Efficient Probabilistic Inference with Partial Ranking Queries
Huang, Jonathan; Guestrin, Carlos E
2012-01-01
Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distributions. We approach the modeling problem from the computational principle that one should make structural assumptions which allow for efficient calculation of typical probabilistic queries. For ranking models, "typical" queries predominantly take the form of partial ranking queries (e.g., given a user's top-k favorite movies, what are his preferences over remaining movies?). In this paper, we argue that riffled independence factorizations proposed in recent literature [7, 8] are a natural structural assumption for ranking distributions, allowing for particularly efficient processing of partial ranking queries.
Shi, Changfa; Cheng, Yuanzhi; Wang, Jinke; Wang, Yadong; Mori, Kensaku; Tamura, Shinichi
2017-02-22
One major limiting factor that prevents the accurate delineation of human organs has been the presence of severe pathology and pathology affecting organ borders. Overcoming these limitations is exactly what we are concerned in this study. We propose an automatic method for accurate and robust pathological organ segmentation from CT images. The method is grounded in the active shape model (ASM) framework. It leverages techniques from low-rank and sparse decomposition (LRSD) theory to robustly recover a subspace from grossly corrupted data. We first present a population-specific LRSD-based shape prior model, called LRSD-SM, to handle non-Gaussian gross errors caused by weak and misleading appearance cues of large lesions, complex shape variations, and poor adaptation to the finer local details in a unified framework. For the shape model initialization, we introduce a method based on patient-specific LRSD-based probabilistic atlas (PA), called LRSD-PA, to deal with large errors in atlas-to-target registration and low likelihood of the target organ. Furthermore, to make our segmentation framework more efficient and robust against local minima, we develop a hierarchical ASM search strategy. Our method is tested on the SLIVER07 database for liver segmentation competition, and ranks 3rd in all the published state-of-the-art automatic methods. Our method is also evaluated on some pathological organs (pathological liver and right lung) from 95 clinical CT scans and its results are compared with the three closely related methods. The applicability of the proposed method to segmentation of the various pathological organs (including some highly severe cases) is demonstrated with good results on both quantitative and qualitative experimentation; our segmentation algorithm can delineate organ boundaries that reach a level of accuracy comparable with those of human raters.
Expert opinion on landslide susceptibility elicted by probabilistic inversion from scenario rankings
Lee, Katy; Dashwood, Claire; Lark, Murray
2016-04-01
For many natural hazards the opinion of experts, with experience in assessing susceptibility under different circumstances, is a valuable source of information on which to base risk assessments. This is particularly important where incomplete process understanding, and limited data, limit the scope to predict susceptibility by mechanistic or statistical modelling. The expert has a tacit model of a system, based on their understanding of processes and their field experience. This model may vary in quality, depending on the experience of the expert. There is considerable interest in how one may elicit expert understanding by a process which is transparent and robust, to provide a basis for decision support. One approach is to provide experts with a set of scenarios, and then to ask them to rank small overlapping subsets of these with respect to susceptibility. Methods of probabilistic inversion have been used to compute susceptibility scores for each scenario, implicit in the expert ranking. It is also possible to model these scores as functions of measurable properties of the scenarios. This approach has been used to assess susceptibility of animal populations to invasive diseases, to assess risk to vulnerable marine environments and to assess the risk in hypothetical novel technologies for food production. We will present the results of a study in which a group of geologists with varying degrees of expertise in assessing landslide hazards were asked to rank sets of hypothetical simplified scenarios with respect to land slide susceptibility. We examine the consistency of their rankings and the importance of different properties of the scenarios in the tacit susceptibility model that their rankings implied. Our results suggest that this is a promising approach to the problem of how experts can communicate their tacit model of uncertain systems to those who want to make use of their expertise.
HISTORY BASED PROBABILISTIC BACKOFF ALGORITHM
Narendran Rajagopalan
2012-01-01
Full Text Available Performance of Wireless LAN can be improved at each layer of the protocol stack with respect to energy efficiency. The Media Access Control layer is responsible for the key functions like access control and flow control. During contention, Backoff algorithm is used to gain access to the medium with minimum probability of collision. After studying different variations of back off algorithms that have been proposed, a new variant called History based Probabilistic Backoff Algorithm is proposed. Through mathematical analysis and simulation results using NS-2, it is seen that proposed History based Probabilistic Backoff algorithm performs better than Binary Exponential Backoff algorithm.
Borgonovo, Emanuele
2008-08-01
In this work, we study the effect of epistemic uncertainty in the ranking and categorization of elements of probabilistic safety assessment (PSA) models. We show that, while in a deterministic setting a PSA element belongs to a given category univocally, in the presence of epistemic uncertainty, a PSA element belongs to a given category only with a certain probability. We propose an approach to estimate these probabilities, showing that their knowledge allows to appreciate "the sensitivity of component categorizations to uncertainties in the parameter values" (U.S. NRC Regulatory Guide 1.174). We investigate the meaning and utilization of an assignment method based on the expected value of importance measures. We discuss the problem of evaluating changes in quality assurance, maintenance activities prioritization, etc. in the presence of epistemic uncertainty. We show that the inclusion of epistemic uncertainly in the evaluation makes it necessary to evaluate changes through their effect on PSA model parameters. We propose a categorization of parameters based on the Fussell-Vesely and differential importance (DIM) measures. In addition, issues in the calculation of the expected value of the joint importance measure are present when evaluating changes affecting groups of components. We illustrate that the problem can be solved using DIM. A numerical application to a case study concludes the work.
Probabilistic Models over Ordered Partitions with Application in Learning to Rank
Truyen, Tran The; Venkatesh, Svetha
2010-01-01
This paper addresses the general problem of modelling and learning rank data with ties. We propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on the problem of learning to rank with the data from the recently held Yahoo! challenge, and demonstrate that the models are competitive against well-known rivals.
Revising incompletely specified convex probabilistic belief bases
Rens, G
2016-04-01
Full Text Available International Workshop on Non-Monotonic Reasoning (NMR), 22-24 April 2016, Cape Town, South Africa Revising Incompletely Specified Convex Probabilistic Belief Bases Gavin Rens CAIR_, University of KwaZulu-Natal, School of Mathematics, Statistics...
Citation graph based ranking in Invenio
Marian, Ludmila; Rajman, Martin; Vesely, Martin
2010-01-01
Invenio is the web-based integrated digital library system developed at CERN. Within this framework, we present four types of ranking models based on the citation graph that complement the simple approach based on citation counts: time-dependent citation counts, a relevancy ranking which extends the PageRank model, a time-dependent ranking which combines the freshness of citations with PageRank and a ranking that takes into consideration the external citations. We present our analysis and results obtained on two main data sets: Inspire and CERN Document Server. Our main contributions are: (i) a study of the currently available ranking methods based on the citation graph; (ii) the development of new ranking methods that correct some of the identified limitations of the current methods such as treating all citations of equal importance, not taking time into account or considering the citation graph complete; (iii) a detailed study of the key parameters for these ranking methods. (The original publication is ava...
SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking
Shams, Bita; Haratizadeh, Saman
2016-09-01
Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.
A network-based dynamical ranking system
Motegi, Shun
2012-01-01
Ranking players or teams in sports is of practical interests. From the viewpoint of networks, a ranking system is equivalent a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score (i.e., strength) of a player, for example, depends on time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. Our ranking system, also interpreted as a centrality measure for directed temporal networks, has two parameters. One parameter represents the exponential decay rate of the past score, and the other parameter controls the effect of indirect wins on the score. We derive a set of linear online update equ...
Rank-based camera spectral sensitivity estimation.
Finlayson, Graham; Darrodi, Maryam Mohammadzadeh; Mackiewicz, Michal
2016-04-01
In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab-a difficult and lengthy procedure-or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have "raw mode." Experiments validate our method.
Fuzzy Logic Based Power System Contingency Ranking
A. Y. Abdelaziz
2013-02-01
Full Text Available Voltage stability is a major concern in planning and operations of power systems. It is well known that voltage instability and collapse have led to major system failures. Modern transmission networks are more heavily loaded than ever before to meet the growing demand. One of the major consequences resulted from such a stressed system is voltage collapse or instability. This paper presents maximum loadability identification of a load bus in a power transmission network. In this study, Fast Voltage Stability Index (FVSI is utilized as the indicator of the maximum loadability termed as Qmax. In this technique, reactive power loading will be increased gradually at particular load bus until the FVSI reaches close to unity. Therefore, a critical value of FVSI was set as the maximum loadability point. This value ensures the system from entering voltage-collapse region. The main purpose in the maximum loadability assessment is to plan for the maximum allowable load value to avoid voltage collapse; which is important in power system planning risk assessment.The most important task in security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and ranks them according to their severity. The condition of voltage stability in a power system can be characterized by the use of voltage stability indices. This paper presents fuzzy approach for ranking the contingencies using composite-index based on parallel operated fuzzy inference engine. The Line Flow index (L.F and bus Voltage Magnitude (VM of the load buses are expressed in fuzzy set notation. Further, they are evaluated using Fuzzy rules to obtain overall Criticality Index. Contingencies are ranked based on decreasing order of Criticality Index and then provides the comparison of ranking obtained with FVSI method.
Probabilistic forecasts based on radar rainfall uncertainty
Liguori, S.; Rico-Ramirez, M. A.
2012-04-01
The potential advantages resulting from integrating weather radar rainfall estimates in hydro-meteorological forecasting systems is limited by the inherent uncertainty affecting radar rainfall measurements, which is due to various sources of error [1-3]. The improvement of quality control and correction techniques is recognized to play a role for the future improvement of radar-based flow predictions. However, the knowledge of the uncertainty affecting radar rainfall data can also be effectively used to build a hydro-meteorological forecasting system in a probabilistic framework. This work discusses the results of the implementation of a novel probabilistic forecasting system developed to improve ensemble predictions over a small urban area located in the North of England. An ensemble of radar rainfall fields can be determined as the sum of a deterministic component and a perturbation field, the latter being informed by the knowledge of the spatial-temporal characteristics of the radar error assessed with reference to rain-gauges measurements. This approach is similar to the REAL system [4] developed for use in the Southern-Alps. The radar uncertainty estimate can then be propagated with a nowcasting model, used to extrapolate an ensemble of radar rainfall forecasts, which can ultimately drive hydrological ensemble predictions. A radar ensemble generator has been calibrated using radar rainfall data made available from the UK Met Office after applying post-processing and corrections algorithms [5-6]. One hour rainfall accumulations from 235 rain gauges recorded for the year 2007 have provided the reference to determine the radar error. Statistics describing the spatial characteristics of the error (i.e. mean and covariance) have been computed off-line at gauges location, along with the parameters describing the error temporal correlation. A system has then been set up to impose the space-time error properties to stochastic perturbations, generated in real-time at
Probabilistic Model-Based Safety Analysis
Güdemann, Matthias; 10.4204/EPTCS.28.8
2010-01-01
Model-based safety analysis approaches aim at finding critical failure combinations by analysis of models of the whole system (i.e. software, hardware, failure modes and environment). The advantage of these methods compared to traditional approaches is that the analysis of the whole system gives more precise results. Only few model-based approaches have been applied to answer quantitative questions in safety analysis, often limited to analysis of specific failure propagation models, limited types of failure modes or without system dynamics and behavior, as direct quantitative analysis is uses large amounts of computing resources. New achievements in the domain of (probabilistic) model-checking now allow for overcoming this problem. This paper shows how functional models based on synchronous parallel semantics, which can be used for system design, implementation and qualitative safety analysis, can be directly re-used for (model-based) quantitative safety analysis. Accurate modeling of different types of proba...
Rank-based Tests of the Cointegrating Rank in Semiparametric Error Correction Models
Hallin, M.; van den Akker, R.; Werker, B.J.M.
2012-01-01
Abstract: This paper introduces rank-based tests for the cointegrating rank in an Error Correction Model with i.i.d. elliptical innovations. The tests are asymptotically distribution-free, and their validity does not depend on the actual distribution of the innovations. This result holds despite the
Rank-based Tests of the Cointegrating Rank in Semiparametric Error Correction Models
Hallin, M.; van den Akker, R.; Werker, B.J.M.
2012-01-01
Abstract: This paper introduces rank-based tests for the cointegrating rank in an Error Correction Model with i.i.d. elliptical innovations. The tests are asymptotically distribution-free, and their validity does not depend on the actual distribution of the innovations. This result holds despite the
Staged decision making based on probabilistic forecasting
Booister, Nikéh; Verkade, Jan; Werner, Micha; Cranston, Michael; Cumiskey, Lydia; Zevenbergen, Chris
2016-04-01
Flood forecasting systems reduce, but cannot eliminate uncertainty about the future. Probabilistic forecasts explicitly show that uncertainty remains. However, as - compared to deterministic forecasts - a dimension is added ('probability' or 'likelihood'), with this added dimension decision making is made slightly more complicated. A technique of decision support is the cost-loss approach, which defines whether or not to issue a warning or implement mitigation measures (risk-based method). With the cost-loss method a warning will be issued when the ratio of the response costs to the damage reduction is less than or equal to the probability of the possible flood event. This cost-loss method is not widely used, because it motivates based on only economic values and is a technique that is relatively static (no reasoning, yes/no decision). Nevertheless it has high potential to improve risk-based decision making based on probabilistic flood forecasting because there are no other methods known that deal with probabilities in decision making. The main aim of this research was to explore the ways of making decision making based on probabilities with the cost-loss method better applicable in practice. The exploration began by identifying other situations in which decisions were taken based on uncertain forecasts or predictions. These cases spanned a range of degrees of uncertainty: from known uncertainty to deep uncertainty. Based on the types of uncertainties, concepts of dealing with situations and responses were analysed and possible applicable concepts where chosen. Out of this analysis the concepts of flexibility and robustness appeared to be fitting to the existing method. Instead of taking big decisions with bigger consequences at once, the idea is that actions and decisions are cut-up into smaller pieces and finally the decision to implement is made based on economic costs of decisions and measures and the reduced effect of flooding. The more lead-time there is in
Rank-based decompositions of morphological templates.
Sussner, P; Ritter, G X
2000-01-01
Methods for matrix decomposition have found numerous applications in image processing, in particular for the problem of template decomposition. Since existing matrix decomposition techniques are mainly concerned with the linear domain, we consider it timely to investigate matrix decomposition techniques in the nonlinear domain with applications in image processing. The mathematical basis for these investigations is the new theory of rank within minimax algebra. Thus far, only minimax decompositions of rank 1 and rank 2 matrices into outer product expansions are known to the image processing community. We derive a heuristic algorithm for the decomposition of matrices having arbitrary rank.
Document Ranking Based upon Markov Chains.
Danilowicz, Czeslaw; Balinski, Jaroslaw
2001-01-01
Considers how the order of documents in information retrieval responses are determined and introduces a method that uses a probabilistic model of a document set where documents are regarded as states of a Markov chain and where transition probabilities are directly proportional to similarities between documents. (Author/LRW)
Non-steroidal Anti-inflammatory Drugs Ranking by Nondeterministic Assessments of Probabilistic Type
Madalina luiza MOLDOVEANU
2012-09-01
Full Text Available With a number of common therapeutic prescriptions, common mechanisms, common pharmacological effects - analgesic, antipyretic and anti-inflammatory (acetaminophen excepted, common side effects (SE (platelet dysfunction, gastritis and peptic ulcers, renal insufficiency in susceptible patients, water and sodium retention, edemas, nephropathies, and only a few different characteristics – different chemical structures, pharmacokinetics and different therapeutic possibility, different selectivities according to cyclooxygenase pathway 1 and 2, non-steroidal anti-inflammatory drugs (NSAIDs similarities are more apparent than differences. Being known that in a correct treatment benefits would exceed risks, the question “Which anti-inflammatory drug presents the lowest risks for a patient?” is just natural. By the Global Risk Method (GRM and the Maximum Risk Method (MRM we have determined the ranking of fourteen NSAIDs considering the risks presented by each particular NSAID. Nimesulide, Etoricoxib and Celecoxib safety level came superior to the other NSAIDs, whereas Etodolac and Indomethacin present an increased side effects risk.
Probabilistic Model-Based Diagnosis for Electrical Power Systems
National Aeronautics and Space Administration — We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system,...
Probabilistic prediction of fatigue damage based on linear fracture mechanics
M. Krejsa
2017-01-01
Full Text Available Paper describes in detail and gives example of the probabilistic assessment of a steel structural element subject to fatigue load, particular attention being paid to cracks from the edge and those from surface. Fatigue crack damage depends on a number of stress range cycles. Three sizes are important for the characteristics of the propagation of fatigue cracks - the initial size, detectable size and acceptable size. The theoretical model of fatigue crack progression in paper is based on a linear fracture mechanics. When determining the required degree of reliability, it is possible to specify the time of the first inspection of the construction which will focus on the fatigue damage. Using a conditional probability, times for subsequent inspections can be determined. For probabilistic calculation of fatigue crack progression was used the original and new probabilistic methods - the Direct Optimized Probabilistic Calculation (“DOProC”, which is based on optimized numerical integration. The algorithm of the probabilistic calculation was applied in the FCProbCalc code (“Fatigue Crack Probabilistic Calculation”, using which is possible to carry out the probabilistic modelling of propagation of fatigue cracks in a user friendly environment very effectively.
Probabilistic reasoning for assembly-based 3D modeling
Chaudhuri, Siddhartha
2011-01-01
Assembly-based modeling is a promising approach to broadening the accessibility of 3D modeling. In assembly-based modeling, new models are assembled from shape components extracted from a database. A key challenge in assembly-based modeling is the identification of relevant components to be presented to the user. In this paper, we introduce a probabilistic reasoning approach to this problem. Given a repository of shapes, our approach learns a probabilistic graphical model that encodes semantic and geometric relationships among shape components. The probabilistic model is used to present components that are semantically and stylistically compatible with the 3D model that is being assembled. Our experiments indicate that the probabilistic model increases the relevance of presented components. © 2011 ACM.
Probabilistic liquefaction triggering based on the cone penetration test
Moss, R.E.S.; Seed, R.B.; Kayen, R.E.; Stewart, J.P.; Tokimatsu, K.
2005-01-01
Performance-based earthquake engineering requires a probabilistic treatment of potential failure modes in order to accurately quantify the overall stability of the system. This paper is a summary of the application portions of the probabilistic liquefaction triggering correlations proposed recently proposed by Moss and co-workers. To enable probabilistic treatment of liquefaction triggering, the variables comprising the seismic load and the liquefaction resistance were treated as inherently uncertain. Supporting data from an extensive Cone Penetration Test (CPT)-based liquefaction case history database were used to develop a probabilistic correlation. The methods used to measure the uncertainty of the load and resistance variables, how the interactions of these variables were treated using Bayesian updating, and how reliability analysis was applied to produce curves of equal probability of liquefaction are presented. The normalization for effective overburden stress, the magnitude correlated duration weighting factor, and the non-linear shear mass participation factor used are also discussed.
Quantum probability ranking principle for ligand-based virtual screening
Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal
2017-02-01
Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.
Quantum probability ranking principle for ligand-based virtual screening.
Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal
2017-04-01
Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.
ONTOPARK: ONTOLOGY BASED PAGE RANKING FRAMEWORK USING RESOURCE DESCRIPTION FRAMEWORK
S. Yasodha
2014-01-01
Full Text Available Traditional search engines like Google and Yahoo fail to rank the relevant information for users’ query. This is because such search engines rely on keywords for searching and they fail to consider the semantics of the query. More sophisticated methods that do provide the relevant information for the query is the need of the time. The Semantic Web that stores metadata as ontology could be used to solve this problem. The major drawback of the PageRank algorithm of Google is that ranking is based not only on the page ranks produced but also on the number of hits to the Web page. This paved way for illegitimate means of boosting page ranks. As a result, Web pages whose page rank is zero are also ranked in top-order. This drawback of PageRank algorithm motivated us to contribute to the Web community to provide semantic search results. So we propose ONTOPARK, an ontology based framework for ranking Web pages. The proposed framework combines the Vector Space Model of Information Retrieval with Ontology. The framework constructs semantically annotated Resource Description Framework (RDF files which form the RDF knowledgebase for each query. The proposed framework has been evaluated by two measures, precision and recall. The proposed framework improves the precision of both single-word and multi-word queries which infer that replacing Web database by semantic knowledgebase will definitely improve the quality of search. The surfing time of the surfers will also be minimized.
A New Page Ranking Algorithm Based On WPRVOL Algorithm
Roja Javadian Kootenae; Seyyed Mohsen Hashemi; mehdi afzali
2013-01-01
The amount of information on the web is always growing, thus powerful search tools are needed to search for such a large collection. Search engines in this direction help users so they can find their desirable information among the massive volume of information in an easier way. But what is important in the search engines and causes a distinction between them is page ranking algorithm used in them. In this paper a new page ranking algorithm based on "Weighted Page Ranking based on Visits of ...
INTEL: Intel based systems move up in supercomputing ranks
2002-01-01
"The TOP500 supercomputer rankings released today at the Supercomputing 2002 conference show a dramatic increase in the number of Intel-based systems being deployed in high-performance computing (HPC) or supercomputing areas" (1/2 page).
CAD Parts-Based Assembly Modeling by Probabilistic Reasoning
Zhang, Kai-Ke
2016-04-11
Nowadays, increasing amount of parts and sub-assemblies are publicly available, which can be used directly for product development instead of creating from scratch. In this paper, we propose an interactive design framework for efficient and smart assembly modeling, in order to improve the design efficiency. Our approach is based on a probabilistic reasoning. Given a collection of industrial assemblies, we learn a probabilistic graphical model from the relationships between the parts of assemblies. Then in the modeling stage, this probabilistic model is used to suggest the most likely used parts compatible with the current assembly. Finally, the parts are assembled under certain geometric constraints. We demonstrate the effectiveness of our framework through a variety of assembly models produced by our prototype system. © 2015 IEEE.
Ensemble-based Probabilistic Forecasting at Horns Rev
Pinson, Pierre; Madsen, Henrik
2009-01-01
of probabilistic forecasts, the resolution of which may be maximized by using meteorological ensemble predictions as input. The paper concentrates on the test case of the Horns Rev wind form over a period of approximately 1 year, in order to describe, apply and discuss a complete ensemble-based probabilistic...... the benefit of yielding predictive distributions that are of increased reliability (in a probabilistic sense) in comparison with the raw ensemble forecasts, at the some time taking advantage of their high resolution. Copyright (C) 2008 John Wiley & Sons, Ltd....... are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which ore recursively estimated in order to maximize the overall skill of obtained predictive distributions. Such a methodology has...
Probabilistic contour extraction based on shape prior model
FAN Xin; LIANG De-qun
2005-01-01
Statistical shape prior model is employed to construct the dynamics in probabilistic contour estimation.By applying principal component analysis,plausible shape samples are efficiently generated to predict contour samples.Based on the shape-dependent dynamics and probabilistic image model,a particle filter is used to estimate the contour with a specific shape.Compared with the deterministic approach with shape information,the proposed method is simple yet more effective in extracting contours from images with shape variations and occlusion.
A network-based dynamical ranking system for competitive sports
Motegi, Shun; Masuda, Naoki
2012-12-01
From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.
A Syntactic Classification based Web Page Ranking Algorithm
Mukhopadhyay, Debajyoti; Kim, Young-Chon
2011-01-01
The existing search engines sometimes give unsatisfactory search result for lack of any categorization of search result. If there is some means to know the preference of user about the search result and rank pages according to that preference, the result will be more useful and accurate to the user. In the present paper a web page ranking algorithm is being proposed based on syntactic classification of web pages. Syntactic Classification does not bother about the meaning of the content of a web page. The proposed approach mainly consists of three steps: select some properties of web pages based on user's demand, measure them, and give different weightage to each property during ranking for different types of pages. The existence of syntactic classification is supported by running fuzzy c-means algorithm and neural network classification on a set of web pages. The change in ranking for difference in type of pages but for same query string is also being demonstrated.
Weighted Discriminative Dictionary Learning based on Low-rank Representation
Chang, Heyou; Zheng, Hao
2017-01-01
Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.
Differential evolution with ranking-based mutation operators.
Gong, Wenyin; Cai, Zhihua
2013-12-01
Differential evolution (DE) has been proven to be one of the most powerful global numerical optimization algorithms in the evolutionary algorithm family. The core operator of DE is the differential mutation operator. Generally, the parents in the mutation operator are randomly chosen from the current population. In nature, good species always contain good information, and hence, they have more chance to be utilized to guide other species. Inspired by this phenomenon, in this paper, we propose the ranking-based mutation operators for the DE algorithm, where some of the parents in the mutation operators are proportionally selected according to their rankings in the current population. The higher ranking a parent obtains, the more opportunity it will be selected. In order to evaluate the influence of our proposed ranking-based mutation operators on DE, our approach is compared with the jDE algorithm, which is a highly competitive DE variant with self-adaptive parameters, with different mutation operators. In addition, the proposed ranking-based mutation operators are also integrated into other advanced DE variants to verify the effect on them. Experimental results indicate that our proposed ranking-based mutation operators are able to enhance the performance of the original DE algorithm and the advanced DE algorithms.
Reachability-based Analysis for Probabilistic Roadmap Planners
Geraerts, R.J.; Overmars, M.H.
2007-01-01
In the last fifteen years, sampling-based planners like the Probabilistic Roadmap Method (PRM) have proved to be successful in solving complex motion planning problems. While theoretically, the complexity of the motion planning problem is exponential in the number of degrees of freedom, sampling-bas
A sampling-based approach to probabilistic pursuit evasion
Mahadevan, Aditya
2012-05-01
Probabilistic roadmaps (PRMs) are a sampling-based approach to motion-planning that encodes feasible paths through the environment using a graph created from a subset of valid positions. Prior research has shown that PRMs can be augmented with useful information to model interesting scenarios related to multi-agent interaction and coordination. © 2012 IEEE.
Reachability-based Analysis for Probabilistic Roadmap Planners
Geraerts, R.J.; Overmars, M.H.
2007-01-01
In the last fifteen years, sampling-based planners like the Probabilistic Roadmap Method (PRM) have proved to be successful in solving complex motion planning problems. While theoretically, the complexity of the motion planning problem is exponential in the number of degrees of freedom,
A New Page Ranking Algorithm Based On WPRVOL Algorithm
Roja Javadian Kootenae
2013-03-01
Full Text Available The amount of information on the web is always growing, thus powerful search tools are needed to search for such a large collection. Search engines in this direction help users so they can find their desirable information among the massive volume of information in an easier way. But what is important in the search engines and causes a distinction between them is page ranking algorithm used in them. In this paper a new page ranking algorithm based on "Weighted Page Ranking based on Visits of Links (WPRVOL Algorithm" for search engines is being proposed which is called WPR'VOL for short. The proposed algorithm considers the number of visits of first and second level in-links. The original WPRVOL algorithm takes into account the number of visits of first level in-links of the pages and distributes rank scores based on the popularity of the pages whereas the proposed algorithm considers both in-links of that page (first level in-links and in-links of the pages that point to it (second level in-links in order to calculation of rank of the page, hence more related pages are displayed at the top of search result list. In the summary it is said that the proposed algorithm assigns higher rank to pages that both themselves and pages that point to them be important.
Rank-based deactivation model for networks with age
Wang Xue-Wen; Yang Guo-Hong; Li Xiao-Lin; Xu Xin-Jian
2013-01-01
We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones.During evolution,each node experiences two stages:active and inactive.The transition from the active state to the inactive one is based on the rank of the node.In this paper,we adopt age as a criterion of ranking,and propose two deactivation models that generalize previous research.In model A,the older active node possesses the higher rank,whereas in model B,the younger active node takes the higher rank.We make a comparative study between the two models through the node-degree distribution.
van Raan, Anthony F J; Visser, Martijn S
2010-01-01
We applied a set of standard bibliometric indicators to monitor the scientific state-of-arte of 500 universities worldwide and constructed a ranking on the basis of these indicators (Leiden Ranking 2010). We find a dramatic and hitherto largely underestimated language effect in the bibliometric, citation-based measurement of research performance when comparing the ranking based on all Web of Science (WoS) covered publications and on only English WoS covered publications, particularly for Germany and France.
van Raan, Anthony F J; van Leeuwen, Thed N; Visser, Martijn S
2011-08-01
We applied a set of standard bibliometric indicators to monitor the scientific state-of-arte of 500 universities worldwide and constructed a ranking on the basis of these indicators (Leiden Ranking 2010). We find a dramatic and hitherto largely underestimated language effect in the bibliometric, citation-based measurements of research performance when comparing the ranking based on all Web of Science (WoS) covered publications and on only English WoS covered publications, particularly for Germany and France.
International Conference on Robust Rank-Based and Nonparametric Methods
McKean, Joseph
2016-01-01
The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...
A network-based ranking system for American college football
Park, J; Park, Juyong
2005-01-01
American college football faces a conflict created by the desire to stage national championship games between the best teams of a season when there is no conventional playoff system to decide which those teams are. Instead, ranking of teams is based on their record of wins and losses during the season, but each team plays only a small fraction of eligible opponents, making the system underdetermined or contradictory or both. It is an interesting challenge to create a ranking system that at once is mathematically well-founded, gives results in general accord with received wisdom concerning the relative strengths of the teams, and is based upon intuitive principles, allowing it to be accepted readily by fans and experts alike. Here we introduce a one-parameter ranking method that satisfies all of these requirements and is based on a network representation of college football schedules.
Result Diversification Based on Query-Specific Cluster Ranking
J. He (Jiyin); E. Meij; M. de Rijke
2011-01-01
htmlabstractResult diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking,
Result diversification based on query-specific cluster ranking
He, J.; Meij, E.; de Rijke, M.
2011-01-01
Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification
Adaptive Game Level Creation through Rank-based Interactive Evolution
Liapis, Antonios; Martínez, Héctor Pérez; Togelius, Julian
2013-01-01
This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as f...
Result diversification based on query-specific cluster ranking
He, J.; Meij, E.; de Rijke, M.
2011-01-01
Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification
Ranking Institutional Settings Based on Publications in Community Psychology Journals
Jason, Leonard A.; Pokorny, Steven B.; Patka, Mazna; Adams, Monica; Morello, Taylor
2007-01-01
Two primary outlets for community psychology research, the "American Journal of Community Psychology" and the "Journal of Community Psychology", were assessed to rank institutions based on publication frequency and scientific influence of publications over a 32-year period. Three specific periods were assessed (1973-1983, 1984-1994, 1995-2004).…
Probabilistic confidence for decisions based on uncertain reliability estimates
Reid, Stuart G.
2013-05-01
Reliability assessments are commonly carried out to provide a rational basis for risk-informed decisions concerning the design or maintenance of engineering systems and structures. However, calculated reliabilities and associated probabilities of failure often have significant uncertainties associated with the possible estimation errors relative to the 'true' failure probabilities. For uncertain probabilities of failure, a measure of 'probabilistic confidence' has been proposed to reflect the concern that uncertainty about the true probability of failure could result in a system or structure that is unsafe and could subsequently fail. The paper describes how the concept of probabilistic confidence can be applied to evaluate and appropriately limit the probabilities of failure attributable to particular uncertainties such as design errors that may critically affect the dependability of risk-acceptance decisions. This approach is illustrated with regard to the dependability of structural design processes based on prototype testing with uncertainties attributable to sampling variability.
Research on Transformer Fault Based on Probabilistic Neural Network
Li Yingshun
2015-01-01
Full Text Available With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production process by the use of bionic algorithm. This paper makes a diagnosis of transformer fault by the use of an expert system developed by this paper, and verifies that the probabilistic neural network has a good convergence, fault-tolerant ability and big data handling capability in the fault diagnosis. It is suitable for industrial production, which can provide a reliable mathematical model for the construction of fault diagnosis expert system in the industrial production.
An Individual-based Probabilistic Model for Fish Stock Simulation
Federico Buti
2010-08-01
Full Text Available We define an individual-based probabilistic model of a sole (Solea solea behaviour. The individual model is given in terms of an Extended Probabilistic Discrete Timed Automaton (EPDTA, a new formalism that is introduced in the paper and that is shown to be interpretable as a Markov decision process. A given EPDTA model can be probabilistically model-checked by giving a suitable translation into syntax accepted by existing model-checkers. In order to simulate the dynamics of a given population of soles in different environmental scenarios, an agent-based simulation environment is defined in which each agent implements the behaviour of the given EPDTA model. By varying the probabilities and the characteristic functions embedded in the EPDTA model it is possible to represent different scenarios and to tune the model itself by comparing the results of the simulations with real data about the sole stock in the North Adriatic sea, available from the recent project SoleMon. The simulator is presented and made available for its adaptation to other species.
Probabilistic Model-Based Background Subtraction
Krüger, Volker; Andersen, Jakob; Prehn, Thomas
2005-01-01
manner. Bayesian propagation over time is used for proper model selection and tracking during model-based background subtraction. Bayes propagation is attractive in our application as it allows to deal with uncertainties during tracking. We have tested our approach on suitable outdoor video data....... is the correlation between pixels. In this paper we introduce a model-based background subtraction approach which facilitates prior knowledge of pixel correlations for clearer and better results. Model knowledge is being learned from good training video data, the data is stored for fast access in a hierarchical...
Uncertainty Analysis of Method-Based Operating Event Groups Ranking
Zdenko Šimić
2014-01-01
Full Text Available Safe operation and industrial improvements are coming from the technology development and operational experience (OE feedback. A long life span for many industrial facilities makes OE very important. Proper assessment and understanding of OE remains a challenge because of organization system relations, complexity, and number of OE events acquired. One way to improve OE events understanding is to focus their investigation and analyze in detail the most important. The OE ranking method is developed to select the most important events based on the basic event parameters and the analytical hierarchy process applied at the level of event groups. This paper investigates further how uncertainty in the model affects ranking results. An analysis was performed on the set of the two databases from the 20 years of nuclear power plants in France and Germany. From all uncertainties the presented analysis selected ranking indexes as the most relevant for consideration. Here the presented analysis of uncertainty clearly shows that considering uncertainty is important for all results, especially for event groups ranked closely and next to the most important one. Together with the previously performed sensitivity analysis, uncertainty assessment provides additional insights and a better judgment of the event groups’ importance in further detailed investigation.
A Smart Approach for GPT Cryptosystem Based on Rank Codes
Rashwan, Haitham; Honary, Bahram
2010-01-01
The concept of Public- key cryptosystem was innovated by McEliece's cryptosystem. The public key cryptosystem based on rank codes was presented in 1991 by Gabidulin -Paramonov-Trejtakov(GPT). The use of rank codes in cryptographic applications is advantageous since it is practically impossible to utilize combinatoric decoding. This has enabled using public keys of a smaller size. Respective structural attacks against this system were proposed by Gibson and recently by Overbeck. Overbeck's attacks break many versions of the GPT cryptosystem and are turned out to be either polynomial or exponential depending on parameters of the cryptosystem. In this paper, we introduce a new approach, called the Smart approach, which is based on a proper choice of the distortion matrix X. The Smart approach allows for withstanding all known attacks even if the column scrambler matrix P over the base field Fq.
Flow-based reputation: more than just ranking
Simone, Antonino; Zannone, Nicola
2012-01-01
The last years have seen a growing interest in collaborative systems like electronic marketplaces and P2P file sharing systems where people are intended to interact with other people. Those systems, however, are subject to security and operational risks because of their open and distributed nature. Reputation systems provide a mechanism to reduce such risks by building trust relationships among entities and identifying malicious entities. A popular reputation model is the so called flow-based model. Most existing reputation systems based on such a model provide only a ranking, without absolute reputation values; this makes it difficult to determine whether entities are actually trustworthy or untrustworthy. In addition, those systems ignore a significant part of the available information; as a consequence, reputation values may not be accurate. In this paper, we present a flow-based reputation metric that gives absolute values instead of merely a ranking. Our metric makes use of all the available information....
Ranking grey numbers based on dominance grey degrees
Yong Liu; Jeffrey Forrest; Naiming Xie
2014-01-01
With respect to the decision making problems where a lot of fuzzy and grey information always exists in the real-life deci-sion making information system, it is difficult for such uncertainty methods as fuzzy mathematics, probability, and interval numbers to deal with. To this end, based on the thought and method of grey numbers, grey degrees and interval numbers, the concept of dominance grey degree is defined. And then a method of rank-ing interval grey numbers based on the dominance grey degree is proposed. After discussing the relevant properties, the paper final y uses an example to demonstrate the effectiveness and ap-plicability of the model. The result shows that the proposed model can more accurately describe uncertainty decision making prob-lems, and realize the total ordering process for multiple-attribute decision-making problems.
Adaptive Game Level Creation through Rank-based Interactive Evolution
Liapis, Antonios; Martínez, Héctor Pérez; Togelius, Julian
2013-01-01
as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using...... artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches....
Performance based Ranking Model for Cloud SaaS Services
Sahar Abdalla Elmubarak
2017-01-01
Full Text Available Cloud computing systems provide virtualized resources that can be provisioned on demand basis. Enormous number of cloud providers are offering diverse number of services. The performance of these services is a critical factor for clients to determine the cloud provider that they will choose. However, determining a provider with efficient and effective services is a challenging task. There is a need for an efficient model that help clients to select the best provider based on the performance attributes and measurements. Cloud service ranking is a standard method used to perform this task. It is the process of arranging and classifying several cloud services within the cloud, then compute the relative ranking values of them based on the quality of service required by clients and the features of the cloud services. The objective of this study is to propose an enhanced performance based ranking model to help users choose the best service they need. The proposed model combines the attributes and measurements from cloud computing field and the welldefined and established software engineering field. SMICloud Toolkit has been used to test the applicability of the proposed model. The experimentation results of the proposed model were promising.
Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs
Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan
2016-04-01
Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more
Case-Based Reasoning for Explaining Probabilistic Machine Learning
T omas Olsson
2014-04-01
Full Text Available This paper describes a generic fram e w ork for e xplaining the prediction of probabilistic machine learning algorithms using cases. The fram e w ork consists of t w o components: a similarity metric between cases th at is defined relat i v e to a probability model and an n ov el case - based approach to justifying the probabilistic prediction by estimating the prediction error using case - based reasoning. As basis for der i ving similarity metrics, we define similarity in terms of the principle of inte r c han g eability that t w o cases are considered similar or identical if t w o probability distri b utions, der i v ed from e xcluding either one or the other case in the case base, are identical. Lastl y , we sh o w the applicability of the propo sed approach by der i ving a metric for linear r e gression, and apply the proposed approach for e xplaining predictions of the ene r gy performance of households
MATILDA: A Military Laser Range Safety Tool Based on Probabilistic Risk Assessment (PRA) Techniques
2014-08-01
AFRL-RH-FS-TR-2014-0035 MATILDA: A Military Laser Range Safety Tool Based on Probabilistic Risk Assessment (PRA) Techniques Paul...the Government’s approval or disapproval of its ideas or findings. MATILDA: A Military Laser Range Safety Tool Based on Probabilistic Risk Assessment... Probabilistic Risk Assessment (PRA) techniques to perform laser safety and hazard analysis for high output lasers in outdoor environments has become
Do PageRank-based author rankings outperform simple citation counts?
Fiala, Dalibor; Žitnik, Slavko; Bajec, Marko
2015-01-01
The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts. These metrics are clear, straightforward, and easy to obtain. When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists over others. However, due to their nature, there is a danger of oversimplifying scientific achievements. Therefore, many other indicators have been proposed including the usage of the PageRank algorithm known for the ranking of webpages and its modifications suited to citation networks. Nevertheless, this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity, its application should be well justified, particularly when compared to the standard citation counts. In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering,...
Probabilistic performance-based design for high performance control systems
Micheli, Laura; Cao, Liang; Gong, Yongqiang; Cancelli, Alessandro; Laflamme, Simon; Alipour, Alice
2017-04-01
High performance control systems (HPCS) are advanced damping systems capable of high damping performance over a wide frequency bandwidth, ideal for mitigation of multi-hazards. They include active, semi-active, and hybrid damping systems. However, HPCS are more expensive than typical passive mitigation systems, rely on power and hardware (e.g., sensors, actuators) to operate, and require maintenance. In this paper, a life cycle cost analysis (LCA) approach is proposed to estimate the economic benefit these systems over the entire life of the structure. The novelty resides in the life cycle cost analysis in the performance based design (PBD) tailored to multi-level wind hazards. This yields a probabilistic performance-based design approach for HPCS. Numerical simulations are conducted on a building located in Boston, MA. LCA are conducted for passive control systems and HPCS, and the concept of controller robustness is demonstrated. Results highlight the promise of the proposed performance-based design procedure.
Separating Stars and Galaxies Probabilistically Based on Color
Strait, Victoria
2015-01-01
Using photometric data from the Deep Lens Survey (DLS) we develop a star-galaxy separation algorithm based on objects' colors in six bands (B,V,R,z,J,K). Using a training set selected from a catalog of stars classified via their DLS shapes, we fit a third order polynomial to the filtered color-color data to approximate the stellar locus. Our algorithm produces a weighted probability of an object being a star. Based on each object's distance from the stellar locus in color-color space, we fit the resulting histogram as the sum of two Gaussians. We find that near-infrared information (J and K) provide the best separation, but explore using optical information alone to determine the classification as well. Our results demonstrate that the use of color information in a probabilistic algorithm has the potential to dramatically improve star-galaxy classification when used in conjunction with existing shape-based algorithms.
Global Infrasound Association Based on Probabilistic Clutter Categorization
Arora, Nimar; Mialle, Pierrick
2016-04-01
The IDC advances its methods and continuously improves its automatic system for the infrasound technology. The IDC focuses on enhancing the automatic system for the identification of valid signals and the optimization of the network detection threshold by identifying ways to refine signal characterization methodology and association criteria. An objective of this study is to reduce the number of associated infrasound arrivals that are rejected from the automatic bulletins when generating the reviewed event bulletins. Indeed, a considerable number of signal detections are due to local clutter sources such as microbaroms, waterfalls, dams, gas flares, surf (ocean breaking waves) etc. These sources are either too diffuse or too local to form events. Worse still, the repetitive nature of this clutter leads to a large number of false event hypotheses due to the random matching of clutter at multiple stations. Previous studies, for example [1], have worked on categorization of clutter using long term trends on detection azimuth, frequency, and amplitude at each station. In this work we continue the same line of reasoning to build a probabilistic model of clutter that is used as part of NETVISA [2], a Bayesian approach to network processing. The resulting model is a fusion of seismic, hydroacoustic and infrasound processing built on a unified probabilistic framework. References: [1] Infrasound categorization Towards a statistics based approach. J. Vergoz, P. Gaillard, A. Le Pichon, N. Brachet, and L. Ceranna. ITW 2011 [2] NETVISA: Network Processing Vertically Integrated Seismic Analysis. N. S. Arora, S. Russell, and E. Sudderth. BSSA 2013
Probabilistic atlas based labeling of the cerebral vessel tree
Van de Giessen, Martijn; Janssen, Jasper P.; Brouwer, Patrick A.; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke
2015-03-01
Preoperative imaging of the cerebral vessel tree is essential for planning therapy on intracranial stenoses and aneurysms. Usually, a magnetic resonance angiography (MRA) or computed tomography angiography (CTA) is acquired from which the cerebral vessel tree is segmented. Accurate analysis is helped by the labeling of the cerebral vessels, but labeling is non-trivial due to anatomical topological variability and missing branches due to acquisition issues. In recent literature, labeling the cerebral vasculature around the Circle of Willis has mainly been approached as a graph-based problem. The most successful method, however, requires the definition of all possible permutations of missing vessels, which limits application to subsets of the tree and ignores spatial information about the vessel locations. This research aims to perform labeling using probabilistic atlases that model spatial vessel and label likelihoods. A cerebral vessel tree is aligned to a probabilistic atlas and subsequently each vessel is labeled by computing the maximum label likelihood per segment from label-specific atlases. The proposed method was validated on 25 segmented cerebral vessel trees. Labeling accuracies were close to 100% for large vessels, but dropped to 50-60% for small vessels that were only present in less than 50% of the set. With this work we showed that using solely spatial information of the vessel labels, vessel segments from stable vessels (>50% presence) were reliably classified. This spatial information will form the basis for a future labeling strategy with a very loose topological model.
Rank Based Clustering For Document Retrieval From Biomedical Databases
Manicassamy, Jayanthi
2009-01-01
Now a day's, search engines are been most widely used for extracting information's from various resources throughout the world. Where, majority of searches lies in the field of biomedical for retrieving related documents from various biomedical databases. Currently search engines lacks in document clustering and representing relativeness level of documents extracted from the databases. In order to overcome these pitfalls a text based search engine have been developed for retrieving documents from Medline and PubMed biomedical databases. The search engine has incorporated page ranking bases clustering concept which automatically represents relativeness on clustering bases. Apart from this graph tree construction is made for representing the level of relatedness of the documents that are networked together. This advance functionality incorporation for biomedical document based search engine found to provide better results in reviewing related documents based on relativeness.
Rank Based Clustering For Document Retrieval From Biomedical Databases
Jayanthi Manicassamy
2009-09-01
Full Text Available Now a day's, search engines are been most widely used for extracting information's from various resources throughout the world. Where, majority of searches lies in the field of biomedical for retrieving related documents from various biomedical databases. Currently search engines lacks in document clustering and representing relativeness level of documents extracted from the databases. In order to overcome these pitfalls a text based search engine have been developed for retrieving documents from Medline and PubMed biomedical databases. The search engine has incorporated page ranking bases clustering concept which automatically represents relativeness on clustering bases. Apart from this graph tree construction is made for representing the level of relatedness of the documents that are networked together. This advance functionality incorporation for biomedical document based search engine found to provide better results in reviewing related documents based on relativeness.
Rank-Based Analysis of Unbalanced Repeated Measures Data
M. Mushﬁqur Rashid
2012-07-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} In this article, we have developed a rank (intra-subject based analysis of clinical trials with unbalanced repeated measures data. We assume that the errors within each patient are exchangeable and continuous random variables. This rank-based inference is valid when the unbalanced data are missing either completely at random or by design. A drop in dispersion test is developed for general linear hypotheses. A numerical example is given to illustrate the procedure.
Jensen, Finn Verner; Lauritzen, Steffen Lilholt
2001-01-01
This article describes the basic ideas and algorithms behind specification and inference in probabilistic networks based on directed acyclic graphs, undirected graphs, and chain graphs.......This article describes the basic ideas and algorithms behind specification and inference in probabilistic networks based on directed acyclic graphs, undirected graphs, and chain graphs....
Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study.
Tîrnăucă, Cristina; Montaña, José L; Ontañón, Santiago; González, Avelino J; Pardo, Luis M
2016-06-24
Imagine an agent that performs tasks according to different strategies. The goal of Behavioral Recognition (BR) is to identify which of the available strategies is the one being used by the agent, by simply observing the agent's actions and the environmental conditions during a certain period of time. The goal of Behavioral Cloning (BC) is more ambitious. In this last case, the learner must be able to build a model of the behavior of the agent. In both settings, the only assumption is that the learner has access to a training set that contains instances of observed behavioral traces for each available strategy. This paper studies a machine learning approach based on Probabilistic Finite Automata (PFAs), capable of achieving both the recognition and cloning tasks. We evaluate the performance of PFAs in the context of a simulated learning environment (in this case, a virtual Roomba vacuum cleaner robot), and compare it with a collection of other machine learning approaches.
Probabilistic composition of cone-based cardinal direction relations
2008-01-01
Composition tables play a significant role in qualitative spatial reasoning (QSR). At present,a couple of composition tables focusing on various spatial relations have been developed in a qualitative approach. However,the spatial reasoning proc-esses are usually not purely qualitative in everyday life,where probability is one important issue that should be considered. In this paper,the probabilistic compo-sitions of cone-based cardinal direction relations (CDR) are discussed and esti-mated by making some assumptions. Consequently,the form of composition result turns to be {(R1,P1),(R2,P2),…,(Rn,Pn)},where Pi is the probability associated with relation Ri. Employing the area integral method,the probabilities in each composi-tion case can be computed with the assumption that the target object is uniformly distributed in the corresponding cone regions.
A probabilistic model for component-based shape synthesis
Kalogerakis, Evangelos
2012-07-01
We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis. © 2012 ACM 0730-0301/2012/08-ART55.
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.
Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
Ndukwu, Ukachukwu
2009-01-01
This paper presents a novel approach for augmenting proof-based verification with performance-style analysis of the kind employed in state-of-the-art model checking tools for probabilistic systems. Quantitative safety properties usually specified as probabilistic system invariants and modeled in proof-based environments are evaluated using bounded model checking techniques. Our specific contributions include the statement of a theorem that is central to model checking safety properties of proof-based systems, the establishment of a procedure; and its full implementation in a prototype system (YAGA) which readily transforms a probabilistic model specified in a proof-based environment to its equivalent verifiable PRISM model equipped with reward structures. The reward structures capture the exact interpretation of the probabilistic invariants and can reveal succinct information about the model during experimental investigations. Finally, we demonstrate the novelty of the technique on a probabilistic library cas...
Sailaukhanuly, Yerbolat; Zhakupbekova, Arai; Amutova, Farida; Carlsen, Lars
2013-01-01
Knowledge of the environmental behavior of chemicals is a fundamental part of the risk assessment process. The present paper discusses various methods of ranking of a series of persistent organic pollutants (POPs) according to the persistence, bioaccumulation and toxicity (PBT) characteristics. Traditionally ranking has been done as an absolute (total) ranking applying various multicriteria data analysis methods like simple additive ranking (SAR) or various utility functions (UFs) based rankings. An attractive alternative to these ranking methodologies appears to be partial order ranking (POR). The present paper compares different ranking methods like SAR, UF and POR. Significant discrepancies between the rankings are noted and it is concluded that partial order ranking, as a method without any pre-assumptions concerning possible relation between the single parameters, appears as the most attractive ranking methodology. In addition to the initial ranking partial order methodology offers a wide variety of analytical tools to elucidate the interplay between the objects to be ranked and the ranking parameters. In the present study is included an analysis of the relative importance of the single P, B and T parameters.
A Project Risk Ranking Approach Based on Set Pair Analysis
Gao Feng; Chen Yingwu
2006-01-01
Set Pair Analysis (SPA) is a new methodology to describe and process system uncertainty. It is different from stochastic or fuzzy methods in reasoning and operation, and it has been applied in many areas recently. In this paper, the application of SPA in risk ranking is presented, which includes review of risk ranking, introduction of Connecting Degree (CD) that is a key role in SPA., Arithmetic and Tendency Grade (TG) of CDs, and a risk ranking approach proposed. Finally a case analysis is presented to illustrate the reasonability of this approach. It is found that this approach is very convenient to operate, while the ranking result is more comprehensible.
Note: A manifold ranking based saliency detection method for camera
Zhang, Libo; Sun, Yihan; Luo, Tiejian; Rahman, Mohammad Muntasir
2016-09-01
Research focused on salient object region in natural scenes has attracted a lot in computer vision and has widely been used in many applications like object detection and segmentation. However, an accurate focusing on the salient region, while taking photographs of the real-world scenery, is still a challenging task. In order to deal with the problem, this paper presents a novel approach based on human visual system, which works better with the usage of both background prior and compactness prior. In the proposed method, we eliminate the unsuitable boundary with a fixed threshold to optimize the image boundary selection which can provide more precise estimations. Then, the object detection, which is optimized with compactness prior, is obtained by ranking with background queries. Salient objects are generally grouped together into connected areas that have compact spatial distributions. The experimental results on three public datasets demonstrate that the precision and robustness of the proposed algorithm have been improved obviously.
Crime Busting Model Based on Dynamic Ranking Algorithms
Yang Cao
2013-01-01
Full Text Available This paper proposed a crime busting model with two dynamic ranking algorithms to detect the likelihood of a suspect and the possibility of a leader in a complex social network. Signally, in order to obtain the priority list of suspects, an advanced network mining approach with a dynamic cumulative nominating algorithm is adopted to rapidly reduce computational expensiveness than most other topology-based approaches. Our method can also greatly increase the accuracy of solution with the enhancement of semantic learning filtering at the same time. Moreover, another dynamic algorithm of node contraction is also presented to help identify the leader among conspirators. Test results are given to verify the theoretical results, which show the great performance for either small or large datasets.
Manifold-Ranking-Based Keyword Propagation for Image Retrieval
Li Mingjing
2006-01-01
Full Text Available A novel keyword propagation method is proposed for image retrieval based on a recently developed manifold-ranking algorithm. In contrast to existing methods which train a binary classifier for each keyword, our keyword model is constructed in a straightforward manner by exploring the relationship among all images in the feature space in the learning stage. In relevance feedback, the feedback information can be naturally incorporated to refine the retrieval result by additional propagation processes. In order to speed up the convergence of the query concept, we adopt two active learning schemes to select images during relevance feedback. Furthermore, by means of keyword model update, the system can be self-improved constantly. The updating procedure can be performed online during relevance feedback without extra offline training. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images validate the effectiveness of the proposed method.
Who Should Rank Our Journals...And Based on What?
Cherkowski, Sabre; Currie, Russell; Hilton, Sandy
2012-01-01
Purpose: This study aims to establish the use of active scholar assessment (ASA) in the field of education leadership as a new methodology in ranking administration and leadership journals. The secondary purpose of this study is to respond to the paucity of research on journal ranking in educational administration and leadership.…
AN IMPROVED PDR INDOOR LOCAION ALGORITHM BASED ON PROBABILISTIC CONSTRAINTS
Y. You
2017-09-01
Full Text Available In this paper, we proposed an indoor pedestrian positioning method which is probabilistic constrained by "multi-target encounter" when the initial position is known. The method is based on the Pedestrian Dead Reckoning (PDR method. According to the PDR method of positioning error size and indoor road network structure, the buffer distance is determined reasonably and the buffer centering on the PDR location is generated. At the same time, key nodes are selected based on indoor network. In the premise of knowing the distance between multiple key nodes, the forward distance of pedestrians which entered from different nodes can be calculated and then we sum their distances and compared with the known distance between the key nodes, which determines whether pedestrians meet. When pedestrians meet, each two are seen as a cluster. The algorithm determines whether the range of the intersection of the buffer meet the conditions. When the condition is satisfied, the centre of the intersection area is taken as the pedestrian position. At the same time, based on the angle mutation of pedestrian which caused by the special structure of the indoor staircase, the pedestrian's location is matched to the real location of the key landmark (staircase. Then the cumulative error of the PDR method is eliminated. The method can locate more than one person at the same time, as long as you know the true location of a person, you can also know everyone’s real location in the same cluster and efficiently achieve indoor pedestrian positioning.
Probabilistic thinking and death anxiety: a terror management based study.
Hayslip, Bert; Schuler, Eric R; Page, Kyle S; Carver, Kellye S
2014-01-01
Terror Management Theory has been utilized to understand how death can change behavioral outcomes and social dynamics. One area that is not well researched is why individuals willingly engage in risky behavior that could accelerate their mortality. One method of distancing a potential life threatening outcome when engaging in risky behaviors is through stacking probability in favor of the event not occurring, termed probabilistic thinking. The present study examines the creation and psychometric properties of the Probabilistic Thinking scale in a sample of young, middle aged, and older adults (n = 472). The scale demonstrated adequate internal consistency reliability for each of the four subscales, excellent overall internal consistency, and good construct validity regarding relationships with measures of death anxiety. Reliable age and gender effects in probabilistic thinking were also observed. The relationship of probabilistic thinking as part of a cultural buffer against death anxiety is discussed, as well as its implications for Terror Management research.
Entropy-based Probabilistic Fatigue Damage Prognosis and Algorithmic Performance Comparison
National Aeronautics and Space Administration — In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an...
Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison
National Aeronautics and Space Administration — In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an...
Knowledge-based diagnostic system with probabilistic approach
Adina COCU
2007-12-01
Full Text Available This paper presents a knowledge learning diagnostic approach implemented in an educational system. Probabilistic inference is used here to diagnose knowledge understanding level and to reason about probable cause of learner’s misconceptions. When one learner takes an assessment, the system use probabilistic reasoning and will advice the learner about the most appropriate error cause and will also provide, the conforming part of theory which treats errors related to his misconceptions.
Content-based image retrieval with ontological ranking
Tsai, Shen-Fu; Tsai, Min-Hsuan; Huang, Thomas S.
2010-02-01
Images are a much more powerful medium of expression than text, as the adage says: "One picture is worth a thousand words." It is because compared with text consisting of an array of words, an image has more degrees of freedom and therefore a more complicated structure. However, the less limited structure of images presents researchers in the computer vision community a tough task of teaching machines to understand and organize images, especially when a limit number of learning examples and background knowledge are given. The advance of internet and web technology in the past decade has changed the way human gain knowledge. People, hence, can exchange knowledge with others by discussing and contributing information on the web. As a result, the web pages in the internet have become a living and growing source of information. One is therefore tempted to wonder whether machines can learn from the web knowledge base as well. Indeed, it is possible to make computer learn from the internet and provide human with more meaningful knowledge. In this work, we explore this novel possibility on image understanding applied to semantic image search. We exploit web resources to obtain links from images to keywords and a semantic ontology constituting human's general knowledge. The former maps visual content to related text in contrast to the traditional way of associating images with surrounding text; the latter provides relations between concepts for machines to understand to what extent and in what sense an image is close to the image search query. With the aid of these two tools, the resulting image search system is thus content-based and moreover, organized. The returned images are ranked and organized such that semantically similar images are grouped together and given a rank based on the semantic closeness to the input query. The novelty of the system is twofold: first, images are retrieved not only based on text cues but their actual contents as well; second, the grouping
A web-based tool for ranking landslide mitigation measures
Lacasse, S.; Vaciago, G.; Choi, Y. J.; Kalsnes, B.
2012-04-01
brief description, guidance on design, schematic details, practical examples and references for each mitigation measure. Each of the measures was given a score on its ability and applicability for different types of landslides and boundary conditions, and a decision support matrix was established. The web-based toolbox organizes the information in the compendium and provides an algorithm to rank the measures on the basis of the decision support matrix, and on the basis of the risk level estimated at the site. The toolbox includes a description of the case under study and offers a simplified option for estimating the hazard and risk levels of the slide at hand. The user selects the mitigation measures to be included in the assessment. The toolbox then ranks, with built-in assessment factors and weights and/or with user-defined ranking values and criteria, the mitigation measures included in the analysis. The toolbox includes data management, e.g. saving data half-way in an analysis, returning to an earlier case, looking up prepared examples or looking up information on mitigation measures. The toolbox also generates a report and has user-forum and help features. The presentation will give an overview of the mitigation measures considered and examples of the use of the toolbox, and will take the attendees through the application of the toolbox.
Reproducibility of thalamic segmentation based on probabilistic tractography.
Traynor, Catherine; Heckemann, Rolf A; Hammers, Alexander; O'Muircheartaigh, Jonathan; Crum, William R; Barker, Gareth J; Richardson, Mark P
2010-08-01
Reliable identification of thalamic nuclei is required to improve targeting of electrodes used in Deep Brain Stimulation (DBS), and for exploring the role of thalamus in health and disease. A previously described method using probabilistic tractography to segment the thalamus based on connections to cortical target regions was implemented. Both within- and between-subject reproducibility were quantitatively assessed by the overlap of the resulting segmentations; the effect of two different numbers of target regions (6 and 31) on reproducibility of the segmentation results was also investigated. Very high reproducibility was observed when a single dataset was processed multiple times using different starting conditions. Thalamic segmentation was also very reproducible when multiple datasets from the same subject were processed using six cortical target regions. Within-subject reproducibility was reduced when the number of target regions was increased, particularly in medial and posterior regions of the thalamus. A large degree of overlap in segmentation results from different subjects was obtained, particularly in thalamic regions classified as connecting to frontal, parietal, temporal and pre-central cortical target regions.
Stimulus discriminability may bias value-based probabilistic learning
Slagter, Heleen A.; Collins, Anne G. E.; Frank, Michael J.; Kenemans, J. Leon
2017-01-01
Reinforcement learning tasks are often used to assess participants’ tendency to learn more from the positive or more from the negative consequences of one’s action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning. PMID:28481915
Stimulus discriminability may bias value-based probabilistic learning.
Schutte, Iris; Slagter, Heleen A; Collins, Anne G E; Frank, Michael J; Kenemans, J Leon
2017-01-01
Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning.
Hyper-local, directions-based ranking of places
Venetis, Petros; Gonzalez, Hector; Jensen, Christian S.;
2011-01-01
they are numerous and contain precise locations. Specifically, the paper proposes a framework that takes a user location and a collection of near-by places as arguments, producing a ranking of the places. The framework enables a range of aspects of directions queries to be exploited for the ranking of places......, including the frequency with which places have been referred to in directions queries. Next, the paper proposes an algorithm and accompanying data structures capable of ranking places in response to hyper-local web queries. Finally, an empirical study with very large directions query logs offers insight...... into the potential of directions queries for the ranking of places and suggests that the proposed algorithm is suitable for use in real web search engines....
Ranking streamflow model performance based on Information theory metrics
Martinez, Gonzalo; Pachepsky, Yakov; Pan, Feng; Wagener, Thorsten; Nicholson, Thomas
2016-04-01
The accuracy-based model performance metrics not necessarily reflect the qualitative correspondence between simulated and measured streamflow time series. The objective of this work was to use the information theory-based metrics to see whether they can be used as complementary tool for hydrologic model evaluation and selection. We simulated 10-year streamflow time series in five watersheds located in Texas, North Carolina, Mississippi, and West Virginia. Eight model of different complexity were applied. The information-theory based metrics were obtained after representing the time series as strings of symbols where different symbols corresponded to different quantiles of the probability distribution of streamflow. The symbol alphabet was used. Three metrics were computed for those strings - mean information gain that measures the randomness of the signal, effective measure complexity that characterizes predictability and fluctuation complexity that characterizes the presence of a pattern in the signal. The observed streamflow time series has smaller information content and larger complexity metrics than the precipitation time series. Watersheds served as information filters and and streamflow time series were less random and more complex than the ones of precipitation. This is reflected the fact that the watershed acts as the information filter in the hydrologic conversion process from precipitation to streamflow. The Nash Sutcliffe efficiency metric increased as the complexity of models increased, but in many cases several model had this efficiency values not statistically significant from each other. In such cases, ranking models by the closeness of the information-theory based parameters in simulated and measured streamflow time series can provide an additional criterion for the evaluation of hydrologic model performance.
Full ranking procedure based on best and worst frontiers
Feng Yang; Fei Du; Liang Liang; Liuyi Ling
2015-01-01
In the traditional data envelopment analysis (DEA) structure, the efficiency score for one decision making unit (DMU) is calculated by measuring the distance of the evaluated DMU to the best practice frontier. Recent researches have provided the reasonability of considering the worst practice frontier as a supple-ment to the traditional DEA techniques. The existing researches take only one type of frontier into account, and they cannot com-pare the evaluated DMU with both the best and the worst perform-ing DMUs. A DEA-based procedure is developed to consider the best and the worst frontiers in the same scenario where the ratio of two distances (RDS) measure is proposed. The principal appli-cation of this approach is for ranking, and, as a complement tool, for performance evaluation. The proposed approach can be used in a wide range of applications such as the performance evaluation of employees and others. Final y, a bookstore data set is used to il ustrate the proposed approach.
A rank-based Prediction Algorithm of Learning User's Intention
Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing
Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.
Loss Function Based Ranking in Two-Stage, Hierarchical Models
Lin, Rongheng; Louis, Thomas A.; Paddock, Susan M.; Ridgeway, Greg
2009-01-01
Performance evaluations of health services providers burgeons. Similarly, analyzing spatially related health information, ranking teachers and schools, and identification of differentially expressed genes are increasing in prevalence and importance. Goals include valid and efficient ranking of units for profiling and league tables, identification of excellent and poor performers, the most differentially expressed genes, and determining “exceedances” (how many and which unit-specific true parameters exceed a threshold). These data and inferential goals require a hierarchical, Bayesian model that accounts for nesting relations and identifies both population values and random effects for unit-specific parameters. Furthermore, the Bayesian approach coupled with optimizing a loss function provides a framework for computing non-standard inferences such as ranks and histograms. Estimated ranks that minimize Squared Error Loss (SEL) between the true and estimated ranks have been investigated. The posterior mean ranks minimize SEL and are “general purpose,” relevant to a broad spectrum of ranking goals. However, other loss functions and optimizing ranks that are tuned to application-specific goals require identification and evaluation. For example, when the goal is to identify the relatively good (e.g., in the upper 10%) or relatively poor performers, a loss function that penalizes classification errors produces estimates that minimize the error rate. We construct loss functions that address this and other goals, developing a unified framework that facilitates generating candidate estimates, comparing approaches and producing data analytic performance summaries. We compare performance for a fully parametric, hierarchical model with Gaussian sampling distribution under Gaussian and a mixture of Gaussians prior distributions. We illustrate approaches via analysis of standardized mortality ratio data from the United States Renal Data System. Results show that SEL
Decision Bayes Criteria for Optimal Classifier Based on Probabilistic Measures
Wissal Drira; Faouzi Ghorbel
2014-01-01
This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.
Rank-based model selection for multiple ions quantum tomography
Guţă, Mădălin; Kypraios, Theodore; Dryden, Ian
2012-10-01
The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the ‘sparsity’ properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods—the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)—two models consisting of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of four ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a four ions experiment aimed at creating a Smolin state of rank 4. By applying the two methods together with the Pearson χ2 test we conclude that the data can be suitably described with a model whose rank is between 7 and 9. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements.
The Evolution with Age of Probabilistic, Intuitively Based Misconceptions.
Fischbein, Efraim; Schnarch, Ditza
1997-01-01
Describes a study that investigated probabilistic intuitions held by students (N=98) from grade 7 through college through the use of a questionnaire. Of the misconceptions that were investigated, availability was the only one that was stable across age groups. Contains 20 references. (DDR)
Probabilistic Anomaly Detection Based On System Calls Analysis
Przemysław Maciołek
2007-01-01
Full Text Available We present an application of probabilistic approach to the anomaly detection (PAD. Byanalyzing selected system calls (and their arguments, the chosen applications are monitoredin the Linux environment. This allows us to estimate “(abnormality” of their behavior (bycomparison to previously collected profiles. We’ve attached results of threat detection ina typical computer environment.
Integration of Evidence Base into a Probabilistic Risk Assessment
Saile, Lyn; Lopez, Vilma; Bickham, Grandin; Kerstman, Eric; FreiredeCarvalho, Mary; Byrne, Vicky; Butler, Douglas; Myers, Jerry; Walton, Marlei
2011-01-01
INTRODUCTION: A probabilistic decision support model such as the Integrated Medical Model (IMM) utilizes an immense amount of input data that necessitates a systematic, integrated approach for data collection, and management. As a result of this approach, IMM is able to forecasts medical events, resource utilization and crew health during space flight. METHODS: Inflight data is the most desirable input for the Integrated Medical Model. Non-attributable inflight data is collected from the Lifetime Surveillance for Astronaut Health study as well as the engineers, flight surgeons, and astronauts themselves. When inflight data is unavailable cohort studies, other models and Bayesian analyses are used, in addition to subject matters experts input on occasion. To determine the quality of evidence of a medical condition, the data source is categorized and assigned a level of evidence from 1-5; the highest level is one. The collected data reside and are managed in a relational SQL database with a web-based interface for data entry and review. The database is also capable of interfacing with outside applications which expands capabilities within the database itself. Via the public interface, customers can access a formatted Clinical Findings Form (CLiFF) that outlines the model input and evidence base for each medical condition. Changes to the database are tracked using a documented Configuration Management process. DISSCUSSION: This strategic approach provides a comprehensive data management plan for IMM. The IMM Database s structure and architecture has proven to support additional usages. As seen by the resources utilization across medical conditions analysis. In addition, the IMM Database s web-based interface provides a user-friendly format for customers to browse and download the clinical information for medical conditions. It is this type of functionality that will provide Exploratory Medicine Capabilities the evidence base for their medical condition list
Bibliometric Rankings of Journals Based on the Thomson Reuters Citations Database
C-L. Chang (Chia-Lin); M.J. McAleer (Michael)
2015-01-01
markdownabstract__Abstract__ Virtually all rankings of journals are based on citations, including self citations by journals and individual academics. The gold standard for bibliometric rankings based on citations data is the widely-used Thomson Reuters Web of Science (2014) citations database, whi
Bibliometric Rankings of Journals based on the Thomson Reuters Citations Database
C-L. Chang (Chia-Lin); M.J. McAleer (Michael)
2015-01-01
markdownabstract__Abstract__ Virtually all rankings of journals are based on citations, including self citations by journals and individual academics. The gold standard for bibliometric rankings based on citations data is the widely-used Thomson Reuters Web of Science (2014) citations database, whi
Probabilistic hypergraph based hash codes for social image search
Yi XIE; Hui-min YU; Roland HU
2014-01-01
With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing (SH) has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order repre-sentation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hy-peredges. Experiments on Flickr image datasets verify the performance of our proposed approach.
SELECTING PERFECT INTERESTINGNESS MEASURES BY COEFFICIENT OF VARIATION BASED RANKING ALGORITHM
K. Selvarangam
2014-01-01
Full Text Available Ranking interestingness measure is an active and essential research domain in the process of knowledge discovery from the extracted rules. Since various measures proposed by many researchers in various situations increases the list of measures and these are not able to use as a common measures to evaluate the rules, knowledge finders are not able to identify a perfect measure to ensure the actual knowledge on database. In this study, we presented about a ranking method to identify a perfect measure, which also reduces the number of measures. Ranking will be done by increasing order of Coefficient of Variation (CV and not applicable measures are eliminated. Also we introduced heuristic association measures, U cost, S cost, R cost, T combined cost and ranked with existing measures using CV based ranking algorithm, our measures are placed in better position on ranking, compared with the existing measures.
Low rank extremal PPT states and unextendible product bases
Leinaas, Jon Magne; Sollid, Per Øyvind
2010-01-01
It is known how to construct, in a bipartite quantum system, a unique low rank entangled mixed state with positive partial transpose (a PPT state) from an unextendible product basis (a UPB), defined as an unextendible set of orthogonal product vectors. We point out that a state constructed in this way belongs to a continuous family of entangled PPT states of the same rank, all related by non-singular product transformations, unitary or non-unitary. The characteristic property of a state $\\rho$ in such a family is that its kernel $\\Ker\\rho$ has a generalized UPB, a basis of product vectors, not necessarily orthogonal, with no product vector in $\\Im\\rho$, the orthogonal complement of $\\Ker\\rho$. The generalized UPB in $\\Ker\\rho$ has the special property that it can be transformed to orthogonal form by a product transformation. In the case of a system of dimension $3\\times 3$, we give a complete parametrization of orthogonal UPBs. This is then a parametrization of families of rank 4 entangled (and extremal) PPT ...
Dorogovtsev, S N
2015-01-01
Currently the ranking of scientists is based on the $h$-index, which is widely perceived as an imprecise and simplistic though still useful metric. We find that the $h$-index actually favours modestly performing researchers and propose a simple criterion for proper ranking.
GeoSearcher: Location-Based Ranking of Search Engine Results.
Watters, Carolyn; Amoudi, Ghada
2003-01-01
Discussion of Web queries with geospatial dimensions focuses on an algorithm that assigns location coordinates dynamically to Web sites based on the URL. Describes a prototype search system that uses the algorithm to re-rank search engine results for queries with a geospatial dimension, thus providing an alternative ranking order for search engine…
Quantification of health states with rank-based nonmetric multidimensional scaling
Krabbe, Paul F M; Salomon, Joshua A; Murray, Christopher J L
2007-01-01
OBJECTIVES: An alternative methodology is introduced to generate cardinal valuations of health states. This methodology is based on the ranking of differences between health states combined with an associated scaling model that transforms the individual rank data into group values on the interval le
Dominance-based ranking functions for interval-valued intuitionistic fuzzy sets.
Chen, Liang-Hsuan; Tu, Chien-Cheng
2014-08-01
The ranking of interval-valued intuitionistic fuzzy sets (IvIFSs) is difficult since they include the interval values of membership and nonmembership. This paper proposes ranking functions for IvIFSs based on the dominance concept. The proposed ranking functions consider the degree to which an IvIFS dominates and is not dominated by other IvIFSs. Based on the bivariate framework and the dominance concept, the functions incorporate not only the boundary values of membership and nonmembership, but also the relative relations among IvIFSs in comparisons. The dominance-based ranking functions include bipolar evaluations with a parameter that allows the decision-maker to reflect his actual attitude in allocating the various kinds of dominance. The relationship for two IvIFSs that satisfy the dual couple is defined based on four proposed ranking functions. Importantly, the proposed ranking functions can achieve a full ranking for all IvIFSs. Two examples are used to demonstrate the applicability and distinctiveness of the proposed ranking functions.
Eugene Garfield, Francis Narin, and PageRank: The Theoretical Bases of the Google Search Engine
Bensman, Stephen J
2013-01-01
This paper presents a test of the validity of using Google Scholar to evaluate the publications of researchers by comparing the premises on which its search engine, PageRank, is based, to those of Garfield's theory of citation indexing. It finds that the premises are identical and that PageRank and Garfield's theory of citation indexing validate each other.
Optimization-Based Approaches to Control of Probabilistic Boolean Networks
Koichi Kobayashi
2017-02-01
Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
Probabilistic Priority Message Checking Modeling Based on Controller Area Networks
Lin, Cheng-Min
Although the probabilistic model checking tool called PRISM has been applied in many communication systems, such as wireless local area network, Bluetooth, and ZigBee, the technique is not used in a controller area network (CAN). In this paper, we use PRISM to model the mechanism of priority messages for CAN because the mechanism has allowed CAN to become the leader in serial communication for automobile and industry control. Through modeling CAN, it is easy to analyze the characteristic of CAN for further improving the security and efficiency of automobiles. The Markov chain model helps us to model the behaviour of priority messages.
Image Inpainting Algorithm Based on Low-Rank Approximation and Texture Direction
Jinjiang Li
2014-01-01
Full Text Available Existing image inpainting algorithm based on low-rank matrix approximation cannot be suitable for complex, large-scale, damaged texture image. An inpainting algorithm based on low-rank approximation and texture direction is proposed in the paper. At first, we decompose the image using low-rank approximation method. Then the area to be repaired is interpolated by level set algorithm, and we can reconstruct a new image by the boundary values of level set. In order to obtain a better restoration effect, we make iteration for low-rank decomposition and level set interpolation. Taking into account the impact of texture direction, we segment the texture and make low-rank decomposition at texture direction. Experimental results show that the new algorithm is suitable for texture recovery and maintaining the overall consistency of the structure, which can be used to repair large-scale damaged image.
Mohammad Ali Abdolvand
2013-02-01
Full Text Available Both financial and non financial information is needed for decision making by many businesses. Accurate ranks of business branches, is very important information for management and many outsiders as owners, potential investors, labor union, government agencies, bankers, other creditors and the public, because all these groups have some interest in the business that will be served by information about its position and ranks. MCDM method is useful to rank businesses’ branches and 3-stage procedure (S.E.T can be used to rank service organizations such as insurance firms, travel agencies and banking. The present study aims to evaluate and rank a business’ branches based on the clients’ perception of their shopping experience. For this purpose, a sample of 270 clients who has had the experience of shopping from XYZ insurance firm in Shiraz-Iran was used in order to collect data and 240 questionnaires were returned and used in this study. So service quality based on the clients’ perception by SERVPERF was evaluated, then for calculating the criteria weights Entropy method was applied and finally, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS used to achieve the final ranking results. Branches final ranks are: C, A, B. full results of ranking branches are shown in Table 7 and 8.
Microblog Hot Spot Mining Based on PAM Probabilistic Topic Model
Zheng Yaxin
2015-01-01
Full Text Available Microblogs are short texts carried with limited information, which will increase the difficulty of topic mining. This paper proposes the use of PAM (Pachinko Allocation Model probabilistic topic model to extract the generative model of text’s implicit theme for microblog hot spot mining. First, three categories of microblog and the main contribution of this paper are illustrated. Second, for there are four topic models which are respectively explained, the PAM model is introduced in detail in terms of how to generate a document, the accuracy of document classification and the topic correlation in PAM. Finally, MapReduce is described. For the number of microblogs is huge as well as the number of contactors, the totally number of words is relatively small. With MapReduce, microblogs data are split by contactor, document-topic count matrix and contactor-topic count matrix can be locally stored while the word-topic count matrix must be globally stored. Thus, the hot spot mining can be achieved on the basis of PAM probabilistic topic model.
Web Image Search Re-ranking with Click-based Similarity and Typicality.
Yang, Xiaopeng; Mei, Tao; Zhang, Yong Dong; Liu, Jie; Satoh, Shin'ichi
2016-07-20
In image search re-ranking, besides the well known semantic gap, intent gap, which is the gap between the representation of users' query/demand and the real intent of the users, is becoming a major problem restricting the development of image retrieval. To reduce human effects, in this paper, we use image click-through data, which can be viewed as the "implicit feedback" from users, to help overcome the intention gap, and further improve the image search performance. Generally, the hypothesis visually similar images should be close in a ranking list and the strategy images with higher relevance should be ranked higher than others are widely accepted. To obtain satisfying search results, thus, image similarity and the level of relevance typicality are determinate factors correspondingly. However, when measuring image similarity and typicality, conventional re-ranking approaches only consider visual information and initial ranks of images, while overlooking the influence of click-through data. This paper presents a novel re-ranking approach, named spectral clustering re-ranking with click-based similarity and typicality (SCCST). First, to learn an appropriate similarity measurement, we propose click-based multi-feature similarity learning algorithm (CMSL), which conducts metric learning based on clickbased triplets selection, and integrates multiple features into a unified similarity space via multiple kernel learning. Then based on the learnt click-based image similarity measure, we conduct spectral clustering to group visually and semantically similar images into same clusters, and get the final re-rank list by calculating click-based clusters typicality and withinclusters click-based image typicality in descending order. Our experiments conducted on two real-world query-image datasets with diverse representative queries show that our proposed reranking approach can significantly improve initial search results, and outperform several existing re-ranking approaches.
Group Recommendation Based on the PageRank
Jing Wang
2012-12-01
Full Text Available Social network greatly improve the social recommendation application, especially the study of group recommendation. The group recommendation, analyze the social factors of the group, such as social and trust relationship between users, as the factors for the prediction model established. In this paper, PageRank algorithm is introduced in the recommendation method to calculate the member’s importance in the group respectively, and to amend the aggregate function of individual preferences. The aggregate function consider the relationship between various users in the group, and optimize the aggregate function according to users different influence on the group, which can better reflect the social characteristics of group. In short, the study on group recommended model and algorithm can take the initiative to find the user's needs. Extensive experiments demonstrate the effectiveness and efficiency of the methods, which improve the prediction accuracy of the group recommended algorithms.
Population based ranking of frameless CT-MRI registration methods
Opposits, Gabor; Kis, Sandor A.; Tron, Lajos; Emri, Miklos [Debrecen Univ. (Hungary). Dept. of Nuclear Medicine; Berenyi, Ervin [Debrecen Univ. (Hungary). Dept. of Biomedical Laboratory and Imaging Science; Takacs, Endre [Rotating Gamma Ltd., Debrecen (Hungary); Dobai, Jozsef G.; Bognar, Laszlo [Debrecen Univ., Medical Center (Hungary). Dept. of Neurosurgery; Szuecs, Bernadett [ScanoMed Ltd., Debrecen (Hungary)
2015-07-01
Clinical practice often requires simultaneous information obtained by two different imaging modalities. Registration algorithms are commonly used for this purpose. Automated procedures are very helpful in cases when the same kind of registration has to be performed on images of a high number of subjects. Radiotherapists would prefer to use the best automated method to assist therapy planning, however there are not accepted procedures for ranking the different registration algorithms. We were interested in developing a method to measure the population level performance of CT-MRI registration algorithms by a parameter of values in the [0,1] interval. Pairs of CT and MRI images were collected from 1051 subjects. Results of an automated registration were corrected manually until a radiologist and a neurosurgeon expert both accepted the result as good. This way 1051 registered MRI images were produced by the same pair of experts to be used as gold standards for the evaluation of the performance of other registration algorithms. Pearson correlation coefficient, mutual information, normalized mutual information, Kullback-Leibler divergence, L{sub 1} norm and square L{sub 2} norm (dis)similarity measures were tested for sensitivity to indicate the extent of (dis)similarity of a pair of individual mismatched images. The square Hellinger distance proved suitable to grade the performance of registration algorithms at population level providing the developers with a valuable tool to rank algorithms. The developed procedure provides an objective method to find the registration algorithm performing the best on the population level out of newly constructed or available preselected ones.
A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook
Ji Yae Shin
2016-01-01
Full Text Available Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF model was designed using the past, current, and forecasted drought condition. In this study, the drought conditions were represented by the standardized precipitation index (SPI. The accuracy of forecasted SPIs was assessed by comparing the observed SPIs and confidence intervals (CIs, exhibiting the associated uncertainty. Then, this study suggested the drought outlook framework based on probabilistic drought forecasting results. The overall results provided sufficient agreement between the observed and forecasted drought conditions in the outlook framework.
Convergence rates for rank-based models with applications to portfolio theory
Ichiba, Tomoyuki; Shkolnikov, Mykhaylo
2011-01-01
We determine rates of convergence of rank-based interacting diffusions and semimartingale reflecting Brownian motions to equilibrium. Convergence rate for the total variation metric is derived using Lyapunov functions. Sharp fluctuations of additive functionals are obtained using Transportation Cost-Information inequalities for Markov processes. We work out various applications to the rank-based abstract equity markets used in Stochastic Portfolio Theory. For example, we produce quantitative bounds, including constants, for fluctuations of market weights and occupation times of various ranks for individual coordinates. Another important application is the comparison of performance between symmetric functionally generated portfolios and the market portfolio. This produces estimates of probabilities of "beating the market".
VIDEO MULTI-TARGET TRACKING BASED ON PROBABILISTIC GRAPHICAL MODEL
Xu Feng; Huang Chenrong; Wu Zhengjun; Xu Lizhong
2011-01-01
In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Experimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.
Priority Based Prediction Mechanism for Ranking Providers in Federated Cloud Architecture
G.Vadivel
2017-01-01
Full Text Available In Current trends the Cloud computing has a lot of potential to help hospitals cut costs, a new report says. Here‟s some help overcoming some of the challenges hiding in the cloud. Previously there several methods are available for this like Broker based trust architecture and etc. Health care framework which are patient, doctor, symptom and disease. In this paper we are going to discuss the broker based architecture for federated cloud and its Service Measurement Index considered for evaluating the providers,construction of Grade Distribution Table(GDT,concept of ranking the providers based on prediction weights comparison, optimal service provider selection and results discussion compared with existing techniques available for ranking the providers. In this paper we are going to propose, two different ranking mechanisms to sort the providers and select the optimal provider automatically. Grade distribution ranking model is proposed by assigning the grade for the providers based on the values of SMI attributes, based on the total grade value, providers falls on either Gold, Silver or Bronze. Each category applies the quick sort to sort the providers and find the provider at the top is optimal. If there is more than one provider at the top, apply priority feedback based decision tree and find the optimal provider for the request. In the second ranking mechanism, joint probability distribution mechanism is used to rank the providers, two providers having same score, apply priority feedback based decision tree and find the optimal provider for the request.
University rankings: The web ranking
Isidro F. Aguillo
2012-03-01
Full Text Available The publication in 2003 of the Ranking of Universities by Jiao Tong University of Shanghai has revolutionized not only academic studies on Higher Education, but has also had an important impact on the national policies and the individual strategies of the sector. The work gathers the main characteristics of this and other global university rankings, paying special attention to their potential benefits and limitations. The Web Ranking is analyzed in depth, presenting the model on which its compound indicator is based and analyzing its different variables. ------- Rankings de universidades: El ranking web Resumen La publicación en 2003 del Ranking de Universidades de la Universidad Jiao Tong de Shanghai ha revolucionado no sólo los estudios académicos sobre la Educación Superior, sino que también ha tenido un importante impacto sobre las políticas nacionales y las estrategias individuales del sector. El trabajo recoge las principales características de este y otros rankings mundiales de universidades, prestando especial atención a sus potencialidades y limitaciones. Se analiza en profundidad el Ranking Web, presentando el modelo en el que se basa su indicador compuesto y analizando sus diferentes variables y principales resultados. DOI: 10.18870/hlrc.v2i1.56 PDF document contains both the original in Spanish and an English translation.
Astronomical Site Ranking Based on Tropospheric Wind Statistics
García-Lorenzo, B; Muñoz-Tunón, C; Mendizabal, E
2004-01-01
We present comprehensive and reliable statistics of high altitude wind speeds and the tropospheric flows at the location of five important astronomical observatories. Statistical analysis exclusively of high altitude winds point to La Palma as the most suitable site for adaptive optics, with a mean value of 22.13 m/s at the 200 mbar pressure level. La Silla is at the bottom of the ranking, with the largest average value 200 mbar wind speed(33.35 m/s). We have found a clear annual periodicity of high altitude winds for the five sites in study. We have also explored the connection of high to low altitude atmospheric winds as a first approach of the linear relationship between the average velocity of the turbulence and high altitude winds (Sarazin & Tokovinin 2001). We may conclude that high and low altitude winds show good linear relationships at the five selected sites. The highest correlation coefficients correspond to Paranal and San Pedro Martir, while La Palma and La Silla show similar high to low alti...
Dilger, Alexander; Müller, Harry
2013-01-01
Rankings of academics can be constructed in two different ways, either based on journal rankings or based on citations. Although citation-based rankings promise some fundamental advantages they are still not common in German-speaking business administration. However, the choice of the underlying database is crucial. This article argues that for…
A Probabilistic Analysis of the Sacco and Vanzetti Evidence
Kadane, Joseph B
2011-01-01
A Probabilistic Analysis of the Sacco and Vanzetti Evidence is a Bayesian analysis of the trial and post-trial evidence in the Sacco and Vanzetti case, based on subjectively determined probabilities and assumed relationships among evidential events. It applies the ideas of charting evidence and probabilistic assessment to this case, which is perhaps the ranking cause celebre in all of American legal history. Modern computation methods applied to inference networks are used to show how the inferential force of evidence in a complicated case can be graded. The authors employ probabilistic assess
Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine
Yiming Xing
2017-01-01
Full Text Available Real-time and accurate prediction of traffic flow is the key to intelligent transportation systems (ITS. However, due to the nonstationarity of traffic flow data, traditional point forecasting can hardly be accurate, so probabilistic forecasting methods are essential for quantification of the potential risks and uncertainties for traffic management. A probabilistic forecasting model of traffic flow based on a multikernel extreme learning machine (MKELM is proposed. Moreover, the optimal output weights of MKELM are obtained by utilizing Quantum-behaved particle swarm optimization (QPSO algorithm. To verify its effectiveness, traffic flow probabilistic prediction using QPSO-MKELM was compared with other learning methods. Experimental results show that QPSO-MKELM is more effective for practical applications. And it will help traffic managers to make right decisions.
Stability Analysis of Ranking Alternatives Based on Subjective and Objective Weight
JIANG Yan; ZUO Bao-he; YUE Chao-yuan
2002-01-01
Weights of criteria are used to assess the relative importance of the different criteria in multicriteria analysis, which can influence ranking result more or less depending on the multicriteria decisionmaking method used. In this paper, the influences of alternatives' ranking result associated with the change of weight are discussed by making use of the concept of weight stability intervals based on subjective and objective integrated weighting approach. Meamwhile, A model of weight proportion stability intervals is proposed. a numeral example is used to illuminate how many increment of objective weight can change the ranking results determined by subjective weight.
A Probabilistic Feature Map-Based Localization System Using a Monocular Camera
Hyungjin Kim
2015-08-01
Full Text Available Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments
Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER meas...
L.D.F. Venderbos (Lionne); M.J. Roobol-Bouts (Monique); C.H. Bangma (Chris); R.C.N. van den Bergh (Roderick); L.P. Bokhorst (Leonard); D. Nieboer (Daan); Godtman, R; J. Hugosson (Jonas); van der Kwast, T; E.W. Steyerberg (Ewout)
2016-01-01
textabstractTo study whether probabilistic selection by the use of a nomogram could improve patient selection for active surveillance (AS) compared to the various sets of rule-based AS inclusion criteria currently used. We studied Dutch and Swedish patients participating in the European Randomized s
Probabilistic distance-based quantizer design for distributed estimation
Kim, Yoon Hak
2016-12-01
We consider an iterative design of independently operating local quantizers at nodes that should cooperate without interaction to achieve application objectives for distributed estimation systems. We suggest as a new cost function a probabilistic distance between the posterior distribution and its quantized one expressed as the Kullback Leibler (KL) divergence. We first present the analysis that minimizing the KL divergence in the cyclic generalized Lloyd design framework is equivalent to maximizing the logarithmic quantized posterior distribution on the average which can be further computationally reduced in our iterative design. We propose an iterative design algorithm that seeks to maximize the simplified version of the posterior quantized distribution and discuss that our algorithm converges to a global optimum due to the convexity of the cost function and generates the most informative quantized measurements. We also provide an independent encoding technique that enables minimization of the cost function and can be efficiently simplified for a practical use of power-constrained nodes. We finally demonstrate through extensive experiments an obvious advantage of improved estimation performance as compared with the typical designs and the novel design techniques previously published.
HellRank: A Hellinger-based Centrality Measure for Bipartite Social Networks
Taheri, Seyed Mohammad; Mahyar, Hamidreza; Firouzi, Mohammad; K., Elahe Ghalebi; Grosu, Radu; Movaghar, Ali
2016-01-01
Measuring centrality in a social network, especially in bipartite mode, poses several challenges such as requirement of full knowledge of the network topology and lack of properly detection of top-k behavioral representative users. In this paper, to overcome the aforementioned challenging issues, we propose an accurate centrality measure, called HellRank, to identify central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a...
SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating.
Lee, Young-Joo; Cho, Soojin
2016-03-02
Fatigue life prediction for a bridge should be based on the current condition of the bridge, and various sources of uncertainty, such as material properties, anticipated vehicle loads and environmental conditions, make the prediction very challenging. This paper presents a new approach for probabilistic fatigue life prediction for bridges using finite element (FE) model updating based on structural health monitoring (SHM) data. Recently, various types of SHM systems have been used to monitor and evaluate the long-term structural performance of bridges. For example, SHM data can be used to estimate the degradation of an in-service bridge, which makes it possible to update the initial FE model. The proposed method consists of three steps: (1) identifying the modal properties of a bridge, such as mode shapes and natural frequencies, based on the ambient vibration under passing vehicles; (2) updating the structural parameters of an initial FE model using the identified modal properties; and (3) predicting the probabilistic fatigue life using the updated FE model. The proposed method is demonstrated by application to a numerical model of a bridge, and the impact of FE model updating on the bridge fatigue life is discussed.
CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential
Moss, R.E.S.; Seed, R.B.; Kayen, R.E.; Stewart, J.P.; Der Kiureghian, A.; Cetin, K.O.
2006-01-01
This paper presents a complete methodology for both probabilistic and deterministic assessment of seismic soil liquefaction triggering potential based on the cone penetration test (CPT). A comprehensive worldwide set of CPT-based liquefaction field case histories were compiled and back analyzed, and the data then used to develop probabilistic triggering correlations. Issues investigated in this study include improved normalization of CPT resistance measurements for the influence of effective overburden stress, and adjustment to CPT tip resistance for the potential influence of "thin" liquefiable layers. The effects of soil type and soil character (i.e., "fines" adjustment) for the new correlations are based on a combination of CPT tip and sleeve resistance. To quantify probability for performancebased engineering applications, Bayesian "regression" methods were used, and the uncertainties of all variables comprising both the seismic demand and the liquefaction resistance were estimated and included in the analysis. The resulting correlations were developed using a Bayesian framework and are presented in both probabilistic and deterministic formats. The results are compared to previous probabilistic and deterministic correlations. ?? 2006 ASCE.
A Dynamic Probabilistic Based Broadcasting Scheme for MANETs
Shanmugam, Kannan; Subburathinam, Karthik; Velayuthampalayam Palanisamy, Arunachalam
2016-01-01
MANET is commonly known as Mobile Ad Hoc Network in which cluster of mobile nodes can communicate with each other without having any basic infrastructure. The basic characteristic of MANET is dynamic topology. Due to the dynamic behavior nature, the topology of the network changes very frequently, and this will lead to the failure of the valid route repeatedly. Thus, the process of finding the valid route leads to notable drop in the throughput of the network. To identify a new valid path to the targeted mobile node, available proactive routing protocols use simple broadcasting method known as simple flooding. The simple flooding method broadcasts the RREQ packet from the source to the rest of the nodes in mobile network. But the problem with this method is disproportionate repetitive retransmission of RREQ packet which could result in high contention on the available channel and packet collision due to extreme traffic in the network. A reasonable number of routing algorithms have been suggested for reducing the lethal impact of flooding the RREQ packets. However, most of the algorithms have resulted in considerable amount of complexity and deduce the throughput by depending on special hardware components and maintaining complex information which will be less frequently used. By considering routing complexity with the goal of increasing the throughput of the network, in this paper, we have introduced a new approach called Dynamic Probabilistic Route (DPR) discovery. The Node's Forwarding Probability (NFP) is dynamically calculated by the DPR mobile nodes using Probability Function (PF) which depends on density of local neighbor nodes and the cumulative number of its broadcast covered neighbors. PMID:27019868
A Dynamic Probabilistic Based Broadcasting Scheme for MANETs.
Shanmugam, Kannan; Subburathinam, Karthik; Palanisamy, Arunachalam Velayuthampalayam
2016-01-01
MANET is commonly known as Mobile Ad Hoc Network in which cluster of mobile nodes can communicate with each other without having any basic infrastructure. The basic characteristic of MANET is dynamic topology. Due to the dynamic behavior nature, the topology of the network changes very frequently, and this will lead to the failure of the valid route repeatedly. Thus, the process of finding the valid route leads to notable drop in the throughput of the network. To identify a new valid path to the targeted mobile node, available proactive routing protocols use simple broadcasting method known as simple flooding. The simple flooding method broadcasts the RREQ packet from the source to the rest of the nodes in mobile network. But the problem with this method is disproportionate repetitive retransmission of RREQ packet which could result in high contention on the available channel and packet collision due to extreme traffic in the network. A reasonable number of routing algorithms have been suggested for reducing the lethal impact of flooding the RREQ packets. However, most of the algorithms have resulted in considerable amount of complexity and deduce the throughput by depending on special hardware components and maintaining complex information which will be less frequently used. By considering routing complexity with the goal of increasing the throughput of the network, in this paper, we have introduced a new approach called Dynamic Probabilistic Route (DPR) discovery. The Node's Forwarding Probability (NFP) is dynamically calculated by the DPR mobile nodes using Probability Function (PF) which depends on density of local neighbor nodes and the cumulative number of its broadcast covered neighbors.
A Dynamic Probabilistic Based Broadcasting Scheme for MANETs
Kannan Shanmugam
2016-01-01
Full Text Available MANET is commonly known as Mobile Ad Hoc Network in which cluster of mobile nodes can communicate with each other without having any basic infrastructure. The basic characteristic of MANET is dynamic topology. Due to the dynamic behavior nature, the topology of the network changes very frequently, and this will lead to the failure of the valid route repeatedly. Thus, the process of finding the valid route leads to notable drop in the throughput of the network. To identify a new valid path to the targeted mobile node, available proactive routing protocols use simple broadcasting method known as simple flooding. The simple flooding method broadcasts the RREQ packet from the source to the rest of the nodes in mobile network. But the problem with this method is disproportionate repetitive retransmission of RREQ packet which could result in high contention on the available channel and packet collision due to extreme traffic in the network. A reasonable number of routing algorithms have been suggested for reducing the lethal impact of flooding the RREQ packets. However, most of the algorithms have resulted in considerable amount of complexity and deduce the throughput by depending on special hardware components and maintaining complex information which will be less frequently used. By considering routing complexity with the goal of increasing the throughput of the network, in this paper, we have introduced a new approach called Dynamic Probabilistic Route (DPR discovery. The Node’s Forwarding Probability (NFP is dynamically calculated by the DPR mobile nodes using Probability Function (PF which depends on density of local neighbor nodes and the cumulative number of its broadcast covered neighbors.
Evaluating user reputation in online rating systems via an iterative group-based ranking method
Gao, Jian
2015-01-01
Reputation is a valuable asset in online social lives and it has drawn increased attention. How to evaluate user reputation in online rating systems is especially significant due to the existence of spamming attacks. To address this issue, so far, a variety of methods have been proposed, including network-based methods, quality-based methods and group-based ranking method. In this paper, we propose an iterative group-based ranking (IGR) method by introducing an iterative reputation-allocation process into the original group-based ranking (GR) method. More specifically, users with higher reputation have higher weights in dominating the corresponding group sizes. The reputation of users and the corresponding group sizes are iteratively updated until they become stable. Results on two real data sets suggest that the proposed IGR method has better performance and its robustness is considerably improved comparing with the original GR method. Our work highlights the positive role of users' grouping behavior towards...
Correlated Topic Model for Web Services Ranking
Mustapha AZNAG
2013-07-01
Full Text Available With the increasing number of published Web services providing similar functionalities, it’s very tedious for a service consumer to make decision to select the appropriate one according to her/his needs. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA, Latent Dirichlet Allocation (LDA and Correlated Topic Model (CTM to extract latent factors from web service descriptions. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering and ranking web services using latent factors. In our experiment, we evaluated our Service Discovery and Ranking approach by calculating the precision (P@n and normalized discounted cumulative gain (NDCGn.
Sari, Dwi Ivayana; Budayasa, I. Ketut; Juniati, Dwi
2017-08-01
Probabilistic thinking is very important in human life especially in responding to situation which possibly occured or situation containing uncertainty elements. It is necessary to develop students' probabilistic thinking since in elementary school by teaching probability. Based on mathematics curriculum in Indonesia, probability is firstly introduced to ninth grade students. Though, some research showed that low-grade students were successful in solving probability tasks, even in pre school. This study is aimed to explore students' probabilistic thinking of elementary school; high and low math ability in solving probability tasks. Qualitative approach was chosen to describe in depth related to students' probabilistic thinking. The results showed that high and low math ability students were difference in responding to 1 and 2 dimensional sample space tasks, and probability comparison tasks of drawing marker and contextual. Representation used by high and low math ability students were also difference in responding to contextual probability of an event task and probability comparison task of rotating spinner. This study is as reference to mathematics curriculum developers of elementary school in Indonesia. In this case to introduce probability material and teach probability through spinner, as media in learning.
Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; van Andel, Schalk-Jan; Wood, Andy
2014-05-01
Probabilistic streamflow forecasts have been increasingly used or requested by practitioners in the operation of multipurpose water reservoirs. They usually integrate hydrologic inflow forecasts to their operational management rules to optimize water allocation or its economic value, to mitigate droughts, for flood and ecological control, among others. In this paper, we present an experiment conducted to investigate the use of probabilistic forecasts to make decisions on water reservoir outflows. The experiment was set up as a risk-based decision-making game. In the game, each participant acted as a water manager. A sequence of probabilistic inflow forecasts was presented to be used to make a reservoir release decision at a monthly time step, subject to a few constraints. After each decision, the actual inflow was presented and the consequences of the decisions made were discussed. Results from the application of the game to different groups of scientists and operational managers during conferences and meetings in 2013 (a total of about 150 participants) illustrate the different strategies adopted by the players. This game experiment allowed participants to experience first hand the challenges of probabilistic, quantitative decision-making.
无
2007-01-01
With the fast growth of Chinese economic,more and more capital will be invested in environmental projects.How to select the environmental investment projects(alternatives)for obtaining the best environmental quality and economic benefits is an important problem for the decision makers.The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria.A model for ranking the projects of municipal sewage treatment plants is proposed by using exports' information and the data of the real projects.And,the ranking result is given based on the PROMETHEE method. Furthermore,by means of the concept of the weight stability intervals(WSI),the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed.The result shows that some criteria,such as"proportion of benefit to projoct cost",will influence the ranking result of alternatives very strong while others not.The influence are not only from the value of criterion but also from the changing the weight of criterion.So,some criteria such as"proportion of benefit to projoct cost" are key critera for ranking the projects. Decision makers must be cautious to them.
Context-Based Moving Ob ject Tra jectory Uncertainty Reduction and Ranking in Road Network
Jian Dai; Zhi-Ming Ding; Jia-Jie Xu
2016-01-01
To support a large amount of GPS data generated from various moving objects, the back-end servers usually store low-sampling-rate trajectories. Therefore, no precise position information can be obtained directly from the back-end servers and uncertainty is an inherent characteristic of the spatio-temporal data. How to deal with the uncertainty thus becomes a basic and challenging problem. A lot of researches have been rigidly conducted on the uncertainty of a moving object itself and isolated from the context where it is derived. However, we discover that the uncertainty of moving objects can be eﬃciently reduced and effectively ranked using the context-aware information. In this paper, we focus on context-aware information and propose an integrated framework, Context-Based Uncertainty Reduction and Ranking (CURR), to reduce and rank the uncertainty of trajectories. Specifically, given two consecutive samplings, we aim to infer and rank the possible trajectories in accordance with the information extracted from context. Since some context-aware information can be used to reduce the uncertainty while some context-aware information can be used to rank the uncertainty, to leverage them accordingly, CURR naturally consists of two stages: reduction stage and ranking stage which complement each other. We also implement a prototype system to validate the effectiveness of our solution. Extensive experiments are conducted and the evaluation results demonstrate the eﬃciency and high accuracy of CURR.
Ranking Models in Conjoint Analysis
K.Y. Lam (Kar Yin); A.J. Koning (Alex); Ph.H.B.F. Franses (Philip Hans)
2010-01-01
textabstractIn this paper we consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we do not need to compute high-dimensional integrals. We extend the approximation technique proposed by
Efficient Radio Map Construction Based on Low-Rank Approximation for Indoor Positioning
Yongli Hu
2013-01-01
Full Text Available Fingerprint-based positioning in a wireless local area network (WLAN environment has received much attention recently. One key issue for the positioning method is the radio map construction, which generally requires significant effort to collect enough measurements of received signal strength (RSS. Based on the observation that RSSs have high spatial correlation, we propose an efficient radio map construction method based on low-rank approximation. Different from the conventional interpolation methods, the proposed method represents the distribution of RSSs as a low-rank matrix and constructs the dense radio map from relative sparse measurements by a revised low-rank matrix completion method. To evaluate the proposed method, both simulation tests and field experiments have been conducted. The experimental results indicate that the proposed method can reduce the RSS measurements evidently. Moreover, using the constructed radio maps for positioning, the positioning accuracy is also improved.
Ranking Activities Based on Their Impact and Threat
2009-07-01
11] states that: “Threat Assessment involves assessing situations to determine whether detrimental events are likely to occur. Per the JDL Data...a combined reference model based on many years of research in this area. The model, as shown in Figure 1, was built by combining the JDL Data...Fusion model and Endsley’s SA Model. As part of [7], we’ve refined how one can think of JDL Levels 1 and 2 as well as described differences between
Sarrouti, Mourad; Ouatik El Alaoui, Said
2017-04-01
Passage retrieval, the identification of top-ranked passages that may contain the answer for a given biomedical question, is a crucial component for any biomedical question answering (QA) system. Passage retrieval in open-domain QA is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in biomedical QA. In this paper, we present a new biomedical passage retrieval method based on Stanford CoreNLP sentence/passage length, probabilistic information retrieval (IR) model and UMLS concepts. In the proposed method, we first use our document retrieval system based on PubMed search engine and UMLS similarity to retrieve relevant documents to a given biomedical question. We then take the abstracts from the retrieved documents and use Stanford CoreNLP for sentence splitter to make a set of sentences, i.e., candidate passages. Using stemmed words and UMLS concepts as features for the BM25 model, we finally compute the similarity scores between the biomedical question and each of the candidate passages and keep the N top-ranked ones. Experimental evaluations performed on large standard datasets, provided by the BioASQ challenge, show that the proposed method achieves good performances compared with the current state-of-the-art methods. The proposed method significantly outperforms the current state-of-the-art methods by an average of 6.84% in terms of mean average precision (MAP). We have proposed an efficient passage retrieval method which can be used to retrieve relevant passages in biomedical QA systems with high mean average precision. Copyright © 2017 Elsevier Inc. All rights reserved.
Introducing trimming and function ranking to Solid Works based on function analysis
Chechurin, Leonid S.; Wits, Wessel W.; Bakker, Hans M.; Vaneker, Tom H.J.; Cascini, G.; Vaneker, T.H.J.
2011-01-01
TRIZ based Function Analysis models existing products based on functional interactions between product parts. Such a function model description is the ideal starting point for product innovation. Design engineers can apply (TRIZ) methods such as trimming and function ranking to this function model t
Introducing Trimming and Function Ranking to SolidWorks based on Function Analysis
Chechurin, L.S.; Wits, W.W.; Bakker, H.M.; Vaneker, T.H.J.
2015-01-01
TRIZ based Function Analysis models existing products based on functional interactions between product parts. Such a function model description is the ideal starting point for product innovation. Design engineers can apply (TRIZ) methods such as trimming and function ranking to this function model t
Introducing Trimming and Function Ranking to SolidWorks based on Function Analysis
Chechurin, L.S.; Wits, Wessel Willems; Bakker, Hans M.; Vaneker, Thomas H.J.
2015-01-01
TRIZ based Function Analysis models existing products based on functional interactions between product parts. Such a function model description is the ideal starting point for product innovation. Design engineers can apply (TRIZ) methods such as trimming and function ranking to this function model
Hyper-local, directions-based ranking of places
Venetis, Petros; Gonzalez, Hector; Jensen, Christian S.
2011-01-01
, enables so-called hyper-local web querying where the location of a user is accurate at a much finer granularity than with IP-based positioning. This paper addresses the problem of determining the importance of points of interest, or places, in local-search results. In doing so, the paper proposes......Studies find that at least 20% of web queries have local intent; and the fraction of queries with local intent that originate from mobile properties may be twice as high. The emergence of standardized support for location providers in web browsers, as well as of providers of accurate locations...... techniques that exploit logged directions queries. A query that asks for directions from a location a to a location b is taken to suggest that a user is interested in traveling to b and thus is a vote that location b is interesting. Such user-generated directions queries are particularly interesting because...
Improved NSGA-II Based on a Novel Ranking Scheme
D'Souza, Rio G L; Kandasamy, A
2010-01-01
Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a benchmark algorithm for Multiobjective Optimization. The determination of pareto-optimal solutions is the key to its success. However the basic algorithm suffers from a high order of complexity, which renders it less useful for practical applications. Among the variants of NSGA, several attempts have been made to reduce the complexity. Though successful in reducing the runtime complexity, there is scope for further improvements, especially considering that the populations involved are frequently of large size. We propose a variant which reduces the run-time complexity using the simple principle of space-time trade-off. The improved algorithm is applied to the problem of classifying types of leukemia based on microarray data. Results of comparative tests are presented showing that the improved algorithm performs well on large populations.
SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering
Zhang, Xiangrong; Yang, Jie; Hou, Biao; Jiao, Licheng
2010-10-01
Spectral clustering has become one of the most popular modern clustering algorithms in recent years. In this paper, a new algorithm named entropy ranking based adaptive semi-supervised spectral clustering for SAR image segmentation is proposed. We focus not only on finding a suitable scaling parameter but also determining automatically the cluster number with the entropy ranking theory. Also, two kinds of constrains must-link and cannot-link based semi-supervised spectral clustering is applied to gain better segmentation results. Experimental results on SAR images show that the proposed method outperforms other spectral clustering algorithms.
ZHANG Chunsen
2015-08-01
Full Text Available A hyperspectral images classification method based on the weighted probabilistic fusion of multiple spectral-spatial features was proposed in this paper. First, the minimum noise fraction (MNF approach was employed to reduce the dimension of hyperspectral image and extract the spectral feature from the image, then combined the spectral feature with the texture feature extracted based on gray level co-occurrence matrix (GLCM, the multi-scale morphological feature extracted based on OFC operator and the end member feature extracted based on sequential maximum angle convex cone (SMACC method to form three spectral-spatial features. Afterwards, support vector machine (SVM classifier was used for the classification of each spectral-spatial feature separately. Finally, we established the weighted probabilistic fusion model and applied the model to fuse the SVM outputs for the final classification result. In order to verify the proposed method, the ROSIS and AVIRIS image were used in our experiment and the overall accuracy reached 97.65% and 96.62% separately. The results indicate that the proposed method can not only overcome the limitations of traditional single-feature based hyperspectral image classification, but also be superior to conventional VS-SVM method and probabilistic fusion method. The classification accuracy of hyperspectral images was improved effectively.
A learning framework for age rank estimation based on face images with scattering transform.
Chang, Kuang-Yu; Chen, Chu-Song
2015-03-01
This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age estimation based on face images. The proposed approach exploits relative-order information among the age labels for rank prediction. In our approach, the age rank is obtained by aggregating a series of binary classification results, where cost sensitivities among the labels are introduced to improve the aggregating performance. In addition, we give a theoretical analysis on designing the cost of individual binary classifier so that the misranking cost can be bounded by the total misclassification costs. An efficient descriptor, scattering transform, which scatters the Gabor coefficients and pooled with Gaussian smoothing in multiple layers, is evaluated for facial feature extraction. We show that this descriptor is a generalization of conventional bioinspired features and is more effective for face-based age inference. Experimental results demonstrate that our method outperforms the state-of-the-art age estimation approaches.
Low-rank and eigenface based sparse representation for face recognition.
Yi-Fu Hou
Full Text Available In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC. Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA to alleviate the influence of noises (e.g., illumination difference and occlusions. Secondly, Singular Value Decomposition (SVD is applied to extract the eigenfaces from these low-rank and approximate images. Finally, we utilize these eigenfaces to construct a compact and discriminative dictionary for sparse representation. We evaluate our method on five popular databases. Experimental results demonstrate the effectiveness and robustness of our method.
Low-rank and eigenface based sparse representation for face recognition.
Hou, Yi-Fu; Sun, Zhan-Li; Chong, Yan-Wen; Zheng, Chun-Hou
2014-01-01
In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC). Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA) to alleviate the influence of noises (e.g., illumination difference and occlusions). Secondly, Singular Value Decomposition (SVD) is applied to extract the eigenfaces from these low-rank and approximate images. Finally, we utilize these eigenfaces to construct a compact and discriminative dictionary for sparse representation. We evaluate our method on five popular databases. Experimental results demonstrate the effectiveness and robustness of our method.
A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network
Jing Xu
2016-05-01
Full Text Available Due to the traditional state recognition approaches for complex electromechanical equipment having had the disadvantages of excessive reliance on complete expert knowledge and insufficient training sets, real-time state identification system was always difficult to be established. The running efficiency cannot be guaranteed and the fault rate cannot be reduced fundamentally especially in some extreme working conditions. To solve these problems, an online state recognition method for complex equipment based on a fuzzy probabilistic neural network (FPNN was proposed in this paper. The fuzzy rule base for complex equipment was established and a multi-level state space model was constructed. Moreover, a probabilistic neural network (PNN was applied in state recognition, and the fuzzy functions and quantification matrix were presented. The flowchart of proposed approach was designed. Finally, a simulation example of shearer state recognition and the industrial application with an accuracy of 90.91% were provided and the proposed approach was feasible and efficient.
Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint
Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-31
Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.
CT image sequence restoration based on sparse and low-rank decomposition.
Shuiping Gou
Full Text Available Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA, Linearized Alternating Direction Method with Adaptive Penalty (LADMAP and Go Decomposition (GoDec. Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images.
Annihilating Filter-Based Low-Rank Hankel Matrix Approach for Image Inpainting.
Jin, Kyong Hwan; Ye, Jong Chul
2015-11-01
In this paper, we propose a patch-based image inpainting method using a low-rank Hankel structured matrix completion approach. The proposed method exploits the annihilation property between a shift-invariant filter and image data observed in many existing inpainting algorithms. In particular, by exploiting the commutative property of the convolution, the annihilation property results in a low-rank block Hankel structure data matrix, and the image inpainting problem becomes a low-rank structured matrix completion problem. The block Hankel structured matrices are obtained patch-by-patch to adapt to the local changes in the image statistics. To solve the structured low-rank matrix completion problem, we employ an alternating direction method of multipliers with factorization matrix initialization using the low-rank matrix fitting algorithm. As a side product of the matrix factorization, locally adaptive dictionaries can be also easily constructed. Despite the simplicity of the algorithm, the experimental results using irregularly subsampled images as well as various images with globally missing patterns showed that the proposed method outperforms existing state-of-the-art image inpainting methods.
Statistical reliability and path diversity based PageRank algorithm improvements
Hong, Dohy
2012-01-01
In this paper we present new improvement ideas of the original PageRank algorithm. The first idea is to introduce an evaluation of the statistical reliability of the ranking score of each node based on the local graph property and the second one is to introduce the notion of the path diversity. The path diversity can be exploited to dynamically modify the increment value of each node in the random surfer model or to dynamically adapt the damping factor. We illustrate the impact of such modifications through examples and simple simulations.
Tools for Evaluating Research Output: Are Citation-based Rankings of Economics Journals Stable?
Sutter, Matthias; Kocher, Martin G.
2001-01-01
Studied the assignment of economics journals to different quartiles of citation-based rankings, a measure of journal quality that has implications for evaluating research output and academic promotions. Found that about 60% of journals remain in the same quartile, and about 95% remain the same or move up within 5- to 10-year intervals, suggesting…
Performance prediction for Grid workflow activities based on features-ranked RBF network
Wang Jie; Duan Rubing; Farrukh Nadeem
2009-01-01
Accurate performance prediction of Grid workflow activities can help Grid schedulers map activities to appropriate Grid sites. This paper describes an approach based on features-ranked RBF neural network to predict the performance of Grid workflow activities. Experimental results for two kinds of real world Grid workflow activities are presented to show effectiveness of our approach.
Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation
Qiaoyu Tan
2017-01-01
Full Text Available Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels. To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR. MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover. We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.
AHMER ALI
2014-10-01
Full Text Available The probabilistic seismic performance of a standard Korean nuclear power plant (NPP with an idealized isolation is investigated in the present work. A probabilistic seismic hazard analysis (PSHA of the Wolsong site on the Korean peninsula is performed by considering peak ground acceleration (PGA as an earthquake intensity measure. A procedure is reported on the categorization and selection of two sets of ground motions of the Tohoku earthquake, i.e. long-period and common as Set A and Set B respectively, for the nonlinear time history response analysis of the base-isolated NPP. Limit state values as multiples of the displacement responses of the NPP base isolation are considered for the fragility estimation. The seismic risk of the NPP is further assessed by incorporation of the rate of frequency exceedance and conditional failure probability curves. Furthermore, this framework attempts to show the unacceptable performance of the isolated NPP in terms of the probabilistic distribution and annual probability of limit states. The comparative results for long and common ground motions are discussed to contribute to the future safety of nuclear facilities against drastic events like Tohoku.
L. Mediero
2012-12-01
Full Text Available Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.
Han, Guangjie; Li, Shanshan; Zhu, Chunsheng; Jiang, Jinfang; Zhang, Wenbo
2017-02-08
Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.
Probabilistic Data Modeling and Querying for Location-Based Data Warehouses
Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach
2005-01-01
Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical...... and uncertain data, e.g., data from the location-based services domain such as transportation infrastructures and the attached static and dynamic content such as speed limits and vehicle positions. The paper also presents algebraic operators that support querying of such data. The work is motivated...... with a realworld case study, based on our collaboration with a leading Danish vendor of location-based services....
Shahrokh Shojaeian
2014-01-01
Full Text Available There are always some uncertainties in prediction and estimation of distribution systems loads. These uncertainties impose some undesirable impacts and deviations on power flow of the system which may cause reduction in accuracy of the results obtained by system analysis. Thus, probabilistic analysis of distribution system is very important. This paper proposes a probabilistic power flow technique by applying a normal probabilistic distribution in seven standard deviations on forward-backward algorithm. The losses and voltage of IEEE 33-bus test distribution network is investigated by our new algorithm and the results are compared with the conventional algorithm i.e., based on deterministic methods.
On revision of partially specified convex probabilistic belief bases
Rens, G
2016-08-01
Full Text Available a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted...
DSRank: A New Hyper-Linked Based Method to Rank Datasets in LOD Cloud
Hamidreza Fardad
2014-12-01
Full Text Available The increase of available datasets in web of data, causes the ranking of the datasets become very important. The present article, the famous PageRank algorithm is extended and a new link-based method is proposed for ranking the datasets in web of data. In this method, the number of links to the dataset, the type of the links, and the number of each type of the links has been considered and a new hyper-linked based approach name as DSRank is proposed. The suggested algorithm has been implemented on datasets through collecting from the web amounting to 20 GB. All of the datasets are arranged by using suggested method. In order to evaluate, the access log files of Dbpedia, DBTune, and Dog Food are used. The number of requests by users in one day for these datasets are calculated and then datasets are organized based on user’s opinion. The results of comparing our suggested algorithm with the number of the requests by the users in a day indicate that the order of the assigned ranks in the dataset through using the proposed method is correct.
Sikkema, A.; Ruitenbeek, van H.C.G.; Gerritsma, W.; Wouters, P.
2014-01-01
In een academische wereld die steeds competitiever wordt, willen we graag weten wat ‘de beste’ universiteit is. Verschillende rankings bedienen ons op onze wenken, waaronder Times Higher Education, Sjanghai, QS en Leiden. De kritiek op die lijsten is echter niet mals, ook omdat universiteiten graag
Kayen, R.; Moss, R.E.S.; Thompson, E.M.; Seed, R.B.; Cetin, K.O.; Der Kiureghian, A.; Tanaka, Y.; Tokimatsu, K.
2013-01-01
Shear-wave velocity (Vs) offers a means to determine the seismic resistance of soil to liquefaction by a fundamental soil property. This paper presents the results of an 11-year international project to gather new Vs site data and develop probabilistic correlations for seismic soil liquefaction occurrence. Toward that objective, shear-wave velocity test sites were identified, and measurements made for 301 new liquefaction field case histories in China, Japan, Taiwan, Greece, and the United States over a decade. The majority of these new case histories reoccupy those previously investigated by penetration testing. These new data are combined with previously published case histories to build a global catalog of 422 case histories of Vs liquefaction performance. Bayesian regression and structural reliability methods facilitate a probabilistic treatment of the Vs catalog for performance-based engineering applications. Where possible, uncertainties of the variables comprising both the seismic demand and the soil capacity were estimated and included in the analysis, resulting in greatly reduced overall model uncertainty relative to previous studies. The presented data set and probabilistic analysis also help resolve the ancillary issues of adjustment for soil fines content and magnitude scaling factors.
Strategic Entrepreneurship Based Model of Catch-up University in Global Rankings
Kozlov Mikhail
2016-01-01
Full Text Available The paper will help answer the question, why only few universities managed to succeed significantly in their global ranking advancement, while most of their competitors fail. For this purpose it will introduce a new strategically entrepreneurial catch-up university framework, based on the combination of the resource based view, dynamic capabilities, strategic entrepreneurship and latecomer organization concepts. The new framework logics explains the advantages of being ambidextrous for ranking oriented universities and pursuing new potentially more favorable opportunities for research development. It will propose that substantial increase in the level of dynamic capabilities of the universities and their resource base accumulation is based on the use of the new combination of financial, human and social capital combined with strategic management of these resources in the process of identification and exploitation of greater opportunities.
Lithner, Delilah, E-mail: delilah.lithner@gmail.com; Larsson, Ake; Dave, Goeran
2011-08-15
Plastics constitute a large material group with a global annual production that has doubled in 15 years (245 million tonnes in 2008). Plastics are present everywhere in society and the environment, especially the marine environment, where large amounts of plastic waste accumulate. The knowledge of human and environmental hazards and risks from chemicals associated with the diversity of plastic products is very limited. Most chemicals used for producing plastic polymers are derived from non-renewable crude oil, and several are hazardous. These may be released during the production, use and disposal of the plastic product. In this study the environmental and health hazards of chemicals used in 55 thermoplastic and thermosetting polymers were identified and compiled. A hazard ranking model was developed for the hazard classes and categories in the EU classification and labelling (CLP) regulation which is based on the UN Globally Harmonized System. The polymers were ranked based on monomer hazard classifications, and initial assessments were made. The polymers that ranked as most hazardous are made of monomers classified as mutagenic and/or carcinogenic (category 1A or 1B). These belong to the polymer families of polyurethanes, polyacrylonitriles, polyvinyl chloride, epoxy resins, and styrenic copolymers. All have a large global annual production (1-37 million tonnes). A considerable number of polymers (31 out of 55) are made of monomers that belong to the two worst of the ranking model's five hazard levels, i.e. levels IV-V. The polymers that are made of level IV monomers and have a large global annual production (1-5 million tonnes) are phenol formaldehyde resins, unsaturated polyesters, polycarbonate, polymethyl methacrylate, and urea-formaldehyde resins. This study has identified hazardous substances used in polymer production for which the risks should be evaluated for decisions on the need for risk reduction measures, substitution, or even phase out
Probabilistic Data Modeling and Querying for Location-Based Data Warehouses
Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach
2005-01-01
Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical...... and uncertain data, e.g., data from the location-based services domain such as transportation infrastructures and the attached static and dynamic content such as speed limits and vehicle positions. The paper also presents algebraic operators that support querying of such data. The work is motivated...
Probabilistic Data Modeling and Querying for Location-Based Data Warehouses
Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach
Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical...... and uncertain data, e.g., data from the location-based services domain such as transportation infrastructures and the attached static and dynamic content such as speed limits and vehicle positions. The paper also presents algebraic operators that support querying of such data. Use of pre...
A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.
Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe
2012-04-01
We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.
Ilmonen, Pauliina; 10.1214/11-AOS906
2012-01-01
We consider semiparametric location-scatter models for which the $p$-variate observation is obtained as $X=\\Lambda Z+\\mu$, where $\\mu$ is a $p$-vector, $\\Lambda$ is a full-rank $p\\times p$ matrix and the (unobserved) random $p$-vector $Z$ has marginals that are centered and mutually independent but are otherwise unspecified. As in blind source separation and independent component analysis (ICA), the parameter of interest throughout the paper is $\\Lambda$. On the basis of $n$ i.i.d. copies of $X$, we develop, under a symmetry assumption on $Z$, signed-rank one-sample testing and estimation procedures for $\\Lambda$. We exploit the uniform local and asymptotic normality (ULAN) of the model to define signed-rank procedures that are semiparametrically efficient under correctly specified densities. Yet, as is usual in rank-based inference, the proposed procedures remain valid (correct asymptotic size under the null, for hypothesis testing, and root-$n$ consistency, for point estimation) under a very broad range of ...
A Multiobjective Programming Method for Ranking All Units Based on Compensatory DEA Model
Haifang Cheng
2014-01-01
Full Text Available In order to rank all decision making units (DMUs on the same basis, this paper proposes a multiobjective programming (MOP model based on a compensatory data envelopment analysis (DEA model to derive a common set of weights that can be used for the full ranking of all DMUs. We first revisit a compensatory DEA model for ranking all units, point out the existing problem for solving the model, and present an improved algorithm for which an approximate global optimal solution of the model can be obtained by solving a sequence of linear programming. Then, we applied the key idea of the compensatory DEA model to develop the MOP model in which the objectives are to simultaneously maximize all common weights under constraints that the sum of efficiency values of all DMUs is equal to unity and the sum of all common weights is also equal to unity. In order to solve the MOP model, we transform it into a single objective programming (SOP model using a fuzzy programming method and solve the SOP model using the proposed approximation algorithm. To illustrate the ranking method using the proposed method, two numerical examples are solved.
Rosner, Bernard; Glynn, Robert J
2007-02-10
The Spearman (rho(s)) and Kendall (tau) rank correlation coefficient are routinely used as measures of association between non-normally distributed random variables. However, confidence limits for rho(s) are only available under the assumption of bivariate normality and for tau under the assumption of asymptotic normality of tau. In this paper, we introduce another approach for obtaining confidence limits for rho(s) or tau based on the arcsin transformation of sample probit score correlations. This approach is shown to be applicable for an arbitrary bivariate distribution. The arcsin-based estimators for rho(s) and tau (denoted by rho(s,a), tau(a)) are shown to have asymptotic relative efficiency (ARE) of 9/pi2 compared with the usual estimators rho(s) and tau when rho(s) and tau are, respectively, 0. In some nutritional applications, the Spearman rank correlation between nutrient intake as assessed by a reference instrument versus nutrient intake as assessed by a surrogate instrument is used as a measure of validity of the surrogate instrument. However, if only a single replicate (or a few replicates) are available for the reference instrument, then the estimated Spearman rank correlation will be downwardly biased due to measurement error. In this paper, we use the probit transformation as a tool for specifying an ANOVA-type model for replicate ranked data resulting in a point and interval estimate of a measurement error corrected rank correlation. This extends previous work by Rosner and Willett for obtaining point and interval estimates of measurement error corrected Pearson correlations.
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
Erkan, G; 10.1613/jair.1523
2011-01-01
We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluatio...
RESEARCH ON SOLVING TRAVELLING SALESMAN PROBLEM USING RANK BASED ANT SYSTEM ON GPU
Khushbu Khatri
2015-10-01
Full Text Available Ant Colony Optimization (ACO is meta-heuristic algorithm inspired from nature to solve many combinatorial optimization problems such as Travelling Salesman Problem (TSP. There are many versions of ACO used to solve TSP like, Ant System, Elitist Ant System, Max-Min Ant System, Rank based Ant System algorithm. For improved performance, these methods can be implemented in parallel architecture like GPU, CUDA architecture. Graphics Processing Unit (GPU provides highly parallel and fully programmable platform. GPUs which have many processing units with an off-chip global memory can be used for general purpose parallel computation. This paper presents a parallel Rank Based Ant System algorithm to solve TSP by use of Pre Roulette Wheel Selection Method.
Zhang, Xin; Juan, Anna de; Tauler, Romà
2016-01-01
This paper shows the effect of using local rank and selectivity constraints based on spatial information of spectroscopic images to increase the performance of Multivariate Curve Resolution (MCR) methods and to decrease the ambiguity of final results. Fixed Size Image Window-Evolving Factor Analysis (FSIW-EFA) is applied to discover which pixels are more suitable for the application of local rank constraints. An automated method to help in setting appropriate threshold values for the application of FSIW-EFA, based on global and local use of Singular Value Decomposition (SVD) is proposed. Additional use of correlation coefficients between selected reference spectra and pixel spectra of the image is shown to provide an alternative way for the application of the selectivity constraint in spectroscopic images for the first time. This alternative method resulted to be satisfactory when pure pixels exist. Copyright © 2015 Elsevier B.V. All rights reserved.
Rounded Data Analysis Based on Multi-Layer Ranked Set Sampling
Wei Ming LI; Zhi Dong BAI
2011-01-01
Observations of sampling are often subject to rounding,but are modeled as though they were unrounded.This paper examines the impact of rounding errors on parameter estimation with multi-layer ranked set sampling.It shows that the rounding errors seriously distort the behavior of covariance matrix estimate,and lead to inconsistent estimation.Taking this into account,we present a new approach to implement the estimation for this model,and further establish the strong consistency and asymptotic normality of the proposed estimators.Simulation experiments show that our estimates based on rounded multi-layer ranked set sampling are always more efficient than those based on rounded simple random sampling.
FEATURE RANKING BASED NESTED SUPPORT VECTOR MACHINE ENSEMBLE FOR MEDICAL IMAGE CLASSIFICATION.
Varol, Erdem; Gaonkar, Bilwaj; Erus, Guray; Schultz, Robert; Davatzikos, Christos
2012-01-01
This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based on the rank value using a forward feature selection scheme. Finally, an SVM classifier is trained on each subset of image voxels. The class label of a test subject is calculated by combining individual decisions of the SVM classifiers using a voting mechanism. The method is applied for classifying patients with neurological diseases such as Alzheimer's disease (AD) and autism spectrum disorder (ASD). The results on both datasets demonstrate superior performance as compared to two state of the art methods for medical image classification.
Ranking Operations Management Conferences
Steenhuis, Harm-Jan; Bruijn, de Erik Joost; Gupta, Sushil; Laptaned, U.
2007-01-01
Several publications have appeared in the field of Operations Management which rank Operations Management related journals. Several ranking systems exist for journals based on , for example, perceived relevance and quality, citation, and author affiliation. Many academics also publish at conferences
Kumar, Y Satish; Talarico, Claudio; Wang, Janet; 10.1109/DATE.2005.31
2011-01-01
Since the advent of new nanotechnologies, the variability of gate delay due to process variations has become a major concern. This paper proposes a new gate delay model that includes impact from both process variations and multiple input switching. The proposed model uses orthogonal polynomial based probabilistic collocation method to construct a delay analytical equation from circuit timing performance. From the experimental results, our approach has less that 0.2% error on the mean delay of gates and less than 3% error on the standard deviation.
Novel Opportunistic Network Routing Based on Social Rank for Device-to-Device Communication
Tong Wang
2017-01-01
Full Text Available In recent years, there has been dramatic proliferation of research concerned with fifth-generation (5G mobile communication networks, among which device-to-device (D2D communication is one of the key technologies. Due to the intermittent connection of nodes, the D2D network topology may be disconnected frequently, which will lead to failure in transmission of large data files. In opportunistic networks, in case of encountering nodes which never meet before a flood message blindly to cause tremendous network overhead, a novel opportunistic network routing protocol based on social rank and intermeeting time (SRIT is proposed in this paper. An improved utility approach applied in utility replication based on encounter durations and intermeeting time is put forward to enhance the routing efficiency. Meanwhile, in order to select better candidate nodes in the network, a social graph among people is established when they socially relate to each other in social rank replication. The results under the scenario show an advantage of the proposed opportunistic network routing based on social rank and intermeeting time (SRIT over the compared algorithms in terms of delivery ratio, average delivery latency, and overhead ratio.
Hybrid Packet-Pheromone-Based Probabilistic Routing for Mobile Ad Hoc Networks
Kashkouli Nejad, Keyvan; Shawish, Ahmed; Jiang, Xiaohong; Horiguchi, Susumu
Ad-Hoc networks are collections of mobile nodes communicating using wireless media without any fixed infrastructure. Minimal configuration and quick deployment make Ad-Hoc networks suitable for emergency situations like natural disasters or military conflicts. The current Ad-Hoc networks can only support either high mobility or high transmission rate at a time because they employ static approaches in their routing schemes. However, due to the continuous expansion of the Ad-Hoc network size, node-mobility and transmission rate, the development of new adaptive and dynamic routing schemes has become crucial. In this paper we propose a new routing scheme to support high transmission rates and high node-mobility simultaneously in a big Ad-Hoc network, by combining a new proposed packet-pheromone-based approach with the Hint Based Probabilistic Protocol (HBPP) for congestion avoidance with dynamic path selection in packet forwarding process. Because of using the available feedback information, the proposed algorithm does not introduce any additional overhead. The extensive simulation-based analysis conducted in this paper indicates that the proposed algorithm offers small packet-latency and achieves a significantly higher delivery probability in comparison with the available Hint-Based Probabilistic Protocol (HBPP).
Ranking alternatives based on imprecise multi-criteria data and pairwise overlap dominance relations
Franco de los Rios, Camilo Andres; Hougaard, Jens Leth; Nielsen, Kurt
This paper explores a multi-criteria outranking methodology that is designed to both handle uncertain and imprecise data in describing alternatives as well as treating the decision maker's preference information in a sensible way that re flects the difficulties in articulating preferences. Based...... on fuzzy interval degrees, representing and measuring data imprecision, this procedure obtains a set of semi-equivalence classes assigning an intransitive order on the alternatives. Relevance measures are then explored for ranking alternatives with respect to the semi-equivalence classes, and a final...... illustrative example is given for comparison with standard methods like PROMETHEE. The proposed methodology takes into account the risk attitudes of decision makers, organizing the alternatives and ranking them according to their relevance. The whole interactive decision support allows understanding...
ROCIT : a visual object recognition algorithm based on a rank-order coding scheme.
Gonzales, Antonio Ignacio; Reeves, Paul C.; Jones, John J.; Farkas, Benjamin D.
2004-06-01
This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to eliminate the inconsistency between model parameters and states. The performance of RAPCKF is tested with numerical cases of unsaturated flow models. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
Ho, Clifford Kuofei
2004-06-01
Chemical transport through human skin can play a significant role in human exposure to toxic chemicals in the workplace, as well as to chemical/biological warfare agents in the battlefield. The viability of transdermal drug delivery also relies on chemical transport processes through the skin. Models of percutaneous absorption are needed for risk-based exposure assessments and drug-delivery analyses, but previous mechanistic models have been largely deterministic. A probabilistic, transient, three-phase model of percutaneous absorption of chemicals has been developed to assess the relative importance of uncertain parameters and processes that may be important to risk-based assessments. Penetration routes through the skin that were modeled include the following: (1) intercellular diffusion through the multiphase stratum corneum; (2) aqueous-phase diffusion through sweat ducts; and (3) oil-phase diffusion through hair follicles. Uncertainty distributions were developed for the model parameters, and a Monte Carlo analysis was performed to simulate probability distributions of mass fluxes through each of the routes. Sensitivity analyses using stepwise linear regression were also performed to identify model parameters that were most important to the simulated mass fluxes at different times. This probabilistic analysis of percutaneous absorption (PAPA) method has been developed to improve risk-based exposure assessments and transdermal drug-delivery analyses, where parameters and processes can be highly uncertain.
Robust salt-dome detection using the ranking of texture-based attributes
Deriche, Mohamed
2016-09-01
The accurate interpretation and analysis of seismic data heavily depends on the robustness of the algorithms used. We focus on the robust detection of salt domes from seismic surveys. We discuss a novel feature-ranking classification model for saltdome detection for seismic images using an optimal set of texture attributes. The proposed algorithm overcomes the limitations of existing texture attribute-based techniques, which heavily depend on the relevance of the attributes to the geological nature of salt domes and the number of attributes used for accurate detection. The algorithm combines the attributes from the Gray-Level Co-occurrence Matrix (GLCM), the Gabor filters, and the eigenstructure of the covariance matrix with feature ranking using the information content. The top-ranked attributes are combined to form the optimal feature set, which ensures that the algorithm works well even in the absence of strong reflectors along the salt-dome boundaries. Contrary to existing salt-dome detection techniques, the proposed algorithm is robust and computationally efficient, and works with small-sized feature sets. I used the Netherlands F3 block to evaluate the performance of the proposed algorithm. The experimental results suggest that the proposed workflow based on information theory can detect salt domes with accuracy superior to existing salt-dome detection techniques.
An Improved Fuzzy Based Missing Value Estimation in DNA Microarray Validated by Gene Ranking
Sujay Saha
2016-01-01
Full Text Available Most of the gene expression data analysis algorithms require the entire gene expression matrix without any missing values. Hence, it is necessary to devise methods which would impute missing data values accurately. There exist a number of imputation algorithms to estimate those missing values. This work starts with a microarray dataset containing multiple missing values. We first apply the modified version of the fuzzy theory based existing method LRFDVImpute to impute multiple missing values of time series gene expression data and then validate the result of imputation by genetic algorithm (GA based gene ranking methodology along with some regular statistical validation techniques, like RMSE method. Gene ranking, as far as our knowledge, has not been used yet to validate the result of missing value estimation. Firstly, the proposed method has been tested on the very popular Spellman dataset and results show that error margins have been drastically reduced compared to some previous works, which indirectly validates the statistical significance of the proposed method. Then it has been applied on four other 2-class benchmark datasets, like Colorectal Cancer tumours dataset (GDS4382, Breast Cancer dataset (GSE349-350, Prostate Cancer dataset, and DLBCL-FL (Leukaemia for both missing value estimation and ranking the genes, and the results show that the proposed method can reach 100% classification accuracy with very few dominant genes, which indirectly validates the biological significance of the proposed method.
Lithner, Delilah; Larsson, Ake; Dave, Göran
2011-08-15
Plastics constitute a large material group with a global annual production that has doubled in 15 years (245 million tonnes in 2008). Plastics are present everywhere in society and the environment, especially the marine environment, where large amounts of plastic waste accumulate. The knowledge of human and environmental hazards and risks from chemicals associated with the diversity of plastic products is very limited. Most chemicals used for producing plastic polymers are derived from non-renewable crude oil, and several are hazardous. These may be released during the production, use and disposal of the plastic product. In this study the environmental and health hazards of chemicals used in 55 thermoplastic and thermosetting polymers were identified and compiled. A hazard ranking model was developed for the hazard classes and categories in the EU classification and labelling (CLP) regulation which is based on the UN Globally Harmonized System. The polymers were ranked based on monomer hazard classifications, and initial assessments were made. The polymers that ranked as most hazardous are made of monomers classified as mutagenic and/or carcinogenic (category 1A or 1B). These belong to the polymer families of polyurethanes, polyacrylonitriles, polyvinyl chloride, epoxy resins, and styrenic copolymers. All have a large global annual production (1-37 million tonnes). A considerable number of polymers (31 out of 55) are made of monomers that belong to the two worst of the ranking model's five hazard levels, i.e. levels IV-V. The polymers that are made of level IV monomers and have a large global annual production (1-5 million tonnes) are phenol formaldehyde resins, unsaturated polyesters, polycarbonate, polymethyl methacrylate, and urea-formaldehyde resins. This study has identified hazardous substances used in polymer production for which the risks should be evaluated for decisions on the need for risk reduction measures, substitution, or even phase out.
Evaluation of feature-based 3-d registration of probabilistic volumetric scenes
Restrepo, Maria I.; Ulusoy, Ali O.; Mundy, Joseph L.
2014-12-01
Automatic estimation of the world surfaces from aerial images has seen much attention and progress in recent years. Among current modeling technologies, probabilistic volumetric models (PVMs) have evolved as an alternative representation that can learn geometry and appearance in a dense and probabilistic manner. Recent progress, in terms of storage and speed, achieved in the area of volumetric modeling, opens the opportunity to develop new frameworks that make use of the PVM to pursue the ultimate goal of creating an entire map of the earth, where one can reason about the semantics and dynamics of the 3-d world. Aligning 3-d models collected at different time-instances constitutes an important step for successful fusion of large spatio-temporal information. This paper evaluates how effectively probabilistic volumetric models can be aligned using robust feature-matching techniques, while considering different scenarios that reflect the kind of variability observed across aerial video collections from different time instances. More precisely, this work investigates variability in terms of discretization, resolution and sampling density, errors in the camera orientation, and changes in illumination and geographic characteristics. All results are given for large-scale, outdoor sites. In order to facilitate the comparison of the registration performance of PVMs to that of other 3-d reconstruction techniques, the registration pipeline is also carried out using Patch-based Multi-View Stereo (PMVS) algorithm. Registration performance is similar for scenes that have favorable geometry and the appearance characteristics necessary for high quality reconstruction. In scenes containing trees, such as a park, or many buildings, such as a city center, registration performance is significantly more accurate when using the PVM.
A computer based living probabilistic safety assessment (LPSA) method for nuclear power plants
Zubair, Muhammad, E-mail: zubairheu@gmail.com [Department of Nuclear Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 446-701 (Korea, Republic of); Department of Basic Sciences, University of Engineering and Technology, Taxila (Pakistan); Zhijian, Zhang [College of Nuclear Science and Technology, Harbin Engineering University (China); Heo, Gyunyoung [Department of Nuclear Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 446-701 (Korea, Republic of); Ahmed, Iftikhar [College of Mathematics and Statics, Chongqing University, 401331 (China); Aamir, Muhammad [Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Chongqing 400030 (China)
2013-12-15
Highlights: • A computer based LPSA method named, online risk monitor system (ORMS) has been proposed. • The essential features and functions of ORMS have been described. • A case study of emergency diesel generator (EDG) of Daya Bay NPP had carried out. • By using ORMS operational failure rate and demand failure probability of EDG has been calculated. - Abstract: To update PSA (probabilistic safety assessment) model this paper presents a computer based living probabilistic safety assessment (LPSA) method named as online risk monitor system (ORMS). The essential features and functions of ORMS have been described in this research. A case study of emergency diesel generator (EDG) of Daya Bay nuclear power plant (NPP) has been done; operational failure rate and demand failure probability of EDG has been calculated with the help of ORMS. The results of ORMS are well matched with data obtained from Daya Bay NPP. ORMS is capable of automatically update the online risk models and reliability parameters of equipment in time. ORMS can support in decision making process of operator and manager in nuclear power plant.
A simulation-based probabilistic design method for arctic sea transport systems
Martin, Bergström; Ove, Erikstad Stein; Sören, Ehlers
2016-12-01
When designing an arctic cargo ship, it is necessary to consider multiple stochastic factors. This paper evaluates the merits of a simulation-based probabilistic design method specifically developed to deal with this challenge. The outcome of the paper indicates that the incorporation of simulations and probabilistic design parameters into the design process enables more informed design decisions. For instance, it enables the assessment of the stochastic transport capacity of an arctic ship, as well as of its long-term ice exposure that can be used to determine an appropriate level of ice-strengthening. The outcome of the paper also indicates that significant gains in transport system cost-efficiency can be obtained by extending the boundaries of the design task beyond the individual vessel. In the case of industrial shipping, this allows for instance the consideration of port-based cargo storage facilities allowing for temporary shortages in transport capacity and thus a reduction in the required fleet size / ship capacity.
Building systems technical condition assessment based on the multilevel probabilistic analysis (rus
Sokolov V.A.
2011-11-01
Full Text Available Methods based on the stochastic apparatus technical diagnostics are put forward to solve problems concerning determination of the structural elements technical condition. Diagnosis is performed using probabilistic methods of recognition of technical conditions of complex technical systems. The diagnosis is conducted with the help of probabilistic methods of the complex engineering systems condition recognition based on the Bayesian analysis. The aforementioned approach and information theory methods are applied to run the multilevel diagnostics of elements and systems of floor slabs in old urban buildings. Multilevel diagnostics was done in the concepts of information entropy. During the analysis of the results of forward and reverse traces on the hierarchical diagnostics structure the next conclusion was made. The forward trace allows to determine the technical condition and category for the each element of each level in the hierarchical structure and the reverse trace gives a possibility to evaluate the contribution of each element condition to the information about the whole building condition. The last point can be an argumentative basis for the account of physical depreciation of building structures.
Probabilistic Routing Based on Two-Hop Information in Delay/Disruption Tolerant Networks
Xu Wang
2015-01-01
Full Text Available We investigate an opportunistic routing protocol in delay/disruption tolerant networks (DTNs where the end-to-end path between source and destination nodes may not exist for most of the time. Probabilistic routing protocol using history of encounters and transitivity (PRoPHET is an efficient history-based routing protocol specifically proposed for DTNs, which only utilizes the delivery predictability of one-hop neighbors to make a decision for message forwarding. In order to further improve the message delivery rate and to reduce the average overhead of PRoPHET, in this paper we propose an improved probabilistic routing algorithm (IPRA, where the history information of contacts for the immediate encounter and two-hop neighbors has been jointly used to make an informed decision for message forwarding. Based on the Opportunistic Networking Environment (ONE simulator, the performance of IPRA has been evaluated via extensive simulations. The results show that IPRA can significantly improve the average delivery rate while achieving a better or comparable performance with respect to average overhead, average delay, and total energy consumption compared with the existing algorithms.
Tiwari, Pallavi; Rosen, Mark; Reed, Galen; Kurhanewicz, John; Madabhushi, Anant
2009-01-01
The major challenge with classifying high dimensional biomedical data is in identifying the appropriate feature representation to (a) overcome the curse of dimensionality, and (b) facilitate separation between the data classes. Another challenge is to integrate information from two disparate modalities, possibly existing in different dimensional spaces, for improved classification. In this paper, we present a novel data representation, integration and classification scheme, Spectral Embedding based Probabilistic boosting Tree (ScEPTre), which incorporates Spectral Embedding (SE) for data representation and integration and a Probabilistic Boosting Tree classifier for data classification. SE provides an alternate representation of the data by non-linearly transforming high dimensional data into a low dimensional embedding space such that the relative adjacencies between objects are preserved. We demonstrate the utility of ScEPTre to classify and integrate Magnetic Resonance (MR) Spectroscopy (MRS) and Imaging (MRI) data for prostate cancer detection. Area under the receiver operating Curve (AUC) obtained via randomized cross validation on 15 prostate MRI-MRS studies suggests that (a) ScEPTre on MRS significantly outperforms a Haar wavelets based classifier, (b) integration of MRI-MRS via ScEPTre performs significantly better compared to using MRI and MRS alone, and (c) data integration via ScEPTre yields superior classification results compared to combining decisions from individual classifiers (or modalities).
A Simulation-Based Probabilistic Design Method for Arctic Sea Transport Systems
Bergstrm Martin; Erikstad Stein Ove; and Ehlers Sren
2016-01-01
When designing an arctic cargo ship, it is necessary to consider multiple stochastic factors. This paper evaluates the merits of a simulation-based probabilistic design method specifically developed to deal with this challenge. The outcome of the paper indicates that the incorporation of simulations and probabilistic design parameters into the design process enables more informed design decisions. For instance, it enables the assessment of the stochastic transport capacity of an arctic ship, as well as of its long-term ice exposure that can be used to determine an appropriate level of ice-strengthening. The outcome of the paper also indicates that significant gains in transport system cost-efficiency can be obtained by extending the boundaries of the design task beyond the individual vessel. In the case of industrial shipping, this allows for instance the consideration of port-based cargo storage facilities allowing for temporary shortages in transport capacity and thus a reduction in the required fleet size / ship capacity.
Probabilistic Data Modeling and Querying for Location-Based Data Warehouses
Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach
Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical...... and uncertain data, e.g., data from the location-based services domain such as transportation infrastructures and the attached static and dynamic content such as speed limits and vehicle positions. The paper also presents algebraic operators that support querying of such data. Use of pre......-aggregation for implementation of the operators is also discussed. The work is motivated with a real-world case study, based on our collaboration with a leading Danish vendor of location-based services....
A discriminative kernel-based approach to rank images from text queries.
Grangier, David; Bengio, Samy
2008-08-01
This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The proposed model hence addresses the retrieval problem directly and does not rely on an intermediate image annotation task, which contrasts with previous research. Moreover, our learning procedure builds upon recent work on the online learning of kernel-based classifiers. This yields an efficient, scalable algorithm, which can benefit from recent kernels developed for image comparison. The experiments performed over stock photography data show the advantage of our discriminative ranking approach over state-of-the-art alternatives (e.g. our model yields 26.3% average precision over the Corel dataset, which should be compared to 22.0%, for the best alternative model evaluated). Further analysis of the results shows that our model is especially advantageous over difficult queries such as queries with few relevant pictures or multiple-word queries.
Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation
Pandiarajan K.
2014-09-01
Full Text Available This paper presents an effective method of network overload management in power systems. The three competing objectives 1 generation cost 2 transmission line overload and 3 real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO and Differential evolution (DE. Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem
Ranking CCR-efficient units based on the indicator with limited resources
Amir Reza Khaki
2012-10-01
Full Text Available Data Envelopment Analysis (DEA is one of the most popular techniques for measuring the relative efficiencies of a set of decision making units (DMUs, which use different inputs producing various outputs. Ranking of efficient DMUs is one of the most interesting DEA perspectives. However, there are cases where we see some limitations on available resources and the proposed model of this paper is associated with Indicator with Limited Sources (ILS, which affects ranking methods. The ILS exists as fixed amount in a community and the DMUs can own it with their abilities. When a DMU loses the same amount of the indicator, the rest of the DMUs are able to own some without even changing their capacities of other indicators and or vice versa. If a DMU looks for more of the same amount of the indicator, the rest of the DMUs have to supply it without even changing their capacity of other indicators. This paper develops a ranking method based on the ILS for the efficient DMUs, when there is changes either in inputs/ outputs ILS. The implementation of the proposed model is applied for a case study of banking system.
Reichenbach, P.; Mondini, A.; Guzzetti, F.; Rossi, M.; Ardizzone, F.; Cardinali, M.
2009-04-01
Probabilistic landslide susceptibility assessments attempt to predict the location and threat posed by known landslides. Under the assumption that landslides will occur in the future because of the same conditions that produced them in the past, geomorphologists use susceptibility assessments to predict the location of future landslides. We present an attempt to exploit satellite data to prepare a landslide susceptibility zonation for a the Collazzone area that extends for 79 sq km in the Umbria region, Central Italy. For the study area we have prepared a map of the Normalized Difference Vegetation Index (NDVI) obtained by processing raw NIR and RED channels (b2 and b3 bands) at 15 m x 15 m resolution of an image acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), on board the TERRA satellite, and a map of Land Surface Temperature (LST) obtained by processing raw TIR channels (b11 to b15 bands) at 90 m × 90 m resolution from the same image. Both maps, in general proxy for soil moisture maps, were obtained through standard algorithms. As expected, there is a strong correspondence between NDVI and LST, but, when the NDVI does not change, elevation effects and others are predominant in LST. For the Collazzone area we prepared two different susceptibility models. The first was prepared through multivariate analysis of thematic data (including morphometry, lithology, structure and land use) obtained through traditional methods, primarily the interpretation of aerial photographs and field work. The second susceptibility model was prepared using terrain morphology and information obtained processing satellite data. The two models were compared in term of model fit and model performance and were validated exploiting landslide inventories not used to build the models. The two susceptibility models are very similar from a geographic and a classification point of view. This is good news, as it tells us that for landslide susceptibility
Case and Relation (CARE based Page Rank Algorithm for Semantic Web Search Engines
N. Preethi
2012-05-01
Full Text Available Web information retrieval deals with a technique of finding relevant web pages for any given query from a collection of documents. Search engines have become the most helpful tool for obtaining useful information from the Internet. The next-generation Web architecture, represented by the Semantic Web, provides the layered architecture possibly allowing data to be reused across application. The proposed architecture use a hybrid methodology named Case and Relation (CARE based Page Rank algorithm which uses past problem solving experience maintained in the case base to form a best matching relations and then use them for generating graphs and spanning forests to assign a relevant score to the pages.
Morphological studies of the murine heart based on probabilistic and statistical atlases.
Perperidis, Dimitrios; Bucholz, Elizabeth; Johnson, G Allan; Constantinides, Christakis
2012-03-01
This study directly compares morphological features of the mouse heart in its end-relaxed state based on constructed morphometric maps and atlases using principal component analysis in C57BL/6J (n=8) and DBA (n=5) mice. In probabilistic atlases, a gradient probability exists for both strains in longitudinal locations from base to apex. Based on the statistical atlases, differences in size (49.8%), apical direction (15.6%), basal ventricular blood pool size (13.2%), and papillary muscle shape and position (17.2%) account for the most significant modes of shape variability for the left ventricle of the C57BL/6J mice. For DBA mice, differences in left ventricular size and direction (67.4%), basal size (15.7%), and position of papillary muscles (16.8%) account for significant variability. Copyright © 2011 Elsevier Ltd. All rights reserved.
Katz, Josh M; Winter, Carl K; Buttrey, Samuel E; Fadel, James G
2012-03-01
Western and guideline based diets were compared to determine if dietary improvements resulting from following dietary guidelines reduce acrylamide intake. Acrylamide forms in heat treated foods and is a human neurotoxin and animal carcinogen. Acrylamide intake from the Western diet was estimated with probabilistic techniques using teenage (13-19 years) National Health and Nutrition Examination Survey (NHANES) food consumption estimates combined with FDA data on the levels of acrylamide in a large number of foods. Guideline based diets were derived from NHANES data using linear programming techniques to comport to recommendations from the Dietary Guidelines for Americans, 2005. Whereas the guideline based diets were more properly balanced and rich in consumption of fruits, vegetables, and other dietary components than the Western diets, acrylamide intake (mean±SE) was significantly greater (Plinear programming and results demonstrate that linear programming techniques can be used to model specific diets for the assessment of toxicological and nutritional dietary components.
Willemen, T; Varon, C; Dorado, A Caicedo; Haex, B; Vander Sloten, J; Van Huffel, S
2015-10-01
Current clinical standards to assess sleep and its disorders lack either accuracy or user-friendliness. They are therefore difficult to use in cost-effective population-wide screening or long-term objective follow-up after diagnosis. In order to fill this gap, the use of cardiac and respiratory information was evaluated for discrimination between different sleep stages, and for detection of apneic breathing. Alternative probabilistic visual representations were also presented, referred to as the hypnocorrogram and apneacorrogram. Analysis was performed on the UCD sleep apnea database, available on Physionet. The presence of apneic events proved to have a significant impact on the performance of a cardiac and respiratory based algorithm for sleep stage classification. WAKE versus SLEEP discrimination resulted in a kappa value of κ = 0.0439, while REM versus NREM resulted in κ = 0.298 and light sleep (N1N2) versus deep sleep (N3) in κ = 0.339. The high proportion of hypopneic events led to poor detection of apneic breathing, resulting in a kappa value of κ = 0.272. While the probabilistic representations allow to put classifier output in perspective, further improvements would be necessary to make the classifier reliable for use on patients with sleep apnea.
Dong, C.; Schoups, G.; van de Giesen, N.
2012-04-01
Water resource planning and management is subject to large uncertainties with respect to the impact of climate change and socio-economic development on water systems. In order to deal with these uncertainties, probabilistic climate and socio-economic scenarios were developed based on the Principle of Maximum Entropy, as defined within information theory, and as inputs to hydrological models to construct probabilistic water scenarios using Monte Carlo simulation. Probabilistic scenarios provide more explicit information than equally-likely scenarios for decision-making in water resource management. A case was developed for the Yellow River Basin, China, where future water availability and water demand are affected by both climate change and socio-economic development. Climate scenarios of future precipitation and temperature were developed based on the results of multiple Global climate models; and socio-economic scenarios were downscaled from existing large-scale scenarios. Probability distributions were assigned to these scenarios to explicitly represent a full set of future possibilities. Probabilistic climate scenarios were used as input to a rainfall-runoff model to simulate future river discharge and socio-economic scenarios for calculating water demand. A full set of possible future water supply-demand scenarios and their associated probability distributions were generated. This set can feed the further analysis of the future water balance, which can be used as a basis to plan and manage water resources in the Yellow River Basin. Key words: Probabilistic scenarios, climate change, socio-economic development, water management
Probabilistic relevance ranking for collaborative filtering
Wang, J.; Robertson, S.; De Vries, A.P.; Reinders, M.J.T.
2008-01-01
Collaborative filtering is concerned with making recommendations about items to users. Most formulations of the problem are specifically designed for predicting user ratings, assuming past data of explicit user ratings is available. However, in practice we may only have implicit evidence of user pre
Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G.
2014-09-01
The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).
Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising
Baburaj, M.; George, Sudhish N.
2016-01-01
The t-SVD based Tensor Robust Principal Component Analysis (TRPCA) decomposes low rank multi-linear signal corrupted by gross errors into low multi-rank and sparse component by simultaneously minimizing tensor nuclear norm and l 1 norm. But if the multi-rank of the signal is considerably large and/or large amount of noise is present, the performance of TRPCA deteriorates. To overcome this problem, this paper proposes a new efficient iterative reweighted tensor decomposition scheme based on t-...
Design of Probabilistic Boolean Networks Based on Network Structure and Steady-State Probabilities.
Kobayashi, Koichi; Hiraishi, Kunihiko
2016-06-06
In this brief, we consider the problem of finding a probabilistic Boolean network (PBN) based on a network structure and desired steady-state properties. In systems biology and synthetic biology, such problems are important as an inverse problem. Using a matrix-based representation of PBNs, a solution method for this problem is proposed. The problem of finding a BN has been studied so far. In the problem of finding a PBN, we must calculate not only the Boolean functions, but also the probabilities of selecting a Boolean function and the number of candidates of the Boolean functions. Hence, the problem of finding a PBN is more difficult than that of finding a BN. The effectiveness of the proposed method is presented by numerical examples.
Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference
Tianhua Xu
2013-01-01
Full Text Available Risk-based maintenance (RBM aims to improve maintenance planning and decision making by reducing the probability and consequences of failure of equipment. A new predictive maintenance strategy that integrates dynamic evolution model and risk assessment is proposed which can be used to calculate the optimal maintenance time with minimal cost and safety constraints. The dynamic evolution model provides qualified risks by using probabilistic inference with bucket elimination and gives the prospective degradation trend of a complex system. Based on the degradation trend, an optimal maintenance time can be determined by minimizing the expected maintenance cost per time unit. The effectiveness of the proposed method is validated and demonstrated by a collision accident of high-speed trains with obstacles in the presence of safety and cost constrains.
Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device
Xiang He
2015-12-01
Full Text Available Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer, wireless signal strength indicators (WiFi, Bluetooth, Zigbee, and visual sensors (LiDAR, camera. People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.
Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device.
He, Xiang; Aloi, Daniel N; Li, Jia
2015-12-14
Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.
Fan, Hong; Zhu, Anfeng; Zhang, Weixia
2015-12-01
In order to meet the rapid positioning of 12315 complaints, aiming at the natural language expression of telephone complaints, a semantic retrieval framework is proposed which is based on natural language parsing and geographical names ontology reasoning. Among them, a search result ranking and recommended algorithms is proposed which is regarding both geo-name conceptual similarity and spatial geometry relation similarity. The experiments show that this method can assist the operator to quickly find location of 12,315 complaints, increased industry and commerce customer satisfaction.
Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization
Na Tian
2015-01-01
Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.
Church, Lewis
2010-01-01
This dissertation answers three research questions: (1) What are the characteristics of a combinatoric measure, based on the Average Search Length (ASL), that performs the same as a probabilistic version of the ASL?; (2) Does the combinatoric ASL measure produce the same performance result as the one that is obtained by ranking a collection of…
Mohd Asyraf Zulkifley
2012-11-01
Full Text Available In video analytics, robust observation detection is very important as thecontent of the videos varies a lot, especially for tracking implementation. Contraryto the image processing field, the problems of blurring, moderate deformation, lowillumination surroundings, illumination change and homogenous texture are normallyencountered in video analytics. Patch-Based Observation Detection (PBOD is developed toimprove detection robustness to complex scenes by fusing both feature- and template-basedrecognition methods. While we believe that feature-based detectors are more distinctive,however, for finding the matching between the frames are best achieved by a collectionof points as in template-based detectors. Two methods of PBOD—the deterministic andprobabilistic approaches—have been tested to find the best mode of detection. Bothalgorithms start by building comparison vectors at each detected points of interest. Thevectors are matched to build candidate patches based on their respective coordination. Forthe deterministic method, patch matching is done in 2-level test where threshold-basedposition and size smoothing are applied to the patch with the highest correlation value. Forthe second approach, patch matching is done probabilistically by modelling the histogramsof the patches by Poisson distributions for both RGB and HSV colour models. Then,maximum likelihood is applied for position smoothing while a Bayesian approach is appliedfor size smoothing. The result showed that probabilistic PBOD outperforms the deterministicapproach with average distance error of 10.03% compared with 21.03%. This algorithm is best implemented as a complement to other simpler detection methods due to heavyprocessing requirement.
Wakker, P. P.; Thaler, R.H.; Tversky, A.
1997-01-01
textabstractProbabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in the premium to compensate for a 1% default risk. While these preferences are intuitively appealing they are difficult to reconcile with expected utility theory. Under highly plausible assumptions about the utility function, willingness to pay for probabilistic i...
Yoshida, Mizuki; Matsuyama, Yutaka
2016-01-01
Fleming and Harrington's G(ρ,γ) class of weighted log-rank tests is appropriate for detecting delayed treatment effects such as those seen in cancer vaccines. A conditional power (CP) and an alpha spending function (ASF) approach are useful for interim analyses that are conducted with the aim of early termination due to futility and efficacy, respectively. However, calculation of the CP and the total Type I error probability are often not considered for delayed effects under the staggered patient entry. In this article, we first propose methods for calculating the CP analytically based on the weighted log-rank test. We compared the performances of the proposed methods with two other methods (i.e., usual log-rank test and optimal one) under the delayed alternatives. Our simulations demonstrated that the CP based on the weighted log-rank test was more powerful than that of the usual log-rank test and was comparable to the CP based on the optimal log-rank test. Second, we quantitatively evaluated the degree to which the Type I error probability was inflated when an ASF approach with forced independent increments assumption was applied to the weighted log-rank test. The proposed method will provide valuable tools in the decision-making stage of the interim analysis.
Galster, Matthias; Eberlein, Armin; Sprinkle, J; Sterritt, R; Breitman, K
2011-01-01
Ranking software requirements helps decide what requirements to implement during a software development project, and when. Currently, requirements ranking techniques focus on resource constraints or stakeholder priorities and neglect the effect of requirements on the software architecture process. H
Galster, Matthias; Eberlein, Armin; Sprinkle, J; Sterritt, R; Breitman, K
2011-01-01
Ranking software requirements helps decide what requirements to implement during a software development project, and when. Currently, requirements ranking techniques focus on resource constraints or stakeholder priorities and neglect the effect of requirements on the software architecture process.
无
2011-01-01
Declining recognition of top university lists prompts China to look for new ways to evaluate its higher learning institutions Zhejiang University for the first time has overtaken Peking University and Tsinghua University to rank No.1 on the latest list of Chinese college rankings.The rankings are an important part of the book Picking Your University and
Application of the rank-based method to DNA methylation for cancer diagnosis.
Li, Hongdong; Hong, Guini; Xu, Hui; Guo, Zheng
2015-01-25
Detecting aberrant DNA methylation as diagnostic or prognostic biomarkers for cancer has been a topic of considerable interest recently. However, current classifiers based on absolute methylation values detected from a cohort of samples are typically difficult to be transferable to other cohorts of samples. Here, focusing on relative methylation levels, we employed a modified rank-based method to extract reversal pairs of CpG sites whose relative methylation level orderings differ between disease samples and normal controls for cancer diagnosis. The reversal pairs identified for five cancer types respectively show excellent prediction performance with the accuracy above 95%. Furthermore, when evaluating the reversal pairs identified for one cancer type in an independent cohorts of samples, we found that they could distinguish different subtypes of this cancer or different malignant stages including early stage of this cancer from normal controls. The identified reversal pairs also appear to be specific to cancer type. In conclusion, the reversal pairs detected by the rank-based method could be used for accurate cancer diagnosis, which are transferable to independent cohorts of samples.
An Automatic Web Service Composition Framework Using QoS-Based Web Service Ranking Algorithm.
Mallayya, Deivamani; Ramachandran, Baskaran; Viswanathan, Suganya
2015-01-01
Web service has become the technology of choice for service oriented computing to meet the interoperability demands in web applications. In the Internet era, the exponential addition of web services nominates the "quality of service" as essential parameter in discriminating the web services. In this paper, a user preference based web service ranking (UPWSR) algorithm is proposed to rank web services based on user preferences and QoS aspect of the web service. When the user's request cannot be fulfilled by a single atomic service, several existing services should be composed and delivered as a composition. The proposed framework allows the user to specify the local and global constraints for composite web services which improves flexibility. UPWSR algorithm identifies best fit services for each task in the user request and, by choosing the number of candidate services for each task, reduces the time to generate the composition plans. To tackle the problem of web service composition, QoS aware automatic web service composition (QAWSC) algorithm proposed in this paper is based on the QoS aspects of the web services and user preferences. The proposed framework allows user to provide feedback about the composite service which improves the reputation of the services.
Abu Bakar, Ahmad Syafadhli Bin
2015-01-01
The concept of ranking fuzzy numbers has received significant attention from the research community due to its successful applications for decision making. It complements the decision maker exercise their subjective judgments under situations that are vague, imprecise, ambiguous and uncertain in nature. The literature on ranking fuzzy numbers show that numerous ranking methods for fuzzy numbers are established where all of them aim to correctly rank all sets of fuzzy numbers that mimic real d...
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs
Zhang, Baichuan; Choudhury, Sutanay; Al-Hasan, Mohammad; Ning, Xia; Agarwal, Khushbu; Purohit, Sumit; Pesantez, Paola
2016-02-01
Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in terms of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.
Mehta, Piyush M.; Kubicek, Martin; Minisci, Edmondo; Vasile, Massimiliano
2017-01-01
Well-known tools developed for satellite and debris re-entry perform break-up and trajectory simulations in a deterministic sense and do not perform any uncertainty treatment. The treatment of uncertainties associated with the re-entry of a space object requires a probabilistic approach. A Monte Carlo campaign is the intuitive approach to performing a probabilistic analysis, however, it is computationally very expensive. In this work, we use a recently developed approach based on a new derivation of the high dimensional model representation method for implementing a computationally efficient probabilistic analysis approach for re-entry. Both aleatoric and epistemic uncertainties that affect aerodynamic trajectory and ground impact location are considered. The method is applicable to both controlled and un-controlled re-entry scenarios. The resulting ground impact distributions are far from the typically used Gaussian or ellipsoid distributions.
Gabay, Yafit; Goldfarb, Liat
2017-07-01
Although Attention-Deficit Hyperactivity Disorder (ADHD) is closely linked to executive function deficits, it has recently been attributed to procedural learning impairments that are quite distinct from the former. These observations challenge the ability of the executive function framework solely to account for the diverse range of symptoms observed in ADHD. A recent neurocomputational model emphasizes the role of striatal dopamine (DA) in explaining ADHD's broad range of deficits, but the link between this model and procedural learning impairments remains unclear. Significantly, feedback-based procedural learning is hypothesized to be disrupted in ADHD because of the involvement of striatal DA in this type of learning. In order to test this assumption, we employed two variants of a probabilistic category learning task known from the neuropsychological literature. Feedback-based (FB) and paired associate-based (PA) probabilistic category learning were employed in a non-medicated sample of ADHD participants and neurotypical participants. In the FB task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of the response. In the PA learning task, participants viewed the cue and its associated outcome simultaneously without receiving an overt response or corrective feedback. In both tasks, participants were trained across 150 trials. Learning was assessed in a subsequent test without a presentation of the outcome or corrective feedback. Results revealed an interesting disassociation in which ADHD participants performed as well as control participants in the PA task, but were impaired compared with the controls in the FB task. The learning curve during FB training differed between the two groups. Taken together, these results suggest that the ability to incrementally learn by feedback is selectively disrupted in ADHD participants. These results are discussed in relation to both
Ranking Theory and Conditional Reasoning.
Skovgaard-Olsen, Niels
2016-05-01
Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's (2013) ranking theoretic approach to conditionals.
Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study †
Tîrnăucă, Cristina; Montaña, José L.; Ontañón, Santiago; González, Avelino J.; Pardo, Luis M.
2016-01-01
Imagine an agent that performs tasks according to different strategies. The goal of Behavioral Recognition (BR) is to identify which of the available strategies is the one being used by the agent, by simply observing the agent’s actions and the environmental conditions during a certain period of time. The goal of Behavioral Cloning (BC) is more ambitious. In this last case, the learner must be able to build a model of the behavior of the agent. In both settings, the only assumption is that the learner has access to a training set that contains instances of observed behavioral traces for each available strategy. This paper studies a machine learning approach based on Probabilistic Finite Automata (PFAs), capable of achieving both the recognition and cloning tasks. We evaluate the performance of PFAs in the context of a simulated learning environment (in this case, a virtual Roomba vacuum cleaner robot), and compare it with a collection of other machine learning approaches. PMID:27347956
Remote-Sensing Image Classification Based on an Improved Probabilistic Neural Network
Lenan Wu
2009-09-01
Full Text Available This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the GLCM-based texture features. Then, a probabilistic neural network (PNN was adopted for classification, and a novel algorithm proposed to enhance its performance. Principle component analysis (PCA was chosen to reduce feature dimensions, random division to reduce the number of neurons, and Brent’s search (BS to find the optimal bias values. The results on San Francisco and Flevoland sites are compared to that using a 3-layer BPNN to demonstrate the validity of our algorithm in terms of confusion matrix and overall accuracy. In addition, the importance of each improvement of the algorithm was proven.
Constant Jacobian Matrix-Based Stochastic Galerkin Method for Probabilistic Load Flow
Yingyun Sun
2016-03-01
Full Text Available An intrusive spectral method of probabilistic load flow (PLF is proposed in the paper, which can handle the uncertainties arising from renewable energy integration. Generalized polynomial chaos (gPC expansions of dependent random variables are utilized to build a spectral stochastic representation of PLF model. Instead of solving the coupled PLF model with a traditional, cumbersome method, a modified stochastic Galerkin (SG method is proposed based on the P-Q decoupling properties of load flow in power system. By introducing two pre-calculated constant sparse Jacobian matrices, the computational burden of the SG method is significantly reduced. Two cases, IEEE 14-bus and IEEE 118-bus systems, are used to verify the computation speed and efficiency of the proposed method.
Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques
Salvador Manuel Malagon-Soldara
2015-03-01
Full Text Available This work presents a comprehensive review of current probabilistic developments used to calculate position by mobile robots in indoor environments. In this calculation, best known as localization, it is necessary to develop most of the tasks delegated to the mobile robot. It is then crucial that the methods used for position calculations be as precise as possible, and accurately represent the location of the robot within a given environment. The research community has devoted a considerable amount of time to provide solutions for the localization problem. Several methodologies have been proposed the most common of which are based in the Bayes rule. Other methodologies include the Kalman filter and the Monte Carlo localization filter wich will be addressed in next sections. The major contribution of this review rests in offering a wide array of techniques that researchers can choose. Therefore, method-sensor combinations and their main advantages are displayed.
Zhongxiang Liu
2016-04-01
Full Text Available Fatigue fracture of bridge stay-cables is usually a multiscale process as the crack grows from micro-scale to macro-scale. Such a process, however, is highly uncertain. In order to make a rational prediction of the residual life of bridge cables, a probabilistic fatigue approach is proposed, based on a comprehensive vehicle load model, finite element analysis and multiscaling and mesoscopic fracture mechanics. Uncertainties in both material properties and external loads are considered. The proposed method is demonstrated through the fatigue life prediction of cables of the Runyang Cable-Stayed Bridge in China, and it is found that cables along the bridge spans may have significantly different fatigue lives, and due to the variability, some of them may have shorter lives than those as expected from the design.
Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary
Yubin Niu
2016-03-01
Full Text Available In this paper, a novel hyperspectral anomaly detector based on low-rank representation (LRR and learned dictionary (LD has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low rank matrix representing the background and a sparse matrix standing for the anomalies. The direct application of LRR model is sensitive to a tradeoff parameter that balances the two parts. To mitigate this problem, a learned dictionary is introduced into the decomposition process. The dictionary is learned from the whole image with a random selection process and therefore can be viewed as the spectra of the background only. It also requires a less computational cost with the learned dictionary. The statistic characteristic of the sparse matrix allows the application of basic anomaly detection method to obtain detection results. Experimental results demonstrate that, compared to other anomaly detection methods, the proposed method based on LRR and LD shows its robustness and has a satisfactory anomaly detection result.
Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms
Xu Chen; Wenli Du; Feng Qian
2016-01-01
Dynamic optimization problems (DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are in-valid. In this article, a technology named ranking-based mutation operator (RMO) is presented to enhance the previous differential evolution (DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.
Motion adaptive patch-based low-rank approach for compressed sensing cardiac cine MRI.
Yoon, Huisu; Kim, Kyung Sang; Kim, Daniel; Bresler, Yoram; Ye, Jong Chul
2014-11-01
One of the technical challenges in cine magnetic resonance imaging (MRI) is to reduce the acquisition time to enable the high spatio-temporal resolution imaging of a cardiac volume within a short scan time. Recently, compressed sensing approaches have been investigated extensively for highly accelerated cine MRI by exploiting transform domain sparsity using linear transforms such as wavelets, and Fourier. However, in cardiac cine imaging, the cardiac volume changes significantly between frames, and there often exist abrupt pixel value changes along time. In order to effectively sparsify such temporal variations, it is necessary to exploit temporal redundancy along motion trajectories. This paper introduces a novel patch-based reconstruction method to exploit geometric similarities in the spatio-temporal domain. In particular, we use a low rank constraint for similar patches along motion, based on the observation that rank structures are relatively less sensitive to global intensity changes, but make it easier to capture moving edges. A Nash equilibrium formulation with relaxation is employed to guarantee convergence. Experimental results show that the proposed algorithm clearly reconstructs important anatomical structures in cardiac cine image and provides improved image quality compared to existing state-of-the-art methods such as k-t FOCUSS, k-t SLR, and MASTeR.
Quantified Risk Ranking Model for Condition-Based Risk and Reliability Centered Maintenance
Chattopadhyaya, Pradip Kumar; Basu, Sushil Kumar; Majumdar, Manik Chandra
2017-06-01
In the recent past, risk and reliability centered maintenance (RRCM) framework is introduced with a shift in the methodological focus from reliability and probabilities (expected values) to reliability, uncertainty and risk. In this paper authors explain a novel methodology for risk quantification and ranking the critical items for prioritizing the maintenance actions on the basis of condition-based risk and reliability centered maintenance (CBRRCM). The critical items are identified through criticality analysis of RPN values of items of a system and the maintenance significant precipitating factors (MSPF) of items are evaluated. The criticality of risk is assessed using three risk coefficients. The likelihood risk coefficient treats the probability as a fuzzy number. The abstract risk coefficient deduces risk influenced by uncertainty, sensitivity besides other factors. The third risk coefficient is called hazardous risk coefficient, which is due to anticipated hazards which may occur in the future and the risk is deduced from criteria of consequences on safety, environment, maintenance and economic risks with corresponding cost for consequences. The characteristic values of all the three risk coefficients are obtained with a particular test. With few more tests on the system, the values may change significantly within controlling range of each coefficient, hence `random number simulation' is resorted to obtain one distinctive value for each coefficient. The risk coefficients are statistically added to obtain final risk coefficient of each critical item and then the final rankings of critical items are estimated. The prioritization in ranking of critical items using the developed mathematical model for risk assessment shall be useful in optimization of financial losses and timing of maintenance actions.
Quantified Risk Ranking Model for Condition-Based Risk and Reliability Centered Maintenance
Chattopadhyaya, Pradip Kumar; Basu, Sushil Kumar; Majumdar, Manik Chandra
2016-03-01
In the recent past, risk and reliability centered maintenance (RRCM) framework is introduced with a shift in the methodological focus from reliability and probabilities (expected values) to reliability, uncertainty and risk. In this paper authors explain a novel methodology for risk quantification and ranking the critical items for prioritizing the maintenance actions on the basis of condition-based risk and reliability centered maintenance (CBRRCM). The critical items are identified through criticality analysis of RPN values of items of a system and the maintenance significant precipitating factors (MSPF) of items are evaluated. The criticality of risk is assessed using three risk coefficients. The likelihood risk coefficient treats the probability as a fuzzy number. The abstract risk coefficient deduces risk influenced by uncertainty, sensitivity besides other factors. The third risk coefficient is called hazardous risk coefficient, which is due to anticipated hazards which may occur in the future and the risk is deduced from criteria of consequences on safety, environment, maintenance and economic risks with corresponding cost for consequences. The characteristic values of all the three risk coefficients are obtained with a particular test. With few more tests on the system, the values may change significantly within controlling range of each coefficient, hence `random number simulation' is resorted to obtain one distinctive value for each coefficient. The risk coefficients are statistically added to obtain final risk coefficient of each critical item and then the final rankings of critical items are estimated. The prioritization in ranking of critical items using the developed mathematical model for risk assessment shall be useful in optimization of financial losses and timing of maintenance actions.
Probabilistic Fatigue Life Updating for Railway Bridges Based on Local Inspection and Repair
Lee, Young-Joo; Kim, Robin E.; Suh, Wonho; Park, Kiwon
2017-01-01
Railway bridges are exposed to repeated train loads, which may cause fatigue failure. As critical links in a transportation network, railway bridges are expected to survive for a target period of time, but sometimes they fail earlier than expected. To guarantee the target bridge life, bridge maintenance activities such as local inspection and repair should be undertaken properly. However, this is a challenging task because there are various sources of uncertainty associated with aging bridges, train loads, environmental conditions, and maintenance work. Therefore, to perform optimal risk-based maintenance of railway bridges, it is essential to estimate the probabilistic fatigue life of a railway bridge and update the life information based on the results of local inspections and repair. Recently, a system reliability approach was proposed to evaluate the fatigue failure risk of structural systems and update the prior risk information in various inspection scenarios. However, this approach can handle only a constant-amplitude load and has limitations in considering a cyclic load with varying amplitude levels, which is the major loading pattern generated by train traffic. In addition, it is not feasible to update the prior risk information after bridges are repaired. In this research, the system reliability approach is further developed so that it can handle a varying-amplitude load and update the system-level risk of fatigue failure for railway bridges after inspection and repair. The proposed method is applied to a numerical example of an in-service railway bridge, and the effects of inspection and repair on the probabilistic fatigue life are discussed. PMID:28441768
Probabilistic Fatigue Life Updating for Railway Bridges Based on Local Inspection and Repair.
Lee, Young-Joo; Kim, Robin E; Suh, Wonho; Park, Kiwon
2017-04-24
Railway bridges are exposed to repeated train loads, which may cause fatigue failure. As critical links in a transportation network, railway bridges are expected to survive for a target period of time, but sometimes they fail earlier than expected. To guarantee the target bridge life, bridge maintenance activities such as local inspection and repair should be undertaken properly. However, this is a challenging task because there are various sources of uncertainty associated with aging bridges, train loads, environmental conditions, and maintenance work. Therefore, to perform optimal risk-based maintenance of railway bridges, it is essential to estimate the probabilistic fatigue life of a railway bridge and update the life information based on the results of local inspections and repair. Recently, a system reliability approach was proposed to evaluate the fatigue failure risk of structural systems and update the prior risk information in various inspection scenarios. However, this approach can handle only a constant-amplitude load and has limitations in considering a cyclic load with varying amplitude levels, which is the major loading pattern generated by train traffic. In addition, it is not feasible to update the prior risk information after bridges are repaired. In this research, the system reliability approach is further developed so that it can handle a varying-amplitude load and update the system-level risk of fatigue failure for railway bridges after inspection and repair. The proposed method is applied to a numerical example of an in-service railway bridge, and the effects of inspection and repair on the probabilistic fatigue life are discussed.
Karanki, Durga Rao; Kushwaha, Hari Shankar; Verma, Ajit Kumar; Ajit, Srividya
2009-05-01
A wide range of uncertainties will be introduced inevitably during the process of performing a safety assessment of engineering systems. The impact of all these uncertainties must be addressed if the analysis is to serve as a tool in the decision-making process. Uncertainties present in the components (input parameters of model or basic events) of model output are propagated to quantify its impact in the final results. There are several methods available in the literature, namely, method of moments, discrete probability analysis, Monte Carlo simulation, fuzzy arithmetic, and Dempster-Shafer theory. All the methods are different in terms of characterizing at the component level and also in propagating to the system level. All these methods have different desirable and undesirable features, making them more or less useful in different situations. In the probabilistic framework, which is most widely used, probability distribution is used to characterize uncertainty. However, in situations in which one cannot specify (1) parameter values for input distributions, (2) precise probability distributions (shape), and (3) dependencies between input parameters, these methods have limitations and are found to be not effective. In order to address some of these limitations, the article presents uncertainty analysis in the context of level-1 probabilistic safety assessment (PSA) based on a probability bounds (PB) approach. PB analysis combines probability theory and interval arithmetic to produce probability boxes (p-boxes), structures that allow the comprehensive propagation through calculation in a rigorous way. A practical case study is also carried out with the developed code based on the PB approach and compared with the two-phase Monte Carlo simulation results.
Probabilistic precipitation forecasts based on a convection-permitting high-resolution NWP model
Bentzien, S.; Friederichs, P.
2011-12-01
High-resolution limited-area numerical weather prediction (NWP) models are particularly developed in order to predict high-impact weather. Due to their high resolution of a few km and their non-hydrostatic dynamics, they are able to describe mesoscale processes in a more detailed and explicit way. Although high-resolution model forecasts lead to more realistic mesoscale structures, forecasts especially for precipitation are still affected by systematic biases, displacement errors, and fast error growth. Due to the large uncertainties, probabilistic prediction is likely to be the best choice to forecast precipitation. Ensemble predictions systems (EPS) have become the prime instrument to assess the uncertainty in mesoscale NWP. EPS can describe uncertainty due to errors in initial and boundary conditions, or physical parameterizations. However, EPS are unable to account for all sources of uncertainty, and are therefore underdispersive. A statistical postprocessing is necessary in order to obtain calibrated and reliable forecasts. A low-cost ensemble can be generated from high-resolution operational NWP forecasts which are frequently updated by data assimilation. Several successively started operational forecasts that cover a limited common time period build a time-lagged ensemble (TLE) forecasts. TLE come at low costs, are often available for several years and define a suitable baseline in order to assess the benefit of an EPS. We present a statistical postprocessing for precipitation forecast based on the COSMO-DE TLE. The COSMO-DE model has a horizontal grid spacing of 2.8 km and runs operationally at the German meteorological service (Deutscher Wetterdienst, DWD) eight times a day. In order to obtain calibrated probabilistic precipitation forecasts, several semi-parametric and parametric techniques are employed. Semi-parametric approaches like logistic or quantile regression are used to estimate probabilities of threshold exceedance (PoT) and quantiles
Wakker, P.P.; Thaler, R.H.; Tversky, A.
1997-01-01
Probabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in premium to compensate for a 1% default risk. These observations cannot be
P.P. Wakker (Peter); R.H. Thaler (Richard); A. Tversky (Amos)
1997-01-01
textabstractProbabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in the premium to compensate for a 1% default risk. While these
P.P. Wakker (Peter); R.H. Thaler (Richard); A. Tversky (Amos)
1997-01-01
textabstractProbabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in the premium to compensate for a 1% default risk. While these pref
Serafini Maria
2003-11-01
Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.
A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests
Bingbing Xia
2016-01-01
Full Text Available This paper proposed a novel voting ranking random forests (VRRF method for solving hepatocellular carcinoma (HCC image classification problem. Firstly, in preprocessing stage, this paper used bilateral filtering for hematoxylin-eosin (HE pathological images. Next, this paper segmented the bilateral filtering processed image and got three different kinds of images, which include single binary cell image, single minimum exterior rectangle cell image, and single cell image with a size of n⁎n. After that, this paper defined atypia features which include auxiliary circularity, amendment circularity, and cell symmetry. Besides, this paper extracted some shape features, fractal dimension features, and several gray features like Local Binary Patterns (LBP feature, Gray Level Cooccurrence Matrix (GLCM feature, and Tamura features. Finally, this paper proposed a HCC image classification model based on random forests and further optimized the model by voting ranking method. The experiment results showed that the proposed features combined with VRRF method have a good performance in HCC image classification problem.
A comparison of average-based, percentile rank, and other citation impact indicators
Ruiz-Castillo, J.; Albarran, P.
2016-07-01
The main aim of this paper is to defend the view that, in spite of the broad agreement in favor of the MNCS and the percentile rank indicators, there are two other citation indicators with desirable properties that the above indicators do not posses: (i) a member of the family of high-impact indicators introduced in Albarránet al. (2011), and (ii) a new indicator, based in the work of Herrero & Villar (2013), which measures the relative performance of the different research units in terms of a series of tournaments in which each research unit is confronted with all others repeatedly. We compare indicators from the point of view of their discriminatory power, measured by the range and the coefficient of variation. Using a large dataset indexed by Thomson Reuters, we consider 40 countries that have published at least 10,000 articles in all sciences in 1998-2003. There are two main findings. First, the new indicator exhibits a greater discriminatory power than percentile rank indicators. Second, the high-impact indicator exhibits the greatest discriminatory power. (Author)
Akkaya, Ali Volkan [Department of Mechanical Engineering, Yildiz Technical University, 34349 Besiktas, Istanbul (Turkey)
2009-02-15
In this paper, multiple nonlinear regression models for estimation of higher heating value of coals are developed using proximate analysis data obtained generally from the low rank coal samples as-received basis. In this modeling study, three main model structures depended on the number of proximate analysis parameters, which are named the independent variables, such as moisture, ash, volatile matter and fixed carbon, are firstly categorized. Secondly, sub-model structures with different arrangements of the independent variables are considered. Each sub-model structure is analyzed with a number of model equations in order to find the best fitting model using multiple nonlinear regression method. Based on the results of nonlinear regression analysis, the best model for each sub-structure is determined. Among them, the models giving highest correlation for three main structures are selected. Although the selected all three models predicts HHV rather accurately, the model involving four independent variables provides the most accurate estimation of HHV. Additionally, when the chosen model with four independent variables and a literature model are tested with extra proximate analysis data, it is seen that that the developed model in this study can give more accurate prediction of HHV of coals. It can be concluded that the developed model is effective tool for HHV estimation of low rank coals. (author)
A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests.
Xia, Bingbing; Jiang, Huiyan; Liu, Huiling; Yi, Dehui
2015-01-01
This paper proposed a novel voting ranking random forests (VRRF) method for solving hepatocellular carcinoma (HCC) image classification problem. Firstly, in preprocessing stage, this paper used bilateral filtering for hematoxylin-eosin (HE) pathological images. Next, this paper segmented the bilateral filtering processed image and got three different kinds of images, which include single binary cell image, single minimum exterior rectangle cell image, and single cell image with a size of n⁎n. After that, this paper defined atypia features which include auxiliary circularity, amendment circularity, and cell symmetry. Besides, this paper extracted some shape features, fractal dimension features, and several gray features like Local Binary Patterns (LBP) feature, Gray Level Co-occurrence Matrix (GLCM) feature, and Tamura features. Finally, this paper proposed a HCC image classification model based on random forests and further optimized the model by voting ranking method. The experiment results showed that the proposed features combined with VRRF method have a good performance in HCC image classification problem.
Low-Rank Decomposition Based Restoration of Compressed Images via Adaptive Noise Estimation.
Zhang, Xinfeng; Lin, Weisi; Xiong, Ruiqin; Liu, Xianming; Ma, Siwei; Gao, Wen
2016-07-07
Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estimation and reduction for compression noise via low-rank decomposition of similar image patches. We first formulate the framework of compression noise reduction based upon low-rank decomposition. Compression noises are removed by soft-thresholding the singular values in singular value decomposition (SVD) of every group of similar image patches. For each group of similar patches, the thresholds are adaptively determined according to compression noise levels and singular values. We analyze the relationship of image statistical characteristics in spatial and transform domains, and estimate compression noise level for every group of similar patches from statistics in both domains jointly with quantization steps. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method not only improves the quality of compressed images obviously for post-processing, but are also helpful for computer vision tasks as a pre-processing method.
Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko
We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.
Alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution.
He, Xin; Ling, Xu; Sinha, Saurabh
2009-03-01
Cross-species comparison has emerged as a powerful paradigm for predicting cis-regulatory modules (CRMs) and understanding their evolution. The comparison requires reliable sequence alignment, which remains a challenging task for less conserved noncoding sequences. Furthermore, the existing models of DNA sequence evolution generally do not explicitly treat the special properties of CRM sequences. To address these limitations, we propose a model of CRM evolution that captures different modes of evolution of functional transcription factor binding sites (TFBSs) and the background sequences. A particularly novel aspect of our work is a probabilistic model of gains and losses of TFBSs, a process being recognized as an important part of regulatory sequence evolution. We present a computational framework that uses this model to solve the problems of CRM alignment and prediction. Our alignment method is similar to existing methods of statistical alignment but uses the conserved binding sites to improve alignment. Our CRM prediction method deals with the inherent uncertainties of binding site annotations and sequence alignment in a probabilistic framework. In simulated as well as real data, we demonstrate that our program is able to improve both alignment and prediction of CRM sequences over several state-of-the-art methods. Finally, we used alignments produced by our program to study binding site conservation in genome-wide binding data of key transcription factors in the Drosophila blastoderm, with two intriguing results: (i) the factor-bound sequences are under strong evolutionary constraints even if their neighboring genes are not expressed in the blastoderm and (ii) binding sites in distal bound sequences (relative to transcription start sites) tend to be more conserved than those in proximal regions. Our approach is implemented as software, EMMA (Evolutionary Model-based cis-regulatory Module Analysis), ready to be applied in a broad biological context.
Alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution.
Xin He
2009-03-01
Full Text Available Cross-species comparison has emerged as a powerful paradigm for predicting cis-regulatory modules (CRMs and understanding their evolution. The comparison requires reliable sequence alignment, which remains a challenging task for less conserved noncoding sequences. Furthermore, the existing models of DNA sequence evolution generally do not explicitly treat the special properties of CRM sequences. To address these limitations, we propose a model of CRM evolution that captures different modes of evolution of functional transcription factor binding sites (TFBSs and the background sequences. A particularly novel aspect of our work is a probabilistic model of gains and losses of TFBSs, a process being recognized as an important part of regulatory sequence evolution. We present a computational framework that uses this model to solve the problems of CRM alignment and prediction. Our alignment method is similar to existing methods of statistical alignment but uses the conserved binding sites to improve alignment. Our CRM prediction method deals with the inherent uncertainties of binding site annotations and sequence alignment in a probabilistic framework. In simulated as well as real data, we demonstrate that our program is able to improve both alignment and prediction of CRM sequences over several state-of-the-art methods. Finally, we used alignments produced by our program to study binding site conservation in genome-wide binding data of key transcription factors in the Drosophila blastoderm, with two intriguing results: (i the factor-bound sequences are under strong evolutionary constraints even if their neighboring genes are not expressed in the blastoderm and (ii binding sites in distal bound sequences (relative to transcription start sites tend to be more conserved than those in proximal regions. Our approach is implemented as software, EMMA (Evolutionary Model-based cis-regulatory Module Analysis, ready to be applied in a broad biological context.
Performance of cumulant-based rank reduction estimator in presence of unexpected modeling errors
王鼎
2015-01-01
Compared with the rank reduction estimator (RARE) based on second-order statistics (called SOS-RARE), the RARE based on fourth-order cumulants (referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival (DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding (DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error (MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.
Accurate and reliable cancer classification based on probabilistic inference of pathway activity.
Junjie Su
Full Text Available With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput measurements, and the heterogeneity across different samples, often make it difficult to find reliable gene markers. To overcome this problem, several studies have proposed the use of pathway-based markers, instead of individual gene markers, for building the classifier. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes, and use the pathway activities for classification. It has been shown that pathway-based classifiers typically yield more reliable results compared to traditional gene-based classifiers. In this paper, we propose a new classification method based on probabilistic inference of pathway activities. For a given sample, we compute the log-likelihood ratio between different disease phenotypes based on the expression level of each gene. The activity of a given pathway is then inferred by combining the log-likelihood ratios of the constituent genes. We apply the proposed method to the classification of breast cancer metastasis, and show that it achieves higher accuracy and identifies more reproducible pathway markers compared to several existing pathway activity inference methods.
A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain
Francesca Gagliardi
2017-07-01
Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.
Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing
Majid Shakhsi Dastgahian
2016-11-01
Full Text Available Millimeter-wave communication (mmWC is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS and mobile sets (MS. Unlike the conventional MIMO systems, Millimeter-wave (mmW systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.
Feasibility of developing risk-based rankings of pressure boundary systems for inservice inspection
Vo, T.V.; Smith, B.W.; Simonen, F.A.; Gore, B.F.
1994-08-01
The goals of the Evaluation and Improvement of Non-destructive Examination Reliability for the In-service Inspection of Light Water Reactors Program sponsored by the Nuclear Regulatory Commission at Pacific Northwest Laboratory (PNL) are to (1) assess current ISI techniques and requirements for all pressure boundary systems and components, (2) determine if improvements to the requirements are needed, and (3) if necessary, develop recommendations for revising the applicable ASME Codes and regulatory requirements. In evaluating approaches that could be used to provide a technical basis for improved inservice inspection plans, PNL has developed and applied a method that uses results of probabilistic risk assessment (PRA) to establish piping system ISI requirements. In the PNL program, the feasibility of generic ISI requirements is being addressed in two phases. Phase I involves identifying and prioritizing the systems most relevant to plant safety. The results of these evaluations will be later consolidated into requirements for comprehensive inservice inspection of nuclear power plant components that will be developed in Phase II. This report presents Phase I evaluations for eight selected plants and attempts to compare these PRA-based inspection priorities with current ASME Section XI requirements for Class 1, 2 and 3 systems. These results show that there are generic insights that can be extrapolated from the selected plants to specific classes of light water reactors.
FOGCAST: Probabilistic fog forecasting based on operational (high-resolution) NWP models
Masbou, M.; Hacker, M.; Bentzien, S.
2013-12-01
The presence of fog and low clouds in the lower atmosphere can have a critical impact on both airborne and ground transports and is often connected with serious accidents. The improvement of localization, duration and variations in visibility therefore holds an immense operational value. Fog is generally a small scale phenomenon and mostly affected by local advective transport, radiation, turbulent mixing at the surface as well as its microphysical structure. Sophisticated three-dimensional fog models, based on advanced microphysical parameterization schemes and high vertical resolution, have been already developed and give promising results. Nevertheless, the computational time is beyond the range of an operational setup. Therefore, mesoscale numerical weather prediction models are generally used for forecasting all kinds of weather situations. In spite of numerous improvements, a large uncertainty of small scale weather events inherent in deterministic prediction cannot be evaluated adequately. Probabilistic guidance is necessary to assess these uncertainties and give reliable forecasts. In this study, fog forecasts are obtained by a diagnosis scheme similar to Fog Stability Index (FSI) based on COSMO-DE model outputs. COSMO-DE I the German-focused high-resolution operational weather prediction model of the German Meteorological Service. The FSI and the respective fog occurrence probability is optimized and calibrated with statistical postprocessing in terms of logistic regression. In a second step, the predictor number of the FOGCAST model has been optimized by use of the LASSO-method (Least Absolute Shrinkage and Selection Operator). The results will present objective out-of-sample verification based on the Brier score and is performed for station data over Germany. Furthermore, the probabilistic fog forecast approach, FOGCAST, serves as a benchmark for the evaluation of more sophisticated 3D fog models. Several versions have been set up based on different
Simulation-Based Probabilistic Tsunami Hazard Analysis: Empirical and Robust Hazard Predictions
De Risi, Raffaele; Goda, Katsuichiro
2017-08-01
Probabilistic tsunami hazard analysis (PTHA) is the prerequisite for rigorous risk assessment and thus for decision-making regarding risk mitigation strategies. This paper proposes a new simulation-based methodology for tsunami hazard assessment for a specific site of an engineering project along the coast, or, more broadly, for a wider tsunami-prone region. The methodology incorporates numerous uncertain parameters that are related to geophysical processes by adopting new scaling relationships for tsunamigenic seismic regions. Through the proposed methodology it is possible to obtain either a tsunami hazard curve for a single location, that is the representation of a tsunami intensity measure (such as inundation depth) versus its mean annual rate of occurrence, or tsunami hazard maps, representing the expected tsunami intensity measures within a geographical area, for a specific probability of occurrence in a given time window. In addition to the conventional tsunami hazard curve that is based on an empirical statistical representation of the simulation-based PTHA results, this study presents a robust tsunami hazard curve, which is based on a Bayesian fitting methodology. The robust approach allows a significant reduction of the number of simulations and, therefore, a reduction of the computational effort. Both methods produce a central estimate of the hazard as well as a confidence interval, facilitating the rigorous quantification of the hazard uncertainties.
Grinberg, Michael; Ohr, Florian
2010-01-01
One of the big challenges in multi-target tracking is the track management and correct data association between measurements and tracks. Major reason for tracking errors are detection failures such as merged, split, incomplete or missed detections as well as clutter-based detections (phantom objects). Those effects combined with uncertainties in existence and number of objects in the scene as well as uncertainties in their observability and dynamic object state lead to gross tracking errors. In this contribution we present an algorithm for visual detection and tracking of multiple extended targets which is capable of coping with occlusions and split and merge effects. Unlike most of the state-of-the-art approaches we utilize information about the measurements' composition gained through tracking dedicated feature points in the image and in 3D space, which allows us to reconstruct the desired object characteristics from the data even in the case of detection errors due to above-mentioned reasons. The proposed Feature-Based Probabilistic Data Association approach resolves data association ambiguities in a soft threshold-free decision based not only on target state prediction but also on the existence and observability estimation modeled as two additional Markov chains. A novel measurement reconstruction scheme allows for a correct innovation in case of split, merged and incomplete measurements realizing thus a detection-by-tracking approach. This process is assisted by a grid based object representation which offers a lower abstraction level of targets extent and is used for detailed occlusion analysis.
Diagnosis of students' ability in a statistical course based on Rasch probabilistic outcome
Mahmud, Zamalia; Ramli, Wan Syahira Wan; Sapri, Shamsiah; Ahmad, Sanizah
2017-06-01
Measuring students' ability and performance are important in assessing how well students have learned and mastered the statistical courses. Any improvement in learning will depend on the student's approaches to learning, which are relevant to some factors of learning, namely assessment methods carrying out tasks consisting of quizzes, tests, assignment and final examination. This study has attempted an alternative approach to measure students' ability in an undergraduate statistical course based on the Rasch probabilistic model. Firstly, this study aims to explore the learning outcome patterns of students in a statistics course (Applied Probability and Statistics) based on an Entrance-Exit survey. This is followed by investigating students' perceived learning ability based on four Course Learning Outcomes (CLOs) and students' actual learning ability based on their final examination scores. Rasch analysis revealed that students perceived themselves as lacking the ability to understand about 95% of the statistics concepts at the beginning of the class but eventually they had a good understanding at the end of the 14 weeks class. In terms of students' performance in their final examination, their ability in understanding the topics varies at different probability values given the ability of the students and difficulty of the questions. Majority found the probability and counting rules topic to be the most difficult to learn.
Computer vision-based limestone rock-type classification using probabilistic neural network
Ashok Kumar Patel; Snehamoy Chatterjee
2016-01-01
Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network (PNN) where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rock-types. Overall the error of mis-classification is below 6%. When compared with other three classifica-tion algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.
Computer vision-based limestone rock-type classification using probabilistic neural network
Ashok Kumar Patel
2016-01-01
Full Text Available Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network (PNN where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rock-types. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.
Padil, Khairul H.; Bakhary, Norhisham; Hao, Hong
2017-01-01
The effectiveness of artificial neural networks (ANNs) when applied to pattern recognition in vibration-based damage detection has been demonstrated in many studies because they are capable of providing accurate results and the reliable identification of structural damage based on modal data. However, the use of ANNs has been questioned in terms of its reliability in the face of uncertainties in measurement and modeling data. Attempts to incorporate a probabilistic method into an ANN by treating the uncertainties as normally distributed random variables has delivered promising solutions to this problem, but the probabilistic method is less straightforward in practice because it is often not possible to obtain unbiased probabilistic distributions of the uncertainties. Moreover, the probabilistic ANN method is computationally complex, especially when generating output data. In this study, a non-probabilistic ANN is proposed to address the problem of uncertainty in vibration damage detection using ANNs. The input data for the network consist of natural frequencies and mode shapes, and the output is the Young's modulus (E values), which acts as an elemental stiffness parameter (ESP). Through the interval analysis method, the noise in measured frequencies and mode shapes are considered to be coupled rather than statistically distributed. This method calculates the interval bound (lower and upper bounds) of the ESP changes based on an interval analysis method. The ANN is used to predict the output of this interval bound by considering the uncertainties in the input parameters. To establish the relationship between the input parameters and output parameters, a possibility of damage existence (PoDE) parameter is defined for the undamaged and damaged states. A stiffness reduction factor (SRF) is also used to represent changes in the stiffness parameter. A numerical model and a laboratory-tested steel portal frame demonstrate the efficacy of the method in improving the
Towards a Complexity Theory of Randomized Search Heuristics: Ranking-Based Black-Box Complexity
Doerr, Benjamin
2011-01-01
Randomized search heuristics are a broadly used class of general-purpose algorithms. Analyzing them via classical methods of theoretical computer science is a growing field. A big step forward would be a useful complexity theory for such algorithms. We enrich the two existing black-box complexity notions due to Wegener and other authors by the restrictions that not actual objective values, but only the relative quality of the previously evaluated solutions may be taken into account by the algorithm. Many randomized search heuristics belong to this class of algorithms. We show that the new ranking-based model gives more realistic complexity estimates for some problems, while for others the low complexities of the previous models still hold.
Design and Analysis of a Ranking Approach to Private Location-Based Services
Yiu, Man Lung; Jensen, Christian Søndergaard; Møller, Jesper
2011-01-01
Users of mobile services wish to retrieve nearby points of interest without disclosing their locations to the services. This article addresses the challenge of optimizing the query performance while satisfying given location privacy and query accuracy requirements. The article's proposal, Space......Twist, aims to offer location privacy for k nearest neighbor (kNN) queries at low communication cost without requiring a trusted anonymizer. The solution can be used with a conventional DBMS as well as with a server optimized for location-based services. In particular, we believe that this is the first...... guarantees for obtaining better performance. We extend SpaceTwist with so-called ring ranking, which improves the communication cost, delayed termination, which improves the privacy afforded the user, and the ability to function in spatial networks in addition to Euclidean space. We report on analytical...
A Direct Elliptic Solver Based on Hierarchically Low-Rank Schur Complements
Chávez, Gustavo
2017-03-17
A parallel fast direct solver for rank-compressible block tridiagonal linear systems is presented. Algorithmic synergies between Cyclic Reduction and Hierarchical matrix arithmetic operations result in a solver with O(Nlog2N) arithmetic complexity and O(NlogN) memory footprint. We provide a baseline for performance and applicability by comparing with well-known implementations of the $$\\\\mathcal{H}$$ -LU factorization and algebraic multigrid within a shared-memory parallel environment that leverages the concurrency features of the method. Numerical experiments reveal that this method is comparable with other fast direct solvers based on Hierarchical Matrices such as $$\\\\mathcal{H}$$ -LU and that it can tackle problems where algebraic multigrid fails to converge.
Suciu, Dan; Koch, Christop
2011-01-01
Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for rep
A rank-based sequence aligner with applications in phylogenetic analysis.
Liviu P Dinu
Full Text Available Recent tools for aligning short DNA reads have been designed to optimize the trade-off between correctness and speed. This paper introduces a method for assigning a set of short DNA reads to a reference genome, under Local Rank Distance (LRD. The rank-based aligner proposed in this work aims to improve correctness over speed. However, some indexing strategies to speed up the aligner are also investigated. The LRD aligner is improved in terms of speed by storing [Formula: see text]-mer positions in a hash table for each read. Another improvement, that produces an approximate LRD aligner, is to consider only the positions in the reference that are likely to represent a good positional match of the read. The proposed aligner is evaluated and compared to other state of the art alignment tools in several experiments. A set of experiments are conducted to determine the precision and the recall of the proposed aligner, in the presence of contaminated reads. In another set of experiments, the proposed aligner is used to find the order, the family, or the species of a new (or unknown organism, given only a set of short Next-Generation Sequencing DNA reads. The empirical results show that the aligner proposed in this work is highly accurate from a biological point of view. Compared to the other evaluated tools, the LRD aligner has the important advantage of being very accurate even for a very low base coverage. Thus, the LRD aligner can be considered as a good alternative to standard alignment tools, especially when the accuracy of the aligner is of high importance. Source code and UNIX binaries of the aligner are freely available for future development and use at http://lrd.herokuapp.com/aligners. The software is implemented in C++ and Java, being supported on UNIX and MS Windows.
A quantum-inspired genetic algorithm based on probabilistic coding for multiple sequence alignment.
Huo, Hong-Wei; Stojkovic, Vojislav; Xie, Qiao-Luan
2010-02-01
Quantum parallelism arises from the ability of a quantum memory register to exist in a superposition of base states. Since the number of possible base states is 2(n), where n is the number of qubits in the quantum memory register, one operation on a quantum computer performs what an exponential number of operations on a classical computer performs. The power of quantum algorithms comes from taking advantages of quantum parallelism. Quantum algorithms are exponentially faster than classical algorithms. Genetic optimization algorithms are stochastic search algorithms which are used to search large, nonlinear spaces where expert knowledge is lacking or difficult to encode. QGMALIGN--a probabilistic coding based quantum-inspired genetic algorithm for multiple sequence alignment is presented. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The experimental results show that QGMALIGN can compete with the popular methods, such as CLUSTALX and SAGA, and performs well on the presenting biological data. Moreover, the addition of genetic operators to the quantum-inspired algorithm lowers the cost of overall running time.
ProbMetab: an R package for Bayesian probabilistic annotation of LC–MS-based metabolomics
Silva, Ricardo R.; Jourdan, Fabien; Salvanha, Diego M.; Letisse, Fabien; Jamin, Emilien L.; Guidetti-Gonzalez, Simone; Labate, Carlos A.; Vêncio, Ricardo Z. N.
2014-01-01
Summary: We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography–mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions. Availability and implementation: ProbMetab was implemented in a modular manner to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/. Contact: rvencio@usp.br Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24443383
Basant, Nikita; Gupta, Shikha; Singh, Kunwar P
2016-03-01
Organic solvents are widely used chemicals and the neurotoxic properties of some are well established. In this study, we established nonlinear qualitative and quantitative structure-toxicity relationship (STR) models for predicting neurotoxic classes and neurotoxicity of structurally diverse solvents in rodent test species following OECD guideline principles for model development. Probabilistic neural network (PNN) based qualitative and generalized regression neural network (GRNN) based quantitative STR models were constructed using neurotoxicity data from rat and mouse studies. Further, interspecies correlation based quantitative activity-activity relationship (QAAR) and global QSTR models were also developed using the combined data set of both rodent species for predicting the neurotoxicity of solvents. The constructed models were validated through deriving several statistical coefficients for the test data and the prediction and generalization abilities of these models were evaluated. The qualitative STR models (rat and mouse) yielded classification accuracies of 92.86% in the test data sets, whereas, the quantitative STRs yielded correlation (R(2)) of >0.93 between the measured and model predicted toxicity values in both the test data (rat and mouse). The prediction accuracies of the QAAR (R(2) 0.859) and global STR (R(2) 0.945) models were comparable to those of the independent local STR models. The results suggest the ability of the developed QSTR models to reliably predict binary neurotoxicity classes and the endpoint neurotoxicities of the structurally diverse organic solvents.
Conditioning Probabilistic Databases
Koch, Christoph
2008-01-01
Past research on probabilistic databases has studied the problem of answering queries on a static database. Application scenarios of probabilistic databases however often involve the conditioning of a database using additional information in the form of new evidence. The conditioning problem is thus to transform a probabilistic database of priors into a posterior probabilistic database which is materialized for subsequent query processing or further refinement. It turns out that the conditioning problem is closely related to the problem of computing exact tuple confidence values. It is known that exact confidence computation is an NP-hard problem. This has lead researchers to consider approximation techniques for confidence computation. However, neither conditioning nor exact confidence computation can be solved using such techniques. In this paper we present efficient techniques for both problems. We study several problem decomposition methods and heuristics that are based on the most successful search techn...
A third-rank tensor field based on a U(1) gauge theory in loop space
Deguchi, Shinichi; Nakajima, Tadahito [Nihon Univ., Tokyo (Japan). Dept. of Physics
1995-08-01
We derive the Stueckelberg formalism extended to a third-rank tensor field from a U(1) gauge theory in loop space, the space of all loops in space-time. The third-rank tensor field is regarded as a constrained U(1) gauge field on the loop space. (author).
Safety Parameter Management in Astrium Based on Ranking of Product Characteristics Process
Meredith, Laurence; Magnin, Cedric
2013-09-01
Economic constraints are one of the major drivers in systems development. Because safety is a major constraint that cannot be neglected, industries must find a way to build safe designs without overdesign or superfluous activities and costs.The purpose is to provide sufficient effort on actual safety critical items and not to waste effort (time and money).Via its multi-systems experience in space transportation, space vehicles and satellites, ASTRIUM has developed dedicated processes to optimize safety costs without decreasing the level of safety of its systems.The process is based on an iterative and exhaustive identification of items involved in systems safety thanks to risk analysis right from the beginning of the projects. Safety critical items and their parameters/characteristics that contribute to potential safety issues are ranked depending on the criticality of their failures and their probability of occurrence and these are then treated through the dedicated safety process. Referred to as Ranking Of Product Characteristics (ROPC) in ASTRIUM SPACE TRANSPORTATION or safety Critical Items management in ASTRIUM SA TELLITE, the different terms reflect primarily the divergence between types of safety critical items present on a space vehicle or on a satellite.Each identified safety parameter of a given element of a system is earmarked as such throughout the design, manufacturing, supply, assembly, anomaly control... and end usage and maintenance of the systems. Safety characteristics are controlled and monitored at each step of the development through dedicated checks, keypoints and tests until its last possible test and maintenance plan. The process also deals with systems evolutions and safety non regression. It ensures safety of a system through analysis but also actually verifies that the design is compliant to specified safety parameters: safety built as specified without extra costs due to emphasis put on non-critical parameters.
Caccavale, Mauro; Matano, Fabio; Sacchi, Marco
2017-10-01
's displacements (DN) under different probability of exceeding or return periods (probabilistic seismic scenarios). As a further step, in order to estimate the earthquake-induced landslide hazard, we defined three DN threshold values that have considered capable to trigger shallow seismic-induced landslides in the regional context, and mapped the sectors with DN values exceeding such thresholds. On this basis, we constructed frequency-magnitude curves to estimate the probability of slope failures at the source areas, as a function of DN, by correlating the annual probability of landslide occurrence with the number of terrain cells associated with DN values greater than the selected threshold. Finally, based on the estimated annual landslide frequency of the seismic triggering event for each terrain cell, we implemented a 1:5000 scale map of Earthquake-induced Landslide Hazard for Ischia, Procida and Vivara Islands. The map reports the zoning and ranking of study area into sub-zones, on a pixel basis, according to the degree of the potential hazard from landslides derived by the frequency of the triggering event.
S. Raia
2014-03-01
Full Text Available Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. These models extend spatially the static stability models adopted in geotechnical engineering, and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the operation of the existing models lays in the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of rainfall-induced shallow landslides. For this purpose, we have modified the transient rainfall infiltration and grid-based regional slope-stability analysis (TRIGRS code. The new code (TRIGRS-P adopts a probabilistic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs
Keen, A. S.; Lynett, P. J.; Ayca, A.
2016-12-01
Because of the damage resulting from the 2010 Chile and 2011 Japanese tele-tsunamis, the tsunami risk to the small craft marinas in California has become an important concern. The talk will outline an assessment tool which can be used to assess the tsunami hazard to small craft harbors. The methodology is based on the demand and structural capacity of the floating dock system, composed of floating docks/fingers and moored vessels. The structural demand is determined using a Monte Carlo methodology. Monte Carlo methodology is a probabilistic computational tool where the governing might be well known, but the independent variables of the input (demand) as well as the resisting structural components (capacity) may not be completely known. The Monte Carlo approach uses a distribution of each variable, and then uses that random variable within the described parameters, to generate a single computation. The process then repeats hundreds or thousands of times. The numerical model "Method of Splitting Tsunamis" (MOST) has been used to determine the inputs for the small craft harbors within California. Hydrodynamic model results of current speed, direction and surface elevation were incorporated via the drag equations to provide the bases of the demand term. To determine the capacities, an inspection program was developed to identify common features of structural components. A total of six harbors have been inspected ranging from Crescent City in Northern California to Oceanside Harbor in Southern California. Results from the inspection program were used to develop component capacity tables which incorporated the basic specifications of each component (e.g. bolt size and configuration) and a reduction factor (which accounts for the component reduction in capacity with age) to estimate in situ capacities. Like the demand term, these capacities are added probabilistically into the model. To date the model has been applied to Santa Cruz Harbor as well as Noyo River. Once
Satellite Based Probabilistic Snow Cover Extent Mapping (SCE) at Hydro-Québec
Teasdale, Mylène; De Sève, Danielle; Angers, Jean-François; Perreault, Luc
2016-04-01
Over 40% of Canada's water resources are in Quebec and Hydro-Quebec has developed potential to become one of the largest producers of hydroelectricity in the world, with a total installed capacity of 36,643 MW. The Hydro-Québec fleet park includes 27 large reservoirs with a combined storage capacity of 176 TWh, and 668 dams and 98 controls. Thus, over 98% of all electricity used to supply the domestic market comes from water resources and the excess output is sold on the wholesale markets. In this perspective the efficient management of water resources is needed and it is based primarily on a good river flow estimation including appropriate hydrological data. Snow on ground is one of the significant variables representing 30% to 40% of its annual energy reserve. More specifically, information on snow cover extent (SCE) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in northern regions since the snowmelt provides the water that fills the reservoirs and is subsequently used for hydropower generation. For several years Hydro Quebec's research institute ( IREQ) developed several algorithms to map SCE and SWE. So far all the methods were deterministic. However, given the need to maximize the efficient use of all resources while ensuring reliability, the electrical systems must now be managed taking into account all risks. Since snow cover estimation is based on limited spatial information, it is important to quantify and handle its uncertainty in the hydrological forecasting system. This paper presents the first results of a probabilistic algorithm for mapping SCE by combining Bayesian mixture of probability distributions and multiple logistic regression models applied to passive microwave data. This approach allows assigning for each grid point, probabilities to the set of the mutually exclusive discrete outcomes: "snow" and "no snow". Its performance was evaluated using the Brier score since it is particularly appropriate to
Renard, Benjamin; Vidal, Jean-Philippe
2016-04-01
In recent years, the climate modeling community has put a lot of effort into releasing the outputs of multimodel experiments for use by the wider scientific community. In such experiments, several structurally distinct GCMs are run using the same observed forcings (for the historical period) or the same projected forcings (for the future period). In addition, several members are produced for a single given model structure, by running each GCM with slightly different initial conditions. This multiplicity of GCM outputs offers many opportunities in terms of uncertainty quantification or GCM comparisons. In this presentation, we propose a new procedure to weight GCMs according to their ability to reproduce the observed climate. Such weights can be used to combine the outputs of several models in a way that rewards good-performing models and discards poorly-performing ones. The proposed procedure has the following main properties: 1. It is based on explicit probabilistic models describing the time series produced by the GCMs and the corresponding historical observations, 2. It can use several members whenever available, 3. It accounts for the uncertainty in observations, 4. It assigns a weight to each GCM (all weights summing up to one), 5. It can also assign a weight to the "H0 hypothesis" that all GCMs in the multimodel ensemble are not compatible with observations. The application of the weighting procedure is illustrated with several case studies including synthetic experiments, simple cases where the target GCM output is a simple univariate variable and more realistic cases where the target GCM output is a multivariate and/or a spatial variable. These case studies illustrate the generality of the procedure which can be applied in a wide range of situations, as long as the analyst is prepared to make an explicit probabilistic assumption on the target variable. Moreover, these case studies highlight several interesting properties of the weighting procedure. In
A heuristic biomarker selection approach based on professional tennis player ranking strategy.
Han, Bin; Xie, Ruifei; Li, Lihua; Zhu, Lei; Wang, Shen
2014-01-01
Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Michał Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.
A symmetry-based method to infer structural brain networks from probabilistic tractography data
Kamal Shadi
2016-11-01
Full Text Available Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs from probabilistic tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA, does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold. Instead, we exploit a basic limitation of the tractography process: the observed streamlines from a source to a target do not provide any information about the polarity of the underlying white matter, and so if there are some fibers connecting two voxels (or two ROIs X and Y, tractography should be able in principle to follow this connection in both directions, from X to Y and from Y to X. We leverage this limitation to formulate the network inference process as an optimization problem that minimizes the (appropriately normalized asymmetry of the observed network. We evaluate the proposed method using both the FiberCup dataset and based on a noise model that randomly corrupts the observed connectivity of synthetic networks. As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are associated with various neuropsychiatric conditions including depression, anxiety and addiction.
Oh, Kye Min [KHNP Central Research Institute, Daejeon (Korea, Republic of); Han, Sang Hoon; Park, Jin Hee; Lim, Ho Gon; Yang, Joon Yang [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Heo, Gyun Young [Kyung Hee University, Yongin (Korea, Republic of)
2017-06-15
In Korea, many nuclear power plants operate at a single site based on geographical characteristics, but the population density near the sites is higher than that in other countries. Thus, multiunit accidents are a more important consideration than in other countries and should be addressed appropriately. Currently, there are many issues related to a multiunit probabilistic safety assessment (PSA). One of them is the quantification of a multiunit PSA model. A traditional PSA uses a Boolean manipulation of the fault tree in terms of the minimal cut set. However, such methods have some limitations when rare event approximations cannot be used effectively or a very small truncation limit should be applied to identify accident sequence combinations for a multiunit site. In particular, it is well known that seismic risk in terms of core damage frequency can be overestimated because there are many events that have a high failure probability. In this study, we propose a quantification method based on a Monte Carlo approach for a multiunit PSA model. This method can consider all possible accident sequence combinations in a multiunit site and calculate a more exact value for events that have a high failure probability. An example model for six identical units at a site was also developed and quantified to confirm the applicability of the proposed method.
Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio
2015-12-01
This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.
Qu, Xueyong
Uncertainties exist practically everywhere from structural design to manufacturing, product lifetime service, and maintenance. Uncertainties can be introduced by errors in modeling and simulation; by manufacturing imperfections (such as variability in material properties and structural geometric dimensions); and by variability in loading. Structural design by safety factors using nominal values without considering uncertainties may lead to designs that are either unsafe, or too conservative and thus not efficient. The focus of this dissertation is reliability-based design optimization (RBDO) of composite structures. Uncertainties are modeled by the probabilistic distributions of random variables. Structural reliability is evaluated in term of the probability of failure. RBDO minimizes cost such as structural weight subject to reliability constraints. Since engineering structures usually have multiple failure modes, Monte Carlo simulation (MCS) was used employed to calculate the system probability of failure. Response surface (RS) approximation techniques were used to solve the difficulties associated with MCS. The high computational cost of a large number of MCS samples was alleviated by analysis RS, and numerical noise in the results of MCS was filtered out by design RS. RBDO of composite laminates is investigated for use in hydrogen tanks in cryogenic environments. The major challenge is to reduce the large residual strains developed due to thermal mismatch between matrix and fibers while maintaining the load carrying capacity. RBDO is performed to provide laminate designs, quantify the effects of uncertainties on the optimum weight, and identify those parameters that have the largest influence on optimum design. Studies of weight and reliability tradeoffs indicate that the most cost-effective measure for reducing weight and increasing reliability is quality control. A probabilistic sufficiency factor (PSF) approach was developed to improve the computational
Vigna, Sebastiano
2009-01-01
This note tries to attempt a sketch of the history of spectral ranking, a general umbrella name for techniques that apply the theory of linear maps (in particular, eigenvalues and eigenvectors) to matrices that do not represent geometric transformations, but rather some kind of relationship between entities. Albeit recently made famous by the ample press coverage of Google's PageRank algorithm, spectral ranking was devised more than fifty years ago, almost exactly in the same terms, and has been studied in psychology and social sciences. I will try to describe it in precise and modern mathematical terms, highlighting along the way the contributions given by previous scholars.
A risk-based methodology for ranking environmental chemical stressors at the regional scale.
Giubilato, Elisa; Zabeo, Alex; Critto, Andrea; Giove, Silvio; Bierkens, Johan; Den Hond, Elly; Marcomini, Antonio
2014-04-01
A "Risk-based Tool for the Regional Ranking of Environmental Chemical Stressors" has been developed, aimed at supporting decision-makers in the identification of priority environmental contaminants, as well as priority areas, to be further assessed. The tool implements a methodology based on a quantitative Weight-of-Evidence approach, integrating three types of information, identified as "Lines-of-Evidence" (LoE), namely: LoE "Environmental Contamination" (including data on chemical contamination in environmental matrices in the region, thus providing information on potential population exposure), LoE "Intake" (including results from human biomonitoring studies, i.e. concentration of chemicals in human biological matrices, thus providing an integrated estimation of exposure) and LoE "Observed Effects" (including information on the incidence of adverse health outcomes associated with environmental exposure to chemicals). A Multi-Criteria Decision Analysis (MCDA) methodology based on fuzzy logic has been developed to support the integration of information related to these three LoEs for each chemical stressor. The tool allows one to rank chemical stressors at different spatial scales, such as at the regional level as well as within each sub-area (e.g., counties). Moreover, it supports the identification of priority sub-areas within the region, where environmental and health data suggest possible adverse health effects and thus more investigation efforts are needed. To evaluate the performance of this newly developed tool, a case-study in the Flemish region (north of Belgium) has been selected. In the case-study, data on soil contamination by metals and organic contaminants were integrated with data on exposure and effect biomarkers measured in adolescents within the framework of the human biomonitoring study performed by the Flemish Centre of Expertise on Environment and Health in the period 2002-2006. The case-study demonstrated the performance of the tool in
A probabilistic coding based quantum genetic algorithm for multiple sequence alignment.
Huo, Hongwei; Xie, Qiaoluan; Shen, Xubang; Stojkovic, Vojislav
2008-01-01
This paper presents an original Quantum Genetic algorithm for Multiple sequence ALIGNment (QGMALIGN) that combines a genetic algorithm and a quantum algorithm. A quantum probabilistic coding is designed for representing the multiple sequence alignment. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The features of implicit parallelism and state superposition in quantum mechanics and the global search capability of the genetic algorithm are exploited to get efficient computation. A set of well known test cases from BAliBASE2.0 is used as reference to evaluate the efficiency of the QGMALIGN optimization. The QGMALIGN results have been compared with the most popular methods (CLUSTALX, SAGA, DIALIGN, SB_PIMA, and QGMALIGN) results. The QGMALIGN results show that QGMALIGN performs well on the presenting biological data. The addition of genetic operators to the quantum algorithm lowers the cost of overall running time.
Xue, L.; Dai, C.; Zhang, D.; Guadagnini, A.
2015-12-01
It is critical to predict contaminant plume in an aquifer under uncertainty, which can help assess environmental risk and design rational management strategies. An accurate prediction of contaminant plume requires the collection of data to help characterize the system. Due to the limitation of financial resources, ones should estimate the expectative value of data collected from each optional monitoring scheme before carried out. Data-worth analysis is believed to be an effective approach to identify the value of the data in some problems, which quantifies the uncertainty reduction assuming that the plausible data has been collected. However, it is difficult to apply the data-worth analysis to a dynamic simulation of contaminant transportation model owning to its requirement of large number of inverse-modeling. In this study, a novel efficient data-worth analysis framework is proposed by developing the Probabilistic Collocation Method based Ensemble Kalman Filter (PCKF). The PCKF constructs polynomial chaos expansion surrogate model to replace the original complex numerical model. Consequently, the inverse modeling can perform on the proxy rather than the original model. An illustrative example, considering the dynamic change of the contaminant concentration, is employed to demonstrate the proposed approach. The Results reveal that schemes with different sampling frequencies, monitoring networks location, prior data content will have significant impact on the uncertainty reduction of the estimation of contaminant plume. Our proposition is validated to provide the reasonable value of data from various schemes.
Jaka Sembiring
2015-08-01
Full Text Available The business value of information technology (IT services is often difficult to assess, especially from the point of view of a non-IT manager. This condition could severely impact organizational IT strategic decisions. Various approaches have been proposed to quantify the business value, but some are trapped in technical complexity while others misguide managers into directly and subjectively judging some technical entities outside their domain of expertise. This paper describes a method on how to properly capture both perspectives based on a probabilistic business-driven model. The proposed model presents a procedure to calculate the business value of IT services. The model also covers IT security services and their business value as an important aspect of IT services that is not covered in previously published researches. The impact of changes in the quality of IT services on business value will also be discussed. A simulation and a case illustration are provided to show the possible application of the proposed model for a simple business process in an enterprise.
Regional gold potential mapping in Kelantan (Malaysia) using probabilistic based models and GIS
Yusoff, Suhaimizi; Pradhan, Biswajeet; Manap, Mohamad Abd; Shafri, Helmi Zulhaidi Mohd
2015-06-01
The aim of this study is to test and compare two probabilistic based models (frequency ratio and weightsof- evidence) with regard to regional gold potential mapping at Kelantan, Malaysia. Until now these models have not been used for the purpose of mapping gold potential areas in Malaysia. This study analyzed the spatial relationship between gold deposits and geological factors such as lithology, faults, geochemical and geophysical data in geographical information system (GIS) software. About eight (8) gold deposits and five (5) related factors are identified and quantified for their spatial relationships. Then, all factors were combined to generate a predictive gold potential map. The predictive maps were then validated by comparing them with known gold deposits using receiver operating characteristics (ROC) and "area under the curve" (AUC) graphs. The results of validation showed accuracies of 80% for the frequency ratio and 74% for the weightsof- evidence model, respectively. The results demonstrated the usefulness of frequency ratio and weights-of-evidence modeling techniques in mineral exploration work to discover unknown gold deposits in Kelantan, Malaysia.
$C^3$-index: A PageRank based multi-faceted metric for authors' performance measurement
Pradhan, Dinesh; Paul, Partha Sarathi; Maheswari, Umesh; Nandi, Subrata; Chakraborty, Tanmoy
2016-01-01
Ranking scientific authors is an important but challenging task, mostly due to the dynamic nature of the evolving scientific publications. The basic indicators of an author's productivity and impact are still the number of publications and the citation count (leading to the popular metrics such as h-index, g-index etc.). H-index and its popular variants are mostly effective in ranking highly-cited authors, thus fail to resolve ties while ranking medium-cited and low-cited authors who are majo...
Sakthivel, Rathinasamy; Selvi, Subramaniam; Mathiyalagan, Kalidass; Shi, Peng
2015-12-01
This paper is concerned with the problem of reliable mixed H ∞ and passivity-based control for a class of stochastic Takagi-Sugeno (TS) fuzzy systems with Markovian switching and probabilistic time varying delays. Different from the existing works, the H∞ and passivity control problem with probabilistic occurrence of time-varying delays and actuator failures is considered in a unified framework, which is more general in some practical situations. The main aim of this paper is to design a reliable mixed H∞ and passivity-based controller such that the stochastic TS fuzzy system with Markovian switching is stochastically stable with a prescribed mixed H∞ and passivity performance level γ > 0 . Based on the Lyapunov-Krasovskii functional (LKF) involving lower and upper bound of probabilistic time delay and convex combination technique, a new set of delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is established for obtaining the required result. Finally, a numerical example based on the modified truck-trailer model is given to demonstrate the effectiveness and applicability of the proposed design techniques.
van Ours, J.C.; Vermeulen, F.M.P.
2007-01-01
This paper ranks Dutch economists using information about publications and citations. Rankings involve the aggregation of several performance dimensions. Instead of using a cardinal approach, where each dimension is weighted based on impact factors of journals for example, we use an ordinal approach
Maryam, Syeda; McCrackin, Laura; Crowley, Mark; Rathi, Yogesh; Michailovich, Oleg
2017-03-01
The world's aging population has given rise to an increasing awareness towards neurodegenerative disorders, including Alzheimers Disease (AD). Treatment options for AD are currently limited, but it is believed that future success depends on our ability to detect the onset of the disease in its early stages. The most frequently used tools for this include neuropsychological assessments, along with genetic, proteomic, and image-based diagnosis. Recently, the applicability of Diffusion Magnetic Resonance Imaging (dMRI) analysis for early diagnosis of AD has also been reported. The sensitivity of dMRI to the microstructural organization of cerebral tissue makes it particularly well-suited to detecting changes which are known to occur in the early stages of AD. Existing dMRI approaches can be divided into two broad categories: region-based and tract-based. In this work, we propose a new approach, which extends region-based approaches to the simultaneous characterization of multiple brain regions. Given a predefined set of features derived from dMRI data, we compute the probabilistic distances between different brain regions and treat the resulting connectivity pattern as an undirected, fully-connected graph. The characteristics of this graph are then used as markers to discriminate between AD subjects and normal controls (NC). Although in this preliminary work we omit subjects in the prodromal stage of AD, mild cognitive impairment (MCI), our method demonstrates perfect separability between AD and NC subject groups with substantial margin, and thus holds promise for fine-grained stratification of NC, MCI and AD populations.
Efficient Multi-keyword Ranked Search over Outsourced Cloud Data based on Homomorphic Encryption
Nie Mengxi
2016-01-01
Full Text Available With the development of cloud computing, more and more data owners are motivated to outsource their data to the cloud server for great flexibility and less saving expenditure. Because the security of outsourced data must be guaranteed, some encryption methods should be used which obsoletes traditional data utilization based on plaintext, e.g. keyword search. To solve the search of encrypted data, some schemes were proposed to solve the search of encrypted data, e.g. top-k single or multiple keywords retrieval. However, the efficiency of these proposed schemes is not high enough to be impractical in the cloud computing. In this paper, we propose a new scheme based on homomorphic encryption to solve this challenging problem of privacy-preserving efficient multi-keyword ranked search over outsourced cloud data. In our scheme, the inner product is adopted to measure the relevance scores and the technique of relevance feedback is used to reflect the search preference of the data users. Security analysis shows that the proposed scheme can meet strict privacy requirements for such a secure cloud data utilization system. Performance evaluation demonstrates that the proposed scheme can achieve low overhead on both computation and communication.
Othman, Muhammad Murtadha; Abd Rahman, Nurulazmi; Musirin, Ismail; Fotuhi-Firuzabad, Mahmud; Rajabi-Ghahnavieh, Abbas
2015-01-01
This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.
Can We Do Better in Unimodal Biometric Systems? A Rank-Based Score Normalization Framework.
Moutafis, Panagiotis; Kakadiaris, Ioannis A
2015-12-01
Biometric systems use score normalization techniques and fusion rules to improve recognition performance. The large amount of research on score fusion for multimodal systems raises an important question: can we utilize the available information from unimodal systems more effectively? In this paper, we present a rank-based score normalization framework that addresses this problem. Specifically, our approach consists of three algorithms: 1) partition the matching scores into subsets and normalize each subset independently; 2) utilize the gallery versus gallery matching scores matrix (i.e., gallery-based information); and 3) dynamically augment the gallery in an online fashion. We invoke the theory of stochastic dominance along with results of prior research to demonstrate when and why our approach yields increased performance. Our framework: 1) can be used in conjunction with any score normalization technique and any fusion rule; 2) is amenable to parallel programming; and 3) is suitable for both verification and open-set identification. To assess the performance of our framework, we use the UHDB11 and FRGC v2 face datasets. Specifically, the statistical hypothesis tests performed illustrate that the performance of our framework improves as we increase the number of samples per subject. Furthermore, the corresponding statistical analysis demonstrates that increased separation between match and nonmatch scores is obtained for each probe. Besides the benefits and limitations highlighted by our experimental evaluation, results under optimal and pessimal conditions are also presented to offer better insights.
Xu, Qianyao; Kang, Chongqing; Zhang, Ning
2016-01-01
When conducting the wind power (WP) planning, it is very important for electric power companies to evaluate the penetration limit of the grid-accommodable WP. This paper proposes a probabilistic method for determining grid-accommodable WP capacity based on the multiscenario analysis. Typical power....... The validity and effectiveness of the new method are demonstrated in two cases, i.e., the IEEE 39-bus test system and a real large power system in China, respectively....
Gueddana, Amor; Attia, Moez; Chatta, Rihab
2014-05-01
In this work, we simulate a fiber-based Quantum Key Distribution Protocol (QKDP) BB84 working at the telecoms wavelength 1550 nm with taking into consideration an optimized attack strategy. We consider in our work a quantum channel composed by probabilistic Single Photon Source (SPS), single mode optical Fiber and quantum detector with high efficiency. We show the advantages of using the Quantum Dots (QD) embedded in micro-cavity compared to the Heralded Single Photon Sources (HSPS). Second, we show that Eve is always getting some information depending on the mean photon number per pulse of the used SPS and therefore, we propose an optimized version of the QKDP BB84 based on Quantum Dense Coding (QDC) that could be implemented by quantum CNOT gates. We evaluate the success probability of implementing the optimized QKDP BB84 when using nowadays probabilistic quantum optical devices for circuit realization. We use for our modeling an abstract probabilistic model of a CNOT gate based on linear optical components and having a success probability of sqrt (4/27), we take into consideration the best SPSs realizations, namely the QD and the HSPS, generating a single photon per pulse with a success probability of 0.73 and 0.37, respectively. We show that the protocol is totally secure against attacks but could be correctly implemented only with a success probability of few percent.
Myers, Catherine E; Sheynin, Jony; Balsdon, Tarryn; Luzardo, Andre; Beck, Kevin D; Hogarth, Lee; Haber, Paul; Moustafa, Ahmed A
2016-01-01
Addiction is the continuation of a habit in spite of negative consequences. A vast literature gives evidence that this poor decision-making behavior in individuals addicted to drugs also generalizes to laboratory decision making tasks, suggesting that the impairment in decision-making is not limited to decisions about taking drugs. In the current experiment, opioid-addicted individuals and matched controls with no history of illicit drug use were administered a probabilistic classification task that embeds both reward-based and punishment-based learning trials, and a computational model of decision making was applied to understand the mechanisms describing individuals' performance on the task. Although behavioral results showed that opioid-addicted individuals performed as well as controls on both reward- and punishment-based learning, the modeling results suggested subtle differences in how decisions were made between the two groups. Specifically, the opioid-addicted group showed decreased tendency to repeat prior responses, meaning that they were more likely to "chase reward" when expectancies were violated, whereas controls were more likely to stick with a previously-successful response rule, despite occasional expectancy violations. This tendency to chase short-term reward, potentially at the expense of developing rules that maximize reward over the long term, may be a contributing factor to opioid addiction. Further work is indicated to better understand whether this tendency arises as a result of brain changes in the wake of continued opioid use/abuse, or might be a pre-existing factor that may contribute to risk for addiction.
Fan, Jung-Wei; Friedman, Carol
2011-10-01
Biomedical natural language processing (BioNLP) is a useful technique that unlocks valuable information stored in textual data for practice and/or research. Syntactic parsing is a critical component of BioNLP applications that rely on correctly determining the sentence and phrase structure of free text. In addition to dealing with the vast amount of domain-specific terms, a robust biomedical parser needs to model the semantic grammar to obtain viable syntactic structures. With either a rule-based or corpus-based approach, the grammar engineering process requires substantial time and knowledge from experts, and does not always yield a semantically transferable grammar. To reduce the human effort and to promote semantic transferability, we propose an automated method for deriving a probabilistic grammar based on a training corpus consisting of concept strings and semantic classes from the Unified Medical Language System (UMLS), a comprehensive terminology resource widely used by the community. The grammar is designed to specify noun phrases only due to the nominal nature of the majority of biomedical terminological concepts. Evaluated on manually parsed clinical notes, the derived grammar achieved a recall of 0.644, precision of 0.737, and average cross-bracketing of 0.61, which demonstrated better performance than a control grammar with the semantic information removed. Error analysis revealed shortcomings that could be addressed to improve performance. The results indicated the feasibility of an approach which automatically incorporates terminology semantics in the building of an operational grammar. Although the current performance of the unsupervised solution does not adequately replace manual engineering, we believe once the performance issues are addressed, it could serve as an aide in a semi-supervised solution.
Yugsi Molina, F. X.; Oppikofer, T.; Fischer, L.; Hermanns, R. L.; Taurisano, A.
2012-04-01
Traditional techniques to assess rockfall hazard are partially based on probabilistic analysis. Stochastic methods has been used for run-out analysis of rock blocks to estimate the trajectories that a detached block will follow during its fall until it stops due to kinetic energy loss. However, the selection of rockfall source areas is usually defined either by multivariate analysis or by field observations. For either case, a physically based approach is not used for the source area detection. We present an example of rockfall hazard assessment that integrates a probabilistic rockfall run-out analysis with a stochastic assessment of the rockfall source areas using kinematic stability analysis in a GIS environment. The method has been tested for a steep more than 200 m high rock wall, located in the municipality of Norddal (Møre og Romsdal county, Norway), where a large number of people are either exposed to snow avalanches, rockfalls, or debris flows. The area was selected following the recently published hazard mapping plan of Norway. The cliff is formed by medium to coarse-grained quartz-dioritic to granitic gneisses of Proterozoic age. Scree deposits product of recent rockfall activity are found at the bottom of the rock wall. Large blocks can be found several tens of meters away from the cliff in Sylte, the main locality in the Norddal municipality. Structural characterization of the rock wall was done using terrestrial laser scanning (TLS) point clouds in the software Coltop3D (www.terranum.ch), and results were validated with field data. Orientation data sets from the structural characterization were analyzed separately to assess best-fit probability density functions (PDF) for both dip angle and dip direction angle of each discontinuity set. A GIS-based stochastic kinematic analysis was then carried out using the discontinuity set orientations and the friction angle as random variables. An airborne laser scanning digital elevation model (ALS-DEM) with 1 m
Bailey Timothy L
2006-02-01
Full Text Available Abstract Background The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.
A Rank-Constrained Matrix Representation for Hypergraph-Based Subspace Clustering
Yubao Sun
2015-01-01
Full Text Available This paper presents a novel, rank-constrained matrix representation combined with hypergraph spectral analysis to enable the recovery of the original subspace structures of corrupted data. Real-world data are frequently corrupted with both sparse error and noise. Our matrix decomposition model separates the low-rank, sparse error, and noise components from the data in order to enhance robustness to the corruption. In order to obtain the desired rank representation of the data within a dictionary, our model directly utilizes rank constraints by restricting the upper bound of the rank range. An alternative projection algorithm is proposed to estimate the low-rank representation and separate the sparse error from the data matrix. To further capture the complex relationship between data distributed in multiple subspaces, we use hypergraph to represent the data by encapsulating multiple related samples into one hyperedge. The final clustering result is obtained by spectral decomposition of the hypergraph Laplacian matrix. Validation experiments on the Extended Yale Face Database B, AR, and Hopkins 155 datasets show that the proposed method is a promising tool for subspace clustering.
Bradshaw, Corey J A; Brook, Barry W
2016-01-01
There are now many methods available to assess the relative citation performance of peer-reviewed journals. Regardless of their individual faults and advantages, citation-based metrics are used by researchers to maximize the citation potential of their articles, and by employers to rank academic track records. The absolute value of any particular index is arguably meaningless unless compared to other journals, and different metrics result in divergent rankings. To provide a simple yet more objective way to rank journals within and among disciplines, we developed a κ-resampled composite journal rank incorporating five popular citation indices: Impact Factor, Immediacy Index, Source-Normalized Impact Per Paper, SCImago Journal Rank and Google 5-year h-index; this approach provides an index of relative rank uncertainty. We applied the approach to six sample sets of scientific journals from Ecology (n = 100 journals), Medicine (n = 100), Multidisciplinary (n = 50); Ecology + Multidisciplinary (n = 25), Obstetrics & Gynaecology (n = 25) and Marine Biology & Fisheries (n = 25). We then cross-compared the κ-resampled ranking for the Ecology + Multidisciplinary journal set to the results of a survey of 188 publishing ecologists who were asked to rank the same journals, and found a 0.68-0.84 Spearman's ρ correlation between the two rankings datasets. Our composite index approach therefore approximates relative journal reputation, at least for that discipline. Agglomerative and divisive clustering and multi-dimensional scaling techniques applied to the Ecology + Multidisciplinary journal set identified specific clusters of similarly ranked journals, with only Nature & Science separating out from the others. When comparing a selection of journals within or among disciplines, we recommend collecting multiple citation-based metrics for a sample of relevant and realistic journals to calculate the composite rankings and their relative uncertainty windows.
Hierarchical partial order ranking.
Carlsen, Lars
2008-09-01
Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritization of polluted sites is given.
Rank-Based Methods for Selection of Landscape Metrics for Land Cover Pattern Change Detection
Priyakant Sinha
2016-02-01
Full Text Available Often landscape metrics are not thoroughly evaluated with respect to remote sensing data characteristics, such as their behavior in relation to variation in spatial and temporal resolution, number of land cover classes or dominant land cover categories. In such circumstances, it may be difficult to ascertain whether a change in a metric is due to landscape pattern change or due to the inherent variability in multi-temporal data. This study builds on this important consideration and proposes a rank-based metric selection process through computation of four difference-based indices (β, γ, ξ and θ using a Max–Min/Max normalization approach. Land cover classification was carried out for two contrasting provinces, the Liverpool Range (LR and Liverpool Plains (LP, of the Brigalow Belt South Bioregion (BBSB of NSW, Australia. Landsat images, Multi Spectral Scanner (MSS of 1972–1973 and TM of 1987–1988, 1993–1994, 1999–2000 and 2009–2010 were classified using object-based image analysis methods. A total of 30 landscape metrics were computed and their sensitivities towards variation in spatial and temporal resolutions, number of land cover classes and dominant land cover categories were evaluated by computing a score based on Max–Min/Max normalization. The landscape metrics selected on the basis of the proposed methods (Diversity index (MSIDI, Area weighted mean patch fractal dimension (SHAPE_AM, Mean core area (CORE_MN, Total edge (TE, No. of patches (NP, Contagion index (CONTAG, Mean nearest neighbor index (ENN_MN and Mean patch fractal dimension (FRAC_MN were successful and effective in identifying changes over five different change periods. Major changes in land cover pattern after 1993 were observed, and though the trends were similar in both cases, the LP region became more fragmented than the LR. The proposed method was straightforward to apply, and can deal with multiple metrics when selection of an appropriate set can become
Omranian, Nooshin; Mueller-Roeber, Bernd; Nikoloski, Zoran
2012-04-01
The levels of cellular organization, from gene transcription to translation to protein-protein interaction and metabolism, operate via tightly regulated mutual interactions, facilitating organismal adaptability and various stress responses. Characterizing the mutual interactions between genes, transcription factors, and proteins involved in signaling, termed crosstalk, is therefore crucial for understanding and controlling cells' functionality. We aim at using high-throughput transcriptomics data to discover previously unknown links between signaling networks. We propose and analyze a novel method for crosstalk identification which relies on transcriptomics data and overcomes the lack of complete information for signaling pathways in Arabidopsis thaliana. Our method first employs a network-based transformation of the results from the statistical analysis of differential gene expression in given groups of experiments under different signal-inducing conditions. The stationary distribution of a random walk (similar to the PageRank algorithm) on the constructed network is then used to determine the putative transcripts interrelating different signaling pathways. With the help of the proposed method, we analyze a transcriptomics data set including experiments from four different stresses/signals: nitrate, sulfur, iron, and hormones. We identified promising gene candidates, downstream of the transcription factors (TFs), associated to signaling crosstalk, which were validated through literature mining. In addition, we conduct a comparative analysis with the only other available method in this field which used a biclustering-based approach. Surprisingly, the biclustering-based approach fails to robustly identify any candidate genes involved in the crosstalk of the analyzed signals. We demonstrate that our proposed method is more robust in identifying gene candidates involved downstream of the signaling crosstalk for species for which large transcriptomics data sets
De March, I; Sironi, E; Taroni, F
2016-09-01
Analysis of marks recovered from different crime scenes can be useful to detect a linkage between criminal cases, even though a putative source for the recovered traces is not available. This particular circumstance is often encountered in the early stage of investigations and thus, the evaluation of evidence association may provide useful information for the investigators. This association is evaluated here from a probabilistic point of view: a likelihood ratio based approach is suggested in order to quantify the strength of the evidence of trace association in the light of two mutually exclusive propositions, namely that the n traces come from a common source or from an unspecified number of sources. To deal with this kind of problem, probabilistic graphical models are used, in form of Bayesian networks and object-oriented Bayesian networks, allowing users to intuitively handle with uncertainty related to the inferential problem.
Darjan Karabasevic
2016-05-01
Full Text Available Corporate sector and companies have recognized the importance of implementation of strategy of corporate social responsibility in order to increase the company's image and responsibility towards society and the communities where they operate. Multinational companies in their everyday activities and operations pay more attention to sustainable models of corporate social responsibility. The focus of this paper is to identify the indicators of corporate social responsibility and to rank companies according to the indicators. Proposed framework for evaluation and ranking is based on the SWARA and the ARAS methods. The usability and efficiency of the proposed framework is shown on an illustrative example.
Mao, Shasha; Xiong, Lin; Jiao, Licheng; Feng, Tian; Yeung, Sai-Kit
2016-07-26
Riemannian optimization has been widely used to deal with the fixed low-rank matrix completion problem, and Riemannian metric is a crucial factor of obtaining the search direction in Riemannian optimization. This paper proposes a new Riemannian metric via simultaneously considering the Riemannian geometry structure and the scaling information, which is smoothly varying and invariant along the equivalence class. The proposed metric can make a tradeoff between the Riemannian geometry structure and the scaling information effectively. Essentially, it can be viewed as a generalization of some existing metrics. Based on the proposed Riemanian metric, we also design a Riemannian nonlinear conjugate gradient algorithm, which can efficiently solve the fixed low-rank matrix completion problem. By experimenting on the fixed low-rank matrix completion, collaborative filtering, and image and video recovery, it illustrates that the proposed method is superior to the state-of-the-art methods on the convergence efficiency and the numerical performance.
Rank-Based miRNA Signatures for Early Cancer Detection
Mario Lauria
2014-01-01
Full Text Available We describe a new signature definition and analysis method to be used as biomarker for early cancer detection. Our new approach is based on the construction of a reference map of transcriptional signatures of both healthy and cancer affected individuals using circulating miRNA from a large number of subjects. Once such a map is available, the diagnosis for a new patient can be performed by observing the relative position on the map of his/her transcriptional signature. To demonstrate its efficacy for this specific application we report the results of the application of our method to published datasets of circulating miRNA, and we quantify its performance compared to current state-of-the-art methods. A number of additional features make this method an ideal candidate for large-scale use, for example, as a mass screening tool for early cancer detection or for at-home diagnostics. Specifically, our method is minimally invasive (because it works well with circulating miRNA, it is robust with respect to lab-to-lab protocol variability and batch effects (it requires that only the relative ranking of expression value of miRNA in a profile be accurate not their absolute values, and it is scalable to a large number of subjects. Finally we discuss the need for HPC capability in a widespread application of our or similar methods.
Muhammad Rafi
2015-10-01
Full Text Available It is very common for a customer to read reviews about the product before making a final decision to buy it. Customers are always eager to get the best and the most objective information about the product theywish to purchase and reviews are the major source to obtain this information. Although reviews are easily accessible from the web, but since most of them carry ambiguous opinion and different structure, it is often very difficult for a customer to filter the information he actually needs. This paper suggests a framework, which provides a single user interface solution to this problem based on sentiment analysis of reviews. First, it extracts all the reviews from different websites carrying varying structure, and gathers information about relevant aspects of that product. Next, it does sentiment analysis around those aspects and gives them sentiment scores. Finally, it ranks all extracted aspects and clusters them into positive and negative class. The final output is a graphical visualization of all positive and negative aspects, which provide the customer easy, comparable, and visual information about the important aspects of the product. The experimental results on five different products carrying 5000 reviewsshow 78% accuracy. Moreover, the paper also explained the effect of Negation, Valence Shifter, and Diminisher with sentiment lexiconon sentiment analysis, andconcluded that they all are independent of the case problem , and have no effect on the accuracy of sentiment analysis.
Ranking of Reactions Based on Sensitivity of Protein Noise Depends on the Choice of Noise Measure.
Sucheta Gokhale
Full Text Available Gene expression is a stochastic process. Identification of the step maximally affecting noise in the protein level is an important aspect of investigation of gene product distribution. There are numerous experimental and theoretical studies that seek to identify this important step. However, these studies have used two different measures of noise, viz. coefficient of variation and Fano factor, and have compared different processes leading to contradictory observations regarding the important step. In this study, we performed systematic global and local sensitivity analysis on two models of gene expression to investigate relative contribution of reaction rate parameters to steady state noise in the protein level using both the measures of noise. We analytically and computationally showed that the ranking of parameters based on the sensitivity of the noise to variation in a given parameter is a strong function of the choice of the noise measure. If the Fano factor is used as the noise measure, translation is the important step whereas for coefficient of variation, transcription is the important step. We derived an analytical expression for local sensitivity and used it to explain the distinct contributions of each reaction parameter to the two measures of noise. We extended the analysis to a generic linear catalysis reaction system and observed that the reaction network topology was an important factor influencing the local sensitivity of the two measures of noise. Our study suggested that, for the analysis of contributions of reactions to the noise, consideration of both the measures of noise is important.
Zijian Li
2017-07-01
Full Text Available To control the risk of human exposure to pesticides, about 50 nations have promulgated pesticide soil regulatory guidance values (RGVs, and 104 nations have provided pesticide drinking water maximum concentration levels (MCLs. In addition, 90 nations have regulated pesticide agricultural commodity maximum residue limits (MRLs. Pesticide standard values (PSVs for one single pesticide varied in a range of six, seven, or even eight orders of magnitude. Some PSVs are too large to prevent the impact of pesticides on human health. Many nations have not provided PSVs for some commonly used pesticides until now. This research has introduced several completeness values and numerical values methods to evaluate the national jurisdiction’s performance on PSVs on a nation base. The national jurisdiction ranking system developed by these methods will be beneficial to the environmental regulation makers in the management of PSVs. Results also indicate that European countries perform better in the regulation of pesticide soil RGVs, drinking water MCLs, and agricultural commodity MRLs.
Conflict-cost based random sampling design for parallel MRI with low rank constraints
Kim, Wan; Zhou, Yihang; Lyu, Jingyuan; Ying, Leslie
2015-05-01
In compressed sensing MRI, it is very important to design sampling pattern for random sampling. For example, SAKE (simultaneous auto-calibrating and k-space estimation) is a parallel MRI reconstruction method using random undersampling. It formulates image reconstruction as a structured low-rank matrix completion problem. Variable density (VD) Poisson discs are typically adopted for 2D random sampling. The basic concept of Poisson disc generation is to guarantee samples are neither too close to nor too far away from each other. However, it is difficult to meet such a condition especially in the high density region. Therefore the sampling becomes inefficient. In this paper, we present an improved random sampling pattern for SAKE reconstruction. The pattern is generated based on a conflict cost with a probability model. The conflict cost measures how many dense samples already assigned are around a target location, while the probability model adopts the generalized Gaussian distribution which includes uniform and Gaussian-like distributions as special cases. Our method preferentially assigns a sample to a k-space location with the least conflict cost on the circle of the highest probability. To evaluate the effectiveness of the proposed random pattern, we compare the performance of SAKEs using both VD Poisson discs and the proposed pattern. Experimental results for brain data show that the proposed pattern yields lower normalized mean square error (NMSE) than VD Poisson discs.
Rank-based biomarker index to assess cadmium ecotoxicity on the earthworm Eisenia andrei.
Panzarino, O; Hyršl, P; Dobeš, P; Vojtek, L; Vernile, P; Bari, G; Terzano, R; Spagnuolo, M; de Lillo, E
2016-02-01
A proper soil risk assessment needs to estimate the processes that affect the fate and the behaviour of a contaminant, which are influenced by soil biotic and abiotic components. For this reason, the measurement of biomarkers in soil bioindicator organisms, such as earthworms, has recently received increasing attention. In this study, the earthworm Eisenia andrei was used to assess the pollutant-induced stress syndrome after exposure to sublethal concentrations of Cd (10 or 100 μg g(-1)) in OECD soil, after 14 d of exposure. Cadmium bioaccumulation and potential biomarkers such as catalase (CAT), hydrogen peroxide (H2O2), glutathione-S-transferase (GST), malondialdehyde (MDA), phenoloxidase (PO), metallothioneins (MTs) and genotoxic damage were determined. Results suggested that the exposure to 10 and 100 μg g(-1) Cd significantly increased Cd bioaccumulation, MTs and MDA; 100 μg g(-1) Cd contamination evidenced significantly higher values of H2O2 content and PO activity; CAT activity was inhibited at the higher concentration while GST and Comet assay did not show any significant differences from the control. Rank-based biomarker index showed that both different contaminated soils had an effect on the earthworms and allowed to validate the ecotoxicological relevance of this battery of biomarkers for a promising integrated multi-marker approach in soil monitoring and assessment. Copyright © 2015 Elsevier Ltd. All rights reserved.
Improving the Ranking Capability of the Hyperlink Based Search Engines Using Heuristic Approach
Haider A. Ramadhan
2006-01-01
Full Text Available To evaluate the informative content of a Web page, the Web structure has to be carefully analyzed. Hyperlink analysis, which is capable of measuring the potential information contained in a Web page with respect to the Web space, is gaining more attention. The links to and from Web pages are an important resource that has largely gone unused in existing search engines. Web pages differ from general text in that they posses external and internal structure. The Web links between documents can provide useful information in finding pages for a given set of topics. Making use of the Web link information would allow the construction of more powerful tools for answering user queries. Google has been among the first search engines to utilize hyper links in page ranking. Still two main flaws in Google need to be tackled. First, all the backlinks to a page are assigned equal weights. Second, less content rich pages, such as intermediate and transient pages, are not differentiated from more content rich pages. To overcome these pitfalls, this paper proposes a heuristic based solution to differentiate the significance of various backlinks by assigning a different weight factor to them depending on their location in the directory tree of the Web space.
STUDY ON EFFICIENT COMPUTATION AND PERFORMANCE OF AV-BASED REDUCED-RANK FILTERING
Xu Bin; Yang Chenyang; Mao Shiyi
2005-01-01
In this paper, the complexity and performance of the Auxiliary Vector (AV) based reduced-rank filtering are addressed. The AV filters presented in the previous papers have the general form of the sum of the signature vector of the desired signal and a set of weighted AVs,which can be classified as three categories according to the orthogonality of their AVs and the optimality of the weight coefficients of the AVs. The AV filter with orthogonal AVs and optimal weight coefficients has the best performance, but requires considerable computational complexity and suffers from the numerical unstable operation. In order to reduce its computational load while keeping the superior performance, several low complexity algorithms are proposed to efficiently calculate the AVs and their weight coefficients. The diagonal loading technique is also introduced to solve the numerical unstability problem without complexity increase. The performance of the three types of AV filters is also compared through their application to Direct Sequence Code Division Multiple Access (DS-CDM A) systems for interference suppression.
TERM WEIGHTING BASED ON POSITIVE IMPACT FACTOR QUERY FOR ARABIC FIQH DOCUMENT RANKING
Rizka Sholikah
2017-02-01
Full Text Available Query becomes one of the most decisive factor on documents searching. A query contains several words, where one of them will become a key term. Key term is a word that has higher information and value than the others in query. It can be used in any kind of text documents, including Arabic Fiqh documents. Using key term in term weighting process could led to an improvement on result’s relevancy. In Arabic Fiqh document searching, not using the proper method in term weighting will relieve important value of key term. In this paper, we propose a new term weighting method based on Positive Impact Factor Query (PIFQ for Arabic Fiqh documents ranking. PIFQ calculated using key term’s frequency on each category (mazhab on Fiqh. The key term that frequently appear on a certain mazhab will get higher score on that mazhab, and vice versa. After PIFQ values are acquired, TF.IDF calculation will be done to each words. Then, PIFQ weight will be combine with the result from TF.IDF so that the new weight values for each words will be produced. Experimental result performed on a number of queries using 143 Arabic Fiqh documents show that the proposed method is better than traditional TF.IDF, with 77.9%, 83.1%, and 80.1% of precision, recall, and F-measure respectively.
PosMed: Ranking genes and bioresources based on Semantic Web Association Study.
Makita, Yuko; Kobayashi, Norio; Yoshida, Yuko; Doi, Koji; Mochizuki, Yoshiki; Nishikata, Koro; Matsushima, Akihiro; Takahashi, Satoshi; Ishii, Manabu; Takatsuki, Terue; Bhatia, Rinki; Khadbaatar, Zolzaya; Watabe, Hajime; Masuya, Hiroshi; Toyoda, Tetsuro
2013-07-01
Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.
Ranking Grid-sites based on their Reliability for Successfully Executing Jobs of Given Durations
Farrukh Nadeem
2015-04-01
Full Text Available Today's Grids include resources (referred as Grid-site s from different domains including dedicated production resources, resources from university labs, and even P2P en¬vironments. Grid high level services, like schedulers, resource managers, etc. need to know the reliability of the available Grid-sites to select the most suitable from them. Modeling reliability of a Grid-site for successful execution of a job requires prediction of Grid-site availability for the given duration of job execution as well as possibility of successful execution of the job. Predicting Grid-site availability is complex due to different availability patterns, resource sharing policies implemented by resource owners, nature of domain the resource belongs to (e.g. P2P etc., and its maintenance etc. To give a solution, we model reliability of Grid-site in terms of prediction of its availability and possibility of job success. Our availability predictions incorporate past patterns of the Grid-site availability using pattern recognition methods. To estimate possibility of job success, we consider historical traces of job execution. The experiments conducted on a trace of real Grid demonstrate the effectiveness of our approach for ranking Grid-sites based on their reliability for executing jobs successfully.
Predicting Postfire Hillslope Erosion with a Web-based Probabilistic Model
Robichaud, P. R.; Elliot, W. J.; Pierson, F. B.; Hall, D. E.; Moffet, C. A.
2005-12-01
Modeling erosion after major disturbances, such as wildfire, has major challenges that need to be overcome. Fire-induced changes include increased erosion due to loss of the protective litter and duff, loss of soil water storage, and in some cases, creation of water repellent soil conditions. These conditions increase the potential for flooding, and sedimentation, which are of special concern to people who live and mange resources in the areas adjacent to burned areas. A web-based Erosion Risk Management Tool (ERMiT), has been developed to predict surface erosion from postfire hillslopes and to evaluate the potential effectiveness of various erosion mitigation practices. The model uses a probabilistic approach that incorporates variability in weather, soil properties, and burn severity for forests, rangeland, and chaparral hillslopes. The Water Erosion Prediction Project (WEPP) is the erosion prediction engine used in a Monte Carlo simulation mode to provide event-based erosion rate probabilities. The one-page custom interface is targeted for hydrologists and soil scientists. The interface allows users to select climate, soil texture, burn severity, and hillslope topography. For a given hillslope, the model uses a single 100-year run to obtain weather variability and then twenty 5- to 10-year runs to incorporate soil property, cover, and spatial burn severity variability. The output, in both tabular and graphical form, relates the probability of soil erosion exceeding a given amount in each of the first five years following the fire. Event statistics are provided to show the magnitude and rainfall intensity of the storms used to predict erosion rates. ERMiT also allows users to compare the effects of various mitigation treatments (mulches, seeding, and barrier treatments such as contour-felled logs or straw wattles) on the erosion rate probability. Data from rainfall simulation and concentrated flow (rill) techniques were used to parameterize ERMiT for these varied
Data-worth analysis through probabilistic collocation-based Ensemble Kalman Filter
Dai, Cheng; Xue, Liang; Zhang, Dongxiao; Guadagnini, Alberto
2016-09-01
We propose a new and computationally efficient data-worth analysis and quantification framework keyed to the characterization of target state variables in groundwater systems. We focus on dynamically evolving plumes of dissolved chemicals migrating in randomly heterogeneous aquifers. An accurate prediction of the detailed features of solute plumes requires collecting a substantial amount of data. Otherwise, constraints dictated by the availability of financial resources and ease of access to the aquifer system suggest the importance of assessing the expected value of data before these are actually collected. Data-worth analysis is targeted to the quantification of the impact of new potential measurements on the expected reduction of predictive uncertainty based on a given process model. Integration of the Ensemble Kalman Filter method within a data-worth analysis framework enables us to assess data worth sequentially, which is a key desirable feature for monitoring scheme design in a contaminant transport scenario. However, it is remarkably challenging because of the (typically) high computational cost involved, considering that repeated solutions of the inverse problem are required. As a computationally efficient scheme, we embed in the data-worth analysis framework a modified version of the Probabilistic Collocation Method-based Ensemble Kalman Filter proposed by Zeng et al. (2011) so that we take advantage of the ability to assimilate data sequentially in time through a surrogate model constructed via the polynomial chaos expansion. We illustrate our approach on a set of synthetic scenarios involving solute migrating in a two-dimensional random permeability field. Our results demonstrate the computational efficiency of our approach and its ability to quantify the impact of the design of the monitoring network on the reduction of uncertainty associated with the characterization of a migrating contaminant plume.
A first passage based model for probabilistic fracture of polycrystalline silicon MEMS structures
Xu, Zhifeng; Le, Jia-Liang
2017-02-01
Experiments have shown that the failure loads of Microelectromechanical Systems (MEMS) devices usually exhibit a considerable level of variability, which is believed to be caused by the random material strength and the geometry-induced random stress field. Understanding the strength statistics of MEMS devices is of paramount importance for the device design guarding against a tolerable failure risk. In this study, we develop a continuum-based probabilistic model for polycrystalline silicon (poly-Si) MEMS structures within the framework of first passage analysis. The failure of poly-Si MEMS structures is considered to be triggered by fracture initiation from the sidewalls governed by a nonlocal failure criterion. The model takes into account an autocorrelated random field of material tensile strength. The nonlocal random stress field is obtained by stochastic finite element simulations based on the information of the uncertainties of the sidewall geometry. The model is formulated within the contexts of both stationary and non-stationary stochastic processes for MEMS structures of various geometries and under different loading configurations. It is shown that the model agrees well with the experimentally measured strength distributions of uniaxial tensile poly-Si MEMS specimens of different gauge lengths. The model is further used to predict the strength distribution of poly-Si MEMS beams under three-point bending, and the result is compared with the Monte Carlo simulation. The present model predicts strong size effects on both the strength distribution and the mean structural strength. It is shown that the mean size effect curve consists of three power-law asymptotes in the small, intermediate, and large-size regimes. By matching these three asymptotes, an approximate size effect equation is proposed. The present model is shown to be a generalization of the classical weakest-link statistical model, and it provides a physical interpretation of the material length
Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game
Arnal, Louise; Ramos, Maria-Helena; Coughlan de Perez, Erin; Cloke, Hannah Louise; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk Jan; Pappenberger, Florian
2016-08-01
Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.
Dzhioev, Alan A
2011-01-01
The approach to study properties of charge-exchange excitations in hot nuclei is presented. The approach is based on the extension of the finite rank separable approximation for Skyrme interactions to finite temperatures employing the TFD formalism. We present the formulae to obtain charge-exchange strength distributions within the Thermal Quasiparticle Random Phase Approximation (TQRPA).
Chermak, Edrisse
2014-12-21
Summary: Herein, we present CONSRANK, a web tool for analyzing, comparing and ranking protein–protein and protein–nucleic acid docking models, based on the conservation of inter-residue contacts and its visualization in 2D and 3D interactive contact maps.
2014-11-01
Cholera . Fig. 1. An example Wikipedia article of ICD-10 concept Cholera3 2 http...apps.who.int/classifications/icd10/browse/2010/en 3 http://en.wikipedia.org/wiki/ Cholera Based on the selected concepts, ranking is performed by scoring...Conference (TREC), (2011). 9. Koopman, B. and Zuccon, G. Understanding negation and family history to improve clinical information retrieval. Proceedings
The optimized expansion based low-rank method for wavefield extrapolation
Wu, Zedong
2014-03-01
Spectral methods are fast becoming an indispensable tool for wavefield extrapolation, especially in anisotropic media because it tends to be dispersion and artifact free as well as highly accurate when solving the wave equation. However, for inhomogeneous media, we face difficulties in dealing with the mixed space-wavenumber domain extrapolation operator efficiently. To solve this problem, we evaluated an optimized expansion method that can approximate this operator with a low-rank variable separation representation. The rank defines the number of inverse Fourier transforms for each time extrapolation step, and thus, the lower the rank, the faster the extrapolation. The method uses optimization instead of matrix decomposition to find the optimal wavenumbers and velocities needed to approximate the full operator with its explicit low-rank representation. As a result, we obtain lower rank representations compared with the standard low-rank method within reasonable accuracy and thus cheaper extrapolations. Additional bounds set on the range of propagated wavenumbers to adhere to the physical wave limits yield unconditionally stable extrapolations regardless of the time step. An application on the BP model provided superior results compared to those obtained using the decomposition approach. For transversely isotopic media, because we used the pure P-wave dispersion relation, we obtained solutions that were free of the shear wave artifacts, and the algorithm does not require that n > 0. In addition, the required rank for the optimization approach to obtain high accuracy in anisotropic media was lower than that obtained by the decomposition approach, and thus, it was more efficient. A reverse time migration result for the BP tilted transverse isotropy model using this method as a wave propagator demonstrated the ability of the algorithm.
Paz, A.; Godinez, V.; Lopez, R., E-mail: abpaz@cnsns.gob.m [Comision Nacional de Seguridad Nuclear y Salvaguardias, Dr. Barragan No. 779, Col. Narvarte, 03020 Mexico D. F. (Mexico)
2010-10-15
The present work describes the implementation process and main results of the risk assessment to the radiotherapy practices with Linear Accelerators (Linac), with cobalt 60, and with brachytherapy. These evaluations were made throughout the risk assessment tool for radiotherapy practices SEVRRA (risk evaluation system for radiotherapy), developed at the Mexican National Commission in Nuclear Safety and Safeguards derived from the outcome obtained with the Probabilistic Safety Analysis developed at the Ibero-American Regulators Forum for these radiotherapy facilities. The methodology used is supported by risk matrices method, a mathematical tool that estimates the risk to the patient, radiation workers and public from mechanical failures, mis calibration of the devices, human mistakes, and so. The initiating events are defined as those undesirable events that, together with other failures, can produce a delivery of an over-dose or an under-dose of the medical prescribed dose, to the planned target volume, or a significant dose to non prescribed human organs. Initiating events frequency and reducer of its frequency (actions intended to avoid the accident) are estimated as well as robustness of barriers to those actions, such as mechanical switches, which detect and prevent the accident from occurring. The spectrum of the consequences is parameterized, and the actions performed to reduce the consequences are identified. Based on this analysis, a software tool was developed in order to simplify the evaluations to radiotherapy installations and it has been applied as a first step forward to some Mexican installations, as part of a national implementation process, the final goal is evaluation of all Mexican facilities in the near future. The main target and benefits of the SEVRRA implementation are presented in this paper. (Author)
Greg Thoma; John Veil; Fred Limp; Jackson Cothren; Bruce Gorham; Malcolm Williamson; Peter Smith; Bob Sullivan
2009-05-31
This report describes work performed during the initial period of the project 'Probabilistic Risk Based Decision Support for Oil and Gas Exploration and Production Facilities in Sensitive Ecosystems.' The specific region that is within the scope of this study is the Fayetteville Shale Play. This is an unconventional, tight formation, natural gas play that currently has approximately 1.5 million acres under lease, primarily to Southwestern Energy Incorporated and Chesapeake Energy Incorporated. The currently active play encompasses a region from approximately Fort Smith, AR east to Little Rock, AR approximately 50 miles wide (from North to South). The initial estimates for this field put it almost on par with the Barnett Shale play in Texas. It is anticipated that thousands of wells will be drilled during the next several years; this will entail installation of massive support infrastructure of roads and pipelines, as well as drilling fluid disposal pits and infrastructure to handle millions of gallons of fracturing fluids. This project focuses on gas production in Arkansas as the test bed for application of proactive risk management decision support system for natural gas exploration and production. The activities covered in this report include meetings with representative stakeholders, development of initial content and design for an educational web site, and development and preliminary testing of an interactive mapping utility designed to provide users with information that will allow avoidance of sensitive areas during the development of the Fayetteville Shale Play. These tools have been presented to both regulatory and industrial stakeholder groups, and their feedback has been incorporated into the project.
Yusof, Norbazlan M.; Pradhan, Biswajeet
2014-06-01
PLUS Berhad holds the concession for a total of 987 km of toll expressways in Malaysia, the longest of which is the North-South Expressway or NSE. Acting as the backbone' of the west coast of the peninsula, the NSE stretches from the Malaysian-Thai border in the north to the border with neighbouring Singapore in the south, linking several major cities and towns along the way. North-South Expressway in Malaysia contributes to the country economic development through trade, social and tourism sector. Presently, the highway is good in terms of its condition and connection to every state but some locations need urgent attention. Stability of slopes at these locations is of most concern as any instability can cause danger to the motorist. In this paper, two study locations have been analysed; they are Gua Tempurung (soil slope) and Jelapang (rock slope) which are obviously having two different characteristics. These locations passed through undulating terrain with steep slopes where landslides are common and the probability of slope instability due to human activities in surrounding areas is high. A combination of twelve (12) landslide conditioning factors database on slope stability such as slope degree and slope aspect were extracted from IFSAR (interoferometric synthetic aperture radar) while landuse, lithology and structural geology were constructed from interpretation of high resolution satellite data from World View II, Quickbird and Ikonos. All this information was analysed in geographic information system (GIS) environment for landslide susceptibility mapping using probabilistic based frequency ratio model. Consequently, information on the slopes such as inventories, condition assessments and maintenance records were assessed through total expressway maintenance management system or better known as TEMAN. The above mentioned system is used by PLUS as an asset management and decision support tools for maintenance activities along the highways as well as for data
Bellier Joseph
2016-01-01
Full Text Available Hydrological ensemble forecasting performances are analysed over 5 basins up to 2000 km2 in the French Upper Rhone region. Streamflow forecasts are issued at an hourly time step from lumped ARX rainfall-runoff models forced by different precipitation forecasts. Ensemble meteorological forecasts from ECMWF and NCEP are considered, as well as analogue-based forecasts fed by their corresponding control forecast. Analogue forecasts are rearranged using an adaptation of the Schaake-Shuffle method in order to ensure the temporal coherence. A new evaluation approach is proposed, separating forecasting performances on peak amplitudes and peak timings for high flow events. Evaluation is conducted against both simulated and observed streamflow (so that relative meteorological and hydrological uncertainties can be assessed, by means of CRPS and rank histograms, over the 2007-2014 period. Results show a general agreement of the forecasting performances when averaged over the 5 basins. However, ensemble-based and analogue-based streamflow forecasts produce a different signature on peak events in terms of bias, spread and reliability. Strengths and weaknesses of both approaches are discussed as well as potential improvements, notably towards their merging.
The Stag Hunt Game: An Example of an Excel-Based Probabilistic Game
Bridge, Dave
2016-01-01
With so many role-playing simulations already in the political science education literature, the recent repeated calls for new games is both timely and appropriate. This article answers and extends those calls by advocating the creation of probabilistic games using Microsoft Excel. I introduce the example of the Stag Hunt Game--a short, effective,…
Kindermans, Pieter-Jan; Verschore, Hannes; Schrauwen, Benjamin
2013-10-01
In recent years, in an attempt to maximize performance, machine learning approaches for event-related potential (ERP) spelling have become more and more complex. In this paper, we have taken a step back as we wanted to improve the performance without building an overly complex model, that cannot be used by the community. Our research resulted in a unified probabilistic model for ERP spelling, which is based on only three assumptions and incorporates language information. On top of that, the probabilistic nature of our classifier yields a natural dynamic stopping strategy. Furthermore, our method uses the same parameters across 25 subjects from three different datasets. We show that our classifier, when enhanced with language models and dynamic stopping, improves the spelling speed and accuracy drastically. Additionally, we would like to point out that as our model is entirely probabilistic, it can easily be used as the foundation for complex systems in future work. All our experiments are executed on publicly available datasets to allow for future comparison with similar techniques.
Douven, Igor; Horsten, Leon; Romeijn, Jan-Willem
2010-01-01
Until now, antirealists have offered sketches of a theory of truth, at best. In this paper, we present a probabilist account of antirealist truth in some formal detail, and we assess its ability to deal with the problems that are standardly taken to beset antirealism.
Sørensen, John Dalsgaard; Burcharth, H. F.
This chapter describes how partial safety factors can be used in design of vertical wall breakwaters and an example of a code format is presented. The partial safety factors are calibrated on a probabilistic basis. The code calibration process used to calibrate some of the partial safety factors...
Bod, R.; Heine, B.; Narrog, H.
2010-01-01
Probabilistic linguistics takes all linguistic evidence as positive evidence and lets statistics decide. It allows for accurate modelling of gradient phenomena in production and perception, and suggests that rule-like behaviour is no more than a side effect of maximizing probability. This chapter
A Novel Ranking Algorithm Based Network Selection For Heterogeneous Wireless Access
Mohamed Lahby
2013-02-01
Full Text Available In order to provide ubiquitous access for the users, future generation network integrate a multitude of radio access technologies (RAT’S which can interoperate between them. However, the most challenging problem is the selection of an optimal radio access network, in terms of quality of service anywhere at anytime. This paper proposes a novel ranking algorithm, which combines multi attribute decision making (MADM and Mahalanobis distance. Firstly, a classification method is applied to build a classes which having the homogeneous criteria. Afterwards, the Fuzzy AHP, MADM method is applied to determine weights of inter-classes and intraclasses. Finally, Mahalanobis distance is used to rank the alternatives. The simulation results show that the proposed algorithm can effectively reduce the ranking abnormality and the number of handoffs.
Passage Retrieval: A Probabilistic Technique.
Melucci, Massimo
1998-01-01
Presents a probabilistic technique to retrieve passages from texts having a large size or heterogeneous semantic content. Results of experiments comparing the probabilistic technique to one based on a text segmentation algorithm revealed that the passage size affects passage retrieval performance; text organization and query generality may have an…
Probabilistic seismic hazard study based on active fault and finite element geodynamic models
Kastelic, Vanja; Carafa, Michele M. C.; Visini, Francesco
2016-04-01
We present a probabilistic seismic hazard analysis (PSHA) that is exclusively based on active faults and geodynamic finite element input models whereas seismic catalogues were used only in a posterior comparison. We applied the developed model in the External Dinarides, a slow deforming thrust-and-fold belt at the contact between Adria and Eurasia.. is the Our method consists of establishing s two earthquake rupture forecast models: (i) a geological active fault input (GEO) model and, (ii) a finite element (FEM) model. The GEO model is based on active fault database that provides information on fault location and its geometric and kinematic parameters together with estimations on its slip rate. By default in this model all deformation is set to be released along the active faults. The FEM model is based on a numerical geodynamic model developed for the region of study. In this model the deformation is, besides along the active faults, released also in the volumetric continuum elements. From both models we calculated their corresponding activity rates, its earthquake rates and their final expected peak ground accelerations. We investigated both the source model and the earthquake model uncertainties by varying the main active fault and earthquake rate calculation parameters through constructing corresponding branches of the seismic hazard logic tree. Hazard maps and UHS curves have been produced for horizontal ground motion on bedrock conditions VS 30 ≥ 800 m/s), thereby not considering local site amplification effects. The hazard was computed over a 0.2° spaced grid considering 648 branches of the logic tree and the mean value of 10% probability of exceedance in 50 years hazard level, while the 5th and 95th percentiles were also computed to investigate the model limits. We conducted a sensitivity analysis to control which of the input parameters influence the final hazard results in which measure. The results of such comparison evidence the deformation model and
Liu, Xiaoming; Yang, Zhou; Wang, Jia; Liu, Jun; Zhang, Kai; Hu, Wei
2017-01-01
Image denoising is a crucial step before performing segmentation or feature extraction on an image, which affects the final result in image processing. In recent years, utilizing the self-similarity characteristics of the images, many patch-based image denoising methods have been proposed, but most of them, named the internal denoising methods, utilized the noisy image only where the performances are constrained by the limited information they used. We proposed a patch-based method, which uses a low-rank technique and targeted database, to denoise the optical coherence tomography (OCT) image. When selecting the similar patches for the noisy patch, our method combined internal and external denoising, utilizing the other images relevant to the noisy image, in which our targeted database is made up of these two kinds of images and is an improvement compared with the previous methods. Next, we leverage the low-rank technique to denoise the group matrix consisting of the noisy patch and the corresponding similar patches, for the fact that a clean image can be seen as a low-rank matrix and rank of the noisy image is much larger than the clean image. After the first-step denoising is accomplished, we take advantage of Gabor transform, which considered the layer characteristic of the OCT retinal images, to construct a noisy image before the second step. Experimental results demonstrate that our method compares favorably with the existing state-of-the-art methods.
Ranking provinces based on development scale in agriculture sector using taxonomy technique
Shahram Rostampour
2012-08-01
Full Text Available The purpose of this paper is to determine comparative ranking of agricultural development in different provinces of Iran using taxonomy technique. The independent variables are amount of annual rainfall amount, the number of permanent rivers, the width of pastures and forest, cultivated level of agricultural harvests and garden harvests, number of beehives, the number of fish farming ranches, the number of tractors and combines, the number of cooperative production societies, the number of industrial cattle breeding and aviculture. The results indicate that the maximum development coefficient value is associated with Razavi Khorasan province followed by Mazandaran, East Azarbayjan while the minimum ranking value belongs to Bushehr province.
A Distributed Taxation Based Rank Adaptation Scheme for 5G Small Cells
Catania, Davide; Cattoni, Andrea Fabio; Mahmood, Nurul Huda
2015-01-01
The further densification of small cells impose high and undesirable levels of inter-cell interference. Multiple Input Multiple Output (MIMO) systems along with advanced receiver techniques provide us with extra degrees of freedom to combat such a problem. With such tools, rank adaptation algorit...
Stakeholder Perspectives on Citation and Peer-Based Rankings of Higher Education Journals
Wilkins, Stephen; Huisman, Jeroen
2015-01-01
The purpose of this article is to identify and discuss the possible uses of higher education journal rankings, and the associated advantages and disadvantages of using them. The research involved 40 individuals--lecturers, university managers, journal editors and publishers--who represented a range of stakeholders involved with research into…
Stakeholder Perspectives on Citation and Peer-Based Rankings of Higher Education Journals
Wilkins, Stephen; Huisman, Jeroen
2015-01-01
The purpose of this article is to identify and discuss the possible uses of higher education journal rankings, and the associated advantages and disadvantages of using them. The research involved 40 individuals--lecturers, university managers, journal editors and publishers--who represented a range of stakeholders involved with research into…
Physics-based Probabilistic Seismic Hazard Analysis for Seismicity Induced by Fluid Injection
Foxall, W.; Hutchings, L. J.; Johnson, S.; Savy, J. B.
2011-12-01
Risk associated with induced seismicity (IS) is a significant factor in the design, permitting and operation of enhanced geothermal, geological CO2 sequestration and other fluid injection projects. Whereas conventional probabilistic seismic hazard and risk analysis (PSHA, PSRA) methods provide an overall framework, they require adaptation to address specific characteristics of induced earthquake occurrence and ground motion estimation, and the nature of the resulting risk. The first problem is to predict the earthquake frequency-magnitude distribution of induced events for PSHA required at the design and permitting stage before the start of injection, when an appropriate earthquake catalog clearly does not exist. Furthermore, observations and theory show that the occurrence of earthquakes induced by an evolving pore-pressure field is time-dependent, and hence does not conform to the assumption of Poissonian behavior in conventional PSHA. We present an approach to this problem based on generation of an induced seismicity catalog using numerical simulation of pressure-induced shear failure in a model of the geologic structure and stress regime in and surrounding the reservoir. The model is based on available measurements of site-specific in-situ properties as well as generic earthquake source parameters. We also discuss semi-empirical analysis to sequentially update hazard and risk estimates for input to management and mitigation strategies using earthquake data recorded during and after injection. The second important difference from conventional PSRA is that in addition to potentially damaging ground motions a significant risk associated with induce seismicity in general is the perceived nuisance caused in nearby communities by small, local felt earthquakes, which in general occur relatively frequently. Including these small, usually shallow earthquakes in the hazard analysis requires extending the ground motion frequency band considered to include the high
Ranking multiple docking solutions based on the conservation of inter-residue contacts
Oliva, Romina M.
2013-06-17
Molecular docking is the method of choice for investigating the molecular basis of recognition in a large number of functional protein complexes. However, correctly scoring the obtained docking solutions (decoys) to rank native-like (NL) conformations in the top positions is still an open problem. Herein we present CONSRANK, a simple and effective tool to rank multiple docking solutions, which relies on the conservation of inter-residue contacts in the analyzed decoys ensemble. First it calculates a conservation rate for each inter-residue contact, then it ranks decoys according to their ability to match the more frequently observed contacts. We applied CONSRANK to 102 targets from three different benchmarks, RosettaDock, DOCKGROUND, and Critical Assessment of PRedicted Interactions (CAPRI). The method performs consistently well, both in terms of NL solutions ranked in the top positions and of values of the area under the receiver operating characteristic curve. Its ideal application is to solutions coming from different docking programs and procedures, as in the case of CAPRI targets. For all the analyzed CAPRI targets where a comparison is feasible, CONSRANK outperforms the CAPRI scorers. The fraction of NL solutions in the top ten positions in the RosettaDock, DOCKGROUND, and CAPRI benchmarks is enriched on average by a factor of 3.0, 1.9, and 9.9, respectively. Interestingly, CONSRANK is also able to specifically single out the high/medium quality (HMQ) solutions from the docking decoys ensemble: it ranks 46.2 and 70.8% of the total HMQ solutions available for the RosettaDock and CAPRI targets, respectively, within the top 20 positions. © 2013 Wiley Periodicals, Inc.
Information retrieval for OCR documents: a content-based probabilistic correction model
Jin, Rong; Zhai, ChangXiang; Hauptmann, Alexander
2003-01-01
The difficulty with information retrieval for OCR documents lies in the fact that OCR documents contain a significant amount of erroneous words and unfortunately most information retrieval techniques rely heavily on word matching between documents and queries. In this paper, we propose a general content-based correction model that can work on top of an existing OCR correction tool to "boost" retrieval performance. The basic idea of this correction model is to exploit the whole content of a document to supplement any other useful information provided by an existing OCR correction tool for word corrections. Instead of making an explicit correction decision for each erroneous word as typically done in a traditional approach, we consider the uncertainties in such correction decisions and compute an estimate of the original "uncorrupted" document language model accordingly. The document language model can then be used for retrieval with a language modeling retrieval approach. Evaluation using the TREC standard testing collections indicates that our method significantly improves the performance compared with simple word correction approaches such as using only the top ranked correction.
Ranking Economic History Journals
Di Vaio, Gianfranco; Weisdorf, Jacob Louis
This study ranks - for the first time - 12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We...... also compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential...
Ranking economic history journals
Di Vaio, Gianfranco; Weisdorf, Jacob Louis
2010-01-01
This study ranks-for the first time-12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We also...... compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential for economic...
Mengmeng Ma
2015-01-01
Full Text Available To solve the invalidation problem of Dempster-Shafer theory of evidence (DS with high conflict in multisensor data fusion, this paper presents a novel combination approach of conflict evidence with different weighting factors using a new probabilistic dissimilarity measure. Firstly, an improved probabilistic transformation function is proposed to map basic belief assignments (BBAs to probabilities. Then, a new dissimilarity measure integrating fuzzy nearness and introduced correlation coefficient is proposed to characterize not only the difference between basic belief functions (BBAs but also the divergence degree of the hypothesis that two BBAs support. Finally, the weighting factors used to reassign conflicts on BBAs are developed and Dempster’s rule is chosen to combine the discounted sources. Simple numerical examples are employed to demonstrate the merit of the proposed method. Through analysis and comparison of the results, the new combination approach can effectively solve the problem of conflict management with better convergence performance and robustness.
Towards History-based Grammars Using Richer Models for Probabilistic Parsing
Black, E; Lafferty, G D; Magerman, D M; Mercer, R; Roukos, S; Black, Ezra; Jelinek, Fred; Lafferty, John; Magerman, David M.; Mercer, Robert; Roukos, Salim
1994-01-01
We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse. In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error.
无
2006-01-01
In order to resolve the multisensor multiplied maneuvering target tracking problem, this paper presents a distributed interacted multiple model multisensor joint probabilistic data association algorithm (DIMM-MSJPDA). First of all, the interacted multiple model joint probabilistic data association algorithm is applied to each sensor, and then the state estimation, estimation covariance, model probability, combined innovation, innovation covariance are delivered to the fusion center. Then, the tracks from each sensor are correlated and the D-S evidence theory is used to gain the model probability of an identical target. Finally, the ultimate state estimation of each target is calculated according to the new model probability, and the state estimation is transmitted to each sensor. Simulations are designed to test the tracking performance of DIMM-MSJPDA algorithm. The results show that the use of DIMM-MSJPDA algorithm enables the distributed multisensor system to track multiplied maneuvering targets and its tracking performance is much better than that of IMMJPDA algorithm.
Roldsgaard, Joan Hee; Kiremidjian, A.; Georgakis, Christos T.;
The scope of the present paper is to present a framework for assessment of the probability of occurrence of ice/snow accretion on bridge cables. The framework utilizes Bayesian Probabilistic Networks and the methodology is illustrated with an example of the cable-stayed Øresund Bridge. The case...... study focuses on the ice/snow accretion due to the in-cloud icing or precipitation icing mechanisms and includes probabilistic assessments of the meteorological variables influencing the ice/snow accretion on the stay cables. Different probability distribution functions are utilized...... for the representation of the meteorological variables and evaluated both by goodness-of-fit test and qualitatively. Conditional probability curves are developed to predict the amount of ice accretion given a set of meteorological conditions using the Gaussian Kernel Smoothing method. The fitted probability distribution...
Rankings from Fuzzy Pairwise Comparisons
Broek, van den Pim; Noppen, Joost; Mohammadian, M.
2006-01-01
We propose a new method for deriving rankings from fuzzy pairwise comparisons. It is based on the observation that quantification of the uncertainty of the pairwise comparisons should be used to obtain a better crisp ranking, instead of a fuzzified version of the ranking obtained from crisp pairwise
2014-12-24
in energy and sensor applications , and carbon nanotubes (CNT) also ranked relatively high based on worker inha- lation from CNTs used in research and...accidental expo- sures to carbon nanotubes and copper flakes incorporated into energy and obscurant materiel by Army workers rank highest relative to...that inhalation from accidental exposures to carbon nanotubes and copper flakes incorporated into energy and obscurant materiel by Army workers rank
Ranking of sabotage/tampering avoidance technology alternatives
Andrews, W.B.; Tabatabai, A.S.; Powers, T.B.; Daling, P.M.; Fecht, B.A.; Gore, B.F.; Overcast, T.D.; Rankin, W.R.; Schreiber, R.E.; Tawil, J.J.
1986-01-01
Pacific Northwest Laboratory conducted a study to evaluate alternatives to the design and operation of nuclear power plants, emphasizing a reduction of their vulnerability to sabotage. Estimates of core melt accident frequency during normal operations and from sabotage/tampering events were used to rank the alternatives. Core melt frequency for normal operations was estimated using sensitivity analysis of results of probabilistic risk assessments. Core melt frequency for sabotage/tampering was estimated by developing a model based on probabilistic risk analyses, historic data, engineering judgment, and safeguards analyses of plant locations where core melt events could be initiated. Results indicate the most effective alternatives focus on large areas of the plant, increase safety system redundancy, and reduce reliance on single locations for mitigation of transients. Less effective options focus on specific areas of the plant, reduce reliance on some plant areas for safe shutdown, and focus on less vulnerable targets.
Probabilistic Approach to Rough Set Theory
Wojciech Ziarko
2006-01-01
The presentation introduces the basic ideas and investigates the probabilistic approach to rough set theory. The major aspects of the probabilistic approach to rough set theory to be explored during the presentation are: the probabilistic view of the approximation space, the probabilistic approximations of sets, as expressed via variable precision and Bayesian rough set models, and probabilistic dependencies between sets and multi-valued attributes, as expressed by the absolute certainty gain and expected certainty gain measures, respectively. The probabilis-tic dependency measures allow for representation of subtle stochastic associations between attributes. They also allow for more comprehensive evaluation of rules computed from data and for computation of attribute reduct, core and significance factors in probabilistic decision tables. It will be shown that the probabilistic dependency measure-based attribute reduction techniques are also extendible to hierarchies of decision tables. The presentation will include computational examples to illustrate pre-sented concepts and to indicate possible practical applications.
S. Raia
2013-02-01
Full Text Available Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS code. The new code (TRIGRS-P adopts a stochastic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying
Johnson, S.; Foxall, W.; Savy, J. B.; Hutchings, L. J.
2012-12-01
Risk associated with induced seismicity is a significant factor in the design, permitting and operation of enhanced geothermal, geological CO2 sequestration, wastewater disposal, and other fluid injection projects. The conventional probabilistic seismic hazard analysis (PSHA) approach provides a framework for estimation of induced seismicity hazard but requires adaptation to address the particular occurrence characteristics of induced earthquakes and to estimation of the ground motions they generate. The assumption often made in conventional PSHA of Poissonian earthquake occurrence in both space and time is clearly violated by seismicity induced by an evolving pore pressure field. Our project focuses on analyzing hazard at the pre-injection design and permitting stage, before an induced earthquake catalog can be recorded. In order to accommodate the commensurate lack of pre-existing data, we have adopted a numerical physics-based approach to synthesizing and estimating earthquake frequency-magnitude distributions. Induced earthquake sequences are generated using the program RSQSIM (Dieterich and Richards-Dinger, PAGEOPH, 2010) augmented to simulate pressure-induced shear failure on faults and fractures embedded in a 3D geological structure under steady-state tectonic shear loading. The model uses available site-specific data on rock properties and in-situ stress, and generic values of frictional properties appropriate to the shallow reservoir depths at which induced events usually occur. The space- and time-evolving pore pressure field is coupled into the simulation from a multi-phase flow model. In addition to potentially damaging ground motions, induced seismicity poses a risk of perceived nuisance in nearby communities caused by relatively frequent, low magnitude earthquakes. Including these shallow local earthquakes in the hazard analysis requires extending the magnitude range considered to as low as M2 and the frequency band to include the short
A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking
Shi Chuan; Kang Li-shan; Li Yan; Yan Zhen-yu
2003-01-01
Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare to front, retain the diversity of the population, and use less time.
Microseismic Event Grouping Based on PageRank Linkage at the Newberry Volcano Geothermal Site
Aguiar, A. C.; Myers, S. C.
2016-12-01
The Newberry Volcano DOE FORGE site in Central Oregon has been stimulated two times using high-pressure fluid injection to study the Enhanced Geothermal Systems (EGS) technology. Several hundred microseismic events were generated during the first stimulation in the fall of 2012. Initial locations of this microseismicity do not show well defined subsurface structure in part because event location uncertainties are large (Foulger and Julian, 2013). We focus on this stimulation to explore the spatial and temporal development of microseismicity, which is key to understanding how subsurface stimulation modifies stress, fractures rock, and increases permeability. We use PageRank, Google's initial search algorithm, to determine connectivity within the events (Aguiar and Beroza, 2014) and assess signal-correlation topology for the micro-earthquakes. We then use this information to create signal families and compare these to the spatial and temporal proximity of associated earthquakes. We relocate events within families (identified by PageRank linkage) using the Bayesloc approach (Myers et al., 2007). Preliminary relocations show tight spatial clustering of event families as well as evidence of events relocating to a different cluster than originally reported. We also find that signal similarity (linkage) at several stations, not just one or two, is needed in order to determine that events are in close proximity to one another. We show that indirect linkage of signals using PageRank is a reliable way to increase the number of events that are confidently determined to be similar to one another, which may lead to efficient and effective grouping of earthquakes with similar physical characteristics, such as focal mechanisms and stress drop. Our ultimate goal is to determine whether changes in the state of stress and/or changes in the generation of subsurface fracture networks can be detected using PageRank topology as well as aid in the event relocation to obtain more accurate
Xiao-Jun Wang; Chen Yang; Zhi-Ping Qiu
2013-01-01
Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification (SMI) and two-step model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence (PoDE) in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.
Should we really use post-hoc tests based on mean-ranks?
Benavoli, Alessio; Mangili, Francesca
2015-01-01
The statistical comparison of multiple algorithms over multiple data sets is fundamental in machine learning. This is typically carried out by the Friedman test. When the Friedman test rejects the null hypothesis, multiple comparisons are carried out to establish which are the significant differences among algorithms. The multiple comparisons are usually performed using the mean-ranks test. The aim of this technical note is to discuss the inconsistencies of the mean-ranks post-hoc test with the goal of discouraging its use in machine learning as well as in medicine, psychology, etc.. We show that the outcome of the mean-ranks test depends on the pool of algorithms originally included in the experiment. In other words, the outcome of the comparison between algorithms A and B depends also on the performance of the other algorithms included in the original experiment. This can lead to paradoxical situations. For instance the difference between A and B could be declared significant if the pool comprises algorithm...
基于PageRank，HITS和SALSA算法的学术论文评价%Academic Publication Ranking Based on PageRank, HITS and SALSA
苏成; Hee-Sop KIM
2015-01-01
For evaluation of researchers and scientific research management, evaluation of scientific papers is very important. In this paper, first we presented experiments using the PageRank, HITS and SALSA by rank“library and information science” related papers. Second we also compared for different citation ranking algorithms by common elements of each pair of the above algorithms and Spearman rho. We discussed several algorithms' feature how to influence the ranking results. Finally, the merits, the demerits and applicable scope of each method are discussed, and some useful conclusions are given.%科技论文评价对于科研工作者的评价以及相关科研管理工作至关重要。利用PageRank、HITS和SALSA算法进行了“图书馆与信息科学”领域的论文排序实验，比较了不同算法排序列表前100共同论文数和Spearman相关系数，讨论了不同算法特性对排序结果的影响，最后讨论了不同算法的优缺点、适用范围。
Gao, Zhijun; Bu, Wei; Zheng, Yalin; Wu, Xiangqian
2017-01-01
Using the graph-based a simple linear iterative clustering (SLIC) superpixels and manifold ranking technology, a novel automated intra-retinal layer segmentation method is proposed in this paper. Eleven boundaries of ten retinal layers in optical coherence tomography (OCT) images are exactly, fast and reliably quantified. Instead of considering the intensity or gradient features of the single-pixel in most existing segmentation methods, the proposed method focuses on the superpixels and the connected components-based image cues. The image is represented as some weighted graphs with superpixels or connected components as nodes. Each node is ranked with the gradient and spatial distance cues via graph-based Dijkstra's method or manifold ranking. So that it can effectively overcome speckle noise, organic texture and blood vessel artifacts issues. Segmentation is carried out in a three-stage scheme to extract eleven boundaries efficiently. The segmentation algorithm is validated on 2D and 3D OCT images in three databases, and is compared with the manual tracings of two independent observers. It demonstrates promising results in term of the mean unsigned boundaries errors, the mean signed boundaries errors, and layers thickness errors.
Bhanu Pratap Soni
2016-12-01
Full Text Available This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs: line MVA performance index (PIMVA and Voltage-reactive power performance index (PIVQ. SVM works in two steps. Step I is the estimation of both standard indices (PIMVA and PIVQ that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system. The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.
Zeng, Xiang-tian; Li, Deng-feng; Yu, Gao-feng
2014-01-01
The aim of this paper is to develop a method for ranking trapezoidal intuitionistic fuzzy numbers (TrIFNs) in the process of decision making in the intuitionistic fuzzy environment. Firstly, the concept of TrIFNs is introduced. Arithmetic operations and cut sets over TrIFNs are investigated. Then, the values and ambiguities of the membership degree and the nonmembership degree for TrIFNs are defined as well as the value-index and ambiguity-index. Finally, a value and ambiguity-based ranking method is developed and applied to solve multiattribute decision making problems in which the ratings of alternatives on attributes are expressed using TrIFNs. A numerical example is examined to demonstrate the implementation process and applicability of the method proposed in this paper. Furthermore, comparison analysis of the proposed method is conducted to show its advantages over other similar methods. PMID:25147854
Xiang-tian Zeng
2014-01-01
Full Text Available The aim of this paper is to develop a method for ranking trapezoidal intuitionistic fuzzy numbers (TrIFNs in the process of decision making in the intuitionistic fuzzy environment. Firstly, the concept of TrIFNs is introduced. Arithmetic operations and cut sets over TrIFNs are investigated. Then, the values and ambiguities of the membership degree and the nonmembership degree for TrIFNs are defined as well as the value-index and ambiguity-index. Finally, a value and ambiguity-based ranking method is developed and applied to solve multiattribute decision making problems in which the ratings of alternatives on attributes are expressed using TrIFNs. A numerical example is examined to demonstrate the implementation process and applicability of the method proposed in this paper. Furthermore, comparison analysis of the proposed method is conducted to show its advantages over other similar methods.
Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images
Singan, Vasanth R
2011-10-21
Abstract Background Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial. Results We have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells. Conclusions This algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets
An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices
Yasmine Rezgui
2017-01-01
Full Text Available This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM. Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF, Signal Strength Difference (SSD, and the Hyperbolic Location Fingerprinting (HLF based SVM. The results show that the NR-SVM outperforms these popular methods.
Ranking method for the reciprocal judgment matrix based on the unascertained three-valued judgments
Wan Yucheng; Ma Baoguo; Sheng Zhaohan
2006-01-01
The ranking problem is studied when the pairwise comparisons values are uncertain in the analytic hierarchy process (AHP). The method of constructing the judgment matrix is presented when the pairwise comparisons values are denoted by the unascrtained three-valued reciprocal scales. By turning the reciprocal judgment matrix into attribute judgment matrix, the method to check the consistency of the pairwise comparisons judgment matrix and the calculation method of weighting coefficients are given. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.
Yi, Wen-Bin; Shen, Li; Qi, Yin-Feng; Tang, Hong
2011-09-01
The paper introduces the Probabilistic Latent Semantic Analysis (PLSA) to the image clustering and an effective image clustering algorithm using the semantic information from PLSA is proposed which is used for hyperspectral images. Firstly, the ISODATA algorithm is used to obtain the initial clustering result of hyperspectral image and the clusters of the initial clustering result are considered as the visual words of the PLSA. Secondly, the object-oriented image segmentation algorithm is used to partition the hyperspectral image and segments with relatively pure pixels are regarded as documents in PLSA. Thirdly, a variety of identification methods which can estimate the best number of cluster centers is combined to get the number of latent semantic topics. Then the conditional distributions of visual words in topics and the mixtures of topics in different documents are estimated by using PLSA. Finally, the conditional probabilistic of latent semantic topics are distinguished using statistical pattern recognition method, the topic type for each visual in each document will be given and the clustering result of hyperspectral image are then achieved. Experimental results show the clusters of the proposed algorithm are better than K-MEANS and ISODATA in terms of object-oriented property and the clustering result is closer to the distribution of real spatial distribution of surface.
A Dynamic Probabilistic Broadcasting Scheme based on Cross-Layer design for MANETs
Qing-wen WANG
2010-11-01
Full Text Available Broadcasting plays a fundamental role in transmitting a message from the sender to the rest of the network nodes in Mobile Ad hoc Networks (MANETs. The blind flooding scheme causes a broadcast storm problem, which leads to significant network performance degradation. In order to solve the problem, a dynamic probabilistic broadcasting scheme cross-layer design for MANETs (DPBSC is proposed. DPBSC adopts the cross-layer design, which lets routing layer share the received signal power information at MAC layer while still maintaining separation between the two layers. The additional transmission range that can benefit from rebroadcast is calculated according to the received signal power, which is applied to dynamically adjust the rebroadcast probability. DPBSC reduces the redundant retransmission and the chance of the contention and collision in the networks. Simulation results reveal that the DPBSC achieves better performance in terms of the saved-rebroadcast, the average packet drop fraction, the average number of collisions and average end-to-end delay at expense of the throughput, which is respectively compared with the blind flooding and fixed probabilistic flooding applied at the routing layer while IEEE 802.11 at the MAC layer.
Using a consensus approach based on the conservation of inter-residue contacts to rank CAPRI models
Vangone, Anna
2013-10-17
Herein we propose the use of a consensus approach, CONSRANK, for ranking CAPRI models. CONSRANK relies on the conservation of inter-residue contacts in the analyzed decoys ensemble. Models are ranked according to their ability to match the most frequently observed contacts. We applied CONSRANK to 19 CAPRI protein-protein targets, covering a wide range of prediction difficulty and involved in a variety of biological functions. CONSRANK results are consistently good, both in terms of native-like (NL) solutions ranked in the top positions and of values of the Area Under the receiver operating characteristic Curve (AUC). For targets having a percentage of NL solutions above 3%, an excellent performance is found, with AUC values approaching 1. For the difficult target T46, having only 3.4% NL solutions, the number of NL solutions in the top 5 and 10 ranked positions is enriched by a factor 30, and the AUC value is as high as 0.997. AUC values below 0.8 are only found for targets featuring a percentage of NL solutions within 1.1%. Remarkably, a false consensus emerges only in one case, T42, which happens to be an artificial protein, whose assembly details remain uncertain, based on controversial experimental data. We also show that CONSRANK still performs very well on a limited number of models, provided that more than 1 NL solution is included in the ensemble, thus extending its applicability to cases where few dozens of models are available.© 2013 Wiley Periodicals, Inc.
KaM_CRK: Clustering and Ranking Knowledge for Reasonable Results Based on Behaviors and Contexts
Changhong Hu
2013-01-01
Full Text Available A model named KaM_CRK is proposed, which can supply the clustered and ranked knowledge to the users on different contexts. By comparing the attributes of contexts and JANs, our findings indicate that our model can accumulate the JANs, whose attributes are similar with the user’s contexts, together. By applying the KaM_CLU algorithm and Centre rank strategy into the KaM_CRK model, the model boosts a significant promotion on the accuracy of provision of user's knowledge. By analyzing the users' behaviors, the dynamic coefficient BehaviorF is first presented in KaM_CLU. Compared to traditional approaches of K_means and DBSCAN, the KaM_CLU algorithm does not need to initialize the number of clusters. Additionally, its synthetic results are more accurate, reasonable, and fit than other approaches for users. It is known from our evaluation through real data that our strategy performs better on time efficiency and user's satisfaction, which will save by 30% and promote by 5%, respectively.
不确定环境下飞行器航迹规划%Route Planning Based on Probabilistic Distributing
王光源; 汲万峰; 章尧卿; 于嘉晖
2012-01-01
Flight planning can be compartmentalized certain route planning and uncertain route planning according to the assured degree. The article presents a method of route planning based on probabilistic expression for aerocraft in allusion to the uncertain environment. The probabilistic function model of uncertain threat has been proposed according to the model of threat presence funcion, position distributing function and static threat probability function. Genetic algorithm has been used in the route planning and finally the validity of the model and algorithm has been proved by the simulation results.%根据环境的确定性程度,飞行规划可划分为确定性航迹规划和不确定性航迹规划.针对不确定战场环境航迹规划问题进行了研究,提出了一种基于概率表示的军用飞行器航迹规划方法.根据威胁源的存在概率函数、位置分布概率函数和静态威胁概率函数模型,建立了不确定威胁的威胁概率函数模型.将遗传算法运用到航迹规划中,并进行了仿真实验,仿真结果验证了模型及算法的有效性.
Lemaire, Jean-Jacques; Coste, Jérôme; Ouchchane, Lemlih; Caire, François; Nuti, Christophe; Derost, Philippe; Cristini, Vittorio; Gabrillargues, Jean; Hemm, Simone; Durif, Franck; Chazal, Jean
2007-01-01
In this article, we briefly review the concept of brain mapping in stereotactic surgery taking into account recent advances in stereotactic imaging. The gold standard continues to rely on probabilistic and indirect targeting, relative to a stereotactic reference, i.e., mostly the anterior (AC) and the posterior (PC) commissures. The theoretical position of a target defined on an atlas is transposed into the stereotactic space of a patient's brain; final positioning depends on electrophysiological analysis. The method is also used to analyze final electrode or lesion position for a patient or group of patients, by projection on an atlas. Limitations are precision of definition of the AC-PC line, probabilistic location and reliability of the electrophysiological guidance. Advances in MR imaging, as from 1.5-T machines, make stereotactic references no longer mandatory and allow an anatomic mapping based on an individual patient's brain. Direct targeting is enabled by high-quality images, an advanced anatomic knowledge and dedicated surgical software. Labeling associated with manual segmentation can help for the position analysis along non-conventional, interpolated planes. Analysis of final electrode or lesion position, for a patient or group of patients, could benefit from the concept of membership, the attribution of a weighted membership degree to a contact or a structure according to its level of involvement. In the future, more powerful MRI machines, diffusion tensor imaging, tractography and computational modeling will further the understanding of anatomy and deep brain stimulation effects.
Estimating Independent Locally Shifted Random Utility Models for Ranking Data
Lam, Kar Yin; Koning, Alex J.; Franses, Philip Hans
2011-01-01
We consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we avoided the computation of high-dimensional integrals. We extended the approximation technique proposed by Henery (1981) in the context of the Thurstone-Mosteller-Daniels model to any…
Mu Zhou
2015-09-01
Full Text Available Due to the wide deployment of wireless local area networks (WLAN, received signal strength (RSS-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM. Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization.
A novel approximation of basic probability assignment based on rank-level fusion
Yang Yi; Han Deqiang; Han Chongzhao; Cao Feng
2013-01-01
Belief functions theory is an important tool in the field of information fusion.However,when the cardinality of the frame of discernment becomes large,the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications.The basic probability assignment (BPA) approximations,which can reduce the complexity of the BPAs,are always used to reduce the computational cost of evidence combination.In this paper,both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction.The two criteria are jointly used by using rank-level fusion.Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.
Raia, S; Rossi, M; Baum, R L; Godt, J W; Guzzetti, F
2013-01-01
Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are deterministic. These models extend spatially the static stability models adopted in geotechnical engineering and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the existing models is the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of shallow rainfall-induced landslides. For the purpose, we have modified the TRIG...
Shayesteh, E. [Islamic Azad University, Garmsar Branch, Garmsar (Iran); Yousefi, A.; Parsa Moghaddam, M. [Department of Electrical Engineering, Tarbiat Modares University (TMU), Tehran (Iran)
2010-05-15
Spinning Reserve is one of the ancillary services which is essential to satisfy system security constraints when the power system faces with a contingency. In this paper, Day Ahead Demand Response Program as one of the incentive-based Demand Response programs is implemented as a source of spinning reserve. In this regard, certain number of demands are selected according to a sensitivity analysis, and simulated as virtual generation units. The reserve market is cleared for Spinning Reserve allocation considering a probabilistic technique. A comparison is performed between the absence and existence of Day Ahead Demand Response Program from both economical and reliability viewpoints. Numerical studies based on IEEE 57 bus test system is conducted for evaluation of the proposed method. (author)
Bouhadad, Youcef
2017-04-01
Northern Algeria is an interplate area where the African and the Eurasian tectonic plates are converging in the NW-SE direction. Therefore, earthquakes are not distributed randomly but directly related to the activity of active faults. The seismotectonic conditions of occurrence of strong damaging earthquakes in the area are well understood following the numerous detailed studies that followed the El-Asnam October 10th , 1980 earthquake (Ms=7.3) and the Zemmouri May 1st , 2003 (Mw=6.8) earthquake. The potentially active structures consist of active folds or asymmetrical folds underlined by thrust faults. Some of the faults are blind as revealed by the Chenoua 29th , 1989 (Ms=6.0) and the Ain Temouchent 1999 (Ms=5.6) earthquakes. We applied the probabilistic approach to assess seismic hazard in the area of Mostaganem, western Algeria. The following steps are performed (i) Seismic sources are identified on the basis of field geological/geophysical investigations,(ii) Source parameters such as b-values, slip rate and maximum magnitude are assessed for each seismic source, and then given a weight in the framework of a logic tree model, (iii) Attenuation relations which fit Algerian strong motion records are used, (iv) Results are presented as annual frequencies of exceedance versus peak ground acceleration (PGA) as well as maps of hazard for different return periods. Finally, we quantified and discussed the scientific uncertainties related to the state of knowledge and the used alternative models and values. Keywords: seismic hazard- active faults- probabilistic approach- uncertainties-Algeria
Mikaël Cozic
2016-11-01
Full Text Available The modeling of awareness and unawareness is a significant topic in the doxastic logic literature, where it is usually tackled in terms of full belief operators. The present paper aims at a treatment in terms of partial belief operators. It draws upon the modal probabilistic logic that was introduced by Aumann (1999 at the semantic level, and then axiomatized by Heifetz and Mongin (2001. The paper embodies in this framework those properties of unawareness that have been highlighted in the seminal paper by Modica and Rustichini (1999. Their paper deals with full belief, but we argue that the properties in question also apply to partial belief. Our main result is a (soundness and completeness theorem that reunites the two strands—modal and probabilistic—of doxastic logic.
14 CFR 1214.1105 - Final ranking.
2010-01-01
... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Final ranking. 1214.1105 Section 1214.1105... Recruitment and Selection Program § 1214.1105 Final ranking. Final rankings will be based on a combination of... preference will be included in this final ranking in accordance with applicable regulations....
Higher rank case of Dwork's conjecture
Wan, D
2000-01-01
This is the final version of ANT-0142 ("An embedding approach to Dwork's conjecture"). It reduces the higher rank case of the conjecture over a general base variety to the rank one case over the affine space. The general rank one case is completed in ANT-0235 "Rank one case of Dwork's conjecture". Both papers will appear in JAMS.
Geert Verdoolaege
2015-07-01
Full Text Available In regression analysis for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS is the most popular. In many situations, the assumptions underlying OLS are not fulfilled, and several other approaches have been proposed. However, most techniques address only part of the shortcomings of OLS. We here discuss a new and more general regression method, which we call geodesic least squares regression (GLS. The method is based on minimization of the Rao geodesic distance on a probabilistic manifold. For the case of a power law, we demonstrate the robustness of the method on synthetic data in the presence of significant uncertainty on both the data and the regression model. We then show good performance of the method in an application to a scaling law in magnetic confinement fusion.
Khan, F I; Iqbal, A; Ramesh, N; Abbasi, S A
2001-10-12
As it is conventionally done, strategies for incorporating accident--prevention measures in any hazardous chemical process industry are developed on the basis of input from risk assessment. However, the two steps-- risk assessment and hazard reduction (or safety) measures--are not linked interactively in the existing methodologies. This prevents a quantitative assessment of the impacts of safety measures on risk control. We have made an attempt to develop a methodology in which risk assessment steps are interactively linked with implementation of safety measures. The resultant system tells us the extent of reduction of risk by each successive safety measure. It also tells based on sophisticated maximum credible accident analysis (MCAA) and probabilistic fault tree analysis (PFTA) whether a given unit can ever be made 'safe'. The application of the methodology has been illustrated with a case study.
Refunjol, B.T. [Lagoven, S.A., Pdvsa (Venezuela); Lake, L.W. [Univ. of Texas, Austin, TX (United States)
1997-08-01
Quantification of the spatial distribution of properties is important for many reservoir-engineering applications. But, before applying any reservoir-characterization technique, the type of problem to be tackled and the information available should be analyzed. This is important because difficulties arise in reservoirs where production records are the only information for analysis. This paper presents the results of a practical technique to determine preferential flow trends in a reservoir. The technique is a combination of reservoir geology, tracer data, and Spearman rank correlation coefficient analysis. The Spearman analysis, in particular, will prove to be important because it appears to be insightful and uses injection/production data that are prevalent in circumstances where other data are nonexistent. The technique is applied to the North Buck Draw field, Campbell County, Wyoming. This work provides guidelines to assess information about reservoir continuity in interwell regions from widely available measurements of production and injection rates at existing wells. The information gained from the application of this technique can contribute to both the daily reservoir management and the future design, control, and interpretation of subsequent projects in the reservoir, without the need for additional data.
Ahmed, Qasim Zeeshan
2013-12-18
This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of L sensors. As the destination has the capacity of processing only U out of these L signals, the strongest U signals are selected while the remaining (L?U) signals are suppressed. A preprocessing block similar to channel-shortening is proposed in this contribution. However, this preprocessing block employs a rank-reduction technique instead of channel-shortening. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit error rate (BER) performance. From our simulations, it can be shown that these schemes outperform the channel-shortening schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared to channel-shortening schemes when sensors employ fixed gain amplification. However, for sensors which employ variable gain amplification, a tradeoff exists in terms of BER performance between the channel-shortening and these schemes. These schemes outperform channel-shortening scheme for lower signal-to-noise ratio.
Ranking of enabling technologies for oxy-fuel based carbon capture
Ochs, T.L.; Oryshchyn, D.L.; Ciferno, J.P.
2007-06-01
The USDOE National Energy Technology Laboratory (NETL) has begun a process to identify and rank enabling technologies that have significant impacts on pulverized coal oxy-fuel systems. Oxy-fuel combustion has been identified as a potential method for effectively capturing carbon in coal fired power plants. Presently there are a number of approaches for carbon capture via oxy-fuel combustion and it is important to order those approaches so that new research can concentrate on those technologies with high potentials to substantially lower the cost of reduced carbon electricity generation. NETL evaluates these technologies using computer models to determine the energy use of each technology and the potential impact of improvements in the technologies on energy production by a power plant. Near-term sub-critical boiler technologies are targeted for this analysis because: • most of the world continues to build single reheat sub-critical plants; • the overwhelming number of coal fired power plants requiring retrofit for CO2 capture are sub-critical plants. In addition, even in the realm of new construction, subcritical plants are common because they are well understood, easy to operate and maintain, fuel tolerant, and reliable. Following the initial investigation into sub-critical oxy-fuel technology, future investigations will move into the supercritical range.
Sampling scheme optimization for diffuse optical tomography based on data and image space rankings
Sabir, Sohail; Kim, Changhwan; Cho, Sanghoon; Heo, Duchang; Kim, Kee Hyun; Ye, Jong Chul; Cho, Seungryong
2016-10-01
We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source-detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling.
14th International Probabilistic Workshop
Taerwe, Luc; Proske, Dirk
2017-01-01
This book presents the proceedings of the 14th International Probabilistic Workshop that was held in Ghent, Belgium in December 2016. Probabilistic methods are currently of crucial importance for research and developments in the field of engineering, which face challenges presented by new materials and technologies and rapidly changing societal needs and values. Contemporary needs related to, for example, performance-based design, service-life design, life-cycle analysis, product optimization, assessment of existing structures and structural robustness give rise to new developments as well as accurate and practically applicable probabilistic and statistical engineering methods to support these developments. These proceedings are a valuable resource for anyone interested in contemporary developments in the field of probabilistic engineering applications.
Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game
Arnal, Louise; Ramos, Maria-Helena; Coughlan, Erin; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk-Jan; Pappenberger, Florian
2016-04-01
Forecast uncertainty is a twofold issue, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic forecasts over deterministic forecasts for a diversity of activities in the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. The setup and the results of a risk-based decision-making experiment, designed as a game on the topic of flood protection mitigation, called ``How much are you prepared to pay for a forecast?'', will be presented. The game was played at several workshops in 2015, including during this session at the EGU conference in 2015, and a total of 129 worksheets were collected and analysed. The aim of this experiment was to contribute to the understanding of the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game showed that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers. Balancing avoided costs and the cost (or the benefit) of having forecasts available for making decisions is not straightforward, even in a simplified game situation, and is a topic that deserves more attention from the hydrological forecasting community in the future.
Ranking Economic History Journals
Di Vaio, Gianfranco; Weisdorf, Jacob Louis
This study ranks - for the first time - 12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We...... also compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential...... for economic history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....
基于LDA模型的餐馆评论排序%Ranks of Restaurant Reviews Based on LDA Model
吕韶华; 杨亮; 林鸿飞
2011-01-01
In order to solve the problems of implicit aspects in review text and the inconsistency between rank of review and review text, this paper makes use of LDA on restaurant reviews to get the useful topics and discard unrelated ones, then gets the scores of some aspects based on them, and at last a model, which can predict ranks of restaurants based on new reviews, is trained with logistic regression using these scores. Experimental results show that the effectiveness of this method is better.%在餐馆评论中,存在评论文本未明确指出评价等级及评论文本不一致等问题.为此,提出一种基于LDA模型的餐馆评论排序方法.利用LDA模型对评论文本进行主题抽取,过滤掉不相关评论,基于过滤后的用户评论和用户给出的评论等级计算餐馆评论若干方面的得分,在该得分的基础上,利用逻辑回归进行训练,得到餐馆评论排序模型.实验结果表明,该方法的排序效果较优.
Probabilistic Planning with Imperfect Sensing Actions Using Hybrid Probabilistic Logic Programs
Saad, Emad
Effective planning in uncertain environment is important to agents and multi-agents systems. In this paper, we introduce a new logic based approach to probabilistic contingent planning (probabilistic planning with imperfect sensing actions), by relating probabilistic contingent planning to normal hybrid probabilistic logic programs with probabilistic answer set semantics [24]. We show that any probabilistic contingent planning problem can be encoded as a normal hybrid probabilistic logic program. We formally prove the correctness of our approach. Moreover, we show that the complexity of finding a probabilistic contingent plan in our approach is NP-complete. In addition, we show that any probabilistic contingent planning problem, \\cal PP, can be encoded as a classical normal logic program with answer set semantics, whose answer sets corresponds to valid trajectories in \\cal PP. We show that probabilistic contingent planning problems can be encoded as SAT problems. We present a new high level probabilistic action description language that allows the representation of sensing actions with probabilistic outcomes.
Probabilistic authenticated quantum dialogue
Hwang, Tzonelih; Luo, Yi-Ping
2015-12-01
This work proposes a probabilistic authenticated quantum dialogue (PAQD) based on Bell states with the following notable features. (1) In our proposed scheme, the dialogue is encoded in a probabilistic way, i.e., the same messages can be encoded into different quantum states, whereas in the state-of-the-art authenticated quantum dialogue (AQD), the dialogue is encoded in a deterministic way; (2) the pre-shared secret key between two communicants can be reused without any security loophole; (3) each dialogue in the proposed PAQD can be exchanged within only one-step quantum communication and one-step classical communication. However, in the state-of-the-art AQD protocols, both communicants have to run a QKD protocol for each dialogue and each dialogue requires multiple quantum as well as classical communicational steps; (4) nevertheless, the proposed scheme can resist the man-in-the-middle attack, the modification attack, and even other well-known attacks.
Sarrafi, Aral; Mao, Zhu
2016-04-01
In the application of Structural Health Monitoring (SHM), processing the online-acquired data plays a very important role, among which wavelet transform is an outstanding tool and compared to Fourier transform, it handles the nonstationary behaviors in the time series in an adaptive fashion. When dealing with time-variant data, there are uncertainties from numerous resources inherent to the feature estimation, such as measurement noise, operational and environmental variability, hardware limitation, etc. The corruption from uncertainty will make the data interpretation ambiguous and thereby dramatically degrades the decision quality with regard to the occurrence, location, severity, and extent of damages. This paper derives a probabilistic model to quantify analytically the uncertainty of wavelet transform feature as a random variable, and variance is derived analytically in this work. Considering central limit theorem, Gaussian probability density function characterizes the distribution and this has been validated via Monte Carlo testing. By fully characterizing the uncertainty, the damage detection implementations may be facilitated with a quantified false alarm rate and miss catch rate.
Cetin, K.O.; Seed, R.B.; Der Kiureghian, A.; Tokimatsu, K.; Harder, L.F.; Kayen, R.E.; Moss, R.E.S.
2004-01-01
This paper presents'new correlations for assessment of the likelihood of initiation (or triggering) of soil liquefaction. These new correlations eliminate several sources of bias intrinsic to previous, similar correlations, and provide greatly reduced overall uncertainty and variance. Key elements in the development of these new correlations are (1) accumulation of a significantly expanded database of field performance case histories; (2) use of improved knowledge and understanding of factors affecting interpretation of standard penetration test data; (3) incorporation of improved understanding of factors affecting site-specific earthquake ground motions (including directivity effects, site-specific response, etc.); (4) use of improved methods for assessment of in situ cyclic shear stress ratio; (5) screening of field data case histories on a quality/uncertainty basis; and (6) use of high-order probabilistic tools (Bayesian updating). The resulting relationships not only provide greatly reduced uncertainty, they also help to resolve a number of corollary issues that have long been difficult and controversial including: (1) magnitude-correlated duration weighting factors, (2) adjustments for fines content, and (3) corrections for overburden stress. ?? ASCE.
A mathematical framework for probabilistic choice based on information theory and psychophysics.
Takahashi, Taiki
2006-01-01
Risky decision-making (e.g. reward dependency) has been associated with substance abuse, psychopathy and pathological gambling; conversely, marked sensitivity to risk and uncertainty have been observed in anxiety disorder patients. In economic decision theory, probability and uncertainty have been dissociated. Frank Knight defined uncertainty as loss of information on the probability distribution of outcomes for choices (i.e., unpredictability), which is referred to as Knightian uncertainty (also as ambiguity). However, even when the probability distribution of outcomes is known, there are different degrees of predictability. In information theory, this type of degrees of uncertainty/unpredictability has been parametrized by introducing Shannon entropy. In the present paper, we show: (i) a mathematical framework combining Shannon entropy in information theory and Weber's law in psychophysics is capable of parametrizing subject's level of both aversion to probabilistic uncertainty (exaggerated in anxiety disorder patients) and reward dependency (enhanced in drug addicts and pathological gamblers), and (ii) this framework has an analogue in thermodynamics, therefore this can readily be utilized in studies in the nascent fields of neuroeconomics and econophysics as well. Future study directions for elucidating maladaptive personality characteristics in neuropsychiatric patients by using the present framework are discussed.
Aita, Takuyo; Ichihashi, Norikazu; Yomo, Tetsuya
2013-12-01
To analyze the evolutionary dynamics of a mutant population in an evolutionary experiment, it is necessary to sequence a vast number of mutants by high-throughput (next-generation) sequencing technologies, which enable rapid and parallel analysis of multikilobase sequences. However, the observed sequences include many errors of base call. Therefore, if next-generation sequencing is applied to analysis of a heterogeneous population of various mutant sequences, it is necessary to discriminate between true bases as point mutations and errors of base call in the observed sequences, and to subject the sequences to error-correction processes. To address this issue, we have developed a novel method of error correction based on the Potts model and a maximum a posteriori probability (MAP) estimate of its parameters corresponding to the "true sequences". Our method of error correction utilizes (1) the "quality scores" which are assigned to individual bases in the observed sequences and (2) the neighborhood relationship among the observed sequences mapped in sequence space. The computer experiments of error correction of artificially generated sequences supported the effectiveness of our method, showing that 50-90% of errors were removed. Interestingly, this method is analogous to a probabilistic model based method of image restoration developed in the field of information engineering.
Mattila, Keijo Kalervo; Hegele Júnior, Luiz Adolfo; Philippi, Paulo Cesar
2014-01-01
We propose isotropic finite differences for high-accuracy approximation of high-rank derivatives. These finite differences are based on direct application of lattice-Boltzmann stencils. The presented finite-difference expressions are valid in any dimension, particularly in two and three dimensions, and any lattice-Boltzmann stencil isotropic enough can be utilized. A theoretical basis for the proposed utilization of lattice-Boltzmann stencils in the approximation of high-rank derivatives is established. In particular, the isotropy and accuracy properties of the proposed approximations are derived directly from this basis. Furthermore, in this formal development, we extend the theory of Hermite polynomial tensors in the case of discrete spaces and present expressions for the discrete inner products between monomials and Hermite polynomial tensors. In addition, we prove an equivalency between two approaches for constructing lattice-Boltzmann stencils. For the numerical verification of the presented finite differences, we introduce 5th-, 6th-, and 8th-order two-dimensional lattice-Boltzmann stencils.
Probabilistic household forecasts based on register data- the case of Denmark and Finland
Solveig Christiansen
2013-06-01
Full Text Available BACKGROUND Household forecasts are important for public planning and for predicting consumer demand. OBJECTIVE The purpose of this paper is to compute probabilistic household forecasts for Finland and Denmark, taking advantage of unique housing register data covering the whole populations dating back to the 1980s. A major advantage is that we do not have to rely on small population samples, and we can get quite reliable estimates even for infrequent transitions. A further merit is having time series containing the population in different household positions (dependent child, living with a spouse, living in a consensual union, living alone, lone parent, living in other private household and institutional households by age and sex. METHODS These series enable us to estimate the uncertainty in the future distribution of the population across household positions. Combining these uncertainty parameters with expected shares computed in a deterministic household forecast, we simulate 3000 sample paths for the household shares for each age and sex. These paths are then combined with 3000 simulations from a stochastic population forecast covering the same period to obtain the predicted number of households and persons in each household position by age and sex. RESULTS According to our forecasts, we expect a strong growth in the number of private households during a 30-year period, of 27Š in Finland and 13Š in Denmark. The number of households consisting of a married couple or a person who lives alone are the most certain, and single parents and other private households are the most uncertain.
Probabilistic seismic hazard in the San Francisco Bay area based on seismicity simulation
Pollitz, F. F.
2008-12-01
Understanding how fault systems evolve in time under a relevant set of governing physical laws is a needed critical step towards reliable earthquake forecasting. We can address issues relevant to probabilistic seismic hazard analysis (e.g. recurrence time, coefficient of variation, probability of multi-segment rupture) with numerical simulations of seismicity. A seismicity simulator essentially provides a means of tracking the increasing tectonic stress as it loads the faults and determines how stress is redistributed among the network faults as the result of an earthquake. I implement a seismicity simulator that includes the effects of: (1) tectonic loading of a plate boundary zone; (2) static stress transfer; (3) viscoelasticity of the ductile lower crust and mantle; (4) length- and depth-dependent fault slip. I apply it to a network of multiple interacting faults in the San Francisco Bay area. Earthquake initiation, propagation, and termination are governed by a cascade model using a Coulomb failure function. 30000 years of simulated seismicity yield probability density functions of inter-event times on all major faults at practically a continuum of magnitude thresholds. At a threshold of M6.5, reasonable combinations of controlling parameters yield mean inter-event times of ~ 140 years for the southern Hayward and Rodgers Creek faults and ~ 250 years for the northern Hayward and northern Calaveras faults. To help interpret simulation results I explore systematic covariations among mean characteristic magnitude, coefficient of variation (typical values are 0.4 to 0.6), degree of dynamic overshoot, and mantle viscosity.
Probabilistic simulation of fire scenarios
Hostikka, Simo E-mail: simo.bostikka@vtt.fi; Keski-Rahkonen, Olavi
2003-10-01
A risk analysis tool is developed for computation of the distributions of fire model output variables. The tool, called Probabilistic Fire Simulator (PFS), combines Monte Carlo simulation and CFAST, a two-zone fire model. In this work, the tool is used to estimate the failure probability of redundant cables in a cable tunnel fire, and the failure and smoke filling probabilities in an electronics room during an electronics cabinet fire. Sensitivity of the output variables to the input variables is calculated in terms of the rank order correlations. The use of the rank order correlations allows the user to identify both modelling parameters and actual facility properties that have the most influence on the results. Various steps of the simulation process, i.e. data collection, generation of the input distributions, modelling assumptions, definition of the output variables and the actual simulation, are described.
Cunha, S.
2015-12-01
Full Text Available Nowadays, considering the high variety of construction products, adequate material selection, based on their properties and function, becomes increasingly important. In this research, a ranking procedure developed by Czarnecki and Lukowski is applied in mortars with incorporation of phase change materials (PCM. The ranking procedure transforms experimental results of properties into one numerical value. The products can be classified according to their individual properties or even an optimized combination of different properties. The main purpose of this study was the ranking of mortars with incorporation of different contents of PCM based in different binders. Aerial lime, hydraulic lime, gypsum and cement were the binders studied. For each binder, three different mortars were developed. Reference mortars, mortars with incorporation of 40% of PCM and mortars with incorporation of 40% of PCM and 1% of fibers, were tested. Results show that the incorporation of PCM in mortars changes their global performance.Actualmente, existen varios productos de construcción, siendo importante una adecuada selección, con base en sus principales propiedades y funciones. En esta investigación se aplicó un procedimiento de clasificación desarrollado por Czarnecki y Lukowski, en morteros con incorporación de materiales de cambio de fase (PCM. Este procedimiento transforma los resultados experimentales de las propiedades en un único valor numérico. Los productos se clasifican de acuerdo con sus propiedades individuales o en una combinación optimizada de diferentes propiedades. El principal objetivo de este estudio fue la clasificación de morteros basado en los diferentes aglutinantes con incorporación de diferentes cantidades de PCM. Los aglutinantes utilizados fueran la cal aérea, cal hidráulica, yeso y cemento. Para cada aglutinante se han desarrollado tres morteros, siendo morteros de referencia, con incorporación de 40% de PCM y con incorporaci
Multiple graph regularized protein domain ranking
Wang, Jim Jing-Yan
2012-11-19
Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.
Consistent ranking of volatility models
Hansen, Peter Reinhard; Lunde, Asger
2006-01-01
We show that the empirical ranking of volatility models can be inconsistent for the true ranking if the evaluation is based on a proxy for the population measure of volatility. For example, the substitution of a squared return for the conditional variance in the evaluation of ARCH-type models can...
Haagsma Juanita A
2008-12-01
Full Text Available Abstract Background Burden of disease estimates, which combine mortality and morbidity into a single measure, are used increasingly for priority setting in disease control, prevention and surveillance. However, because there is no clear exclusion criterion for highly prevalent minimal disease in burden of disease studies its application may be restricted. The aim of this study was to apply a newly developed relevance criterion based on preferences of a population panel, and to compare burden of disease estimates of five foodborne pathogens calculated with and without application of this criterion. Methods Preferences for twenty health states associated with foodborne disease were obtained from a population panel (n = 107 with the Visual Analogue Scale and the Time Trade-off (TTO technique. The TTO preferences were used to derive the relevance criterion: if at least 50% of a panel of judges is willing to trade-off time in order to be restored to full health the health state is regarded as relevant, i.e. TTO median is greater than 0. Subsequently, the burden of disease of each of the five foodborne pathogens was calculated both with and without the relevance criterion. Results The panel ranked the health states consistently. Of the twenty health states, three did not meet the preference-based relevance criterion. Application of the relevance criterion reduced the burden of disease estimate of all five foodborne pathogens. The reduction was especially significant for norovirus and rotavirus, decreasing with 94% and 78% respectively. Conclusion Individual preferences elicited with the TTO from a population panel can be used to empirically derive a relevance criterion for burden of disease estimates. Application of this preference-based relevance criterion results in considerable changes in ranking of foodborne pathogens.
Liu, Xian; Xu, Yuan; Li, Shanshan; Wang, Yulan; Peng, Jianlong; Luo, Cheng; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang
2014-01-01
Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound. We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities. With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.
Probabilistic Tsunami Hazard Analysis
Thio, H. K.; Ichinose, G. A.; Somerville, P. G.; Polet, J.
2006-12-01
The recent tsunami disaster caused by the 2004 Sumatra-Andaman earthquake has focused our attention to the hazard posed by large earthquakes that occur under water, in particular subduction zone earthquakes, and the tsunamis that they generate. Even though these kinds of events are rare, the very large loss of life and material destruction caused by this earthquake warrant a significant effort towards the mitigation of the tsunami hazard. For ground motion hazard, Probabilistic Seismic Hazard Analysis (PSHA) has become a standard practice in the evaluation and mitigation of seismic hazard to populations in particular with respect to structures, infrastructure and lifelines. Its ability to condense the complexities and variability of seismic activity into a manageable set of parameters greatly facilitates the design of effective seismic resistant buildings but also the planning of infrastructure projects. Probabilistic Tsunami Hazard Analysis (PTHA) achieves the same goal for hazards posed by tsunami. There are great advantages of implementing such a method to evaluate the total risk (seismic and tsunami) to coastal communities. The method that we have developed is based on the traditional PSHA and therefore completely consistent with standard seismic practice. Because of the strong dependence of tsunami wave heights on bathymetry, we use a full waveform tsunami waveform computation in lieu of attenuation relations that are common in PSHA. By pre-computing and storing the tsunami waveforms at points along the coast generated for sets of subfaults that comprise larger earthquake faults, we can efficiently synthesize tsunami waveforms for any slip distribution on those faults by summing the individual subfault tsunami waveforms (weighted by their slip). This efficiency make it feasible to use Green's function summation in lieu of attenuation relations to provide very accurate estimates of tsunami height for probabilistic calculations, where one typically computes
Relevance ranking for vertical search engines
Chang, Yi
2014-01-01
In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Search Engines teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications. This reference book for professionals covers concepts and theories from the fundamental to the advanced, such as relevance, query intention, location-based relevance ranking, and cross-property ranking. It covers the most recent developments in vertical search ranking applications, such as freshness-based relevance theory for new search applications, location-based relevance theory for local search applications, and cross-property ranking theory for applications involving multiple verticals. It introduces ranking algorithms and teaches readers how to manipulate ranking algorithms for the best results. It covers concepts and theories from the fundamental to the advanced. It discusses the state of the art: development of ...
Probabilistic models of language processing and acquisition.
Chater, Nick; Manning, Christopher D
2006-07-01
Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.
Renata Maciel de Melo
2015-03-01
Full Text Available The quality of the construction production process may be improved using several different methods such as Lean Construction, ISO 9001, ISO 14001 or ISO 18001. Construction companies need a preliminary study and systematic implementation of changes to become more competitive and efficient. This paper presents a multicriteria decision model for the selection and ranking of such alternatives for improvement approaches regarding the aspects of quality, sustainability and safety, based on the PROMETHEE II method. The adoption of this model provides more confidence and visibility for decision makers. One of the differentiators of this model is the use of a fragmented set of improvement alternatives. These alternatives were combined with some restrictions to create a global set of alternatives. An application to three scenarios, considering realistic data, was developed. The results of the application show that the model should be incorporated into the strategic planning process of organizations.
Ranking coastal flood protection designs from engineered to nature-based
Nat, van der A.; Vellinga, P.; Leemans, R.; Slobbe, van E.
2016-01-01
Compared to traditional hard engineering, nature-based flood protection can be more cost effective, use up less raw materials, increase system adaptability and present opportunities to improve ecosystem functioning. However, high flood safety standards cause the need to combine nature-based struc
张广利; 赵新伟; 罗金恒; 张良
2012-01-01
A kind of probabilistic assessment method was worked out in order to improve the reliability of the safety assessment of corroded pipeline. Three corroded pipeline assessment criteria including NG - 18 , B31G and PCORRC were compared, and their bulging factors were analyzed. Based on the comparison results, a probabilistic assessment method was worked out for the corroded pipeline. The variation trends of reliability of corroded pipeline with the defect size were discussed using Monte-Carlo simulation method. Furthermore, a sensitivity analysis method was worked out to rank the influences of assessment parameters on the reliability. A determination method of critical defect size was worked out.%为提高含腐蚀缺陷管道安全评价的可靠性,建立一种概率评价方法.分析3种常用的含腐蚀缺陷管道评价准则(NG-18,B31G及PCORRC评价准则),对比评价准则表达式中的鼓胀因子.引入可靠性理论,利用蒙特卡洛模拟技术,研究管道可靠度随缺陷尺寸变化的规律,以及通过敏感性分析,对比评价参数对管道安全可靠性的影响,并进行排序,建立含腐蚀缺陷管道评价准则的概率评价方法.在3种评价准则中,影响管道安全可靠性的关键参数均为管材力学性能的分散性.运行压力的波动,对管道的可靠性也有较大影响.
Diversifying customer review rankings.
Krestel, Ralf; Dokoohaki, Nima
2015-06-01
E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything. Current approaches to display reviews to users or recommend an individual review for a product are based on the recency or helpfulness of each review. In this paper, we present a framework to rank product reviews by optimizing the coverage of the ranking with respect to sentiment or aspects, or by summarizing all reviews with the top-K reviews in the ranking. To accomplish this, we make use of the assigned star rating for a product as an indicator for a review's sentiment polarity and compare bag-of-words (language model) with topic models (latent Dirichlet allocation) as a mean to represent aspects. Our evaluation on manually annotated review data from a commercial review Web site demonstrates the effectiveness of our approach, outperforming plain recency ranking by 30% and obtaining best results by combining language and topic model representations.
Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.; Sun, Alexander Y.; Xia, Youlong
2016-11-01
Prediction of drought plays an important role in drought preparedness and mitigation, especially because of large impacts of drought and increasing demand for water resources. An important aspect for improving drought prediction skills is the identification of drought predictability sources. In general, a drought originates from precipitation deficit and thus the antecedent meteorological drought may provide predictive information for other types of drought. In this study, a hydrological drought (represented by Standardized Runoff Index (SRI)) prediction method is proposed based on the meta-Gaussian model taking into account the persistence and its prior meteorological drought condition (represented by Standardized Precipitation Index (SPI)). Considering the inherent nature of standardized drought indices, the meta-Gaussian model arises as a suitable model for constructing the joint distribution of multiple drought indices. Accordingly, the conditional distribution of hydrological drought can be derived analytically, which enables the probabilistic prediction of hydrological drought in the target period and uncertainty quantifications. Based on monthly precipitation and surface runoff of climate divisions of Texas, U.S., 1-month and 2-month lead predictions of hydrological drought are illustrated and compared to the prediction from Ensemble Streamflow Prediction (ESP). Results, based on 10 climate divisions in Texas, show that the proposed meta-Gaussian model provides useful drought prediction information with performance depending on regions and seasons.
K S Vipin; T G Sitharam
2013-06-01
The delineation of seismic source zones plays an important role in the evaluation of seismic hazard. In most of the studies the seismic source delineation is done based on geological features. In the present study, an attempt has been made to delineate seismic source zones in the study area (south India) based on the seismicity parameters. Seismicity parameters and the maximum probable earthquake for these source zones were evaluated and were used in the hazard evaluation. The probabilistic evaluation of seismic hazard for south India was carried out using a logic tree approach. Two different types of seismic sources, linear and areal, were considered in the present study to model the seismic sources in the region more precisely. In order to properly account for the attenuation characteristics of the region, three different attenuation relations were used with different weightage factors. Seismic hazard evaluation was done for the probability of exceedance (PE) of 10% and 2% in 50 years. The spatial variation of rock level peak horizontal acceleration (PHA) and spectral acceleration (Sa) values corresponding to return periods of 475 and 2500 years for the entire study area are presented in this work. The peak ground acceleration (PGA) values at ground surface level were estimated based on different NEHRP site classes by considering local site effects.
Zendehrouh, Sareh
2015-11-01
Recent work on decision-making field offers an account of dual-system theory for decision-making process. This theory holds that this process is conducted by two main controllers: a goal-directed system and a habitual system. In the reinforcement learning (RL) domain, the habitual behaviors are connected with model-free methods, in which appropriate actions are learned through trial-and-error experiences. However, goal-directed behaviors are associated with model-based methods of RL, in which actions are selected using a model of the environment. Studies on cognitive control also suggest that during processes like decision-making, some cortical and subcortical structures work in concert to monitor the consequences of decisions and to adjust control according to current task demands. Here a computational model is presented based on dual system theory and cognitive control perspective of decision-making. The proposed model is used to simulate human performance on a variant of probabilistic learning task. The basic proposal is that the brain implements a dual controller, while an accompanying monitoring system detects some kinds of conflict including a hypothetical cost-conflict one. The simulation results address existing theories about two event-related potentials, namely error related negativity (ERN) and feedback related negativity (FRN), and explore the best account of them. Based on the results, some testable predictions are also presented. Copyright © 2015 Elsevier Ltd. All rights reserved.
The Asymptotics of Ranking Algorithms
Duchi, John C; Jordan, Michael I
2012-01-01
We consider the predictive problem of supervised ranking, where the task is to rank sets of candidate items returned in response to queries. Although there exist statistical procedures that come with guarantees of consistency in this setting, these procedures require that individuals provide a complete ranking of all items, which is rarely feasible in practice. Instead, individuals routinely provide partial preference information, such as pairwise comparisons of items, and more practical approaches to ranking have aimed at modeling this partial preference data directly. As we show, however, such an approach has serious theoretical shortcomings. Indeed, we demonstrate that many commonly used surrogate losses for pairwise comparison data do not yield consistency; surprisingly, we show inconsistency even in low-noise settings. With these negative results as motivation, we present a new approach to supervised ranking based on aggregation of partial preferences and develop $U$-statistic-based empirical risk minimi...
Improving Ranking Using Quantum Probability
Melucci, Massimo
2011-01-01
The paper shows that ranking information units by quantum probability differs from ranking them by classical probability provided the same data used for parameter estimation. As probability of detection (also known as recall or power) and probability of false alarm (also known as fallout or size) measure the quality of ranking, we point out and show that ranking by quantum probability yields higher probability of detection than ranking by classical probability provided a given probability of false alarm and the same parameter estimation data. As quantum probability provided more effective detectors than classical probability within other domains that data management, we conjecture that, the system that can implement subspace-based detectors shall be more effective than a system which implements a set-based detectors, the effectiveness being calculated as expected recall estimated over the probability of detection and expected fallout estimated over the probability of false alarm.
ZHANG; Yunqiu; GUO; Kelei
2009-01-01
Based on the analysis of the existing ranking terminology or subject relevancy of documents methods through an intermediary collection as a catalyst(designated as Group B collection)for the purpose of of non-interactive literature-based discovery,this article proposes a bi-directional document occurrence frequency based ranking method according to the"concurrence theory"and the degree and extent of the subject relevancy.This method explores and further refines the ranking method that is based on the occurrence frequency of the usage of certain terminologies and documents and injects a new insightful perspective of the concurrence of appropriate terminologies/documents in the"low occurrence frequency component"of three non-interactive document collections.A preliminary experiment was conducted to analyze and to test the significance and viability of our newly designed operational method.
Schweizer, B
2005-01-01
Topics include special classes of probabilistic metric spaces, topologies, and several related structures, such as probabilistic normed and inner-product spaces. 1983 edition, updated with 3 new appendixes. Includes 17 illustrations.
Ghosh, Saswata; Hornby, Sidney; Grove, Gary; Zerwick, Charles; Appa, Yohini; Blankschtein, Daniel
2007-01-01
We propose that skin electrical current measurements can be used in vitro to effectively rank aqueous solutions containing surfactants and humectants (the enhancer) contacting the skin, relative to a PBS aqueous solution (the control) contacting the skin, based on their ability to perturb the skin aqueous pores. Specifically, we develop an in vitro ranking metric using the increase in the skin electrical current induced by an enhancer relative to the control. Aqueous contacting solutions containing (i) surfactants [SDS (sodium dodecyl sulfate)] and C(12)E(6) [dodecyl hexa (ethylene oxide)], (ii) humectants (glycerol and propylene glycol), and (iii) a control (PBS) were studied. Utilizing the new in vitro ranking metric, these aqueous contacting solutions were ranked as follows (from the mildest to the harshest): glycerol aqueous surfactant-humectant solutions described above. The results of these in vivo measurements were found to be consistent with the ranking results obtained using the in vitro ranking metric. To further explore the validity of our model and to verify the skin barrier mitigating effect of glycerol, in vivo soap chamber measurements were carried out for aqueous SDS solutions containing 10 wt% added glycerol. These in vivo measurements support our recent in vitro finding that glycerol reduces the average radius and the pore number density of the skin aqueous pores, such that SDS micelles are hindered from penetrating into the skin and inducing skin barrier perturbation.
Poongavanam, Vasanthanathan; Svendsen, Casper Steinmann; Kongsted, Jacob
2014-01-01
Quantum mechanical (QM) calculations have been used to predict the binding affinity of a set of ligands towards HIV-1 RT associated RNase H (RNH). The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function. Furt....... Thus, the computational models tested in this study could be useful as high throughput filters for searching HIV-1 RNase H active-site molecules in the virtual screening process.......Quantum mechanical (QM) calculations have been used to predict the binding affinity of a set of ligands towards HIV-1 RT associated RNase H (RNH). The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function...... of the methods based on the use of a training set of molecules, QM based chelation calculations were used as filter in virtual screening of compounds in the ZINC database. By this, we find, compared to regular docking, QM based chelation calculations to significantly reduce the large number of false positives...
Jing Xu
2015-10-01
Full Text Available In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD and Probabilistic Neural Network (PNN is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method.
Mahmud, Zamalia; Porter, Anne; Salikin, Masniyati; Ghani, Nor Azura Md
2015-12-01
Students' understanding of probability concepts have been investigated from various different perspectives. Competency on the other hand is often measured separately in the form of test structure. This study was set out to show that perceived understanding and competency can be calibrated and assessed together using Rasch measurement tools. Forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW have volunteered to participate in the study. Rasch measurement which is based on a probabilistic model is used to calibrate the responses from two survey instruments and investigate the interactions between them. Data were captured from the e-learning platform Moodle where students provided their responses through an online quiz. The study shows that majority of the students perceived little understanding about conditional and independent events prior to learning about it but tend to demonstrate a slightly higher competency level afterward. Based on the Rasch map, there is indication of some increase in learning and knowledge about some probability concepts at the end of the two weeks lessons on probability concepts.
Mahata, Avik; Mukhopadhyay, Tanmoy; Adhikari, Sondipon
2016-03-01
Nano-twinned structures are mechanically stronger, ductile and stable than its non-twinned form. We have investigated the effect of varying twin spacing and twin boundary width (TBW) on the yield strength of the nano-twinned copper in a probabilistic framework. An efficient surrogate modelling approach based on polynomial chaos expansion has been proposed for the analysis. Effectively utilising 15 sets of expensive molecular dynamics simulations, thousands of outputs have been obtained corresponding to different sets of twin spacing and twin width using virtual experiments based on the surrogates. One of the major outcomes of this work is that there exists an optimal combination of twin boundary spacing and twin width until which the strength can be increased and after that critical point the nanowires weaken. This study also reveals that the yield strength of nano-twinned copper is more sensitive to TBW than twin spacing. Such robust inferences have been possible to be drawn only because of applying the surrogate modelling approach, which makes it feasible to obtain results corresponding to 40 000 combinations of different twin boundary spacing and twin width in a computationally efficient framework.
Sivathayalan S.
2012-01-01
Full Text Available The current practice of detailed seismic risk assessment cannot be easily applied to all the bridges in a large transportation networks due to limited resources. This paper presents a new approach for seismic risk assessment of large bridge inventories in a city or national bridge network based on the framework of probabilistic performance based seismic risk assessment. To account for the influences of local site effects, a procedure to generate site-specific hazard curves that includes seismic hazard microzonation information has been developed for seismic risk assessment of bridge inventories. Simulated ground motions compatible with the site specific seismic hazard are used as input excitations in nonlinear time history analysis of representative bridges for calibration. A normalizing procedure to obtain generalized fragility relationships in terms of structural characteristic parameters of bridge span and size and longitudinal and transverse reinforcement ratios is presented. The seismic risk of bridges in a large inventory can then be easily evaluated using the normalized fragility relationships without the requirement of carrying out detailed nonlinear time history analysis.
Zeeshan Ali Siddiqui
2016-01-01
Full Text Available Component-based software system (CBSS development technique is an emerging discipline that promises to take software development into a new era. As hardware systems are presently being constructed from kits of parts, software systems may also be assembled from components. It is more reliable to reuse software than to create. It is the glue code and individual components reliability that contribute to the reliability of the overall system. Every component contributes to overall system reliability according to the number of times it is being used, some components are of critical usage, known as usage frequency of component. The usage frequency decides the weight of each component. According to their weights, each component contributes to the overall reliability of the system. Therefore, ranking of components may be obtained by analyzing their reliability impacts on overall application. In this paper, we propose the application of fuzzy multi-objective optimization on the basis of ratio analysis, Fuzzy-MOORA. The method helps us find the best suitable alternative, software component, from a set of available feasible alternatives named software components. It is an accurate and easy to understand tool for solving multi-criteria decision making problems that have imprecise and vague evaluation data. By the use of ratio analysis, the proposed method determines the most suitable alternative among all possible alternatives, and dimensionless measurement will realize the job of ranking of components for estimating CBSS reliability in a non-subjective way. Finally, three case studies are shown to illustrate the use of the proposed technique.
Vasanthanathan Poongavanam
Full Text Available Quantum mechanical (QM calculations have been used to predict the binding affinity of a set of ligands towards HIV-1 RT associated RNase H (RNH. The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function. Furthermore, full protein fragment molecular orbital (FMO calculations were conducted and subsequently analysed for individual residue stabilization/destabilization energy contributions to the overall binding affinity in order to better understand the true and false predictions. After a successful assessment of the methods based on the use of a training set of molecules, QM based chelation calculations were used as filter in virtual screening of compounds in the ZINC database. By this, we find, compared to regular docking, QM based chelation calculations to significantly reduce the large number of false positives. Thus, the computational models tested in this study could be useful as high throughput filters for searching HIV-1 RNase H active-site molecules in the virtual screening process.
Ranking alternatives based on imprecise multi-criteria data and pairwise overlap dominance relations
Franco de los Rios, Camilo Andres; Hougaard, Jens Leth; Nielsen, Kurt
This paper explores a multi-criteria outranking methodology that is designed to both handle uncertain and imprecise data in describing alternatives as well as treating the decision maker's preference information in a sensible way that re flects the difficulties in articulating preferences. Based ...
Design and analysis of a ranking approach to private location-based services
Yiu, Ma Lung; Jensen, Christian S.; Møller, Jesper
2011-01-01
Twist, aims to offer location privacy for k nearest neighbor (kNN) queries at low communication cost without requiring a trusted anonymizer. The solution can be used with a conventional DBMS as well as with a server optimized for location-based services. In particular, we believe that this is the first...
Probabilistic Concurrent Kleene Algebra
Annabelle McIver
2013-06-01
Full Text Available We provide an extension of concurrent Kleene algebras to account for probabilistic properties. The algebra yields a unified framework containing nondeterminism, concurrency and probability and is sound with respect to the set of probabilistic automata modulo probabilistic simulation. We use the resulting algebra to generalise the algebraic formulation of a variant of Jones' rely/guarantee calculus.
Li, Linlin; Switzer, Adam D.; Wang, Yu; Chan, Chung-Han; Qiu, Qiang; Weiss, Robert
2017-04-01
Current tsunami inundation maps are commonly generated using deterministic scenarios, either for real-time forecasting or based on hypothetical "worst-case" events. Such maps are mainly used for emergency response and evacuation planning and do not include the information of return period. However, in practice, probabilistic tsunami inundation maps are required in a wide variety of applications, such as land-use planning, engineer design and for insurance purposes. In this study, we present a method to develop the probabilistic tsunami inundation map using a stochastic earthquake source model. To demonstrate the methodology, we take Macau a coastal city in the South China Sea as an example. Two major advances of this method are: it incorporates the most updated information of seismic tsunamigenic sources along the Manila megathrust; it integrates a stochastic source model into a Monte Carlo-type simulation in which a broad range of slip distribution patterns are generated for large numbers of synthetic earthquake events. When aggregated the large amount of inundation simulation results, we analyze the uncertainties associated with variability of earthquake rupture location and slip distribution. We also explore how tsunami hazard evolves in Macau in the context of sea level rise. Our results suggest Macau faces moderate tsunami risk due to its low-lying elevation, extensive land reclamation, high coastal population and major infrastructure density. Macau consists of four districts: Macau Peninsula, Taipa Island, Coloane island and Cotai strip. Of these Macau Peninsula is the most vulnerable to tsunami due to its low-elevation and exposure to direct waves and refracted waves from the offshore region and reflected waves from mainland. Earthquakes with magnitude larger than Mw8.0 in the northern Manila trench would likely cause hazardous inundation in Macau. Using a stochastic source model, we are able to derive a spread of potential tsunami impacts for earthquakes
Learning Probabilistic Decision Graphs
Jaeger, Manfred; Dalgaard, Jens; Silander, Tomi
2004-01-01
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence relations that cannot be captured in a Bayesian network structure, and can sometimes provide computationally more...... efficient representations than Bayesian networks. In this paper we present an algorithm for learning PDGs from data. First experiments show that the algorithm is capable of learning optimal PDG representations in some cases, and that the computational efficiency of PDG models learned from real-life data...
Tensor Rank and Stochastic Entanglement Catalysis for Multipartite Pure States
Chen, Lin; Duan, Runyao; Ji, Zhengfeng; Winter, Andreas
2010-01-01
The tensor rank (aka generalized Schmidt rank) of multipartite pure states plays an important role in the study of entanglement classifications and transformations. We employ powerful tools from the theory of homogeneous polynomials to investigate the tensor rank of symmetric states such as the tripartite state $\\ket{W_3}=\\tfrac{1}{\\sqrt{3}}(\\ket{100}+\\ket{010}+\\ket{001})$ and its $N$-partite generalization $\\ket{W_N}$. Previous tensor rank estimates are dramatically improved and we show that (i) three copies of $\\ket{W_3}$ has rank either 15 or 16, (ii) two copies of $\\ket{W_N}$ has rank $3N-2$, and (iii) $n$ copies of $\\ket{W_N}$ has rank O(N). A remarkable consequence of these results is that certain multipartite transformations, impossible even probabilistically, can become possible when performed in multiple copy bunches or when assisted by some catalyzing state. This novel effect is impossible for bipartite pure states.
Aggregate ranking of the world's leading universities
Vladimir M. Moskovkin
2015-06-01
Full Text Available The paper presents a methodology for calculating the aggregate global university ranking (Aggregated Global University Ranking, or AGUR, which consists of an automated presentation of the comparable lists of names for different universities from particular global university rankings (using Machine Learning and Mining Data algorithms and a simple procedure of aggregating particular global university rankings (summing up the university ranking positions from different particular rankings and their subsequent ranking. The second procedure makes it possible to bring lists of universities from particular rankings, which are nonidentical by length, to one size. The paper includes a sample AGUR for six particular global university rankings as of 2013, as well as cross-correlation matrices and intersection matrices for AGUR for 2011-2013, all created by means of using the Python-based software.
Hano, Takeshi; Ito, Katsutoshi; Mochida, Kazuhiko; Ohkubo, Nobuyuki; Kono, Kumiko; Onduka, Toshimitsu; Ito, Mana; Ichihashi, Hideki; Fujii, Kazunori; Tanaka, Hiroyuki
2015-07-01
The primary ecological risk of dimethyldithiocarbamate (DMDC), a dithiocarbamate fungicide (DTC) metabolite, was evaluated based on their probabilistic environmental concentration distributions (ECDs) in the coastal environment, Hiroshima Bay, Japan. And their behavior and temporal trends was further considered. This is the first report of the identification of DMDC from environmental seawater and sediment samples. DMDC concentrations in bottom seawater were substantially higher than those in surface seawater, which are associated with the leachability from sediments in bottom seawaters, and with photodegradation in surface seawaters. Furthermore, seasonal risks are dominated by higher concentrations from April to June, indicating temporal variation in the risk to exposed species. Hierarchical Bayesian analysis offered DMDC ECD medians and range (5th to 95th percentiles) of 0.85 ng L(-1) (0.029, 22), 12 ng L(-1) (3.2, 48) and 110 ng kg dry(-1) (9.5, 1200) in surface seawater, bottom seawater and sediment, respectively. Considering that DMDC and DTCs have similar toxicological potential to aquatic organisms, the occurrence of the compound in water is likely to be of biological relevance. In summary, this work provides the first demonstration that the ecological risk of DMDC and its derived DTCs in Hiroshima Bay is relatively high, and that DTCs should be a high priority for future research on marine contamination, especially in bottom seawaters.
Rank Based Selection of Electrode Positions for a Multi-Lead ECG Electrode Array.
Hintermuller, Christoph; Fischer, Gerald; Seger, Michael; Pfeifer, Bernhard; Modre, Robert; Tilg, Bernhard
2005-01-01
Methods for noninvasive imaging of electrical function of the heart seem to become a clinical standard procedure the next years. Thus, the overall procedure has to meet clinical requirements as easy and fast application. In this study we propose a new electrode array meeting clinical requirements such as easy to apply and compatibility with routine leads. Within body surface regions of high sensitivity, identified in a prior, information content based study, the number of required electrodes was optimized using effort-gain plots. These plots were generated by applying a so called type one detector criterion. The optimal array was selected from a set of 12 electrode arrays. Each of them consists of two L-shaped regular spaced parts. The optimal array was found by comparing several layouts and electrode densities to the electrode array we use for clinical studies. It consists of 125 electrodes with a regular spacing between 2cm and 3cm.
U.S. Natural Gas Storage Risk-Based Ranking Methodology and Results
Folga, Steve [Argonne National Lab. (ANL), Argonne, IL (United States); Portante, Edgar [Argonne National Lab. (ANL), Argonne, IL (United States); Shamsuddin, Shabbir [Argonne National Lab. (ANL), Argonne, IL (United States); Tompkins, Angeli [Argonne National Lab. (ANL), Argonne, IL (United States); Talaber, Leah [Argonne National Lab. (ANL), Argonne, IL (United States); McLamore, Mike [Argonne National Lab. (ANL), Argonne, IL (United States); Kavicky, Jim [Argonne National Lab. (ANL), Argonne, IL (United States); Conzelmann, Guenter [Argonne National Lab. (ANL), Argonne, IL (United States); Levin, Todd [Argonne National Lab. (ANL), Argonne, IL (United States)
2016-10-01
This report summarizes the methodology and models developed to assess the risk to energy delivery from the potential loss of underground gas storage (UGS) facilities located within the United States. The U.S. has a total of 418 existing storage fields, of which 390 are currently active. The models estimate the impacts of a disruption of each of the active UGS facilities on their owners/operators, including (1) local distribution companies (LDCs), (2) directly connected transporting pipelines and thus on the customers in downstream States, and (3) third-party entities and thus on contracted customers expecting the gas shipment. Impacts are measured across all natural gas customer classes. For the electric sector, impacts are quantified in terms of natural gas-fired electric generation capacity potentially affected from the loss of a UGS facility. For the purpose of calculating the overall supply risk, the overall consequence of the disruption of an UGS facility across all customer classes is expressed in terms of the number of expected equivalent residential customer outages per year, which combines the unit business interruption cost per customer class and the estimated number of affected natural gas customers with estimated probabilities of UGS disruptions. All models and analyses are based on publicly available data. The report presents a set of findings and recommendations in terms of data, further analyses, regulatory requirements and standards, and needs to improve gas/electric industry coordination for electric reliability.
Ranking of tree-ring based temperature reconstructions of the past millennium
Esper, Jan; Krusic, Paul J.; Ljungqvist, Fredrik C.; Luterbacher, Jürg; Carrer, Marco; Cook, Ed; Davi, Nicole K.; Hartl-Meier, Claudia; Kirdyanov, Alexander; Konter, Oliver; Myglan, Vladimir; Timonen, Mauri; Treydte, Kerstin; Trouet, Valerie; Villalba, Ricardo; Yang, Bao; Büntgen, Ulf
2016-08-01
Tree-ring chronologies are widely used to reconstruct high-to low-frequency variations in growing season temperatures over centuries to millennia. The relevance of these timeseries in large-scale climate reconstructions is often determined by the strength of their correlation against instrumental temperature data. However, this single criterion ignores several important quantitative and qualitative characteristics of tree-ring chronologies. Those characteristics are (i) data homogeneity, (ii) sample replication, (iii) growth coherence, (iv) chronology development, and (v) climate signal including the correlation with instrumental data. Based on these 5 characteristics, a reconstruction-scoring scheme is proposed and applied to 39 published, millennial-length temperature reconstructions from Asia, Europe, North America, and the Southern Hemisphere. Results reveal no reconstruction scores highest in every category and each has their own strengths and weaknesses. Reconstructions that perform better overall include N-Scan and Finland from Europe, E-Canada from North America, Yamal and Dzhelo from Asia. Reconstructions performing less well include W-Himalaya and Karakorum from Asia, Tatra and S-Finland from Europe, and Great Basin from North America. By providing a comprehensive set of criteria to evaluate tree-ring chronologies we hope to improve the development of large-scale temperature reconstructions spanning the past millennium. All reconstructions and their corresponding scores are provided at http://www.blogs.uni-mainz.de/fb09climatology.
Quantum probabilistic logic programming
Balu, Radhakrishnan
2015-05-01
We describe a quantum mechanics based logic programming language that supports Horn clauses, random variables, and covariance matrices to express and solve problems in probabilistic logic. The Horn clauses of the language wrap random variables, including infinite valued, to express probability distributions and statistical correlations, a powerful feature to capture relationship between distributions that are not independent. The expressive power of the language is based on a mechanism to implement statistical ensembles and to solve the underlying SAT instances using quantum mechanical machinery. We exploit the fact that classical random variables have quantum decompositions to build the Horn clauses. We establish the semantics of the language in a rigorous fashion by considering an existing probabilistic logic language called PRISM with classical probability measures defined on the Herbrand base and extending it to the quantum context. In the classical case H-interpretations form the sample space and probability measures defined on them lead to consistent definition of probabilities for well formed formulae. In the quantum counterpart, we define probability amplitudes on Hinterpretations facilitating the model generations and verifications via quantum mechanical superpositions and entanglements. We cast the well formed formulae of the language as quantum mechanical observables thus providing an elegant interpretation for their probabilities. We discuss several examples to combine statistical ensembles and predicates of first order logic to reason with situations involving uncertainty.
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...
Ranking Support Vector Machine with Kernel Approximation.
Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi
2017-01-01
Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.
Probabilistic aspects of Wigner function
Usenko, C V
2004-01-01
The Wigner function of quantum systems is an effective instrument to construct the approximate classical description of the systems for which the classical approximation is possible. During the last time, the Wigner function formalism is applied as well to seek indications of specific quantum properties of quantum systems leading to impossibility of the classical approximation construction. Most of all, as such an indication the existence of negative values in Wigner function for specific states of the quantum system being studied is used. The existence of such values itself prejudices the probabilistic interpretation of the Wigner function, though for an arbitrary observable depending jointly on the coordinate and the momentum of the quantum system just the Wigner function gives an effective instrument to calculate the average value and the other statistical characteristics. In this paper probabilistic interpretation of the Wigner function based on coordination of theoretical-probabilistic definition of the ...
A common fixed point for operators in probabilistic normed spaces
Ghaemi, M.B. [Faculty of Mathematics, Iran University of Science and Technology, Narmak, Tehran (Iran, Islamic Republic of)], E-mail: mghaemi@iust.ac.ir; Lafuerza-Guillen, Bernardo [Department of Applied Mathematics, University of Almeria, Almeria (Spain)], E-mail: blafuerz@ual.es; Razani, A. [Department of Mathematics, Faculty of Science, I. Kh. International University, P.O. Box 34194-288, Qazvin (Iran, Islamic Republic of)], E-mail: razani@ikiu.ac.ir
2009-05-15
Probabilistic Metric spaces was introduced by Karl Menger. Alsina, Schweizer and Sklar gave a general definition of probabilistic normed space based on the definition of Menger [Alsina C, Schweizer B, Sklar A. On the definition of a probabilistic normed spaces. Aequationes Math 1993;46:91-8]. Here, we consider the equicontinuity of a class of linear operators in probabilistic normed spaces and finally, a common fixed point theorem is proved. Application to quantum Mechanic is considered.0.
Probabilistic active recognition of multiple objects using Hough-based geometric matching features
Govender, N
2015-01-01
Full Text Available be recognized simultaneously, and occlusion and clutter (through distracter objects) is common. We propose a representation for object viewpoints using Hough transform based geometric matching features, which are robust in such circumstances. We show how...
U.S. Environmental Protection Agency — This dataset provides the city-specific air exchange rate measurements, modeled, literature-based as well as housing characteristics. This dataset is associated with...
Fasan, Marco; Noè, Salavore; Panza, Giuliano; Magrin, Andrea; Romanelli, Fabio; Vaccari, Franco
2015-01-01
In this paper, a new seismic Performance Based Design (PBD) process based on a deterministic definition of the seismic input is presented. The proposed procedure aims to address the following considerations, arisen from the analysis of seismic phenomena, which cannot be taken in account using standard probabilistic seismic input (PSHA): a) any structure at a given location, regardless of its importance, is subject to the same shaking as a result of a given earthquake, b) it is impossible to determine when a future earthquake of a given intensity/magnitude will occur, c) insufficient data are available to develop reliable statistics with regards to earthquakes. On the basis of these considerations, the seismic input at a given site - determined on the basis of the seismic history, the seismogenic zones and the seismogenic nodes - is defined using the Neo Deterministic Seismic Hazard Assessment (NDSHA). Two different analysis are carried out at different levels of detail. The first one (RSA) provides the Maximu...
Kolyshkin A.
2014-06-01
Full Text Available The VHCF behaviour of metallic materials containing microstructural defects such as non-metallic inclusions is determined by the size and distribution of the damage dominating defects. In the present paper, the size and location of about 60.000 inclusions measured on the longitudinal and transversal cross sections of AISI 304 sheet form a database for the probabilistic determination of failure-relevant inclusion distribution in fatigue specimens and their corresponding fatigue lifes. By applying the method of Murakami et al. the biggest measured inclusions were used in order to predict the size of failure-relevant inclusions in the fatigue specimens. The location of the crack initiating inclusions was defined based on the modeled inclusion population and the stress distribution in the fatigue specimen, using the probabilistic Monte Carlo framework. Reasonable agreement was obtained between modeling and experimental results.
Patel, Ameera X; Bullmore, Edward T
2016-11-15
Connectome mapping using techniques such as functional magnetic resonance imaging (fMRI) has become a focus of systems neuroscience. There remain many statistical challenges in analysis of functional connectivity and network architecture from BOLD fMRI multivariate time series. One key statistic for any time series is its (effective) degrees of freedom, df, which will generally be less than the number of time points (or nominal degrees of freedom, N). If we know the df, then probabilistic inference on other fMRI statistics, such as the correlation between two voxel or regional time series, is feasible. However, we currently lack good estimators of df in fMRI time series, especially after the degrees of freedom of the "raw" data have been modified substantially by denoising algorithms for head movement. Here, we used a wavelet-based method both to denoise fMRI data and to estimate the (effective) df of the denoised process. We show that seed voxel correlations corrected for locally variable df could be tested for false positive connectivity with better control over Type I error and greater specificity of anatomical mapping than probabilistic connectivity maps using the nominal degrees of freedom. We also show that wavelet despiked statistics can be used to estimate all pairwise correlations between a set of regional nodes, assign a P value to each edge, and then iteratively add edges to the graph in order of increasing P. These probabilistically thresholded graphs are likely more robust to regional variation in head movement effects than comparable graphs constructed by thresholding correlations. Finally, we show that time-windowed estimates of df can be used for probabilistic connectivity testing or dynamic network analysis so that apparent changes in the functional connectome are appropriately corrected for the effects of transient noise bursts. Wavelet despiking is both an algorithm for fMRI time series denoising and an estimator of the (effective) df of denoised
Centrality based Document Ranking
2014-11-01
summarization. In ECIR ’09: Proceedings of the 31th European Conference on IR Research, Lecture Notes in Computer Science , pages 724–728, Toulouse, France...Proceedings, Part I, volume 8796 of Lecture Notes in Computer Science . Springer, 2014. 16. J. Otterbacher, G. Erkan, and D. R. Radev. Using random walks for
A probabilistic risk assessment for dengue fever by a threshold based-quantile regression
Chiu, Chuan-Hung; Tan, Yih-Chi; Wen, Tzai-Hung; Chien, Lung-Chang; Yu, Hwa-Lung
2014-05-01
This article introduces an important concept "return period" to analyze potential incident rate of dengue fever by bringing together two models: the quantile regression model and the threshold-based method. The return period provided the frequency of incidence of dengue fever, and established the risk maps for potential incidence of dengue fever to point out highest risk in certain areas. A threshold-based linear quantile regression model was constructed to find significantly main effects and interactions based on collinearity test and stepwise selection, and also showed the performance of our model via pseudo R2. Finally, the spatial risk maps of the specified return periods and average incident rates were given, and indicated that high population density place (e.g., residential area), water conservancy facilities, and corresponding interactions could lead to a positive influence on dengue fever. These factors would be the key point to disease protection in a given study area.
Yi, Ming; Stephens, Robert M
2008-09-26
Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data.
Ming Yi
Full Text Available Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data.
Gramatica, Paola; Papa, Ester
2007-04-15
Persistence in the environment is an important criterion in prioritizing hazardous chemicals and in identifying new persistent organic pollutants (POPs). Degradation half-life in various compartments is among the more commonly used criteria for studying environmental persistence, but the limited availability of experimental data or reliable estimates is a serious problem. Available half-life data for degradation in air, water, sediment, and soil, for a set of 250 organic POP-type chemicals, were combined in a multivariate approach by principal component analysis to obtain a ranking of the studied organic pollutants according to their relative overall half-life. A global half-life index (GHLI) applicable for POP screening purposes is proposed. The reliability of this index was verified in comparison with multimedia model results. This global index was then modeled as a cumulative end-point using a QSAR approach based on few theoretical molecular descriptors, and a simple and robust regression model externally validated for its predictive ability was derived. The application of this model could allow a fast preliminary identification and prioritization of not yet known POPs, just from the knowledge of their molecular structure. This model can be applied a priori also in the chemical design of safer and alternative non-POP compounds.
MRPrimer: a MapReduce-based method for the thorough design of valid and ranked primers for PCR.
Kim, Hyerin; Kang, NaNa; Chon, Kang-Wook; Kim, Seonho; Lee, NaHye; Koo, JaeHyung; Kim, Min-Soo
2015-11-16
Primer design is a fundamental technique that is widely used for polymerase chain reaction (PCR). Although many methods have been proposed for primer design, they require a great deal of manual effort to generate feasible and valid primers, including homology tests on off-target sequences using BLAST-like tools. That approach is inconvenient for many target sequences of quantitative PCR (qPCR) due to considering the same stringent and allele-invariant constraints. To address this issue, we propose an entirely new method called MRPrimer that can design all feasible and valid primer pairs existing in a DNA database at once, while simultaneously checking a multitude of filtering constraints and validating primer specificity. Furthermore, MRPrimer suggests the best primer pair for each target sequence, based on a ranking method. Through qPCR analysis using 343 primer pairs and the corresponding sequencing and comparative analyses, we showed that the primer pairs designed by MRPrimer are very stable and effective for qPCR. In addition, MRPrimer is computationally efficient and scalable and therefore useful for quickly constructing an entire collection of feasible and valid primers for frequently updated databases like RefSeq. Furthermore, we suggest that MRPrimer can be utilized conveniently for experiments requiring primer design, especially real-time qPCR. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
Dastgheib, A.; Rajabalinejad, M.R.; Ranasinghe, R.; Roelvink, D.
2012-01-01
This paper demonstrates the sensitivity of morphological process-based models to the chronology of input wave conditions. In this research the effect of an emerged offshore breakwater on the morphology of the beach is investigated. A 30 day long morphological simulation with real time history of the
Probabilistic View-based 3D Curve Skeleton Computation on the GPU
Kustra, Jacek; Jalba, Andrei; Telea, Alexandru
2013-01-01
Computing curve skeletons of 3D shapes is a challenging task. Recently, a high-potential technique for this task was proposed, based on integrating medial information obtained from several 2D projections of a 3D shape. However effective, this technique is strongly influenced in terms of complexity b
A Probabilistic Ontology Development Methodology
2014-06-01
Model-Based Systems Engineering (MBSE) Methodologies," Seattle, 2008. [17] Jeffrey O. Grady, System Requirements Analysis. New York: McGraw-Hill, Inc...software. [Online]. http://www.norsys.com/index.html [26] Lise Getoor, Nir Friedman, Daphne Koller, Avi Pfeffer , and Ben Taskar, "Probabilistic
Wang, Haoyu
Uncertainty is inevitable in structural design. This research presents an efficient uncertainty analysis technique based on stochastic response surfaces (SRS). The focus is on calculating uncertainty propagation using fewer number of function evaluations. Due to sensitivity analysis, the gradient information of the performance is efficiently calculated and used in constructing SRS. Based on SRS, reliability-based design optimization (RBDO) is studied intensively in this research. Probability sensitivity analysis using the sampling technique is also proposed. Since the computational cost of RBDO increases significantly proportional to the increasing number of random variables, global sensitivity analysis is introduced to adaptively reduce unessential random variables. It has been shown that the global sensitivity indices can be calculated analytically because the SRS employs the Hermite polynomials as bases. Traditional structural design focuses on designing a reliable structure under well characterized random factors (dimensions, shape, material properties, etc). Variations of these parameters are relatively small and well characterized. However, everyday engineering life tends to use the existing structural part in a different applications instead of designing a completely new part. In this research, a reliability-based safety envelope concept for load tolerance is introduced. This shows the capacity of the current design as a future reference for design upgrade, maintenance and control. The safety envelope is applied to estimate the load tolerance of a structural part with respect to the reliability of fatigue life. Stochastic response surface is also applied on robust design in this research. It is shown that the polynomial chaos expansion with appropriate bases provides an accurate and efficient tool in evaluating the performance variance. In addition, the sensitivity of the output variance, which is critical in the mathematical programming method, is
Hasle, Grethe Rytter; Lundholm, N.
2005-01-01
Pseudo-nitzschia seriata f. obtusa (Hasle) Hasle is raised in rank to P. obtusa (Hasle) Hasle & Lundholm, based on morphological, phylogenetic and distributional features. The most prominent distinctive morphological feature is the shape of the valve ends, which in girdle view are truncate in P...
El Gharamti, Mohamad
2014-02-01
The accuracy of groundwater flow and transport model predictions highly depends on our knowledge of subsurface physical parameters. Assimilation of contaminant concentration data from shallow dug wells could help improving model behavior, eventually resulting in better forecasts. In this paper, we propose a joint state-parameter estimation scheme which efficiently integrates a low-rank extended Kalman filtering technique, namely the Singular Evolutive Extended Kalman (SEEK) filter, with the prominent complex-step method (CSM). The SEEK filter avoids the prohibitive computational burden of the Extended Kalman filter by updating the forecast along the directions of error growth only, called filter correction directions. CSM is used within the SEEK filter to efficiently compute model derivatives with respect to the state and parameters along the filter correction directions. CSM is derived using complex Taylor expansion and is second order accurate. It is proven to guarantee accurate gradient computations with zero numerical round-off errors, but requires complexifying the numerical code. We perform twin-experiments to test the performance of the CSM-based SEEK for estimating the state and parameters of a subsurface contaminant transport model. We compare the efficiency and the accuracy of the proposed scheme with two standard finite difference-based SEEK filters as well as with the ensemble Kalman filter (EnKF). Assimilation results suggest that the use of the CSM in the context of the SEEK filter may provide up to 80% more accurate solutions when compared to standard finite difference schemes and is competitive with the EnKF, even providing more accurate results in certain situations. We analyze the results based on two different observation strategies. We also discuss the complexification of the numerical code and show that this could be efficiently implemented in the context of subsurface flow models. © 2013 Elsevier B.V.
NESTOR: A Computer-Based Medical Diagnostic Aid That Integrates Causal and Probabilistic Knowledge.
1984-11-01
Ox class I Sequential Multiple dx CASNET Exhaustive I Single dx Sequential Multiple dx ABEL Triggers Multiple dx Exhaustive I Multiple dx CADUCEUS ...general domains of medicine where there are typically hundreds of diseases and therefore many billions of possible multiple disease hypotheses. CADUCEUS ...The CADUCEUS program has been designed by Pople as a successor of INTERNIST [Pople 82]. It is based on a knowledge representation which consists of a
BuSeog Ju
2015-08-01
Full Text Available This paper presents the seismic fragility of piping systems based on monotonic experimental tests. The fragility, conditional probability of failure for a given level of intensity measure, generally used to evaluate the structural safety and indentify the fault tree with respect to seismically induced failures. The nonlinear FE models of threaded T-joint systems based on monotonic experimental results were developed in OpenSees, using the ElasticPPGap material. In order to evaluate the seismic fragility of Tjoint piping systems, 1-inch and 2-inch threaded T-joint systems, were incorporated with main hospital piping system, and multiple nonlinear time history analyses related to Monte Carlo simulation were conducted. Consequently, seismic fragility of piping system at 1-inch and 2-inch threaded T-joint connections corresponding to ductility level was developed. It was interesting to observe that 2-inch threaded T-joint was more fragile rather than 1-inch threaded T-joint, based on the nonlinear FE model by monotonic tests and also location 1 was more fragile rather than location 2 for both 1-inch and 2-inch T-joint.
Analyzing Dynamic Probabilistic Risk Assessment Data through Topology-Based Clustering
Diego Mandelli; Dan Maljovec; BeiWang; Valerio Pascucci; Peer-Timo Bremer
2013-09-01
We investigate the use of a topology-based clustering technique on the data generated by dynamic event tree methodologies. The clustering technique we utilizes focuses on a domain-partitioning algorithm based on topological structures known as the Morse-Smale complex, which partitions the data points into clusters based on their uniform gradient flow behavior. We perform both end state analysis and transient analysis to classify the set of nuclear scenarios. We demonstrate our methodology on a dataset generated for a sodium-cooled fast reactor during an aircraft crash scenario. The simulation tracks the temperature of the reactor as well as the time for a recovery team to fix the passive cooling system. Combined with clustering results obtained previously through mean shift methodology, we present the user with complementary views of the data that help illuminate key features that may be otherwise hidden using a single methodology. By clustering the data, the number of relevant test cases to be selected for further analysis can be drastically reduced by selecting a representative from each cluster. Identifying the similarities of simulations within a cluster can also aid in the drawing of important conclusions with respect to safety analysis.
Universal scaling in sports ranking
Deng, Weibing; Cai, Xu; Bulou, Alain; Wang, Qiuping A
2011-01-01
Ranking is a ubiquitous phenomenon in the human society. By clicking the web pages of Forbes, you may find all kinds of rankings, such as world's most powerful people, world's richest people, top-paid tennis stars, and so on and so forth. Herewith, we study a specific kind, sports ranking systems in which players' scores and prize money are calculated based on their performances in attending various tournaments. A typical example is tennis. It is found that the distributions of both scores and prize money follow universal power laws, with exponents nearly identical for most sports fields. In order to understand the origin of this universal scaling we focus on the tennis ranking systems. By checking the data we find that, for any pair of players, the probability that the higher-ranked player will top the lower-ranked opponent is proportional to the rank difference between the pair. Such a dependence can be well fitted to a sigmoidal function. By using this feature, we propose a simple toy model which can simul...
Yu Shimizu
Full Text Available Diagnosis of psychiatric disorders based on brain imaging data is highly desirable in clinical applications. However, a common problem in applying machine learning algorithms is that the number of imaging data dimensions often greatly exceeds the number of available training samples. Furthermore, interpretability of the learned classifier with respect to brain function and anatomy is an important, but non-trivial issue. We propose the use of logistic regression with a least absolute shrinkage and selection operator (LASSO to capture the most critical input features. In particular, we consider application of group LASSO to select brain areas relevant to diagnosis. An additional advantage of LASSO is its probabilistic output, which allows evaluation of diagnosis certainty. To verify our approach, we obtained semantic and phonological verbal fluency fMRI data from 31 depression patients and 31 control subjects, and compared the performances of group LASSO (gLASSO, and sparse group LASSO (sgLASSO to those of standard LASSO (sLASSO, Support Vector Machine (SVM, and Random Forest. Over 90% classification accuracy was achieved with gLASSO, sgLASSO, as well as SVM; however, in contrast to SVM, LASSO approaches allow for identification of the most discriminative weights and estimation of prediction reliability. Semantic task data revealed contributions to the classification from left precuneus, left precentral gyrus, left inferior frontal cortex (pars triangularis, and left cerebellum (c rus1. Weights for the phonological task indicated contributions from left inferior frontal operculum, left post central gyrus, left insula, left middle frontal cortex, bilateral middle temporal cortices, bilateral precuneus, left inferior frontal cortex (pars triangularis, and left precentral gyrus. The distribution of normalized odds ratios further showed, that predictions with absolute odds ratios higher than 0.2 could be regarded as certain.
Shimizu, Yu; Yoshimoto, Junichiro; Toki, Shigeru; Takamura, Masahiro; Yoshimura, Shinpei; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji
2015-01-01
Diagnosis of psychiatric disorders based on brain imaging data is highly desirable in clinical applications. However, a common problem in applying machine learning algorithms is that the number of imaging data dimensions often greatly exceeds the number of available training samples. Furthermore, interpretability of the learned classifier with respect to brain function and anatomy is an important, but non-trivial issue. We propose the use of logistic regression with a least absolute shrinkage and selection operator (LASSO) to capture the most critical input features. In particular, we consider application of group LASSO to select brain areas relevant to diagnosis. An additional advantage of LASSO is its probabilistic output, which allows evaluation of diagnosis certainty. To verify our approach, we obtained semantic and phonological verbal fluency fMRI data from 31 depression patients and 31 control subjects, and compared the performances of group LASSO (gLASSO), and sparse group LASSO (sgLASSO) to those of standard LASSO (sLASSO), Support Vector Machine (SVM), and Random Forest. Over 90% classification accuracy was achieved with gLASSO, sgLASSO, as well as SVM; however, in contrast to SVM, LASSO approaches allow for identification of the most discriminative weights and estimation of prediction reliability. Semantic task data revealed contributions to the classification from left precuneus, left precentral gyrus, left inferior frontal cortex (pars triangularis), and left cerebellum (c rus1). Weights for the phonological task indicated contributions from left inferior frontal operculum, left post central gyrus, left insula, left middle frontal cortex, bilateral middle temporal cortices, bilateral precuneus, left inferior frontal cortex (pars triangularis), and left precentral gyrus. The distribution of normalized odds ratios further showed, that predictions with absolute odds ratios higher than 0.2 could be regarded as certain.
Fontanarosa, Davide; van der Laan, Hans Paul; Witte, Marnix; Shakirin, Georgy; Roelofs, Erik; Langendijk, Johannes; Larnbin, Philippe; van Herk, Marcel
2013-01-01
Background and purpose: To apply target probabilistic planning (TPP) approach to intensity modulated radiotherapy (IMRT) plans for head and neck cancer (HNC) patients. Material and methods: Twenty plans of HNC patients were re-planned replacing the simultaneous integrated boost IMRT optimization obj
PBPK-Based Probabilistic Risk Assessment for Total Chlorotriazines in Drinking Water.
Breckenridge, Charles B; Campbell, Jerry L; Clewell, Harvey J; Andersen, Melvin E; Valdez-Flores, Ciriaco; Sielken, Robert L
2016-04-01
The risk of human exposure to total chlorotriazines (TCT) in drinking water was evaluated using a physiologically based pharmacokinetic (PBPK) model. Daily TCT (atrazine, deethylatrazine, deisopropylatrazine, and diaminochlorotriazine) chemographs were constructed for 17 frequently monitored community water systems (CWSs) using linear interpolation and Krieg estimates between observed TCT values. Synthetic chemographs were created using a conservative bias factor of 3 to generate intervening peaks between measured values. Drinking water consumption records from 24-h diaries were used to calculate daily exposure. Plasma TCT concentrations were updated every 30 minutes using the PBPK model output for each simulated calendar year from 2006 to 2010. Margins of exposure (MOEs) were calculated (MOE = [Human Plasma TCTPOD] ÷ [Human Plasma TCTEXP]) based on the toxicological point of departure (POD) and the drinking water-derived exposure to TCT. MOEs were determined based on 1, 2, 3, 4, 7, 14, 28, or 90 days of rolling average exposures and plasma TCT Cmax, or the area under the curve (AUC). Distributions of MOE were determined and the 99.9th percentile was used for risk assessment. MOEs for all 17 CWSs were >1000 at the 99.9(th)percentile. The 99.9(th)percentile of the MOE distribution was 2.8-fold less when the 3-fold synthetic chemograph bias factor was used. MOEs were insensitive to interpolation method, the consumer's age, the water consumption database used and the duration of time over which the rolling average plasma TCT was calculated, for up to 90 days. MOEs were sensitive to factors that modified the toxicological, or hyphenated appropriately no-observed-effects level (NOEL), including rat strain, endpoint used, method of calculating the NOEL, and the pharmacokinetics of elimination, as well as the magnitude of exposure (CWS, calendar year, and use of bias factors).
Validation of probabilistic fracture models in mullite based ceramics using experimental data
Pascual Cosp, J.; Zapatero Arenzana, J.; Ramirez del Valle, A. [Dpto. de Ingenieria Civil, de Materiales y Fabricacion. E.T.S.I.I. Campus de El Ejido, s/n. Univ. de Malaga. Malaga (Spain); Galiano Serrano, J.C. [Unidad Asociada ' ' Lab. de Materiales y Superficies' ' , Inst. de Ciencia de Materiales, UNSE-CSIC-Univ. de Malaga (Spain)
2004-07-01
Mullite based ceramic materials of different types have been obtained using various firing conditions. Strength of ceramics has been measured in four point bending test. Weibull distribution function has been used to characterize statistically the variation of the mechanical strength. A surface flaws mapping is established by scanning electron microscopy and distributions of pore-size, orientation and shape factor are obtained for each sample. A study of strength has been done using Weibull's theory and the surface flaws mapping. (orig.)
Dong-Hoon eLee
2016-01-01
Full Text Available The purpose of this study is to acquire accurate diffusion tensor tractography (DTT results for arcuate fasciculus (AF fiber tract using Brodmann's area (BA template for region of interest (ROI setting. Thirteen healthy subjects were participated in this study. Fractional anisotropy (FA map of each subject was calculated using diffusion tensor data, and T1w template was co-registered to FA map. The BA template was also co-registered using the transformation matrix. The ROIs were drawn in the co-registered BA template, and AF fiber tract was extracted. To generate the probabilistic pathway map, a binary mask image was generated based on the fiber tract image and co-registered to T1w template image. We also measured relative location of the AF fiber tract. The location of the probability pathway map of each subject’s AF fiber tract was well defined in the brain. By using this probabilistic map, the mediolateral position ratio of AF was measured 18%, and the anteroposterior position ratio of AF was measured 35%, respectively. This study demonstrated that the AF fiber tract can be extracted using BA template for ROI setting and probabilistic pathway of fiber tract. Our results and analytical approaches can helpful for accurate fiber tracking and application of perspective clinical researches.
M.H. Amini
2013-12-01
Full Text Available High penetration of distributed generations and the increasing demand for using electric vehicles provide a lot of issues for the utilities. If these two effective elements of the future power system are used in an unscheduled manner, it may lead to the loss increment in distribution networks, dramatically. In this paper, the simultaneous allocation of distributed generations (DGs and electric vehicles (EVs parking lots has been studied in a radial distribution network. A distribution network which is upgrading and equipped with DGs is considered and these resources' optimal placement is achieved in interaction with the EV parking lots in order to minimize the power distribution network loss. The proposed method considers not only the loss minimization but also the reliability of the parking lot from the investor's point of view. The output of this model is the daily electricity demand of parking lot. The proposed method includes two levels. At the first level of the proposed algorithm, the parking lot investor makes decision and selects three candidate buses for each parking based on three main criteria. After making decision by the parking lot investor, the candidate buses are introduced to the distribution network operator. At the second stage, the distribution network operator allocates the DGs and EV parking lots based on the candidate buses of investor in order to achieve the minimum loss of the distribution network. Finally, the effectiveness of the proposed method is evaluated by allocating of DGs and EV parking lots simultaneously on the standard distribution test system.
Interspecies extrapolation based on the RepDose database--a probabilistic approach.
Escher, Sylvia E; Batke, Monika; Hoffmann-Doerr, Simone; Messinger, Horst; Mangelsdorf, Inge
2013-04-12
Repeated dose toxicity studies from the RepDose database (DB) were used to determine interspecies differences for rats and mice. NOEL (no observed effect level) ratios based on systemic effects were investigated for three different types of exposure: inhalation, oral food/drinking water and oral gavage. Furthermore, NOEL ratios for local effects in inhalation studies were evaluated. On the basis of the NOEL ratio distributions, interspecies assessment factors (AF) are evaluated. All data sets were best described by a lognormal distribution. No difference was seen between inhalation and oral exposure for systemic effects. Rats and mice were on average equally sensitive at equipotent doses with geometric mean (GM) values of 1 and geometric standard deviation (GSD) values ranging from 2.30 to 3.08. The local AF based on inhalation exposure resulted in a similar distribution with GM values of 1 and GSD values between 2.53 and 2.70. Our analysis confirms former analyses on interspecies differences, including also dog and human data. Furthermore it supports the principle of allometric scaling according to caloric demand in the case that body doses are applied. In conclusion, an interspecies distribution animal/human with a GM equal to allometric scaling and a GSD of 2.5 was derived.
INSYDE: a synthetic, probabilistic flood damage model based on explicit cost analysis
Dottori, Francesco; Figueiredo, Rui; Martina, Mario L. V.; Molinari, Daniela; Scorzini, Anna Rita
2016-12-01
Methodologies to estimate economic flood damages are increasingly important for flood risk assessment and management. In this work, we present a new synthetic flood damage model based on a component-by-component analysis of physical damage to buildings. The damage functions are designed using an expert-based approach with the support of existing scientific and technical literature, loss adjustment studies, and damage surveys carried out for past flood events in Italy. The model structure is designed to be transparent and flexible, and therefore it can be applied in different geographical contexts and adapted to the actual knowledge of hazard and vulnerability variables. The model has been tested in a recent flood event in northern Italy. Validation results provided good estimates of post-event damages, with similar or superior performances when compared with other damage models available in the literature. In addition, a local sensitivity analysis was performed in order to identify the hazard variables that have more influence on damage assessment results.
Microscopic and probabilistic approach to thermal steady state based on a dice and coin toy model
Onorato, Pasquale; Malgieri, Massimiliano; Moggio, Lorenzo; Oss, Stefano
2017-07-01
In this article we present an educational approach to thermal equilibrium which was tested on a group of 13 undergraduate students at the University of Trento. The approach is based on a stochastic toy model, in which bodies in thermal contact are represented by rows of squares on a cardboard table, which exchange coins placed on the squares based on the roll of two dice. The discussion of several physical principles, such as the exponential approach to equilibrium, the determination of the equilibrium temperature, and the interpretation of the equilibrium state as the most probable macrostate, proceeds through a continual comparison between the outcomes obtained with the toy model and the results of a real experiment on the thermal contact of two masses of water at different temperatures. At the end of the sequence, a re-analysis of the experimental results in view of both the Boltzmann and Clausius definitions of entropy reveals some limits of the toy model, but also allows for a critical discussion of the concepts of temperature and entropy. In order to provide the reader with a feeling of how the sequence was received by students, and how it helped them understand the topics introduced, we discuss some excerpts from their answers to a conceptual item given at the end of the sequence.