic probability and neighborhood density on children's production of newly learned words.

    Science.gov (United States)

    Heisler, Lori; Goffman, Lisa

    A word learning paradigm was used to teach children novel words that varied in phonotactic probability and neighborhood density. The effects of frequency and density on speech production were examined when phonetic forms were non-referential (i.e., when no referent was attached) and when phonetic forms were referential (i.e., when a referent was attached through fast mapping). Two methods of analysis were included: (1) kinematic variability of speech movement patterning; and (2) measures of segmental accuracy. Results showed that phonotactic frequency influenced the stability of movement patterning whereas neighborhood density influenced phoneme accuracy. Motor learning was observed in both non-referential and referential novel words. Forms with low phonotactic probability and low neighborhood density showed a word learning effect when a referent was assigned during fast mapping. These results elaborate on and specify the nature of interactivity observed across lexical, phonological, and articulatory domains.

  9. Comprehensive decision tree models in bioinformatics.

    Directory of Open Access Journals (Sweden)

    Gregor Stiglic

    Full Text Available PURPOSE: Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. METHODS: This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. RESULTS: The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. CONCLUSIONS: The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets

  10. Comprehensive decision tree models in bioinformatics.

    Science.gov (United States)

    Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

    2012-01-01

    Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly

  11. Tree-average distances on certain phylogenetic networks have their weights uniquely determined.

    Science.gov (United States)

    Willson, Stephen J

    2012-01-01

    A phylogenetic network N has vertices corresponding to species and arcs corresponding to direct genetic inheritance from the species at the tail to the species at the head. Measurements of DNA are often made on species in the leaf set, and one seeks to infer properties of the network, possibly including the graph itself. In the case of phylogenetic trees, distances between extant species are frequently used to infer the phylogenetic trees by methods such as neighbor-joining. This paper proposes a tree-average distance for networks more general than trees. The notion requires a weight on each arc measuring the genetic change along the arc. For each displayed tree the distance between two leaves is the sum of the weights along the path joining them. At a hybrid vertex, each character is inherited from one of its parents. We will assume that for each hybrid there is a probability that the inheritance of a character is from a specified parent. Assume that the inheritance events at different hybrids are independent. Then for each displayed tree there will be a probability that the inheritance of a given character follows the tree; this probability may be interpreted as the probability of the tree. The tree-average distance between the leaves is defined to be the expected value of their distance in the displayed trees. For a class of rooted networks that includes rooted trees, it is shown that the weights and the probabilities at each hybrid vertex can be calculated given the network and the tree-average distances between the leaves. Hence these weights and probabilities are uniquely determined. The hypotheses on the networks include that hybrid vertices have indegree exactly 2 and that vertices that are not leaves have a tree-child.

  12. Teaching Probability to Pre-Service Teachers with Argumentation Based Science Learning Approach

    Science.gov (United States)

    Can, Ömer Sinan; Isleyen, Tevfik

    2016-01-01

    The aim of this study is to explore the effects of the argumentation based science learning (ABSL) approach on the teaching probability to pre-service teachers. The sample of the study included 41 students studying at the Department of Elementary School Mathematics Education in a public university during the 2014-2015 academic years. The study is…

  13. Talking Trees

    Science.gov (United States)

    Tolman, Marvin

    2005-01-01

    Students love outdoor activities and will love them even more when they build confidence in their tree identification and measurement skills. Through these activities, students will learn to identify the major characteristics of trees and discover how the pace--a nonstandard measuring unit--can be used to estimate not only distances but also the…

  14. Constructing multi-labelled decision trees for junction design using the predicted probabilities

    NARCIS (Netherlands)

    Bezembinder, Erwin M.; Wismans, Luc J. J.; Van Berkum, Eric C.

    2017-01-01

    In this paper, we evaluate the use of traditional decision tree algorithms CRT, CHAID and QUEST to determine a decision tree which can be used to predict a set of (Pareto optimal) junction design alternatives (e.g. signal or roundabout) for a given traffic demand pattern and available space. This is

  15. STRIDE: Species Tree Root Inference from Gene Duplication Events.

    Science.gov (United States)

    Emms, David M; Kelly, Steven

    2017-12-01

    The correct interpretation of any phylogenetic tree is dependent on that tree being correctly rooted. We present STRIDE, a fast, effective, and outgroup-free method for identification of gene duplication events and species tree root inference in large-scale molecular phylogenetic analyses. STRIDE identifies sets of well-supported in-group gene duplication events from a set of unrooted gene trees, and analyses these events to infer a probability distribution over an unrooted species tree for the location of its root. We show that STRIDE correctly identifies the root of the species tree in multiple large-scale molecular phylogenetic data sets spanning a wide range of timescales and taxonomic groups. We demonstrate that the novel probability model implemented in STRIDE can accurately represent the ambiguity in species tree root assignment for data sets where information is limited. Furthermore, application of STRIDE to outgroup-free inference of the origin of the eukaryotic tree resulted in a root probability distribution that provides additional support for leading hypotheses for the origin of the eukaryotes. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  16. ANALYSIS OF EFFECTIVENESS OF METHODOLOGICAL SYSTEM FOR PROBABILITY AND STOCHASTIC PROCESSES COMPUTER-BASED LEARNING FOR PRE-SERVICE ENGINEERS

    Directory of Open Access Journals (Sweden)

    E. Chumak

    2015-04-01

    Full Text Available The author substantiates that only methodological training systems of mathematical disciplines with implementation of information and communication technologies (ICT can meet the requirements of modern educational paradigm and make possible to increase the educational efficiency. Due to this fact, the necessity of developing the methodology of theory of probability and stochastic processes computer-based learning for pre-service engineers is underlined in the paper. The results of the experimental study for analysis of the efficiency of methodological system of theory of probability and stochastic processes computer-based learning for pre-service engineers are shown. The analysis includes three main stages: ascertaining, searching and forming. The key criteria of the efficiency of designed methodological system are the level of probabilistic and stochastic skills of students and their learning motivation. The effect of implementing the methodological system of probability theory and stochastic processes computer-based learning on the level of students’ IT literacy is shown in the paper. The expanding of the range of objectives of ICT applying by students is described by author. The level of formation of students’ learning motivation on the ascertaining and forming stages of the experiment is analyzed. The level of intrinsic learning motivation for pre-service engineers is defined on these stages of the experiment. For this purpose, the methodology of testing the students’ learning motivation in the chosen specialty is presented in the paper. The increasing of intrinsic learning motivation of the experimental group students (E group against the control group students (C group is demonstrated.

  17. What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

    Science.gov (United States)

    Binder, Harald

    2014-07-01

    This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. MFAULT: a computer program for analyzing fault trees

    International Nuclear Information System (INIS)

    Pelto, P.J.; Purcell, W.L.

    1977-11-01

    A description and user instructions are presented for MFAULT, a FORTRAN computer program for fault tree analysis. MFAULT identifies the cut sets of a fault tree, calculates their probabilities, and screens the cut sets on the basis of specified cut-offs on probability and/or cut set length. MFAULT is based on an efficient upward-working algorithm for cut set identification. The probability calculations are based on the assumption of small probabilities and constant hazard rates (i.e., exponential failure distributions). Cut sets consisting of repairable components (basic events) only, non-repairable components only, or mixtures of both types can be evaluated. Components can be on-line or standby. Unavailability contributions from pre-existing failures, failures on demand, and testing and maintenance down-time can be handled. MFAULT can analyze fault trees with AND gates, OR gates, inhibit gates, on switches (houses) and off switches. The code is presently capable of finding up to ten event cut sets from a fault tree with up to 512 basic events and 400 gates. It is operational on the CONTROL DATA CYBER 74 computer. 11 figures

  19. Tree mortality estimates and species distribution probabilities in southeastern United States forests

    Science.gov (United States)

    Martin A. Spetich; Zhaofei Fan; Zhen Sui; Michael Crosby; Hong S. He; Stephen R. Shifley; Theodor D. Leininger; W. Keith Moser

    2017-01-01

    Stresses to trees under a changing climate can lead to changes in forest tree survival, mortality and distribution.  For instance, a study examining the effects of human-induced climate change on forest biodiversity by Hansen and others (2001) predicted a 32% reduction in loblolly–shortleaf pine habitat across the eastern United States.  However, they also...

  20. Predicting incomplete gene microarray data with the use of supervised learning algorithms

    CSIR Research Space (South Africa)

    Twala, B

    2010-10-01

    Full Text Available that prediction using supervised learning can be improved in probabilistic terms given incomplete microarray data. This imputation approach is based on the a priori probability of each value determined from the instances at that node of a decision tree (PDT...

  1. The Effect of Incremental Changes in Phonotactic Probability and Neighborhood Density on Word Learning by Preschool Children

    Science.gov (United States)

    Storkel, Holly L.; Bontempo, Daniel E.; Aschenbrenner, Andrew J.; Maekawa, Junko; Lee, Su-Yeon

    2013-01-01

    Purpose: Phonotactic probability or neighborhood density has predominately been defined through the use of gross distinctions (i.e., low vs. high). In the current studies, the authors examined the influence of finer changes in probability (Experiment 1) and density (Experiment 2) on word learning. Method: The authors examined the full range of…

  2. Multivariate analysis of flow cytometric data using decision trees.

    Science.gov (United States)

    Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

    2012-01-01

    Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

  3. Pareto Efficient Solutions of Attack-Defence Trees

    DEFF Research Database (Denmark)

    Aslanyan, Zaruhi; Nielson, Flemming

    2015-01-01

    Attack-defence trees are a promising approach for representing threat scenarios and possible countermeasures in a concise and intuitive manner. An attack-defence tree describes the interaction between an attacker and a defender, and is evaluated by assigning parameters to the nodes, such as proba......Attack-defence trees are a promising approach for representing threat scenarios and possible countermeasures in a concise and intuitive manner. An attack-defence tree describes the interaction between an attacker and a defender, and is evaluated by assigning parameters to the nodes......, such as probability or cost of attacks and defences. In case of multiple parameters most analytical methods optimise one parameter at a time, e.g., minimise cost or maximise probability of an attack. Such methods may lead to sub-optimal solutions when optimising conflicting parameters, e.g., minimising cost while...... maximising probability. In order to tackle this challenge, we devise automated techniques that optimise all parameters at once. Moreover, in the case of conflicting parameters our techniques compute the set of all optimal solutions, defined in terms of Pareto efficiency. The developments are carried out...

  4. The Problem of Predecessors on Spanning Trees

    Directory of Open Access Journals (Sweden)

    V. S. Poghosyan

    2011-01-01

    Full Text Available We consider the equiprobable distribution of spanning trees on the square lattice. All bonds of each tree can be oriented uniquely with respect to an arbitrary chosen site called the root. The problem of predecessors is to find the probability that a path along the oriented bonds passes sequentially fixed sites i and j. The conformal field theory for the Potts model predicts the fractal dimension of the path to be 5/4. Using this result, we show that the probability in the predecessors problem for two sites separated by large distance r decreases as P(r ∼ r −3/4. If sites i and j are nearest neighbors on the square lattice, the probability P(1 = 5/16 can be found from the analytical theory developed for the sandpile model. The known equivalence between the loop erased random walk (LERW and the directed path on the spanning tree states that P(1 is the probability for the LERW started at i to reach the neighboring site j. By analogy with the self-avoiding walk, P(1 can be called the return probability. Extensive Monte-Carlo simulations confirm the theoretical predictions.

  5. A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data

    Directory of Open Access Journals (Sweden)

    Sebastian Lamprecht

    2017-05-01

    Full Text Available Determining the exact position of a forest inventory plot—and hence the position of the sampled trees—is often hampered by a poor Global Navigation Satellite System (GNSS signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation. This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011–2012 within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the Waldplaner 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations.

  6. From exemplar to grammar: a probabilistic analogy-based model of language learning.

    Science.gov (United States)

    Bod, Rens

    2009-07-01

    While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase-structure trees should be assigned to initial sentences, s/he allows (implicitly) for all possible trees and lets linguistic experience decide which is the "best" tree for each sentence. The best tree is obtained by maximizing "structural analogy" between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus-based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar-based and rule-based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children's language development from item-based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions. Copyright © 2009 Cognitive Science Society, Inc.

  7. Integrating cyber attacks within fault trees

    International Nuclear Information System (INIS)

    Nai Fovino, Igor; Masera, Marcelo; De Cian, Alessio

    2009-01-01

    In this paper, a new method for quantitative security risk assessment of complex systems is presented, combining fault-tree analysis, traditionally used in reliability analysis, with the recently introduced Attack-tree analysis, proposed for the study of malicious attack patterns. The combined use of fault trees and attack trees helps the analyst to effectively face the security challenges posed by the introduction of modern ICT technologies in the control systems of critical infrastructures. The proposed approach allows considering the interaction of malicious deliberate acts with random failures. Formal definitions of fault tree and attack tree are provided and a mathematical model for the calculation of system fault probabilities is presented.

  8. Integrating cyber attacks within fault trees

    Energy Technology Data Exchange (ETDEWEB)

    Nai Fovino, Igor [Joint Research Centre - EC, Institute for the Protection and Security of the Citizen, Ispra, VA (Italy)], E-mail: igor.nai@jrc.it; Masera, Marcelo [Joint Research Centre - EC, Institute for the Protection and Security of the Citizen, Ispra, VA (Italy); De Cian, Alessio [Department of Electrical Engineering, University di Genova, Genoa (Italy)

    2009-09-15

    In this paper, a new method for quantitative security risk assessment of complex systems is presented, combining fault-tree analysis, traditionally used in reliability analysis, with the recently introduced Attack-tree analysis, proposed for the study of malicious attack patterns. The combined use of fault trees and attack trees helps the analyst to effectively face the security challenges posed by the introduction of modern ICT technologies in the control systems of critical infrastructures. The proposed approach allows considering the interaction of malicious deliberate acts with random failures. Formal definitions of fault tree and attack tree are provided and a mathematical model for the calculation of system fault probabilities is presented.

  9. Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.

    Science.gov (United States)

    Vock, David M; Wolfson, Julian; Bandyopadhyay, Sunayan; Adomavicius, Gediminas; Johnson, Paul E; Vazquez-Benitez, Gabriela; O'Connor, Patrick J

    2016-06-01

    Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include (1) discarding those observations, (2) treating them as non-events, (3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Multivariate analysis of flow cytometric data using decision trees

    Directory of Open Access Journals (Sweden)

    Svenja eSimon

    2012-04-01

    Full Text Available Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called 'induction of decision trees' in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees's quality, we used stratified 5-fold cross-validation and chose the 'best' tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

  11. Automated reasoning with dynamic event trees: a real-time, knowledge-based decision aide

    International Nuclear Information System (INIS)

    Touchton, R.A.; Gunter, A.D.; Subramanyan, N.

    1988-01-01

    The models and data contained in a probabilistic risk assessment (PRA) Event Sequence Analysis represent a wealth of information that can be used for dynamic calculation of event sequence likelihood. In this paper we report a new and unique computerization methodology which utilizes these data. This sub-system (referred to as PREDICTOR) has been developed and tested as part of a larger system. PREDICTOR performs a real-time (re)calculation of the estimated likelihood of core-melt as a function of plant status. This methodology uses object-oriented programming techniques from the artificial intelligence discipline that enable one to codify event tree and fault tree logic models and associated probabilities developed in a PRA study. Existence of off-normal conditions is reported to PREDICTOR, which then updates the relevant failure probabilities throughout the event tree and fault tree models by dynamically replacing the off-the-shelf (or prior) probabilities with new probabilities based on the current situation. The new event probabilities are immediately propagated through the models (using 'demons') and an updated core-melt probability is calculated. Along the way, the dominant non-success path of each event tree is determined and highlighted. (author)

  12. Unrealistic phylogenetic trees may improve phylogenetic footprinting.

    Science.gov (United States)

    Nettling, Martin; Treutler, Hendrik; Cerquides, Jesus; Grosse, Ivo

    2017-06-01

    The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily. Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting. The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo. : martin.nettling@informatik.uni-halle.de. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

  13. A scenario tree model for the Canadian Notifiable Avian Influenza Surveillance System and its application to estimation of probability of freedom and sample size determination.

    Science.gov (United States)

    Christensen, Jette; Stryhn, Henrik; Vallières, André; El Allaki, Farouk

    2011-05-01

    In 2008, Canada designed and implemented the Canadian Notifiable Avian Influenza Surveillance System (CanNAISS) with six surveillance activities in a phased-in approach. CanNAISS was a surveillance system because it had more than one surveillance activity or component in 2008: passive surveillance; pre-slaughter surveillance; and voluntary enhanced notifiable avian influenza surveillance. Our objectives were to give a short overview of two active surveillance components in CanNAISS; describe the CanNAISS scenario tree model and its application to estimation of probability of populations being free of NAI virus infection and sample size determination. Our data from the pre-slaughter surveillance component included diagnostic test results from 6296 serum samples representing 601 commercial chicken and turkey farms collected from 25 August 2008 to 29 January 2009. In addition, we included data from a sub-population of farms with high biosecurity standards: 36,164 samples from 55 farms sampled repeatedly over the 24 months study period from January 2007 to December 2008. All submissions were negative for Notifiable Avian Influenza (NAI) virus infection. We developed the CanNAISS scenario tree model, so that it will estimate the surveillance component sensitivity and the probability of a population being free of NAI at the 0.01 farm-level and 0.3 within-farm-level prevalences. We propose that a general model, such as the CanNAISS scenario tree model, may have a broader application than more detailed models that require disease specific input parameters, such as relative risk estimates. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

  14. Lognormal Approximations of Fault Tree Uncertainty Distributions.

    Science.gov (United States)

    El-Shanawany, Ashraf Ben; Ardron, Keith H; Walker, Simon P

    2018-01-26

    Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed-form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling-based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed-form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models. © 2018 Society for Risk Analysis.

  15. Calculating method on human error probabilities considering influence of management and organization

    International Nuclear Information System (INIS)

    Gao Jia; Huang Xiangrui; Shen Zupei

    1996-01-01

    This paper is concerned with how management and organizational influences can be factored into quantifying human error probabilities on risk assessments, using a three-level Influence Diagram (ID) which is originally only as a tool for construction and representation of models of decision-making trees or event trees. An analytical model of human errors causation has been set up with three influence levels, introducing a method for quantification assessments (of the ID), which can be applied into quantifying probabilities) of human errors on risk assessments, especially into the quantification of complex event trees (system) as engineering decision-making analysis. A numerical case study is provided to illustrate the approach

  16. Tree Transduction Tools for Cdec

    Directory of Open Access Journals (Sweden)

    Austin Matthews

    2014-09-01

    Full Text Available We describe a collection of open source tools for learning tree-to-string and tree-to-tree transducers and the extensions to the cdec decoder that enable translation with these. Our modular, easy-to-extend tools extract rules from trees or forests aligned to strings and trees subject to different structural constraints. A fast, multithreaded implementation of the Cohn and Blunsom (2009 model for extracting compact tree-to-string rules is also included. The implementation of the tree composition algorithm used by cdec is described, and translation quality and decoding time results are presented. Our experimental results add to the body of evidence suggesting that tree transducers are a compelling option for translation, particularly when decoding speed and translation model size are important.

  17. Time-dependent methodology for fault tree evaluation

    International Nuclear Information System (INIS)

    Vesely, W.B.

    1976-01-01

    Any fault tree may be evaluated applying the method called the kinetic theory of fault trees. The basic feature of this method as presented here is in that any information on primary failure, type failure or peak failure is derived from three characteristics: probability of existence, failure intensity and failure density. The determination of the said three characteristics for a given phenomenon yields the remaining probabilistic information on the individual aspects of the failure and on their totality for the whole observed period. The probabilistic characteristics are determined by applying the analysis of phenomenon probability. The total time dependent information on the peak failure is obtained by using the type failures (critical paths) of the fault tree. By applying the said process the total time dependent information is obtained for every primary failure and type failure of the fault tree. In the application of the method of the kinetic theory of fault trees represented by the PREP and KITT programmes, the type failures are first obtained using the deterministic testing method or using the Monte Carlo simulation (PREP programme). The respective characteristics are then determined using the kinetic theory of fault trees (KITT programmes). (Oy)

  18. Boosted decision trees and applications

    International Nuclear Information System (INIS)

    Coadou, Y.

    2013-01-01

    Decision trees are a machine learning technique more and more commonly used in high energy physics, while it has been widely used in the social sciences. After introducing the concepts of decision trees, this article focuses on its application in particle physics. (authors)

  19. Psychomotor development and learning difficulties in preschool children with probable attention deficit hyperactivity disorder: An epidemiological study in Navarre and La Rioja.

    Science.gov (United States)

    Marín-Méndez, J J; Borra-Ruiz, M C; Álvarez-Gómez, M J; Soutullo Esperón, C

    2017-10-01

    ADHD symptoms begin to appear at preschool age. ADHD may have a significant negative impact on academic performance. In Spain, there are no standardized tools for detecting ADHD at preschool age, nor is there data about the incidence of this disorder. To evaluate developmental factors and learning difficulties associated with probable ADHD and to assess the impact of ADHD in school performance. We conducted a population-based study with a stratified multistage proportional cluster sample design. We found significant differences between probable ADHD and parents' perception of difficulties in expressive language, comprehension, and fine motor skills, as well as in emotions, concentration, behaviour, and relationships. Around 34% of preschool children with probable ADHD showed global learning difficulties, mainly in patients with the inattentive type. According to the multivariate analysis, learning difficulties were significantly associated with both delayed psychomotor development during the first 3 years of life (OR: 5.57) as assessed by parents, and probable ADHD (OR: 2.34) CONCLUSIONS: There is a connection between probable ADHD in preschool children and parents' perception of difficulties in several dimensions of development and learning. Early detection of ADHD at preschool ages is necessary to start prompt and effective clinical and educational interventions. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

  20. Classification tree for the assessment of sedentary lifestyle among hypertensive.

    Science.gov (United States)

    Castelo Guedes Martins, Larissa; Venícios de Oliveira Lopes, Marcos; Gomes Guedes, Nirla; Paixão de Menezes, Angélica; de Oliveira Farias, Odaleia; Alves Dos Santos, Naftale

    2016-04-01

    To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

  1. Classification tree for the assessment of sedentary lifestyle among hypertensive

    Directory of Open Access Journals (Sweden)

    Larissa Castelo Guedes Martins

    Full Text Available Objective.To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL in people with high blood pressure (HTN. Methods. A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection. Results. The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Conclusion. Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

  2. Improved detection probability of low level light and infrared image fusion system

    Science.gov (United States)

    Luo, Yuxiang; Fu, Rongguo; Zhang, Junju; Wang, Wencong; Chang, Benkang

    2018-02-01

    Low level light(LLL) image contains rich information on environment details, but is easily affected by the weather. In the case of smoke, rain, cloud or fog, much target information will lose. Infrared image, which is from the radiation produced by the object itself, can be "active" to obtain the target information in the scene. However, the image contrast and resolution is bad, the ability of the acquisition of target details is very poor, and the imaging mode does not conform to the human visual habit. The fusion of LLL and infrared image can make up for the deficiency of each sensor and give play to the advantages of single sensor. At first, we show the hardware design of fusion circuit. Then, through the recognition probability calculation of the target(one person) and the background image(trees), we find that the trees detection probability of LLL image is higher than that of the infrared image, and the person detection probability of the infrared image is obviously higher than that of LLL image. The detection probability of fusion image for one person and trees is higher than that of single detector. Therefore, image fusion can significantly enlarge recognition probability and improve detection efficiency.

  3. A Comprehensive Probability Project for the Upper Division One-Semester Probability Course Using Yahtzee

    Science.gov (United States)

    Wilson, Jason; Lawman, Joshua; Murphy, Rachael; Nelson, Marissa

    2011-01-01

    This article describes a probability project used in an upper division, one-semester probability course with third-semester calculus and linear algebra prerequisites. The student learning outcome focused on developing the skills necessary for approaching project-sized math/stat application problems. These skills include appropriately defining…

  4. Phylogenomics reveal a robust fungal tree of life

    NARCIS (Netherlands)

    Kuramae, Eiko E.; Robert, Vincent; Snel, Berend; Weiß, Michael; Boekhout, Teun

    2006-01-01

    Our understanding of the tree of life (TOL) is still fragmentary. Until recently, molecular phylogeneticists have built trees based on ribosomal RNA sequences and selected protein sequences, which, however, usually suffered from lack of support for the deeper branches and inconsistencies probably

  5. Building of fuzzy decision trees using ID3 algorithm

    Science.gov (United States)

    Begenova, S. B.; Avdeenko, T. V.

    2018-05-01

    Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.

  6. Dependencies in event trees analyzed by Petri nets

    International Nuclear Information System (INIS)

    Nývlt, Ondřej; Rausand, Marvin

    2012-01-01

    This paper discusses how non-marked Petri nets can be used to model and analyze event trees where the pivotal (branching) events are dependent and modeled by fault trees. The dependencies may, for example, be caused by shared utilities, shared components, or general common cause failures that are modeled by beta-factor models. These dependencies are cumbersome to take into account when using standard event-/fault tree modeling techniques, and may lead to significant errors in the calculated end-state probabilities of the event tree if they are not properly analyzed. A new approach is proposed in this paper, where the whole event tree is modeled by a non-marked Petri net and where P-invariants, representing the structural properties of the Petri net, are used to obtain the frequency of each end-state of the event tree with dependencies. The new approach is applied to a real example of an event tree analysis of the Strahov highway tunnel in Prague, Czech Republic, including two types of dependencies (shared Programmable Logic Controllers and Common Cause Failures). - Highlights: ► In this paper, we model and analyze event trees (ET) using Petri nets. ► The pivotal events of the modeled event trees are dependent (e.g., shared PLCs, CCF). ► A new method based on P-invariants to obtain probabilities of end states is proposed. ► Method is shown in the case study of the Stahov tunnel in the Czech Republic.

  7. ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

    Science.gov (United States)

    Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

    2016-07-07

    Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

  8. Probabilistic Properties of Rectilinear Steiner Minimal Trees

    Directory of Open Access Journals (Sweden)

    V. N. Salnikov

    2015-01-01

    Full Text Available This work concerns the properties of Steiner minimal trees for the manhattan plane in the context of introducing a probability measure. This problem is important because exact algorithms to solve the Steiner problem are computationally expensive (NP-hard and the solution (especially in the case of big number of points to be connected has a diversity of practical applications. That is why the work considers a possibility to rank the possible topologies of the minimal trees with respect to a probability of their usage. For this, the known facts about the structural properties of minimal trees for selected metrics have been analyzed to see their usefulness for the problem in question. For the small amount of boundary (fixed vertices, the paper offers a way to introduce a probability measure as a corollary of proved theorem about some structural properties of the minimal trees.This work is considered to further the previous similar activity concerning a problem of searching for minimal fillings, and it is a door opener to the more general (complicated task. The stated method demonstrates the possibility to reach the final result analytically, which gives a chance of its applicability to the case of the bigger number of boundary vertices (probably, with the use of computer engineering.The introducing definition of an essential Steiner point allowed a considerable restriction of the ambiguity of initial problem solution and, at the same time, comparison of such an approach with more classical works in the field concerned. The paper also lists main barriers of classical approaches, preventing their use for the task of introducing a probability measure.In prospect, application areas of the described method are expected to be wider both in terms of system enlargement (the number of boundary vertices and in terms of other metric spaces (the Euclidean case is of especial interest. The main interest is to find the classes of topologies with significantly

  9. Thinning of Tree Stands in the Arctic Zone of Krasnoyarsk Territory With Different Ecological Conditions

    Directory of Open Access Journals (Sweden)

    V. I. Polyakov

    2014-10-01

    Full Text Available In 2001 six permanent sample plots (PSP were established in forest stands differing in degrees of damage by pollution from the Norilsk industrial region. In 2004 the second forest inventory was carried out at these PSP for evaluation of pollutant impacts on stand condition changes. During both inventory procedures the vigor state of every tree was visually categorized according to 6-points scale of «Forest health regulations in Russian Federation». The changeover of tree into fall was also taken into account. Two types of Markov’s models simulating thinning process in tree stands within different ecological conditions has been developed: 1 based on assessment for probability of tree survival during three years; 2 in terms of evaluation of matrix for probability on change of vigor state category in the same period. The reconstruction of tree mortality from 1979 after industrial complex «Nadezda» setting into operation was realized on the basis of probability estimation of dead standing trees conservation during three years observed. The forecast of situation was carried out up to 2030. Using logistic regression the probability of tree survival was established depending on four factors: degree of tree damage by pollutants, tree species, stand location in relief and tree age. The acquired results make it possible to single out an impact of pollutants to tree stands’ resistance from other factors. There was revealed the percent of tree fall, resulted by pollution. The evaluation scale of SO2 gas resistance of tree species was constructed: birch, spruce, larch. Larch showed the highest percent of fall because of pollution.

  10. Predicting gene function using hierarchical multi-label decision tree ensembles

    Directory of Open Access Journals (Sweden)

    Kocev Dragi

    2010-01-01

    Full Text Available Abstract Background S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability. Results We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO. We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use. Conclusions Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.

  11. Analysis of event tree with imprecise inputs by fuzzy set theory

    International Nuclear Information System (INIS)

    Ahn, Kwang Il; Chun, Moon Hyun

    1990-01-01

    Fuzzy set theory approach is proposed as a method to analyze event trees with imprecise or linguistic input variables such as 'likely' or 'improbable' instead of the numerical probability. In this paper, it is shown how the fuzzy set theory can be applied to the event tree analysis. The result of this study shows that the fuzzy set theory approach can be applied as an acceptable and effective tool for analysis of the event tree with fuzzy type of inputs. Comparisons of the fuzzy theory approach with the probabilistic approach of computing probabilities of final states of the event tree through subjective weighting factors and LHS technique show that the two approaches have common factors and give reasonable results

  12. How to Identify and Interpret Evolutionary Tree Diagrams

    Science.gov (United States)

    Kong, Yi; Anderson, Trevor; Pelaez, Nancy

    2016-01-01

    Evolutionary trees are key tools for modern biology and are commonly portrayed in textbooks to promote learning about biological evolution. However, many people have difficulty in understanding what evolutionary trees are meant to portray. In fact, some ideas that current professional biologists depict with evolutionary trees are neither clearly…

  13. Uncertainties related to the fault tree reliability data

    International Nuclear Information System (INIS)

    Apostol, Minodora; Nitoi, Mirela; Farcasiu, M.

    2003-01-01

    Uncertainty analyses related to the fault trees evaluate the system variability which appears from the uncertainties of the basic events probabilities. Having a logical model which describes a system, to obtain outcomes means to evaluate it, using estimations for each basic event of the model. If the model has basic events that incorporate uncertainties, then the results of the model should incorporate the uncertainties of the events. Uncertainties estimation in the final result of the fault tree means first the uncertainties evaluation for the basic event probabilities and then combination of these uncertainties, to calculate the top event uncertainty. To calculate the propagating uncertainty, a knowledge of the probability density function as well as the range of possible values of the basic event probabilities is required. The following data are defined, using suitable probability density function: the components failure rates; the human error probabilities; the initiating event frequencies. It was supposed that the possible value distribution of the basic event probabilities is given by the lognormal probability density function. To know the range of possible value of the basic event probabilities, the error factor or the uncertainty factor is required. The aim of this paper is to estimate the error factor for the failure rates and for the human errors probabilities from the reliability data base used in Cernavoda Probabilistic Safety Evaluation. The top event chosen as an example is FEED3, from the Pressure and Inventory Control System. The quantitative evaluation of this top event was made by using EDFT code, developed in Institute for Nuclear Research Pitesti (INR). It was supposed that the error factors for the component failures are the same as for the failure rates. Uncertainty analysis was made with INCERT application, which uses the moment method and Monte Carlo method. The reliability data base used at INR Pitesti does not contain the error factors (ef

  14. Success tree method of resources evaluation

    International Nuclear Information System (INIS)

    Chen Qinglan; Sun Wenpeng

    1994-01-01

    By applying the reliability theory in system engineering, the success tree method is used to transfer the expert's recognition on metallogenetic regularities into the form of the success tree. The aim of resources evaluation is achieved by means of calculating the metallogenetic probability or favorability of the top event of the success tree. This article introduces in detail, the source, principle of the success tree method and three kinds of calculation methods, expounds concretely how to establish the success tree of comprehensive uranium metallogenesis as well as the procedure from which the resources evaluation is performed. Because this method has not restrictions on the number of known deposits and calculated area, it is applicable to resources evaluation for different mineral species, types and scales and possesses good prospects of development

  15. Algorithms for Decision Tree Construction

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    The study of algorithms for decision tree construction was initiated in 1960s. The first algorithms are based on the separation heuristic [13, 31] that at each step tries dividing the set of objects as evenly as possible. Later Garey and Graham [28] showed that such algorithm may construct decision trees whose average depth is arbitrarily far from the minimum. Hyafil and Rivest in [35] proved NP-hardness of DT problem that is constructing a tree with the minimum average depth for a diagnostic problem over 2-valued information system and uniform probability distribution. Cox et al. in [22] showed that for a two-class problem over information system, even finding the root node attribute for an optimal tree is an NP-hard problem. © Springer-Verlag Berlin Heidelberg 2011.

  16. Supervised Learning of Two-Layer Perceptron under the Existence of External Noise — Learning Curve of Boolean Functions of Two Variables in Tree-Like Architecture —

    Science.gov (United States)

    Uezu, Tatsuya; Kiyokawa, Shuji

    2016-06-01

    We investigate the supervised batch learning of Boolean functions expressed by a two-layer perceptron with a tree-like structure. We adopt continuous weights (spherical model) and the Gibbs algorithm. We study the Parity and And machines and two types of noise, input and output noise, together with the noiseless case. We assume that only the teacher suffers from noise. By using the replica method, we derive the saddle point equations for order parameters under the replica symmetric (RS) ansatz. We study the critical value αC of the loading rate α above which the learning phase exists for cases with and without noise. We find that αC is nonzero for the Parity machine, while it is zero for the And machine. We derive the exponents barβ of order parameters expressed as (α - α C)bar{β} when α is near to αC. Furthermore, in the Parity machine, when noise exists, we find a spin glass solution, in which the overlap between the teacher and student vectors is zero but that between student vectors is nonzero. We perform Markov chain Monte Carlo simulations by simulated annealing and also by exchange Monte Carlo simulations in both machines. In the Parity machine, we study the de Almeida-Thouless stability, and by comparing theoretical and numerical results, we find that there exist parameter regions where the RS solution is unstable, and that the spin glass solution is metastable or unstable. We also study asymptotic learning behavior for large α and derive the exponents hat{β } of order parameters expressed as α - hat{β } when α is large in both machines. By simulated annealing simulations, we confirm these results and conclude that learning takes place for the input noise case with any noise amplitude and for the output noise case when the probability that the teacher's output is reversed is less than one-half.

  17. Understanding the challenges of municipal tree planting

    Science.gov (United States)

    E.G. McPherson; R. Young

    2010-01-01

    Nine of the twelve largest cities in the U.S. have mayoral tree planting initiatives (TPIs), with pledges to plant nearly 20 million trees. Although executive-level support for trees has never been this widespread, many wonder if this support will endure as administrations change and budgets tighten. In an effort to share lessons learned from successes and setbacks, a...

  18. Alignment-free genome tree inference by learning group-specific distance metrics.

    Science.gov (United States)

    Patil, Kaustubh R; McHardy, Alice C

    2013-01-01

    Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.

  19. Frankincense production is determined by tree size and tapping frequency and intensity

    NARCIS (Netherlands)

    Eshete, A.; Sterck, F.J.; Bongers, F.

    2012-01-01

    Resin production in trees probably depends on trade-offs within the tree, its environment and on tapping activities. Frankincense, the highly esteemed resin from dry woodland frankincense trees of Boswellia papyrifera is exploited in traditional ways for millennia. New exploitation practices lead to

  20. Usefulness of problem tree, objective tree and logical framework ...

    African Journals Online (AJOL)

    The discussion has led to the conclusion that higher learning institutions are not adequately preparing graduates to face the increasing labor market demands in terms of skills and competitiveness. Having outlined the roots of the problem through the problem tree, the researchers proposed potential strategies to handle the ...

  1. Relating phylogenetic trees to transmission trees of infectious disease outbreaks.

    Science.gov (United States)

    Ypma, Rolf J F; van Ballegooijen, W Marijn; Wallinga, Jacco

    2013-11-01

    Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.

  2. Active learning strategies for the deduplication of electronic patient data using classification trees.

    Science.gov (United States)

    Sariyar, M; Borg, A; Pommerening, K

    2012-10-01

    Supervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Based on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002). On the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. In record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings. Copyright © 2012 Elsevier Inc. All rights reserved.

  3. Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood.

    Science.gov (United States)

    Wu, Yufeng

    2012-03-01

    Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets. © 2011 The Author. Evolution© 2011 The Society for the Study of Evolution.

  4. Univariate decision tree induction using maximum margin classification

    OpenAIRE

    Yıldız, Olcay Taner

    2012-01-01

    In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier performs at least as good as C4.5 and linear discriminant tree (LDT) with a similar time complexity. F...

  5. Mathematical foundations of event trees

    International Nuclear Information System (INIS)

    Papazoglou, Ioannis A.

    1998-01-01

    A mathematical foundation from first principles of event trees is presented. The main objective of this formulation is to offer a formal basis for developing automated computer assisted construction techniques for event trees. The mathematical theory of event trees is based on the correspondence between the paths of the tree and the elements of the outcome space of a joint event. The concept of a basic cylinder set is introduced to describe joint event outcomes conditional on specific outcomes of basic events or unconditional on the outcome of basic events. The concept of outcome space partition is used to describe the minimum amount of information intended to be preserved by the event tree representation. These concepts form the basis for an algorithm for systematic search for and generation of the most compact (reduced) form of an event tree consistent with the minimum amount of information the tree should preserve. This mathematical foundation allows for the development of techniques for automated generation of event trees corresponding to joint events which are formally described through other types of graphical models. Such a technique has been developed for complex systems described by functional blocks and it is reported elsewhere. On the quantification issue of event trees, a formal definition of a probability space corresponding to the event tree outcomes is provided. Finally, a short discussion is offered on the relationship of the presented mathematical theory with the more general use of event trees in reliability analysis of dynamic systems

  6. Interactive design of probability density functions for shape grammars

    KAUST Repository

    Dang, Minh; Lienhard, Stefan; Ceylan, Duygu; Neubert, Boris; Wonka, Peter; Pauly, Mark

    2015-01-01

    A shape grammar defines a procedural shape space containing a variety of models of the same class, e.g. buildings, trees, furniture, airplanes, bikes, etc. We present a framework that enables a user to interactively design a probability density

  7. Spacetime quantum probabilities II: Relativized descriptions and Popperian propensities

    Science.gov (United States)

    Mugur-Schächter, M.

    1992-02-01

    In the first part of this work(1) we have explicated the spacetime structure of the probabilistic organization of quantum mechanics. We have shown that each quantum mechanical state, in consequence of the spacetime characteristics of the epistemic operations by which the observer produces the state to be studied and the processes of qualification of these, brings in a tree-like spacetime structure, a “quantum mechanical probability tree,” that transgresses the theory of probabilities as it now stands. In this second part we develop the general implications of these results. Starting from the lowest level of cognitive action and creating an appropriate symbolism, we construct a “relativizing epistemic syntax,” a “general method of relativized conceptualization” where—systematically—each description is explicitly referred to the epistemic operations by which the observer produces the entity to be described and obtains qualifications of it. The method generates a typology of increasingly complex relativized descriptions where the question of realism admits of a particularly clear pronouncement. Inside this typology the epistemic processes that lie—UNIVERSALLY—at the basis of any conceptualization, reveal a tree-like spacetime structure. It appears in particular that the spacetime structure of the relativized representation of a probabilistic description, which transgresses the nowadays theory of probabilities, is the general mould of which the quantum mechanical probability trees are only particular realizations. This entails a clear definition of the descriptional status of quantum mechanics. While the recognition of the universal cognitive content of the quantum mechanical formalism opens up vistas toward mathematical developments of the relativizing epistemic syntax. The relativized representation of a probabilistic description leads with inner necessity to a “morphic” interpretation of probabilities that can be regarded as a formalized and

  8. Coalescent methods for estimating phylogenetic trees.

    Science.gov (United States)

    Liu, Liang; Yu, Lili; Kubatko, Laura; Pearl, Dennis K; Edwards, Scott V

    2009-10-01

    We review recent models to estimate phylogenetic trees under the multispecies coalescent. Although the distinction between gene trees and species trees has come to the fore of phylogenetics, only recently have methods been developed that explicitly estimate species trees. Of the several factors that can cause gene tree heterogeneity and discordance with the species tree, deep coalescence due to random genetic drift in branches of the species tree has been modeled most thoroughly. Bayesian approaches to estimating species trees utilizes two likelihood functions, one of which has been widely used in traditional phylogenetics and involves the model of nucleotide substitution, and the second of which is less familiar to phylogeneticists and involves the probability distribution of gene trees given a species tree. Other recent parametric and nonparametric methods for estimating species trees involve parsimony criteria, summary statistics, supertree and consensus methods. Species tree approaches are an appropriate goal for systematics, appear to work well in some cases where concatenation can be misleading, and suggest that sampling many independent loci will be paramount. Such methods can also be challenging to implement because of the complexity of the models and computational time. In addition, further elaboration of the simplest of coalescent models will be required to incorporate commonly known issues such as deviation from the molecular clock, gene flow and other genetic forces.

  9. Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

    Science.gov (United States)

    Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

    2012-01-01

    In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999

  10. Risk estimation using probability machines

    Science.gov (United States)

    2014-01-01

    Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

  11. Undergraduate Students’ Difficulties in Reading and Constructing Phylogenetic Tree

    Science.gov (United States)

    Sa'adah, S.; Tapilouw, F. S.; Hidayat, T.

    2017-02-01

    Representation is a very important communication tool to communicate scientific concepts. Biologists produce phylogenetic representation to express their understanding of evolutionary relationships. The phylogenetic tree is visual representation depict a hypothesis about the evolutionary relationship and widely used in the biological sciences. Phylogenetic tree currently growing for many disciplines in biology. Consequently, learning about phylogenetic tree become an important part of biological education and an interesting area for biology education research. However, research showed many students often struggle with interpreting the information that phylogenetic trees depict. The purpose of this study was to investigate undergraduate students’ difficulties in reading and constructing a phylogenetic tree. The method of this study is a descriptive method. In this study, we used questionnaires, interviews, multiple choice and open-ended questions, reflective journals and observations. The findings showed students experiencing difficulties, especially in constructing a phylogenetic tree. The students’ responds indicated that main reasons for difficulties in constructing a phylogenetic tree are difficult to placing taxa in a phylogenetic tree based on the data provided so that the phylogenetic tree constructed does not describe the actual evolutionary relationship (incorrect relatedness). Students also have difficulties in determining the sister group, character synapomorphy, autapomorphy from data provided (character table) and comparing among phylogenetic tree. According to them building the phylogenetic tree is more difficult than reading the phylogenetic tree. Finding this studies provide information to undergraduate instructor and students to overcome learning difficulties of reading and constructing phylogenetic tree.

  12. Rooted triple consensus and anomalous gene trees

    Directory of Open Access Journals (Sweden)

    Schmidt Heiko A

    2008-04-01

    Full Text Available Abstract Background Anomalous gene trees (AGTs are gene trees with a topology different from a species tree that are more probable to observe than congruent gene trees. In this paper we propose a rooted triple approach to finding the correct species tree in the presence of AGTs. Results Based on simulated data we show that our method outperforms the extended majority rule consensus strategy, while still resolving the species tree. Applying both methods to a metazoan data set of 216 genes, we tested whether AGTs substantially interfere with the reconstruction of the metazoan phylogeny. Conclusion Evidence of AGTs was not found in this data set, suggesting that erroneously reconstructed gene trees are the most significant challenge in the reconstruction of phylogenetic relationships among species with current data. The new method does however rule out the erroneous reconstruction of deep or poorly resolved splits in the presence of lineage sorting.

  13. Indexing Density Models for Incremental Learning and Anytime Classification on Data Streams

    DEFF Research Database (Denmark)

    Seidl, Thomas; Assent, Ira; Kranen, Philipp

    2009-01-01

    Classification of streaming data faces three basic challenges: it has to deal with huge amounts of data, the varying time between two stream data items must be used best possible (anytime classification) and additional training data must be incrementally learned (anytime learning) for applying...... to the individual object to be classified) a hierarchy of mixture densities that represent kernel density estimators at successively coarser levels. Our probability density queries together with novel classification improvement strategies provide the necessary information for very effective classification at any...... point of interruption. Moreover, we propose a novel evaluation method for anytime classification using Poisson streams and demonstrate the anytime learning performance of the Bayes tree....

  14. External validity of individual differences in multiple cue probability learning: The case of pilot training

    Directory of Open Access Journals (Sweden)

    Nadine Matton

    2013-09-01

    Full Text Available Individuals differ in their ability to deal with unpredictable environments. Could impaired performances on learning an unpredictable cue-criteria relationship in a laboratory task be associated with impaired learning of complex skills in a natural setting? We focused on a multiple-cue probability learning (MCPL laboratory task and on the natural setting of pilot training. We used data from three selection sessions and from the three corresponding selected pilot student classes of a national airline pilot selection and training system. First, applicants took an MCPL task at the selection stage (N=556; N=701; N=412. Then, pilot trainees selected from the applicant pools (N=44; N=60; N=28 followed the training for 2.5 to 3 yrs. Differences in final MCPL performance were associated with pilot training difficulties. Indeed, poor MCPL performers experienced almost twice as many pilot training difficulties as better MCPL performers (44.0% and 25.0%, respectively.

  15. Hide and vanish: data sets where the most parsimonious tree is known but hard to find, and their implications for tree search methods.

    Science.gov (United States)

    Goloboff, Pablo A

    2014-10-01

    Three different types of data sets, for which the uniquely most parsimonious tree can be known exactly but is hard to find with heuristic tree search methods, are studied. Tree searches are complicated more by the shape of the tree landscape (i.e. the distribution of homoplasy on different trees) than by the sheer abundance of homoplasy or character conflict. Data sets of Type 1 are those constructed by Radel et al. (2013). Data sets of Type 2 present a very rugged landscape, with narrow peaks and valleys, but relatively low amounts of homoplasy. For such a tree landscape, subjecting the trees to TBR and saving suboptimal trees produces much better results when the sequence of clipping for the tree branches is randomized instead of fixed. An unexpected finding for data sets of Types 1 and 2 is that starting a search from a random tree instead of a random addition sequence Wagner tree may increase the probability that the search finds the most parsimonious tree; a small artificial example where these probabilities can be calculated exactly is presented. Data sets of Type 3, the most difficult data sets studied here, comprise only congruent characters, and a single island with only one most parsimonious tree. Even if there is a single island, missing entries create a very flat landscape which is difficult to traverse with tree search algorithms because the number of equally parsimonious trees that need to be saved and swapped to effectively move around the plateaus is too large. Minor modifications of the parameters of tree drifting, ratchet, and sectorial searches allow travelling around these plateaus much more efficiently than saving and swapping large numbers of equally parsimonious trees with TBR. For these data sets, two new related criteria for selecting taxon addition sequences in Wagner trees (the "selected" and "informative" addition sequences) produce much better results than the standard random or closest addition sequences. These new methods for Wagner

  16. Probability Distribution of Long-run Indiscriminate Felling of Trees in ...

    African Journals Online (AJOL)

    Bright

    conditionally independent of every prior state given the current state (Obodos, ... of events or experiments in which the probability of occurrence for an event ... represent the exhaustive and mutually exclusive outcomes (states) of a system at.

  17. Does probability of occurrence relate to population dynamics?

    Science.gov (United States)

    Thuiller, Wilfried; Münkemüller, Tamara; Schiffers, Katja H; Georges, Damien; Dullinger, Stefan; Eckhart, Vincent M; Edwards, Thomas C; Gravel, Dominique; Kunstler, Georges; Merow, Cory; Moore, Kara; Piedallu, Christian; Vissault, Steve; Zimmermann, Niklaus E; Zurell, Damaris; Schurr, Frank M

    2014-12-01

    Hutchinson defined species' realized niche as the set of environmental conditions in which populations can persist in the presence of competitors. In terms of demography, the realized niche corresponds to the environments where the intrinsic growth rate ( r ) of populations is positive. Observed species occurrences should reflect the realized niche when additional processes like dispersal and local extinction lags do not have overwhelming effects. Despite the foundational nature of these ideas, quantitative assessments of the relationship between range-wide demographic performance and occurrence probability have not been made. This assessment is needed both to improve our conceptual understanding of species' niches and ranges and to develop reliable mechanistic models of species geographic distributions that incorporate demography and species interactions. The objective of this study is to analyse how demographic parameters (intrinsic growth rate r and carrying capacity K ) and population density ( N ) relate to occurrence probability ( P occ ). We hypothesized that these relationships vary with species' competitive ability. Demographic parameters, density, and occurrence probability were estimated for 108 tree species from four temperate forest inventory surveys (Québec, Western US, France and Switzerland). We used published information of shade tolerance as indicators of light competition strategy, assuming that high tolerance denotes high competitive capacity in stable forest environments. Interestingly, relationships between demographic parameters and occurrence probability did not vary substantially across degrees of shade tolerance and regions. Although they were influenced by the uncertainty in the estimation of the demographic parameters, we found that r was generally negatively correlated with P occ , while N, and for most regions K, was generally positively correlated with P occ . Thus, in temperate forest trees the regions of highest occurrence

  18. FTA, Fault Tree Analysis for Minimal Cut Sets, Graphics for CALCOMP

    International Nuclear Information System (INIS)

    Van Slyke, W.J.; Griffing, D.E.; Diven, J.

    1978-01-01

    1 - Description of problem or function: The FTA (Fault Tree Analysis) system was designed to predict probabilities of the modes of failure for complex systems and to graphically present the structure of systems. There are three programs in the system. Program ALLCUTS performs the calculations. Program KILMER constructs a CalComp plot file of the system fault tree. Program BRANCH builds a cross-reference list of the system fault tree. 2 - Method of solution: ALLCUTS employs a top-down set expansion algorithm to find fault tree cut-sets and then optionally calculates their probability using a currently accepted cut-set quantification method. The methodology is adapted from that in WASH-1400 (draft), August 1974. 3 - Restrictions on the complexity of the problem: Maxima of: 175 basic events, 425 rate events. ALLCUTS may be expanded to solve larger problems depending on available core memory

  19. Improved Membership Probability for Moving Groups: Bayesian and Machine Learning Approaches

    Science.gov (United States)

    Lee, Jinhee; Song, Inseok

    2018-01-01

    Gravitationally unbound loose stellar associations (i.e., young nearby moving groups: moving groups hereafter) have been intensively explored because they are important in planet and disk formation studies, exoplanet imaging, and age calibration. Among the many efforts devoted to the search for moving group members, a Bayesian approach (e.g.,using the code BANYAN) has become popular recently because of the many advantages it offers. However, the resultant membership probability needs to be carefully adopted because of its sensitive dependence on input models. In this study, we have developed an improved membership calculation tool focusing on the beta-Pic moving group. We made three improvements for building models used in BANYAN II: (1) updating a list of accepted members by re-assessing memberships in terms of position, motion, and age, (2) investigating member distribution functions in XYZ, and (3) exploring field star distribution functions in XYZUVW. Our improved tool can change membership probability up to 70%. Membership probability is critical and must be better defined. For example, our code identifies only one third of the candidate members in SIMBAD that are believed to be kinematically associated with beta-Pic moving group.Additionally, we performed cluster analysis of young nearby stars using an unsupervised machine learning approach. As more moving groups and their members are identified, the complexity and ambiguity in moving group configuration has been increased. To clarify this issue, we analyzed ~4,000 X-ray bright young stellar candidates. Here, we present the preliminary results. By re-identifying moving groups with the least human intervention, we expect to understand the composition of the solar neighborhood. Moreover better defined moving group membership will help us understand star formation and evolution in relatively low density environments; especially for the low-mass stars which will be identified in the coming Gaia release.

  20. Counterexamples in probability

    CERN Document Server

    Stoyanov, Jordan M

    2013-01-01

    While most mathematical examples illustrate the truth of a statement, counterexamples demonstrate a statement's falsity. Enjoyable topics of study, counterexamples are valuable tools for teaching and learning. The definitive book on the subject in regards to probability, this third edition features the author's revisions and corrections plus a substantial new appendix.

  1. Decision tree modeling using R.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-08-01

    In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

  2. Probably not future prediction using probability and statistical inference

    CERN Document Server

    Dworsky, Lawrence N

    2008-01-01

    An engaging, entertaining, and informative introduction to probability and prediction in our everyday lives Although Probably Not deals with probability and statistics, it is not heavily mathematical and is not filled with complex derivations, proofs, and theoretical problem sets. This book unveils the world of statistics through questions such as what is known based upon the information at hand and what can be expected to happen. While learning essential concepts including "the confidence factor" and "random walks," readers will be entertained and intrigued as they move from chapter to chapter. Moreover, the author provides a foundation of basic principles to guide decision making in almost all facets of life including playing games, developing winning business strategies, and managing personal finances. Much of the book is organized around easy-to-follow examples that address common, everyday issues such as: How travel time is affected by congestion, driving speed, and traffic lights Why different gambling ...

  3. Distribution of cavity trees in midwestern old-growth and second-growth forests

    Science.gov (United States)

    Zhaofei Fan; Stephen R. Shifley; Martin A. Spetich; Frank R. Thompson; David R. Larsen

    2003-01-01

    We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in...

  4. Learning Extended Finite State Machines

    Science.gov (United States)

    Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard

    2014-01-01

    We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

  5. Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

    Science.gov (United States)

    Liu, Yang; Traskin, Mikhail; Lorch, Scott A; George, Edward I; Small, Dylan

    2015-03-01

    A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.

  6. Reversing a tree regeneration crisis in an endangered ecoregion.

    Science.gov (United States)

    Fischer, Joern; Stott, Jenny; Zerger, Andre; Warren, Garth; Sherren, Kate; Forrester, Robert I

    2009-06-23

    Global food demand is growing rapidly. Livestock grazing can provide a valuable source of protein, but conventional grazing is often unsustainable. We studied an 800,000-ha section of a threatened ecoregion in southeastern Australia. Conventional management in the region involves continuous livestock grazing with few rest periods and regular fertilizer application. By using remotely sensed data on tree cover and extensive field data on livestock grazing regimes, soil chemistry, tree diameters, and tree regeneration, we show that the region is facing a tree regeneration crisis. Under conventional management, across the region, millions of hectares of land currently supporting tens of millions of trees will be treeless within decades from now. This would have severe negative ramifications for biodiversity and key ecosystem services, including water infiltration and shade provision for livestock. However, we identified an unexpected win-win solution for tree regeneration and commercial grazing. A relatively new practice in the region is fast-rotational grazing, characterized by prolonged rest periods in between short, intensive grazing events. The probability of regeneration under fast-rotational grazing was up to 4-fold higher than under conventional grazing, and it did not differ significantly from the probability of regeneration in ungrazed areas. In addition, trees were more likely to regenerate where soil nutrient levels were low. These findings suggest that the tree regeneration crisis can be reversed by applying low-input, fast-rotational grazing. New policy settings supporting these practices could signal a turning point for the region, from ecological decline to ecological recovery.

  7. A machine learning approach to galaxy-LSS classification - I. Imprints on halo merger trees

    Science.gov (United States)

    Hui, Jianan; Aragon, Miguel; Cui, Xinping; Flegal, James M.

    2018-04-01

    The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here, we present a machine learning approach to study imprints of environmental effects on the mass assembly of haloes. We present a galaxy-LSS machine learning classifier based on galaxy properties sensitive to the environment. We then use the classifier to assess the relevance of each property. Correlations between galaxy properties and their cosmic environment can be used to predict galaxy membership to void/wall or filament/cluster with an accuracy of 93 per cent. Our study unveils environmental information encoded in properties of haloes not normally considered directly dependent on the cosmic environment such as merger history and complexity. Understanding the physical mechanism by which the cosmic web is imprinted in a halo can lead to significant improvements in galaxy formation models. This is accomplished by extracting features from galaxy properties and merger trees, computing feature scores for each feature and then applying support vector machine (SVM) to different feature sets. To this end, we have discovered that the shape and depth of the merger tree, formation time, and density of the galaxy are strongly associated with the cosmic environment. We describe a significant improvement in the original classification algorithm by performing LU decomposition of the distance matrix computed by the feature vectors and then using the output of the decomposition as input vectors for SVM.

  8. Are baboons learning "orthographic" representations? Probably not.

    Directory of Open Access Journals (Sweden)

    Maja Linke

    Full Text Available The ability of Baboons (papio papio to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. However, given that pigeons (columba livia, whose brain morphology is drastically different, can also be trained to distinguish between English words and nonwords, it appears that a less species-specific learning algorithm may be required to explain this behavior. Accordingly, we examined whether the learning model of Rescorla and Wagner, which has proved to be amazingly fruitful in understanding animal and human learning could account for these data. We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior-including key lexical similarity effects and the ups and downs in accuracy as learning unfolds-with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. By contrast, our results suggest that in learning to perform lexical decision tasks, baboons and pigeons do not construct a hierarchy of lexical units. Rather, they make optimal use of low-level information obtained through the massively parallel processing of gradient orientation features. Accordingly, we suggest that reading in humans first involves initially learning a high-level system building on letter representations acquired from explicit instruction in literacy, which is then integrated into a conventionalized oral communication system, and that like the latter, fluent reading involves the massively parallel processing of the low-level features encoding semantic contrasts.

  9. Empirical relationships between tree fall and landscape-level amounts of logging and fire.

    Science.gov (United States)

    Lindenmayer, David B; Blanchard, Wade; Blair, David; McBurney, Lachlan; Stein, John; Banks, Sam C

    2018-01-01

    Large old trees are critically important keystone structures in forest ecosystems globally. Populations of these trees are also in rapid decline in many forest ecosystems, making it important to quantify the factors that influence their dynamics at different spatial scales. Large old trees often occur in forest landscapes also subject to fire and logging. However, the effects on the risk of collapse of large old trees of the amount of logging and fire in the surrounding landscape are not well understood. Using an 18-year study in the Mountain Ash (Eucalyptus regnans) forests of the Central Highlands of Victoria, we quantify relationships between the probability of collapse of large old hollow-bearing trees at a site and the amount of logging and the amount of fire in the surrounding landscape. We found the probability of collapse increased with an increasing amount of logged forest in the surrounding landscape. It also increased with a greater amount of burned area in the surrounding landscape, particularly for trees in highly advanced stages of decay. The most likely explanation for elevated tree fall with an increasing amount of logged or burned areas in the surrounding landscape is change in wind movement patterns associated with cutblocks or burned areas. Previous studies show that large old hollow-bearing trees are already at high risk of collapse in our study area. New analyses presented here indicate that additional logging operations in the surrounding landscape will further elevate that risk. Current logging prescriptions require the protection of large old hollow-bearing trees on cutblocks. We suggest that efforts to reduce the probability of collapse of large old hollow-bearing trees on unlogged sites will demand careful landscape planning to limit the amount of timber harvesting in the surrounding landscape.

  10. Analytical propagation of uncertainties through fault trees

    International Nuclear Information System (INIS)

    Hauptmanns, Ulrich

    2002-01-01

    A method is presented which enables one to propagate uncertainties described by uniform probability density functions through fault trees. The approach is analytical. It is based on calculating the expected value and the variance of the top event probability. These two parameters are then equated with the corresponding ones of a beta-distribution. An example calculation comparing the analytically calculated beta-pdf (probability density function) with the top event pdf obtained using the Monte-Carlo method shows excellent agreement at a much lower expense of computing time

  11. IND - THE IND DECISION TREE PACKAGE

    Science.gov (United States)

    Buntine, W.

    1994-01-01

    A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which has good prediction of classes on new data. Standard algorithms are CART (by Breiman Friedman, Olshen and Stone) and ID3 and its successor C4 (by Quinlan). As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis. IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or it may be omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection. IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in a CART-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like the early version of C4. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves. IND also comes with a comprehensive experimental control suite. IND consists of four basic kinds of routines: data manipulation routines, tree generation routines, tree testing routines, and tree display routines. The data manipulation routines are used to partition a single large data set into smaller training and test sets. The

  12. Tritium concentrations in tree ring cellulose

    International Nuclear Information System (INIS)

    Kaji, Toshio; Momoshima, Noriyuki; Takashima, Yoshimasa.

    1989-01-01

    Measurements of tritium (tissue bound tritium; TBT) concentration in tree rings are presented and discussed. Such measurement is expected to provide a useful means of estimating the tritium level in the environment in the past. The concentration of tritium bound in the tissue (TBT) in a tree ring considered to reflect the environmental tritium level in the area at the time of the formation of the ring, while the concentration of tritium in the free water in the tissue represents the current environmental tritium level. First, tritium concentration in tree ring cellulose sampled from a cedar tree grown in a typical environment in Fukuoka Prefecture is compared with the tritium concentration in precipitation in Tokyo. Results show that the year-to-year variations in the tritium concentration in the tree rings agree well with those in precipitation. The maximum concentration, which occurred in 1963, is attibuted to atmospheric nuclear testing which was performed frequently during the 1961 - 1963 period. Measurement is also made of the tritium concentration in tree ring cellulose sampled from a pine tree grown near the Isotope Center of Kyushu University (Fukuoka). Results indicate that the background level is higher probably due to the release of tritium from the facilities around the pine tree. Thus, measurement of tritium in tree ring cellulose clearly shows the year-to-year variation in the tritium concentration in the atmosphere. (N.K.)

  13. Gap probability - Measurements and models of a pecan orchard

    Science.gov (United States)

    Strahler, Alan H.; Li, Xiaowen; Moody, Aaron; Liu, YI

    1992-01-01

    Measurements and models are compared for gap probability in a pecan orchard. Measurements are based on panoramic photographs of 50* by 135 view angle made under the canopy looking upwards at regular positions along transects between orchard trees. The gap probability model is driven by geometric parameters at two levels-crown and leaf. Crown level parameters include the shape of the crown envelope and spacing of crowns; leaf level parameters include leaf size and shape, leaf area index, and leaf angle, all as functions of canopy position.

  14. Wind-Induced Reconfigurations in Flexible Branched Trees

    Science.gov (United States)

    Ojo, Oluwafemi; Shoele, Kourosh

    2017-11-01

    Wind induced stresses are the major mechanical cause of failure in trees. We know that the branching mechanism has an important effect on the stress distribution and stability of a tree in the wind. Eloy in PRL 2011, showed that Leonardo da Vinci's original observation which states the total cross section of branches is conserved across branching nodes is the best configuration for resisting wind-induced fracture in rigid trees. However, prediction of the fracture risk and pattern of a tree is also a function of their reconfiguration capabilities and how they mitigate large wind-induced stresses. In this studies through developing an efficient numerical simulation of flexible branched trees, we explore the role of the tree flexibility on the optimal branching. Our results show that the probability of a tree breaking at any point depends on both the cross-section changes in the branching nodes and the level of tree flexibility. It is found that the branching mechanism based on Leonardo da Vinci's original observation leads to a uniform stress distribution over a wide range of flexibilities but the pattern changes for more flexible systems.

  15. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

    Science.gov (United States)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

    2012-03-15

    To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

  16. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    Energy Technology Data Exchange (ETDEWEB)

    Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

    2012-03-15

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  17. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    International Nuclear Information System (INIS)

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

    2012-01-01

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  18. Mobbing call experiment suggests the enhancement of forest bird movement by tree cover in urban landscapes across seasons

    Directory of Open Access Journals (Sweden)

    Atsushi Shimazaki

    2017-06-01

    Full Text Available Local scale movement behavior is an important basis to predict large-scale bird movements in heterogeneous landscapes. Here we conducted playback experiments using mobbing calls to estimate the probability that forest birds would cross a 50-m urban area during three seasons (breeding, dispersal, and wintering seasons with varying amounts of tree cover, building area, and electric wire density. We examined the responses of four forest resident species: Marsh Tit (Poecile palustris, Varied Tit (Sittiparus varius, Japanese Tit (P. minor, and Eurasian Nuthatch (Sitta europaea in central Hokkaido, northern Japan. We carried out and analyzed 250 playback experiments that attracted 618 individuals. Our results showed that tree cover increased the crossing probability of three species other than Varied Tit. Building area and electric wire density had no detectable effect on crossing probability for four species. Seasonal difference in the crossing probability was found only for Varied Tit, and the probability was the highest in the breeding season. These results suggest that the positive effect of tree cover on the crossing probability would be consistent across seasons. We therefore conclude that planting trees would be an effective way to promote forest bird movement within an urban landscape.

  19. Application Research of Fault Tree Analysis in Grid Communication System Corrective Maintenance

    Science.gov (United States)

    Wang, Jian; Yang, Zhenwei; Kang, Mei

    2018-01-01

    This paper attempts to apply the fault tree analysis method to the corrective maintenance field of grid communication system. Through the establishment of the fault tree model of typical system and the engineering experience, the fault tree analysis theory is used to analyze the fault tree model, which contains the field of structural function, probability importance and so on. The results show that the fault tree analysis can realize fast positioning and well repairing of the system. Meanwhile, it finds that the analysis method of fault tree has some guiding significance to the reliability researching and upgrading f the system.

  20. Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia.

    Science.gov (United States)

    Gabryś, Hubert S; Buettner, Florian; Sterzing, Florian; Hauswald, Henrik; Bangert, Mark

    2018-01-01

    The purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands. A cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0-6 months (early), 6-15 months (late), 15-24 months (long-term), and at any time (a longitudinal model) after radiotherapy. Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC) of univariate logistic regression models. The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively. We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post hoc analysis. NTCP models based on the parotid mean dose failed to predict xerostomia (AUCs  0.85), dose gradients in the right-left (AUCs > 0.78), and the anterior-posterior (AUCs > 0.72) direction. Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose-volume histogram (DVH) spread with the generalization AUCs ranging from 0.74 to 0.88. On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection. We found no advantage in using data cleaning or class balancing methods. We demonstrated that incorporation of organ- and dose-shape descriptors is beneficial for xerostomia prediction in highly conformal radiotherapy treatments. Due to strong reliance on patient-specific, dose-independent factors, our results underscore the need for development of personalized data-driven risk profiles for NTCP models of xerostomia. The facilitated

  1. Human reliability analysis using event trees

    International Nuclear Information System (INIS)

    Heslinga, G.

    1983-01-01

    The shut-down procedure of a technologically complex installation as a nuclear power plant consists of a lot of human actions, some of which have to be performed several times. The procedure is regarded as a chain of modules of specific actions, some of which are analyzed separately. The analysis is carried out by making a Human Reliability Analysis event tree (HRA event tree) of each action, breaking down each action into small elementary steps. The application of event trees in human reliability analysis implies more difficulties than in the case of technical systems where event trees were mainly used until now. The most important reason is that the operator is able to recover a wrong performance; memory influences play a significant role. In this study these difficulties are dealt with theoretically. The following conclusions can be drawn: (1) in principle event trees may be used in human reliability analysis; (2) although in practice the operator will recover his fault partly, theoretically this can be described as starting the whole event tree again; (3) compact formulas have been derived, by which the probability of reaching a specific failure consequence on passing through the HRA event tree after several times of recovery is to be calculated. (orig.)

  2. On algorithm for building of optimal α-decision trees

    KAUST Repository

    Alkhalid, Abdulaziz

    2010-01-01

    The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.

  3. On the number of vertices of each rank in phylogenetic trees and their generalizations

    OpenAIRE

    Bóna, Miklós

    2015-01-01

    We find surprisingly simple formulas for the limiting probability that the rank of a randomly selected vertex in a randomly selected phylogenetic tree or generalized phylogenetic tree is a given integer.

  4. Interpreting CNNs via Decision Trees

    OpenAIRE

    Zhang, Quanshi; Yang, Yu; Wu, Ying Nian; Zhu, Song-Chun

    2018-01-01

    This paper presents a method to learn a decision tree to quantitatively explain the logic of each prediction of a pre-trained convolutional neural networks (CNNs). Our method boosts the following two aspects of network interpretability. 1) In the CNN, each filter in a high conv-layer must represent a specific object part, instead of describing mixed patterns without clear meanings. 2) People can explain each specific prediction made by the CNN at the semantic level using a decision tree, i.e....

  5. Does probability of occurrence relate to population dynamics?

    Science.gov (United States)

    Thuiller, Wilfried; Münkemüller, Tamara; Schiffers, Katja H.; Georges, Damien; Dullinger, Stefan; Eckhart, Vincent M.; Edwards, Thomas C.; Gravel, Dominique; Kunstler, Georges; Merow, Cory; Moore, Kara; Piedallu, Christian; Vissault, Steve; Zimmermann, Niklaus E.; Zurell, Damaris; Schurr, Frank M.

    2014-01-01

    Hutchinson defined species' realized niche as the set of environmental conditions in which populations can persist in the presence of competitors. In terms of demography, the realized niche corresponds to the environments where the intrinsic growth rate (r) of populations is positive. Observed species occurrences should reflect the realized niche when additional processes like dispersal and local extinction lags do not have overwhelming effects. Despite the foundational nature of these ideas, quantitative assessments of the relationship between range-wide demographic performance and occurrence probability have not been made. This assessment is needed both to improve our conceptual understanding of species' niches and ranges and to develop reliable mechanistic models of species geographic distributions that incorporate demography and species interactions.The objective of this study is to analyse how demographic parameters (intrinsic growth rate r and carrying capacity K ) and population density (N ) relate to occurrence probability (Pocc ). We hypothesized that these relationships vary with species' competitive ability. Demographic parameters, density, and occurrence probability were estimated for 108 tree species from four temperate forest inventory surveys (Québec, western USA, France and Switzerland). We used published information of shade tolerance as indicators of light competition strategy, assuming that high tolerance denotes high competitive capacity in stable forest environments.Interestingly, relationships between demographic parameters and occurrence probability did not vary substantially across degrees of shade tolerance and regions. Although they were influenced by the uncertainty in the estimation of the demographic parameters, we found that r was generally negatively correlated with Pocc, while N, and for most regions K, was generally positively correlated with Pocc. Thus, in temperate forest trees the regions of highest occurrence

  6. Deduction of probable events of lateral gene transfer through comparison of phylogenetic trees by recursive consolidation and rearrangement

    Directory of Open Access Journals (Sweden)

    Charlebois Robert L

    2005-04-01

    Full Text Available Abstract Background When organismal phylogenies based on sequences of single marker genes are poorly resolved, a logical approach is to add more markers, on the assumption that weak but congruent phylogenetic signal will be reinforced in such multigene trees. Such approaches are valid only when the several markers indeed have identical phylogenies, an issue which many multigene methods (such as the use of concatenated gene sequences or the assembly of supertrees do not directly address. Indeed, even when the true history is a mixture of vertical descent for some genes and lateral gene transfer (LGT for others, such methods produce unique topologies. Results We have developed software that aims to extract evidence for vertical and lateral inheritance from a set of gene trees compared against an arbitrary reference tree. This evidence is then displayed as a synthesis showing support over the tree for vertical inheritance, overlaid with explicit lateral gene transfer (LGT events inferred to have occurred over the history of the tree. Like splits-tree methods, one can thus identify nodes at which conflict occurs. Additionally one can make reasonable inferences about vertical and lateral signal, assigning putative donors and recipients. Conclusion A tool such as ours can serve to explore the reticulated dimensionality of molecular evolution, by dissecting vertical and lateral inheritance at high resolution. By this, we mean that individual nodes can be examined not only for congruence, but also for coherence in light of LGT. We assert that our tools will facilitate the comparison of phylogenetic trees, and the interpretation of conflicting data.

  7. The Efficacy of Consensus Tree Methods for Summarizing Phylogenetic Relationships from a Posterior Sample of Trees Estimated from Morphological Data.

    Science.gov (United States)

    O'Reilly, Joseph E; Donoghue, Philip C J

    2018-03-01

    Consensus trees are required to summarize trees obtained through MCMC sampling of a posterior distribution, providing an overview of the distribution of estimated parameters such as topology, branch lengths, and divergence times. Numerous consensus tree construction methods are available, each presenting a different interpretation of the tree sample. The rise of morphological clock and sampled-ancestor methods of divergence time estimation, in which times and topology are coestimated, has increased the popularity of the maximum clade credibility (MCC) consensus tree method. The MCC method assumes that the sampled, fully resolved topology with the highest clade credibility is an adequate summary of the most probable clades, with parameter estimates from compatible sampled trees used to obtain the marginal distributions of parameters such as clade ages and branch lengths. Using both simulated and empirical data, we demonstrate that MCC trees, and trees constructed using the similar maximum a posteriori (MAP) method, often include poorly supported and incorrect clades when summarizing diffuse posterior samples of trees. We demonstrate that the paucity of information in morphological data sets contributes to the inability of MCC and MAP trees to accurately summarise of the posterior distribution. Conversely, majority-rule consensus (MRC) trees represent a lower proportion of incorrect nodes when summarizing the same posterior samples of trees. Thus, we advocate the use of MRC trees, in place of MCC or MAP trees, in attempts to summarize the results of Bayesian phylogenetic analyses of morphological data.

  8. Systems analysis approach to probabilistic modeling of fault trees

    International Nuclear Information System (INIS)

    Bartholomew, R.J.; Qualls, C.R.

    1985-01-01

    A method of probabilistic modeling of fault tree logic combined with stochastic process theory (Markov modeling) has been developed. Systems are then quantitatively analyzed probabilistically in terms of their failure mechanisms including common cause/common mode effects and time dependent failure and/or repair rate effects that include synergistic and propagational mechanisms. The modeling procedure results in a state vector set of first order, linear, inhomogeneous, differential equations describing the time dependent probabilities of failure described by the fault tree. The solutions of this Failure Mode State Variable (FMSV) model are cumulative probability distribution functions of the system. A method of appropriate synthesis of subsystems to form larger systems is developed and applied to practical nuclear power safety systems

  9. RAFT: a computer program for fault tree risk calculations

    International Nuclear Information System (INIS)

    Seybold, G.D.

    1977-11-01

    A description and user instructions are presented for RAFT, a FORTRAN computer code for calculation of a risk measure for fault tree cut sets. RAFT calculates release quantities and a risk measure based on the product of probability and release quantity for cut sets of fault trees modeling the accidental release of radioactive material from a nuclear fuel cycle facility. Cut sets and their probabilities are supplied as input to RAFT from an external fault tree analysis code. Using the total inventory available of radioactive material, along with release fractions for each event in a cut set, the release terms are calculated for each cut set. Each release term is multiplied by the cut set probability to yield the cut set risk measure. RAFT orders the dominant cut sets on the risk measure. The total risk measure of processed cut sets and their fractional contributions are supplied as output. Input options are available to eliminate redundant cut sets, apply threshold values on cut set probability and risk, and control the total number of cut sets output. Hash addressing is used to remove redundant cut sets from the analysis. Computer hardware and software restrictions are given along with a sample problem and cross-reference table of the code. Except for the use of file management utilities, RAFT is written exclusively in FORTRAN language and is operational on a Control Data, CYBER 74-18--series computer system. 4 figures

  10. A knowledge-based approach to the evaluation of fault trees

    International Nuclear Information System (INIS)

    Hwang, Yann-Jong; Chow, Louis R.; Huang, Henry C.

    1996-01-01

    A list of critical components is useful for determining the potential problems of a complex system. However, to find this list through evaluating the fault trees is expensive and time consuming. This paper intends to propose an integrated software program which consists of a fault tree constructor, a knowledge base, and an efficient algorithm for evaluating minimal cut sets of a large fault tree. The proposed algorithm uses the approaches of top-down heuristic searching and the probability-based truncation. That makes the evaluation of fault trees obviously efficient and provides critical components for solving the potential problems in complex systems. Finally, some practical fault trees are included to illustrate the results

  11. Quantitative analysis of a fault tree with priority AND gates

    International Nuclear Information System (INIS)

    Yuge, T.; Yanagi, S.

    2008-01-01

    A method for calculating the exact top event probability of a fault tree with priority AND gates and repeated basic events is proposed when the minimal cut sets are given. A priority AND gate is an AND gate where the input events must occur in a prescribed order for the occurrence of the output event. It is known that the top event probability of such a dynamic fault tree is obtained by converting the tree into an equivalent Markov model. However, this method is not realistic for a complex system model because the number of states which should be considered in the Markov analysis increases explosively as the number of basic events increases. To overcome the shortcomings of the Markov model, we propose an alternative method to obtain the top event probability in this paper. We assume that the basic events occur independently, exponentially distributed, and the component whose failure corresponds to the occurrence of the basic event is non-repairable. First, we obtain the probability of occurrence of the output event of a single priority AND gate by Markov analysis. Then, the top event probability is given by a cut set approach and the inclusion-exclusion formula. An efficient procedure to obtain the probabilities corresponding to logical products in the inclusion-exclusion formula is proposed. The logical product which is composed of two or more priority AND gates having at least one common basic event as their inputs is transformed into the sum of disjoint events which are equivalent to a priority AND gate in the procedure. Numerical examples show that our method works well for complex systems

  12. Automated fault tree analysis: the GRAFTER system

    International Nuclear Information System (INIS)

    Sancaktar, S.; Sharp, D.R.

    1985-01-01

    An inherent part of probabilistic risk assessment (PRA) is the construction and analysis of detailed fault trees. For this purpose, a fault tree computer graphics code named GRAFTER has been developed. The code system centers around the GRAFTER code. This code is used interactively to construct, store, update and print fault trees of small or large sizes. The SIMON code is used to provide data for the basic event probabilities. ENCODE is used to process the GRAFTER files to prepare input for the WAMCUT code. WAMCUT is used to quantify the top event probability and to identify the cutsets. This code system has been extensively used in various PRA projects. It has resulted in reduced manpower costs, increased QA capability, ease of documentation and it has simplified sensitivity analyses. Because of its automated nature, it is also suitable for LIVING PRA Studies which require updating and modifications during the lifetime of the plant. Brief descriptions and capabilities of the GRAFTER, SIMON and ENCODE codes are provided; an application of the GRAFTER system is outlined; and conclusions and comments on the code system are given

  13. Uncertainties and quantification of common cause failure rates and probabilities for system analyses

    International Nuclear Information System (INIS)

    Vaurio, Jussi K.

    2005-01-01

    Simultaneous failures of multiple components due to common causes at random times are modelled by constant multiple-failure rates. A procedure is described for quantification of common cause failure (CCF) basic event probabilities for system models using plant-specific and multiple-plant failure-event data. Methodology is presented for estimating CCF-rates from event data contaminated with assessment uncertainties. Generalised impact vectors determine the moments for the rates of individual systems or plants. These moments determine the effective numbers of events and observation times to be input to a Bayesian formalism to obtain plant-specific posterior CCF-rates. The rates are used to determine plant-specific common cause event probabilities for the basic events of explicit fault tree models depending on test intervals, test schedules and repair policies. Three methods are presented to determine these probabilities such that the correct time-average system unavailability can be obtained with single fault tree quantification. Recommended numerical values are given and examples illustrate different aspects of the methodology

  14. DeepSAT: A Deep Learning Approach to Tree-cover Delineation in 1-m NAIP Imagery for the Continental United States

    Science.gov (United States)

    Ganguly, S.; Basu, S.; Nemani, R. R.; Mukhopadhyay, S.; Michaelis, A.; Votava, P.

    2016-12-01

    High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with 60 million pixels) and has a total size of 65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to 8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the

  15. DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

    Science.gov (United States)

    Ganguly, Sangram; Basu, Saikat; Nemani, Ramakrishna R.; Mukhopadhyay, Supratik; Michaelis, Andrew; Votava, Petr

    2016-01-01

    High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with 60 million pixels) and has a total size of 65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to 8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the

  16. Assessing and Improving Student Understanding of Tree-Thinking

    Science.gov (United States)

    Kummer, Tyler A.

    Evolution is the unifying theory of biology. The importance of understanding evolution by those who study the origins, diversification and diversity life cannot be overstated. Because of its importance, in addition to a scientific study of evolution, many researchers have spent time studying the acceptance and the teaching of evolution. Phylogenetic Systematics is the field of study developed to understand the evolutionary history of organisms, traits, and genes. Tree-thinking is the term by which we identify concepts related to the evolutionary history of organisms. It is vital that those who undertake a study of biology be able to understand and interpret what information these phylogenies are meant to convey. In this project, we evaluated the current impact a traditional study of biology has on the misconceptions students hold by assessing tree-thinking in freshman biology students to those nearing the end of their studies. We found that the impact of studying biology was varied with some misconceptions changing significantly while others persisted. Despite the importance of tree-thinking no appropriately developed concept inventory exists to measure student understanding of these important concepts. We developed a concept inventory capable of filling this important need and provide evidence to support its use among undergraduate students. Finally, we developed and modified activities as well as courses based on best practices to improve teaching and learning of tree-thinking and organismal diversity. We accomplished this by focusing on two key questions. First, how do we best introduce students to tree-thinking and second does tree-thinking as a course theme enhance student understanding of not only tree-thinking but also organismal diversity. We found important evidence suggesting that introducing students to tree-thinking via building evolutionary trees was less successful than introducing the concept via tree interpretation and may have in fact introduced or

  17. Spatial Probability Cuing and Right Hemisphere Damage

    Science.gov (United States)

    Shaqiri, Albulena; Anderson, Britt

    2012-01-01

    In this experiment we studied statistical learning, inter-trial priming, and visual attention. We assessed healthy controls and right brain damaged (RBD) patients with and without neglect, on a simple visual discrimination task designed to measure priming effects and probability learning. All participants showed a preserved priming effect for item…

  18. The probable effect of integrated reporting on audit quality

    Directory of Open Access Journals (Sweden)

    Tamer A. El Nashar

    2016-06-01

    Full Text Available This paper examines a probable effect of integrated reporting on improving the audit quality of organizations. I correlate the hypothesis of this paper in relation to the current trends of protecting the economies, the financial markets and the societies. I predict an improvement of the audit quality, as a result to an estimated percentage of organizations’ reliance on the integrated reporting in their accountability perspective. I used a decision tree and a Bayes’ theorem approach, to predict the probabilities of the significant effect on improving the auditing quality. I find the overall result of this paper, indicates that the probability of organizations to rely on the integrated reporting by a significant percentage, predicts also a significant improvement in audit quality.

  19. A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

    Science.gov (United States)

    Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Votava, Petr; Roy, Anshuman; Mukhopadhyay, Supratik; Nemani, Ramakrishna

    2015-01-01

    Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

  20. A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery using a Probabilistic Learning Framework

    Science.gov (United States)

    Basu, S.; Ganguly, S.; Michaelis, A.; Votava, P.; Roy, A.; Mukhopadhyay, S.; Nemani, R. R.

    2015-12-01

    Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

  1. Visualizing the Bayesian 2-test case: The effect of tree diagrams on medical decision making.

    Science.gov (United States)

    Binder, Karin; Krauss, Stefan; Bruckmaier, Georg; Marienhagen, Jörg

    2018-01-01

    In medicine, diagnoses based on medical test results are probabilistic by nature. Unfortunately, cognitive illusions regarding the statistical meaning of test results are well documented among patients, medical students, and even physicians. There are two effective strategies that can foster insight into what is known as Bayesian reasoning situations: (1) translating the statistical information on the prevalence of a disease and the sensitivity and the false-alarm rate of a specific test for that disease from probabilities into natural frequencies, and (2) illustrating the statistical information with tree diagrams, for instance, or with other pictorial representation. So far, such strategies have only been empirically tested in combination for "1-test cases", where one binary hypothesis ("disease" vs. "no disease") has to be diagnosed based on one binary test result ("positive" vs. "negative"). However, in reality, often more than one medical test is conducted to derive a diagnosis. In two studies, we examined a total of 388 medical students from the University of Regensburg (Germany) with medical "2-test scenarios". Each student had to work on two problems: diagnosing breast cancer with mammography and sonography test results, and diagnosing HIV infection with the ELISA and Western Blot tests. In Study 1 (N = 190 participants), we systematically varied the presentation of statistical information ("only textual information" vs. "only tree diagram" vs. "text and tree diagram in combination"), whereas in Study 2 (N = 198 participants), we varied the kinds of tree diagrams ("complete tree" vs. "highlighted tree" vs. "pruned tree"). All versions were implemented in probability format (including probability trees) and in natural frequency format (including frequency trees). We found that natural frequency trees, especially when the question-related branches were highlighted, improved performance, but that none of the corresponding probabilistic visualizations did.

  2. Probability tales

    CERN Document Server

    Grinstead, Charles M; Snell, J Laurie

    2011-01-01

    This book explores four real-world topics through the lens of probability theory. It can be used to supplement a standard text in probability or statistics. Most elementary textbooks present the basic theory and then illustrate the ideas with some neatly packaged examples. Here the authors assume that the reader has seen, or is learning, the basic theory from another book and concentrate in some depth on the following topics: streaks, the stock market, lotteries, and fingerprints. This extended format allows the authors to present multiple approaches to problems and to pursue promising side discussions in ways that would not be possible in a book constrained to cover a fixed set of topics. To keep the main narrative accessible, the authors have placed the more technical mathematical details in appendices. The appendices can be understood by someone who has taken one or two semesters of calculus.

  3. ArborZ: PHOTOMETRIC REDSHIFTS USING BOOSTED DECISION TREES

    International Nuclear Information System (INIS)

    Gerdes, David W.; Sypniewski, Adam J.; McKay, Timothy A.; Hao, Jiangang; Weis, Matthew R.; Wechsler, Risa H.; Busha, Michael T.

    2010-01-01

    Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper, we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning technique of boosted decision trees. We study the algorithm using galaxies from the Sloan Digital Sky Survey (SDSS) and from mock catalogs intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show that it improves upon the performance of existing algorithms. Moreover, the method naturally leads to the reconstruction of a full probability density function (PDF) for the photometric redshift of each galaxy, not merely a single 'best estimate' and error, and also provides a photo-z quality figure of merit for each galaxy that can be used to reject outliers. We show that the stacked PDFs yield a more accurate reconstruction of the redshift distribution N(z). We discuss limitations of the current algorithm and ideas for future work.

  4. Gravity and count probabilities in an expanding universe

    Science.gov (United States)

    Bouchet, Francois R.; Hernquist, Lars

    1992-01-01

    The time evolution of nonlinear clustering on large scales in cold dark matter, hot dark matter, and white noise models of the universe is investigated using N-body simulations performed with a tree code. Count probabilities in cubic cells are determined as functions of the cell size and the clustering state (redshift), and comparisons are made with various theoretical models. We isolate the features that appear to be the result of gravitational instability, those that depend on the initial conditions, and those that are likely a consequence of numerical limitations. More specifically, we study the development of skewness, kurtosis, and the fifth moment in relation to variance, the dependence of the void probability on time as well as on sparseness of sampling, and the overall shape of the count probability distribution. Implications of our results for theoretical and observational studies are discussed.

  5. Development of a new model to evaluate the probability of automatic plant trips for pressurized water reactors

    Energy Technology Data Exchange (ETDEWEB)

    Shimada, Yoshio [Institute of Nuclear Safety System Inc., Mihama, Fukui (Japan); Kawai, Katsunori; Suzuki, Hiroshi [Mitsubishi Heavy Industries Ltd., Tokyo (Japan)

    2001-09-01

    In order to improve the reliability of plant operations for pressurized water reactors, a new fault tree model was developed to evaluate the probability of automatic plant trips. This model consists of fault trees for sixteen systems. It has the following features: (1) human errors and transmission line incidents are modeled by the existing data, (2) the repair of failed components is considered to calculate the failure probability of components, (3) uncertainty analysis is performed by an exact method. From the present results, it is confirmed that the obtained upper and lower bound values of the automatic plant trip probability are within the existing data bound in Japan. Thereby this model can be applicable to the prediction of plant performance and reliability. (author)

  6. Study on probability distribution of fire scenarios in risk assessment to emergency evacuation

    International Nuclear Information System (INIS)

    Chu Guanquan; Wang Jinhui

    2012-01-01

    Event tree analysis (ETA) is a frequently-used technique to analyze the probability of probable fire scenario. The event probability is usually characterized by definite value. It is not appropriate to use definite value as these estimates may be the result of poor quality statistics and limited knowledge. Without addressing uncertainties, ETA will give imprecise results. The credibility of risk assessment will be undermined. This paper presents an approach to address event probability uncertainties and analyze probability distribution of probable fire scenario. ETA is performed to construct probable fire scenarios. The activation time of every event is characterized as stochastic variable by considering uncertainties of fire growth rate and other input variables. To obtain probability distribution of probable fire scenario, Markov Chain is proposed to combine with ETA. To demonstrate the approach, a case study is presented.

  7. Fault tree analysis for urban flooding

    NARCIS (Netherlands)

    Ten Veldhuis, J.A.E.; Clemens, F.H.L.R.; Van Gelder, P.H.A.J.M.

    2008-01-01

    Traditional methods to evaluate flood risk mostly focus on storm events as the main cause of flooding. Fault tree analysis is a technique that is able to model all potential causes of flooding and to quantify both the overall probability of flooding and the contributions of all causes of flooding to

  8. An intensive tree-ring experience

    NARCIS (Netherlands)

    Sánchez-Salguero, Raúl; Hevia, Andrea; Camarero, J.J.; Treydte, Kerstin; Frank, Dave; Crivellaro, Alan; Domínguez-Delmás, Marta; Hellman, Lena; Kaczka, Ryszard J.; Kaye, Margot; Akhmetzyanov, Linar; Ashiq, Muhammad Waseem; Bhuyan, Upasana; Bondarenko, Olesia; Camisón, Álvaro; Camps, Sien; García, Vicenta Constante; Vaz, Filipe Costa; Gavrila, Ionela G.; Gulbranson, Erik; Huhtamaa, Heli; Janecka, Karolina; Jeffers, Darren; Jochner, Matthias; Koutecký, Tomáš; Lamrani-Alaoui, Mostafa; Lebreton-Anberrée, Julie; Seijo, María Martín; Matulewski, Pawel; Metslaid, Sandra; Miron, Sergiu; Morrisey, Robert; Opdebeeck, Jorgen; Ovchinnikov, Svyatoslav; Peters, Richard; Petritan, Any M.; Popkova, Margarita; Rehorkova, Stepanka; Ariza, María O.R.; Sánchez-Miranda, Ángela; Linden, Van der Marjolein; Vannoppen, Astrid; Volařík, Daniel

    2017-01-01

    The European Dendroecologial Fieldweek (EDF) provides an intensive learning experience in tree-ring research that challenges any participant to explore new multidisciplinary dendro-sciences approaches within the context of field and laboratory settings. Here we present the 25th EDF, held in

  9. Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering

    Directory of Open Access Journals (Sweden)

    Matthew Parkan

    2018-02-01

    Full Text Available Individual tree crown segmentation from Airborne Laser Scanning data is a nodal problem in forest remote sensing. Focusing on single layered spruce and fir dominated coniferous forests, this article addresses the problem of directly estimating 3D segment shape uncertainty (i.e., without field/reference surveys, using a probabilistic approach. First, a coarse segmentation (marker controlled watershed is applied. Then, the 3D alpha hull and several descriptors are computed for each segment. Based on these descriptors, the alpha hulls are grouped to form ensembles (i.e., groups of similar tree shapes. By examining how frequently regions of a shape occur within an ensemble, it is possible to assign a shape probability to each point within a segment. The shape probability can subsequently be thresholded to obtain improved (filtered tree segments. Results indicate this approach can be used to produce segmentation reliability maps. A comparison to manually segmented tree crowns also indicates that the approach is able to produce more reliable tree shapes than the initial (unfiltered segmentation.

  10. Development of accident sequence precursors methodologies for core damage Probabilities in NPPs

    International Nuclear Information System (INIS)

    Munoz, R.; Minguez, E.; Melendez, E.; Sanchez-Perea, M.; Izquierdo, J.M.

    1998-01-01

    Several licensing programs have focused on the evaluation of the importance of operating events occurred in NPPs. Some have worked the dynamic aspects of the sequence of events involved, reproducing the incidents, while others are based on PSA applications to incident analysis. A method that controls the two above approaches to determine risk analysis derives from the Integrated Safety Assessment methodology (ISA). The dynamics of the event is followed by transient simulation in tree form, building a Setpoint or Deterministic Dynamic Event Tree (DDET). When a setpoint is reached, the actuation of a protection is triggered, then the tree is opened in branches corresponding to different functioning states. The engineering simulator with the new states followers each branch. One of these states is the nominal one, which is the PSA is associated to the success criterion of the system. The probability of the sequence is calculated in parallel to the dynamics. The following tools should perform the couple simulation: 1. A Scheduler that drives the simulation of the different sequences, and open branches upon demand. It will be the unique generator of processes while constructing the tree calculation, and will develop the computation in a distributed computational environment. 2. The Plant Simulator, which models the plant systems and the operator actions throughout a sequence. It receives the state of the equipment in each sequence and must provide information about setpoint crossing to the Scheduler. It will receive decision flags to continue or to stop each sequence, and to send new conditions to other plant simulators. 3. The Probability Calculator, linked only to the Scheduler, is the fault trees associated with each event tree header and performing their Boolean product. (Author)

  11. Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.

    Directory of Open Access Journals (Sweden)

    Michael R W Dawson

    Full Text Available Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.

  12. Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability

    Science.gov (United States)

    2017-01-01

    Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent’s environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned. PMID:28212422

  13. Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.

    Science.gov (United States)

    Dawson, Michael R W; Gupta, Maya

    2017-01-01

    Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.

  14. (Almost) practical tree codes

    KAUST Repository

    Khina, Anatoly

    2016-08-15

    We consider the problem of stabilizing an unstable plant driven by bounded noise over a digital noisy communication link, a scenario at the heart of networked control. To stabilize such a plant, one needs real-time encoding and decoding with an error probability profile that decays exponentially with the decoding delay. The works of Schulman and Sahai over the past two decades have developed the notions of tree codes and anytime capacity, and provided the theoretical framework for studying such problems. Nonetheless, there has been little practical progress in this area due to the absence of explicit constructions of tree codes with efficient encoding and decoding algorithms. Recently, linear time-invariant tree codes were proposed to achieve the desired result under maximum-likelihood decoding. In this work, we take one more step towards practicality, by showing that these codes can be efficiently decoded using sequential decoding algorithms, up to some loss in performance (and with some practical complexity caveats). We supplement our theoretical results with numerical simulations that demonstrate the effectiveness of the decoder in a control system setting.

  15. Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package

    Directory of Open Access Journals (Sweden)

    Alexis Gabadinho

    2016-08-01

    Full Text Available This article presents the PST R package for categorical sequence analysis with probabilistic suffix trees (PSTs, i.e., structures that store variable-length Markov chains (VLMCs. VLMCs allow to model high-order dependencies in categorical sequences with parsimonious models based on simple estimation procedures. The package is specifically adapted to the field of social sciences, as it allows for VLMC models to be learned from sets of individual sequences possibly containing missing values; in addition, the package is extended to account for case weights. This article describes how a VLMC model is learned from one or more categorical sequences and stored in a PST. The PST can then be used for sequence prediction, i.e., to assign a probability to whole observed or artificial sequences. This feature supports data mining applications such as the extraction of typical patterns and outliers. This article also introduces original visualization tools for both the model and the outcomes of sequence prediction. Other features such as functions for pattern mining and artificial sequence generation are described as well. The PST package also allows for the computation of probabilistic divergence between two models and the fitting of segmented VLMCs, where sub-models fitted to distinct strata of the learning sample are stored in a single PST.

  16. A Universal Phylogenetic Tree.

    Science.gov (United States)

    Offner, Susan

    2001-01-01

    Presents a universal phylogenetic tree suitable for use in high school and college-level biology classrooms. Illustrates the antiquity of life and that all life is related, even if it dates back 3.5 billion years. Reflects important evolutionary relationships and provides an exciting way to learn about the history of life. (SAH)

  17. Effects of phentermine and pentobarbital on choice processes during multiple probability learning (MPL) and decision processes manipulated by pay-off conditions

    NARCIS (Netherlands)

    Volkerts, ER; VanLaar, MW; Verbaten, MN; Mulder, G; Maes, RAA

    1997-01-01

    The primary research question in this investigation concerned whether arousal manipulation by a stimulant (phentermine 20 mg) and a depressant (pentobarbital 100 mg) will oppositely affect choice behaviour in a probability learning task and decision processes manipulated by pay-off. A 3-source

  18. Advanced features of the fault tree solver FTREX

    International Nuclear Information System (INIS)

    Jung, Woo Sik; Han, Sang Hoon; Ha, Jae Joo

    2005-01-01

    This paper presents advanced features of a fault tree solver FTREX (Fault Tree Reliability Evaluation eXpert). Fault tree analysis is one of the most commonly used methods for the safety analysis of industrial systems especially for the probabilistic safety analysis (PSA) of nuclear power plants. Fault trees are solved by the classical Boolean algebra, conventional Binary Decision Diagram (BDD) algorithm, coherent BDD algorithm, and Bayesian networks. FTREX could optionally solve fault trees by the conventional BDD algorithm or the coherent BDD algorithm and could convert the fault trees into the form of the Bayesian networks. The algorithm based on the classical Boolean algebra solves a fault tree and generates MCSs. The conventional BDD algorithm generates a BDD structure of the top event and calculates the exact top event probability. The BDD structure is a factorized form of the prime implicants. The MCSs of the top event could be extracted by reducing the prime implicants in the BDD structure. The coherent BDD algorithm is developed to overcome the shortcomings of the conventional BDD algorithm such as the huge memory requirements and a long run time

  19. Modular representation and analysis of fault trees

    Energy Technology Data Exchange (ETDEWEB)

    Olmos, J; Wolf, L [Massachusetts Inst. of Tech., Cambridge (USA). Dept. of Nuclear Engineering

    1978-08-01

    An analytical method to describe fault tree diagrams in terms of their modular compositions is developed. Fault tree structures are characterized by recursively relating the top tree event to all its basic component inputs through a set of equations defining each of the modulus for the fault tree. It is shown that such a modular description is an extremely valuable tool for making a quantitative analysis of fault trees. The modularization methodology has been implemented into the PL-MOD computer code, written in PL/1 language, which is capable of modularizing fault trees containing replicated components and replicated modular gates. PL-MOD in addition can handle mutually exclusive inputs and explicit higher order symmetric (k-out-of-n) gates. The step-by-step modularization of fault trees performed by PL-MOD is demonstrated and it is shown how this procedure is only made possible through an extensive use of the list processing tools available in PL/1. A number of nuclear reactor safety system fault trees were analyzed. PL-MOD performed the modularization and evaluation of the modular occurrence probabilities and Vesely-Fussell importance measures for these systems very efficiently. In particular its execution time for the modularization of a PWR High Pressure Injection System reduced fault tree was 25 times faster than that necessary to generate its equivalent minimal cut-set description using MOCUS, a code considered to be fast by present standards.

  20. Fast Image Texture Classification Using Decision Trees

    Science.gov (United States)

    Thompson, David R.

    2011-01-01

    Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

  1. On the distribution of interspecies correlation for Markov models of character evolution on Yule trees.

    Science.gov (United States)

    Mulder, Willem H; Crawford, Forrest W

    2015-01-07

    Efforts to reconstruct phylogenetic trees and understand evolutionary processes depend fundamentally on stochastic models of speciation and mutation. The simplest continuous-time model for speciation in phylogenetic trees is the Yule process, in which new species are "born" from existing lineages at a constant rate. Recent work has illuminated some of the structural properties of Yule trees, but it remains mostly unknown how these properties affect sequence and trait patterns observed at the tips of the phylogenetic tree. Understanding the interplay between speciation and mutation under simple models of evolution is essential for deriving valid phylogenetic inference methods and gives insight into the optimal design of phylogenetic studies. In this work, we derive the probability distribution of interspecies covariance under Brownian motion and Ornstein-Uhlenbeck models of phenotypic change on a Yule tree. We compute the probability distribution of the number of mutations shared between two randomly chosen taxa in a Yule tree under discrete Markov mutation models. Our results suggest summary measures of phylogenetic information content, illuminate the correlation between site patterns in sequences or traits of related organisms, and provide heuristics for experimental design and reconstruction of phylogenetic trees. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. Posbist fault tree analysis of coherent systems

    International Nuclear Information System (INIS)

    Huang, H.-Z.; Tong Xin; Zuo, Ming J.

    2004-01-01

    When the failure probability of a system is extremely small or necessary statistical data from the system is scarce, it is very difficult or impossible to evaluate its reliability and safety with conventional fault tree analysis (FTA) techniques. New techniques are needed to predict and diagnose such a system's failures and evaluate its reliability and safety. In this paper, we first provide a concise overview of FTA. Then, based on the posbist reliability theory, event failure behavior is characterized in the context of possibility measures and the structure function of the posbist fault tree of a coherent system is defined. In addition, we define the AND operator and the OR operator based on the minimal cut of a posbist fault tree. Finally, a model of posbist fault tree analysis (posbist FTA) of coherent systems is presented. The use of the model for quantitative analysis is demonstrated with a real-life safety system

  3. Induction of Ordinal Decision Trees

    NARCIS (Netherlands)

    J.C. Bioch (Cor); V. Popova (Viara)

    2003-01-01

    textabstractThis paper focuses on the problem of monotone decision trees from the point of view of the multicriteria decision aid methodology (MCDA). By taking into account the preferences of the decision maker, an attempt is made to bring closer similar research within machine learning and MCDA.

  4. From Google Maps to a fine-grained catalog of street trees

    Science.gov (United States)

    Branson, Steve; Wegner, Jan Dirk; Hall, David; Lang, Nico; Schindler, Konrad; Perona, Pietro

    2018-01-01

    Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time, detect significant changes and classify the type of change. We believe this is the first work to exploit publicly available image data for city-scale street tree detection, species recognition and change tracking, exhaustively over several square kilometers, respectively many thousands of trees. Experiments in the city of Pasadena

  5. An ordering heuristic for building Binary Decision Diagrams for fault-trees

    International Nuclear Information System (INIS)

    Bouissou, M.

    1997-01-01

    Binary Decision Diagrams (BDD) have recently made a noticeable entry in the RAMS field. This kind of representation for boolean functions makes possible the assessment of complex fault-trees, both qualitatively (minimal cut-sets search) and quantitatively (exact calculation of top event probability). The object of the paper is to present a pre-processing of the fault-tree which ensures that the results given by different heuristics on the 'optimized' fault-tree are not too sensitive to the way the tree is written. This property is based on a theoretical proof. In contrast with some well known heuristics, the method proposed is not based only on intuition and practical experiments. (author)

  6. Seismic Margin Assessment for Research Reactor using Fragility based Fault Tree Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kwag, Shinyoung; Oh, Jinho; Lee, Jong-Min; Ryu, Jeong-Soo [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2016-10-15

    The research reactor has been often subjected to external hazards during the design lifetime. Especially, a seismic event can be one of significant threats to the failure of structure system of the research reactor. This failure is possibly extended to the direct core damage of the reactor. For this purpose, the fault tree for structural system failure leading to the core damage under an earthquake accident is developed. The failure probabilities of basic events are evaluated as fragility curves of log-normal distributions. Finally, the plant-level seismic margin is investigated by the fault tree analysis combining with fragility data and the critical path is identified. The plant-level probabilistic seismic margin assessment using the fragility based fault tree analysis was performed for quantifying the safety of research reactor to a seismic hazard. For this, the fault tree for structural system failure leading to the core damage of the reactor under a seismic accident was developed. The failure probabilities of basic events were evaluated as fragility curves of log-normal distributions.

  7. Tree manipulation experiment

    Science.gov (United States)

    Nishina, K.; Takenaka, C.; Ishizuka, S.; Hashimoto, S.; Yagai, Y.

    2012-12-01

    Some forest operations such as thinning and harvesting management could cause changes in N cycling and N2O emission from soils, since thinning and harvesting managements are accompanied with changes in aboveground environments such as an increase of slash falling and solar radiation on the forest floor. However, a considerable uncertainty exists in effects of thinning and harvesting on N2O fluxes regarding changes in belowground environments by cutting trees. To focus on the effect of changes in belowground environments on the N2O emissions from soils, we conducted a tree manipulation experiment in Japanese cedar (Cryptomeria japonica) stand without soil compaction and slash falling near the chambers and measured N2O flux at 50 cm and 150 cm distances from the tree trunk (stump) before and after cutting. We targeted 5 trees for the manipulation and established the measurement chambers to the 4 directions around each targeted tree relative to upper slope (upper, left, right, lower positions). We evaluated the effect of logging on the emission by using hierarchical Bayesian model. HB model can evaluate the variability in observed data and their uncertainties in the estimation with various probability distributions. Moreover, the HB model can easily accommodate the non-linear relationship among the N2O emissions and the environmental factors, and explicitly take non-independent data (nested structure of data) for the estimation into account by using random effects in the model. Our results showed tree cutting stimulated N2O emission from soils, and also that the increase of N2O flux depended on the distance from the trunk (stump): the increase of N2O flux at 50 cm from the trunk (stump) was greater than that of 150 cm from the trunk. The posterior simulation of the HB model indicated that the stimulation of N2O emission by tree cut- ting could reach up to 200 cm in our experimental plot. By tree cutting, the estimated N2O emission at 0-40 cm from the trunk doubled

  8. AnswerTree – a hyperplace-based game for collaborative mobile learning

    OpenAIRE

    Moore, Adam; Goulding, James; Brown, Elizabeth; Swan, Jerry

    2009-01-01

    In this paper we present AnswerTree, a collaborative mobile location-based educational game designed to teach 8-12 year olds about trees and wildlife within the University of Nottingham campus. The activity is designed around collecting virtual cards (similar in nature to the popular Top TrumpsTM games) containing graphics and information about notable trees. Each player begins by collecting one card from a game location, but then he or she can only collect further cards by answering question...

  9. A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

    Science.gov (United States)

    Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

    2014-12-01

    Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field (CRF), which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

  10. Can Children Read Evolutionary Trees?

    Science.gov (United States)

    Ainsworth, Shaaron; Saffer, Jessica

    2013-01-01

    Representations of the "tree of life" such as cladograms show the history of lineages and their relationships. They are increasingly found in formal and informal learning settings. Unfortunately, there is evidence that these representations can be challenging to interpret correctly. This study explored the question of whether children…

  11. An analysis of the survivability of sensor darts in impacts with trees.

    Energy Technology Data Exchange (ETDEWEB)

    Prentice, John K. (Sci-Tac, Inc., Boulder, CO.); Gardner, David Randall

    2005-07-01

    A methodology was developed for computing the probability that the sensor dart for the 'Near Real-Time Site Characterization for Assured HDBT Defeat' Grand-Challenge LDRD project will survive deployment over a forested region. The probability can be decomposed into three approximately independent probabilities that account for forest coverage, branch density and the physics of an impact between the dart and a tree branch. The probability that a dart survives an impact with a tree branch was determined from the deflection induced by the impact. If a dart that was deflected so that it impacted the ground at an angle of attack exceeding a user-specified, threshold value, the dart was assumed to not survive the impact with the branch; otherwise it was assumed to have survived. A computer code was developed for calculating dart angle of attack at impact with the ground and a Monte Carlo scheme was used to calculate the probability distribution of a sensor dart surviving an impact with a branch as a function of branch radius, length, and height from the ground. Both an early prototype design and the current dart design were used in these studies. As a general rule of thumb, it we observed that for reasonably generic trees and for a threshold angle of attack of 5{sup o} (which is conservative for dart survival), the probability of reaching the ground with an angle of attack less than the threshold is on the order of 30% for the prototype dart design and 60% for the current dart design, though these numbers should be treated with some caution.

  12. Comparison of greedy algorithms for α-decision tree construction

    KAUST Repository

    Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

    2011-01-01

    A comparison among different heuristics that are used by greedy algorithms which constructs approximate decision trees (α-decision trees) is presented. The comparison is conducted using decision tables based on 24 data sets from UCI Machine Learning Repository [2]. Complexity of decision trees is estimated relative to several cost functions: depth, average depth, number of nodes, number of nonterminal nodes, and number of terminal nodes. Costs of trees built by greedy algorithms are compared with minimum costs calculated by an algorithm based on dynamic programming. The results of experiments assign to each cost function a set of potentially good heuristics that minimize it. © 2011 Springer-Verlag.

  13. A maximum pseudo-likelihood approach for estimating species trees under the coalescent model

    Directory of Open Access Journals (Sweden)

    Edwards Scott V

    2010-10-01

    Full Text Available Abstract Background Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE of the species tree (topology, branch lengths, and population sizes from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE of species trees, with branch lengths of the species tree in coalescent units. Results We show that the MPE of the species tree is statistically consistent as the number M of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate O(M -1. The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals. Conclusions MP-EST can consistently estimate the topology and branch lengths (in coalescent units of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT, the MP-EST method is robust to a small amount of HGT in the

  14. An automated approach to the design of decision tree classifiers

    Science.gov (United States)

    Argentiero, P.; Chin, R.; Beaudet, P.

    1982-01-01

    An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

  15. A fault tree analysis strategy using binary decision diagrams

    International Nuclear Information System (INIS)

    Reay, Karen A.; Andrews, John D.

    2002-01-01

    The use of binary decision diagrams (BDDs) in fault tree analysis provides both an accurate and efficient means of analysing a system. There is a problem, however, with the conversion process of the fault tree to the BDD. The variable ordering scheme chosen for the construction of the BDD has a crucial effect on its resulting size and previous research has failed to identify any scheme that is capable of producing BDDs for all fault trees. This paper proposes an analysis strategy aimed at increasing the likelihood of obtaining a BDD for any given fault tree, by ensuring the associated calculations are as efficient as possible. The method implements simplification techniques, which are applied to the fault tree to obtain a set of 'minimal' subtrees, equivalent to the original fault tree structure. BDDs are constructed for each, using ordering schemes most suited to their particular characteristics. Quantitative analysis is performed simultaneously on the set of BDDs to obtain the top event probability, the system unconditional failure intensity and the criticality of the basic events

  16. Credit Risk Analysis Using Machine and Deep Learning Models

    Directory of Open Access Journals (Sweden)

    Peter Martey Addo

    2018-04-01

    Full Text Available Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises.

  17. WAMCUT, a computer code for fault tree evaluation. Final report

    International Nuclear Information System (INIS)

    Erdmann, R.C.

    1978-06-01

    WAMCUT is a code in the WAM family which produces the minimum cut sets (MCS) for a given fault tree. The MCS are useful as they provide a qualitative evaluation of a system, as well as providing a means of determining the probability distribution function for the top of the tree. The program is very efficient and will produce all the MCS in a very short computer time span. 22 figures, 4 tables

  18. The probability and the management of human error

    International Nuclear Information System (INIS)

    Dufey, R.B.; Saull, J.W.

    2004-01-01

    Embedded within modern technological systems, human error is the largest, and indeed dominant contributor to accident cause. The consequences dominate the risk profiles for nuclear power and for many other technologies. We need to quantify the probability of human error for the system as an integral contribution within the overall system failure, as it is generally not separable or predictable for actual events. We also need to provide a means to manage and effectively reduce the failure (error) rate. The fact that humans learn from their mistakes allows a new determination of the dynamic probability and human failure (error) rate in technological systems. The result is consistent with and derived from the available world data for modern technological systems. Comparisons are made to actual data from large technological systems and recent catastrophes. Best estimate values and relationships can be derived for both the human error rate, and for the probability. We describe the potential for new approaches to the management of human error and safety indicators, based on the principles of error state exclusion and of the systematic effect of learning. A new equation is given for the probability of human error (λ) that combines the influences of early inexperience, learning from experience (ε) and stochastic occurrences with having a finite minimum rate, this equation is λ 5.10 -5 + ((1/ε) - 5.10 -5 ) exp(-3*ε). The future failure rate is entirely determined by the experience: thus the past defines the future

  19. The hydrological vulnerability of western North American boreal tree species based on ground-based observations of tree mortality

    Science.gov (United States)

    Hember, R. A.; Kurz, W. A.; Coops, N. C.

    2017-12-01

    Several studies indicate that climate change has increased rates of tree mortality, adversely affecting timber supply and carbon storage in western North American boreal forests. Statistical models of tree mortality can play a complimentary role in detecting and diagnosing forest change. Yet, such models struggle to address real-world complexity, including expectations that hydrological vulnerability arises from both drought stress and excess-water stress, and that these effects vary by species, tree size, and competitive status. Here, we describe models that predict annual probability of tree mortality (Pm) of common boreal tree species based on tree height (H), biomass of larger trees (BLT), soil water content (W), reference evapotranspiration (E), and two-way interactions. We show that interactions among H and hydrological variables are consistently significant. Vulnerability to extreme droughts consistently increases as H approaches maximum observed values of each species, while some species additionally show increasing vulnerability at low H. Some species additionally show increasing vulnerability to low W under high BLT, or increasing drought vulnerability under low BLT. These results suggest that vulnerability of trees to increasingly severe droughts depends on the hydraulic efficiency, competitive status, and microclimate of individual trees. Static simulations of Pm across a 1-km grid (i.e., with time-independent inputs of H, BLT, and species composition) indicate complex spatial patterns in the time trends during 1965-2014 and a mean change in Pm of 42 %. Lastly, we discuss how the size-dependence of hydrological vulnerability, in concert with increasingly severe drought events, may shape future responses of stand-level biomass production to continued warming and increasing carbon dioxide concentration in the region.

  20. Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

    Directory of Open Access Journals (Sweden)

    Atif Shahzad

    2016-02-01

    Full Text Available A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment.

  1. A Decision Tree for Psychology Majors: Supplying Questions as Well as Answers.

    Science.gov (United States)

    Poe, Retta E.

    1988-01-01

    Outlines the development of a psychology careers decision tree to help faculty advise students plan their program. States that students using the decision tree may benefit by learning more about their career options and by acquiring better question-asking skills. (GEA)

  2. An ordering heuristic for building Binary Decision Diagrams for fault-trees

    Energy Technology Data Exchange (ETDEWEB)

    Bouissou, M. [Electricite de France (EDF), 75 - Paris (France)

    1997-12-31

    Binary Decision Diagrams (BDD) have recently made a noticeable entry in the RAMS field. This kind of representation for boolean functions makes possible the assessment of complex fault-trees, both qualitatively (minimal cut-sets search) and quantitatively (exact calculation of top event probability). The object of the paper is to present a pre-processing of the fault-tree which ensures that the results given by different heuristics on the `optimized` fault-tree are not too sensitive to the way the tree is written. This property is based on a theoretical proof. In contrast with some well known heuristics, the method proposed is not based only on intuition and practical experiments. (author) 12 refs.

  3. Use of fault and decision tree analyses to protect against industrial sabotage

    International Nuclear Information System (INIS)

    Fullwood, R.R.; Erdmann, R.C.

    1975-01-01

    Fault tree and decision tree analyses provide systematic bases for evaluation of safety systems and procedures. Heuristically, this paper shows applications of these methods for industrial sabotage analysis at a reprocessing plant. Fault trees constructed by ''leak path'' analysis for completeness through path inventory. The escape fault tree is readily developed by this method and using the reciprocal character of the trees, the attack fault tree is constructed. After construction, the events on the fault tree are corrected for their nonreciprocal character. The fault trees are algebraically solved and the protection that is afforded is ranked by the number of barriers that must be penetrated. No attempt is made to assess the barrier penetration probabilities or penetration time duration. Event trees are useful for dynamic plant protection analysis through their time-sequencing character. To illustrate their usefulness, a simple attack scenario is devised and event-tree analyzed. Two saboteur success paths and 21 failure paths are found. This example clearly shows the event tree usefulness for concisely presenting the time sequencing of key decision points. However, event trees have the disadvantage of being scenario dependent, therefore requiring a separate event tree for each scenario

  4. Root and Branch Reform: Teaching City Kids about Urban Trees

    Science.gov (United States)

    Walker, Mark

    2017-01-01

    In today's electronic age, suburban and city children are increasingly disconnected with the natural world. Studying trees allows children to learn about the world they live in and can teach a variety of useful topics contained within the National Curriculum in England. Knowledge of trees is specifically required in the science curriculum at key…

  5. The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir

    KAUST Repository

    Prasetyo Utomo, Chandra

    2011-06-01

    Permeability is an important parameter connected with oil reservoir. Predicting the permeability could save millions of dollars. Unfortunately, petroleum engineers have faced numerous challenges arriving at cost-efficient predictions. Much work has been carried out to solve this problem. The main challenge is to handle the high range of permeability in each reservoir. For about a hundred year, mathematicians and engineers have tried to deliver best prediction models. However, none of them have produced satisfying results. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way out of this complexity through the design of a hybrid intelligent model. In this proposal, the system combines classification and regression models to predict the permeability value. These are based on the well logs data. In order to handle the high range of the permeability value, a classification tree is utilized. A benefit of this innovation is that the tree represents knowledge in a clear and succinct fashion and thereby avoids the complexity of all previous models. Finally, it is important to note that the ELM is used as a final predictor. Results demonstrate that this proposed hybrid model performs better when compared with support vector machines (SVM) and ELM in term of correlation coefficient. Moreover, the classification tree model potentially leads to better communication among petroleum engineers concerning this important process and has wider implications for oil reservoir management efficiency.

  6. Evaluation of Decision Trees for Cloud Detection from AVHRR Data

    Science.gov (United States)

    Shiffman, Smadar; Nemani, Ramakrishna

    2005-01-01

    Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

  7. PL-MOD: a computer code for modular fault tree analysis and evaluation

    International Nuclear Information System (INIS)

    Olmos, J.; Wolf, L.

    1978-01-01

    The computer code PL-MOD has been developed to implement the modular methodology to fault tree analysis. In the modular approach, fault tree structures are characterized by recursively relating the top tree event to all basic event inputs through a set of equations, each defining an independent modular event for the tree. The advantages of tree modularization lie in that it is a more compact representation than the minimal cut-set description and in that it is well suited for fault tree quantification because of its recursive form. In its present version, PL-MOD modularizes fault trees and evaluates top and intermediate event failure probabilities, as well as basic component and modular event importance measures, in a very efficient way. Thus, its execution time for the modularization and quantification of a PWR High Pressure Injection System reduced fault tree was 25 times faster than that necessary to generate its equivalent minimal cut-set description using the computer code MOCUS

  8. Probability & Statistics: Modular Learning Exercises. Teacher Edition

    Science.gov (United States)

    Actuarial Foundation, 2012

    2012-01-01

    The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The modules also introduce students to real world math concepts and problems that property and casualty actuaries come across in their work. They are designed to be used by teachers and…

  9. Probability & Statistics: Modular Learning Exercises. Student Edition

    Science.gov (United States)

    Actuarial Foundation, 2012

    2012-01-01

    The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The materials are centered on the fictional town of Happy Shores, a coastal community which is at risk for hurricanes. Actuaries at an insurance company figure out the risks and…

  10. Reliability study of the auxiliary feed-water system of a pressurized water reactor by faults tree and Bayesian Network

    International Nuclear Information System (INIS)

    Lava, Deise Diana; Borges, Diogo da Silva; Guimarães, Antonio Cesar Ferreira; Moreira, Maria de Lourdes

    2017-01-01

    This paper aims to present a study of the reliability of the Auxiliary Feed-water System (AFWS) through the methods of Fault Tree and Bayesian Network. Therefore, the paper consists of a literature review of the history of nuclear energy and the methodologies used. The AFWS is responsible for providing water system to cool the secondary circuit of nuclear reactors of the PWR type when normal feeding water system failure. How this system operates only when the primary system fails, it is expected that the AFWS failure probability is very low. The AFWS failure probability is divided into two cases: the first is the probability of failure in the first eight hours of operation and the second is the probability of failure after eight hours of operation, considering that the system has not failed within the first eight hours. The calculation of the probability of failure of the second case was made through the use of Fault Tree and Bayesian Network, that it was constructed from the Fault Tree. The results of the failure probability obtained were very close, on the order of 10 -3 . (author)

  11. Reliability study of the auxiliary feed-water system of a pressurized water reactor by faults tree and Bayesian Network

    Energy Technology Data Exchange (ETDEWEB)

    Lava, Deise Diana; Borges, Diogo da Silva; Guimarães, Antonio Cesar Ferreira; Moreira, Maria de Lourdes, E-mail: deise_dy@hotmail.com, E-mail: diogosb@outlook.com, E-mail: tony@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

    2017-07-01

    This paper aims to present a study of the reliability of the Auxiliary Feed-water System (AFWS) through the methods of Fault Tree and Bayesian Network. Therefore, the paper consists of a literature review of the history of nuclear energy and the methodologies used. The AFWS is responsible for providing water system to cool the secondary circuit of nuclear reactors of the PWR type when normal feeding water system failure. How this system operates only when the primary system fails, it is expected that the AFWS failure probability is very low. The AFWS failure probability is divided into two cases: the first is the probability of failure in the first eight hours of operation and the second is the probability of failure after eight hours of operation, considering that the system has not failed within the first eight hours. The calculation of the probability of failure of the second case was made through the use of Fault Tree and Bayesian Network, that it was constructed from the Fault Tree. The results of the failure probability obtained were very close, on the order of 10{sup -3}. (author)

  12. Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

    Science.gov (United States)

    Hor, Soheil; Moradi, Mehdi

    2016-12-01

    Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

  13. Long‐term time‐dependent probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3)

    Science.gov (United States)

    Field, Edward; Biasi, Glenn P.; Bird, Peter; Dawson, Timothy E.; Felzer, Karen R.; Jackson, David A.; Johnson, Kaj M.; Jordan, Thomas H.; Madden, Christopher; Michael, Andrew J.; Milner, Kevin; Page, Morgan T.; Parsons, Thomas E.; Powers, Peter; Shaw, Bruce E.; Thatcher, Wayne R.; Weldon, Ray J.; Zeng, Yuehua

    2015-01-01

    The 2014 Working Group on California Earthquake Probabilities (WGCEP 2014) presents time-dependent earthquake probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3). Building on the UCERF3 time-independent model, published previously, renewal models are utilized to represent elastic-rebound-implied probabilities. A new methodology has been developed that solves applicability issues in the previous approach for un-segmented models. The new methodology also supports magnitude-dependent aperiodicity and accounts for the historic open interval on faults that lack a date-of-last-event constraint. Epistemic uncertainties are represented with a logic tree, producing 5,760 different forecasts. Results for a variety of evaluation metrics are presented, including logic-tree sensitivity analyses and comparisons to the previous model (UCERF2). For 30-year M≥6.7 probabilities, the most significant changes from UCERF2 are a threefold increase on the Calaveras fault and a threefold decrease on the San Jacinto fault. Such changes are due mostly to differences in the time-independent models (e.g., fault slip rates), with relaxation of segmentation and inclusion of multi-fault ruptures being particularly influential. In fact, some UCERF2 faults were simply too long to produce M 6.7 sized events given the segmentation assumptions in that study. Probability model differences are also influential, with the implied gains (relative to a Poisson model) being generally higher in UCERF3. Accounting for the historic open interval is one reason. Another is an effective 27% increase in the total elastic-rebound-model weight. The exact factors influencing differences between UCERF2 and UCERF3, as well as the relative importance of logic-tree branches, vary throughout the region, and depend on the evaluation metric of interest. For example, M≥6.7 probabilities may not be a good proxy for other hazard or loss measures. This sensitivity, coupled with the

  14. Learning from examples - Generation and evaluation of decision trees for software resource analysis

    Science.gov (United States)

    Selby, Richard W.; Porter, Adam A.

    1988-01-01

    A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

  15. Fault tree analysis. Implementation of the WAM-codes

    International Nuclear Information System (INIS)

    Bento, J.P.; Poern, K.

    1979-07-01

    The report describes work going on at Studsvik at the implementation of the WAM code package for fault tree analysis. These codes originally developed under EPRI contract by Sciences Applications Inc, allow, in contrast with other fault tree codes, all Boolean operations, thus allowing modeling of ''NOT'' conditions and dependent components. To concretize the implementation of these codes, the auxiliary feed-water system of the Swedish BWR Oskarshamn 2 was chosen for the reliability analysis. For this system, both the mean unavailability and the probability density function of the top event - undesired event - of the system fault tree were calculated, the latter using a Monte-Carlo simulation technique. The present study is the first part of a work performed under contract with the Swedish Nuclear Power Inspectorate. (author)

  16. Learning resistance mutation pathways of HIV

    OpenAIRE

    Ramon, Jan; Dubrovskaya, Snezhana; Blockeel, Hendrik

    2007-01-01

    We propose a novel machine learning algorithm for learning mutation pathways of viruses from a population of viral DNA strands. More specifically, given a number of sequences, the algorithm constructs a phylogenetic tree that expresses the ancestry of the sequences, and at the same time builds a model describing dependencies between mutations that is consistent with the data as well as the phylogenetic tree. Our approach extends existing approaches for phylogenetic tree construction by not as...

  17. Disturbance legacies and climate jointly drive tree growth and mortality in an intensively studied boreal forest

    Energy Technology Data Exchange (ETDEWEB)

    Bond-Lamberty, Benjamin; Rocha, Adrian; Calvin, Katherine V.; Holmes, Bruce; Wang, Chuankuan; Goulden, Michael L.

    2014-01-01

    How will regional growth and mortality change with even relatively small climate shifts, even independent of catastrophic disturbances? This question is particularly acute for the North American boreal forest, which is carbon-dense and subject The goals of this study were to combine dendrochronological sampling, inventory records, and machine-learning algorithms to understand how tree growth and death have changed at one highly studied site (Northern Old Black Spruce, NOBS) in the central Canadian boreal forest. Over the 1999-2012 inventory period, mean DBH increased even as stand density and basal area declined significantly from 41.3 to 37.5 m2 ha-1. Tree mortality averaged 1.4±0.6% yr-1, with most mortality occurring in medium-sized trees. A combined tree ring chronology constructed from 2001, 2004, and 2012 sampling showed several periods of extreme growth depression, with increased mortality lagging depressed growth by ~5 years. Minimum and maximum air temperatures exerted a negative influence on tree growth, while precipitation and climate moisture index had a positive effect; both current- and previous-year data exerted significant effects. Models based on these variables explained 23-44% of the ring-width variability. There have been at least one, and probably two, significant recruitment episodes since stand initiation, and we infer that past climate extremes led to significant NOBS mortality still visible in the current forest structure. These results imply that a combination of successional and demographic processes, along with mortality driven by abiotic factors, continue to affect the stand, with significant implications for our understanding of previous work at NOBS and the sustainable management of regional forests.

  18. LocTree3 prediction of localization

    DEFF Research Database (Denmark)

    Goldberg, T.; Hecht, M.; Hamp, T.

    2014-01-01

    The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria a...

  19. Estimating Coextinction Risks from Epidemic Tree Death: Affiliate Lichen Communities among Diseased Host Tree Populations of Fraxinus excelsior

    Science.gov (United States)

    Jönsson, Mari T.; Thor, Göran

    2012-01-01

    At least 10% of the world’s tree species are threatened with extinction and pathogens are increasingly implicated in tree threats. Coextinction and threats to affiliates as a consequence of the loss or decline of their host trees is a poorly understood phenomenon. Ash dieback is an emerging infectious disease causing severe dieback of common ash Fraxinus excelsior throughout Europe. We utilized available empirical data on affiliate epiphytic lichen diversity (174 species and 17,800 observations) among 20 ash dieback infected host tree populations of F. excelsior on the island Gotland in the Baltic Sea, Sweden. From this, we used structured scenario projections scaled with empirical data of ash dieback disease to generate probabilistic models for estimating local and regional lichen coextinction risks. Average coextinction probabilities (Ā) were 0.38 (95% CI ±0.09) for lichens occurring on F. excelsior and 0.14 (95% CI ±0.03) when considering lichen persistence on all tree species. Ā was strongly linked to local disease incidence levels and generally increasing with lichen host specificity to F. excelsior and decreasing population size. Coextinctions reduced affiliate community viability, with significant local reductions in species richness and shifts in lichen species composition. Affiliates were projected to become locally extirpated before their hosts, illuminating the need to also consider host tree declines. Traditionally managed open wooded meadows had the highest incidence of ash dieback disease and significantly higher proportions of affiliate species projected to go extinct, compared with unmanaged closed forests and semi-open grazed sites. Most cothreatened species were not previously red-listed, which suggest that tree epidemics cause many unforeseen threats to species. Our analysis shows that epidemic tree deaths represent an insidious, mostly overlooked, threat to sessile affiliate communities in forested environments. Current conservation and

  20. Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

    Science.gov (United States)

    Austin, Peter C; Lee, Douglas S

    2011-01-01

    Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

  1. New Splitting Criteria for Decision Trees in Stationary Data Streams.

    Science.gov (United States)

    Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Rutkowski, Leszek; Duda, Piotr; Jaworski, Maciej

    2018-06-01

    The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding's inequality and hundreds of researchers followed this scheme. Recently, we have demonstrated that although the Hoeffding decision trees are an effective tool for dealing with stream data, they are a purely heuristic procedure; for example, classical decision trees such as ID3 or CART cannot be adopted to data stream mining using Hoeffding's inequality. Therefore, there is an urgent need to develop new algorithms, which are both mathematically justified and characterized by good performance. In this paper, we address this problem by developing a family of new splitting criteria for classification in stationary data streams and investigating their probabilistic properties. The new criteria, derived using appropriate statistical tools, are based on the misclassification error and the Gini index impurity measures. The general division of splitting criteria into two types is proposed. Attributes chosen based on type- splitting criteria guarantee, with high probability, the highest expected value of split measure. Type- criteria ensure that the chosen attribute is the same, with high probability, as it would be chosen based on the whole infinite data stream. Moreover, in this paper, two hybrid splitting criteria are proposed, which are the combinations of single criteria based on the misclassification error and Gini index.

  2. Using Tree-Ring Data to Develop Critical Scientific and Mathematical Thinking Skills in Undergraduate Students

    Science.gov (United States)

    Fiondella, F.; Davi, N. K.; Wattenberg, F.; Pringle, P. T.; Greidanus, I.; Oelkers, R.

    2015-12-01

    Tree-ring science provides an engaging, intuitive, and relevant entryway into understanding both climate change and environmental research. It also sheds light on the process of science--from inspiration, to fieldwork, to analysis, to publishing and communication. The basic premise of dendrochronology is that annual rings reflect year-to-year environmental conditions and that by studying long-lived trees we can learn about environmental and climatic conditions going back hundreds to thousands of years. Conceptually, this makes tree-ring studies accessible to students and faculty for a number of reasons. First, in order to collect their data, dendrochronologists often launch expeditions to stunningly picturesque and remote places in search of long-lived, climate sensitive trees. The exciting stories and images that scientists bring back from the field can help connect students to the studies, their motivation, and the data collected. Second, tree rings can be more easily explained as a proxy for climate than ice cores, speleothems and others. Most people have prior knowledge about trees and annual growth rings. It is even possible, for example, for non-expert audiences to see climate variability through time with the naked eye by looking at climate-sensitive tree cores. Third, tree rings are interdisciplinary and illustrate the interplay between the mathematical sciences, the biological sciences, and the geosciences—that is, they show that the biosphere is a fundamental component of the Earth system. Here, we present online, multi-media learning modules for undergraduates that introduce students to several foundational studies in tree-ring science. These include evaluating tree-ring cores from ancient hemlock trees growing on a talus slope in New Paltz, NY to learn about drought in the Northeastern US, evaluating long-term streamflow and drought of the Colorado River based on tree-ring records, and using tree-ring dating techniques to develop construction

  3. Constructing Dynamic Event Trees from Markov Models

    International Nuclear Information System (INIS)

    Paolo Bucci; Jason Kirschenbaum; Tunc Aldemir; Curtis Smith; Ted Wood

    2006-01-01

    In the probabilistic risk assessment (PRA) of process plants, Markov models can be used to model accurately the complex dynamic interactions between plant physical process variables (e.g., temperature, pressure, etc.) and the instrumentation and control system that monitors and manages the process. One limitation of this approach that has prevented its use in nuclear power plant PRAs is the difficulty of integrating the results of a Markov analysis into an existing PRA. In this paper, we explore a new approach to the generation of failure scenarios and their compilation into dynamic event trees from a Markov model of the system. These event trees can be integrated into an existing PRA using software tools such as SAPHIRE. To implement our approach, we first construct a discrete-time Markov chain modeling the system of interest by: (a) partitioning the process variable state space into magnitude intervals (cells), (b) using analytical equations or a system simulator to determine the transition probabilities between the cells through the cell-to-cell mapping technique, and, (c) using given failure/repair data for all the components of interest. The Markov transition matrix thus generated can be thought of as a process model describing the stochastic dynamic behavior of the finite-state system. We can therefore search the state space starting from a set of initial states to explore all possible paths to failure (scenarios) with associated probabilities. We can also construct event trees of arbitrary depth by tracing paths from a chosen initiating event and recording the following events while keeping track of the probabilities associated with each branch in the tree. As an example of our approach, we use the simple level control system often used as benchmark in the literature with one process variable (liquid level in a tank), and three control units: a drain unit and two supply units. Each unit includes a separate level sensor to observe the liquid level in the tank

  4. Event tree analysis for the system of hybrid reactor

    International Nuclear Information System (INIS)

    Yang Yongwei; Qiu Lijian

    1993-01-01

    The application of probabilistic risk assessment for fusion-fission hybrid reactor is introduced. A hybrid reactor system has been analysed using event trees. According to the character of the conceptual design of Hefei Fusion-fission Experimental Hybrid Breeding Reactor, the probabilities of the event tree series induced by 4 typical initiating events were calculated. The results showed that the conceptual design is safe and reasonable. through this paper, the safety character of hybrid reactor system has been understood more deeply. Some suggestions valuable to safety design for hybrid reactor have been proposed

  5. Molecular and physiological responses of trees to waterlogging stress.

    Science.gov (United States)

    Kreuzwieser, Jürgen; Rennenberg, Heinz

    2014-10-01

    One major effect of global climate change will be altered precipitation patterns in many regions of the world. This will cause a higher probability of long-term waterlogging in winter/spring and flash floods in summer because of extreme rainfall events. Particularly, trees not adapted at their natural site to such waterlogging stress can be impaired. Despite the enormous economic, ecological and social importance of forest ecosystems, the effect of waterlogging on trees is far less understood than the effect on many crops or the model plant Arabidopsis. There is only a handful of studies available investigating the transcriptome and metabolome of waterlogged trees. Main physiological responses of trees to waterlogging include the stimulation of fermentative pathways and an accelerated glycolytic flux. Many energy-consuming, anabolic processes are slowed down to overcome the energy crisis mediated by waterlogging. A crucial feature of waterlogging tolerance is the steady supply of glycolysis with carbohydrates, particularly in the roots; stress-sensitive trees fail to maintain sufficient carbohydrate availability resulting in the dieback of the stressed tissues. The present review summarizes physiological and molecular features of waterlogging tolerance of trees; the focus is on carbon metabolism in both, leaves and roots of trees. © 2014 John Wiley & Sons Ltd.

  6. Tree cover and species composition effects on academic performance of primary school students.

    Science.gov (United States)

    Sivarajah, Sivajanani; Smith, Sandy M; Thomas, Sean C

    2018-01-01

    Human exposure to green space and vegetation is widely recognized to result in physical and mental health benefits; however, to date, the specific effects of tree cover, diversity, and species composition on student academic performance have not been investigated. We compiled standardized performance scores in Grades 3 and 6 for the collective student body in 387 schools across the Toronto District School Board (TDSB), and examined variation in relation to tree cover, tree diversity, and tree species composition based on comprehensive inventories of trees on school properties combined with aerial-photo-based assessments of tree cover. Analyses accounted for variation due to socioeconomic factors using the learning opportunity index (LOI), a regional composite index of external challenges to learning that incorporates income and other factors, such as students with English as a second language. As expected, LOI had the greatest influence on student academic performance; however, the proportion of tree cover, as distinct from other types of "green space" such as grass, was found to be a significant positive predictor of student performance, accounting for 13% of the variance explained in a statistical model predicting mean student performance assessments. The effects of tree cover and species composition were most pronounced in schools that showed the highest level of external challenges, suggesting the importance of urban forestry investments in these schools.

  7. PDE-Foam - a probability-density estimation method using self-adapting phase-space binning

    CERN Document Server

    Dannheim, Dominik; Voigt, Alexander; Grahn, Karl-Johan; Speckmayer, Peter

    2009-01-01

    Probability-Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. To efficiently use large event samples to estimate the probability density, a binary search tree (range searching) is used in the PDE-RS implementation. It is a generalisation of standard likelihood methods and a powerful classification tool for problems with highly non-linearly correlated observables. In this paper, we present an innovative improvement of the PDE method that uses a self-adapting binning method to divide the multi-dimensional phase space in a finite number of hyper-rectangles (cells). The binning algorithm adjusts the size and position of a predefined number of cells inside the multidimensional phase space, minimizing the variance of the signal and background densities inside the cells. The binned density information is stored in binary trees, allowing for a very ...

  8. Visualizing and Understanding Probability and Statistics: Graphical Simulations Using Excel

    Science.gov (United States)

    Gordon, Sheldon P.; Gordon, Florence S.

    2009-01-01

    The authors describe a collection of dynamic interactive simulations for teaching and learning most of the important ideas and techniques of introductory statistics and probability. The modules cover such topics as randomness, simulations of probability experiments such as coin flipping, dice rolling and general binomial experiments, a simulation…

  9. Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Dawei Li

    2017-01-01

    Full Text Available This study develops a tree augmented naive Bayesian (TAN classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN based and multilayer feed forward (MLF neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

  10. Retrieval system for emplaced spent unreprocessed fuel (SURF) in salt bed depository: accident event analysis and mechanical failure probabilities. Final report

    International Nuclear Information System (INIS)

    Bhaskaran, G.; McCleery, J.E.

    1979-10-01

    This report provides support in developing an accident prediction event tree diagram, with an analysis of the baseline design concept for the retrieval of emplaced spent unreprocessed fuel (SURF) contained in a degraded Canister. The report contains an evaluation check list, accident logic diagrams, accident event tables, fault trees/event trees and discussions of failure probabilities for the following subsystems as potential contributors to a failure: (a) Canister extraction, including the core and ram units; (b) Canister transfer at the hoist area; and (c) Canister hoisting. This report is the second volume of a series. It continues and expands upon the report Retrieval System for Emplaced Spent Unreprocessed Fuel (SURF) in Salt Bed Depository: Baseline Concept Criteria Specifications and Mechanical Failure Probabilities. This report draws upon the baseline conceptual specifications contained in the first report

  11. Water-Tree Modelling and Detection for Underground Cables

    Science.gov (United States)

    Chen, Qi

    is used to model water-tree in large system. Both empirical measurements and the mathematical model show that the impedance of early-stage water-tree is extremely large. As the result, traditional detection methods such Tan-Delta or Partial Discharge are not effective due to the excessively high accuracy requirement. A high-frequency pulse detection method is developed instead. The water-tree impedance is capacitive in nature and it can be reduced to manageable level by high-frequency inputs. The method is able to determine the location of early-stage water-tree in long-distance cables using economically feasible equipment. A pattern recognition method is developed to estimate the severity of water-tree using its pulse response from the high-frequency test method. The early-warning system for water-tree appearance is a tool developed to assist the practical implementation of the high-frequency pulse detection method. Although the equipment used by the detection method is economically feasible, it is still a specialized test and not designed for constant monitoring of the system. The test also place heavy stress on the cable and it is most effective when the cable is taken offline. As the result, utilities need a method to estimate the likelihood of water-tree presence before subjecting the cable to the specialized test. The early-warning system takes advantage of naturally occurring high-frequency events in the system and uses a deviation-comparison method to estimate the probability of water-tree presence on the cable. If the likelihood is high, then the utility can use the high-frequency pulse detection method to obtain accurate results. Specific pulse response patterns can be used to calculate the capacitance of water-tree. The calculated result, however, is subjected to margins of error due to limitations from the real system. There are both long-term and short-term methods to improve the accuracy. Computation algorithm improvement allows immediate improvement on

  12. Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

    Science.gov (United States)

    Shiffman, Smadar

    2004-01-01

    Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

  13. Failures probability calculation of the energy supply of the Angra-1 reactor rods assembly

    International Nuclear Information System (INIS)

    Borba, P.R.

    1978-01-01

    This work analyses the electric power system of the Angra I PWR plant. It is demonstrated that this system is closely coupled with the safety engineering features, which are the equipments provided to prevent, limit, or mitigate the release of radioactive material and to permit the safe reactor shutdown. Event trees are used to analyse the operation of those systems which can lead to the release of radioactivity following a specified initial event. The fault trees technique is used to calculate the failure probability of the on-site electric power system [pt

  14. Probability theory a foundational course

    CERN Document Server

    Pakshirajan, R P

    2013-01-01

    This book shares the dictum of J. L. Doob in treating Probability Theory as a branch of Measure Theory and establishes this relation early. Probability measures in product spaces are introduced right at the start by way of laying the ground work to later claim the existence of stochastic processes with prescribed finite dimensional distributions. Other topics analysed in the book include supports of probability measures, zero-one laws in product measure spaces, Erdos-Kac invariance principle, functional central limit theorem and functional law of the iterated logarithm for independent variables, Skorohod embedding, and the use of analytic functions of a complex variable in the study of geometric ergodicity in Markov chains. This book is offered as a text book for students pursuing graduate programs in Mathematics and or Statistics. The book aims to help the teacher present the theory with ease, and to help the student sustain his interest and joy in learning the subject.

  15. Diffusion on a disordered Cayley tree

    International Nuclear Information System (INIS)

    Brezini, A.; Olivier, G.

    1983-08-01

    The model proposed recently by Brezini to calculate the average probability and the average size of the localization domain for an electron being localized at a given site in a disordered Cayley tree, is extended to the case of a uniform distribution for site energies. Thus, numerical results are presented in the limit of weak disorder and particular attention is paid to the states near the mobility edge. (author)

  16. Categorizing ideas about trees: a tree of trees.

    Science.gov (United States)

    Fisler, Marie; Lecointre, Guillaume

    2013-01-01

    The aim of this study is to explore whether matrices and MP trees used to produce systematic categories of organisms could be useful to produce categories of ideas in history of science. We study the history of the use of trees in systematics to represent the diversity of life from 1766 to 1991. We apply to those ideas a method inspired from coding homologous parts of organisms. We discretize conceptual parts of ideas, writings and drawings about trees contained in 41 main writings; we detect shared parts among authors and code them into a 91-characters matrix and use a tree representation to show who shares what with whom. In other words, we propose a hierarchical representation of the shared ideas about trees among authors: this produces a "tree of trees." Then, we categorize schools of tree-representations. Classical schools like "cladists" and "pheneticists" are recovered but others are not: "gradists" are separated into two blocks, one of them being called here "grade theoreticians." We propose new interesting categories like the "buffonian school," the "metaphoricians," and those using "strictly genealogical classifications." We consider that networks are not useful to represent shared ideas at the present step of the study. A cladogram is made for showing who is sharing what with whom, but also heterobathmy and homoplasy of characters. The present cladogram is not modelling processes of transmission of ideas about trees, and here it is mostly used to test for proximity of ideas of the same age and for categorization.

  17. Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

    Directory of Open Access Journals (Sweden)

    Hubert S. Gabryś

    2018-03-01

    Full Text Available PurposeThe purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP models based on the mean radiation dose to parotid glands.Material and methodsA cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0–6 months (early, 6–15 months (late, 15–24 months (long-term, and at any time (a longitudinal model after radiotherapy. Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC of univariate logistic regression models. The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively. We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post hoc analysis.ResultsNTCP models based on the parotid mean dose failed to predict xerostomia (AUCs < 0.60. The most informative predictors were found for late and long-term xerostomia. Late xerostomia correlated with the contralateral dose gradient in the anterior–posterior (AUC = 0.72 and the right–left (AUC = 0.68 direction, whereas long-term xerostomia was associated with parotid volumes (AUCs > 0.85, dose gradients in the right–left (AUCs > 0.78, and the anterior–posterior (AUCs > 0.72 direction. Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose–volume histogram (DVH spread with the generalization AUCs ranging from 0.74 to 0.88. On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection. We found no advantage in using data cleaning or class balancing

  18. Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types

    Science.gov (United States)

    Austin, Peter C.; Tu, Jack V.; Ho, Jennifer E.; Levy, Daniel; Lee, Douglas S.

    2014-01-01

    Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF. PMID:23384592

  19. New approaches to evaluating fault trees

    International Nuclear Information System (INIS)

    Sinnamon, R.M.; Andrews, J.D.

    1997-01-01

    Fault Tree Analysis is now a widely accepted technique to assess the probability and frequency of system failure in many industries. For complex systems an analysis may produce hundreds of thousands of combinations of events which can cause system failure (minimal cut sets). The determination of these cut sets can be a very time consuming process even on modern high speed digital computers. Computerised methods, such as bottom-up or top-down approaches, to conduct this analysis are now so well developed that further refinement is unlikely to result in vast reductions in computer time. It is felt that substantial improvement in computer utilisation will only result from a completely new approach. This paper describes the use of a Binary Decision Diagram for Fault Tree Analysis and some ways in which it can be efficiently implemented on a computer. In particular, attention is given to the production of a minimum form of the Binary Decision Diagram by considering the ordering that has to be given to the basic events of the fault tree

  20. Learning of Behavior Trees for Autonomous Agents

    OpenAIRE

    Colledanchise, Michele; Parasuraman, Ramviyas; Ögren, Petter

    2015-01-01

    Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, ...

  1. Multi-test decision tree and its application to microarray data classification.

    Science.gov (United States)

    Czajkowski, Marcin; Grześ, Marek; Kretowski, Marek

    2014-05-01

    The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. Optimization and analysis of decision trees and rules: Dynamic programming approach

    KAUST Repository

    Alkhalid, Abdulaziz

    2013-08-01

    This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.

  3. Optimization and analysis of decision trees and rules: Dynamic programming approach

    KAUST Repository

    Alkhalid, Abdulaziz; Amin, Talha M.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.

  4. Effects of Phylogenetic Tree Style on Student Comprehension

    Science.gov (United States)

    Dees, Jonathan Andrew

    Phylogenetic trees are powerful tools of evolutionary biology that have become prominent across the life sciences. Consequently, learning to interpret and reason from phylogenetic trees is now an essential component of biology education. However, students often struggle to understand these diagrams, even after explicit instruction. One factor that has been observed to affect student understanding of phylogenetic trees is style (i.e., diagonal or bracket). The goal of this dissertation research was to systematically explore effects of style on student interpretations and construction of phylogenetic trees in the context of an introductory biology course. Before instruction, students were significantly more accurate with bracket phylogenetic trees for a variety of interpretation and construction tasks. Explicit instruction that balanced the use of diagonal and bracket phylogenetic trees mitigated some, but not all, style effects. After instruction, students were significantly more accurate for interpretation tasks involving taxa relatedness and construction exercises when using the bracket style. Based on this dissertation research and prior studies on style effects, I advocate for introductory biology instructors to use only the bracket style. Future research should examine causes of style effects and variables other than style to inform the development of research-based instruction that best supports student understanding of phylogenetic trees.

  5. Spatial probability aids visual stimulus discrimination

    Directory of Open Access Journals (Sweden)

    Michael Druker

    2010-08-01

    Full Text Available We investigated whether the statistical predictability of a target's location would influence how quickly and accurately it was classified. Recent results have suggested that spatial probability can be a cue for the allocation of attention in visual search. One explanation for probability cuing is spatial repetition priming. In our two experiments we used probability distributions that were continuous across the display rather than relying on a few arbitrary screen locations. This produced fewer spatial repeats and allowed us to dissociate the effect of a high probability location from that of short-term spatial repetition. The task required participants to quickly judge the color of a single dot presented on a computer screen. In Experiment 1, targets were more probable in an off-center hotspot of high probability that gradually declined to a background rate. Targets garnered faster responses if they were near earlier target locations (priming and if they were near the high probability hotspot (probability cuing. In Experiment 2, target locations were chosen on three concentric circles around fixation. One circle contained 80% of targets. The value of this ring distribution is that it allowed for a spatially restricted high probability zone in which sequentially repeated trials were not likely to be physically close. Participant performance was sensitive to the high-probability circle in addition to the expected effects of eccentricity and the distance to recent targets. These two experiments suggest that inhomogeneities in spatial probability can be learned and used by participants on-line and without prompting as an aid for visual stimulus discrimination and that spatial repetition priming is not a sufficient explanation for this effect. Future models of attention should consider explicitly incorporating the probabilities of targets locations and features.

  6. Undergraduate Students’ Initial Ability in Understanding Phylogenetic Tree

    Science.gov (United States)

    Sa'adah, S.; Hidayat, T.; Sudargo, Fransisca

    2017-04-01

    The Phylogenetic tree is a visual representation depicts a hypothesis about the evolutionary relationship among taxa. Evolutionary experts use this representation to evaluate the evidence for evolution. The phylogenetic tree is currently growing for many disciplines in biology. Consequently, learning about the phylogenetic tree has become an important part of biological education and an interesting area of biology education research. Skill to understanding and reasoning of the phylogenetic tree, (called tree thinking) is an important skill for biology students. However, research showed many students have difficulty in interpreting, constructing, and comparing among the phylogenetic tree, as well as experiencing a misconception in the understanding of the phylogenetic tree. Students are often not taught how to reason about evolutionary relationship depicted in the diagram. Students are also not provided with information about the underlying theory and process of phylogenetic. This study aims to investigate the initial ability of undergraduate students in understanding and reasoning of the phylogenetic tree. The research method is the descriptive method. Students are given multiple choice questions and an essay that representative by tree thinking elements. Each correct answer made percentages. Each student is also given questionnaires. The results showed that the undergraduate students’ initial ability in understanding and reasoning phylogenetic tree is low. Many students are not able to answer questions about the phylogenetic tree. Only 19 % undergraduate student who answered correctly on indicator evaluate the evolutionary relationship among taxa, 25% undergraduate student who answered correctly on indicator applying concepts of the clade, 17% undergraduate student who answered correctly on indicator determines the character evolution, and only a few undergraduate student who can construct the phylogenetic tree.

  7. Preventing KPI Violations in Business Processes based on Decision Tree Learning and Proactive Runtime Adaptation

    Directory of Open Access Journals (Sweden)

    Dimka Karastoyanova

    2012-01-01

    Full Text Available The performance of business processes is measured and monitored in terms of Key Performance Indicators (KPIs. If the monitoring results show that the KPI targets are violated, the underlying reasons have to be identified and the process should be adapted accordingly to address the violations. In this paper we propose an integrated monitoring, prediction and adaptation approach for preventing KPI violations of business process instances. KPIs are monitored continuously while the process is executed. Additionally, based on KPI measurements of historical process instances we use decision tree learning to construct classification models which are then used to predict the KPI value of an instance while it is still running. If a KPI violation is predicted, we identify adaptation requirements and adaptation strategies in order to prevent the violation.

  8. Sparse suffix tree construction in small space

    DEFF Research Database (Denmark)

    Bille, Philip; Fischer, Johannes; Gørtz, Inge Li

    2013-01-01

    the correct tree with high probability. We then give a Las-Vegas algorithm which also uses O(b) space and runs in the same time bounds with high probability when b = O(√n). Furthermore, additional tradeoffs between the space usage and the construction time for the Monte-Carlo algorithm are given......., which may be of independent interest, that allows to efficiently answer b longest common prefix queries on suffixes of T, using only O(b) space. We expect that this technique will prove useful in many other applications in which space usage is a concern. Our first solution is Monte-Carlo and outputs...

  9. Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.

    Directory of Open Access Journals (Sweden)

    Suyu Mei

    Full Text Available Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM, where support vector machine (SVM is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.

  10. Rule-based detection of intrathoracic airway trees

    International Nuclear Information System (INIS)

    Sonka, M.; Park, W.; Hoffman, E.A.

    1996-01-01

    New sensitive and reliable methods for assessing alterations in regional lung structure and function are critically important for the investigation and treatment of pulmonary diseases. Accurate identification of the airway tree will provide an assessment of airway structure and will provide a means by which multiple volumetric images of the lung at the same lung volume over time can be used to assess regional parenchymal changes. The authors describe a novel rule-based method for the segmentation of airway trees from three-dimensional (3-D) sets of computed tomography (CT) images, and its validation. The presented method takes advantage of a priori anatomical knowledge about pulmonary airway and vascular trees and their interrelationships. The method is based on a combination of 3-D seeded region growing that is used to identify large airways, rule-based two-dimensional (2-D) segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the 3-D set of slices resulting in a tree-like airway structure. The method was validated in 40 3-mm-thick CT sections from five data sets of canine lungs scanned via electron beam CT in vivo with lung volume held at a constant pressure. The method's performance was compared with that of the conventional 3-D region growing method. The method substantially outperformed an existing conventional approach to airway tree detection

  11. Study of the reliability of the Auxiliary Feedwater System of a LWR nuclear power plant through the Fault Tree and Bayesian Network

    International Nuclear Information System (INIS)

    Lava, Deise Diana

    2016-01-01

    This paper aims to present a study of the reliability of the Auxiliary Feedwater System (AFWS) through the methods of Fault Tree and Bayesian Network. Therefore, the paper consists of a literature review of the history of nuclear energy and the methodologies used. The AFWS is responsible for providing water system to cool the secondary circuit of nuclear reactors of the PWR type when normal feeding water system failure. How this system operates only when the primary system fails, it is expected that the AFWS failure probability is very low. The AFWS failure probability is divided into two cases: the first is the probability of failure in the first eight hours of operation and the second is the probability of failure after eight hours of operation, considering that the system has not failed within the first eight hours. The calculation of the probability of failure of the second case was made through the use of Fault Tree and Bayesian Network, that it was constructed from the Fault Tree. The results of the failure probability obtained were very close, on the order of 10 -3 . (author)

  12. Epistemic-based investigation of the probability of hazard scenarios using Bayesian network for the lifting operation of floating objects

    Science.gov (United States)

    Toroody, Ahmad Bahoo; Abaiee, Mohammad Mahdi; Gholamnia, Reza; Ketabdari, Mohammad Javad

    2016-09-01

    Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method (SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types: the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.

  13. Bounds on Average Time Complexity of Decision Trees

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    In this chapter, bounds on the average depth and the average weighted depth of decision trees are considered. Similar problems are studied in search theory [1], coding theory [77], design and analysis of algorithms (e.g., sorting) [38]. For any diagnostic problem, the minimum average depth of decision tree is bounded from below by the entropy of probability distribution (with a multiplier 1/log2 k for a problem over a k-valued information system). Among diagnostic problems, the problems with a complete set of attributes have the lowest minimum average depth of decision trees (e.g, the problem of building optimal prefix code [1] and a blood test study in assumption that exactly one patient is ill [23]). For such problems, the minimum average depth of decision tree exceeds the lower bound by at most one. The minimum average depth reaches the maximum on the problems in which each attribute is "indispensable" [44] (e.g., a diagnostic problem with n attributes and kn pairwise different rows in the decision table and the problem of implementing the modulo 2 summation function). These problems have the minimum average depth of decision tree equal to the number of attributes in the problem description. © Springer-Verlag Berlin Heidelberg 2011.

  14. From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

    Science.gov (United States)

    2010-01-01

    Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for

  15. From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

    Directory of Open Access Journals (Sweden)

    Dawyndt Peter

    2010-01-01

    Full Text Available Abstract Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the

  16. From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

    Science.gov (United States)

    Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

    2010-01-30

    Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial

  17. Unders and Overs: Using a Dice Game to Illustrate Basic Probability Concepts

    Science.gov (United States)

    McPherson, Sandra Hanson

    2015-01-01

    In this paper, the dice game "Unders and Overs" is described and presented as an active learning exercise to introduce basic probability concepts. The implementation of the exercise is outlined and the resulting presentation of various probability concepts are described.

  18. Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees.

    Science.gov (United States)

    Williams, Philip H; Eyles, Rod; Weiller, Georg

    2012-01-01

    MicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require "read count" to be included with the input sequences, which restricts their use to deep-sequencing data. Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set. We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources. A miRNA-predictive decision-tree model has been developed by supervised machine learning. It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected. Some of the most critical features for training the predictor are the miRNA:miRNA(∗) duplex energy and the number of mismatches in the duplex. We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.

  19. Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees

    Directory of Open Access Journals (Sweden)

    Philip H. Williams

    2012-01-01

    Full Text Available MicroRNAs (miRNAs are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require “read count” to be included with the input sequences, which restricts their use to deep-sequencing data. Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set. We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources. A miRNA-predictive decision-tree model has been developed by supervised machine learning. It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected. Some of the most critical features for training the predictor are the miRNA:miRNA∗ duplex energy and the number of mismatches in the duplex. We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.

  20. Learning Binomial Probability Concepts with Simulation, Random Numbers and a Spreadsheet

    Science.gov (United States)

    Rochowicz, John A., Jr.

    2005-01-01

    This paper introduces the reader to the concepts of binomial probability and simulation. A spreadsheet is used to illustrate these concepts. Random number generators are great technological tools for demonstrating the concepts of probability. Ideas of approximation, estimation, and mathematical usefulness provide numerous ways of learning…

  1. Fault-Tree Modeling of Safety-Critical Network Communication in a Digitalized Nuclear Power Plant

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sang Hun; Kang, Hyun Gook [KAIST, Daejeon (Korea, Republic of)

    2015-10-15

    To achieve technical self-reliance for nuclear I and C systems in Korea, the Advanced Power Reactor 1400 (APR-1400) man-machine interface system (MMIS) architecture was developed by the Korea Atomic Energy Research Institute (KAERI). As one of the systems in the developed MMIS architecture, the Engineered Safety Feature-Component Control System (ESF-CCS) employs a network communication system for the transmission of safety-critical information from group controllers (GCs) to loop controllers (LCs) to effectively accommodate the vast number of field controllers. The developed fault-tree model was then applied to several case studies. As an example of the development of a fault-tree model for ESF-CCS signal failure, the fault-tree model of ESF-CCS signal failure for CS pump PP01A in the CSAS condition was designed by considering the identified hazardous states of network failure that would result in a failure to provide input signals to the corresponding LC. The quantitative results for four case studies demonstrated that the probability of overall network communication failure, which was calculated as the sum of the failure probability associated with each failure cause, contributes up to 1.88% of the probability of ESF-CCS signal failure for the CS pump considered in the case studies.

  2. Mapping aerial metal deposition in metropolitan areas from tree bark: a case study in Sheffield, England.

    Science.gov (United States)

    Schelle, E; Rawlins, B G; Lark, R M; Webster, R; Staton, I; McLeod, C W

    2008-09-01

    We investigated the use of metals accumulated on tree bark for mapping their deposition across metropolitan Sheffield by sampling 642 trees of three common species. Mean concentrations of metals were generally an order of magnitude greater than in samples from a remote uncontaminated site. We found trivially small differences among tree species with respect to metal concentrations on bark, and in subsequent statistical analyses did not discriminate between them. We mapped the concentrations of As, Cd and Ni by lognormal universal kriging using parameters estimated by residual maximum likelihood (REML). The concentrations of Ni and Cd were greatest close to a large steel works, their probable source, and declined markedly within 500 m of it and from there more gradually over several kilometres. Arsenic was much more evenly distributed, probably as a result of locally mined coal burned in domestic fires for many years. Tree bark seems to integrate airborne pollution over time, and our findings show that sampling and analysing it are cost-effective means of mapping and identifying sources.

  3. Isoprene emission from tropical tree species

    International Nuclear Information System (INIS)

    Padhy, P.K.; Varshney, C.K.

    2005-01-01

    Foliar emission of isoprene was measured in nine commonly growing tree species of Delhi, India. Dynamic flow enclosure technique was used and gas samples were collected onto Tenax-GC/Carboseive cartridges, which were then attached to the sample injection system in the gas chromatograph (GC). Eluting compounds were analysed using a flame ionisation detector (FID). Out of the nine tree species, isoprene emission was found in six species (Eucalyptus sp., Ficus benghalensis, Ficus religiosa, Mangifera indica, Melia azedarach, and Syzygium jambolanum), whereas, in the remaining three tree species (Alstonia scholaris, Azadirachta indica, and Cassia fistula) no isoprene emission was detected or the levels of emission were negligible or below the detection limit (BDL). Among six tree species, the highest hourly emission (10.2±6.8 μg g -1 leaf dry weight, average of five seasons) was observed in Ficus religiosa, while minimum emission was from Melia azedarach (2.2±4.9 μg g -1 leaf dry weight, average of five seasons). Isoprene emission (average of six species), over five seasons, was found to vary between 3.9 and 8.5 μg g -1 leaf dry weight during the rainy season. In addition, significant diurnal variation in isoprene emission was observed in each species. The preliminary estimate made in this study on the annual biogenic VOC emission from India may probably be the first of its kind from this part of the world. - Isoprene flux (diurnal and seasonal) from some tropical tree species was estimated and a regional comparison was made

  4. Isoprene emission from tropical tree species

    Energy Technology Data Exchange (ETDEWEB)

    Padhy, P.K. [School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110 067 (India)]. E-mail: padhypk2003@yahoo.com; Varshney, C.K. [School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110 067 (India)

    2005-05-01

    Foliar emission of isoprene was measured in nine commonly growing tree species of Delhi, India. Dynamic flow enclosure technique was used and gas samples were collected onto Tenax-GC/Carboseive cartridges, which were then attached to the sample injection system in the gas chromatograph (GC). Eluting compounds were analysed using a flame ionisation detector (FID). Out of the nine tree species, isoprene emission was found in six species (Eucalyptus sp., Ficus benghalensis, Ficus religiosa, Mangifera indica, Melia azedarach, and Syzygium jambolanum), whereas, in the remaining three tree species (Alstonia scholaris, Azadirachta indica, and Cassia fistula) no isoprene emission was detected or the levels of emission were negligible or below the detection limit (BDL). Among six tree species, the highest hourly emission (10.2{+-}6.8 {mu}g g{sup -1} leaf dry weight, average of five seasons) was observed in Ficus religiosa, while minimum emission was from Melia azedarach (2.2{+-}4.9 {mu}g g{sup -1} leaf dry weight, average of five seasons). Isoprene emission (average of six species), over five seasons, was found to vary between 3.9 and 8.5 {mu}g g{sup -1} leaf dry weight during the rainy season. In addition, significant diurnal variation in isoprene emission was observed in each species. The preliminary estimate made in this study on the annual biogenic VOC emission from India may probably be the first of its kind from this part of the world. - Isoprene flux (diurnal and seasonal) from some tropical tree species was estimated and a regional comparison was made.

  5. Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.

    Science.gov (United States)

    Deshwar, Amit G; Vembu, Shankar; Morris, Quaid

    2015-01-01

    Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor…

  6. Elastic modulus of tree frog adhesive toe pads.

    Science.gov (United States)

    Barnes, W Jon P; Goodwyn, Pablo J Perez; Nokhbatolfoghahai, Mohsen; Gorb, Stanislav N

    2011-10-01

    Previous work using an atomic force microscope in nanoindenter mode indicated that the outer, 10- to 15-μm thick, keratinised layer of tree frog toe pads has a modulus of elasticity equivalent to silicone rubber (5-15 MPa) (Scholz et al. 2009), but gave no information on the physical properties of deeper structures. In this study, micro-indentation is used to measure the stiffness of whole toe pads of the tree frog, Litoria caerulea. We show here that tree frog toe pads are amongst the softest of biological structures (effective elastic modulus 4-25 kPa), and that they exhibit a gradient of stiffness, being stiffest on the outside. This stiffness gradient results from the presence of a dense network of capillaries lying beneath the pad epidermis, which probably has a shock absorbing function. Additionally, we compare the physical properties (elastic modulus, work of adhesion, pull-off force) of the toe pads of immature and adult frogs.

  7. Small Worlds in the Tree Topologies of Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Qiao, Li; Lingguo, Cui; Baihai, Zhang

    2010-01-01

    In this study, the characteristics of small worlds are investigated in the context of the tree topologies of wireless sensor networks. Tree topologies, which construct spatial graphs with larger characteristic path lengths than random graphs and small clustering coefficients, are ubiquitous...... in wireless sensor networks. Suffering from the link rewiring or the link addition, the characteristic path length of the tree topology reduces rapidly and the clustering coefficient increases greatly. The variety of characteristic path length influences the time synchronization characteristics of wireless...... sensor networks greatly. With the increase of the link rewiring or the link addition probability, the time synchronization error decreases drastically. Two novel protocols named LEACH-SW and TREEPSI-SW are proposed to improve the performances of the sensor networks, in which the small world...

  8. The steady and vibrating statuses of tulip tree leaves in wind

    Directory of Open Access Journals (Sweden)

    Yuanyuan Zhu

    2017-01-01

    Full Text Available The study of tree leaf aerodynamics is useful to tree protection, solar panel design and development of new power generation technology. 73 tulip leaves were tested in suspended condition and with front as well as back surface of the lamina facing wind. Three types of vibrating statuses, two types of steady statuses, and five critical wind speeds were observed. The existence probabilities of the statuses and criticals, the probability density distribution of every critical over the range of wind speed 0–27 m/s, and the expected values of the criticals were obtained by statistics. The critical Reynolds number, defined by critical wind speed and lamina length, shows an increasing trend with increasing the lamina area or length to width ratio of the lamina, but it shows no trend of increase or decrease with increasing the length ratio of petiole to lamina.

  9. The Studies of Decision Tree in Estimation of Breast Cancer Risk by Using Polymorphism Nucleotide

    Directory of Open Access Journals (Sweden)

    Frida Seyedmir

    2017-07-01

    Full Text Available Abstract Introduction:   Decision tree is the data mining tools to collect, accurate prediction and sift information from massive amounts of data that are used widely in the field of computational biology and bioinformatics. In bioinformatics can be predict on diseases, including breast cancer. The use of genomic data including single nucleotide polymorphisms is a very important factor in predicting the risk of diseases. The number of seven important SNP among hundreds of thousands genetic markers were identified as factors associated with breast cancer. The objective of this study is to evaluate the training data on decision tree predictor error of the risk of breast cancer by using single nucleotide polymorphism genotype. Methods: The risk of breast cancer were calculated associated with the use of SNP formula:xj = fo * In human,  The decision tree can be used To predict the probability of disease using single nucleotide polymorphisms .Seven SNP with different odds ratio associated with breast cancer considered and coding and design of decision tree model, C4.5, by  Csharp2013 programming language were done. In the decision tree created with the coding, the four important associated SNP was considered. The decision tree error in two case of coding and using WEKA were assessment and percentage of decision tree accuracy in prediction of breast cancer were calculated. The number of trained samples was obtained with systematic sampling. With coding, two scenarios as well as software WEKA, three scenarios with different sets of data and the number of different learning and testing, were evaluated. Results: In both scenarios of coding, by increasing the training percentage from 66/66 to 86/42, the error reduced from 55/56 to 9/09. Also by running of WEKA on three scenarios with different sets of data, the number of different education, and different tests by increasing records number from 81 to 2187, the error rate decreased from 48/15 to 13

  10. Decision Tree Approach to Discovering Fraud in Leasing Agreements

    Directory of Open Access Journals (Sweden)

    Horvat Ivan

    2014-09-01

    Full Text Available Background: Fraud attempts create large losses for financing subjects in modern economies. At the same time, leasing agreements have become more and more popular as a means of financing objects such as machinery and vehicles, but are more vulnerable to fraud attempts. Objectives: The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements. Methods/Approach: Real-world data from one Croatian leasing firm was used for creating tow models for fraud detection in leasing. The decision tree method was used for creating a classification model, and the CHAID algorithm was deployed. Results: The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud. Conclusions: In order to enhance the probability of the developed model, it would be necessary to develop software that would enable automated, quick and transparent retrieval of data from the system, processing according to the rules and displaying the results in multiple categories.

  11. Lesser prairie-chicken avoidance of trees in a grassland landscape

    Science.gov (United States)

    Lautenbach, Joseph M.; Plumb, Reid T.; Robinson, Samantha G.; Hagen, Christian A.; Haukos, David A.; Pitman, James C.

    2016-01-01

    Grasslands are among the most imperiled ecosystems in North America. Reasons that grasslands are threatened include conversion to row-crop agriculture, fragmentation, and changes in fire regimes. The reduction of fire processes in remaining prairies has resulted in tree encroachment and establishment in grasslands, further reducing grassland quantity and quality. Grassland birds have been experiencing precipitous population declines in recent decades, commensurate with landscape changes to grasslands. The lesser prairie-chicken (Tympanuchus pallidicinctus Ridgway) is a declining species of prairie grouse of conservation concern. We used second- and third-order habitat selection metrics to test if female lesser prairie-chickens avoid grasslands where trees were present. Our results indicated that female lesser prairie-chickens selected habitats avoiding the nearest trees by 283 m on average, nearly twice as far as would be expected at random. Lesser prairie-chickens were 40 times more likely to use habitats with tree densities of 0 trees ∙ ha− 1 than habitats with 5 trees ∙ ha− 1. Probability of use indicated that lesser prairie-chickens were 19 times more likely to use habitats 1000 m from the nearest tree when compared with using habitats 0 m from the nearest tree. Nest survival was not affected at densities 2 trees ∙ ha− 1. Avoidance of trees could be due to perceived increased predation risk, reduced habitat quality, or a combination of these potentially confounding factors. Preventing further establishment and expansion of trees in landscapes occupied by lesser prairie-chickens could contribute to the continued persistence of the species. Additionally, restoring grasslands through tree removal may facilitate conservation efforts for grassland species such as the lesser prairie-chicken by improving habitat quality and promoting expansion of occupied range.

  12. Probability safety assessment of the Kozloduy-5 and Kozloduy-6 reactors

    Energy Technology Data Exchange (ETDEWEB)

    Boyadzhiev, A; Manchev, B [Risk Engineering Ltd., Sofia (Bulgaria)

    1996-12-31

    A probability safety assessment (PSA) of Level 1 (assessment of plant failures leading to the determination of core damage frequency) has been carried out for the NPP Kozloduy Units 5 and 6 (reactors WWER-1000). The scope of the study includes all significant accident initiators including seismic (earthquake) and fire initiators. Event trees for all initiators and fault trees for front line systems, support systems and major safety systems have been built. A distribution of the different initiators has been established as follows: internal initiators - 85%, seismic initiators - 5%, fire initiators- 10%. The loss of offsite power was identified as main contributor from the internal initiators with frequency 1,1.10{sup -4}/y. It is concluded that the safety functions of WWER-1000 are adequately covered by the safety systems. 4 refs., 2 tabs.

  13. Application of subset simulation methods to dynamic fault tree analysis

    International Nuclear Information System (INIS)

    Liu Mengyun; Liu Jingquan; She Ding

    2015-01-01

    Although fault tree analysis has been implemented in the nuclear safety field over the past few decades, it was recently criticized for the inability to model the time-dependent behaviors. Several methods are proposed to overcome this disadvantage, and dynamic fault tree (DFT) has become one of the research highlights. By introducing additional dynamic gates, DFT is able to describe the dynamic behaviors like the replacement of spare components or the priority of failure events. Using Monte Carlo simulation (MCS) approach to solve DFT has obtained rising attention, because it can model the authentic behaviors of systems and avoid the limitations in the analytical method. In this paper, it provides an overview and MCS information for DFT analysis, including the sampling of basic events and the propagation rule for logic gates. When calculating rare-event probability, large amount of simulations in standard MCS are required. To improve the weakness, subset simulation (SS) approach is applied. Using the concept of conditional probability and Markov Chain Monte Carlo (MCMC) technique, the SS method is able to accelerate the efficiency of exploring the failure region. Two cases are tested to illustrate the performance of SS approach, and the numerical results suggest that it gives high efficiency when calculating complicated systems with small failure probabilities. (author)

  14. Monte-Carlo Tree Search by Best Arm Identification

    NARCIS (Netherlands)

    E. Kaufmann (Emilie); W.M. Koolen-Wijkstra (Wouter)

    2017-01-01

    textabstractRecent advances in bandit tools and techniques for sequential learning are steadily enabling new applications and are promising the resolution of a range of challenging related problems. We study the game tree search problem, where the goal is to quickly identify the optimal move in a

  15. Monte-Carlo tree search by best arm identification

    NARCIS (Netherlands)

    E. Kaufmann (Emilie); W.M. Koolen-Wijkstra (Wouter)

    2017-01-01

    textabstractRecent advances in bandit tools and techniques for sequential learning are steadily enabling new applications and are promising the resolution of a range of challenging related problems. We study the game tree search problem, where the goal is to quickly identify the optimal move in a

  16. Tree compression with top trees

    DEFF Research Database (Denmark)

    Bille, Philip; Gørtz, Inge Li; Landau, Gad M.

    2013-01-01

    We introduce a new compression scheme for labeled trees based on top trees [3]. Our compression scheme is the first to simultaneously take advantage of internal repeats in the tree (as opposed to the classical DAG compression that only exploits rooted subtree repeats) while also supporting fast...

  17. Tree compression with top trees

    DEFF Research Database (Denmark)

    Bille, Philip; Gørtz, Inge Li; Landau, Gad M.

    2015-01-01

    We introduce a new compression scheme for labeled trees based on top trees. Our compression scheme is the first to simultaneously take advantage of internal repeats in the tree (as opposed to the classical DAG compression that only exploits rooted subtree repeats) while also supporting fast...

  18. Radial Growth and Physiological Response of Coniferous Trees to Arctic Amplification

    Science.gov (United States)

    Tei, Shunsuke; Sugimoto, Atsuko; Liang, Maochang; Yonenobu, Hitoshi; Matsuura, Yojiro; Osawa, Akira; Sato, Hisashi; Fujinuma, Junichi; Maximov, Trofim

    2017-11-01

    We describe the physiological responses of boreal conifers to climate change for the past 112 years using ring-width and carbon isotope ratio (δ13C) chronologies at six forest sites in northern Eurasia and Canada. Responses differed among regions, depending on their climatic and/or geographic characteristics. Tree radial growth decreased over the past 52 years in central eastern Siberia with the higher rate of summer temperature increase than other regions, as indicated by the negative correlation between radial growth and summer temperature, but increased in northern Europe and Canada. Changes in tree-ring δ13C indicated that recent climatic conditions have induced stronger drought stress for trees from central eastern Siberia than for those from other regions. The observed tree growth trends were compared to those simulated using a dynamic global vegetation model. Although the modeled annual net primary production (NPP) for trees generally exhibited similar decadal variation to radial growth, simulations did not show a recent decrease in tree growth, even in central eastern Siberia. This was probably due to an overestimation of the sensitivity of modeled tree NPP to precipitation. Our results suggest that the tree NPP forecasted under the expected future increases in temperature and average precipitation might be overestimated, especially in severely dry regions such as central eastern Siberia.

  19. Selective Tree-ring Models: A Novel Method for Reconstructing Streamflow Using Tree Rings

    Science.gov (United States)

    Foard, M. B.; Nelson, A. S.; Harley, G. L.

    2017-12-01

    Surface water is among the most instrumental and vulnerable resources in the Northwest United States (NW). Recent observations show that overall water quantity is declining in streams across the region, while extreme flooding events occur more frequently. Historical streamflow models inform probabilities of extreme flow events (flood or drought) by describing frequency and duration of past events. There are numerous examples of tree-rings being utilized to reconstruct streamflow in the NW. These models confirm that tree-rings are highly accurate at predicting streamflow, however there are many nuances that limit their applicability through time and space. For example, most models predict streamflow from hydrologically altered rivers (e.g. dammed, channelized) which may hinder our ability to predict natural prehistoric flow. They also have a tendency to over/under-predict extreme flow events. Moreover, they often neglect to capture the changing relationships between tree-growth and streamflow over time and space. To address these limitations, we utilized national tree-ring and streamflow archives to investigate the relationships between the growth of multiple coniferous species and free-flowing streams across the NW using novel species-and site-specific streamflow models - a term we coined"selective tree-ring models." Correlation function analysis and regression modeling were used to evaluate the strengths and directions of the flow-growth relationships. Species with significant relationships in the same direction were identified as strong candidates for selective models. Temporal and spatial patterns of these relationships were examined using running correlations and inverse distance weighting interpolation, respectively. Our early results indicate that (1) species adapted to extreme climates (e.g. hot-dry, cold-wet) exhibit the most consistent relationships across space, (2) these relationships weaken in locations with mild climatic variability, and (3) some

  20. Anderson transition on the Cayley tree as a traveling wave critical point for various probability distributions

    International Nuclear Information System (INIS)

    Monthus, Cecile; Garel, Thomas

    2009-01-01

    For Anderson localization on the Cayley tree, we study the statistics of various observables as a function of the disorder strength W and the number N of generations. We first consider the Landauer transmission T N . In the localized phase, its logarithm follows the traveling wave form T N ≅(ln T N )-bar + ln t* where (i) the disorder-averaged value moves linearly (ln(T N ))-bar≅-N/ξ loc and the localization length diverges as ξ loc ∼(W-W c ) -ν loc with ν loc = 1 and (ii) the variable t* is a fixed random variable with a power-law tail P*(t*) ∼ 1/(t*) 1+β(W) for large t* with 0 N are governed by rare events. In the delocalized phase, the transmission T N remains a finite random variable as N → ∞, and we measure near criticality the essential singularity (ln(T ∞ ))-bar∼-|W c -W| -κ T with κ T ∼ 0.25. We then consider the statistical properties of normalized eigenstates Σ x |ψ(x)| 2 = 1, in particular the entropy S = -Σ x |ψ(x)| 2 ln |ψ(x)| 2 and the inverse participation ratios (IPR) I q = Σ x |ψ(x)| 2q . In the localized phase, the typical entropy diverges as S typ ∼( W-W c ) -ν S with ν S ∼ 1.5, whereas it grows linearly as S typ (N) ∼ N in the delocalized phase. Finally for the IPR, we explain how closely related variables propagate as traveling waves in the delocalized phase. In conclusion, both the localized phase and the delocalized phase are characterized by the traveling wave propagation of some probability distributions, and the Anderson localization/delocalization transition then corresponds to a traveling/non-traveling critical point. Moreover, our results point toward the existence of several length scales that diverge with different exponents ν at criticality

  1. Shade tree spatial structure and pod production explain frosty pod rot intensity in cacao agroforests, Costa Rica.

    Science.gov (United States)

    Gidoin, Cynthia; Avelino, Jacques; Deheuvels, Olivier; Cilas, Christian; Bieng, Marie Ange Ngo

    2014-03-01

    Vegetation composition and plant spatial structure affect disease intensity through resource and microclimatic variation effects. The aim of this study was to evaluate the independent effect and relative importance of host composition and plant spatial structure variables in explaining disease intensity at the plot scale. For that purpose, frosty pod rot intensity, a disease caused by Moniliophthora roreri on cacao pods, was monitored in 36 cacao agroforests in Costa Rica in order to assess the vegetation composition and spatial structure variables conducive to the disease. Hierarchical partitioning was used to identify the most causal factors. Firstly, pod production, cacao tree density and shade tree spatial structure had significant independent effects on disease intensity. In our case study, the amount of susceptible tissue was the most relevant host composition variable for explaining disease intensity by resource dilution. Indeed, cacao tree density probably affected disease intensity more by the creation of self-shading rather than by host dilution. Lastly, only regularly distributed forest trees, and not aggregated or randomly distributed forest trees, reduced disease intensity in comparison to plots with a low forest tree density. A regular spatial structure is probably crucial to the creation of moderate and uniform shade as recommended for frosty pod rot management. As pod production is an important service expected from these agroforests, shade tree spatial structure may be a lever for integrated management of frosty pod rot in cacao agroforests.

  2. Heart sounds analysis using probability assessment

    Czech Academy of Sciences Publication Activity Database

    Plešinger, Filip; Viščor, Ivo; Halámek, Josef; Jurčo, Juraj; Jurák, Pavel

    2017-01-01

    Roč. 38, č. 8 (2017), s. 1685-1700 ISSN 0967-3334 R&D Projects: GA ČR GAP102/12/2034; GA MŠk(CZ) LO1212; GA MŠk ED0017/01/01 Institutional support: RVO:68081731 Keywords : heart sounds * FFT * machine learning * signal averaging * probability assessment Subject RIV: FS - Medical Facilities ; Equipment OBOR OECD: Medical engineering Impact factor: 2.058, year: 2016

  3. A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment

    International Nuclear Information System (INIS)

    Purba, Julwan Hendry

    2014-01-01

    Highlights: • We propose a fuzzy-based reliability approach to evaluate basic event reliabilities. • It implements the concepts of failure possibilities and fuzzy sets. • Experts evaluate basic event failure possibilities using qualitative words. • Triangular fuzzy numbers mathematically represent qualitative failure possibilities. • It is a very good alternative for conventional reliability approach. - Abstract: Fault tree analysis has been widely utilized as a tool for nuclear power plant probabilistic safety assessment. This analysis can be completed only if all basic events of the system fault tree have their quantitative failure rates or failure probabilities. However, it is difficult to obtain those failure data due to insufficient data, environment changing or new components. This study proposes a fuzzy-based reliability approach to evaluate basic events of system fault trees whose failure precise probability distributions of their lifetime to failures are not available. It applies the concept of failure possibilities to qualitatively evaluate basic events and the concept of fuzzy sets to quantitatively represent the corresponding failure possibilities. To demonstrate the feasibility and the effectiveness of the proposed approach, the actual basic event failure probabilities collected from the operational experiences of the David–Besse design of the Babcock and Wilcox reactor protection system fault tree are used to benchmark the failure probabilities generated by the proposed approach. The results confirm that the proposed fuzzy-based reliability approach arises as a suitable alternative for the conventional probabilistic reliability approach when basic events do not have the corresponding quantitative historical failure data for determining their reliability characteristics. Hence, it overcomes the limitation of the conventional fault tree analysis for nuclear power plant probabilistic safety assessment

  4. OBEST: The Object-Based Event Scenario Tree Methodology

    International Nuclear Information System (INIS)

    WYSS, GREGORY D.; DURAN, FELICIA A.

    2001-01-01

    Event tree analysis and Monte Carlo-based discrete event simulation have been used in risk assessment studies for many years. This report details how features of these two methods can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology with some of the best features of each. The resultant Object-Based Event Scenarios Tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible (especially those that exhibit inconsistent or variable event ordering, which are difficult to represent in an event tree analysis). Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST method uses a recursive algorithm to solve the object model and identify all possible scenarios and their associated probabilities. Since scenario likelihoods are developed directly by the solution algorithm, they need not be computed by statistical inference based on Monte Carlo observations (as required by some discrete event simulation methods). Thus, OBEST is not only much more computationally efficient than these simulation methods, but it also discovers scenarios that have extremely low probabilities as a natural analytical result--scenarios that would likely be missed by a Monte Carlo-based method. This report documents the OBEST methodology, the demonstration software that implements it, and provides example OBEST models for several different application domains, including interactions among failing interdependent infrastructure systems, circuit analysis for fire risk evaluation in nuclear power plants, and aviation safety studies

  5. Tree-indexed processes: a high level crossing analysis

    Directory of Open Access Journals (Sweden)

    Mark Kelbert

    2003-01-01

    Full Text Available Consider a branching diffusion process on R1 starting at the origin. Take a high level u>0 and count the number R(u,n of branches reaching u by generation n. Let Fk,n(u be the probability P(R(u,nprobabilities Fk(u is introduced and the structure of the set of solutions is analysed. We interpret Fk(u as a potential ruin probability in the situation of a multiple choice of a decision taken at vertices of a ‘logical tree’. It is shown that, unlike the standard risk theory, the above equation has a manifold of solutions. Also an analogue of Lundberg's bound for branching diffusion is derived.

  6. Generalising tree traversals and tree transformations to DAGs

    DEFF Research Database (Denmark)

    Bahr, Patrick; Axelsson, Emil

    2017-01-01

    We present a recursion scheme based on attribute grammars that can be transparently applied to trees and acyclic graphs. Our recursion scheme allows the programmer to implement a tree traversal or a tree transformation and then apply it to compact graph representations of trees instead. The resul......We present a recursion scheme based on attribute grammars that can be transparently applied to trees and acyclic graphs. Our recursion scheme allows the programmer to implement a tree traversal or a tree transformation and then apply it to compact graph representations of trees instead...... as the complementing theory with a number of examples....

  7. Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions.

    Science.gov (United States)

    Dinov, Ivo D; Siegrist, Kyle; Pearl, Dennis K; Kalinin, Alexandr; Christou, Nicolas

    2016-06-01

    Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability distributions. However, a few dozen families of distributions are commonly defined and are frequently used in practice for problem solving, experimental applications, and theoretical studies. In this paper, we present a new computational and graphical infrastructure, the Distributome , which facilitates the discovery, exploration and application of diverse spectra of probability distributions. The extensible Distributome infrastructure provides interfaces for (human and machine) traversal, search, and navigation of all common probability distributions. It also enables distribution modeling, applications, investigation of inter-distribution relations, as well as their analytical representations and computational utilization. The entire Distributome framework is designed and implemented as an open-source, community-built, and Internet-accessible infrastructure. It is portable, extensible and compatible with HTML5 and Web2.0 standards (http://Distributome.org). We demonstrate two types of applications of the probability Distributome resources: computational research and science education. The Distributome tools may be employed to address five complementary computational modeling applications (simulation, data-analysis and inference, model-fitting, examination of the analytical, mathematical and computational properties of specific probability distributions, and exploration of the inter-distributional relations). Many high school and college science, technology, engineering and mathematics (STEM) courses may be enriched by the use of modern pedagogical approaches and technology-enhanced methods. The Distributome resources provide enhancements for blended STEM education by improving student motivation, augmenting the classical curriculum with interactive webapps, and overhauling the

  8. A fast BDD algorithm for large coherent fault trees analysis

    International Nuclear Information System (INIS)

    Jung, Woo Sik; Han, Sang Hoon; Ha, Jaejoo

    2004-01-01

    Although a binary decision diagram (BDD) algorithm has been tried to solve large fault trees until quite recently, they are not efficiently solved in a short time since the size of a BDD structure exponentially increases according to the number of variables. Furthermore, the truncation of If-Then-Else (ITE) connectives by the probability or size limit and the subsuming to delete subsets could not be directly applied to the intermediate BDD structure under construction. This is the motivation for this work. This paper presents an efficient BDD algorithm for large coherent systems (coherent BDD algorithm) by which the truncation and subsuming could be performed in the progress of the construction of the BDD structure. A set of new formulae developed in this study for AND or OR operation between two ITE connectives of a coherent system makes it possible to delete subsets and truncate ITE connectives with a probability or size limit in the intermediate BDD structure under construction. By means of the truncation and subsuming in every step of the calculation, large fault trees for coherent systems (coherent fault trees) are efficiently solved in a short time using less memory. Furthermore, the coherent BDD algorithm from the aspect of the size of a BDD structure is much less sensitive to variable ordering than the conventional BDD algorithm

  9. Calculating Adversarial Risk from Attack Trees: Control Strength and Probabilistic Attackers

    NARCIS (Netherlands)

    Pieters, Wolter; Davarynejad, Mohsen

    2015-01-01

    Attack trees are a well-known formalism for quantitative analysis of cyber attacks consisting of multiple steps and alternative paths. It is possible to derive properties of the overall attacks from properties of individual steps, such as cost for the attacker and probability of success. However, in

  10. Failure analysis of storage tank component in LNG regasification unit using fault tree analysis method (FTA)

    Science.gov (United States)

    Mulyana, Cukup; Muhammad, Fajar; Saad, Aswad H.; Mariah, Riveli, Nowo

    2017-03-01

    Storage tank component is the most critical component in LNG regasification terminal. It has the risk of failure and accident which impacts to human health and environment. Risk assessment is conducted to detect and reduce the risk of failure in storage tank. The aim of this research is determining and calculating the probability of failure in regasification unit of LNG. In this case, the failure is caused by Boiling Liquid Expanding Vapor Explosion (BLEVE) and jet fire in LNG storage tank component. The failure probability can be determined by using Fault Tree Analysis (FTA). Besides that, the impact of heat radiation which is generated is calculated. Fault tree for BLEVE and jet fire on storage tank component has been determined and obtained with the value of failure probability for BLEVE of 5.63 × 10-19 and for jet fire of 9.57 × 10-3. The value of failure probability for jet fire is high enough and need to be reduced by customizing PID scheme of regasification LNG unit in pipeline number 1312 and unit 1. The value of failure probability after customization has been obtained of 4.22 × 10-6.

  11. Introduction of operator actions in the event trees

    International Nuclear Information System (INIS)

    Bars, G.; Lanore, J.M.; Villeroux, C.

    1984-11-01

    In the PRA in progress in France for a 900 MW PWR plant, an effort is done for introducing operator actions during accident sequences. A first approach of this complex problem relies on an extensive use of existing methods an knowledge in diverse fields. Identification of actions is based on the operating procedures, and in particular on the existence of special emergency procedures which define the optimal actions during severe accidents. This approach implies the introduction in the event trees of the notion of procedure failure. Quantification of the corresponding probabilities leads to several problems including physics of the sequences, systems availability and human behaviour for decision making and actions. This treatment is illustrated by the example of the small break event tree

  12. Investigating how students communicate tree-thinking

    Science.gov (United States)

    Boyce, Carrie Jo

    Learning is often an active endeavor that requires students work at building conceptual understandings of complex topics. Personal experiences, ideas, and communication all play large roles in developing knowledge of and understanding complex topics. Sometimes these experiences can promote formation of scientifically inaccurate or incomplete ideas. Representations are tools used to help individuals understand complex topics. In biology, one way that educators help people understand evolutionary histories of organisms is by using representations called phylogenetic trees. In order to understand phylogenetics trees, individuals need to understand the conventions associated with phylogenies. My dissertation, supported by the Tree-Thinking Representational Competence and Word Association frameworks, is a mixed-methods study investigating the changes in students' tree-reading, representational competence and mental association of phylogenetic terminology after participation in varied instruction. Participants included 128 introductory biology majors from a mid-sized southern research university. Participants were enrolled in either Introductory Biology I, where they were not taught phylogenetics, or Introductory Biology II, where they were explicitly taught phylogenetics. I collected data using a pre- and post-assessment consisting of a word association task and tree-thinking diagnostic (n=128). Additionally, I recruited a subset of students from both courses (n=37) to complete a computer simulation designed to teach students about phylogenetic trees. I then conducted semi-structured interviews consisting of a word association exercise with card sort task, a retrospective pre-assessment discussion, a post-assessment discussion, and interview questions. I found that students who received explicit lecture instruction had a significantly higher increase in scores on a tree-thinking diagnostic than students who did not receive lecture instruction. Students who received both

  13. Probability & Perception: The Representativeness Heuristic in Action

    Science.gov (United States)

    Lu, Yun; Vasko, Francis J.; Drummond, Trevor J.; Vasko, Lisa E.

    2014-01-01

    If the prospective students of probability lack a background in mathematical proofs, hands-on classroom activities may work well to help them to learn to analyze problems correctly. For example, students may physically roll a die twice to count and compare the frequency of the sequences. Tools such as graphing calculators or Microsoft Excel®…

  14. Surface tree languages and parallel derivation trees

    NARCIS (Netherlands)

    Engelfriet, Joost

    1976-01-01

    The surface tree languages obtained by top-down finite state transformation of monadic trees are exactly the frontier-preserving homomorphic images of sets of derivation trees of ETOL systems. The corresponding class of tree transformation languages is therefore equal to the class of ETOL languages.

  15. The analysis of probability task completion; Taxonomy of probabilistic thinking-based across gender in elementary school students

    Science.gov (United States)

    Sari, Dwi Ivayana; Budayasa, I. Ketut; Juniati, Dwi

    2017-08-01

    Formulation of mathematical learning goals now is not only oriented on cognitive product, but also leads to cognitive process, which is probabilistic thinking. Probabilistic thinking is needed by students to make a decision. Elementary school students are required to develop probabilistic thinking as foundation to learn probability at higher level. A framework of probabilistic thinking of students had been developed by using SOLO taxonomy, which consists of prestructural probabilistic thinking, unistructural probabilistic thinking, multistructural probabilistic thinking and relational probabilistic thinking. This study aimed to analyze of probability task completion based on taxonomy of probabilistic thinking. The subjects were two students of fifth grade; boy and girl. Subjects were selected by giving test of mathematical ability and then based on high math ability. Subjects were given probability tasks consisting of sample space, probability of an event and probability comparison. The data analysis consisted of categorization, reduction, interpretation and conclusion. Credibility of data used time triangulation. The results was level of boy's probabilistic thinking in completing probability tasks indicated multistructural probabilistic thinking, while level of girl's probabilistic thinking in completing probability tasks indicated unistructural probabilistic thinking. The results indicated that level of boy's probabilistic thinking was higher than level of girl's probabilistic thinking. The results could contribute to curriculum developer in developing probability learning goals for elementary school students. Indeed, teachers could teach probability with regarding gender difference.

  16. From Fractal Trees to Deltaic Networks

    Science.gov (United States)

    Cazanacli, D.; Wolinsky, M. A.; Sylvester, Z.; Cantelli, A.; Paola, C.

    2013-12-01

    Geometric networks that capture many aspects of natural deltas can be constructed from simple concepts from graph theory and normal probability distributions. Fractal trees with symmetrical geometries are the result of replicating two simple geometric elements, line segments whose lengths decrease and bifurcation angles that are commonly held constant. Branches could also have a thickness, which in the case of natural distributary systems is the equivalent of channel width. In river- or wave-dominated natural deltas, the channel width is a function of discharge. When normal variations around the mean values for length, bifurcating angles, and discharge are applied, along with either pruning of 'clashing' branches or merging (equivalent to channel confluence), fractal trees start resembling natural deltaic networks, except that the resulting channels are unnaturally straight. Introducing a bifurcation probability fewer, naturally curved channels are obtained. If there is no bifurcation, the direction of each new segment depends on the direction the previous segment upstream (correlated random walk) and, to a lesser extent, on a general direction of growth (directional bias). When bifurcation occurs, the resulting two directions also depend on the bifurcation angle and the discharge split proportions, with the dominant branch following the direction of the upstream parent channel closely. The bifurcation probability controls the channel density and, in conjunction with the variability of the directional angles, the overall curvature of the channels. The growth of the network in effect is associated with net delta progradation. The overall shape and shape evolution of the delta depend mainly on the bifurcation angle average size and angle variability coupled with the degree of dominant direction dependency (bias). The proposed algorithm demonstrates how, based on only a few simple rules, a wide variety of channel networks resembling natural deltas, can be replicated

  17. Information-theoretic methods for estimating of complicated probability distributions

    CERN Document Server

    Zong, Zhi

    2006-01-01

    Mixing up various disciplines frequently produces something that are profound and far-reaching. Cybernetics is such an often-quoted example. Mix of information theory, statistics and computing technology proves to be very useful, which leads to the recent development of information-theory based methods for estimating complicated probability distributions. Estimating probability distribution of a random variable is the fundamental task for quite some fields besides statistics, such as reliability, probabilistic risk analysis (PSA), machine learning, pattern recognization, image processing, neur

  18. TreePics: visualizing trees with pictures

    Directory of Open Access Journals (Sweden)

    Nicolas Puillandre

    2017-09-01

    Full Text Available While many programs are available to edit phylogenetic trees, associating pictures with branch tips in an efficient and automatic way is not an available option. Here, we present TreePics, a standalone software that uses a web browser to visualize phylogenetic trees in Newick format and that associates pictures (typically, pictures of the voucher specimens to the tip of each branch. Pictures are visualized as thumbnails and can be enlarged by a mouse rollover. Further, several pictures can be selected and displayed in a separate window for visual comparison. TreePics works either online or in a full standalone version, where it can display trees with several thousands of pictures (depending on the memory available. We argue that TreePics can be particularly useful in a preliminary stage of research, such as to quickly detect conflicts between a DNA-based phylogenetic tree and morphological variation, that may be due to contamination that needs to be removed prior to final analyses, or the presence of species complexes.

  19. Identifying biological pathway interrupting toxins using multi-tree ensembles

    Directory of Open Access Journals (Sweden)

    Gergo Barta

    2016-08-01

    Full Text Available The pharmaceutical industry constantly seeks new ways to improve current methods that scientists use to evaluate environmental chemicals and develop new medicines. Various automated steps are involved in the process as testing hundreds of thousands of chemicals manually would be infeasible. Our research effort and the Toxicology in the 21st Century Data Challenge focused on cost-effective automation of toxicological testing, a chemical substance screening process looking for possible toxic effects caused by interrupting biological pathways. The computational models we propose in this paper successfully combine various publicly available substance fingerprinting tools with advanced machine learning techniques. In our paper, we explore the significance and utility of assorted feature selection methods as the structural analyzers generate a plethora of features for each substance. Machine learning models were carefully selected and evaluated based on their capability to cope with the high-dimensional high-variety data with multi-tree ensemble methods coming out on top. Techniques like Random forests and Extra trees combine numerous simple tree models and proved to produce reliable predictions on toxic activity while being nearly non-parametric and insensitive to dimensionality extremes. The Tox21 Data Challenge contest offered a great platform to compare a wide range of solutions in a controlled and orderly manner. The results clearly demonstrate that the generic approach presented in this paper is comparable to advanced deep learning and domain-specific solutions. Even surpassing the competition in some nuclear receptor signaling and stress pathway assays and achieving an accuracy of up to 94 percent.

  20. Using decision-tree classifier systems to extract knowledge from databases

    Science.gov (United States)

    St.clair, D. C.; Sabharwal, C. L.; Hacke, Keith; Bond, W. E.

    1990-01-01

    One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described.

  1. Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree.

    Science.gov (United States)

    Lee, Chen-Yu; Gallagher, Patrick; Tu, Zhuowen

    2018-04-01

    In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling methods, we also visualize the learned pooling masks and the embeddings of the internal feature responses for different pooling operations. Our proposed pooling operations are easy to implement and can be applied within various deep neural network architectures.

  2. Review of the severe accident risk reduction program (SARRP) containment event trees

    International Nuclear Information System (INIS)

    1986-05-01

    A part of the Severe Accident Risk Reduction Program, researchers at Sandia National Laboratories have constructed a group of containment event trees to be used in the analysis of key accident sequences for light water reactors (LWR) during postulated severe accidents. The ultimate goal of the program is to provide to the NRC staff a current assessment of the risk from severe reactor accidents for a group of five light water reactors. This review specifically focuses on the development and construction of the containment event trees and the results for containment failure probability, modes and timing. The report first gives the background on the program, the review criteria, and a summary of the observations, findings and recommendations. secondly, the individual reviews of each committee member on the event trees is presented. Finally, a review is provided on the computer model used to construct and evaluate the event trees

  3. Human Factors Reliability Analysis for Assuring Nuclear Safety Using Fuzzy Fault Tree

    International Nuclear Information System (INIS)

    Eisawy, E.A.-F. I.; Sallam, H.

    2016-01-01

    In order to ensure effective prevention of harmful events, the risk assessment process cannot ignore the role of humans in the dynamics of accidental events and thus the seriousness of the consequences that may derive from them. Human reliability analysis (HRA) involves the use of qualitative and quantitative methods to assess the human contribution to risk. HRA techniques have been developed in order to provide human error probability values associated with operators’ tasks to be included within the broader context of system risk assessment, and are aimed at reducing the probability of accidental events. Fault tree analysis (FTA) is a graphical model that displays the various combinations of equipment failures and human errors that can result in the main system failure of interest. FTA is a risk analysis technique to assess likelihood (in a probabilistic context) of an event. The objective data available to estimate the likelihood is often missing, and even if available, is subject to incompleteness and imprecision or vagueness. Without addressing incompleteness and imprecision in the available data, FTA and subsequent risk analysis give a false impression of precision and correctness that undermines the overall credibility of the process. To solve this problem, qualitative justification in the context of failure possibilities can be used as alternative for quantitative justification. In this paper, we introduce the approach of fuzzy reliability as solution for fault tree analysis drawbacks. A new fuzzy fault tree method is proposed for the analysis of human reliability based on fuzzy sets and fuzzy operations t-norms, co-norms, defuzzification, and fuzzy failure probability. (author)

  4. Hyper-parameter tuning of a decision tree induction algorithm

    NARCIS (Netherlands)

    Mantovani, R.G.; Horváth, T.; Cerri, R.; Vanschoren, J.; de Carvalho, A.C.P.L.F.

    2017-01-01

    Supervised classification is the most studied task in Machine Learning. Among the many algorithms used in such task, Decision Tree algorithms are a popular choice, since they are robust and efficient to construct. Moreover, they have the advantage of producing comprehensible models and satisfactory

  5. Improving software requirements specification for safety-related systems using the fault tree developed by an object-based method

    International Nuclear Information System (INIS)

    Cepin, M.; Mavko, B.

    1998-01-01

    A modification of the fault tree analysis is presented. The new fault tree integrates structural and behavioral models of a system. Information on the system structure is captured in the name of each gate and basic event of the fault tree. Information on the system behavior is captured in their description. Behavior is expressed using the axiomatic notation based on first order predicate logic. The new fault tree is a useful model for analysis and improvement of software requirements specification. The benefit of such improvements is reduced probability of failures in specification, which in turn results in increased reliability of the software.(author)

  6. Early evolution without a tree of life.

    Science.gov (United States)

    Martin, William F

    2011-06-30

    Life is a chemical reaction. Three major transitions in early evolution are considered without recourse to a tree of life. The origin of prokaryotes required a steady supply of energy and electrons, probably in the form of molecular hydrogen stemming from serpentinization. Microbial genome evolution is not a treelike process because of lateral gene transfer and the endosymbiotic origins of organelles. The lack of true intermediates in the prokaryote-to-eukaryote transition has a bioenergetic cause.

  7. TU-AB-BRD-03: Fault Tree Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Dunscombe, P. [University of Calgary (Canada)

    2015-06-15

    Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk-based quality management program for intensity-modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program. Learning Objectives: Learn how to design a process map for a radiotherapy process Learn how to

  8. TU-AB-BRD-03: Fault Tree Analysis

    International Nuclear Information System (INIS)

    Dunscombe, P.

    2015-01-01

    Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk-based quality management program for intensity-modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program. Learning Objectives: Learn how to design a process map for a radiotherapy process Learn how to

  9. Tree detection in urban regions from aerial imagery and DSM based on local maxima points

    Science.gov (United States)

    Korkmaz, Özgür; Yardımcı ćetin, Yasemin; Yilmaz, Erdal

    2017-05-01

    In this study, we propose an automatic approach for tree detection and classification in registered 3-band aerial images and associated digital surface models (DSM). The tree detection results can be used in 3D city modelling and urban planning. This problem is magnified when trees are in close proximity to each other or other objects such as rooftops in the scenes. This study presents a method for locating individual trees and estimation of crown size based on local maxima from DSM accompanied by color and texture information. For this purpose, segment level classifier trained for 10 classes and classification results are improved by analyzing the class probabilities of neighbour segments. Later, the tree classes under a certain height were eliminated using the Digital Terrain Model (DTM). For the tree classes, local maxima points are obtained and the tree radius estimate is made from the vertical and horizontal height profiles passing through these points. The final tree list containing the centers and radius of the trees is obtained by selecting from the list of tree candidates according to the overlapping and selection parameters. Although the limited number of train sets are used in this study, tree classification and localization results are competitive.

  10. Application of machine learning methods in big data analytics at management of contracts in the construction industry

    Directory of Open Access Journals (Sweden)

    Valpeters Marina

    2018-01-01

    Full Text Available The number of experts who realize the importance of big data continues to increase in various fields of the economy. Experts begin to use big data more frequently for the solution of their specific objectives. One of the probable big data tasks in the construction industry is the determination of the probability of contract execution at a stage of its establishment. The contract holder cannot guarantee execution of the contract. Therefore it leads to a lot of risks for the customer. This article is devoted to the applicability of machine learning methods to the task of determination of the probability of a successful contract execution. Authors try to reveal the factors influencing the possibility of contract default and then try to define the following corrective actions for a customer. In the problem analysis, authors used the linear and non-linear algorithms, feature extraction, feature transformation and feature selection. The results of investigation include the prognostic models with a predictive force based on the machine learning algorithms such as logistic regression, decision tree, randomize forest. Authors have validated models on available historical data. The developed models have the potential for practical use in the construction organizations while making new contracts.

  11. Generalized fault tree analysis combined with state analysis

    International Nuclear Information System (INIS)

    Caldarola, L.

    1980-02-01

    An analytical theory has been developed which allows one to calculate the occurrence probability of the top event of a fault tree with multistate (two or more than two states) components. It is shown that, in order to correctly describe a system with multistate components, a special type of boolean algebra is required. This is called 'boolean algebra with restrictions on variables' and its basic rules are the same as those of the traditional boolean algebra with some additional restrictions on the variables. These restrictions are extensively discussed in the paper. It is also shown that the boolean algebra with restrictions on variables facilitates the task of formally combining fault tree analysis with state analysis. The computer program MUSTAFA 1 based on the above theory has been developed. It can analyse fault trees of system containing statistically independent as well as dependent components with two or more than two states. MUSTAFA 1 can handle coherent as well as non coherent boolean functions. (orig.) 891 HP/orig. 892 MB [de

  12. Air pollution assessment using tree barks as biomonitors

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Eliane C.; Saiki, Mitiko, E-mail: eliane_csantos@yahoo.com.br, E-mail: mitiko@ipen.br [Instituto de Pesquisas Energéticas e Nucleares (IPEN/CNEN-SP), São Paulo, SP (Brazil)

    2017-07-01

    In the last decades tree barks have become a very common bioindicator of air pollution because of its several advantages over other bioindicators. In the present study, tree barks were collected from different sites of Metropolitan Region of São Paulo (MRSP) and from two control sites far away from MRSP. The barks were analyzed by neutron activation analysis (NAA) for determinations of As, Br, Ca, Cl, Co, Cr, Cs, Fe, K, La, Mg, Mn, Ni, Rb, Sb, Sc, V and Zn and for Cd, Cu and Pb by graphite furnace absorption spectrometry (GF AAS). Results obtained for samples collected in different sampling sites in the MRSP presented wide variability due to the different pollutants levels that each tree was exposed to. High concentrations of Cd, Pb, Sb and Zn were obtained in tree barks sampled close to high vehicular traffic. The principal components analysis (PCA) applied a identify four possible emission sources, soil resuspension plus vehicular emission, industrial, marine aerosols as well as the tree bark structure itself. The enrichment factor (EF) results indicated that all the elements originated from anthropic sources, with the exception of Cs. The cluster analyses indicated no significant differences between MRSP and control sites were observed with regards to characteristics of element emissions, probably due to the control sites are located also in urban areas. The results of certified reference material analyses indicated that NAA and GF AAS provided reliable data for element concentrations with standardized differences, |Z score| < 2. (author)

  13. Air pollution assessment using tree barks as biomonitors

    International Nuclear Information System (INIS)

    Santos, Eliane C.; Saiki, Mitiko

    2017-01-01

    In the last decades tree barks have become a very common bioindicator of air pollution because of its several advantages over other bioindicators. In the present study, tree barks were collected from different sites of Metropolitan Region of São Paulo (MRSP) and from two control sites far away from MRSP. The barks were analyzed by neutron activation analysis (NAA) for determinations of As, Br, Ca, Cl, Co, Cr, Cs, Fe, K, La, Mg, Mn, Ni, Rb, Sb, Sc, V and Zn and for Cd, Cu and Pb by graphite furnace absorption spectrometry (GF AAS). Results obtained for samples collected in different sampling sites in the MRSP presented wide variability due to the different pollutants levels that each tree was exposed to. High concentrations of Cd, Pb, Sb and Zn were obtained in tree barks sampled close to high vehicular traffic. The principal components analysis (PCA) applied a identify four possible emission sources, soil resuspension plus vehicular emission, industrial, marine aerosols as well as the tree bark structure itself. The enrichment factor (EF) results indicated that all the elements originated from anthropic sources, with the exception of Cs. The cluster analyses indicated no significant differences between MRSP and control sites were observed with regards to characteristics of element emissions, probably due to the control sites are located also in urban areas. The results of certified reference material analyses indicated that NAA and GF AAS provided reliable data for element concentrations with standardized differences, |Z score| < 2. (author)

  14. The effects of urban warming on herbivore abundance and street tree condition.

    Directory of Open Access Journals (Sweden)

    Adam G Dale

    Full Text Available Trees are essential to urban habitats because they provide services that benefit the environment and improve human health. Unfortunately, urban trees often have more herbivorous insect pests than rural trees but the mechanisms and consequences of these infestations are not well documented. Here, we examine how temperature affects the abundance of a scale insect, Melanaspis tenebricosa (Comstock (Hemiptera: Diaspididae, on one of the most commonly planted street trees in the eastern U.S. Next, we examine how both pest abundance and temperature are associated with water stress, growth, and condition of 26 urban street trees. Although trees in the warmest urban sites grew the most, they were more water stressed and in worse condition than trees in cooler sites. Our analyses indicate that visible declines in tree condition were best explained by scale-insect infestation rather than temperature. To test the broader relevance of these results, we extend our analysis to a database of more than 2700 Raleigh, US street trees. Plotting these trees on a Landsat thermal image of Raleigh, we found that warmer sites had over 70% more trees in poor condition than those in cooler sites. Our results support previous studies linking warmer urban habitats to greater pest abundance and extend this association to show its effect on street tree condition. Our results suggest that street tree condition and ecosystem services may decline as urban expansion and global warming exacerbate the urban heat island effect. Although our non-probability sampling method limits our scope of inference, our results present a gloomy outlook for urban forests and emphasize the need for management tools. Existing urban tree inventories and thermal maps could be used to identify species that would be most suitable for urban conditions.

  15. Trimming the UCERF2 hazard logic tree

    Science.gov (United States)

    Porter, Keith A.; Field, Edward H.; Milner, Kevin

    2012-01-01

    The Uniform California Earthquake Rupture Forecast 2 (UCERF2) is a fully time‐dependent earthquake rupture forecast developed with sponsorship of the California Earthquake Authority (Working Group on California Earthquake Probabilities [WGCEP], 2007; Field et al., 2009). UCERF2 contains 480 logic‐tree branches reflecting choices among nine modeling uncertainties in the earthquake rate model shown in Figure 1. For seismic hazard analysis, it is also necessary to choose a ground‐motion‐prediction equation (GMPE) and set its parameters. Choosing among four next‐generation attenuation (NGA) relationships results in a total of 1920 hazard calculations per site. The present work is motivated by a desire to reduce the computational effort involved in a hazard analysis without understating uncertainty. We set out to assess which branching points of the UCERF2 logic tree contribute most to overall uncertainty, and which might be safely ignored (set to only one branch) without significantly biasing results or affecting some useful measure of uncertainty. The trimmed logic tree will have all of the original choices from the branching points that contribute significantly to uncertainty, but only one arbitrarily selected choice from the branching points that do not.

  16. Comparison of optical properties of chromophoric dissolved organic matter (CDOM) in alpine lakes above or below the tree line: insights into sources of CDOM.

    Science.gov (United States)

    Su, Yaling; Chen, Feizhou; Liu, Zhengwen

    2015-05-01

    Here we investigated absorption and fluorescence properties of chromophoric dissolved organic matter (CDOM) in 15 alpine lakes located below or above the tree line to determine its source and composition. The results indicate that the concentrations of CDOM in below-tree-line lakes are significantly higher than in above-tree-line lakes, as evidenced from the absorption coefficients of a250 and a365. The intensities of the protein-like and humic-like fluorescence in below-tree-line lakes are higher than in above-tree-line lakes as well. Three fluorescent components were identified using parallel factor analysis (PARAFAC) modelling. Component 1 is probably associated with biological degradation of terrestrial humic component. The terrestrial humic-like component 2 is only found in below-tree-line lakes. The protein-like or phenolic component 3 is dominant in above-tree-line lakes, which is probably more derived from autochthonous origin. In this study, (1) higher a250/a365 and S275-295 values indicate smaller molecular weights of CDOM in above-tree-line lakes than in below-tree-line lakes, and smaller molecular weights at the surface than at 2.0 m depth; (2) SUVA254 and FI255 results provide evidence of lower percent aromaticity of CDOM in above-tree-line lakes; and (3) FI310 and FI370 suggest a strong allochthonous origin at the surface in below-tree-line lakes, and more contribution from autochthonous biological and aquatic bacterial origin in above-tree-line lakes.

  17. Fault tree analysis for reactor systems

    International Nuclear Information System (INIS)

    Crosetti, P.A.

    1971-01-01

    Reliability analysis is playing an increasingly important role in quantitative assessment of system performance for assuring nuclear safety, improving plant performance and plant life, and reducing plant operating costs. The complexity of today's nuclear plants warrant the use of techniques which will provide a comprehensive evaluation of systems in their total context. In particular, fault tree analysis with probability evaluation can play a key role in assuring nuclear safety, in improving plant performance and plant life, and in reducing plant operating costs. The technique provides an all inclusive, versatile mathematical tool for analyzing complex systems. Its application can include a complete plant as well as any of the systems and subsystems. Fault tree analysis provides an objective basis for analyzing system design, performing trade-off studies, analyzing common mode failures, demonstrating compliance with AEC requirements, and justifying system changes or additions. The logic of the approach makes it readily understandable and, therefore, it serves as an effective visibility tool for both engineering and management. (U.S.)

  18. ColorTree: a batch customization tool for phylogenic trees.

    Science.gov (United States)

    Chen, Wei-Hua; Lercher, Martin J

    2009-07-31

    Genome sequencing projects and comparative genomics studies typically aim to trace the evolutionary history of large gene sets, often requiring human inspection of hundreds of phylogenetic trees. If trees are checked for compatibility with an explicit null hypothesis (e.g., the monophyly of certain groups), this daunting task is greatly facilitated by an appropriate coloring scheme. In this note, we introduce ColorTree, a simple yet powerful batch customization tool for phylogenic trees. Based on pattern matching rules, ColorTree applies a set of customizations to an input tree file, e.g., coloring labels or branches. The customized trees are saved to an output file, which can then be viewed and further edited by Dendroscope (a freely available tree viewer). ColorTree runs on any Perl installation as a stand-alone command line tool, and its application can thus be easily automated. This way, hundreds of phylogenic trees can be customized for easy visual inspection in a matter of minutes. ColorTree allows efficient and flexible visual customization of large tree sets through the application of a user-supplied configuration file to multiple tree files.

  19. Improvement of testing and maintenance based on fault tree analysis

    International Nuclear Information System (INIS)

    Cepin, M.

    2000-01-01

    Testing and maintenance of safety equipment is an important issue, which significantly contributes to safe and efficient operation of a nuclear power plant. In this paper a method, which extends the classical fault tree with time, is presented. Its mathematical model is represented by a set of equations, which include time requirements defined in the house event matrix. House events matrix is a representation of house events switched on and off through the discrete points of time. It includes house events, which timely switch on and off parts of the fault tree in accordance with the status of the plant configuration. Time dependent top event probability is calculated by the fault tree evaluations. Arrangement of components outages is determined on base of minimization of mean system unavailability. The results show that application of the method may improve the time placement of testing and maintenance activities of safety equipment. (author)

  20. Treeing phenomenon of thermoplastic polyethylene blends for recyclable cable insulation materials

    Science.gov (United States)

    Li, Lunzhi; Zhang, Kai; Zhong, Lisheng; Gao, Jinghui; Xu, Man; Chen, Guanghui; Fu, Mingli

    2017-02-01

    Owing to its good recyclability and low processing energy consumption, non-crosslinked polyethylene blends (e.g. LLDPE-HDPE blends) are considered as one of potential environmental-friendly substitutions for crosslinked polyethylene (XLPE) as cable insulation material. Although extensive work has been performed for measuring the basic dielectric properties, there is a lack of the investigations on the aging properties for such a material system, which hinders the evaluation of reliability and lifetime of the material for cable insulation. In this paper, we study the electric aging phenomenon of 0.7LLDPE-0.3HDPE blending material by investigating the treeing behavior, and its comparison with XLPE and LLDPE. Treeing tests show that the 0.7LLDPE-0.3HDPE blends have lower probability for treeing as well as smaller treeing dimensions. Further thermal analysis and microstructure study results suggest that the blends exhibit larger proportion of thick lamellae and higher crystallinity with homogeneously-distributed amorphous region, which is responsible for good anti-treeing performance. Our finding provides the evidence that the 0.7LLDPE-0.3HDPE blends exhibits better electric-aging-retardance properties than XLPE, which may result in a potential application for cable insulation.

  1. Treeing phenomenon of thermoplastic polyethylene blends for recyclable cable insulation materials

    Directory of Open Access Journals (Sweden)

    Lunzhi Li

    2017-02-01

    Full Text Available Owing to its good recyclability and low processing energy consumption, non-crosslinked polyethylene blends (e.g. LLDPE-HDPE blends are considered as one of potential environmental-friendly substitutions for crosslinked polyethylene (XLPE as cable insulation material. Although extensive work has been performed for measuring the basic dielectric properties, there is a lack of the investigations on the aging properties for such a material system, which hinders the evaluation of reliability and lifetime of the material for cable insulation. In this paper, we study the electric aging phenomenon of 0.7LLDPE-0.3HDPE blending material by investigating the treeing behavior, and its comparison with XLPE and LLDPE. Treeing tests show that the 0.7LLDPE-0.3HDPE blends have lower probability for treeing as well as smaller treeing dimensions. Further thermal analysis and microstructure study results suggest that the blends exhibit larger proportion of thick lamellae and higher crystallinity with homogeneously-distributed amorphous region, which is responsible for good anti-treeing performance. Our finding provides the evidence that the 0.7LLDPE-0.3HDPE blends exhibits better electric-aging-retardance properties than XLPE, which may result in a potential application for cable insulation.

  2. Generalized Probability-Probability Plots

    NARCIS (Netherlands)

    Mushkudiani, N.A.; Einmahl, J.H.J.

    2004-01-01

    We introduce generalized Probability-Probability (P-P) plots in order to study the one-sample goodness-of-fit problem and the two-sample problem, for real valued data.These plots, that are constructed by indexing with the class of closed intervals, globally preserve the properties of classical P-P

  3. Quantum Probabilities as Behavioral Probabilities

    Directory of Open Access Journals (Sweden)

    Vyacheslav I. Yukalov

    2017-03-01

    Full Text Available We demonstrate that behavioral probabilities of human decision makers share many common features with quantum probabilities. This does not imply that humans are some quantum objects, but just shows that the mathematics of quantum theory is applicable to the description of human decision making. The applicability of quantum rules for describing decision making is connected with the nontrivial process of making decisions in the case of composite prospects under uncertainty. Such a process involves deliberations of a decision maker when making a choice. In addition to the evaluation of the utilities of considered prospects, real decision makers also appreciate their respective attractiveness. Therefore, human choice is not based solely on the utility of prospects, but includes the necessity of resolving the utility-attraction duality. In order to justify that human consciousness really functions similarly to the rules of quantum theory, we develop an approach defining human behavioral probabilities as the probabilities determined by quantum rules. We show that quantum behavioral probabilities of humans do not merely explain qualitatively how human decisions are made, but they predict quantitative values of the behavioral probabilities. Analyzing a large set of empirical data, we find good quantitative agreement between theoretical predictions and observed experimental data.

  4. Identification of immune correlates of protection in Shigella infection by application of machine learning.

    Science.gov (United States)

    Arevalillo, Jorge M; Sztein, Marcelo B; Kotloff, Karen L; Levine, Myron M; Simon, Jakub K

    2017-10-01

    Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model. The proposed methodology utilizes the Random Forests (RF) machine learning algorithm as well as Classification and Regression Trees (CART) to detect immune markers that predict protection, identify interactions between variables, and define optimal cutoffs. Logistic regression modeling is applied to estimate the probability of protection and the confidence interval (CI) for such a probability is computed by bootstrapping the logistic regression models. The results demonstrate that the combination of Classification and Regression Trees and Random Forests complements the standard logistic regression and uncovers subtle immune interactions. Specific levels of immunoglobulin IgG antibody in blood on the day of challenge predicted protection in 75% (95% CI 67-86). Of those subjects that did not have blood IgG at or above a defined threshold, 100% were protected if they had IgA antibody secreting cells above a defined threshold. Comparison with the results obtained by applying only logistic regression modeling with standard Akaike Information Criterion for model selection shows the usefulness of the proposed method. Given the complexity of the immune system, the use of machine learning methods may enhance traditional statistical approaches. When applied together, they offer a novel way to quantify important immune correlates of protection that may help the development of vaccines. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Model trees for identifying exceptional players in the NHL and NBA draft

    OpenAIRE

    Liu, Yejia

    2018-01-01

    Drafting players is crucial for a team’s success. We describe a data-driven interpretable approach for assessing prospects in the National Hockey League and National Basketball Association. Previous approaches have built a predictive model based on player features, or derived performance predictions from comparable players. Our work develops model tree learning, which incorporates strengths of both model-based and cohort-based approaches. A model tree partitions the feature space according to...

  6. Data analysis & probability task sheets : grades pk-2

    CERN Document Server

    Cook, Tanya

    2009-01-01

    For grades PK-2, our Common Core State Standards-based resource meets the data analysis & probability concepts addressed by the NCTM standards and encourages your students to learn and review the concepts in unique ways. Each task sheet is organized around a central problem taken from real-life experiences of the students.

  7. Probability Aggregates in Probability Answer Set Programming

    OpenAIRE

    Saad, Emad

    2013-01-01

    Probability answer set programming is a declarative programming that has been shown effective for representing and reasoning about a variety of probability reasoning tasks. However, the lack of probability aggregates, e.g. {\\em expected values}, in the language of disjunctive hybrid probability logic programs (DHPP) disallows the natural and concise representation of many interesting problems. In this paper, we extend DHPP to allow arbitrary probability aggregates. We introduce two types of p...

  8. Tree species traits but not diversity mitigate stem breakage in a subtropical forest following a rare and extreme ice storm.

    Directory of Open Access Journals (Sweden)

    Karin Nadrowski

    Full Text Available Future climates are likely to include extreme events, which in turn have great impacts on ecological systems. In this study, we investigated possible effects that could mitigate stem breakage caused by a rare and extreme ice storm in a Chinese subtropical forest across a gradient of forest diversity. We used Bayesian modeling to correct stem breakage for tree size and variance components analysis to quantify the influence of taxon, leaf and wood functional traits, and stand level properties on the probability of stem breakage. We show that the taxon explained four times more variance in individual stem breakage than did stand level properties; trees with higher specific leaf area (SLA were less susceptible to breakage. However, a large part of the variation at the taxon scale remained unexplained, implying that unmeasured or undefined traits could be used to predict damage caused by ice storms. When aggregated at the plot level, functional diversity and wood density increased after the ice storm. We suggest that for the adaption of forest management to climate change, much can still be learned from looking at functional traits at the taxon level.

  9. Monitoring the Soil Water Availability of Young Urban Trees in Hamburg, Germany

    Science.gov (United States)

    Titel, Selina; Gröngröft, Alexander; Eschenbach, Annette

    2017-04-01

    In large cities numerous trees have to be planted each year to replace died off or cut down trees or for greening of constructed roads and newly built quarters. The typical age of planted trees is between five and fifteen years. Often the planting takes place in special planting pits to stimulate the tree growth under the restricted urban conditions. Consequently, trees are surrounded by different soil substrates: the soil from the nursery in the root ball, the special planting pit substrate and the surrounding urban soil which is often anthropogenic influenced. Being relocated in the city, trees have to cope with the warmer urban climate, the soil sealing and compaction and the low water storage capacity of the substrate. All factors together increase the probability of dry phases for roadside trees. The aim of this study is to monitor the soil water availability at sites of planted roadside trees during the first years after planting. Therefore, a measuring design was developed, which works automatically and takes the complex below ground structure of the soil into account. This approach consists of 13 soil water tension sensors inside and outside of each planting pit up to one meter depth connected to a data logger. The monitoring devices will finally be installed at 20 roadside trees (amongst others Quercus cerris, Quercus robur, Acer platanoides 'Fairview') in Hamburg, Germany, to identify phases of drought stress. The young trees were mainly planted in spring 2016. Data of the first year of measurements show, that the water tension varied between the different soil substrates and the depth. In the first year of tree growth in the city, soil in the tree root ball became significantly drier than the surrounding soil material. In late summer 2016 the water tension in the topsoil had the potential to cause drought stress below some trees.

  10. Condition-based fault tree analysis (CBFTA): A new method for improved fault tree analysis (FTA), reliability and safety calculations

    International Nuclear Information System (INIS)

    Shalev, Dan M.; Tiran, Joseph

    2007-01-01

    Condition-based maintenance methods have changed systems reliability in general and individual systems in particular. Yet, this change does not affect system reliability analysis. System fault tree analysis (FTA) is performed during the design phase. It uses components failure rates derived from available sources as handbooks, etc. Condition-based fault tree analysis (CBFTA) starts with the known FTA. Condition monitoring (CM) methods applied to systems (e.g. vibration analysis, oil analysis, electric current analysis, bearing CM, electric motor CM, and so forth) are used to determine updated failure rate values of sensitive components. The CBFTA method accepts updated failure rates and applies them to the FTA. The CBFTA recalculates periodically the top event (TE) failure rate (λ TE ) thus determining the probability of system failure and the probability of successful system operation-i.e. the system's reliability. FTA is a tool for enhancing system reliability during the design stages. But, it has disadvantages, mainly it does not relate to a specific system undergoing maintenance. CBFTA is tool for updating reliability values of a specific system and for calculating the residual life according to the system's monitored conditions. Using CBFTA, the original FTA is ameliorated to a practical tool for use during the system's field life phase, not just during system design phase. This paper describes the CBFTA method and its advantages are demonstrated by an example

  11. Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions

    KAUST Repository

    Azad, Mohammad

    2018-01-01

    Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set

  12. On the use of non-coherent fault trees in safety and security studies

    International Nuclear Information System (INIS)

    Contini, S.; Cojazzi, G.G.M.; Renda, G.

    2008-01-01

    This paper gives some insights on the usefulness of non-coherent fault trees in system modelling from both the point of view of safety and security. A safety-related system can evolve from the working states to failed states through degraded states, i.e. working state, but in a degraded mode. In practical applications the degraded states may be of particular interest due e.g. to the associated risk increase or the different types of consequent actions. The top events definitions of such states contain the working conditions of some sub-systems/components. How the use of non-coherent fault trees can greatly simplify both the modelling and quantification of these states is shown in this paper. Some considerations about the interpretation of the importance indexes of negated basic events are also briefly described. When dealing with security applications, there is a need to cope not only with stochastic events, such as component failures and human errors, but also with deliberate intentional actions, whose successes might be characterised by high probability values. Different mutually exclusive attack scenarios may be envisaged for a given system. Hence, the essential feature of a fault tree analyser is the capability to determine the exact value of the top event probability containing mutually exclusive events. It is also shown that in these cases the use of non-coherent fault trees allows solving the problem with limited effort

  13. Tree Colors: Color Schemes for Tree-Structured Data.

    Science.gov (United States)

    Tennekes, Martijn; de Jonge, Edwin

    2014-12-01

    We present a method to map tree structures to colors from the Hue-Chroma-Luminance color model, which is known for its well balanced perceptual properties. The Tree Colors method can be tuned with several parameters, whose effect on the resulting color schemes is discussed in detail. We provide a free and open source implementation with sensible parameter defaults. Categorical data are very common in statistical graphics, and often these categories form a classification tree. We evaluate applying Tree Colors to tree structured data with a survey on a large group of users from a national statistical institute. Our user study suggests that Tree Colors are useful, not only for improving node-link diagrams, but also for unveiling tree structure in non-hierarchical visualizations.

  14. Pointwise probability reinforcements for robust statistical inference.

    Science.gov (United States)

    Frénay, Benoît; Verleysen, Michel

    2014-02-01

    Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

    KAUST Repository

    Azad, Mohammad

    2017-06-16

    We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.

  16. Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2017-01-01

    We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.

  17. Decision support for mitigating the risk of tree induced transmission line failure in utility rights-of-way.

    Science.gov (United States)

    Poulos, H M; Camp, A E

    2010-02-01

    Vegetation management is a critical component of rights-of-way (ROW) maintenance for preventing electrical outages and safety hazards resulting from tree contact with conductors during storms. Northeast Utility's (NU) transmission lines are a critical element of the nation's power grid; NU is therefore under scrutiny from federal agencies charged with protecting the electrical transmission infrastructure of the United States. We developed a decision support system to focus right-of-way maintenance and minimize the potential for a tree fall episode that disables transmission capacity across the state of Connecticut. We used field data on tree characteristics to develop a system for identifying hazard trees (HTs) in the field using limited equipment to manage Connecticut power line ROW. Results from this study indicated that the tree height-to-diameter ratio, total tree height, and live crown ratio were the key characteristics that differentiated potential risk trees (danger trees) from trees with a high probability of tree fall (HTs). Products from this research can be transferred to adaptive right-of-way management, and the methods we used have great potential for future application to other regions of the United States and elsewhere where tree failure can disrupt electrical power.

  18. Machine learning methods for planning

    CERN Document Server

    Minton, Steven

    1993-01-01

    Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

  19. Probabilistic analysis of Millstone Unit 3 ultimate containment failure probability given high pressure: Chapter 14

    International Nuclear Information System (INIS)

    Bickel, J.H.

    1983-01-01

    The quantification of the containment event trees in the Millstone Unit 3 Probabilistic Safety Study utilizes a conditional probability of failure given high pressure which is based on a new approach. The generation of this conditional probability was based on a weakest link failure mode model which considered contributions from a number of overlapping failure modes. This overlap effect was due to a number of failure modes whose mean failure pressures were clustered within a 5 psi range and which had uncertainties due to variances in material strengths and analytical uncertainties which were between 9 and 15 psi. Based on a review of possible probability laws to describe the failure probability of individual structural failure modes, it was determined that a Weibull probability law most adequately described the randomness in the physical process of interest. The resultant conditional probability of failure is found to have a median failure pressure of 132.4 psia. The corresponding 5-95 percentile values are 112 psia and 146.7 psia respectively. The skewed nature of the conditional probability of failure vs. pressure results in a lower overall containment failure probability for an appreciable number of the severe accident sequences of interest, but also probabilities which are more rigorously traceable from first principles

  20. TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

    Science.gov (United States)

    Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald

    2018-01-01

    Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

  1. Discrete probability models and methods probability on graphs and trees, Markov chains and random fields, entropy and coding

    CERN Document Server

    Brémaud, Pierre

    2017-01-01

    The emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory. The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book. .

  2. Equilibrium point control of a monkey arm simulator by a fast learning tree structured artificial neural network.

    Science.gov (United States)

    Dornay, M; Sanger, T D

    1993-01-01

    A planar 17 muscle model of the monkey's arm based on realistic biomechanical measurements was simulated on a Symbolics Lisp Machine. The simulator implements the equilibrium point hypothesis for the control of arm movements. Given initial and final desired positions, it generates a minimum-jerk desired trajectory of the hand and uses the backdriving algorithm to determine an appropriate sequence of motor commands to the muscles (Flash 1987; Mussa-Ivaldi et al. 1991; Dornay 1991b). These motor commands specify a temporal sequence of stable (attractive) equilibrium positions which lead to the desired hand movement. A strong disadvantage of the simulator is that it has no memory of previous computations. Determining the desired trajectory using the minimum-jerk model is instantaneous, but the laborious backdriving algorithm is slow, and can take up to one hour for some trajectories. The complexity of the required computations makes it a poor model for biological motor control. We propose a computationally simpler and more biologically plausible method for control which achieves the benefits of the backdriving algorithm. A fast learning, tree-structured network (Sanger 1991c) was trained to remember the knowledge obtained by the backdriving algorithm. The neural network learned the nonlinear mapping from a 2-dimensional cartesian planar hand position (x,y) to a 17-dimensional motor command space (u1, . . ., u17). Learning 20 training trajectories, each composed of 26 sample points [[x,y], [u1, . . ., u17] took only 20 min on a Sun-4 Sparc workstation. After the learning stage, new, untrained test trajectories as well as the original trajectories of the hand were given to the neural network as input. The network calculated the required motor commands for these movements. The resulting movements were close to the desired ones for both the training and test cases.

  3. Low Tree-Growth Elasticity of Forest Biomass Indicated by an Individual-Based Model

    Directory of Open Access Journals (Sweden)

    Robbie A. Hember

    2018-01-01

    Full Text Available Environmental conditions and silviculture fundamentally alter the metabolism of individual trees and, therefore, need to be studied at that scale. However, changes in forest biomass density (Mg C ha−1 may be decoupled from changes in growth (kg C year−1 when the latter also accelerates the life cycle of trees and strains access to light, nutrients, and water. In this study, we refer to an individual-based model of forest biomass dynamics to constrain the magnitude of system feedbacks associated with ontogeny and competition and estimate the scaling relationship between changes in tree growth and forest biomass density. The model was driven by fitted equations of annual aboveground biomass growth (Gag, probability of recruitment (Pr, and probability of mortality (Pm parameterized against field observations of black spruce (Picea mariana (Mill. BSP, interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn. Franco, and western hemlock (Tsuga heterophylla (Raf. Sarg.. A hypothetical positive step-change in mean tree growth was imposed half way through the simulations and landscape-scale responses were then evaluated by comparing pre- and post-stimulus periods. Imposing a 100% increase in tree growth above calibrated predictions (i.e., contemporary rates only translated into 36% to 41% increases in forest biomass density. This corresponded with a tree-growth elasticity of forest biomass (εG,SB ranging from 0.33 to 0.55. The inelastic nature of stand biomass density was attributed to the dependence of mortality on intensity of competition and tree size, which decreased stand density by 353 to 495 trees ha−1, and decreased biomass residence time by 10 to 23 years. Values of εG,SB depended on the magnitude of the stimulus. For example, a retrospective scenario in which tree growth increased from 50% below contemporary rates up to contemporary rates indicated values of εG,SB ranging from 0.66 to 0.75. We conclude that: (1 effects of

  4. On the numerical solution of fault trees

    International Nuclear Information System (INIS)

    Demichela, M.; Piccinini, N.; Ciarambino, I.; Contini, S.

    2003-01-01

    In this paper an account will be given of the numerical solution of the logic trees directly extracted from the Recursive Operability Analysis. Particular attention will be devoted to the use of the NOT and INH logic gates for correct logical representation of Fault Trees prior to their quantitative resolution. The NOT gate is needed for correct logical representation of events when both non-intervention and correct intervention of a protective system may lead to a Top Event. The INH gate must be used to correctly represent the time link between two events that are both necessary, but must occur in sequence. Some numerical examples will be employed to show both the correct identification of the events entering the INH gates and how use of the AND gate instead of the INH gate leads to overestimation of the probability of occurrence of a Top Event

  5. Tuned by experience: How orientation probability modulates early perceptual processing.

    Science.gov (United States)

    Jabar, Syaheed B; Filipowicz, Alex; Anderson, Britt

    2017-09-01

    Probable stimuli are more often and more quickly detected. While stimulus probability is known to affect decision-making, it can also be explained as a perceptual phenomenon. Using spatial gratings, we have previously shown that probable orientations are also more precisely estimated, even while participants remained naive to the manipulation. We conducted an electrophysiological study to investigate the effect that probability has on perception and visual-evoked potentials. In line with previous studies on oddballs and stimulus prevalence, low-probability orientations were associated with a greater late positive 'P300' component which might be related to either surprise or decision-making. However, the early 'C1' component, thought to reflect V1 processing, was dampened for high-probability orientations while later P1 and N1 components were unaffected. Exploratory analyses revealed a participant-level correlation between C1 and P300 amplitudes, suggesting a link between perceptual processing and decision-making. We discuss how these probability effects could be indicative of sharpening of neurons preferring the probable orientations, due either to perceptual learning, or to feature-based attention. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Construction of a phylogenetic tree of photosynthetic prokaryotes based on average similarities of whole genome sequences.

    Directory of Open Access Journals (Sweden)

    Soichirou Satoh

    Full Text Available Phylogenetic trees have been constructed for a wide range of organisms using gene sequence information, especially through the identification of orthologous genes that have been vertically inherited. The number of available complete genome sequences is rapidly increasing, and many tools for construction of genome trees based on whole genome sequences have been proposed. However, development of a reasonable method of using complete genome sequences for construction of phylogenetic trees has not been established. We have developed a method for construction of phylogenetic trees based on the average sequence similarities of whole genome sequences. We used this method to examine the phylogeny of 115 photosynthetic prokaryotes, i.e., cyanobacteria, Chlorobi, proteobacteria, Chloroflexi, Firmicutes and nonphotosynthetic organisms including Archaea. Although the bootstrap values for the branching order of phyla were low, probably due to lateral gene transfer and saturated mutation, the obtained tree was largely consistent with the previously reported phylogenetic trees, indicating that this method is a robust alternative to traditional phylogenetic methods.

  7. Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

    Science.gov (United States)

    Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

    2018-02-01

    A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

  8. Alternative measures of risk of extreme events in decision trees

    International Nuclear Information System (INIS)

    Frohwein, H.I.; Lambert, J.H.; Haimes, Y.Y.

    1999-01-01

    A need for a methodology to control the extreme events, defined as low-probability, high-consequence incidents, in sequential decisions is identified. A variety of alternative and complementary measures of the risk of extreme events are examined for their usability as objective functions in sequential decisions, represented as single- or multiple-objective decision trees. Earlier work had addressed difficulties, related to non-separability, with the minimization of some measures of the risk of extreme events in sequential decisions. In an extension of these results, it is shown how some non-separable measures of the risk of extreme events can be interpreted in terms of separable constituents of risk, thereby enabling a wider class of measures of the risk of extreme events to be handled in a straightforward manner in a decision tree. Also for extreme events, results are given to enable minimax- and Hurwicz-criterion analyses in decision trees. An example demonstrates the incorporation of different measures of the risk of extreme events in a multi-objective decision tree. Conceptual formulations for optimizing non-separable measures of the risk of extreme events are identified as an important area for future investigation

  9. Two new fern chloroplasts and decelerated evolution linked to the long generation time in tree ferns.

    Science.gov (United States)

    Zhong, Bojian; Fong, Richard; Collins, Lesley J; McLenachan, Patricia A; Penny, David

    2014-04-30

    We report the chloroplast genomes of a tree fern (Dicksonia squarrosa) and a "fern ally" (Tmesipteris elongata), and show that the phylogeny of early land plants is basically as expected, and the estimates of divergence time are largely unaffected after removing the fastest evolving sites. The tree fern shows the major reduction in the rate of evolution, and there has been a major slowdown in the rate of mutation in both families of tree ferns. We suggest that this is related to a generation time effect; if there is a long time period between generations, then this is probably incompatible with a high mutation rate because otherwise nearly every propagule would probably have several lethal mutations. This effect will be especially strong in organisms that have large numbers of cell divisions between generations. This shows the necessity of going beyond phylogeny and integrating its study with other properties of organisms. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  10. A general method dealing with correlations in uncertainty propagation in fault trees

    International Nuclear Information System (INIS)

    Qin Zhang

    1989-01-01

    This paper deals with the correlations among the failure probabilities (frequencies) of not only the identical basic events but also other basic events in a fault tree. It presents a general and simple method to include these correlations in uncertainty propagation. Two examples illustrate this method and show that neglecting these correlations results in large underestimation of the top event failure probability (frequency). One is the failure of the primary pump in a chemical reactor cooling system, the other example is an accident to a road transport truck carrying toxic waste. (author)

  11. The completeness of fault tree analysis in the presence of dependencies

    International Nuclear Information System (INIS)

    Hughes, R.P.

    1989-02-01

    Existing standard fault tree assessments of systems do not include an assessment of the effects of dependencies in an integrated fashion, but simply add on a ''common cause cut-off''. To support the values used for this cut-off, cut-sets involving certain groups of components susceptible to dependent failure can be assessed using the Distributed Failure Probability method. These rank one contributions do not cover all the possibilities, however, so there is an outstanding need for an integrated procedure for dependent failure assessment of systems which allows for all ranks of cut-set. The purpose of this note is to provide such a procedure which builds upon the standard approach to fault tree analysis. In this standard approach, only a limited number of cut-sets is found, and they are evaluated assuming independence of their components. So, some cut-sets are neglected which could be important contributors to the system failure probability if their components are not independent of each other. The procedure developed therefore deals with this truncation problem and with dependency together. The result is a practical and efficient method for bounding system failure probabilities. The method is a progressive one, whereby this bound is reduced as necessary by a more refined analysis. A simple example is used to illustrate the procedure. (author)

  12. Negative probability in the framework of combined probability

    OpenAIRE

    Burgin, Mark

    2013-01-01

    Negative probability has found diverse applications in theoretical physics. Thus, construction of sound and rigorous mathematical foundations for negative probability is important for physics. There are different axiomatizations of conventional probability. So, it is natural that negative probability also has different axiomatic frameworks. In the previous publications (Burgin, 2009; 2010), negative probability was mathematically formalized and rigorously interpreted in the context of extende...

  13. Post-event human decision errors: operator action tree/time reliability correlation

    International Nuclear Information System (INIS)

    Hall, R.E.; Fragola, J.; Wreathall, J.

    1982-11-01

    This report documents an interim framework for the quantification of the probability of errors of decision on the part of nuclear power plant operators after the initiation of an accident. The framework can easily be incorporated into an event tree/fault tree analysis. The method presented consists of a structure called the operator action tree and a time reliability correlation which assumes the time available for making a decision to be the dominating factor in situations requiring cognitive human response. This limited approach decreases the magnitude and complexity of the decision modeling task. Specifically, in the past, some human performance models have attempted prediction by trying to emulate sequences of human actions, or by identifying and modeling the information processing approach applicable to the task. The model developed here is directed at describing the statistical performance of a representative group of hypothetical individuals responding to generalized situations

  14. Post-event human decision errors: operator action tree/time reliability correlation

    Energy Technology Data Exchange (ETDEWEB)

    Hall, R E; Fragola, J; Wreathall, J

    1982-11-01

    This report documents an interim framework for the quantification of the probability of errors of decision on the part of nuclear power plant operators after the initiation of an accident. The framework can easily be incorporated into an event tree/fault tree analysis. The method presented consists of a structure called the operator action tree and a time reliability correlation which assumes the time available for making a decision to be the dominating factor in situations requiring cognitive human response. This limited approach decreases the magnitude and complexity of the decision modeling task. Specifically, in the past, some human performance models have attempted prediction by trying to emulate sequences of human actions, or by identifying and modeling the information processing approach applicable to the task. The model developed here is directed at describing the statistical performance of a representative group of hypothetical individuals responding to generalized situations.

  15. Relationships between depth and number of misclassifications for decision trees

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2011-01-01

    This paper describes a new tool for the study of relationships between depth and number of misclassifications for decision trees. In addition to the algorithm the paper also presents the results of experiments with three datasets from UCI Machine Learning Repository [3]. © 2011 Springer-Verlag.

  16. Using decision trees and their ensembles for analysis of NIR spectroscopic data

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.

    and interpretation of the models. In this presentation, we are going to discuss an applicability of decision trees based methods (including gradient boosting) for solving classification and regression tasks with NIR spectra as predictors. We will cover such aspects as evaluation, optimization and validation......Advanced machine learning methods, like convolutional neural networks and decision trees, became extremely popular in the last decade. This, first of all, is directly related to the current boom in Big data analysis, where traditional statistical methods are not efficient. According to the kaggle.......com — the most popular online resource for Big data problems and solutions — methods based on decision trees and their ensembles are most widely used for solving the problems. It can be noted that the decision trees and convolutional neural networks are not very popular in Chemometrics. One of the reasons...

  17. Big trees, old trees, and growth factor tables

    Science.gov (United States)

    Kevin T. Smith

    2018-01-01

    The potential for a tree to reach a great size and to live a long life frequently captures the public's imagination. Sometimes the desire to know the age of an impressively large tree is simple curiosity. For others, the date-of-tree establishment can make a big diff erence for management, particularly for trees at historic sites or those mentioned in property...

  18. A Suffix Tree Or Not a Suffix Tree?

    DEFF Research Database (Denmark)

    Starikovskaya, Tatiana; Vildhøj, Hjalte Wedel

    2015-01-01

    In this paper we study the structure of suffix trees. Given an unlabeled tree r on n nodes and suffix links of its internal nodes, we ask the question “Is r a suffix tree?”, i.e., is there a string S whose suffix tree has the same topological structure as r? We place no restrictions on S, in part...

  19. A homeostatic-partly dynamic model for 137Cs in trees

    International Nuclear Information System (INIS)

    Frissel, M.

    1994-01-01

    A model has been developed to describe the behaviour of 137 Cs in trees. The core of the model is the assumption that 137 Cs/K ratio in soil determines the 137 Cs/K ratio in various parts of a tree. This is an equilibrium model but taking into account the time dependence of Cs/K ratio in the soil (caused by K-fertilization) it has been extended to a dynamic model. The model desribes a growing tree. Four compartments are considered: soil; easily accessible parts of the tree; woody parts difficult to access; fruits or leaves. The model is homeostatic, i.e. all 137 Cs concentrations and fluxes are controlled by K concentrations and fluxes, respectively. The addition of K-fertilizer to the soil manifests itself in an immediate change of the Cs/K ratio in the soil and in the easily accessible plant parts, but only slowly - in the woody parts. Also an excess of Cs in the woody part is only slowly released. Important processes are the discrimination between Cs and K and the luxurious consumption of K. The cycling of K in the system (throughput of K via falling leaves, branches, etc.) is also important. Furthermore, a good insight in accessibility of the various parts of the tree seems required, the division in only three compartments, as in the model is probably unsufficient. (author)

  20. Potential transgenic routes to increase tree biomass.

    Science.gov (United States)

    Dubouzet, Joseph G; Strabala, Timothy J; Wagner, Armin

    2013-11-01

    Biomass is a prime target for genetic engineering in forestry because increased biomass yield will benefit most downstream applications such as timber, fiber, pulp, paper, and bioenergy production. Transgenesis can increase biomass by improving resource acquisition and product utilization and by enhancing competitive ability for solar energy, water, and mineral nutrients. Transgenes that affect juvenility, winter dormancy, and flowering have been shown to influence biomass as well. Transgenic approaches have increased yield potential by mitigating the adverse effects of prevailing stress factors in the environment. Simultaneous introduction of multiple genes for resistance to various stress factors into trees may help forest trees cope with multiple or changing environments. We propose multi-trait engineering for tree crops, simultaneously deploying multiple independent genes to address a set of genetically uncorrelated traits that are important for crop improvement. This strategy increases the probability of unpredictable (synergistic or detrimental) interactions that may substantially affect the overall phenotype and its long-term performance. The very limited ability to predict the physiological processes that may be impacted by such a strategy requires vigilance and care during implementation. Hence, we recommend close monitoring of the resultant transgenic genotypes in multi-year, multi-location field trials. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  1. Tree mortality from fire and bark beetles following early and late season prescribed fires in a Sierra Nevada mixed-conifer forest

    Science.gov (United States)

    Schwilk, Dylan W.; Knapp, Eric E.; Ferrenberg, Scott; Keeley, Jon E.; Caprio, Anthony C.

    2006-01-01

    Over the last century, fire exclusion in the forests of the Sierra Nevada has allowed surface fuels to accumulate and has led to increased tree density. Stand composition has also been altered as shade tolerant tree species crowd out shade intolerant species. To restore forest structure and reduce the risk of large, intense fires, managers have increasingly used prescription burning. Most fires prior to EuroAmerican settlement occurred during the late summer and early fall and most prescribed burning has taken place during the latter part of this period. Poor air quality and lack of suitable burn windows during the fall, however, have resulted in a need to conduct more prescription burning earlier in the season. Previous reports have suggested that burning during the time when trees are actively growing may increase mortality rates due to fine root damage and/or bark beetle activity. This study examines the effects of fire on tree mortality and bark beetle attacks under prescription burning during early and late season. Replicated early season burn, late season burn and unburned control plots were established in an old-growth mixed conifer forest in the Sierra Nevada that had not experienced a fire in over 120 years. Although prescribed burns resulted in significant mortality of particularly the smallest tree size classes, no difference between early and late season burns was detected. Direct mortality due to fire was associated with fire intensity. Secondary mortality due to bark beetles was not significantly correlated with fire intensity. The probability of bark beetle attack on pines did not differ between early and late season burns, while the probability of bark beetle attack on firs was greater following early season burns. Overall tree mortality appeared to be primarily the result of fire intensity rather than tree phenology at the time of the burns. Early season burns are generally conducted under higher fuel moisture conditions, leading to less fuel

  2. Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit.

    Science.gov (United States)

    Dawson, Michael R W; Dupuis, Brian; Spetch, Marcia L; Kelly, Debbie M

    2009-08-01

    The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network generates responses consistent with probability matching. This behavior was then used to create an operant procedure for network learning. We use the multiarmed bandit (Gittins 1989), a classic problem of choice behavior, to illustrate that operant training balances exploiting the bandit arm expected to pay off most frequently with exploring other arms. Perceptrons provide a medium for relating results from neural networks, genetic algorithms, animal learning, contingency theory, reinforcement learning, and theories of choice.

  3. The fuzzy set theory application to the analysis of accident progression event trees with phenomenological uncertainty issues

    International Nuclear Information System (INIS)

    Chun, Moon-Hyun; Ahn, Kwang-Il

    1991-01-01

    Fuzzy set theory provides a formal framework for dealing with the imprecision and vagueness inherent in the expert judgement, and therefore it can be used for more effective analysis of accident progression of PRA where experts opinion is a major means for quantifying some event probabilities and uncertainties. In this paper, an example application of the fuzzy set theory is first made to a simple portion of a given accident progression event tree with typical qualitative fuzzy input data, and thereby computational algorithms suitable for application of the fuzzy set theory to the accident progression event tree analysis are identified and illustrated with example applications. Then the procedure used in the simple example is extended to extremely complex accident progression event trees with a number of phenomenological uncertainty issues, i.e., a typical plant damage state 'SEC' of the Zion Nuclear Power Plant risk assessment. The results show that the fuzzy averages of the fuzzy outcomes are very close to the mean values obtained by current methods. The main purpose of this paper is to provide a formal procedure for application of the fuzzy set theory to accident progression event trees with imprecise and qualitative branch probabilities and/or with a number of phenomenological uncertainty issues. (author)

  4. A new approach to enhance the performance of decision tree for classifying gene expression data.

    Science.gov (United States)

    Hassan, Md; Kotagiri, Ramamohanarao

    2013-12-20

    Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

  5. Tree-growth analyses to estimate tree species' drought tolerance

    NARCIS (Netherlands)

    Eilmann, B.; Rigling, A.

    2012-01-01

    Climate change is challenging forestry management and practices. Among other things, tree species with the ability to cope with more extreme climate conditions have to be identified. However, while environmental factors may severely limit tree growth or even cause tree death, assessing a tree

  6. Proposal for the classification of scenarios for deep geological repositories in probability classes

    International Nuclear Information System (INIS)

    Beuth, Thomas

    2013-03-01

    The provided report was elaborated in the framework of the project 3609R03210 ''Research and Development for Proof of the long-term Safety of Deep Geological Repositories''. It contains a proposal for a methodology that enables the assignment of developed scenarios in the frame of Safety Cases to defined probability classes. The assignment takes place indirectly through the categorization of the defining relevant factors (so-called FEP: Features, Events and Processes) of the respective scenarios also in probability classes. Therefore, decision trees and criteria were developed for the categorization of relevant factors in classes. Besides the description of the methodology another focal point of the work was the application of the method taking into account a defined scenario. By means of the scenario the different steps of the method and the decision criteria were documented, respectively. In addition, potential subjective influences along the path of decisions regarding the assignment of scenarios in probability classes were identified.

  7. [Hydraulic limitation on photosynthetic rate of old Populus simonii trees in sandy soil of north Shaanxi Province].

    Science.gov (United States)

    Zuo, Li-Xiang; Li, Yang-Yang; Chen, Jia-Cun

    2014-06-01

    'Old and dwarf trees' on the loess plateau region mainly occurred among mature trees rather than among small trees. To elucidate the mechanism of tree age on 'old and dwarf trees' formation, taking Populus simonii, a tree species that accounted for the largest portion of 'old and dwarf trees' on the loess plateau, as an example, the growth, photosynthesis and hydraulic traits of P. simonii trees with different ages (young: 13-15 years, mid-aged: 31-34 years, and old: 49-54 years) were measured. The results showed that the dieback length increased, and net photosynthetic rate, stomatal conductance, transpiration rate, and whole plant hydraulic conductance decreased significantly with the increasing tree age. Both net photosynthetic rate and stomatal conductance measured at different dates were significantly and positively related to the whole plant hydraulic conductance, suggesting that the decreasing photosynthetic rate of old trees was possibly caused by the declined hydraulic conductance. Although the resistance to cavitation in stems and leaves was stronger in old trees than in young and mid-aged trees, there were no differences in midday native stem embolization degree and leaf hydraulic conductance based on the vulnerability curve estimation, suggesting that the increased hydraulic resistance of the soil-root system is probably the most important reason for decreasing the whole plant hydraulic conductance of old trees.

  8. Probability, statistics, and computational science.

    Science.gov (United States)

    Beerenwinkel, Niko; Siebourg, Juliane

    2012-01-01

    In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

  9. TreeNetViz: revealing patterns of networks over tree structures.

    Science.gov (United States)

    Gou, Liang; Zhang, Xiaolong Luke

    2011-12-01

    Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns. © 2011 IEEE

  10. Value and probability coding in a feedback-based learning task utilizing food rewards.

    Science.gov (United States)

    Tricomi, Elizabeth; Lempert, Karolina M

    2015-01-01

    For the consequences of our actions to guide behavior, the brain must represent different types of outcome-related information. For example, an outcome can be construed as negative because an expected reward was not delivered or because an outcome of low value was delivered. Thus behavioral consequences can differ in terms of the information they provide about outcome probability and value. We investigated the role of the striatum in processing probability-based and value-based negative feedback by training participants to associate cues with food rewards and then employing a selective satiety procedure to devalue one food outcome. Using functional magnetic resonance imaging, we examined brain activity related to receipt of expected rewards, receipt of devalued outcomes, omission of expected rewards, omission of devalued outcomes, and expected omissions of an outcome. Nucleus accumbens activation was greater for rewarding outcomes than devalued outcomes, but activity in this region did not correlate with the probability of reward receipt. Activation of the right caudate and putamen, however, was largest in response to rewarding outcomes relative to expected omissions of reward. The dorsal striatum (caudate and putamen) at the time of feedback also showed a parametric increase correlating with the trialwise probability of reward receipt. Our results suggest that the ventral striatum is sensitive to the motivational relevance, or subjective value, of the outcome, while the dorsal striatum codes for a more complex signal that incorporates reward probability. Value and probability information may be integrated in the dorsal striatum, to facilitate action planning and allocation of effort. Copyright © 2015 the American Physiological Society.

  11. Large old trees influence patterns of delta13C and delta15N in forests.

    Science.gov (United States)

    Weber, Pascale; Bol, Roland; Dixon, Liz; Bardgett, Richard D

    2008-06-01

    Large old trees are the dominant primary producers of native pine forest, but their influence on spatial patterns of soil properties and potential feedback to tree regeneration in their neighbourhood is poorly understood. We measured stable isotopes of carbon (delta(13)C) and nitrogen (delta(15)N) in soil and litter taken from three zones of influence (inner, middle and outer zone) around the trunk of freestanding old Scots pine (Pinus sylvestris L.) trees, to determine the trees' influence on below-ground properties. We also measured delta(15)N and delta(13)C in wood cores extracted from the old trees and from regenerating trees growing within their three zones of influence. We found a significant and positive gradient in soil delta(15)N from the inner zone, nearest to the tree centre, to the outer zone beyond the tree crown. This was probably caused by the higher input of (15)N-depleted litter below the tree crown. In contrast, the soil delta(13)C did not change along the gradient of tree influence. Distance-related trends, although weak, were visible in the wood delta(15)N and delta(13)C of regenerating trees. Moreover, the wood delta(15)N of small trees showed a weak negative relationship with soil N content in the relevant zone of influence. Our results indicate that large old trees control below-ground conditions in their immediate surroundings, and that stable isotopes might act as markers for the spatial and temporal extent of these below-ground effects. John Wiley & Sons, Ltd

  12. Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

    Science.gov (United States)

    Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

    2009-01-01

    The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

  13. Automated Decision Tree Classification of Corneal Shape

    Science.gov (United States)

    Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

    2011-01-01

    Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification

  14. Fault tree analysis with multistate components

    International Nuclear Information System (INIS)

    Caldarola, L.

    1979-02-01

    A general analytical theory has been developed which allows one to calculate the occurence probability of the top event of a fault tree with multistate (more than states) components. It is shown that, in order to correctly describe a system with multistate components, a special type of Boolean algebra is required. This is called 'Boolean algebra with restrictions on varibales' and its basic rules are the same as those of the traditional Boolean algebra with some additional restrictions on the variables. These restrictions are extensively discussed in the paper. Important features of the method are the identification of the complete base and of the smallest irredundant base of a Boolean function which does not necessarily need to be coherent. It is shown that the identification of the complete base of a Boolean function requires the application of some algorithms which are not used in today's computer programmes for fault tree analysis. The problem of statistical dependence among primary components is discussed. The paper includes a small demonstrative example to illustrate the method. The example includes also statistical dependent components. (orig.) [de

  15. Dynamic encoding of speech sequence probability in human temporal cortex.

    Science.gov (United States)

    Leonard, Matthew K; Bouchard, Kristofer E; Tang, Claire; Chang, Edward F

    2015-05-06

    Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning. Copyright © 2015 the authors 0270-6474/15/357203-12$15.00/0.

  16. TREE SELECTING AND TREE RING MEASURING IN DENDROCHRONOLOGICAL INVESTIGATIONS

    Directory of Open Access Journals (Sweden)

    Sefa Akbulut

    2004-04-01

    Full Text Available Dendrochronology is a method of dating which makes use of the annual nature of tree growth. Dendrochronology may be divided into a number of subfields, each of which covers one or more aspects of the use of tree ring data: dendroclimatology, dendrogeomorphology, dendrohydrology, dendroecology, dendroarchaelogy, and dendrogylaciology. Basic of all form the analysis of the tree rings. The wood or tree rings can aid to dating past events about climatology, ecology, geology, hydrology. Dendrochronological studies are conducted either on increment cores or on discs. It may be seen abnormalities on tree rings during the measurement like that false rings, missing rings, reaction wood. Like that situation, increment cores must be extracted from four different sides of each tree and be studied as more as on tree.

  17. Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

    Directory of Open Access Journals (Sweden)

    Xu Chen

    2018-01-01

    Full Text Available Teaching-learning-based optimization (TLBO is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO. In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

  18. Assessment of Student Learning Associated with Tree Thinking in an Undergraduate Introductory Organismal Biology Course

    Science.gov (United States)

    Smith, James J.; Cheruvelil, Kendra Spence; Auvenshine, Stacie

    2013-01-01

    Phylogenetic trees provide visual representations of ancestor-descendant relationships, a core concept of evolutionary theory. We introduced "tree thinking" into our introductory organismal biology course (freshman/sophomore majors) to help teach organismal diversity within an evolutionary framework. Our instructional strategy consisted…

  19. Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions

    KAUST Repository

    Azad, Mohammad

    2018-06-06

    Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle

  20. System Analysis by Mapping a Fault-tree into a Bayesian-network

    Science.gov (United States)

    Sheng, B.; Deng, C.; Wang, Y. H.; Tang, L. H.

    2018-05-01

    In view of the limitations of fault tree analysis in reliability assessment, Bayesian Network (BN) has been studied as an alternative technology. After a brief introduction to the method for mapping a Fault Tree (FT) into an equivalent BN, equations used to calculate the structure importance degree, the probability importance degree and the critical importance degree are presented. Furthermore, the correctness of these equations is proved mathematically. Combining with an aircraft landing gear’s FT, an equivalent BN is developed and analysed. The results show that richer and more accurate information have been achieved through the BN method than the FT, which demonstrates that the BN is a superior technique in both reliability assessment and fault diagnosis.

  1. Visualizing Individual Tree Differences in Tree-Ring Studies

    Directory of Open Access Journals (Sweden)

    Mario Trouillier

    2018-04-01

    Full Text Available Averaging tree-ring measurements from multiple individuals is one of the most common procedures in dendrochronology. It serves to filter out noise from individual differences between trees, such as competition, height, and micro-site effects, which ideally results in a site chronology sensitive to regional scale factors such as climate. However, the climate sensitivity of individual trees can be modulated by factors like competition, height, and nitrogen deposition, calling attention to whether average chronologies adequately assess climatic growth-control. In this study, we demonstrate four simple but effective methods to visually assess differences between individual trees. Using individual tree climate-correlations we: (1 employed jitter plots with superimposed metadata to assess potential causes for these differences; (2 plotted the frequency distributions of climate correlations over time as heat maps; (3 mapped the spatial distribution of climate sensitivity over time to assess spatio-temporal dynamics; and (4 used t-distributed Stochastic Neighborhood Embedding (t-SNE to assess which trees were generally more similar in terms of their tree-ring pattern and their correlation with climate variables. This suite of exploratory methods can indicate if individuals in tree-ring datasets respond differently to climate variability, and therefore, should not solely be explored with climate correlations of the mean population chronology.

  2. Subspace Learning via Local Probability Distribution for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Huiwu Luo

    2015-01-01

    Full Text Available The computational procedure of hyperspectral image (HSI is extremely complex, not only due to the high dimensional information, but also due to the highly correlated data structure. The need of effective processing and analyzing of HSI has met many difficulties. It has been evidenced that dimensionality reduction has been found to be a powerful tool for high dimensional data analysis. Local Fisher’s liner discriminant analysis (LFDA is an effective method to treat HSI processing. In this paper, a novel approach, called PD-LFDA, is proposed to overcome the weakness of LFDA. PD-LFDA emphasizes the probability distribution (PD in LFDA, where the maximum distance is replaced with local variance for the construction of weight matrix and the class prior probability is applied to compute the affinity matrix. The proposed approach increases the discriminant ability of the transformed features in low dimensional space. Experimental results on Indian Pines 1992 data indicate that the proposed approach significantly outperforms the traditional alternatives.

  3. Unification of field theory and maximum entropy methods for learning probability densities

    OpenAIRE

    Kinney, Justin B.

    2014-01-01

    The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such approaches. Both have origins in statistical physics, but the relationship between them has remained unclear. Here I unify these two methods by showing that every maximum entropy de...

  4. A basic approach to fire injury of tree stems

    Science.gov (United States)

    R. E. Martin

    1963-01-01

    Fire has come to be widely used as a tool in wildland management, particularly in the South. Its usefulness in fire hazard reduction, removal of undesirable trees, and changing of cover types has been demonstrated. We are continually trying to improve fire use, however, by learning more of the specific effects of fire on different species of plants.

  5. An enhanced component connection method for conversion of fault trees to binary decision diagrams

    International Nuclear Information System (INIS)

    Remenyte-Prescott, R.; Andrews, J.D.

    2008-01-01

    Fault tree analysis (FTA) is widely applied to assess the failure probability of industrial systems. Many computer packages are available, which are based on conventional kinetic tree theory methods. When dealing with large (possibly non-coherent) fault trees, the limitations of the technique in terms of accuracy of the solutions and the efficiency of the processing time become apparent. Over recent years, the binary decision diagram (BDD) method has been developed that solves fault trees and overcomes the disadvantages of the conventional FTA approach. First of all, a fault tree for a particular system failure mode is constructed and then converted to a BDD for analysis. This paper analyses alternative methods for the fault tree to BDD conversion process. For most fault tree to BDD conversion approaches, the basic events of the fault tree are placed in an ordering. This can dramatically affect the size of the final BDD and the success of qualitative and quantitative analyses of the system. A set of rules is then applied to each gate in the fault tree to generate the BDD. An alternative approach can also be used, where BDD constructs for each of the gate types are first built and then merged to represent a parent gate. A powerful and efficient property, sub-node sharing, is also incorporated in the enhanced method proposed in this paper. Finally, a combined approach is developed taking the best features of the alternative methods. The efficiency of the techniques is analysed and discussed

  6. Recent Advances in Predictive (Machine) Learning

    Energy Technology Data Exchange (ETDEWEB)

    Friedman, J

    2004-01-24

    Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

  7. A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education

    Science.gov (United States)

    Christou, Nicolas; Dinov, Ivo D.

    2011-01-01

    Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources. PMID:21603097

  8. A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education.

    Science.gov (United States)

    Christou, Nicolas; Dinov, Ivo D

    2010-09-01

    Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources.

  9. GenNon-h: Generating multiple sequence alignments on nonhomogeneous phylogenetic trees

    Directory of Open Access Journals (Sweden)

    Kedzierska Anna M

    2012-08-01

    Full Text Available Abstract Background A number of software packages are available to generate DNA multiple sequence alignments (MSAs evolved under continuous-time Markov processes on phylogenetic trees. On the other hand, methods of simulating the DNA MSA directly from the transition matrices do not exist. Moreover, existing software restricts to the time-reversible models and it is not optimized to generate nonhomogeneous data (i.e. placing distinct substitution rates at different lineages. Results We present the first package designed to generate MSAs evolving under discrete-time Markov processes on phylogenetic trees, directly from probability substitution matrices. Based on the input model and a phylogenetic tree in the Newick format (with branch lengths measured as the expected number of substitutions per site, the algorithm produces DNA alignments of desired length. GenNon-h is publicly available for download. Conclusion The software presented here is an efficient tool to generate DNA MSAs on a given phylogenetic tree. GenNon-h provides the user with the nonstationary or nonhomogeneous phylogenetic data that is well suited for testing complex biological hypotheses, exploring the limits of the reconstruction algorithms and their robustness to such models.

  10. The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir

    KAUST Repository

    Prasetyo Utomo, Chandra

    2011-01-01

    the permeability value. These are based on the well logs data. In order to handle the high range of the permeability value, a classification tree is utilized. A benefit of this innovation is that the tree represents knowledge in a clear and succinct fashion

  11. Probability

    CERN Document Server

    Shiryaev, A N

    1996-01-01

    This book contains a systematic treatment of probability from the ground up, starting with intuitive ideas and gradually developing more sophisticated subjects, such as random walks, martingales, Markov chains, ergodic theory, weak convergence of probability measures, stationary stochastic processes, and the Kalman-Bucy filter Many examples are discussed in detail, and there are a large number of exercises The book is accessible to advanced undergraduates and can be used as a text for self-study This new edition contains substantial revisions and updated references The reader will find a deeper study of topics such as the distance between probability measures, metrization of weak convergence, and contiguity of probability measures Proofs for a number of some important results which were merely stated in the first edition have been added The author included new material on the probability of large deviations, and on the central limit theorem for sums of dependent random variables

  12. Modular tree automata

    DEFF Research Database (Denmark)

    Bahr, Patrick

    2012-01-01

    Tree automata are traditionally used to study properties of tree languages and tree transformations. In this paper, we consider tree automata as the basis for modular and extensible recursion schemes. We show, using well-known techniques, how to derive from standard tree automata highly modular...

  13. Rate of tree carbon accumulation increases continuously with tree size.

    Science.gov (United States)

    Stephenson, N L; Das, A J; Condit, R; Russo, S E; Baker, P J; Beckman, N G; Coomes, D A; Lines, E R; Morris, W K; Rüger, N; Alvarez, E; Blundo, C; Bunyavejchewin, S; Chuyong, G; Davies, S J; Duque, A; Ewango, C N; Flores, O; Franklin, J F; Grau, H R; Hao, Z; Harmon, M E; Hubbell, S P; Kenfack, D; Lin, Y; Makana, J-R; Malizia, A; Malizia, L R; Pabst, R J; Pongpattananurak, N; Su, S-H; Sun, I-F; Tan, S; Thomas, D; van Mantgem, P J; Wang, X; Wiser, S K; Zavala, M A

    2014-03-06

    Forests are major components of the global carbon cycle, providing substantial feedback to atmospheric greenhouse gas concentrations. Our ability to understand and predict changes in the forest carbon cycle--particularly net primary productivity and carbon storage--increasingly relies on models that represent biological processes across several scales of biological organization, from tree leaves to forest stands. Yet, despite advances in our understanding of productivity at the scales of leaves and stands, no consensus exists about the nature of productivity at the scale of the individual tree, in part because we lack a broad empirical assessment of whether rates of absolute tree mass growth (and thus carbon accumulation) decrease, remain constant, or increase as trees increase in size and age. Here we present a global analysis of 403 tropical and temperate tree species, showing that for most species mass growth rate increases continuously with tree size. Thus, large, old trees do not act simply as senescent carbon reservoirs but actively fix large amounts of carbon compared to smaller trees; at the extreme, a single big tree can add the same amount of carbon to the forest within a year as is contained in an entire mid-sized tree. The apparent paradoxes of individual tree growth increasing with tree size despite declining leaf-level and stand-level productivity can be explained, respectively, by increases in a tree's total leaf area that outpace declines in productivity per unit of leaf area and, among other factors, age-related reductions in population density. Our results resolve conflicting assumptions about the nature of tree growth, inform efforts to undertand and model forest carbon dynamics, and have additional implications for theories of resource allocation and plant senescence.

  14. Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

    Directory of Open Access Journals (Sweden)

    Adrian Fisher

    2016-06-01

    Full Text Available Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l’Observation de la Terre 5 (SPOT5 High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW, an area of over 800,000 km2. Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008–2011 for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC, a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (±0.51% standard error, while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r2 = 0.90 with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and

  15. Computer aided fault tree synthesis

    International Nuclear Information System (INIS)

    Poucet, A.

    1983-01-01

    Nuclear as well as non-nuclear organisations are showing during the past few years a growing interest in the field of reliability analysis. This urges for the development of powerful, state of the art methods and computer codes for performing such analysis on complex systems. In this report an interactive, computer aided approach is discussed, based on the well known fault tree technique. The time consuming and difficut task of manually constructing a system model (one or more fault trees) is replaced by an efficient interactive procedure in which the flexibility and the learning process inherent to the manual approach are combined with the accuracy in the modelling and the speed of the fully automatical approach. The method presented is based upon the use of a library containing component models. The possibility of setting up a standard library of models of general use and the link with a data collection system are discussed. The method has been implemented in the CAFTS-SALP software package which is described shortly in the report

  16. Oak Wilt: People and Trees, A Community Approach to Management

    Science.gov (United States)

    J. Juzwik; S. Cook; L. Haugen; J. Elwell

    2004-01-01

    Version 1.3. This self-paced short course on CD-ROM was designed as a learning tool for urban and community foresters, city administrators, tree inspectors, parks and recreation staff, and others involved in oak wilt management.Click the "View or print this publication" link below to request your Oak Wilt: People and...

  17. Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans

    Science.gov (United States)

    O'Neil, Alison; Beveridge, Erin; Houston, Graeme; McCormick, Lynne; Poole, Ian

    2014-03-01

    This paper reports on arterial tree tracking in fourteen Contrast Enhanced MRA volumetric scans, given the positions of a predefined set of vascular landmarks, by using the A* algorithm to find the optimal path for each vessel based on voxel intensity and a learnt vascular probability atlas. The algorithm is intended for use in conjunction with an automatic landmark detection step, to enable fully automatic arterial tree tracking. The scan is filtered to give two further images using the top-hat transform with 4mm and 8mm cubic structuring elements. Vessels are then tracked independently on the scan in which the vessel of interest is best enhanced, as determined from knowledge of typical vessel diameter and surrounding structures. A vascular probability atlas modelling expected vessel location and orientation is constructed by non-rigidly registering the training scans to the test scan using a 3D thin plate spline to match landmark correspondences, and employing kernel density estimation with the ground truth center line points to form a probability density distribution. Threshold estimation by histogram analysis is used to segment background from vessel intensities. The A* algorithm is run using a linear cost function constructed from the threshold and the vascular atlas prior. Tracking results are presented for all major arteries excluding those in the upper limbs. An improvement was observed when tracking was informed by contextual information, with particular benefit for peripheral vessels.

  18. Mapping topographic structure in white matter pathways with level set trees.

    Directory of Open Access Journals (Sweden)

    Brian P Kent

    Full Text Available Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees--which provide a concise representation of the hierarchical mode structure of probability density functions--offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N = 30, we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber pathways and an efficient segmentation of the pathways that had empirical accuracy comparable to standard nonparametric clustering techniques. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output.

  19. Goal-oriented functional tree structure for nuclear power plant emergency preparedness

    International Nuclear Information System (INIS)

    Calabrese, R.V.; Roush, M.L.

    1987-01-01

    Guidelines for development and implementation of emergency response plans do not provide the planner/implementer with an adequate overview of the functions to be achieved or a measure of their relative importance. To provide a framework in which this importance can be recognized, understood and quantified, a logical goal oriented tree structure has been developed which integrates and gives a clear visual representation of the functions required to meet the emergency preparedness objective. The tree considers a spectrum of both high and low probability events which may require mitigation both onsite and offsite. The ultimate objective: to Minimize the Ill Effects of a Nuclear Power Plant Incident is satisfied by functions concerned with prevention and mitigation of human injury and property damage. A complete and detailed structure which specifies the subfunctions and success paths which satisfy these functions has been developed. Institutional activities such as planning, training, procedure development, monitoring and decision making do not enter the tree directly. Instead, the logic structure defines the extent to which these activities must be considered and the information systems and decision models required for successful implementation of the plan. The top structure of the tree is presented and a few branches are considered in detail. The impact of institutional activities, information systems, etc. is discussed. Tree quantification is considered

  20. Arenal-type pyroclastic flows: A probabilistic event tree risk analysis

    Science.gov (United States)

    Meloy, Anthony F.

    2006-09-01

    A quantitative hazard-specific scenario-modelling risk analysis is performed at Arenal volcano, Costa Rica for the newly recognised Arenal-type pyroclastic flow (ATPF) phenomenon using an event tree framework. These flows are generated by the sudden depressurisation and fragmentation of an active basaltic andesite lava pool as a result of a partial collapse of the crater wall. The deposits of this type of flow include angular blocks and juvenile clasts, which are rarely found in other types of pyroclastic flow. An event tree analysis (ETA) is a useful tool and framework in which to analyse and graphically present the probabilities of the occurrence of many possible events in a complex system. Four event trees are created in the analysis, three of which are extended to investigate the varying individual risk faced by three generic representatives of the surrounding community: a resident, a worker, and a tourist. The raw numerical risk estimates determined by the ETA are converted into a set of linguistic expressions (i.e. VERY HIGH, HIGH, MODERATE etc.) using an established risk classification scale. Three individually tailored semi-quantitative risk maps are then created from a set of risk conversion tables to show how the risk varies for each individual in different areas around the volcano. In some cases, by relocating from the north to the south, the level of risk can be reduced by up to three classes. While the individual risk maps may be broadly applicable, and therefore of interest to the general community, the risk maps and associated probability values generated in the ETA are intended to be used by trained professionals and government agencies to evaluate the risk and effectively manage the long-term development of infrastructure and habitation. With the addition of fresh monitoring data, the combination of both long- and short-term event trees would provide a comprehensive and consistent method of risk analysis (both during and pre-crisis), and as such

  1. Collaborative multi-agent reinforcement learning based on a novel coordination tree frame with dynamic partition

    NARCIS (Netherlands)

    Fang, M.; Groen, F.C.A.; Li, H.; Zhang, J.

    2014-01-01

    In the research of team Markov games, computing the coordinate team dynamically and determining the joint action policy are the main problems. To deal with the first problem, a dynamic team partitioning method is proposed based on a novel coordinate tree frame. We build a coordinate tree with

  2. Computational Modeling of Statistical Learning: Effects of Transitional Probability versus Frequency and Links to Word Learning

    Science.gov (United States)

    Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S.

    2010-01-01

    Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…

  3. Automatic Transformation of the Thai Categorial Grammar Treebank to Dependency Trees

    DEFF Research Database (Denmark)

    Rishøj, Christian; Ruangrajitpakorn, Taneth; Boonkwan, Prachya

    2011-01-01

    on a generic mapping of CG types in case of unknown words. Currently, all but a handful of the trees in the Thai CG bank can unambiguously be transformed into directed dependency trees. Dependency labels can optionally be assigned with a learned classifier, which in a preliminary evaluation with a very small...... training set achieves 76.5% label accuracy. In the process, a number of annotation errors in the CG bank were identified and corrected. Although rather limited in its coverage, excluding e.g. long-distance dependencies, topicalisations and longer sentences, the resulting treebank is believed to be sound...

  4. A bijection between phylogenetic trees and plane oriented recursive trees

    OpenAIRE

    Prodinger, Helmut

    2017-01-01

    Phylogenetic trees are binary nonplanar trees with labelled leaves, and plane oriented recursive trees are planar trees with an increasing labelling. Both families are enumerated by double factorials. A bijection is constructed, using the respective representations a 2-partitions and trapezoidal words.

  5. A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex.

    Science.gov (United States)

    Chan, Stephanie C Y; Niv, Yael; Norman, Kenneth A

    2016-07-27

    The orbitofrontal cortex (OFC) has been implicated in both the representation of "state," in studies of reinforcement learning and decision making, and also in the representation of "schemas," in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or "latent cause" that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes' rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in the OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives, such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes. Our world is governed by hidden (latent) causes that we cannot observe, but which generate the observations we see. A range of high-level cognitive processes require inference of a probability distribution (or "belief distribution") over the possible latent causes that might be generating our current observations. This is true for reinforcement learning and decision making (where the latent cause comprises the true "state" of the task), and for episodic memory (where memories are believed to be organized by the inferred situation or "schema"). Using fMRI, we show that this belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex, an area that has been separately implicated in the representations of both states and schemas. Copyright © 2016 the authors 0270-6474/16/367817-12$15.00/0.

  6. One-year measurements of chloroethenes in tree cores and groundwater at the SAP Mimoň Site, Northern Bohemia

    DEFF Research Database (Denmark)

    Wittlingerova, Z.; Machackova, J.; Petruzelkova, A.

    2013-01-01

    Chlorinated ethenes (CE) are among the most frequent contaminants of soil and groundwater in the Czech Republic. Because conventional methods of subsurface contamination investigation are costly and technically complicated, attention is directed on alternative and innovative field sampling methods...... documented that the results of tree core analyses can be used to indicate the presence of CE in the subsurface. The results may also be helpful to identify the best sampling period for tree coring and to learn about the time it takes until tree core concentrations react to changes in groundwater conditions....... One promising method is sampling of tree cores (plugs of woody tissue extracted from a host tree). Volatile organic compounds can enter into the trunks and other tissues of trees through their root systems. An analysis of the tree core can thus serve as an indicator of the subsurface contamination...

  7. Description et modélisation de la croissance et du développement du pommier (Malus x domestica Borkh. I. Structure la plus probable de l'arbre jeune

    Directory of Open Access Journals (Sweden)

    Planchon V.

    2003-01-01

    Full Text Available Description and modelisation of growth and development of appletree (Malus x domestica Borkh.. I. Most probable structure of young tree. Based on the observation of all or, at least, a large number of shoots growing on unpruned apple trees, cv. Cox's Orange Pippin and Jonagold grafted on EM9, an original codification system has been conceived. It allows to locate and identify quickly and unequivocally any shoot or growth. From a comprehensive collection of such codified observations, the most probable structure of young one to five year old trees has been established, with the amount and characters of the different observed shoot types. Both varieties display very similar gross structures, despite minor differences in form, flowering mode and productivity. The development of the trees occurs in two phases. The first one builds up a frame composed by the trunk and several storied sets of branches. Flowering is rather strictly alternate during this phase. The second one consists in the reinforcement of the branches by axillary growth, but even more by sympodial branching due to increasing and repeated terminal flowering. This basic structure provides a reference to appreciate changes in tree development brought about by rootstock, fruit load or training practices.

  8. Users' manual for fault tree analysis code: CUT-TD

    International Nuclear Information System (INIS)

    Watanabe, Norio; Kiyota, Mikio.

    1992-06-01

    The CUT-TD code has been developed to find minimal cut sets for a given fault tree and to calculate the occurrence probability of its top event. This code uses an improved top-down algorithm which can enhance the efficiency in deriving minimal cut sets. The features in processing techniques incorporated into CUT-TD are as follows: (1) Consecutive OR gates or consecutive AND gates can be coalesced into a single gate. As a result, this processing directly produces cut sets for the redefined single gate with each gate not being developed. (2) The independent subtrees are automatically identified and their respective cut sets are separately found to enhance the efficiency in processing. (3) The minimal cut sets can be obtained for the top event of a fault tree by combining their respective minimal cut sets for several gates of the fault tree. (4) The user can reduce the computing time for finding minimal cut sets and control the size and significance of cut sets by inputting a minimum probability cut off and/or a maximum order cut off. (5) The user can select events that need not to be further developed in the process of obtaining minimal cut sets. This option can reduce the number of minimal cut sets, save the computing time and assists the user in reviewing the result. (6) Computing time is monitored by the CUT-TD code so that it can prevent the running job from abnormally ending due to excessive CPU time and produce an intermediate result. The CUT-TD code has the ability to restart the calculation with use of the intermediate result. This report provides a users' manual for the CUT-TD code. (author)

  9. Review: Evaluation of Foot-and-Mouth Disease Control Using Fault Tree Analysis.

    Science.gov (United States)

    Isoda, N; Kadohira, M; Sekiguchi, S; Schuppers, M; Stärk, K D C

    2015-06-01

    An outbreak of foot-and-mouth disease (FMD) causes huge economic losses and animal welfare problems. Although much can be learnt from past FMD outbreaks, several countries are not satisfied with their degree of contingency planning and aiming at more assurance that their control measures will be effective. The purpose of the present article was to develop a generic fault tree framework for the control of an FMD outbreak as a basis for systematic improvement and refinement of control activities and general preparedness. Fault trees are typically used in engineering to document pathways that can lead to an undesired event, that is, ineffective FMD control. The fault tree method allows risk managers to identify immature parts of the control system and to analyse the events or steps that will most probably delay rapid and effective disease control during a real outbreak. The present developed fault tree is generic and can be tailored to fit the specific needs of countries. For instance, the specific fault tree for the 2001 FMD outbreak in the UK was refined based on control weaknesses discussed in peer-reviewed articles. Furthermore, the specific fault tree based on the 2001 outbreak was applied to the subsequent FMD outbreak in 2007 to assess the refinement of control measures following the earlier, major outbreak. The FMD fault tree can assist risk managers to develop more refined and adequate control activities against FMD outbreaks and to find optimum strategies for rapid control. Further application using the current tree will be one of the basic measures for FMD control worldwide. © 2013 Blackwell Verlag GmbH.

  10. Rate of tree carbon accumulation increases continuously with tree size

    Science.gov (United States)

    Stephenson, N.L.; Das, A.J.; Condit, R.; Russo, S.E.; Baker, P.J.; Beckman, N.G.; Coomes, D.A.; Lines, E.R.; Morris, W.K.; Rüger, N.; Álvarez, E.; Blundo, C.; Bunyavejchewin, S.; Chuyong, G.; Davies, S.J.; Duque, Á.; Ewango, C.N.; Flores, O.; Franklin, J.F.; Grau, H.R.; Hao, Z.; Harmon, M.E.; Hubbell, S.P.; Kenfack, D.; Lin, Y.; Makana, J.-R.; Malizia, A.; Malizia, L.R.; Pabst, R.J.; Pongpattananurak, N.; Su, S.-H.; Sun, I-F.; Tan, S.; Thomas, D.; van Mantgem, P.J.; Wang, X.; Wiser, S.K.; Zavala, M.A.

    2014-01-01

    Forests are major components of the global carbon cycle, providing substantial feedback to atmospheric greenhouse gas concentrations. Our ability to understand and predict changes in the forest carbon cycle—particularly net primary productivity and carbon storage - increasingly relies on models that represent biological processes across several scales of biological organization, from tree leaves to forest stands. Yet, despite advances in our understanding of productivity at the scales of leaves and stands, no consensus exists about the nature of productivity at the scale of the individual tree, in part because we lack a broad empirical assessment of whether rates of absolute tree mass growth (and thus carbon accumulation) decrease, remain constant, or increase as trees increase in size and age. Here we present a global analysis of 403 tropical and temperate tree species, showing that for most species mass growth rate increases continuously with tree size. Thus, large, old trees do not act simply as senescent carbon reservoirs but actively fix large amounts of carbon compared to smaller trees; at the extreme, a single big tree can add the same amount of carbon to the forest within a year as is contained in an entire mid-sized tree. The apparent paradoxes of individual tree growth increasing with tree size despite declining leaf-level and stand-level productivity can be explained, respectively, by increases in a tree’s total leaf area that outpace declines in productivity per unit of leaf area and, among other factors, age-related reductions in population density. Our results resolve conflicting assumptions about the nature of tree growth, inform efforts to understand and model forest carbon dynamics, and have additional implications for theories of resource allocation and plant senescence.

  11. Heads or tails an introduction to limit theorems in probability

    CERN Document Server

    Lesigne, Emmanuel

    2005-01-01

    Everyone knows some of the basics of probability, perhaps enough to play cards. Beyond the introductory ideas, there are many wonderful results that are unfamiliar to the layman, but which are well within our grasp to understand and appreciate. Some of the most remarkable results in probability are those that are related to limit theorems--statements about what happens when the trial is repeated many times. The most famous of these is the Law of Large Numbers, which mathematicians, engineers, economists, and many others use every day. In this book, Lesigne has made these limit theorems accessible by stating everything in terms of a game of tossing of a coin: heads or tails. In this way, the analysis becomes much clearer, helping establish the reader's intuition about probability. Moreover, very little generality is lost, as many situations can be modelled from combinations of coin tosses. This book is suitable for anyone who would like to learn more about mathematical probability and has had a one-year underg...

  12. Interactive Algorithms for Unsupervised Machine Learning

    Science.gov (United States)

    2015-06-01

    in Neural Information Processing Systems, 2013. 14 [3] Louigi Addario-Berry, Nicolas Broutin, Luc Devroye, and Gábor Lugosi. On combinato- rial...Myung Jin Choi, Vincent Y F Tan , Animashree Anandkumar, and Alan S Willsky. Learn- ing Latent Tree Graphical Models. Journal of Machine Learning

  13. Interacting Factors Driving a Major Loss of Large Trees with Cavities in a Forest Ecosystem

    Science.gov (United States)

    Lindenmayer, David B.; Blanchard, Wade; McBurney, Lachlan; Blair, David; Banks, Sam; Likens, Gene E.; Franklin, Jerry F.; Laurance, William F.; Stein, John A. R.; Gibbons, Philip

    2012-01-01

    Large trees with cavities provide critical ecological functions in forests worldwide, including vital nesting and denning resources for many species. However, many ecosystems are experiencing increasingly rapid loss of large trees or a failure to recruit new large trees or both. We quantify this problem in a globally iconic ecosystem in southeastern Australia – forests dominated by the world's tallest angiosperms, Mountain Ash (Eucalyptus regnans). Tree, stand and landscape-level factors influencing the death and collapse of large living cavity trees and the decay and collapse of dead trees with cavities are documented using a suite of long-term datasets gathered between 1983 and 2011. The historical rate of tree mortality on unburned sites between 1997 and 2011 was >14% with a mortality spike in the driest period (2006–2009). Following a major wildfire in 2009, 79% of large living trees with cavities died and 57–100% of large dead trees were destroyed on burned sites. Repeated measurements between 1997 and 2011 revealed no recruitment of any new large trees with cavities on any of our unburned or burned sites. Transition probability matrices of large trees with cavities through increasingly decayed condition states projects a severe shortage of large trees with cavities by 2039 that will continue until at least 2067. This large cavity tree crisis in Mountain Ash forests is a product of: (1) the prolonged time required (>120 years) for initiation of cavities; and (2) repeated past wildfires and widespread logging operations. These latter factors have resulted in all landscapes being dominated by stands ≤72 years and just 1.16% of forest being unburned and unlogged. We discuss how the features that make Mountain Ash forests vulnerable to a decline in large tree abundance are shared with many forest types worldwide. PMID:23071486

  14. Interacting factors driving a major loss of large trees with cavities in a forest ecosystem.

    Directory of Open Access Journals (Sweden)

    David B Lindenmayer

    Full Text Available Large trees with cavities provide critical ecological functions in forests worldwide, including vital nesting and denning resources for many species. However, many ecosystems are experiencing increasingly rapid loss of large trees or a failure to recruit new large trees or both. We quantify this problem in a globally iconic ecosystem in southeastern Australia--forests dominated by the world's tallest angiosperms, Mountain Ash (Eucalyptus regnans. Tree, stand and landscape-level factors influencing the death and collapse of large living cavity trees and the decay and collapse of dead trees with cavities are documented using a suite of long-term datasets gathered between 1983 and 2011. The historical rate of tree mortality on unburned sites between 1997 and 2011 was >14% with a mortality spike in the driest period (2006-2009. Following a major wildfire in 2009, 79% of large living trees with cavities died and 57-100% of large dead trees were destroyed on burned sites. Repeated measurements between 1997 and 2011 revealed no recruitment of any new large trees with cavities on any of our unburned or burned sites. Transition probability matrices of large trees with cavities through increasingly decayed condition states projects a severe shortage of large trees with cavities by 2039 that will continue until at least 2067. This large cavity tree crisis in Mountain Ash forests is a product of: (1 the prolonged time required (>120 years for initiation of cavities; and (2 repeated past wildfires and widespread logging operations. These latter factors have resulted in all landscapes being dominated by stands ≤72 years and just 1.16% of forest being unburned and unlogged. We discuss how the features that make Mountain Ash forests vulnerable to a decline in large tree abundance are shared with many forest types worldwide.

  15. Interacting factors driving a major loss of large trees with cavities in a forest ecosystem.

    Science.gov (United States)

    Lindenmayer, David B; Blanchard, Wade; McBurney, Lachlan; Blair, David; Banks, Sam; Likens, Gene E; Franklin, Jerry F; Laurance, William F; Stein, John A R; Gibbons, Philip

    2012-01-01

    Large trees with cavities provide critical ecological functions in forests worldwide, including vital nesting and denning resources for many species. However, many ecosystems are experiencing increasingly rapid loss of large trees or a failure to recruit new large trees or both. We quantify this problem in a globally iconic ecosystem in southeastern Australia--forests dominated by the world's tallest angiosperms, Mountain Ash (Eucalyptus regnans). Tree, stand and landscape-level factors influencing the death and collapse of large living cavity trees and the decay and collapse of dead trees with cavities are documented using a suite of long-term datasets gathered between 1983 and 2011. The historical rate of tree mortality on unburned sites between 1997 and 2011 was >14% with a mortality spike in the driest period (2006-2009). Following a major wildfire in 2009, 79% of large living trees with cavities died and 57-100% of large dead trees were destroyed on burned sites. Repeated measurements between 1997 and 2011 revealed no recruitment of any new large trees with cavities on any of our unburned or burned sites. Transition probability matrices of large trees with cavities through increasingly decayed condition states projects a severe shortage of large trees with cavities by 2039 that will continue until at least 2067. This large cavity tree crisis in Mountain Ash forests is a product of: (1) the prolonged time required (>120 years) for initiation of cavities; and (2) repeated past wildfires and widespread logging operations. These latter factors have resulted in all landscapes being dominated by stands ≤72 years and just 1.16% of forest being unburned and unlogged. We discuss how the features that make Mountain Ash forests vulnerable to a decline in large tree abundance are shared with many forest types worldwide.

  16. Algorithms and programs for evaluating fault trees with multi-state components

    International Nuclear Information System (INIS)

    Wickenhaeuser, A.

    1989-07-01

    Part 1 and 2 of the report contain a summary overview of methods and algorithms for the solution of fault tree analysis problems. The following points are treated in detail: Treatment of fault tree components with more than two states. Acceleration of the solution algorithms. Decomposition and modularization of extensive systems. Calculation of the structural function and the exact occurrence probability. Treatment of statistical dependencies. A flexible tool to be employed in solving these problems is the method of forming Boolean variables with restrictions. In this way, components with more than two states can be treated, the possibilities of forming modules expanded, and statistical dependencies treated. Part 3 contains descriptions of the MUSTAFA, MUSTAMO, PASPI, and SIMUST computer programs based on these methods. (orig./HP) [de

  17. The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors

    International Nuclear Information System (INIS)

    Duffey, Romney B.; Saull, John W.

    2006-01-01

    Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the 'learning hypothesis' that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new 'best' equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world's commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum

  18. From elongated spanning trees to vicious random walks

    Science.gov (United States)

    Gorsky, A.; Nechaev, S.; Poghosyan, V. S.; Priezzhev, V. B.

    2013-05-01

    Given a spanning forest on a large square lattice, we consider by combinatorial methods a correlation function of k paths (k is odd) along branches of trees or, equivalently, k loop-erased random walks. Starting and ending points of the paths are grouped such that they form a k-leg watermelon. For large distance r between groups of starting and ending points, the ratio of the number of watermelon configurations to the total number of spanning trees behaves as r-ν log r with ν = (k2 - 1) / 2. Considering the spanning forest stretched along the meridian of this watermelon, we show that the two-dimensional k-leg loop-erased watermelon exponent ν is converting into the scaling exponent for the reunion probability (at a given point) of k (1 + 1)-dimensional vicious walkers, ν˜ =k2 / 2. At the end, we express the conjectures about the possible relation to integrable systems.

  19. From elongated spanning trees to vicious random walks

    Energy Technology Data Exchange (ETDEWEB)

    Gorsky, A. [ITEP, B. Cheryomushkinskaya 25, 117218 Moscow (Russian Federation); Nechaev, S., E-mail: nechaev@lptms.u-psud.fr [LPTMS, Université Paris Sud, 91405 Orsay Cedex (France); P.N. Lebedev Physical Institute of the Russian Academy of Sciences, 119991 Moscow (Russian Federation); Poghosyan, V.S. [Institute for Informatics and Automation Problems NAS of Armenia, 375044 Yerevan (Armenia); Priezzhev, V.B. [Bogolubov Laboratory of Theoretical Physics, Joint Institute for Nuclear Research, 141980 Dubna (Russian Federation)

    2013-05-01

    Given a spanning forest on a large square lattice, we consider by combinatorial methods a correlation function of k paths (k is odd) along branches of trees or, equivalently, k loop-erased random walks. Starting and ending points of the paths are grouped such that they form a k-leg watermelon. For large distance r between groups of starting and ending points, the ratio of the number of watermelon configurations to the total number of spanning trees behaves as r{sup −ν}logr with ν=(k{sup 2}−1)/2. Considering the spanning forest stretched along the meridian of this watermelon, we show that the two-dimensional k-leg loop-erased watermelon exponent ν is converting into the scaling exponent for the reunion probability (at a given point) of k(1+1)-dimensional vicious walkers, ν{sup -tilde=}k{sup 2}/2. At the end, we express the conjectures about the possible relation to integrable systems.

  20. From elongated spanning trees to vicious random walks

    International Nuclear Information System (INIS)

    Gorsky, A.; Nechaev, S.; Poghosyan, V.S.; Priezzhev, V.B.

    2013-01-01

    Given a spanning forest on a large square lattice, we consider by combinatorial methods a correlation function of k paths (k is odd) along branches of trees or, equivalently, k loop-erased random walks. Starting and ending points of the paths are grouped such that they form a k-leg watermelon. For large distance r between groups of starting and ending points, the ratio of the number of watermelon configurations to the total number of spanning trees behaves as r −ν logr with ν=(k 2 −1)/2. Considering the spanning forest stretched along the meridian of this watermelon, we show that the two-dimensional k-leg loop-erased watermelon exponent ν is converting into the scaling exponent for the reunion probability (at a given point) of k(1+1)-dimensional vicious walkers, ν -tilde= k 2 /2. At the end, we express the conjectures about the possible relation to integrable systems

  1. IcyTree: rapid browser-based visualization for phylogenetic trees and networks.

    Science.gov (United States)

    Vaughan, Timothy G

    2017-08-01

    IcyTree is an easy-to-use application which can be used to visualize a wide variety of phylogenetic trees and networks. While numerous phylogenetic tree viewers exist already, IcyTree distinguishes itself by being a purely online tool, having a responsive user interface, supporting phylogenetic networks (ancestral recombination graphs in particular), and efficiently drawing trees that include information such as ancestral locations or trait values. IcyTree also provides intuitive panning and zooming utilities that make exploring large phylogenetic trees of many thousands of taxa feasible. IcyTree is a web application and can be accessed directly at http://tgvaughan.github.com/icytree . Currently supported web browsers include Mozilla Firefox and Google Chrome. IcyTree is written entirely in client-side JavaScript (no plugin required) and, once loaded, does not require network access to run. IcyTree is free software, and the source code is made available at http://github.com/tgvaughan/icytree under version 3 of the GNU General Public License. tgvaughan@gmail.com. © The Author(s) 2017. Published by Oxford University Press.

  2. A quantum probability model of causal reasoning

    Directory of Open Access Journals (Sweden)

    Jennifer S Trueblood

    2012-05-01

    Full Text Available People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause with diagnostic judgments (i.e., the conditional probability of a cause given an effect. The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment.

  3. Early evolution without a tree of life

    Directory of Open Access Journals (Sweden)

    Martin William F

    2011-06-01

    Full Text Available Abstract Life is a chemical reaction. Three major transitions in early evolution are considered without recourse to a tree of life. The origin of prokaryotes required a steady supply of energy and electrons, probably in the form of molecular hydrogen stemming from serpentinization. Microbial genome evolution is not a treelike process because of lateral gene transfer and the endosymbiotic origins of organelles. The lack of true intermediates in the prokaryote-to-eukaryote transition has a bioenergetic cause. This article was reviewed by Dan Graur, W. Ford Doolittle, Eugene V. Koonin and Christophe Malaterre.

  4. Using Tree-Ring Data, Research, and Expeditions as an Accessible, Hands-on "Bridge" into Climate Studies for Diverse Audiences

    Science.gov (United States)

    Davi, N. K.; Wattenberg, F.; Pringle, P. T.; Tanenbaum, J.; O'Brien, A.; Greidanus, I.; Perry, M.

    2012-12-01

    Tree-ring research provides an engaging, intuitive, and relevant entryway into understanding both climate-change and environmental research, as well as the process of science from inspiration, to fieldwork, to analysis, to publishing and communicating. The basic premise of dendrochronology is that annual rings reflect environmental conditions year-by-year and that by studying long-lived trees we can learn about past environments and climates for hundreds-to-thousands of years in the past. Conceptually, this makes tree-ring studies accessible to students and faculty for a number of reasons. First, in order to collect their data, dendrochronologists often launch expeditions to stunningly picturesque and remote places in search of long-lived, climate sensitive trees. Scientist exciting stories and images from the field can be leveraged to connect students to the study and the data. Second, tree-rings can be more easily explained as a proxy for climate than other methods (ice cores, carbon-isotope ratios, etc.), and most people have prior-knowledge about trees and annual growth rings. It is even possible, for example, for non-expert audiences to see climate variability through time with the naked eye by looking at climate sensitive tree cores. Third, tree-rings are interdisciplinary and illustrate the interplay between the mathematical sciences, the biological sciences, and the geosciences—that is, they show that the biosphere is a fundamental component of the Earth system. Here, we will present several projects have been initiated for a range of audiences, including; elementary school, where 5th graders visited a local forest to collect samples and apply their samples and what they learned to math and science classes. 5th grade students also leaned how to use Climate Explorer (KNMI), an online tool that allows scientist and students the opportunity to access and visualize global climate data within a few clicks. Geared to 2 and 4 year colleges, we are also

  5. Scaling Qualitative Probability

    OpenAIRE

    Burgin, Mark

    2017-01-01

    There are different approaches to qualitative probability, which includes subjective probability. We developed a representation of qualitative probability based on relational systems, which allows modeling uncertainty by probability structures and is more coherent than existing approaches. This setting makes it possible proving that any comparative probability is induced by some probability structure (Theorem 2.1), that classical probability is a probability structure (Theorem 2.2) and that i...

  6. Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

    Science.gov (United States)

    Mengshoel, Ole J.; Wilkins, David C.; Roth, Dan

    2010-01-01

    For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

  7. TreeScaper: Visualizing and Extracting Phylogenetic Signal from Sets of Trees.

    Science.gov (United States)

    Huang, Wen; Zhou, Guifang; Marchand, Melissa; Ash, Jeremy R; Morris, David; Van Dooren, Paul; Brown, Jeremy M; Gallivan, Kyle A; Wilgenbusch, Jim C

    2016-12-01

    Modern phylogenomic analyses often result in large collections of phylogenetic trees representing uncertainty in individual gene trees, variation across genes, or both. Extracting phylogenetic signal from these tree sets can be challenging, as they are difficult to visualize, explore, and quantify. To overcome some of these challenges, we have developed TreeScaper, an application for tree set visualization as well as the identification of distinct phylogenetic signals. GUI and command-line versions of TreeScaper and a manual with tutorials can be downloaded from https://github.com/whuang08/TreeScaper/releases TreeScaper is distributed under the GNU General Public License. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  8. A review on machine learning principles for multi-view biological data integration.

    Science.gov (United States)

    Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

    2018-03-01

    Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

  9. Algorithm for protecting light-trees in survivable mesh wavelength-division-multiplexing networks

    Science.gov (United States)

    Luo, Hongbin; Li, Lemin; Yu, Hongfang

    2006-12-01

    Wavelength-division-multiplexing (WDM) technology is expected to facilitate bandwidth-intensive multicast applications such as high-definition television. A single fiber cut in a WDM mesh network, however, can disrupt the dissemination of information to several destinations on a light-tree based multicast session. Thus it is imperative to protect multicast sessions by reserving redundant resources. We propose a novel and efficient algorithm for protecting light-trees in survivable WDM mesh networks. The algorithm is called segment-based protection with sister node first (SSNF), whose basic idea is to protect a light-tree using a set of backup segments with a higher priority to protect the segments from a branch point to its children (sister nodes). The SSNF algorithm differs from the segment protection scheme proposed in the literature in how the segments are identified and protected. Our objective is to minimize the network resources used for protecting each primary light-tree such that the blocking probability can be minimized. To verify the effectiveness of the SSNF algorithm, we conduct extensive simulation experiments. The simulation results demonstrate that the SSNF algorithm outperforms existing algorithms for the same problem.

  10. Teaching Probability with the Support of the R Statistical Software

    Science.gov (United States)

    dos Santos Ferreira, Robson; Kataoka, Verônica Yumi; Karrer, Monica

    2014-01-01

    The objective of this paper is to discuss aspects of high school students' learning of probability in a context where they are supported by the statistical software R. We report on the application of a teaching experiment, constructed using the perspective of Gal's probabilistic literacy and Papert's constructionism. The results show improvement…

  11. Flowering Trees

    Indian Academy of Sciences (India)

    IAS Admin

    Flowering Trees. Ailanthus excelsa Roxb. (INDIAN TREE OF. HEAVEN) of Simaroubaceae is a lofty tree with large pinnately compound alternate leaves, which are ... inflorescences, unisexual and greenish-yellow. Fruits are winged, wings many-nerved. Wood is used in making match sticks. 1. Male flower; 2. Female flower.

  12. Probability differently modulating the effects of reward and punishment on visuomotor adaptation.

    Science.gov (United States)

    Song, Yanlong; Smiley-Oyen, Ann L

    2017-12-01

    Recent human motor learning studies revealed that punishment seemingly accelerated motor learning but reward enhanced consolidation of motor memory. It is not evident how intrinsic properties of reward and punishment modulate the potentially dissociable effects of reward and punishment on motor learning and motor memory. It is also not clear what causes the dissociation of the effects of reward and punishment. By manipulating probability of distribution, a critical property of reward and punishment, the present study demonstrated that probability had distinct modulation on the effects of reward and punishment in adapting to a sudden visual rotation and consolidation of the adaptation memory. Specifically, two probabilities of monetary reward and punishment distribution, 50 and 100%, were applied during young adult participants adapting to a sudden visual rotation. Punishment and reward showed distinct effects on motor adaptation and motor memory. The group that received punishments in 100% of the adaptation trials adapted significantly faster than the other three groups, but the group that received rewards in 100% of the adaptation trials showed marked savings in re-adapting to the same rotation. In addition, the group that received punishments in 50% of the adaptation trials that were randomly selected also had savings in re-adapting to the same rotation. Sensitivity to sensory prediction error or difference in explicit process induced by reward and punishment may likely contribute to the distinct effects of reward and punishment.

  13. Detecting long-term growth trends using tree rings: a critical evaluation of methods.

    Science.gov (United States)

    Peters, Richard L; Groenendijk, Peter; Vlam, Mart; Zuidema, Pieter A

    2015-05-01

    Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability

  14. Evaluation of the probability of arrester failure in a high-voltage transmission line using a Q learning artificial neural network model

    International Nuclear Information System (INIS)

    Ekonomou, L; Karampelas, P; Vita, V; Chatzarakis, G E

    2011-01-01

    One of the most popular methods of protecting high voltage transmission lines against lightning strikes and internal overvoltages is the use of arresters. The installation of arresters in high voltage transmission lines can prevent or even reduce the lines' failure rate. Several studies based on simulation tools have been presented in order to estimate the critical currents that exceed the arresters' rated energy stress and to specify the arresters' installation interval. In this work artificial intelligence, and more specifically a Q-learning artificial neural network (ANN) model, is addressed for evaluating the arresters' failure probability. The aims of the paper are to describe in detail the developed Q-learning ANN model and to compare the results obtained by its application in operating 150 kV Greek transmission lines with those produced using a simulation tool. The satisfactory and accurate results of the proposed ANN model can make it a valuable tool for designers of electrical power systems seeking more effective lightning protection, reducing operational costs and better continuity of service

  15. Evaluation of the probability of arrester failure in a high-voltage transmission line using a Q learning artificial neural network model

    Science.gov (United States)

    Ekonomou, L.; Karampelas, P.; Vita, V.; Chatzarakis, G. E.

    2011-04-01

    One of the most popular methods of protecting high voltage transmission lines against lightning strikes and internal overvoltages is the use of arresters. The installation of arresters in high voltage transmission lines can prevent or even reduce the lines' failure rate. Several studies based on simulation tools have been presented in order to estimate the critical currents that exceed the arresters' rated energy stress and to specify the arresters' installation interval. In this work artificial intelligence, and more specifically a Q-learning artificial neural network (ANN) model, is addressed for evaluating the arresters' failure probability. The aims of the paper are to describe in detail the developed Q-learning ANN model and to compare the results obtained by its application in operating 150 kV Greek transmission lines with those produced using a simulation tool. The satisfactory and accurate results of the proposed ANN model can make it a valuable tool for designers of electrical power systems seeking more effective lightning protection, reducing operational costs and better continuity of service.

  16. Element Partition Trees For H-Refined Meshes to Optimize Direct Solver Performance. Part I: Dynamic Programming

    KAUST Repository

    AbouEisha, Hassan M.

    2017-07-13

    We consider a class of two-and three-dimensional h-refined meshes generated by an adaptive finite element method. We introduce an element partition tree, which controls the execution of the multi-frontal solver algorithm over these refined grids. We propose and study algorithms with polynomial computational cost for the optimization of these element partition trees. The trees provide an ordering for the elimination of unknowns. The algorithms automatically optimize the element partition trees using extensions of dynamic programming. The construction of the trees by the dynamic programming approach is expensive. These generated trees cannot be used in practice, but rather utilized as a learning tool to propose fast heuristic algorithms. In this first part of our paper we focus on the dynamic programming approach, and draw a sketch of the heuristic algorithm. The second part will be devoted to a more detailed analysis of the heuristic algorithm extended for the case of hp-adaptive

  17. Element Partition Trees For H-Refined Meshes to Optimize Direct Solver Performance. Part I: Dynamic Programming

    KAUST Repository

    AbouEisha, Hassan M.; Calo, Victor Manuel; Jopek, Konrad; Moshkov, Mikhail; Paszyńka, Anna; Paszyński, Maciej; Skotniczny, Marcin

    2017-01-01

    We consider a class of two-and three-dimensional h-refined meshes generated by an adaptive finite element method. We introduce an element partition tree, which controls the execution of the multi-frontal solver algorithm over these refined grids. We propose and study algorithms with polynomial computational cost for the optimization of these element partition trees. The trees provide an ordering for the elimination of unknowns. The algorithms automatically optimize the element partition trees using extensions of dynamic programming. The construction of the trees by the dynamic programming approach is expensive. These generated trees cannot be used in practice, but rather utilized as a learning tool to propose fast heuristic algorithms. In this first part of our paper we focus on the dynamic programming approach, and draw a sketch of the heuristic algorithm. The second part will be devoted to a more detailed analysis of the heuristic algorithm extended for the case of hp-adaptive

  18. Resource investments in reproductive growth proportionately limit investments in whole-tree vegetative growth in young olive trees with varying crop loads.

    Science.gov (United States)

    Rosati, Adolfo; Paoletti, Andrea; Al Hariri, Raeed; Morelli, Alessio; Famiani, Franco

    2018-02-21

    It has long been debated whether tree growth is source limited, or whether photosynthesis is adjusted to the actual sink demand, directly regulated by internal and environmental factors. Many studies support both possibilities, but no studies have provided quantitative data at the whole-tree level, across different cultivars and fruit load treatments. This study investigated the effect of different levels of reproductive growth on whole-tree biomass growth across two olive cultivars with different growth rates (i.e., Arbequina, slow-growing and Frantoio, fast-growing), over 2 years. Young trees of both cultivars were completely deflowered either in 2014, 2015, both years or never, providing a range of levels of cumulated reproductive growth over the 2 years. Total vegetative dry matter growth over the 2 years was assessed by destructive sampling (whole tree). Vegetative growth increased significantly less in fruiting trees, however, the total of vegetative and reproductive growth did not differ significantly for any treatment or cultivar. Vegetative growth over the 2 years was closely (R2 = 0.89) and inversely related to reproductive growth across all treatments and cultivars. When using data from 2015 only, the regression improved further (i.e., R2 = 0.99). When biomass was converted into grams of glucose equivalents, based on the chemical composition of the different parts, the results indicated that for every gram of glucose equivalent invested in reproductive growth, vegetative growth was reduced by 0.73-0.78 g of glucose equivalent. This indicates that competition for resources played a major role in determining tree growth, but also that photosynthesis was probably also enhanced at increasing fruit load (or downregulated at decreasing fruit load). The leaf area per unit of trunk cross sectional area increased with deflowering (i.e., decreased with reproductive growth), suggesting that water relations might have limited photosynthesis in deflowered plants

  19. Calculating Nuclear Power Plant Vulnerability Using Integrated Geometry and Event/Fault-Tree Models

    International Nuclear Information System (INIS)

    Peplow, Douglas E.; Sulfredge, C. David; Sanders, Robert L.; Morris, Robert H.; Hann, Todd A.

    2004-01-01

    Since the events of September 11, 2001, the vulnerability of nuclear power plants to terrorist attacks has become a national concern. The results of vulnerability analysis are greatly influenced by the computational approaches used. Standard approximations used in fault-tree analysis are not applicable for attacks, where high component failure probabilities are expected; two methods that do work with high failure probabilities are presented. Different blast modeling approaches can also affect the end results. Modeling the structural details of facility buildings and the geometric layout of components within the buildings is required to yield meaningful results

  20. CONSEL: for assessing the confidence of phylogenetic tree selection.

    Science.gov (United States)

    Shimodaira, H; Hasegawa, M

    2001-12-01

    CONSEL is a program to assess the confidence of the tree selection by giving the p-values for the trees. The main thrust of the program is to calculate the p-value of the Approximately Unbiased (AU) test using the multi-scale bootstrap technique. This p-value is less biased than the other conventional p-values such as the Bootstrap Probability (BP), the Kishino-Hasegawa (KH) test, the Shimodaira-Hasegawa (SH) test, and the Weighted Shimodaira-Hasegawa (WSH) test. CONSEL calculates all these p-values from the output of the phylogeny program packages such as Molphy, PAML, and PAUP*. Furthermore, CONSEL is applicable to a wide class of problems where the BPs are available. The programs are written in C language. The source code for Unix and the executable binary for DOS are found at http://www.ism.ac.jp/~shimo/ shimo@ism.ac.jp

  1. Eliciting conditional and unconditional rank correlations from conditional probabilities

    International Nuclear Information System (INIS)

    Morales, O.; Kurowicka, D.; Roelen, A.

    2008-01-01

    Causes of uncertainties may be interrelated and may introduce dependencies. Ignoring these dependencies may lead to large errors. A number of graphical models in probability theory such as dependence trees, vines and (continuous) Bayesian belief nets [Cooke RM. Markov and entropy properties of tree and vine-dependent variables. In: Proceedings of the ASA section on Bayesian statistical science, 1997; Kurowicka D, Cooke RM. Distribution-free continuous Bayesian belief nets. In: Proceedings of mathematical methods in reliability conference, 2004; Bedford TJ, Cooke RM. Vines-a new graphical model for dependent random variables. Ann Stat 2002; 30(4):1031-68; Kurowicka D, Cooke RM. Uncertainty analysis with high dimensional dependence modelling. New York: Wiley; 2006; Hanea AM, et al. Hybrid methods for quantifying and analyzing Bayesian belief nets. In: Proceedings of the 2005 ENBIS5 conference, 2005; Shachter RD, Kenley CR. Gaussian influence diagrams. Manage Sci 1998; 35(5) .] have been developed to capture dependencies between random variables. The input for these models are various marginal distributions and dependence information, usually in the form of conditional rank correlations. Often expert elicitation is required. This paper focuses on dependence representation, and dependence elicitation. The techniques presented are illustrated with an application from aviation safety

  2. Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees

    Directory of Open Access Journals (Sweden)

    Yue Hu

    2017-01-01

    Full Text Available In the context of robot navigation, game AI, and so on, real-time search is extensively used to undertake motion planning. Though it satisfies the requirement of quick response to users’ commands and environmental changes, learning real-time search (LRTS suffers from the heuristic depressions where agents behave irrationally. There have introduced several effective solutions, such as state abstractions. This paper combines LRTS and encoded quad-tree abstraction which represent the search space in multiresolutions. When exploring the environments, agents are enabled to locally repair the quad-tree models and incrementally refine the spatial cognition. By virtue of the idea of state aggregation and heuristic generalization, our EQ LRTS (encoded quad-tree based LRTS possesses the ability of quickly escaping from heuristic depressions with less state revisitations. Experiments and analysis show that (a our encoding principle for quad-trees is a much more memory-efficient method than other data structures expressing quad-trees, (b EQ LRTS differs a lot in several characteristics from classical PR LRTS which represent the space and refine the paths hierarchically, and (c EQ LRTS substantially reduces the planning amount and curtails heuristic updates compared with LRTS on uniform cells.

  3. Why do trees die? Characterizing the drivers of background tree mortality

    Science.gov (United States)

    Das, Adrian J.; Stephenson, Nathan L.; Davis, Kristin P.

    2016-01-01

    The drivers of background tree mortality rates—the typical low rates of tree mortality found in forests in the absence of acute stresses like drought—are central to our understanding of forest dynamics, the effects of ongoing environmental changes on forests, and the causes and consequences of geographical gradients in the nature and strength of biotic interactions. To shed light on factors contributing to background tree mortality, we analyzed detailed pathological data from 200,668 tree-years of observation and 3,729 individual tree deaths, recorded over a 13-yr period in a network of old-growth forest plots in California's Sierra Nevada mountain range. We found that: (1) Biotic mortality factors (mostly insects and pathogens) dominated (58%), particularly in larger trees (86%). Bark beetles were the most prevalent (40%), even though there were no outbreaks during the study period; in contrast, the contribution of defoliators was negligible. (2) Relative occurrences of broad classes of mortality factors (biotic, 58%; suppression, 51%; and mechanical, 25%) are similar among tree taxa, but may vary with tree size and growth rate. (3) We found little evidence of distinct groups of mortality factors that predictably occur together on trees. Our results have at least three sets of implications. First, rather than being driven by abiotic factors such as lightning or windstorms, the “ambient” or “random” background mortality that many forest models presume to be independent of tree growth rate is instead dominated by biotic agents of tree mortality, with potentially critical implications for forecasting future mortality. Mechanistic models of background mortality, even for healthy, rapidly growing trees, must therefore include the insects and pathogens that kill trees. Second, the biotic agents of tree mortality, instead of occurring in a few predictable combinations, may generally act opportunistically and with a relatively large degree of independence from

  4. Flowering Trees

    Indian Academy of Sciences (India)

    Flowering Trees. Gyrocarpus americanus Jacq. (Helicopter Tree) of Hernandiaceae is a moderate size deciduous tree that grows to about 12 m in height with a smooth, shining, greenish-white bark. The leaves are ovate, rarely irregularly ... flowers which are unpleasant smelling. Fruit is a woody nut with two long thin wings.

  5. The first cycle of the reflective pedagogical paradigm implementation in the introduction probability theory course

    Science.gov (United States)

    Julie, Hongki

    2017-08-01

    One of purposes of this study was describing the steps of the teaching and learning process if the teacher in the Introduction Probability Theory course wanted to teach about the event probability by using the reflective pedagogical paradigm (RPP) and describing the results achieved by the students. The study consisted of three cycles, but the results would be presented in this paper was limited to the results obtained in the first cycle. Stages conducted by the researcher in the first cycle could be divided into five stages, namely (1) to know the students' context, (2) to plan and provide student learning experiences, (3) to facilitate students in actions, (4) to ask students to make a reflection and (5) to evaluate. The type of research used in this research was descriptive qualitative and quantitative research. The students' learning experience, the students' action, and the students' reflection would be described qualitatively. The student evaluation results would be described quantitatively. The research subject in this study was 38 students taking the introduction probability theory course in class C. From the students' reflection, still quite a lot of students were not complete in writing concepts that they have learned and / or have not been precise in describing the relationships between concepts that they have learned. From the students' evaluation, 85.29% students got score under 7. If examined more deeply, the most difficulty of students were in the mathematical horizontal process. As a result, they had difficulty in performing the mathematical vertical process.

  6. Improving information extraction using a probability-based approach

    DEFF Research Database (Denmark)

    Kim, S.; Ahmed, Saeema; Wallace, K.

    2007-01-01

    Information plays a crucial role during the entire life-cycle of a product. It has been shown that engineers frequently consult colleagues to obtain the information they require to solve problems. However, the industrial world is now more transient and key personnel move to other companies...... or retire. It is becoming essential to retrieve vital information from archived product documents, if it is available. There is, therefore, great interest in ways of extracting relevant and sharable information from documents. A keyword-based search is commonly used, but studies have shown...... the recall, while maintaining the high precision, a learning approach that makes identification decisions based on a probability model, rather than simply looking up the presence of the pre-defined variations, looks promising. This paper presents the results of developing such a probability-based entity...

  7. Capacity analysis in multi-state synaptic models: a retrieval probability perspective.

    Science.gov (United States)

    Huang, Yibi; Amit, Yali

    2011-06-01

    We define the memory capacity of networks of binary neurons with finite-state synapses in terms of retrieval probabilities of learned patterns under standard asynchronous dynamics with a predetermined threshold. The threshold is set to control the proportion of non-selective neurons that fire. An optimal inhibition level is chosen to stabilize network behavior. For any local learning rule we provide a computationally efficient and highly accurate approximation to the retrieval probability of a pattern as a function of its age. The method is applied to the sequential models (Fusi and Abbott, Nat Neurosci 10:485-493, 2007) and meta-plasticity models (Fusi et al., Neuron 45(4):599-611, 2005; Leibold and Kempter, Cereb Cortex 18:67-77, 2008). We show that as the number of synaptic states increases, the capacity, as defined here, either plateaus or decreases. In the few cases where multi-state models exceed the capacity of binary synapse models the improvement is small.

  8. Identification of radon anomalies in soil gas using decision trees and neural networks

    International Nuclear Information System (INIS)

    Zmazek, B.; Dzeroski, S.; Torkar, D.; Vaupotic, J.; Kobal, I.

    2010-01-01

    The time series of radon ( 222 Rn) concentration in soil gas at a fault, together with the environmental parameters, have been analysed applying two machine learning techniques: (I) decision trees and (II) neural networks, with the aim at identifying radon anomalies caused by seismic events and not simply ascribed to the effect of the environmental parameters. By applying neural networks, 10 radon anomalies were observed for 12 earthquakes, while with decision trees, the anomaly was found for every earthquake, but, undesirably, some anomalies appeared also during periods without earthquakes. (authors)

  9. Cache-Oblivious Search Trees via Binary Trees of Small Height

    DEFF Research Database (Denmark)

    Brodal, G.S.; Fagerberg, R.; Jacob, R.

    2002-01-01

    We propose a version of cache oblivious search trees which is simpler than the previous proposal of Bender, Demaine and Farach-Colton and has the same complexity bounds. In particular, our data structure avoids the use of weight balanced B-trees, and can be implemented as just a single array......, and range queries in worst case O(logB n + k/B) memory transfers, where k is the size of the output.The basic idea of our data structure is to maintain a dynamic binary tree of height log n+O(1) using existing methods, embed this tree in a static binary tree, which in turn is embedded in an array in a cache...... oblivious fashion, using the van Emde Boas layout of Prokop.We also investigate the practicality of cache obliviousness in the area of search trees, by providing an empirical comparison of different methods for laying out a search tree in memory....

  10. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

    Science.gov (United States)

    Khosravi, Khabat; Pham, Binh Thai; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Revhaug, Inge; Prakash, Indra; Tien Bui, Dieu

    2018-06-15

    Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. Flowering Trees

    Indian Academy of Sciences (India)

    Flowering Trees. Boswellia serrata Roxb. ex Colebr. (Indian Frankincense tree) of Burseraceae is a large-sized deciduous tree that is native to India. Bark is thin, greenish-ash-coloured that exfoliates into smooth papery flakes. Stem exudes pinkish resin ... Fruit is a three-valved capsule. A green gum-resin exudes from the ...

  12. Auto-validating von Neumann rejection sampling from small phylogenetic tree spaces

    Directory of Open Access Journals (Sweden)

    York Thomas

    2009-01-01

    Full Text Available Abstract Background In phylogenetic inference one is interested in obtaining samples from the posterior distribution over the tree space on the basis of some observed DNA sequence data. One of the simplest sampling methods is the rejection sampler due to von Neumann. Here we introduce an auto-validating version of the rejection sampler, via interval analysis, to rigorously draw samples from posterior distributions over small phylogenetic tree spaces. Results The posterior samples from the auto-validating sampler are used to rigorously (i estimate posterior probabilities for different rooted topologies based on mitochondrial DNA from human, chimpanzee and gorilla, (ii conduct a non-parametric test of rate variation between protein-coding and tRNA-coding sites from three primates and (iii obtain a posterior estimate of the human-neanderthal divergence time. Conclusion This solves the open problem of rigorously drawing independent and identically distributed samples from the posterior distribution over rooted and unrooted small tree spaces (3 or 4 taxa based on any multiply-aligned sequence data.

  13. Systolic trees and systolic language recognition by tree automata

    Energy Technology Data Exchange (ETDEWEB)

    Steinby, M

    1983-01-01

    K. Culik II, J. Gruska, A. Salomaa and D. Wood have studied the language recognition capabilities of certain types of systolically operating networks of processors (see research reports Cs-81-32, Cs-81-36 and Cs-82-01, Univ. of Waterloo, Ontario, Canada). In this paper, their model for systolic VLSI trees is formalised in terms of standard tree automaton theory, and the way in which some known facts about recognisable forests and tree transductions can be applied in VLSI tree theory is demonstrated. 13 references.

  14. DupTree: a program for large-scale phylogenetic analyses using gene tree parsimony.

    Science.gov (United States)

    Wehe, André; Bansal, Mukul S; Burleigh, J Gordon; Eulenstein, Oliver

    2008-07-01

    DupTree is a new software program for inferring rooted species trees from collections of gene trees using the gene tree parsimony approach. The program implements a novel algorithm that significantly improves upon the run time of standard search heuristics for gene tree parsimony, and enables the first truly genome-scale phylogenetic analyses. In addition, DupTree allows users to examine alternate rootings and to weight the reconciliation costs for gene trees. DupTree is an open source project written in C++. DupTree for Mac OS X, Windows, and Linux along with a sample dataset and an on-line manual are available at http://genome.cs.iastate.edu/CBL/DupTree

  15. RFID Privacy Risk Evaluation Based on Synthetic Method of Extended Attack Tree and Information Feature Entropy

    OpenAIRE

    Li, Peng; Xu, Chao; Chen, Long; Wang, Ruchuan

    2015-01-01

    Evaluation of security risks in radio frequency identification (RFID) systems is a challenging problem in Internet of Things (IoT). This paper proposes an extended attack tree (EAT) model to identify RFID system’s flaws and vulnerabilities. A corresponding formal description of the model is described which adds a probability SAND node together with the probability attribute of the node attack. In addition, we model the process of an RFID data privacy attack based on EAT, taking a sensitive in...

  16. Flowering Trees

    Indian Academy of Sciences (India)

    More Details Fulltext PDF. Volume 8 Issue 8 August 2003 pp 112-112 Flowering Trees. Zizyphus jujuba Lam. of Rhamnaceae · More Details Fulltext PDF. Volume 8 Issue 9 September 2003 pp 97-97 Flowering Trees. Moringa oleifera · More Details Fulltext PDF. Volume 8 Issue 10 October 2003 pp 100-100 Flowering Trees.

  17. Host heterogeneity influences the impact of a non-native disease invasion on populations of a foundation tree species

    Science.gov (United States)

    Jules, Erik S.; Carroll, Allyson L.; Garcia, Andrea M.; Steenbock, Christopher M.; Kauffman, Matthew J.

    2014-01-01

    Invasive pathogens are becoming increasingly important in forested ecosystems, yet they are often difficult to study because of their rapid transmission. The rate and extent of pathogen spread are thought to be partially controlled by variation in host characteristics, such as when host size and location influence susceptibility. Few host-pathogen systems, however, have been used to test this prediction. We used Port Orford cedar (Chamaecyparis lawsoniana), a foundation tree species in riparian areas of California and Oregon (USA), and the invasive oomycete Phytophthora lateralis to assess pathogen impacts and the role of host characteristics on invasion. Across three streams that had been infected for 13–18 years by P. lateralis, we mapped 2241 trees and determined whether they had been infected using dendrochronology. The infection probability of trees was governed by host size (diameter at breast height [DBH]) and geomorphic position (e.g., active channel, stream bank, floodplain, etc.) similarly across streams. For instance, only 23% of trees <20 cm DBH were infected, while 69% of trees ≥20 cm DBH were infected. Presumably, because spores of P. lateralis are transported downstream in water, they are more likely to encounter well-developed root systems of larger trees. Also because of this water-transport of spores, differences in infection probability were found across the geomorphic positions: 59% of cedar in the active channel and the stream bank (combined) were infected, while 23% of trees found on higher geomorphic types were infected. Overall, 32% of cedar had been infected across the three streams. However, 63% of the total cedar basal area had been killed, because the greatest number of trees, and the largest trees, were found in the most susceptible positions. In the active channel and stream bank, 91% of the basal area was infected, while 46% was infected across higher geomorphic positions. The invasion of Port Orford cedar populations by

  18. Embedding complete ternary tree in hypercubes using AVL trees

    NARCIS (Netherlands)

    S.A. Choudum; I. Raman (Indhumathi)

    2008-01-01

    htmlabstractA complete ternary tree is a tree in which every non-leaf vertex has exactly three children. We prove that a complete ternary tree of height h, TTh, is embeddable in a hypercube of dimension . This result coincides with the result of [2]. However, in this paper, the embedding utilizes

  19. Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses.

    Science.gov (United States)

    Fouquier, Jennifer; Rideout, Jai Ram; Bolyen, Evan; Chase, John; Shiffer, Arron; McDonald, Daniel; Knight, Rob; Caporaso, J Gregory; Kelley, Scott T

    2016-02-24

    Fungi play critical roles in many ecosystems, cause serious diseases in plants and animals, and pose significant threats to human health and structural integrity problems in built environments. While most fungal diversity remains unknown, the development of PCR primers for the internal transcribed spacer (ITS) combined with next-generation sequencing has substantially improved our ability to profile fungal microbial diversity. Although the high sequence variability in the ITS region facilitates more accurate species identification, it also makes multiple sequence alignment and phylogenetic analysis unreliable across evolutionarily distant fungi because the sequences are hard to align accurately. To address this issue, we created ghost-tree, a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach starts with a "foundation" phylogeny based on one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families). Then, "extension" phylogenies are built for more closely related organisms (e.g., fungal species or strains) using a second more rapidly evolving genetic marker. These smaller phylogenies are then grafted onto the foundation tree by mapping taxonomic names such that each corresponding foundation-tree tip would branch into its new "extension tree" child. We applied ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. Our analysis of simulated and real fungal ITS data sets found that phylogenetic distances between fungal communities computed using ghost-tree phylogenies explained significantly more variance than non-phylogenetic distances. The phylogenetic metrics also improved our ability to distinguish small differences (effect sizes) between microbial communities, though results were similar to non

  20. Trees are good, but…

    Science.gov (United States)

    E.G. McPherson; F. Ferrini

    2010-01-01

    We know that “trees are good,” and most people believe this to be true. But if this is so, why are so many trees neglected, and so many tree wells empty? An individual’s attitude toward trees may result from their firsthand encounters with specific trees. Understanding how attitudes about trees are shaped, particularly aversion to trees, is critical to the business of...