Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases.

PubMed

Mezlini, Aziz M; Goldenberg, Anna

2017-10-01

Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios.

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

PubMed Central

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available. PMID:23826291

CUFID-query: accurate network querying through random walk based network flow estimation.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

Neutral Community Dynamics and the Evolution of Species Interactions.

PubMed

Coelho, Marco Túlio P; Rangel, Thiago F

2018-04-01

A contemporary goal in ecology is to determine the ecological and evolutionary processes that generate recurring structural patterns in mutualistic networks. One of the great challenges is testing the capacity of neutral processes to replicate observed patterns in ecological networks, since the original formulation of the neutral theory lacks trophic interactions. Here, we develop a stochastic-simulation neutral model adding trophic interactions to the neutral theory of biodiversity. Without invoking ecological differences among individuals of different species, and assuming that ecological interactions emerge randomly, we demonstrate that a spatially explicit multitrophic neutral model is able to capture the recurrent structural patterns of mutualistic networks (i.e., degree distribution, connectance, nestedness, and phylogenetic signal of species interactions). Nonrandom species distribution, caused by probabilistic events of migration and speciation, create nonrandom network patterns. These findings have broad implications for the interpretation of niche-based processes as drivers of ecological networks, as well as for the integration of network structures with demographic stochasticity.

How People Interact in Evolving Online Affiliation Networks

NASA Astrophysics Data System (ADS)

Gallos, Lazaros K.; Rybski, Diego; Liljeros, Fredrik; Havlin, Shlomo; Makse, Hernán A.

2012-07-01

The study of human interactions is of central importance for understanding the behavior of individuals, groups, and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links, and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We show that an accurate estimation of these probabilistic tendencies can be achieved only by following the time evolution of the network. Inferences about the reason for the existence of links using statistical analysis of network snapshots must therefore be made with great caution. Here, we start by characterizing every single link when the tie was established in the network. This information allows us to describe the probabilistic tendencies of tie formation and extract meaningful sociological conclusions. We also find significant differences in behavioral traits in the social tendencies among individuals according to their degree of activity, gender, age, popularity, and other attributes. For instance, in the particular data sets analyzed here, we find that women reciprocate connections 3 times as much as men and that this difference increases with age. Men tend to connect with the most popular people more often than women do, across all ages. On the other hand, triangular tie tendencies are similar, independent of gender, and show an increase with age. These results require further validation in other social settings. Our findings can be useful to build models of realistic social network structures and to discover the underlying laws that govern establishment of ties in evolving social networks.

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time.

PubMed

Galán, S F; Aguado, F; Díez, F J; Mira, J

2002-07-01

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Bayesian networks and information theory for audio-visual perception modeling.

PubMed

Besson, Patricia; Richiardi, Jonas; Bourdin, Christophe; Bringoux, Lionel; Mestre, Daniel R; Vercher, Jean-Louis

2010-09-01

Thanks to their different senses, human observers acquire multiple information coming from their environment. Complex cross-modal interactions occur during this perceptual process. This article proposes a framework to analyze and model these interactions through a rigorous and systematic data-driven process. This requires considering the general relationships between the physical events or factors involved in the process, not only in quantitative terms, but also in term of the influence of one factor on another. We use tools from information theory and probabilistic reasoning to derive relationships between the random variables of interest, where the central notion is that of conditional independence. Using mutual information analysis to guide the model elicitation process, a probabilistic causal model encoded as a Bayesian network is obtained. We exemplify the method by using data collected in an audio-visual localization task for human subjects, and we show that it yields a well-motivated model with good predictive ability. The model elicitation process offers new prospects for the investigation of the cognitive mechanisms of multisensory perception.

Probabilistic QoS Analysis In Wireless Sensor Networks

DTIC Science & Technology

2012-04-01

and A.O. Fapojuwo. TDMA scheduling with optimized energy efficiency and minimum delay in clustered wireless sensor networks . IEEE Trans. on Mobile...Research Computer Science and Engineering, Department of 5-1-2012 Probabilistic QoS Analysis in Wireless Sensor Networks Yunbo Wang University of...Wang, Yunbo, "Probabilistic QoS Analysis in Wireless Sensor Networks " (2012). Computer Science and Engineering: Theses, Dissertations, and Student

The network organization of protein interactions in the spliceosome is reproduced by the simple rules of food-web models

PubMed Central

Pires, Mathias M.; Cantor, Maurício; Guimarães, Paulo R.; de Aguiar, Marcus A. M.; dos Reis, Sérgio F.; Coltri, Patricia P.

2015-01-01

The network structure of biological systems provides information on the underlying processes shaping their organization and dynamics. Here we examined the structure of the network depicting protein interactions within the spliceosome, the macromolecular complex responsible for splicing in eukaryotic cells. We show the interactions of less connected spliceosome proteins are nested subsets of the connections of the highly connected proteins. At the same time, the network has a modular structure with groups of proteins sharing similar interaction patterns. We then investigated the role of affinity and specificity in shaping the spliceosome network by adapting a probabilistic model originally designed to reproduce food webs. This food-web model was as successful in reproducing the structure of protein interactions as it is in reproducing interactions among species. The good performance of the model suggests affinity and specificity, partially determined by protein size and the timing of association to the complex, may be determining network structure. Moreover, because network models allow building ensembles of realistic networks while encompassing uncertainty they can be useful to examine the dynamics and vulnerability of intracelullar processes. Unraveling the mechanisms organizing the spliceosome interactions is important to characterize the role of individual proteins on splicing catalysis and regulation. PMID:26443080

Control of Stochastic Master Equation Models of Genetic Regulatory Networks by Approximating Their Average Behavior

NASA Astrophysics Data System (ADS)

Umut Caglar, Mehmet; Pal, Ranadip

2010-10-01

The central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid.'' However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of data in the cellular level and probabilistic nature of interactions. Probabilistic models like Stochastic Master Equation (SME) or deterministic models like differential equations (DE) can be used to analyze these types of interactions. SME models based on chemical master equation (CME) can provide detailed representation of genetic regulatory system, but their use is restricted by the large data requirements and computational costs of calculations. The differential equations models on the other hand, have low calculation costs and much more adequate to generate control procedures on the system; but they are not adequate to investigate the probabilistic nature of interactions. In this work the success of the mapping between SME and DE is analyzed, and the success of a control policy generated by DE model with respect to SME model is examined. Index Terms--- Stochastic Master Equation models, Differential Equation Models, Control Policy Design, Systems biology

The role of effective connectivity between the task-positive and task-negative network for evidence gathering [Evidence gathering and connectivity].

PubMed

Andreou, Christina; Steinmann, Saskia; Kolbeck, Katharina; Rauh, Jonas; Leicht, Gregor; Moritz, Steffen; Mulert, Christoph

2018-06-01

Reports linking a 'jumping-to-conclusions' bias to delusions have led to growing interest in the neurobiological correlates of probabilistic reasoning. Several brain areas have been implicated in probabilistic reasoning; however, findings are difficult to integrate into a coherent account. The present study aimed to provide additional evidence by investigating, for the first time, effective connectivity among brain areas involved in different stages of evidence gathering. We investigated evidence gathering in 25 healthy individuals using fMRI and a new paradigm (Box Task) designed such as to minimize the effects of cognitive effort and reward processing. Decisions to collect more evidence ('draws') were contrasted to decisions to reach a final choice ('conclusions') with respect to BOLD activity. Psychophysiological interaction analysis was used to investigate effective connectivity. Conclusion events were associated with extensive brain activations in widely distributed brain areas associated with the task-positive network. In contrast, draw events were characterized by higher activation in areas assumed to be part of the task-negative network. Effective connectivity between the two networks decreased during draws and increased during conclusion events. Our findings indicate that probabilistic reasoning may depend on the balance between the task-positive and task-negative network, and that shifts in connectivity between the two may be crucial for evidence gathering. Thus, abnormal connectivity between the two systems may significantly contribute to the jumping-to-conclusions bias. Copyright © 2018 Elsevier Inc. All rights reserved.

Dominating Scale-Free Networks Using Generalized Probabilistic Methods

PubMed Central

Molnár,, F.; Derzsy, N.; Czabarka, É.; Székely, L.; Szymanski, B. K.; Korniss, G.

2014-01-01

We study ensemble-based graph-theoretical methods aiming to approximate the size of the minimum dominating set (MDS) in scale-free networks. We analyze both analytical upper bounds of dominating sets and numerical realizations for applications. We propose two novel probabilistic dominating set selection strategies that are applicable to heterogeneous networks. One of them obtains the smallest probabilistic dominating set and also outperforms the deterministic degree-ranked method. We show that a degree-dependent probabilistic selection method becomes optimal in its deterministic limit. In addition, we also find the precise limit where selecting high-degree nodes exclusively becomes inefficient for network domination. We validate our results on several real-world networks, and provide highly accurate analytical estimates for our methods. PMID:25200937

Data Imputation in Epistatic MAPs by Network-Guided Matrix Completion

PubMed Central

Žitnik, Marinka; Zupan, Blaž

2015-01-01

Abstract Epistatic miniarray profile (E-MAP) is a popular large-scale genetic interaction discovery platform. E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions with greater precision. However, due to the limits of biotechnology, E-MAP studies fail to measure genetic interactions for up to 40% of gene pairs in an assay. Missing measurements can be recovered by computational techniques for data imputation, in this way completing the interaction profiles and enabling downstream analysis algorithms that could otherwise be sensitive to missing data values. We introduce a new interaction data imputation method called network-guided matrix completion (NG-MC). The core part of NG-MC is low-rank probabilistic matrix completion that incorporates prior knowledge presented as a collection of gene networks. NG-MC assumes that interactions are transitive, such that latent gene interaction profiles inferred by NG-MC depend on the profiles of their direct neighbors in gene networks. As the NG-MC inference algorithm progresses, it propagates latent interaction profiles through each of the networks and updates gene network weights toward improved prediction. In a study with four different E-MAP data assays and considered protein–protein interaction and gene ontology similarity networks, NG-MC significantly surpassed existing alternative techniques. Inclusion of information from gene networks also allowed NG-MC to predict interactions for genes that were not included in original E-MAP assays, a task that could not be considered by current imputation approaches. PMID:25658751

A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions.

PubMed

Li, Liyuan; Xu, Qianli; Gan, Tian; Tan, Cheston; Lim, Joo-Hwee

2018-05-01

Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.

Use of limited data to construct Bayesian networks for probabilistic risk assessment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groth, Katrina M.; Swiler, Laura Painton

2013-03-01

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was tomore » establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.« less

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions

PubMed Central

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage. PMID:27468262

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

PubMed

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

PubMed Central

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Management of Risk and Uncertainty in Systems Acquisition: Proceedings of the 1983 Defense Risk and Uncertainty Workshop Held at Fort Belvoir, Virginia on 13-15 July 1983

DTIC Science & Technology

1983-07-15

categories, however, represent the reality in major acquisition and are often overlooked. Although Figure 1 does not reflect tne dynamics and Interactions...networking and improved computer capabili- ties probabilistic network simulation became a reality . The Naval Sea Systems Command became involved in...reasons for using the WBS are plain: 1. Virtually all risk-prone activities are performed by the contractor, not Government. Government is responsible

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

PubMed

Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

2017-05-01

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

PubMed Central

Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

2006-01-01

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network

USGS Publications Warehouse

Singer, D.A.

2006-01-01

A probabilistic neural network is employed to classify 1610 mineral deposits into 18 types using tonnage, average Cu, Mo, Ag, Au, Zn, and Pb grades, and six generalized rock types. The purpose is to examine whether neural networks might serve for integrating geoscience information available in large mineral databases to classify sites by deposit type. Successful classifications of 805 deposits not used in training - 87% with grouped porphyry copper deposits - and the nature of misclassifications demonstrate the power of probabilistic neural networks and the value of quantitative mineral-deposit models. The results also suggest that neural networks can classify deposits as well as experienced economic geologists. ?? International Association for Mathematical Geology 2006.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

PubMed

Pecevski, Dejan; Maass, Wolfgang

2016-01-01

Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

PubMed Central

Pecevski, Dejan

2016-01-01

Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

Stochastic model simulation using Kronecker product analysis and Zassenhaus formula approximation.

PubMed

Caglar, Mehmet Umut; Pal, Ranadip

2013-01-01

Probabilistic Models are regularly applied in Genetic Regulatory Network modeling to capture the stochastic behavior observed in the generation of biological entities such as mRNA or proteins. Several approaches including Stochastic Master Equations and Probabilistic Boolean Networks have been proposed to model the stochastic behavior in genetic regulatory networks. It is generally accepted that Stochastic Master Equation is a fundamental model that can describe the system being investigated in fine detail, but the application of this model is computationally enormously expensive. On the other hand, Probabilistic Boolean Network captures only the coarse-scale stochastic properties of the system without modeling the detailed interactions. We propose a new approximation of the stochastic master equation model that is able to capture the finer details of the modeled system including bistabilities and oscillatory behavior, and yet has a significantly lower computational complexity. In this new method, we represent the system using tensors and derive an identity to exploit the sparse connectivity of regulatory targets for complexity reduction. The algorithm involves an approximation based on Zassenhaus formula to represent the exponential of a sum of matrices as product of matrices. We derive upper bounds on the expected error of the proposed model distribution as compared to the stochastic master equation model distribution. Simulation results of the application of the model to four different biological benchmark systems illustrate performance comparable to detailed stochastic master equation models but with considerably lower computational complexity. The results also demonstrate the reduced complexity of the new approach as compared to commonly used Stochastic Simulation Algorithm for equivalent accuracy.

A decision network account of reasoning about other people's choices

PubMed Central

Jern, Alan; Kemp, Charles

2015-01-01

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. PMID:26010559

Uncertainty Reduction for Stochastic Processes on Complex Networks

NASA Astrophysics Data System (ADS)

Radicchi, Filippo; Castellano, Claudio

2018-05-01

Many real-world systems are characterized by stochastic dynamical rules where a complex network of interactions among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules, the ability to predict system configurations is generally characterized by a large uncertainty. Selecting a fraction of the nodes and observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation in an optimal way is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying interaction pattern. In this paper, we introduce a computationally efficient algorithm to determine quasioptimal solutions to the problem. The method leverages network sparsity to reduce computational complexity from exponential to almost quadratic, thus allowing the straightforward application of the method to mid-to-large-size systems. Although the method is exact only for equilibrium stochastic processes defined on trees, it turns out to be effective also for out-of-equilibrium processes on sparse loopy networks.

A Bayesian network approach to the database search problem in criminal proceedings

PubMed Central

2012-01-01

Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method’s graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication. PMID:22849390

A Prize-Collecting Steiner Tree Approach for Transduction Network Inference

NASA Astrophysics Data System (ADS)

Bailly-Bechet, Marc; Braunstein, Alfredo; Zecchina, Riccardo

Into the cell, information from the environment is mainly propagated via signaling pathways which form a transduction network. Here we propose a new algorithm to infer transduction networks from heterogeneous data, using both the protein interaction network and expression datasets. We formulate the inference problem as an optimization task, and develop a message-passing, probabilistic and distributed formalism to solve it. We apply our algorithm to the pheromone response in the baker’s yeast S. cerevisiae. We are able to find the backbone of the known structure of the MAPK cascade of pheromone response, validating our algorithm. More importantly, we make biological predictions about some proteins whose role could be at the interface between pheromone response and other cellular functions.

A decision network account of reasoning about other people's choices.

PubMed

Jern, Alan; Kemp, Charles

2015-09-01

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. Copyright © 2015 Elsevier B.V. All rights reserved.

Bayesian networks improve causal environmental ...

EPA Pesticide Factsheets

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Identification of hybrid node and link communities in complex networks

PubMed Central

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-01-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

Identification of hybrid node and link communities in complex networks.

PubMed

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-02

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Identification of hybrid node and link communities in complex networks

NASA Astrophysics Data System (ADS)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

An equation-free probabilistic steady-state approximation: dynamic application to the stochastic simulation of biochemical reaction networks.

PubMed

Salis, Howard; Kaznessis, Yiannis N

2005-12-01

Stochastic chemical kinetics more accurately describes the dynamics of "small" chemical systems, such as biological cells. Many real systems contain dynamical stiffness, which causes the exact stochastic simulation algorithm or other kinetic Monte Carlo methods to spend the majority of their time executing frequently occurring reaction events. Previous methods have successfully applied a type of probabilistic steady-state approximation by deriving an evolution equation, such as the chemical master equation, for the relaxed fast dynamics and using the solution of that equation to determine the slow dynamics. However, because the solution of the chemical master equation is limited to small, carefully selected, or linear reaction networks, an alternate equation-free method would be highly useful. We present a probabilistic steady-state approximation that separates the time scales of an arbitrary reaction network, detects the convergence of a marginal distribution to a quasi-steady-state, directly samples the underlying distribution, and uses those samples to accurately predict the state of the system, including the effects of the slow dynamics, at future times. The numerical method produces an accurate solution of both the fast and slow reaction dynamics while, for stiff systems, reducing the computational time by orders of magnitude. The developed theory makes no approximations on the shape or form of the underlying steady-state distribution and only assumes that it is ergodic. We demonstrate the accuracy and efficiency of the method using multiple interesting examples, including a highly nonlinear protein-protein interaction network. The developed theory may be applied to any type of kinetic Monte Carlo simulation to more efficiently simulate dynamically stiff systems, including existing exact, approximate, or hybrid stochastic simulation techniques.

Reconstructing cerebrovascular networks under local physiological constraints by integer programming

DOE PAGES

Rempfler, Markus; Schneider, Matthias; Ielacqua, Giovanna D.; ...

2015-04-23

We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to the probabilistic model. Starting from an over-connected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (µCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of ourmore » probabilistic model. As a result, we perform experiments on micro magnetic resonance angiography (µMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.« less

Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach

PubMed Central

Wang, Yue; Adalý, Tülay; Kung, Sun-Yuan; Szabo, Zsolt

2007-01-01

This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches. PMID:18172510

A building block for hardware belief networks.

PubMed

Behin-Aein, Behtash; Diep, Vinh; Datta, Supriyo

2016-07-21

Belief networks represent a powerful approach to problems involving probabilistic inference, but much of the work in this area is software based utilizing standard deterministic hardware based on the transistor which provides the gain and directionality needed to interconnect billions of them into useful networks. This paper proposes a transistor like device that could provide an analogous building block for probabilistic networks. We present two proof-of-concept examples of belief networks, one reciprocal and one non-reciprocal, implemented using the proposed device which is simulated using experimentally benchmarked models.

An integrated approach to infer dynamic protein-gene interactions - A case study of the human P53 protein.

PubMed

Wang, Junbai; Wu, Qianqian; Hu, Xiaohua Tony; Tian, Tianhai

2016-11-01

Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is a very important but challenging task. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this challenge, a new integrated method is proposed by combining a top-down approach and a bottom-up approach. First, the top-down approach uses probabilistic graphical models to predict the network structure of DNA repair pathway that is regulated by the p53 protein. Two networks are predicted, namely a network of eight genes with eight inferred interactions and an extended network of 21 genes with 17 interactions. Then, the bottom-up approach using differential equation models is developed to study the detailed genetic regulations based on either a fully connected regulatory network or a gene network obtained by the top-down approach. Model simulation error, parameter identifiability and robustness property are used as criteria to select the optimal network. Simulation results together with permutation tests of input gene network structures indicate that the prediction accuracy and robustness property of the two predicted networks using the top-down approach are better than those of the corresponding fully connected networks. In particular, the proposed approach reduces computational cost significantly for inferring model parameters. Overall, the new integrated method is a promising approach for investigating the dynamics of genetic regulation. Copyright © 2016 Elsevier Inc. All rights reserved.

Optimization of the kernel functions in a probabilistic neural network analyzing the local pattern distribution.

PubMed

Galleske, I; Castellanos, J

2002-05-01

This article proposes a procedure for the automatic determination of the elements of the covariance matrix of the gaussian kernel function of probabilistic neural networks. Two matrices, a rotation matrix and a matrix of variances, can be calculated by analyzing the local environment of each training pattern. The combination of them will form the covariance matrix of each training pattern. This automation has two advantages: First, it will free the neural network designer from indicating the complete covariance matrix, and second, it will result in a network with better generalization ability than the original model. A variation of the famous two-spiral problem and real-world examples from the UCI Machine Learning Repository will show a classification rate not only better than the original probabilistic neural network but also that this model can outperform other well-known classification techniques.

Classification of Company Performance using Weighted Probabilistic Neural Network

NASA Astrophysics Data System (ADS)

Yasin, Hasbi; Waridi Basyiruddin Arifin, Adi; Warsito, Budi

2018-05-01

Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the company's performance modeling with the WPNN model has a very high accuracy that reaches 100%.

A neural network model of metaphor understanding with dynamic interaction based on a statistical language analysis: targeting a human-like model.

PubMed

Terai, Asuka; Nakagawa, Masanori

2007-08-01

The purpose of this paper is to construct a model that represents the human process of understanding metaphors, focusing specifically on similes of the form an "A like B". Generally speaking, human beings are able to generate and understand many sorts of metaphors. This study constructs the model based on a probabilistic knowledge structure for concepts which is computed from a statistical analysis of a large-scale corpus. Consequently, this model is able to cover the many kinds of metaphors that human beings can generate. Moreover, the model implements the dynamic process of metaphor understanding by using a neural network with dynamic interactions. Finally, the validity of the model is confirmed by comparing model simulations with the results from a psychological experiment.

Reachability Analysis in Probabilistic Biological Networks.

PubMed

Gabr, Haitham; Todor, Andrei; Dobra, Alin; Kahveci, Tamer

2015-01-01

Extra-cellular molecules trigger a response inside the cell by initiating a signal at special membrane receptors (i.e., sources), which is then transmitted to reporters (i.e., targets) through various chains of interactions among proteins. Understanding whether such a signal can reach from membrane receptors to reporters is essential in studying the cell response to extra-cellular events. This problem is drastically complicated due to the unreliability of the interaction data. In this paper, we develop a novel method, called PReach (Probabilistic Reachability), that precisely computes the probability that a signal can reach from a given collection of receptors to a given collection of reporters when the underlying signaling network is uncertain. This is a very difficult computational problem with no known polynomial-time solution. PReach represents each uncertain interaction as a bi-variate polynomial. It transforms the reachability problem to a polynomial multiplication problem. We introduce novel polynomial collapsing operators that associate polynomial terms with possible paths between sources and targets as well as the cuts that separate sources from targets. These operators significantly shrink the number of polynomial terms and thus the running time. PReach has much better time complexity than the recent solutions for this problem. Our experimental results on real data sets demonstrate that this improvement leads to orders of magnitude of reduction in the running time over the most recent methods. Availability: All the data sets used, the software implemented and the alignments found in this paper are available at http://bioinformatics.cise.ufl.edu/PReach/.

Bayesian Networks Improve Causal Environmental Assessments for Evidence-Based Policy.

PubMed

Carriger, John F; Barron, Mace G; Newman, Michael C

2016-12-20

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.

Evaluation of Sex-Specific Movement Patterns in Judo Using Probabilistic Neural Networks.

PubMed

Miarka, Bianca; Sterkowicz-Przybycien, Katarzyna; Fukuda, David H

2017-10-01

The purpose of the present study was to create a probabilistic neural network to clarify the understanding of movement patterns in international judo competitions by gender. Analysis of 773 male and 638 female bouts was utilized to identify movements during the approach, gripping, attack (including biomechanical designations), groundwork, defense, and pause phases. Probabilistic neural network and chi-square (χ 2 ) tests modeled and compared frequencies (p ≤ .05). Women (mean [interquartile range]: 9.9 [4; 14]) attacked more than men (7.0 [3; 10]) while attempting a greater number of arm/leg lever (women: 2.7 [1; 6]; men: 4.0 [0; 4]) and trunk/leg lever (women: 0.8 [0; 1]; men: 2.4 [0; 4]) techniques but fewer maximal length-moment arm techniques (women: 0.7 [0; 1]; men: 1.0 [0; 2]). Male athletes displayed one-handed gripping of the back and sleeve, whereas female athletes executed a greater number of groundwork techniques. An optimized probabilistic neural network model, using patterns from the gripping, attack, groundwork, and pause phases, produced an overall prediction accuracy of 76% for discrimination between men and women.

Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics.

PubMed

Sardiu, Mihaela E; Gilmore, Joshua M; Carrozza, Michael J; Li, Bing; Workman, Jerry L; Florens, Laurence; Washburn, Michael P

2009-10-06

Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex.

Network inference using informative priors

PubMed Central

Mukherjee, Sach; Speed, Terence P.

2008-01-01

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of “network inference” is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling. PMID:18799736

Network inference using informative priors.

PubMed

Mukherjee, Sach; Speed, Terence P

2008-09-23

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of "network inference" is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling.

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies

PubMed Central

2013-01-01

Background The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the “correlation” of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions. Results Our method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method’s performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature. Conclusions The advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network. PMID:23432934

Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution.

PubMed

Francis, Andrew; Moulton, Vincent

2018-06-07

Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Generating probabilistic Boolean networks from a prescribed transition probability matrix.

PubMed

Ching, W-K; Chen, X; Tsing, N-K

2009-11-01

Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state.

A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks.

PubMed

Zhou, Xiaobo; Wang, Xiaodong; Pal, Ranadip; Ivanov, Ivan; Bittner, Michael; Dougherty, Edward R

2004-11-22

We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes. We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors and the associated probabilities based on a nonlinear perceptron model, using a reversible jump Markov chain Monte Carlo (MCMC) technique, and an MCMC method is employed to search the network configurations to find those with the highest Bayesian scores to construct the PGRN. The Bayesian method has been used to construct a PGRN based on the observed behavior of a set of genes whose expression patterns vary across a set of melanoma samples exhibiting two very different phenotypes with respect to cell motility and invasiveness. Key biological features have been faithfully reflected in the model. Its steady-state distribution contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state. Most interestingly, the connectivity rules for the most optimal generated networks constituting the PGRN are remarkably similar, as would be expected for a network operating on a distributed basis, with strong interactions between the components.

An on-line BCI for control of hand grasp sequence and holding using adaptive probabilistic neural network.

PubMed

Hazrati, Mehrnaz Kh; Erfanian, Abbas

2008-01-01

This paper presents a new EEG-based Brain-Computer Interface (BCI) for on-line controlling the sequence of hand grasping and holding in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. Moreover, for consistency of man-machine interface, it is desirable the intended movement to be what the subject imagines. For this purpose, we developed an on-line BCI which was based on the classification of EEG associated with imagination of the movement of hand grasping and resting state. A classifier based on probabilistic neural network (PNN) was introduced for classifying the EEG. The PNN is a feedforward neural network that realizes the Bayes decision discriminant function by estimating probability density function using mixtures of Gaussian kernels. Two types of classification schemes were considered here for on-line hand control: adaptive and static. In contrast to static classification, the adaptive classifier was continuously updated on-line during recording. The experimental evaluation on six subjects on different days demonstrated that by using the static scheme, a classification accuracy as high as the rate obtained by the adaptive scheme can be achieved. At the best case, an average classification accuracy of 93.0% and 85.8% was obtained using adaptive and static scheme, respectively. The results obtained from more than 1500 trials on six subjects showed that interactive virtual reality environment can be used as an effective tool for subject training in BCI.

Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays.

PubMed

Liu, Hongjian; Wang, Zidong; Shen, Bo; Huang, Tingwen; Alsaadi, Fuad E

2018-06-01

This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) with both leakage delays as well as probabilistic time-varying delays. For the probabilistic delays, a sequence of Bernoulli distributed random variables is utilized to determine within which intervals the time-varying delays fall at certain time instant. The sector-bounded activation function is considered in the addressed DSMNN. By taking into account the state-dependent characteristics of the network parameters and choosing an appropriate Lyapunov-Krasovskii functional, some sufficient conditions are established under which the underlying DSMNN is globally exponentially stable in the mean square. The derived conditions are made dependent on both the leakage and the probabilistic delays, and are therefore less conservative than the traditional delay-independent criteria. A simulation example is given to show the effectiveness of the proposed stability criterion. Copyright © 2018 Elsevier Ltd. All rights reserved.

Probabilistic track coverage in cooperative sensor networks.

PubMed

Ferrari, Silvia; Zhang, Guoxian; Wettergren, Thomas A

2010-12-01

The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.

Three-body system metaphor for the two-slit experiment and Escherichia coli lactose-glucose metabolism.

PubMed

Asano, Masanari; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2016-05-28

We compare the contextual probabilistic structures of the seminal two-slit experiment (quantum interference experiment), the system of three interacting bodies andEscherichia colilactose-glucose metabolism. We show that they have the same non-Kolmogorov probabilistic structure resulting from multi-contextuality. There are plenty of statistical data with non-Kolmogorov features; in particular, the probabilistic behaviour of neither quantum nor biological systems can be described classically. Biological systems (even cells and proteins) are macroscopic systems and one may try to present a more detailed model of interactions in such systems that lead to quantum-like probabilistic behaviour. The system of interactions between three bodies is one of the simplest metaphoric examples for such interactions. By proceeding further in this way (by playing withn-body systems) we shall be able to find metaphoric mechanical models for complex bio-interactions, e.g. signalling between cells, leading to non-Kolmogorov probabilistic data. © 2016 The Author(s).

Three-body system metaphor for the two-slit experiment and Escherichia coli lactose–glucose metabolism

PubMed Central

Asano, Masanari; Ohya, Masanori; Yamato, Ichiro

2016-01-01

We compare the contextual probabilistic structures of the seminal two-slit experiment (quantum interference experiment), the system of three interacting bodies and Escherichia coli lactose–glucose metabolism. We show that they have the same non-Kolmogorov probabilistic structure resulting from multi-contextuality. There are plenty of statistical data with non-Kolmogorov features; in particular, the probabilistic behaviour of neither quantum nor biological systems can be described classically. Biological systems (even cells and proteins) are macroscopic systems and one may try to present a more detailed model of interactions in such systems that lead to quantum-like probabilistic behaviour. The system of interactions between three bodies is one of the simplest metaphoric examples for such interactions. By proceeding further in this way (by playing with n-body systems) we shall be able to find metaphoric mechanical models for complex bio-interactions, e.g. signalling between cells, leading to non-Kolmogorov probabilistic data. PMID:27091163

Scalable Quantum Networks for Distributed Computing and Sensing

DTIC Science & Technology

2016-04-01

probabilistic measurement , so we developed quantum memories and guided-wave implementations of same, demonstrating controlled delay of a heralded single...Second, fundamental scalability requires a method to synchronize protocols based on quantum measurements , which are inherently probabilistic. To meet...AFRL-AFOSR-UK-TR-2016-0007 Scalable Quantum Networks for Distributed Computing and Sensing Ian Walmsley THE UNIVERSITY OF OXFORD Final Report 04/01

Combined diffusion-weighted and functional magnetic resonance imaging reveals a temporal-occipital network involved in auditory-visual object processing

PubMed Central

Beer, Anton L.; Plank, Tina; Meyer, Georg; Greenlee, Mark W.

2013-01-01

Functional magnetic resonance imaging (MRI) showed that the superior temporal and occipital cortex are involved in multisensory integration. Probabilistic fiber tracking based on diffusion-weighted MRI suggests that multisensory processing is supported by white matter connections between auditory cortex and the temporal and occipital lobe. Here, we present a combined functional MRI and probabilistic fiber tracking study that reveals multisensory processing mechanisms that remained undetected by either technique alone. Ten healthy participants passively observed visually presented lip or body movements, heard speech or body action sounds, or were exposed to a combination of both. Bimodal stimulation engaged a temporal-occipital brain network including the multisensory superior temporal sulcus (msSTS), the lateral superior temporal gyrus (lSTG), and the extrastriate body area (EBA). A region-of-interest (ROI) analysis showed multisensory interactions (e.g., subadditive responses to bimodal compared to unimodal stimuli) in the msSTS, the lSTG, and the EBA region. Moreover, sounds elicited responses in the medial occipital cortex. Probabilistic tracking revealed white matter tracts between the auditory cortex and the medial occipital cortex, the inferior occipital cortex (IOC), and the superior temporal sulcus (STS). However, STS terminations of auditory cortex tracts showed limited overlap with the msSTS region. Instead, msSTS was connected to primary sensory regions via intermediate nodes in the temporal and occipital cortex. Similarly, the lSTG and EBA regions showed limited direct white matter connections but instead were connected via intermediate nodes. Our results suggest that multisensory processing in the STS is mediated by separate brain areas that form a distinct network in the lateral temporal and inferior occipital cortex. PMID:23407860

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

A Guide to the Literature on Learning Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Friedland, Peter (Technical Monitor)

1994-01-01

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and more generally, learning probabilistic graphical models. Because many problems in artificial intelligence, statistics and neural networks can be represented as a probabilistic graphical model, this area provides a unifying perspective on learning. This paper organizes the research in this area along methodological lines of increasing complexity.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks.

PubMed

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

PubMed Central

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses. PMID:29163110

Verification and Optimal Control of Context-Sensitive Probabilistic Boolean Networks Using Model Checking and Polynomial Optimization

PubMed Central

Hiraishi, Kunihiko

2014-01-01

One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of GRNs to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of GRNs, and gene expression is expressed by a binary value (ON or OFF). In particular, a context-sensitive probabilistic Boolean network (CS-PBN), which is one of the extended models of BNs, is used. For CS-PBNs, the verification problem and the optimal control problem are considered. For the verification problem, a solution method using the probabilistic model checker PRISM is proposed. For the optimal control problem, a solution method using polynomial optimization is proposed. Finally, a numerical example on the WNT5A network, which is related to melanoma, is presented. The proposed methods provide us useful tools in control theory of GRNs. PMID:24587766

Discriminative Relational Topic Models.

PubMed

Chen, Ning; Zhu, Jun; Xia, Fei; Zhang, Bo

2015-05-01

Relational topic models (RTMs) provide a probabilistic generative process to describe both the link structure and document contents for document networks, and they have shown promise on predicting network structures and discovering latent topic representations. However, existing RTMs have limitations in both the restricted model expressiveness and incapability of dealing with imbalanced network data. To expand the scope and improve the inference accuracy of RTMs, this paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference (RegBayes) with a regularization parameter to deal with the imbalanced link structure issue in real networks and improve the discriminative ability of learned latent representations; and 3) instead of doing variational approximation with strict mean-field assumptions, we present collapsed Gibbs sampling algorithms for the generalized relational topic models by exploring data augmentation without making restricting assumptions. Under the generic RegBayes framework, we carefully investigate two popular discriminative loss functions, namely, the logistic log-loss and the max-margin hinge loss. Experimental results on several real network datasets demonstrate the significance of these extensions on improving prediction performance.

E-nose based rapid prediction of early mouldy grain using probabilistic neural networks

PubMed Central

Ying, Xiaoguo; Liu, Wei; Hui, Guohua; Fu, Jun

2015-01-01

In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction. PMID:25714125

A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks

PubMed Central

Yin, Junming; Ho, Qirong; Xing, Eric P.

2014-01-01

We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction. PMID:25400487

Hybrid Packet-Pheromone-Based Probabilistic Routing for Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

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).

Energy Efficient Probabilistic Broadcasting for Mobile Ad-Hoc Network

NASA Astrophysics Data System (ADS)

Kumar, Sumit; Mehfuz, Shabana

2017-06-01

In mobile ad-hoc network (MANETs) flooding method is used for broadcasting route request (RREQ) packet from one node to another node for route discovery. This is the simplest method of broadcasting of RREQ packets but it often results in broadcast storm problem, originating collisions and congestion of packets in the network. A probabilistic broadcasting is one of the widely used broadcasting scheme for route discovery in MANETs and provides solution for broadcasting storm problem. But it does not consider limited energy of the battery of the nodes. In this paper, a new energy efficient probabilistic broadcasting (EEPB) is proposed in which probability of broadcasting RREQs is calculated with respect to remaining energy of nodes. The analysis of simulation results clearly indicate that an EEPB route discovery scheme in ad-hoc on demand distance vector (AODV) can increase the network lifetime with a decrease in the average power consumption and RREQ packet overhead. It also decreases the number of dropped packets in the network, in comparison to other EEPB schemes like energy constraint gossip (ECG), energy aware gossip (EAG), energy based gossip (EBG) and network lifetime through energy efficient broadcast gossip (NEBG).

Probabilistic Harmonic Analysis on Distributed Photovoltaic Integration Considering Typical Weather Scenarios

NASA Astrophysics Data System (ADS)

Bin, Che; Ruoying, Yu; Dongsheng, Dang; Xiangyan, Wang

2017-05-01

Distributed Generation (DG) integrating to the network would cause the harmonic pollution which would cause damages on electrical devices and affect the normal operation of power system. On the other hand, due to the randomness of the wind and solar irradiation, the output of DG is random, too, which leads to an uncertainty of the harmonic generated by the DG. Thus, probabilistic methods are needed to analyse the impacts of the DG integration. In this work we studied the harmonic voltage probabilistic distribution and the harmonic distortion in distributed network after the distributed photovoltaic (DPV) system integrating in different weather conditions, mainly the sunny day, cloudy day, rainy day and the snowy day. The probabilistic distribution function of the DPV output power in different typical weather conditions could be acquired via the parameter identification method of maximum likelihood estimation. The Monte-Carlo simulation method was adopted to calculate the probabilistic distribution of harmonic voltage content at different frequency orders as well as the harmonic distortion (THD) in typical weather conditions. The case study was based on the IEEE33 system and the results of harmonic voltage content probabilistic distribution as well as THD in typical weather conditions were compared.

A Brain Network Processing the Age of Faces

PubMed Central

Homola, György A.; Jbabdi, Saad; Beckmann, Christian F.; Bartsch, Andreas J.

2012-01-01

Age is one of the most salient aspects in faces and of fundamental cognitive and social relevance. Although face processing has been studied extensively, brain regions responsive to age have yet to be localized. Using evocative face morphs and fMRI, we segregate two areas extending beyond the previously established face-sensitive core network, centered on the inferior temporal sulci and angular gyri bilaterally, both of which process changes of facial age. By means of probabilistic tractography, we compare their patterns of functional activation and structural connectivity. The ventral portion of Wernicke's understudied perpendicular association fasciculus is shown to interconnect the two areas, and activation within these clusters is related to the probability of fiber connectivity between them. In addition, post-hoc age-rating competence is found to be associated with high response magnitudes in the left angular gyrus. Our results provide the first evidence that facial age has a distinct representation pattern in the posterior human brain. We propose that particular face-sensitive nodes interact with additional object-unselective quantification modules to obtain individual estimates of facial age. This brain network processing the age of faces differs from the cortical areas that have previously been linked to less developmental but instantly changeable face aspects. Our probabilistic method of associating activations with connectivity patterns reveals an exemplary link that can be used to further study, assess and quantify structure-function relationships. PMID:23185334

Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks.

PubMed

Saad, E W; Prokhorov, D V; Wunsch, D C

1998-01-01

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience.

A unified probabilistic framework for spontaneous facial action modeling and understanding.

PubMed

Tong, Yan; Chen, Jixu; Ji, Qiang

2010-02-01

Facial expression is a natural and powerful means of human communication. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, frequent head movements, and ambiguous and uncertain facial motion measurements. Because of these challenges, current research in facial expression recognition is limited to posed expressions and often in frontal view. A spontaneous facial expression is characterized by rigid head movements and nonrigid facial muscular movements. More importantly, it is the coherent and consistent spatiotemporal interactions among rigid and nonrigid facial motions that produce a meaningful facial expression. Recognizing this fact, we introduce a unified probabilistic facial action model based on the Dynamic Bayesian network (DBN) to simultaneously and coherently represent rigid and nonrigid facial motions, their spatiotemporal dependencies, and their image measurements. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge. Given the model and the measurements of facial motions, facial action recognition is accomplished through probabilistic inference by systematically integrating visual measurements with the facial action model. Experiments show that compared to the state-of-the-art techniques, the proposed system yields significant improvements in recognizing both rigid and nonrigid facial motions, especially for spontaneous facial expressions.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

PubMed

Ziebarth, Jesse D; Cui, Yan

2017-01-01

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

A Dynamic Bayesian Network Model for the Production and Inventory Control

NASA Astrophysics Data System (ADS)

Shin, Ji-Sun; Takazaki, Noriyuki; Lee, Tae-Hong; Kim, Jin-Il; Lee, Hee-Hyol

In general, the production quantities and delivered goods are changed randomly and then the total stock is also changed randomly. This paper deals with the production and inventory control using the Dynamic Bayesian Network. Bayesian Network is a probabilistic model which represents the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of a lower limit and a ceiling of the total stock to certain values is shown.

Model based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

PubMed Central

Rasmussen, Peter M.; Smith, Amy F.; Sakadžić, Sava; Boas, David A.; Pries, Axel R.; Secomb, Timothy W.; Østergaard, Leif

2017-01-01

Objective In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than five hundred unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large datasets acquired as part of microvascular imaging studies. PMID:27987383

Learning Orthographic Structure With Sequential Generative Neural Networks.

PubMed

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Homophyly/Kinship Model: Naturally Evolving Networks

NASA Astrophysics Data System (ADS)

Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

2015-10-01

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

Homophyly/Kinship Model: Naturally Evolving Networks

PubMed Central

Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

2015-01-01

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network. PMID:26478264

Deterministic Teleportation of Multi-qudit States in a Network via Various Probabilistic Channels

NASA Astrophysics Data System (ADS)

Zhang, Ti-Hang; Jiang, Min; Huang, Xu; Wan, Min

2014-04-01

In this paper, we present a generalized approach to faithfully teleport an unknown state of a multi-qudit system involving multi spatially remote agents via various probabilistic channels. In a quantum teleportation network, there are generally multi spatially remote relay agents between a sender and a distant receiver. With the assistance of the relay agents, it is possible to directly construct a deterministic channel between the sender and the distant receiver. In our scheme, different from previous probabilistic teleportation protocols, the integrity of the unknown multi-qudit state could be maintained even when the construction of faithful channel fails. Our results also show that the required auxiliary particle resources, local operations and classical communications are considerably reduced for the present purpose.

Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

NASA Astrophysics Data System (ADS)

Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

2016-09-01

In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.

ZERO: probabilistic routing for deploy and forget Wireless Sensor Networks.

PubMed

Vilajosana, Xavier; Llosa, Jordi; Pacho, Jose Carlos; Vilajosana, Ignasi; Juan, Angel A; Vicario, Jose Lopez; Morell, Antoni

2010-01-01

As Wireless Sensor Networks are being adopted by industry and agriculture for large-scale and unattended deployments, the need for reliable and energy-conservative protocols become critical. Physical and Link layer efforts for energy conservation are not mostly considered by routing protocols that put their efforts on maintaining reliability and throughput. Gradient-based routing protocols route data through most reliable links aiming to ensure 99% packet delivery. However, they suffer from the so-called "hot spot" problem. Most reliable routes waste their energy fast, thus partitioning the network and reducing the area monitored. To cope with this "hot spot" problem we propose ZERO a combined approach at Network and Link layers to increase network lifespan while conserving reliability levels by means of probabilistic load balancing techniques.

Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

NASA Astrophysics Data System (ADS)

Hatfield, Fraser N.; Dehmeshki, Jamshid

1998-09-01

Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

NASA Technical Reports Server (NTRS)

Baskaran, Subbiah; Ramachandran, Narayanan; Noever, David

1998-01-01

The use of probabilistic (PNN) and multilayer feed forward (MLFNN) neural networks are investigated for calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of networks are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this article.

Probabilistic atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study

PubMed Central

Figley, Teresa D.; Bhullar, Navdeep; Courtney, Susan M.; Figley, Chase R.

2015-01-01

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, “tractography”) approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template—using high-dimensional, non-linear warping methods—we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses. PMID:26578930

On construction of stochastic genetic networks based on gene expression sequences.

PubMed

Ching, Wai-Ki; Ng, Michael M; Fung, Eric S; Akutsu, Tatsuya

2005-08-01

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

Probabilistic Priority Message Checking Modeling Based on Controller Area Networks

NASA Astrophysics Data System (ADS)

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.

Photonic Quantum Networks formed from NV− centers

PubMed Central

Nemoto, Kae; Trupke, Michael; Devitt, Simon J.; Scharfenberger, Burkhard; Buczak, Kathrin; Schmiedmayer, Jörg; Munro, William J.

2016-01-01

In this article we present a simple repeater scheme based on the negatively-charged nitrogen vacancy centre in diamond. Each repeater node is built from modules comprising an optical cavity containing a single NV−, with one nuclear spin from 15N as quantum memory. The module uses only deterministic processes and interactions to achieve high fidelity operations (>99%), and modules are connected by optical fiber. In the repeater node architecture, the processes between modules by photons can be in principle deterministic, however current limitations on optical components lead the processes to be probabilistic but heralded. Our resource-modest repeater architecture contains two modules at each node, and the repeater nodes are then connected by entangled photon pairs. We discuss the performance of such a quantum repeater network with modest resources and then incorporate more resource-intense strategies step by step. Our architecture should allow large-scale quantum information networks with existing or near future technology. PMID:27215433

Photonic Quantum Networks formed from NV(-) centers.

PubMed

Nemoto, Kae; Trupke, Michael; Devitt, Simon J; Scharfenberger, Burkhard; Buczak, Kathrin; Schmiedmayer, Jörg; Munro, William J

2016-05-24

In this article we present a simple repeater scheme based on the negatively-charged nitrogen vacancy centre in diamond. Each repeater node is built from modules comprising an optical cavity containing a single NV(-), with one nuclear spin from (15)N as quantum memory. The module uses only deterministic processes and interactions to achieve high fidelity operations (>99%), and modules are connected by optical fiber. In the repeater node architecture, the processes between modules by photons can be in principle deterministic, however current limitations on optical components lead the processes to be probabilistic but heralded. Our resource-modest repeater architecture contains two modules at each node, and the repeater nodes are then connected by entangled photon pairs. We discuss the performance of such a quantum repeater network with modest resources and then incorporate more resource-intense strategies step by step. Our architecture should allow large-scale quantum information networks with existing or near future technology.

Security-Oriented and Load-Balancing Wireless Data Routing Game in the Integration of Advanced Metering Infrastructure Network in Smart Grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Fulin; Cao, Yang; Zhang, Jun Jason

Ensuring flexible and reliable data routing is indispensable for the integration of Advanced Metering Infrastructure (AMI) networks, we propose a secure-oriented and load-balancing wireless data routing scheme. A novel utility function is designed based on security routing scheme. Then, we model the interactive security-oriented routing strategy among meter data concentrators or smart grid meters as a mixed-strategy network formation game. Finally, such problem results in a stable probabilistic routing scheme with proposed distributed learning algorithm. One contributions is that we studied that different types of applications affect the routing selection strategy and the strategy tendency. Another contributions is that themore » chosen strategy of our mixed routing can adaptively to converge to a new mixed strategy Nash equilibrium (MSNE) during the learning process in the smart grid.« less

Searching for collective behavior in a large network of sensory neurons.

PubMed

Tkačik, Gašper; Marre, Olivier; Amodei, Dario; Schneidman, Elad; Bialek, William; Berry, Michael J

2014-01-01

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.

Searching for Collective Behavior in a Large Network of Sensory Neurons

PubMed Central

Tkačik, Gašper; Marre, Olivier; Amodei, Dario; Schneidman, Elad; Bialek, William; Berry, Michael J.

2014-01-01

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction. PMID:24391485

Poisson-Like Spiking in Circuits with Probabilistic Synapses

PubMed Central

Moreno-Bote, Rubén

2014-01-01

Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex. PMID:25032705

Identifying a Probabilistic Boolean Threshold Network From Samples.

PubMed

Melkman, Avraham A; Cheng, Xiaoqing; Ching, Wai-Ki; Akutsu, Tatsuya

2018-04-01

This paper studies the problem of exactly identifying the structure of a probabilistic Boolean network (PBN) from a given set of samples, where PBNs are probabilistic extensions of Boolean networks. Cheng et al. studied the problem while focusing on PBNs consisting of pairs of AND/OR functions. This paper considers PBNs consisting of Boolean threshold functions while focusing on those threshold functions that have unit coefficients. The treatment of Boolean threshold functions, and triplets and -tuplets of such functions, necessitates a deepening of the theoretical analyses. It is shown that wide classes of PBNs with such threshold functions can be exactly identified from samples under reasonable constraints, which include: 1) PBNs in which any number of threshold functions can be assigned provided that all have the same number of input variables and 2) PBNs consisting of pairs of threshold functions with different numbers of input variables. It is also shown that the problem of deciding the equivalence of two Boolean threshold functions is solvable in pseudopolynomial time but remains co-NP complete.

Fracture mechanics analysis of cracked structures using weight function and neural network method

NASA Astrophysics Data System (ADS)

Chen, J. G.; Zang, F. G.; Yang, Y.; Shi, K. K.; Fu, X. L.

2018-06-01

Stress intensity factors(SIFs) due to thermal-mechanical load has been established by using weight function method. Two reference stress states sere used to determine the coefficients in the weight function. Results were evaluated by using data from literature and show a good agreement between them. So, the SIFs can be determined quickly using the weight function obtained when cracks subjected to arbitrary loads, and presented method can be used for probabilistic fracture mechanics analysis. A probabilistic methodology considering Monte-Carlo with neural network (MCNN) has been developed. The results indicate that an accurate probabilistic characteristic of the KI can be obtained by using the developed method. The probability of failure increases with the increasing of loads, and the relationship between is nonlinear.

A knowledge-driven probabilistic framework for the prediction of protein-protein interaction networks.

PubMed

Browne, Fiona; Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

2010-03-01

This study applied a knowledge-driven data integration framework for the inference of protein-protein interactions (PPI). Evidence from diverse genomic features is integrated using a knowledge-driven Bayesian network (KD-BN). Receiver operating characteristic (ROC) curves may not be the optimal assessment method to evaluate a classifier's performance in PPI prediction as the majority of the area under the curve (AUC) may not represent biologically meaningful results. It may be of benefit to interpret the AUC of a partial ROC curve whereby biologically interesting results are represented. Therefore, the novel application of the assessment method referred to as the partial ROC has been employed in this study to assess predictive performance of PPI predictions along with calculating the True positive/false positive rate and true positive/positive rate. By incorporating domain knowledge into the construction of the KD-BN, we demonstrate improvement in predictive performance compared with previous studies based upon the Naive Bayesian approach. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Probabilistic Usage of the Multi-Factor Interaction Model

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A Multi-Factor Interaction Model (MFIM) is used to predict the insulating foam mass expulsion during the ascending of a space vehicle. The exponents in the MFIM are evaluated by an available approach which consists of least squares and an optimization algorithm. These results were subsequently used to probabilistically evaluate the effects of the uncertainties in each participating factor in the mass expulsion. The probabilistic results show that the surface temperature dominates at high probabilities and the pressure which causes the mass expulsion at low probabil

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

1997-01-01

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added.

Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment

PubMed Central

Legenstein, Robert; Maass, Wolfgang

2014-01-01

It has recently been shown that networks of spiking neurons with noise can emulate simple forms of probabilistic inference through “neural sampling”, i.e., by treating spikes as samples from a probability distribution of network states that is encoded in the network. Deficiencies of the existing model are its reliance on single neurons for sampling from each random variable, and the resulting limitation in representing quickly varying probabilistic information. We show that both deficiencies can be overcome by moving to a biologically more realistic encoding of each salient random variable through the stochastic firing activity of an ensemble of neurons. The resulting model demonstrates that networks of spiking neurons with noise can easily track and carry out basic computational operations on rapidly varying probability distributions, such as the odds of getting rewarded for a specific behavior. We demonstrate the viability of this new approach towards neural coding and computation, which makes use of the inherent parallelism of generic neural circuits, by showing that this model can explain experimentally observed firing activity of cortical neurons for a variety of tasks that require rapid temporal integration of sensory information. PMID:25340749

A Software Tool for Quantitative Seismicity Analysis - ZMAP

NASA Astrophysics Data System (ADS)

Wiemer, S.; Gerstenberger, M.

2001-12-01

Earthquake catalogs are probably the most basic product of seismology, and remain arguably the most useful for tectonic studies. Modern seismograph networks can locate up to 100,000 earthquakes annually, providing a continuous and sometime overwhelming stream of data. ZMAP is a set of tools driven by a graphical user interface (GUI), designed to help seismologists analyze catalog data. ZMAP is primarily a research tool suited to the evaluation of catalog quality and to addressing specific hypotheses; however, it can also be useful in routine network operations. Examples of ZMAP features include catalog quality assessment (artifacts, completeness, explosion contamination), interactive data exploration, mapping transients in seismicity (rate changes, b-values, p-values), fractal dimension analysis and stress tensor inversions. Roughly 100 scientists worldwide have used the software at least occasionally. About 30 peer-reviewed publications have made use of ZMAP. ZMAP code is open source, written in the commercial software language Matlab by the Mathworks, a widely used software in the natural sciences. ZMAP was first published in 1994, and has continued to grow over the past 7 years. Recently, we released ZMAP v.6. The poster will introduce the features of ZMAP. We will specifically focus on ZMAP features related to time-dependent probabilistic hazard assessment. We are currently implementing a ZMAP based system that computes probabilistic hazard maps, which combine the stationary background hazard as well as aftershock and foreshock hazard into a comprehensive time dependent probabilistic hazard map. These maps will be displayed in near real time on the Internet. This poster is also intended as a forum for ZMAP users to provide feedback and discuss the future of ZMAP.

DCMDN: Deep Convolutional Mixture Density Network

NASA Astrophysics Data System (ADS)

D'Isanto, Antonio; Polsterer, Kai Lars

2017-09-01

Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.

FALCON: a toolbox for the fast contextualization of logical networks

PubMed Central

De Landtsheer, Sébastien; Trairatphisan, Panuwat; Lucarelli, Philippe; Sauter, Thomas

2017-01-01

Abstract Motivation Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically. Results We have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. Availability and implementation FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON. FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. Contact thomas.sauter@uni.lu Supplementary information Supplementary data are available at Bioinformatics online. PMID:28673016

FALCON: a toolbox for the fast contextualization of logical networks.

PubMed

De Landtsheer, Sébastien; Trairatphisan, Panuwat; Lucarelli, Philippe; Sauter, Thomas

2017-11-01

Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically. We have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON. FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. thomas.sauter@uni.lu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

White matter pathways and social cognition.

PubMed

Wang, Yin; Metoki, Athanasia; Alm, Kylie H; Olson, Ingrid R

2018-04-20

There is a growing consensus that social cognition and behavior emerge from interactions across distributed regions of the "social brain". Researchers have traditionally focused their attention on functional response properties of these gray matter networks and neglected the vital role of white matter connections in establishing such networks and their functions. In this article, we conduct a comprehensive review of prior research on structural connectivity in social neuroscience and highlight the importance of this literature in clarifying brain mechanisms of social cognition. We pay particular attention to three key social processes: face processing, embodied cognition, and theory of mind, and their respective underlying neural networks. To fully identify and characterize the anatomical architecture of these networks, we further implement probabilistic tractography on a large sample of diffusion-weighted imaging data. The combination of an in-depth literature review and the empirical investigation gives us an unprecedented, well-defined landscape of white matter pathways underlying major social brain networks. Finally, we discuss current problems in the field, outline suggestions for best practice in diffusion-imaging data collection and analysis, and offer new directions for future research. Copyright © 2018 Elsevier Ltd. All rights reserved.

Altered structural connectivity of pain-related brain network in burning mouth syndrome-investigation by graph analysis of probabilistic tractography.

PubMed

Wada, Akihiko; Shizukuishi, Takashi; Kikuta, Junko; Yamada, Haruyasu; Watanabe, Yusuke; Imamura, Yoshiki; Shinozaki, Takahiro; Dezawa, Ko; Haradome, Hiroki; Abe, Osamu

2017-05-01

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome featuring idiopathic oral pain and burning discomfort despite clinically normal oral mucosa. The etiology of chronic pain syndrome is unclear, but preliminary neuroimaging research has suggested the alteration of volume, metabolism, blood flow, and diffusion at multiple brain regions. According to the neuromatrix theory of Melzack, pain sense is generated in the brain by the network of multiple pain-related brain regions. Therefore, the alteration of pain-related network is also assumed as an etiology of chronic pain. In this study, we investigated the brain network of BMS brain by using probabilistic tractography and graph analysis. Fourteen BMS patients and 14 age-matched healthy controls underwent 1.5T MRI. Structural connectivity was calculated in 83 anatomically defined regions with probabilistic tractography of 60-axis diffusion tensor imaging and 3D T1-weighted imaging. Graph theory network analysis was used to evaluate the brain network at local and global connectivity. In BMS brain, a significant difference of local brain connectivity was recognized at the bilateral rostral anterior cingulate cortex, right medial orbitofrontal cortex, and left pars orbitalis which belong to the medial pain system; however, no significant difference was recognized at the lateral system including the somatic sensory cortex. A strengthened connection of the anterior cingulate cortex and medial prefrontal cortex with the basal ganglia, thalamus, and brain stem was revealed. Structural brain network analysis revealed the alteration of the medial system of the pain-related brain network in chronic pain syndrome.

Integrative Identification of Arabidopsis Mitochondrial Proteome and Its Function Exploitation through Protein Interaction Network

PubMed Central

Cui, Jian; Liu, Jinghua; Li, Yuhua; Shi, Tieliu

2011-01-01

Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome. PMID:21297957

Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks.

PubMed

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.

Finding Influential Spreaders from Human Activity beyond Network Location.

PubMed

Min, Byungjoon; Liljeros, Fredrik; Makse, Hernán A

2015-01-01

Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses.

PubMed

Mostafa, Hesham; Cauwenberghs, Gert

2018-06-01

Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

Use of probabilistic neural networks for emitter correlation

NASA Astrophysics Data System (ADS)

Maloney, P. S.

1990-08-01

The Probabilistic Neural Network (PNN) as described by Specht''3 has been successfully applied to a number of emitter correlation problems involving operational data for training and testing of the neural net work. The PNN has been found to be a reliable classification tool for determining emitter type or even identifying specific emitter platforms given appropriate representative data sets for training con sisting only of parametric data from electronic intelligence (ELINT) reports. Four separate feasibility studies have been conducted to prove the usefulness of PNN in this application area: . Hull-to-emitter correlation (HULTEC) for identification of seagoing emitter platforms . Identification of landbased emitters from airborne sensors . Pulse sorting according to emitter of origin . Emitter typing based on a dynamically learning neural network. 1 .

Complex network description of the ionosphere

NASA Astrophysics Data System (ADS)

Lu, Shikun; Zhang, Hao; Li, Xihai; Li, Yihong; Niu, Chao; Yang, Xiaoyun; Liu, Daizhi

2018-03-01

Complex networks have emerged as an essential approach of geoscience to generate novel insights into the nature of geophysical systems. To investigate the dynamic processes in the ionosphere, a directed complex network is constructed, based on a probabilistic graph of the vertical total electron content (VTEC) from 2012. The results of the power-law hypothesis test show that both the out-degree and in-degree distribution of the ionospheric network are not scale-free. Thus, the distribution of the interactions in the ionosphere is homogenous. None of the geospatial positions play an eminently important role in the propagation of the dynamic ionospheric processes. The spatial analysis of the ionospheric network shows that the interconnections principally exist between adjacent geographical locations, indicating that the propagation of the dynamic processes primarily depends on the geospatial distance in the ionosphere. Moreover, the joint distribution of the edge distances with respect to longitude and latitude directions shows that the dynamic processes travel further along the longitude than along the latitude in the ionosphere. The analysis of small-world-ness indicates that the ionospheric network possesses the small-world property, which can make the ionosphere stable and efficient in the propagation of dynamic processes.

Quantum Logic Networks for Probabilistic and Controlled Teleportation of Unknown Quantum States

NASA Astrophysics Data System (ADS)

Gao, Ting

2004-08-01

We present simplification schemes for probabilistic and controlled teleportation of the unknown quantum states of both one particle and two particles and construct efficient quantum logic networks for implementing the new schemes by means of the primitive operations consisting of single-qubit gates, two-qubit controlled-not gates, Von Neumann measurement, and classically controlled operations. In these schemes the teleportation are not always successful but with certain probability. The project supported by National Natural Science Foundation of China under Grant No. 10271081 and the Natural Science Foundation of Hebei Province of China under Grant No. A2004000141

Method and apparatus for detecting concealed weapons

DOEpatents

Kotter, Dale K.; Fluck, Frederick D.

2006-03-14

Apparatus for classifying a ferromagnetic object within a sensing area may include a magnetic field sensor that produces magnetic field data. A signal processing system operatively associated with the magnetic field sensor includes a neural network. The neural network compares the magnetic field data with magnetic field data produced by known ferromagnetic objects to make a probabilistic determination as to the classification of the ferromagnetic object within the sensing area. A user interface operatively associated with the signal processing system produces a user-discernable output indicative of the probabilistic determination of the classification of the ferromagnetic object within a sensing area.

Watch what you say, your computer might be listening: A review of automated speech recognition

NASA Technical Reports Server (NTRS)

Degennaro, Stephen V.

1991-01-01

Spoken language is the most convenient and natural means by which people interact with each other and is, therefore, a promising candidate for human-machine interactions. Speech also offers an additional channel for hands-busy applications, complementing the use of motor output channels for control. Current speech recognition systems vary considerably across a number of important characteristics, including vocabulary size, speaking mode, training requirements for new speakers, robustness to acoustic environments, and accuracy. Algorithmically, these systems range from rule-based techniques through more probabilistic or self-learning approaches such as hidden Markov modeling and neural networks. This tutorial begins with a brief summary of the relevant features of current speech recognition systems and the strengths and weaknesses of the various algorithmic approaches.

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao

2016-01-01

Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm. PMID:27618902

NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis.

PubMed

Sun, Duanchen; Liu, Yinliang; Zhang, Xiang-Sun; Wu, Ling-Yun

2017-09-21

High-throughput experimental techniques have been dramatically improved and widely applied in the past decades. However, biological interpretation of the high-throughput experimental results, such as differential expression gene sets derived from microarray or RNA-seq experiments, is still a challenging task. Gene Ontology (GO) is commonly used in the functional enrichment studies. The GO terms identified via current functional enrichment analysis tools often contain direct parent or descendant terms in the GO hierarchical structure. Highly redundant terms make users difficult to analyze the underlying biological processes. In this paper, a novel network-based probabilistic generative model, NetGen, was proposed to perform the functional enrichment analysis. An additional protein-protein interaction (PPI) network was explicitly used to assist the identification of significantly enriched GO terms. NetGen achieved a superior performance than the existing methods in the simulation studies. The effectiveness of NetGen was explored further on four real datasets. Notably, several GO terms which were not directly linked with the active gene list for each disease were identified. These terms were closely related to the corresponding diseases when accessed to the curated literatures. NetGen has been implemented in the R package CopTea publicly available at GitHub ( http://github.com/wulingyun/CopTea/ ). Our procedure leads to a more reasonable and interpretable result of the functional enrichment analysis. As a novel term combination-based functional enrichment analysis method, NetGen is complementary to current individual term-based methods, and can help to explore the underlying pathogenesis of complex diseases.

Probabilistic estimation of dune retreat on the Gold Coast, Australia

USGS Publications Warehouse

Palmsten, Margaret L.; Splinter, Kristen D.; Plant, Nathaniel G.; Stockdon, Hilary F.

2014-01-01

Sand dunes are an important natural buffer between storm impacts and development backing the beach on the Gold Coast of Queensland, Australia. The ability to forecast dune erosion at a prediction horizon of days to a week would allow efficient and timely response to dune erosion in this highly populated area. Towards this goal, we modified an existing probabilistic dune erosion model for use on the Gold Coast. The original model was trained using observations of dune response from Hurricane Ivan on Santa Rosa Island, Florida, USA (Plant and Stockdon 2012. Probabilistic prediction of barrier-island response to hurricanes, Journal of Geophysical Research, 117(F3), F03015). The model relates dune position change to pre-storm dune elevations, dune widths, and beach widths, along with storm surge and run-up using a Bayesian network. The Bayesian approach captures the uncertainty of inputs and predictions through the conditional probabilities between variables. Three versions of the barrier island response Bayesian network were tested for use on the Gold Coast. One network has the same structure as the original and was trained with the Santa Rosa Island data. The second network has a modified design and was trained using only pre- and post-storm data from 1988-2009 for the Gold Coast. The third version of the network has the same design as the second version of the network and was trained with the combined data from the Gold Coast and Santa Rosa Island. The two networks modified for use on the Gold Coast hindcast dune retreat with equal accuracy. Both networks explained 60% of the observed dune retreat variance, which is comparable to the skill observed by Plant and Stockdon (2012) in the initial Bayesian network application at Santa Rosa Island. The new networks improved predictions relative to application of the original network on the Gold Coast. Dune width was the most important morphologic variable in hindcasting dune retreat, while hydrodynamic variables, surge and run-up elevation, were also important

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions

PubMed Central

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject’s face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject’s face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network. PMID:26859884

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions.

PubMed

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject's face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject's face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network.

Assuring SS7 dependability: A robustness characterization of signaling network elements

NASA Astrophysics Data System (ADS)

Karmarkar, Vikram V.

1994-04-01

Current and evolving telecommunication services will rely on signaling network performance and reliability properties to build competitive call and connection control mechanisms under increasing demands on flexibility without compromising on quality. The dimensions of signaling dependability most often evaluated are the Rate of Call Loss and End-to-End Route Unavailability. A third dimension of dependability that captures the concern about large or catastrophic failures can be termed Network Robustness. This paper is concerned with the dependability aspects of the evolving Signaling System No. 7 (SS7) networks and attempts to strike a balance between the probabilistic and deterministic measures that must be evaluated to accomplish a risk-trend assessment to drive architecture decisions. Starting with high-level network dependability objectives and field experience with SS7 in the U.S., potential areas of growing stringency in network element (NE) dependability are identified to improve against current measures of SS7 network quality, as per-call signaling interactions increase. A sensitivity analysis is presented to highlight the impact due to imperfect coverage of duplex network component or element failures (i.e., correlated failures), to assist in the setting of requirements on NE robustness. A benefit analysis, covering several dimensions of dependability, is used to generate the domain of solutions available to the network architect in terms of network and network element fault tolerance that may be specified to meet the desired signaling quality goals.

Learning Probabilistic Logic Models from Probabilistic Examples

PubMed Central

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2009-01-01

Abstract We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples. PMID:19888348

Learning Probabilistic Logic Models from Probabilistic Examples.

PubMed

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2008-10-01

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.

PubMed

Probst, Dimitri; Petrovici, Mihai A; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons

PubMed Central

Probst, Dimitri; Petrovici, Mihai A.; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems. PMID:25729361

bnstruct: an R package for Bayesian Network structure learning in the presence of missing data.

PubMed

Franzin, Alberto; Sambo, Francesco; Di Camillo, Barbara

2017-04-15

A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks, particularly the manipulation of missing data, which is a common situation in practice. The software is implemented in R and C and is available on CRAN under a GPL licence. francesco.sambo@unipd.it. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

The SH2 domain interaction landscape.

PubMed

Tinti, Michele; Kiemer, Lars; Costa, Stefano; Miller, Martin L; Sacco, Francesca; Olsen, Jesper V; Carducci, Martina; Paoluzi, Serena; Langone, Francesca; Workman, Christopher T; Blom, Nikolaj; Machida, Kazuya; Thompson, Christopher M; Schutkowski, Mike; Brunak, Søren; Mann, Matthias; Mayer, Bruce J; Castagnoli, Luisa; Cesareni, Gianni

2013-04-25

Members of the SH2 domain family modulate signal transduction by binding to short peptides containing phosphorylated tyrosines. Each domain displays a distinct preference for the sequence context of the phosphorylated residue. We have developed a high-density peptide chip technology that allows for probing of the affinity of most SH2 domains for a large fraction of the entire complement of tyrosine phosphopeptides in the human proteome. Using this technique, we have experimentally identified thousands of putative SH2-peptide interactions for more than 70 different SH2 domains. By integrating this rich data set with orthogonal context-specific information, we have assembled an SH2-mediated probabilistic interaction network, which we make available as a community resource in the PepspotDB database. A predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the extracellular signal-regulated kinase activation loop was validated by experiments in living cells. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Quantum Enhanced Inference in Markov Logic Networks

NASA Astrophysics Data System (ADS)

Wittek, Peter; Gogolin, Christian

2017-04-01

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.

Quantum Enhanced Inference in Markov Logic Networks.

PubMed

Wittek, Peter; Gogolin, Christian

2017-04-19

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.

Quantum Enhanced Inference in Markov Logic Networks

PubMed Central

Wittek, Peter; Gogolin, Christian

2017-01-01

Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning. PMID:28422093

Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network.

PubMed

Bui, Ngot; Yen, John; Honavar, Vasant

2016-06-01

Online health communities constitute a useful source of information and social support for patients. American Cancer Society's Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multi-party conversations that often provide a source of social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of an online health community derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a Probabilistic Computation Tree Logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the (i) classification threshold of the sentiment classifier; (ii) and the choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs.

Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network

PubMed Central

Bui, Ngot; Yen, John; Honavar, Vasant

2017-01-01

Online health communities constitute a useful source of information and social support for patients. American Cancer Society’s Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multi-party conversations that often provide a source of social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of an online health community derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a Probabilistic Computation Tree Logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the (i) classification threshold of the sentiment classifier; (ii) and the choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs. PMID:29399599

Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks

PubMed Central

Han, Guangjie; Li, Shanshan; Zhu, Chunsheng; Jiang, Jinfang; Zhang, Wenbo

2017-01-01

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. PMID:28208735

Gene network analysis: from heart development to cardiac therapy.

PubMed

Ferrazzi, Fulvia; Bellazzi, Riccardo; Engel, Felix B

2015-03-01

Networks offer a flexible framework to represent and analyse the complex interactions between components of cellular systems. In particular gene networks inferred from expression data can support the identification of novel hypotheses on regulatory processes. In this review we focus on the use of gene network analysis in the study of heart development. Understanding heart development will promote the elucidation of the aetiology of congenital heart disease and thus possibly improve diagnostics. Moreover, it will help to establish cardiac therapies. For example, understanding cardiac differentiation during development will help to guide stem cell differentiation required for cardiac tissue engineering or to enhance endogenous repair mechanisms. We introduce different methodological frameworks to infer networks from expression data such as Boolean and Bayesian networks. Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies. Collectively, our literature-based analysis indicates that gene network analysis constitutes a promising opportunity to infer therapy-relevant regulatory processes in heart development. However, the use of network-based approaches has so far been limited by the small amount of samples in available datasets. Thus, we propose to acquire high-resolution temporal expression data to improve the mathematical descriptions of regulatory processes obtained with gene network inference methodologies. Especially probabilistic methods that accommodate the intrinsic variability of biological systems have the potential to contribute to a deeper understanding of heart development.

Efficient Probabilistic Diagnostics for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Probabilistic Assessment of High-Throughput Wireless Sensor Networks

PubMed Central

Kim, Robin E.; Mechitov, Kirill; Sim, Sung-Han; Spencer, Billie F.; Song, Junho

2016-01-01

Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved. PMID:27258270

A Model of Network Porosity

DTIC Science & Technology

2016-11-09

the model does not become a full probabilistic attack graph analysis of the network , whose data requirements are currently unrealistic. The second...flow. – Untrustworthy persons may intentionally try to exfiltrate known sensitive data to ex- ternal networks . People may also unintentionally leak...section will provide details on the components, procedures, data requirements, and parameters required to instantiate the network porosity model. These

Stochastic methods for analysis of power flow in electric networks

NASA Astrophysics Data System (ADS)

1982-09-01

The modeling and effects of probabilistic behavior on steady state power system operation were analyzed. A solution to the steady state network flow equations which adhere both to Kirchoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques was obtained. The development of sound techniques for producing meaningful data to serve as input is examined. Electric demand modeling, equipment failure analysis, and algorithm development are investigated. Two major development areas are described: a decomposition of stochastic processes which gives stationarity, ergodicity, and even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.

Computing preimages of Boolean networks.

PubMed

Klotz, Johannes; Bossert, Martin; Schober, Steffen

2013-01-01

In this paper we present an algorithm based on the sum-product algorithm that finds elements in the preimage of a feed-forward Boolean networks given an output of the network. Our probabilistic method runs in linear time with respect to the number of nodes in the network. We evaluate our algorithm for randomly constructed Boolean networks and a regulatory network of Escherichia coli and found that it gives a valid solution in most cases.

Ensemble transcript interaction networks: a case study on Alzheimer's disease.

PubMed

Armañanzas, Rubén; Larrañaga, Pedro; Bielza, Concha

2012-10-01

Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A Face Attention Technique for a Robot Able to Interpret Facial Expressions

NASA Astrophysics Data System (ADS)

Simplício, Carlos; Prado, José; Dias, Jorge

Automatic facial expressions recognition using vision is an important subject towards human-robot interaction. Here is proposed a human face focus of attention technique and a facial expressions classifier (a Dynamic Bayesian Network) to incorporate in an autonomous mobile agent whose hardware is composed by a robotic platform and a robotic head. The focus of attention technique is based on the symmetry presented by human faces. By using the output of this module the autonomous agent keeps always targeting the human face frontally. In order to accomplish this, the robot platform performs an arc centered at the human; thus the robotic head, when necessary, moves synchronized. In the proposed probabilistic classifier the information is propagated, from the previous instant, in a lower level of the network, to the current instant. Moreover, to recognize facial expressions are used not only positive evidences but also negative.

Probabilistic monitoring in intrusion detection module for energy efficiency in mobile ad hoc networks

NASA Astrophysics Data System (ADS)

De Rango, Floriano; Lupia, Andrea

2016-05-01

MANETs allow mobile nodes communicating to each other using the wireless medium. A key aspect of these kind of networks is the security, because their setup is done without an infrastructure, so external nodes could interfere in the communication. Mobile nodes could be compromised, misbehaving during the multi-hop transmission of data, or they could have a selfish behavior to save energy, which is another important constraint in MANETs. The detection of these behaviors need a framework that takes into account the latest interactions among nodes, so malicious or selfish nodes could be detected also if their behavior is changed over time. The monitoring activity increases the energy consumption, so our proposal takes into account this issue reducing the energy required by the monitoring system, keeping the effectiveness of the intrusion detection system. The results show an improvement in the saved energy, improving the detection performance too.

Probabilistic evaluation of n traces with no putative source: A likelihood ratio based approach in an investigative framework.

PubMed

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. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Inference in the brain: Statistics flowing in redundant population codes

PubMed Central

Pitkow, Xaq; Angelaki, Dora E

2017-01-01

It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors. PMID:28595050

Incorporating seismic phase correlations into a probabilistic model of global-scale seismology

NASA Astrophysics Data System (ADS)

Arora, Nimar

2013-04-01

We present a probabilistic model of seismic phases whereby the attributes of the body-wave phases are correlated to those of the first arriving P phase. This model has been incorporated into NET-VISA (Network processing Vertically Integrated Seismic Analysis) a probabilistic generative model of seismic events, their transmission, and detection on a global seismic network. In the earlier version of NET-VISA, seismic phase were assumed to be independent of each other. Although this didn't affect the quality of the inferred seismic bulletin, for the most part, it did result in a few instances of anomalous phase association. For example, an S phase with a smaller slowness than the corresponding P phase. We demonstrate that the phase attributes are indeed highly correlated, for example the uncertainty in the S phase travel time is significantly reduced given the P phase travel time. Our new model exploits these correlations to produce better calibrated probabilities for the events, as well as fewer anomalous associations.

Perceptual learning as improved probabilistic inference in early sensory areas.

PubMed

Bejjanki, Vikranth R; Beck, Jeffrey M; Lu, Zhong-Lin; Pouget, Alexandre

2011-05-01

Extensive training on simple tasks such as fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous models have argued that perceptual learning is due to either sharpening and amplification of tuning curves in early visual areas or to improved probabilistic inference in later visual areas (at the decision stage). However, early theories are inconsistent with the conclusions of psychophysical experiments manipulating external noise, whereas late theories cannot explain the changes in neural responses that have been reported in cortical areas V1 and V4. Here we show that we can capture both the neurophysiological and behavioral aspects of perceptual learning by altering only the feedforward connectivity in a recurrent network of spiking neurons so as to improve probabilistic inference in early visual areas. The resulting network shows modest changes in tuning curves, in line with neurophysiological reports, along with a marked reduction in the amplitude of pairwise noise correlations.

Recent development and biomedical applications of probabilistic Boolean networks

PubMed Central

2013-01-01

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered. A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed. A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels. PMID:23815817

Bayesian networks in neuroscience: a survey.

PubMed

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.

Bayesian networks in neuroscience: a survey

PubMed Central

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied. PMID:25360109

Bayesian networks improve causal environmental assessments for evidence-based policy

EPA Science Inventory

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the p...

Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

PubMed Central

Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

2013-01-01

Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.

PubMed

Herzallah, Randa

2015-03-01

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained. Copyright © 2014 Elsevier Ltd. All rights reserved.

Alterations of white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus identified by probabilistic tractography and connectivity-based analyses.

PubMed

Xu, Man; Tan, Xiangliang; Zhang, Xinyuan; Guo, Yihao; Mei, Yingjie; Feng, Qianjin; Xu, Yikai; Feng, Yanqiu

2017-01-01

Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients.

Revealing the ISO/IEC 9126-1 Clique Tree for COTS Software Evaluation

NASA Technical Reports Server (NTRS)

Morris, A. Terry

2007-01-01

Previous research has shown that acyclic dependency models, if they exist, can be extracted from software quality standards and that these models can be used to assess software safety and product quality. In the case of commercial off-the-shelf (COTS) software, the extracted dependency model can be used in a probabilistic Bayesian network context for COTS software evaluation. Furthermore, while experts typically employ Bayesian networks to encode domain knowledge, secondary structures (clique trees) from Bayesian network graphs can be used to determine the probabilistic distribution of any software variable (attribute) using any clique that contains that variable. Secondary structures, therefore, provide insight into the fundamental nature of graphical networks. This paper will apply secondary structure calculations to reveal the clique tree of the acyclic dependency model extracted from the ISO/IEC 9126-1 software quality standard. Suggestions will be provided to describe how the clique tree may be exploited to aid efficient transformation of an evaluation model.

Probabilistic resource allocation system with self-adaptive capability

NASA Technical Reports Server (NTRS)

Yufik, Yan M. (Inventor)

1996-01-01

A probabilistic resource allocation system is disclosed containing a low capacity computational module (Short Term Memory or STM) and a self-organizing associative network (Long Term Memory or LTM) where nodes represent elementary resources, terminal end nodes represent goals, and directed links represent the order of resource association in different allocation episodes. Goals and their priorities are indicated by the user, and allocation decisions are made in the STM, while candidate associations of resources are supplied by the LTM based on the association strength (reliability). Reliability values are automatically assigned to the network links based on the frequency and relative success of exercising those links in the previous allocation decisions. Accumulation of allocation history in the form of an associative network in the LTM reduces computational demands on subsequent allocations. For this purpose, the network automatically partitions itself into strongly associated high reliability packets, allowing fast approximate computation and display of allocation solutions satisfying the overall reliability and other user-imposed constraints. System performance improves in time due to modification of network parameters and partitioning criteria based on the performance feedback.

Probabilistic resource allocation system with self-adaptive capability

NASA Technical Reports Server (NTRS)

Yufik, Yan M. (Inventor)

1998-01-01

A probabilistic resource allocation system is disclosed containing a low capacity computational module (Short Term Memory or STM) and a self-organizing associative network (Long Term Memory or LTM) where nodes represent elementary resources, terminal end nodes represent goals, and weighted links represent the order of resource association in different allocation episodes. Goals and their priorities are indicated by the user, and allocation decisions are made in the STM, while candidate associations of resources are supplied by the LTM based on the association strength (reliability). Weights are automatically assigned to the network links based on the frequency and relative success of exercising those links in the previous allocation decisions. Accumulation of allocation history in the form of an associative network in the LTM reduces computational demands on subsequent allocations. For this purpose, the network automatically partitions itself into strongly associated high reliability packets, allowing fast approximate computation and display of allocation solutions satisfying the overall reliability and other user-imposed constraints. System performance improves in time due to modification of network parameters and partitioning criteria based on the performance feedback.

Probabilistic representation of gene regulatory networks.

PubMed

Mao, Linyong; Resat, Haluk

2004-09-22

Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. The simulation software is available upon request. Supplementary material will be made available on the OUP server.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

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.

Integrated software health management for aerospace guidance, navigation, and control systems: A probabilistic reasoning approach

NASA Astrophysics Data System (ADS)

Mbaya, Timmy

Embedded Aerospace Systems have to perform safety and mission critical operations in a real-time environment where timing and functional correctness are extremely important. Guidance, Navigation, and Control (GN&C) systems substantially rely on complex software interfacing with hardware in real-time; any faults in software or hardware, or their interaction could result in fatal consequences. Integrated Software Health Management (ISWHM) provides an approach for detection and diagnosis of software failures while the software is in operation. The ISWHM approach is based on probabilistic modeling of software and hardware sensors using a Bayesian network. To meet memory and timing constraints of real-time embedded execution, the Bayesian network is compiled into an Arithmetic Circuit, which is used for on-line monitoring. This type of system monitoring, using an ISWHM, provides automated reasoning capabilities that compute diagnoses in a timely manner when failures occur. This reasoning capability enables time-critical mitigating decisions and relieves the human agent from the time-consuming and arduous task of foraging through a multitude of isolated---and often contradictory---diagnosis data. For the purpose of demonstrating the relevance of ISWHM, modeling and reasoning is performed on a simple simulated aerospace system running on a real-time operating system emulator, the OSEK/Trampoline platform. Models for a small satellite and an F-16 fighter jet GN&C (Guidance, Navigation, and Control) system have been implemented. Analysis of the ISWHM is then performed by injecting faults and analyzing the ISWHM's diagnoses.

Typing mineral deposits using their grades and tonnages in an artificial neural network

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

2003-01-01

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Probabilistic dual heuristic programming-based adaptive critic

NASA Astrophysics Data System (ADS)

Herzallah, Randa

2010-02-01

Adaptive critic (AC) methods have common roots as generalisations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, non-linear and non-stationary environments. In this study, a novel probabilistic dual heuristic programming (DHP)-based AC controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) AC method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterised by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the probabilistic critic network is then calculated and shown to be equal to the analytically derived correct value. Full derivation of the Riccati solution for this non-standard stochastic linear quadratic control problem is also provided. Moreover, the performance of the proposed probabilistic controller is demonstrated on linear and non-linear control examples.

Information Flow in Interaction Networks II: Channels, Path Lengths, and Potentials

PubMed Central

Stojmirović, Aleksandar

2012-01-01

Abstract In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by constructing a potential function facilitating a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework. PMID:22409812

Improving ontology matching with propagation strategy and user feedback

NASA Astrophysics Data System (ADS)

Li, Chunhua; Cui, Zhiming; Zhao, Pengpeng; Wu, Jian; Xin, Jie; He, Tianxu

2015-07-01

Markov logic networks which unify probabilistic graphical model and first-order logic provide an excellent framework for ontology matching. The existing approach requires a threshold to produce matching candidates and use a small set of constraints acting as filter to select the final alignments. We introduce novel match propagation strategy to model the influences between potential entity mappings across ontologies, which can help to identify the correct correspondences and produce missed correspondences. The estimation of appropriate threshold is a difficult task. We propose an interactive method for threshold selection through which we obtain an additional measurable improvement. Running experiments on a public dataset has demonstrated the effectiveness of proposed approach in terms of the quality of result alignment.

Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada

NASA Astrophysics Data System (ADS)

Maurya, S. P.; Singh, K. H.; Singh, N. P.

2018-05-01

In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000-8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region.

Detection of micro solder balls using active thermography and probabilistic neural network

NASA Astrophysics Data System (ADS)

He, Zhenzhi; Wei, Li; Shao, Minghui; Lu, Xingning

2017-03-01

Micro solder ball/bump has been widely used in electronic packaging. It has been challenging to inspect these structures as the solder balls/bumps are often embedded between the component and substrates, especially in flip-chip packaging. In this paper, a detection method for micro solder ball/bump based on the active thermography and the probabilistic neural network is investigated. A VH680 infrared imager is used to capture the thermal image of the test vehicle, SFA10 packages. The temperature curves are processed using moving average technique to remove the peak noise. And the principal component analysis (PCA) is adopted to reconstruct the thermal images. The missed solder balls can be recognized explicitly in the second principal component image. Probabilistic neural network (PNN) is then established to identify the defective bump intelligently. The hot spots corresponding to the solder balls are segmented from the PCA reconstructed image, and statistic parameters are calculated. To characterize the thermal properties of solder bump quantitatively, three representative features are selected and used as the input vector in PNN clustering. The results show that the actual outputs and the expected outputs are consistent in identification of the missed solder balls, and all the bumps were recognized accurately, which demonstrates the viability of the PNN in effective defect inspection in high-density microelectronic packaging.

Neural network approaches to capture temporal information

NASA Astrophysics Data System (ADS)

van Veelen, Martijn; Nijhuis, Jos; Spaanenburg, Ben

2000-05-01

The automated design and construction of neural networks receives growing attention of the neural networks community. Both the growing availability of computing power and development of mathematical and probabilistic theory have had severe impact on the design and modelling approaches of neural networks. This impact is most apparent in the use of neural networks to time series prediction. In this paper, we give our views on past, contemporary and future design and modelling approaches to neural forecasting.

Preoperative DTI and probabilistic tractography in an amputee with deep brain stimulation for lower limb stump pain.

PubMed

Owen, S L F; Heath, J; Kringelbach, M L; Stein, J F; Aziz, T Z

2007-10-01

This study aimed to find out whether preoperative diffusion tensor imaging (DTI) and probabilistic tractography could help with surgical planning for deep brain stimulation in the periaqueductal/periventricular grey area (PAG/PVG) in a patient with lower leg stump pain. A preoperative DTI was obtained from the patient, who then received DBS surgery in the PAG/PVG area with good pain relief. The postoperative MRI scan showing electrode placement was used to calculate four seed areas to represent the contacts on the Medtronic 3387 electrode. Probabilistic tractography was then performed from the pre-operative DTI image. Tracts were seen to connect to many areas within the pain network from the four different contacts. These initial findings suggest that preoperative DTI scanning and probabilistic tractography may be able to assist surgical planning in the future.

NESSUS/EXPERT - An expert system for probabilistic structural analysis methods

NASA Technical Reports Server (NTRS)

Millwater, H.; Palmer, K.; Fink, P.

1988-01-01

An expert system (NESSUS/EXPERT) is presented which provides assistance in using probabilistic structural analysis methods. NESSUS/EXPERT is an interactive menu-driven expert system that provides information to assist in the use of the probabilistic finite element code NESSUS/FEM and the fast probability integrator. NESSUS/EXPERT was developed with a combination of FORTRAN and CLIPS, a C language expert system tool, to exploit the strengths of each language.

Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics.

PubMed

Sokoloski, Sacha

2017-09-01

In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.

Population rate dynamics and multineuron firing patterns in sensory cortex

PubMed Central

Okun, Michael; Yger, Pierre; Marguet, Stephan; Gerard-Mercier, Florian; Benucci, Andrea; Katzner, Steffen; Busse, Laura; Carandini, Matteo; Harris, Kenneth D.

2012-01-01

Cortical circuits encode sensory stimuli through the firing of neuronal ensembles, and also produce spontaneous population patterns in the absence of sensory drive. This population activity is often characterized experimentally by the distribution of multineuron “words” (binary firing vectors), and a match between spontaneous and evoked word distributions has been suggested to reflect learning of a probabilistic model of the sensory world. We analyzed multineuron word distributions in sensory cortex of anesthetized rats and cats, and found that they are dominated by fluctuations in population firing rate rather than precise interactions between individual units. Furthermore, cortical word distributions change when brain state shifts, and similar behavior is seen in simulated networks with fixed, random connectivity. Our results suggest that similarity or dissimilarity in multineuron word distributions could primarily reflect similarity or dissimilarity in population firing rate dynamics, and not necessarily the precise interactions between neurons that would indicate learning of sensory features. PMID:23197704

Foundational perspectives on causality in large-scale brain networks

NASA Astrophysics Data System (ADS)

Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

Foundational perspectives on causality in large-scale brain networks.

PubMed

Mannino, Michael; Bressler, Steven L

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain. Copyright © 2015 Elsevier B.V. All rights reserved.

Probabilistic lifetime strength of aerospace materials via computational simulation

NASA Technical Reports Server (NTRS)

Boyce, Lola; Keating, Jerome P.; Lovelace, Thomas B.; Bast, Callie C.

1991-01-01

The results of a second year effort of a research program are presented. The research included development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic phenomenological constitutive relationship, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects of primitive variables. These primitive variables often originate in the environment and may include stress from loading, temperature, chemical, or radiation attack. This multifactor interaction constitutive equation is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the constitutive equation using actual experimental materials data together with the multiple linear regression of that data.

A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic testing of functional connectivity and brain graphs.

PubMed

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 fMRI time series. Accurate estimation of df offers many potential advantages for probabilistically thresholding functional connectivity and network statistics tested in the context of spatially variant and non-stationary noise. Code for wavelet despiking, seed correlational testing and probabilistic graph construction is freely available to download as part of the BrainWavelet Toolbox at www.brainwavelet.org. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

PubMed Central

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

Controls of the U.S. Virtual Water Transfer Network

NASA Astrophysics Data System (ADS)

Garcia, S.; Mejia, A.

2017-12-01

A complex interplay of human and natural factors shape the economic geography of the U.S., operating through socioeconomic forces that drive the consumption, production, and exchange of commodities. The virtual water content of a commodity represents the water embedded in its production. This work investigates the controls of national bilateral transfers of the virtual water transfer network (VWTN), through a gravity-type spatial interaction model. We use a probabilistic model to predict the binary network and investigate whether the gravity model can explain the topological properties of the empirical weighted network. In general, the gravity model relates transfer flows to the mass of the trading regions and their geographical distance. We hypothesize that properties of the nodes such as population, employment, and availability of land, together with the Euclidean distance between two trading regions, capture the main drivers of the national VWTN. The results from the model are then compared to the empirical weighted network to verify its ability to model the structure of this self-organized system. The proposed empirical model provides insight into the processes that underlie the formation of the VWTN. It can be a promising tool to study how flows are affected by changes in the generating conditions due to shocks and/or stresses.

Probabilistic structural analysis methods for select space propulsion system components

NASA Technical Reports Server (NTRS)

Millwater, H. R.; Cruse, T. A.

1989-01-01

The Probabilistic Structural Analysis Methods (PSAM) project developed at the Southwest Research Institute integrates state-of-the-art structural analysis techniques with probability theory for the design and analysis of complex large-scale engineering structures. An advanced efficient software system (NESSUS) capable of performing complex probabilistic analysis has been developed. NESSUS contains a number of software components to perform probabilistic analysis of structures. These components include: an expert system, a probabilistic finite element code, a probabilistic boundary element code and a fast probability integrator. The NESSUS software system is shown. An expert system is included to capture and utilize PSAM knowledge and experience. NESSUS/EXPERT is an interactive menu-driven expert system that provides information to assist in the use of the probabilistic finite element code NESSUS/FEM and the fast probability integrator (FPI). The expert system menu structure is summarized. The NESSUS system contains a state-of-the-art nonlinear probabilistic finite element code, NESSUS/FEM, to determine the structural response and sensitivities. A broad range of analysis capabilities and an extensive element library is present.

Network reciprocity by coexisting learning and teaching strategies

NASA Astrophysics Data System (ADS)

Tanimoto, Jun; Brede, Markus; Yamauchi, Atsuo

2012-03-01

We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor. Our simulations reveal that the reciprocity is significantly affected by the frequency with which learning and teaching agents coexist in a network and by the structure of the network itself.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, B

This survey gives an overview of popular generative models used in the modeling of stochastic temporal systems. In particular, this survey is organized into two parts. The first part discusses the discrete-time representations of dynamic Bayesian networks and dynamic relational probabilistic models, while the second part discusses the continuous-time representation of continuous-time Bayesian networks.

Novel probabilistic neuroclassifier

NASA Astrophysics Data System (ADS)

Hong, Jiang; Serpen, Gursel

2003-09-01

A novel probabilistic potential function neural network classifier algorithm to deal with classes which are multi-modally distributed and formed from sets of disjoint pattern clusters is proposed in this paper. The proposed classifier has a number of desirable properties which distinguish it from other neural network classifiers. A complete description of the algorithm in terms of its architecture and the pseudocode is presented. Simulation analysis of the newly proposed neuro-classifier algorithm on a set of benchmark problems is presented. Benchmark problems tested include IRIS, Sonar, Vowel Recognition, Two-Spiral, Wisconsin Breast Cancer, Cleveland Heart Disease and Thyroid Gland Disease. Simulation results indicate that the proposed neuro-classifier performs consistently better for a subset of problems for which other neural classifiers perform relatively poorly.

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-09-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-08-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Probabilistic graphs as a conceptual and computational tool in hydrology and water management

NASA Astrophysics Data System (ADS)

Schoups, Gerrit

2014-05-01

Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.

Analysis of structure-function network decoupling in the brain systems of spastic diplegic cerebral palsy.

PubMed

Lee, Dongha; Pae, Chongwon; Lee, Jong Doo; Park, Eun Sook; Cho, Sung-Rae; Um, Min-Hee; Lee, Seung-Koo; Oh, Maeng-Keun; Park, Hae-Jeong

2017-10-01

Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons

NASA Astrophysics Data System (ADS)

Sengupta, Abhronil; Panda, Priyadarshini; Wijesinghe, Parami; Kim, Yusung; Roy, Kaushik

2016-07-01

Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning and cognitive tasks. In this work, we demonstrate the mapping of the probabilistic spiking nature of pyramidal neurons in the cortex to the stochastic switching behavior of a Magnetic Tunnel Junction in presence of thermal noise. We present results to illustrate the efficiency of neuromorphic systems based on such probabilistic neurons for pattern recognition tasks in presence of lateral inhibition and homeostasis. Such stochastic MTJ neurons can also potentially provide a direct mapping to the probabilistic computing elements in Belief Networks for performing regenerative tasks.

Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

PubMed

Fu, H C; Xu, Y Y; Chang, H Y

1999-12-01

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition

PubMed Central

Bill, Johannes; Buesing, Lars; Habenschuss, Stefan; Nessler, Bernhard; Maass, Wolfgang; Legenstein, Robert

2015-01-01

During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. PMID:26284370

Probabilistic Signal Recovery and Random Matrices

DTIC Science & Technology

2016-12-08

applications in statistics , biomedical data analysis, quantization, dimen- sion reduction, and networks science. 1. High-dimensional inference and geometry Our...low-rank approxima- tion, with applications to community detection in networks, Annals of Statistics 44 (2016), 373–400. [7] C. Le, E. Levina, R...approximation, with applications to community detection in networks, Annals of Statistics 44 (2016), 373–400. C. Le, E. Levina, R. Vershynin, Concentration

Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site

NASA Astrophysics Data System (ADS)

Maiti, Saumen; Tiwari, Ram Krishna

2010-10-01

A new probabilistic approach based on the concept of Bayesian neural network (BNN) learning theory is proposed for decoding litho-facies boundaries from well-log data. We show that how a multi-layer-perceptron neural network model can be employed in Bayesian framework to classify changes in litho-log successions. The method is then applied to the German Continental Deep Drilling Program (KTB) well-log data for classification and uncertainty estimation in the litho-facies boundaries. In this framework, a posteriori distribution of network parameter is estimated via the principle of Bayesian probabilistic theory, and an objective function is minimized following the scaled conjugate gradient optimization scheme. For the model development, we inflict a suitable criterion, which provides probabilistic information by emulating different combinations of synthetic data. Uncertainty in the relationship between the data and the model space is appropriately taken care by assuming a Gaussian a priori distribution of networks parameters (e.g., synaptic weights and biases). Prior to applying the new method to the real KTB data, we tested the proposed method on synthetic examples to examine the sensitivity of neural network hyperparameters in prediction. Within this framework, we examine stability and efficiency of this new probabilistic approach using different kinds of synthetic data assorted with different level of correlated noise. Our data analysis suggests that the designed network topology based on the Bayesian paradigm is steady up to nearly 40% correlated noise; however, adding more noise (˜50% or more) degrades the results. We perform uncertainty analyses on training, validation, and test data sets with and devoid of intrinsic noise by making the Gaussian approximation of the a posteriori distribution about the peak model. We present a standard deviation error-map at the network output corresponding to the three types of the litho-facies present over the entire litho-section of the KTB. The comparisons of maximum a posteriori geological sections constructed here, based on the maximum a posteriori probability distribution, with the available geological information and the existing geophysical findings suggest that the BNN results reveal some additional finer details in the KTB borehole data at certain depths, which appears to be of some geological significance. We also demonstrate that the proposed BNN approach is superior to the conventional artificial neural network in terms of both avoiding "over-fitting" and aiding uncertainty estimation, which are vital for meaningful interpretation of geophysical records. Our analyses demonstrate that the BNN-based approach renders a robust means for the classification of complex changes in the litho-facies successions and thus could provide a useful guide for understanding the crustal inhomogeneity and the structural discontinuity in many other tectonically complex regions.

Parallel computing for probabilistic fatigue analysis

NASA Technical Reports Server (NTRS)

Sues, Robert H.; Lua, Yuan J.; Smith, Mark D.

1993-01-01

This paper presents the results of Phase I research to investigate the most effective parallel processing software strategies and hardware configurations for probabilistic structural analysis. We investigate the efficiency of both shared and distributed-memory architectures via a probabilistic fatigue life analysis problem. We also present a parallel programming approach, the virtual shared-memory paradigm, that is applicable across both types of hardware. Using this approach, problems can be solved on a variety of parallel configurations, including networks of single or multiprocessor workstations. We conclude that it is possible to effectively parallelize probabilistic fatigue analysis codes; however, special strategies will be needed to achieve large-scale parallelism to keep large number of processors busy and to treat problems with the large memory requirements encountered in practice. We also conclude that distributed-memory architecture is preferable to shared-memory for achieving large scale parallelism; however, in the future, the currently emerging hybrid-memory architectures will likely be optimal.

Individual wealth-based selection supports cooperation in spatial public goods games

NASA Astrophysics Data System (ADS)

Chen, Xiaojie; Szolnoki, Attila

2016-09-01

In a social dilemma game group members are allowed to decide if they contribute to the joint venture or not. As a consequence, defectors, who do not invest but only enjoy the mutual benefit, prevail and the system evolves onto the tragedy of the common state. This unfortunate scenario can be avoided if participation is not obligatory but only happens with a given probability. But what if we also consider a player’s individual wealth when to decide about participation? To address this issue we propose a model in which the probabilistic participation in the public goods game is combined with a conditional investment mode that is based on individual wealth: if a player’s wealth exceeds a threshold value then it is qualified and can participate in the joint venture. Otherwise, the participation is forbidden in the investment interactions. We show that if only probabilistic participation is considered, spatially structured populations cannot support cooperation better than well-mixed populations where full defection state can also be avoided for small participation probabilities. By adding the wealth-based criterion of participation, however, structured populations are capable to augment network reciprocity relevantly and allow cooperator strategy to dominate in a broader parameter interval.

A model to assess the Mars Telecommunications Network relay robustness

NASA Technical Reports Server (NTRS)

Girerd, Andre R.; Meshkat, Leila; Edwards, Charles D., Jr.; Lee, Charles H.

2005-01-01

The relatively long mission durations and compatible radio protocols of current and projected Mars orbiters have enabled the gradual development of a heterogeneous constellation providing proximity communication services for surface assets. The current and forecasted capability of this evolving network has reached the point that designers of future surface missions consider complete dependence on it. Such designers, along with those architecting network requirements, have a need to understand the robustness of projected communication service. A model has been created to identify the robustness of the Mars Network as a function of surface location and time. Due to the decade-plus time horizon considered, the network will evolve, with emerging productive nodes and nodes that cease or fail to contribute. The model is a flexible framework to holistically process node information into measures of capability robustness that can be visualized for maximum understanding. Outputs from JPL's Telecom Orbit Analysis Simulation Tool (TOAST) provide global telecom performance parameters for current and projected orbiters. Probabilistic estimates of orbiter fuel life are derived from orbit keeping burn rates, forecasted maneuver tasking, and anomaly resolution budgets. Orbiter reliability is estimated probabilistically. A flexible scheduling framework accommodates the projected mission queue as well as potential alterations.

Diffusion imaging of cerebral white matter in persons who stutter: evidence for network-level anomalies

PubMed Central

Cai, Shanqing; Tourville, Jason A.; Beal, Deryk S.; Perkell, Joseph S.; Guenther, Frank H.; Ghosh, Satrajit S.

2013-01-01

Deficits in brain white matter have been a main focus of recent neuroimaging studies on stuttering. However, no prior study has examined brain connectivity on the global level of the cerebral cortex in persons who stutter (PWS). In the current study, we analyzed the results from probabilistic tractography between regions comprising the cortical speech network. An anatomical parcellation scheme was used to define 28 speech production-related ROIs in each hemisphere. We used network-based statistic (NBS) and graph theory to analyze the connectivity patterns obtained from tractography. At the network-level, the probabilistic corticocortical connectivity from the PWS group were significantly weaker than that from persons with fluent speech (PFS). NBS analysis revealed significant components in the bilateral speech networks with negative correlations with stuttering severity. To facilitate comparison with previous studies, we also performed tract-based spatial statistics (TBSS) and regional fractional anisotropy (FA) averaging. Results from tractography, TBSS and regional FA averaging jointly highlight the importance of several regions in the left peri-Rolandic sensorimotor and premotor areas, most notably the left ventral premotor cortex (vPMC) and middle primary motor cortex, in the neuroanatomical basis of stuttering. PMID:24611042

Diffusion imaging of cerebral white matter in persons who stutter: evidence for network-level anomalies.

PubMed

Cai, Shanqing; Tourville, Jason A; Beal, Deryk S; Perkell, Joseph S; Guenther, Frank H; Ghosh, Satrajit S

2014-01-01

Deficits in brain white matter have been a main focus of recent neuroimaging studies on stuttering. However, no prior study has examined brain connectivity on the global level of the cerebral cortex in persons who stutter (PWS). In the current study, we analyzed the results from probabilistic tractography between regions comprising the cortical speech network. An anatomical parcellation scheme was used to define 28 speech production-related ROIs in each hemisphere. We used network-based statistic (NBS) and graph theory to analyze the connectivity patterns obtained from tractography. At the network-level, the probabilistic corticocortical connectivity from the PWS group were significantly weaker than that from persons with fluent speech (PFS). NBS analysis revealed significant components in the bilateral speech networks with negative correlations with stuttering severity. To facilitate comparison with previous studies, we also performed tract-based spatial statistics (TBSS) and regional fractional anisotropy (FA) averaging. Results from tractography, TBSS and regional FA averaging jointly highlight the importance of several regions in the left peri-Rolandic sensorimotor and premotor areas, most notably the left ventral premotor cortex (vPMC) and middle primary motor cortex, in the neuroanatomical basis of stuttering.

fMRI evidence for strategic decision-making during resolution of pronoun reference

PubMed Central

McMillan, Corey T.; Clark, Robin; Gunawardena, Delani; Ryant, Neville; Grossman, Murray

2012-01-01

Pronouns are extraordinarily common in daily language yet little is known about the neural mechanisms that support decisions about pronoun reference. We propose a large-scale neural network for resolving pronoun reference that consists of two components. First, a core language network in peri-Sylvian cortex supports syntactic and semantic resources for interpreting pronoun meaning in sentences. Second, a frontal-parietal network that supports strategic decision-making is recruited to support probabilistic and risk-related components of resolving a pronoun’s referent. In an fMRI study of healthy young adults, we observed activation of left inferior frontal and superior temporal cortex, consistent with a language network. We also observed activation of brain regions not associated with traditional language areas. By manipulating the context of the pronoun, we were able to demonstrate recruitment of dorsolateral prefrontal cortex during probabilistic evaluation of a pronoun’s reference, and orbital frontal activation when a pronoun must adopt a risky referent. Together, these findings are consistent with a two-component model for resolving a pronoun’s reference that includes neuroanatomic regions supporting core linguistic and decision-making mechanisms. PMID:22245014

Building an ecosystem model using mismatched and fragmented data: A probabilistic network of early marine survival for coho salmon Oncorhynchus kisutch in the Strait of Georgia

NASA Astrophysics Data System (ADS)

Andres Araujo, H.; Holt, Carrie; Curtis, Janelle M. R.; Perry, R. I.; Irvine, James R.; Michielsens, Catherine G. J.

2013-08-01

We evaluated the effects of biophysical conditions and hatchery production on the early marine survival of coho salmon Oncorhynchus kisutch in the Strait of Georgia, British Columbia, Canada. Due to a paucity of balanced multivariate ecosystem data, we developed a probabilistic network that integrated physical and ecological data and information from literature, expert opinion, oceanographic models, and in situ observations. This approach allowed us to evaluate alternate hypotheses about drivers of early marine survival while accounting for uncertainties in relationships among variables. Probabilistic networks allow users to explore multiple environmental settings and evaluate the consequences of management decisions under current and projected future states. We found that the zooplankton biomass anomaly, calanoid copepod biomass, and herring biomass were the best indicators of early marine survival. It also appears that concentrating hatchery supplementation during periods of negative PDO and ENSO (Pacific Decadal and El Niño Southern Oscillation respectively), indicative of generally favorable ocean conditions for salmon, tends to increase survival of hatchery coho salmon while minimizing negative impacts on the survival of wild juveniles. Scientists and managers can benefit from the approach presented here by exploring multiple scenarios, providing a basis for open and repeatable ecosystem-based risk assessments when data are limited.

Complex life cycles in a pond food web: effects of life stage structure and parasites on network properties, trophic positions and the fit of a probabilistic niche model.

PubMed

Preston, Daniel L; Jacobs, Abigail Z; Orlofske, Sarah A; Johnson, Pieter T J

2014-03-01

Most food webs use taxonomic or trophic species as building blocks, thereby collapsing variability in feeding linkages that occurs during the growth and development of individuals. This issue is particularly relevant to integrating parasites into food webs because parasites often undergo extreme ontogenetic niche shifts. Here, we used three versions of a freshwater pond food web with varying levels of node resolution (from taxonomic species to life stages) to examine how complex life cycles and parasites alter web properties, the perceived trophic position of organisms, and the fit of a probabilistic niche model. Consistent with prior studies, parasites increased most measures of web complexity in the taxonomic species web; however, when nodes were disaggregated into life stages, the effects of parasites on several network properties (e.g., connectance and nestedness) were reversed, due in part to the lower trophic generality of parasite life stages relative to free-living life stages. Disaggregation also reduced the trophic level of organisms with either complex or direct life cycles and was particularly useful when including predation on parasites, which can inflate trophic positions when life stages are collapsed. Contrary to predictions, disaggregation decreased network intervality and did not enhance the fit of a probabilistic niche model to the food webs with parasites. Although the most useful level of biological organization in food webs will vary with the questions of interest, our results suggest that disaggregating species-level nodes may refine our perception of how parasites and other complex life cycle organisms influence ecological networks.

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harwood, Caroline S

The goal of this project is to identify gene networks that are critical for efficient biohydrogen production by leveraging variation in gene content and gene expression in independently isolated Rhodopseudomonas palustris strains. Coexpression methods were applied to large data sets that we have collected to define probabilistic causal gene networks. To our knowledge this a first systems level approach that takes advantage of strain-to strain variability to computationally define networks critical for a particular bacterial phenotypic trait.

Modeling Women's Menstrual Cycles using PICI Gates in Bayesian Network.

PubMed

Zagorecki, Adam; Łupińska-Dubicka, Anna; Voortman, Mark; Druzdzel, Marek J

2016-03-01

A major difficulty in building Bayesian network (BN) models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with this problem is through parametric conditional probability distributions that usually require only a number of parameters that is linear in the number of parents. In this paper, we introduce a new class of parametric models, the Probabilistic Independence of Causal Influences (PICI) models, that aim at lowering the number of parameters required to specify local probability distributions, but are still capable of efficiently modeling a variety of interactions. A subset of PICI models is decomposable and this leads to significantly faster inference as compared to models that cannot be decomposed. We present an application of the proposed method to learning dynamic BNs for modeling a woman's menstrual cycle. We show that PICI models are especially useful for parameter learning from small data sets and lead to higher parameter accuracy than when learning CPTs.

Fast, Nonlinear, Fully Probabilistic Inversion of Large Geophysical Problems

NASA Astrophysics Data System (ADS)

Curtis, A.; Shahraeeni, M.; Trampert, J.; Meier, U.; Cho, G.

2010-12-01

Almost all Geophysical inverse problems are in reality nonlinear. Fully nonlinear inversion including non-approximated physics, and solving for probability distribution functions (pdf’s) that describe the solution uncertainty, generally requires sampling-based Monte-Carlo style methods that are computationally intractable in most large problems. In order to solve such problems, physical relationships are usually linearized leading to efficiently-solved, (possibly iterated) linear inverse problems. However, it is well known that linearization can lead to erroneous solutions, and in particular to overly optimistic uncertainty estimates. What is needed across many Geophysical disciplines is a method to invert large inverse problems (or potentially tens of thousands of small inverse problems) fully probabilistically and without linearization. This talk shows how very large nonlinear inverse problems can be solved fully probabilistically and incorporating any available prior information using mixture density networks (driven by neural network banks), provided the problem can be decomposed into many small inverse problems. In this talk I will explain the methodology, compare multi-dimensional pdf inversion results to full Monte Carlo solutions, and illustrate the method with two applications: first, inverting surface wave group and phase velocities for a fully-probabilistic global tomography model of the Earth’s crust and mantle, and second inverting industrial 3D seismic data for petrophysical properties throughout and around a subsurface hydrocarbon reservoir. The latter problem is typically decomposed into 104 to 105 individual inverse problems, each solved fully probabilistically and without linearization. The results in both cases are sufficiently close to the Monte Carlo solution to exhibit realistic uncertainty, multimodality and bias. This provides far greater confidence in the results, and in decisions made on their basis.

A Bayesian Framework for False Belief Reasoning in Children: A Rational Integration of Theory-Theory and Simulation Theory

PubMed Central

Asakura, Nobuhiko; Inui, Toshio

2016-01-01

Two apparently contrasting theories have been proposed to account for the development of children's theory of mind (ToM): theory-theory and simulation theory. We present a Bayesian framework that rationally integrates both theories for false belief reasoning. This framework exploits two internal models for predicting the belief states of others: one of self and one of others. These internal models are responsible for simulation-based and theory-based reasoning, respectively. The framework further takes into account empirical studies of a developmental ToM scale (e.g., Wellman and Liu, 2004): developmental progressions of various mental state understandings leading up to false belief understanding. By representing the internal models and their interactions as a causal Bayesian network, we formalize the model of children's false belief reasoning as probabilistic computations on the Bayesian network. This model probabilistically weighs and combines the two internal models and predicts children's false belief ability as a multiplicative effect of their early-developed abilities to understand the mental concepts of diverse beliefs and knowledge access. Specifically, the model predicts that children's proportion of correct responses on a false belief task can be closely approximated as the product of their proportions correct on the diverse belief and knowledge access tasks. To validate this prediction, we illustrate that our model provides good fits to a variety of ToM scale data for preschool children. We discuss the implications and extensions of our model for a deeper understanding of developmental progressions of children's ToM abilities. PMID:28082941

A Bayesian Framework for False Belief Reasoning in Children: A Rational Integration of Theory-Theory and Simulation Theory.

PubMed

Asakura, Nobuhiko; Inui, Toshio

2016-01-01

Two apparently contrasting theories have been proposed to account for the development of children's theory of mind (ToM): theory-theory and simulation theory. We present a Bayesian framework that rationally integrates both theories for false belief reasoning. This framework exploits two internal models for predicting the belief states of others: one of self and one of others. These internal models are responsible for simulation-based and theory-based reasoning, respectively. The framework further takes into account empirical studies of a developmental ToM scale (e.g., Wellman and Liu, 2004): developmental progressions of various mental state understandings leading up to false belief understanding. By representing the internal models and their interactions as a causal Bayesian network, we formalize the model of children's false belief reasoning as probabilistic computations on the Bayesian network. This model probabilistically weighs and combines the two internal models and predicts children's false belief ability as a multiplicative effect of their early-developed abilities to understand the mental concepts of diverse beliefs and knowledge access. Specifically, the model predicts that children's proportion of correct responses on a false belief task can be closely approximated as the product of their proportions correct on the diverse belief and knowledge access tasks. To validate this prediction, we illustrate that our model provides good fits to a variety of ToM scale data for preschool children. We discuss the implications and extensions of our model for a deeper understanding of developmental progressions of children's ToM abilities.

Dynamic competitive probabilistic principal components analysis.

PubMed

López-Rubio, Ezequiel; Ortiz-DE-Lazcano-Lobato, Juan Miguel

2009-04-01

We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.

Wlan-Based Indoor Localization Using Neural Networks

NASA Astrophysics Data System (ADS)

Saleem, Fasiha; Wyne, Shurjeel

2016-07-01

Wireless indoor localization has generated recent research interest due to its numerous applications. This work investigates Wi-Fi based indoor localization using two variants of the fingerprinting approach. Specifically, we study the application of an artificial neural network (ANN) for implementing the fingerprinting approach and compare its localization performance with a probabilistic fingerprinting method that is based on maximum likelihood estimation (MLE) of the user location. We incorporate spatial correlation of fading into our investigations, which is often neglected in simulation studies and leads to erroneous location estimates. The localization performance is quantified in terms of accuracy, precision, robustness, and complexity. Multiple methods for handling the case of missing APs in online stage are investigated. Our results indicate that ANN-based fingerprinting outperforms the probabilistic approach for all performance metrics considered in this work.

Connectome sensitivity or specificity: which is more important?

PubMed

Zalesky, Andrew; Fornito, Alex; Cocchi, Luca; Gollo, Leonardo L; van den Heuvel, Martijn P; Breakspear, Michael

2016-11-15

Connectomes with high sensitivity and high specificity are unattainable with current axonal fiber reconstruction methods, particularly at the macro-scale afforded by magnetic resonance imaging. Tensor-guided deterministic tractography yields sparse connectomes that are incomplete and contain false negatives (FNs), whereas probabilistic methods steered by crossing-fiber models yield dense connectomes, often with low specificity due to false positives (FPs). Densely reconstructed probabilistic connectomes are typically thresholded to improve specificity at the cost of a reduction in sensitivity. What is the optimal tradeoff between connectome sensitivity and specificity? We show empirically and theoretically that specificity is paramount. Our evaluations of the impact of FPs and FNs on empirical connectomes indicate that specificity is at least twice as important as sensitivity when estimating key properties of brain networks, including topological measures of network clustering, network efficiency and network modularity. Our asymptotic analysis of small-world networks with idealized modular structure reveals that as the number of nodes grows, specificity becomes exactly twice as important as sensitivity to the estimation of the clustering coefficient. For the estimation of network efficiency, the relative importance of specificity grows linearly with the number of nodes. The greater importance of specificity is due to FPs occurring more prevalently between network modules rather than within them. These spurious inter-modular connections have a dramatic impact on network topology. We argue that efforts to maximize the sensitivity of connectome reconstruction should be realigned with the need to map brain networks with high specificity. Copyright © 2016 Elsevier Inc. All rights reserved.

Learning In networks

NASA Technical Reports Server (NTRS)

Buntine, Wray L.

1995-01-01

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.

Probabilistic sizing of laminates with uncertainties

NASA Technical Reports Server (NTRS)

Shah, A. R.; Liaw, D. G.; Chamis, C. C.

1993-01-01

A reliability based design methodology for laminate sizing and configuration for a special case of composite structures is described. The methodology combines probabilistic composite mechanics with probabilistic structural analysis. The uncertainties of constituent materials (fiber and matrix) to predict macroscopic behavior are simulated using probabilistic theory. Uncertainties in the degradation of composite material properties are included in this design methodology. A multi-factor interaction equation is used to evaluate load and environment dependent degradation of the composite material properties at the micromechanics level. The methodology is integrated into a computer code IPACS (Integrated Probabilistic Assessment of Composite Structures). Versatility of this design approach is demonstrated by performing a multi-level probabilistic analysis to size the laminates for design structural reliability of random type structures. The results show that laminate configurations can be selected to improve the structural reliability from three failures in 1000, to no failures in one million. Results also show that the laminates with the highest reliability are the least sensitive to the loading conditions.

Probabilistic diffusion tractography reveals improvement of structural network in musicians.

PubMed

Li, Jianfu; Luo, Cheng; Peng, Yueheng; Xie, Qiankun; Gong, Jinnan; Dong, Li; Lai, Yongxiu; Li, Hong; Yao, Dezhong

2014-01-01

Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.

A Study of Students' Reasoning about Probabilistic Causality: Implications for Understanding Complex Systems and for Instructional Design

ERIC Educational Resources Information Center

Grotzer, Tina A.; Solis, S. Lynneth; Tutwiler, M. Shane; Cuzzolino, Megan Powell

2017-01-01

Understanding complex systems requires reasoning about causal relationships that behave or appear to behave probabilistically. Features such as distributed agency, large spatial scales, and time delays obscure co-variation relationships and complex interactions can result in non-deterministic relationships between causes and effects that are best…

Neural networks and fault probability evaluation for diagnosis issues.

PubMed

Kourd, Yahia; Lefebvre, Dimitri; Guersi, Noureddine

2014-01-01

This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.

Crystal surface analysis using matrix textural features classified by a probabilistic neural network

NASA Astrophysics Data System (ADS)

Sawyer, Curry R.; Quach, Viet; Nason, Donald; van den Berg, Lodewijk

1991-12-01

A system is under development in which surface quality of a growing bulk mercuric iodide crystal is monitored by video camera at regular intervals for early detection of growth irregularities. Mercuric iodide single crystals are employed in radiation detectors. A microcomputer system is used for image capture and processing. The digitized image is divided into multiple overlapping sub-images and features are extracted from each sub-image based on statistical measures of the gray tone distribution, according to the method of Haralick. Twenty parameters are derived from each sub-image and presented to a probabilistic neural network (PNN) for classification. This number of parameters was found to be optimal for the system. The PNN is a hierarchical, feed-forward network that can be rapidly reconfigured as additional training data become available. Training data is gathered by reviewing digital images of many crystals during their growth cycle and compiling two sets of images, those with and without irregularities.

Evaluation of a Cyber Security System for Hospital Network.

PubMed

Faysel, Mohammad A

2015-01-01

Most of the cyber security systems use simulated data in evaluating their detection capabilities. The proposed cyber security system utilizes real hospital network connections. It uses a probabilistic data mining algorithm to detect anomalous events and takes appropriate response in real-time. On an evaluation using real-world hospital network data consisting of incoming network connections collected for a 24-hour period, the proposed system detected 15 unusual connections which were undetected by a commercial intrusion prevention system for the same network connections. Evaluation of the proposed system shows a potential to secure protected patient health information on a hospital network.

An Information Theoretic Approach for Measuring Data Discovery and Utilization During Analytical and Decision Making Processes

DTIC Science & Technology

2015-07-31

and make the expected decision outcomes. The scenario is based around a scripted storyboard where an organized crime network is operating in a city to...interdicted by law enforcement to disrupt the network. The scenario storyboard was used to develop a probabilistic vehicle traffic model in order to

Sustainability and collapse in a coevolutionary model of local resource stocks and behavioral patterns on a social network

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Heitzig, Jobst; Kurths, Jürgen

2014-05-01

When investigating the causes and consequences of global change, the collective behavior of human beings is considered as having a considerable impact on natural systems. In our work, we propose a conceptual coevolutionary model simulating the dynamics of local renewable resources in interaction with simplistic societal agents exploiting those resources. The society is represented by a social network on which social traits may be transmitted between agents. These traits themselves induce a certain rate of exploitation of the resource, leading either to its depletion or sustainable existence. Traits are exchanged probabilistically according to their instantaneous individual payoff, and hence this process depends on the status of the natural resource. At the same time agents may adaptively restructure their set of acquaintances. Connections with agents having a different trait may be broken while new connections with agents of the same trait are established. We investigate which choices of social parameters, like the frequency of social interaction, rationality and rate of social network adaptation, cause the system to end in a sustainable state and, hence, what can be done to avoid a collapse of the entire system. The importance and influence of the social network structure is analyzed by the variation of link-densities in the underlying network topology and shows significant influence on the expected outcome of the model. For a static network with no adaptation we find a robust phase transition between the two different regimes, sustainable and non-sustainable, which co-exist in parameter space. High connectivity within the social network, e.g., high link-densities, in combination with a fast rate of social learning lead to a likely collapse of the entire co-evolutionary system, whereas slow learning and small network connectivity very likely result in the sustainable existence of the natural resources. Collapse may be avoided by an intelligent rewiring, e.g. adaptation, of the social network that may also lead to the isolation of misbehaving parts of the society. Our results may suggest that with the current trend to faster imitation and ever increasing global network connectivity, societies are becoming more vulnerable to environmental collapse if they remain myopic at the same time.

Our Selections and Decisions: Inherent Features of the Nervous System?

NASA Astrophysics Data System (ADS)

Rösler, Frank

The chapter summarizes findings on the neuronal bases of decisionmaking. Taking the phenomenon of selection it will be explained that systems built only from excitatory and inhibitory neuron (populations) have the emergent property of selecting between different alternatives. These considerations suggest that there exists a hierarchical architecture with central selection switches. However, in such a system, functions of selection and decision-making are not localized, but rather emerge from an interaction of several participating networks. These are, on the one hand, networks that process specific input and output representations and, on the other hand, networks that regulate the relative activation/inhibition of the specific input and output networks. These ideas are supported by recent empirical evidence. Moreover, other studies show that rather complex psychological variables, like subjective probability estimates, expected gains and losses, prediction errors, etc., do have biological correlates, i.e., they can be localized in time and space as activation states of neural networks and single cells. These findings suggest that selections and decisions are consequences of an architecture which, seen from a biological perspective, is fully deterministic. However, a transposition of such nomothetic functional principles into the idiographic domain, i.e., using them as elements for comprehensive 'mechanistic' explanations of individual decisions, seems not to be possible because of principle limitations. Therefore, individual decisions will remain predictable by means of probabilistic models alone.

Probabilistic liquefaction triggering based on the cone penetration test

USGS Publications Warehouse

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.

A Novel TRM Calculation Method by Probabilistic Concept

NASA Astrophysics Data System (ADS)

Audomvongseree, Kulyos; Yokoyama, Akihiko; Verma, Suresh Chand; Nakachi, Yoshiki

In a new competitive environment, it becomes possible for the third party to access a transmission facility. From this structure, to efficiently manage the utilization of the transmission network, a new definition about Available Transfer Capability (ATC) has been proposed. According to the North American ElectricReliability Council (NERC)’s definition, ATC depends on several parameters, i. e. Total Transfer Capability (TTC), Transmission Reliability Margin (TRM), and Capacity Benefit Margin (CBM). This paper is focused on the calculation of TRM which is one of the security margin reserved for any uncertainty of system conditions. The TRM calculation by probabilistic method is proposed in this paper. Based on the modeling of load forecast error and error in transmission line limitation, various cases of transmission transfer capability and its related probabilistic nature can be calculated. By consideration of the proposed concept of risk analysis, the appropriate required amount of TRM can be obtained. The objective of this research is to provide realistic information on the actual ability of the network which may be an alternative choice for system operators to make an appropriate decision in the competitive market. The advantages of the proposed method are illustrated by application to the IEEJ-WEST10 model system.

A Network Coverage Information-Based Sensor Registry System for IoT Environments

PubMed Central

Jung, Hyunjun; Jeong, Dongwon; Lee, Sukhoon; On, Byung-Won; Baik, Doo-Kwon

2016-01-01

The Internet of Things (IoT) is expected to provide better services through the interaction of physical objects via the Internet. However, its limitations cause an interoperability problem when the sensed data are exchanged between the sensor nodes in wireless sensor networks (WSNs), which constitute the core infrastructure of the IoT. To address this problem, a Sensor Registry System (SRS) is used. By using a SRS, the information of the heterogeneous sensed data remains pure. If users move along a road, their mobile devices predict their next positions and obtain the sensed data for that position from the SRS. If the WSNs in the location in which the users move are unstable, the sensed data will be lost. Consider a situation where the user passes through dangerous areas. If the user’s mobile device cannot receive information, they cannot be warned about the dangerous situation. To avoid this, two novel SRSs that use network coverage information have been proposed: one uses OpenSignal and the other uses the probabilistic distribution of the users accessing SRS. The empirical study showed that the proposed method can seamlessly provide services related to sensing data under any abnormal circumstance. PMID:27463717

A Network Coverage Information-Based Sensor Registry System for IoT Environments.

PubMed

Jung, Hyunjun; Jeong, Dongwon; Lee, Sukhoon; On, Byung-Won; Baik, Doo-Kwon

2016-07-25

The Internet of Things (IoT) is expected to provide better services through the interaction of physical objects via the Internet. However, its limitations cause an interoperability problem when the sensed data are exchanged between the sensor nodes in wireless sensor networks (WSNs), which constitute the core infrastructure of the IoT. To address this problem, a Sensor Registry System (SRS) is used. By using a SRS, the information of the heterogeneous sensed data remains pure. If users move along a road, their mobile devices predict their next positions and obtain the sensed data for that position from the SRS. If the WSNs in the location in which the users move are unstable, the sensed data will be lost. Consider a situation where the user passes through dangerous areas. If the user's mobile device cannot receive information, they cannot be warned about the dangerous situation. To avoid this, two novel SRSs that use network coverage information have been proposed: one uses OpenSignal and the other uses the probabilistic distribution of the users accessing SRS. The empirical study showed that the proposed method can seamlessly provide services related to sensing data under any abnormal circumstance.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

NASA Astrophysics Data System (ADS)

Hargrave, C.; Moores, M.; Deegan, T.; Gibbs, A.; Poulsen, M.; Harden, F.; Mengersen, K.

2014-03-01

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

Revealing and analyzing networks of environmental systems

NASA Astrophysics Data System (ADS)

Eveillard, D.; Bittner, L.; Chaffron, S.; Guidi, L.; Raes, J.; Karsenti, E.; Bowler, C.; Gorsky, G.

2015-12-01

Understanding the interactions between microbial communities and their environment well enough to be able to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is a complicated task, because (i) communities are complex, (ii) most are described qualitatively, and (iii) quantitative understanding of the way communities interacts with their surroundings remains incomplete. Within this seminar, we will illustrate two complementary approaches that aim to overcome these points in different manners. First, we will present a network analysis that focus on the biological carbon pump in the global ocean. The biological carbon pump is the process by which photosynthesis transforms CO2 to organic carbon sinking to the deep-ocean as particles where it is sequestered. While the intensity of the pump correlate to plankton community composition, the underlying ecosystem structure and interactions driving this process remain largely uncharacterized Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve understanding of these drivers. We show that specific plankton communities correlate with carbon export and highlight unexpected and overlooked taxa such as Radiolaria, alveolate parasites and bacterial pathogens, as well as Synechococcus and their phages, as key players in the biological pump. Additionally, we show that the abundances of just a few bacterial and viral genes predict most of the global ocean carbon export's variability. Together these findings help elucidate ecosystem drivers of the biological carbon pump and present a case study for scaling from genes-to-ecosystems. Second, we will show preliminary results on a probabilistic modeling that predicts microbial community structure across observed physicochemical data, from a putative network and partial quantitative knowledge. This modeling shows that, despite distinct quantitative environmental perturbations, the constraints on community structure could remain stable.

Spatiotemporal movement planning and rapid adaptation for manual interaction.

PubMed

Huber, Markus; Kupferberg, Aleksandra; Lenz, Claus; Knoll, Alois; Brandt, Thomas; Glasauer, Stefan

2013-01-01

Many everyday tasks require the ability of two or more individuals to coordinate their actions with others to increase efficiency. Such an increase in efficiency can often be observed even after only very few trials. Previous work suggests that such behavioral adaptation can be explained within a probabilistic framework that integrates sensory input and prior experience. Even though higher cognitive abilities such as intention recognition have been described as probabilistic estimation depending on an internal model of the other agent, it is not clear whether much simpler daily interaction is consistent with a probabilistic framework. Here, we investigate whether the mechanisms underlying efficient coordination during manual interactions can be understood as probabilistic optimization. For this purpose we studied in several experiments a simple manual handover task concentrating on the action of the receiver. We found that the duration until the receiver reacts to the handover decreases over trials, but strongly depends on the position of the handover. We then replaced the human deliverer by different types of robots to further investigate the influence of the delivering movement on the reaction of the receiver. Durations were found to depend on movement kinematics and the robot's joint configuration. Modeling the task was based on the assumption that the receiver's decision to act is based on the accumulated evidence for a specific handover position. The evidence for this handover position is collected from observing the hand movement of the deliverer over time and, if appropriate, by integrating this sensory likelihood with prior expectation that is updated over trials. The close match of model simulations and experimental results shows that the efficiency of handover coordination can be explained by an adaptive probabilistic fusion of a-priori expectation and online estimation.

Application of probabilistic fiber-tracking method of MR imaging to measure impact of cranial irradiation on structural brain connectivity in children treated for medulloblastoma

NASA Astrophysics Data System (ADS)

Duncan, Elizabeth C.; Reddick, Wilburn E.; Glass, John O.; Hyun, Jung Won; Ji, Qing; Li, Yimei; Gajjar, Amar

2016-03-01

We applied a modified probabilistic fiber-tracking method for the extraction of fiber pathways to quantify decreased white matter integrity as a surrogate of structural loss in connectivity due to cranial radiation therapy (CRT) as treatment for pediatric medulloblastoma. Thirty subjects were examined (n=8 average-risk, n=22 high-risk) and the groups did not differ significantly in age at examination. The pathway analysis created a structural connectome focused on sub-networks within the central executive network (CEN) for comparison between baseline and post-CRT scans and for comparison between standard and high dose CRT. A paired-wise comparison of the connectivity between baseline and post-CRT scans showed the irradiation did have a significant detrimental impact on white matter integrity (decreased fractional anisotropy (FA) and decreased axial diffusivity (AX)) in most of the CEN sub-networks. Group comparisons of the change in the connectivity revealed that patients receiving high dose CRT experienced significant AX decreases in all sub-networks while the patients receiving standard dose CRT had relatively stable AX measures across time. This study on pediatric patients with medulloblastoma demonstrated the utility of this method to identify specific sub-networks within the developing brain affected by CRT.

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

NASA Technical Reports Server (NTRS)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

Global Infrasound Association Based on Probabilistic Clutter Categorization

NASA Astrophysics Data System (ADS)

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

Interacting with an artificial partner: modeling the role of emotional aspects.

PubMed

Cattinelli, Isabella; Goldwurm, Massimiliano; Borghese, N Alberto

2008-12-01

In this paper we introduce a simple model based on probabilistic finite state automata to describe an emotional interaction between a robot and a human user, or between simulated agents. Based on the agent's personality, attitude, and nature, and on the emotional inputs it receives, the model will determine the next emotional state displayed by the agent itself. The probabilistic and time-varying nature of the model yields rich and dynamic interactions, and an autonomous adaptation to the interlocutor. In addition, a reinforcement learning technique is applied to have one agent drive its partner's behavior toward desired states. The model may also be used as a tool for behavior analysis, by extracting high probability patterns of interaction and by resorting to the ergodic properties of Markov chains.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Probabilistic vs linear blending approaches to shared control for wheelchair driving.

PubMed

Ezeh, Chinemelu; Trautman, Pete; Devigne, Louise; Bureau, Valentin; Babel, Marie; Carlson, Tom

2017-07-01

Some people with severe mobility impairments are unable to operate powered wheelchairs reliably and effectively, using commercially available interfaces. This has sparked a body of research into "smart wheelchairs", which assist users to drive safely and create opportunities for them to use alternative interfaces. Various "shared control" techniques have been proposed to provide an appropriate level of assistance that is satisfactory and acceptable to the user. Most shared control techniques employ a traditional strategy called linear blending (LB), where the user's commands and wheelchair's autonomous commands are combined in some proportion. In this paper, however, we implement a more generalised form of shared control called probabilistic shared control (PSC). This probabilistic formulation improves the accuracy of modelling the interaction between the user and the wheelchair by taking into account uncertainty in the interaction. In this paper, we demonstrate the practical success of PSC over LB in terms of safety, particularly for novice users.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

Incorporating Resilience into Dynamic Social Models

DTIC Science & Technology

2016-07-20

solved by simply using the information provided by the scenario. Instead, additional knowledge is required from relevant fields that study these...resilience function by leveraging Bayesian Knowledge Bases (BKBs), a probabilistic reasoning network framework[5],[6]. BKBs allow for inferencing...reasoning network framework based on Bayesian Knowledge Bases (BKBs). BKBs are central to our social resilience framework as they are used to

fMRI evidence for strategic decision-making during resolution of pronoun reference.

PubMed

McMillan, Corey T; Clark, Robin; Gunawardena, Delani; Ryant, Neville; Grossman, Murray

2012-04-01

Pronouns are extraordinarily common in daily language yet little is known about the neural mechanisms that support decisions about pronoun reference. We propose a large-scale neural network for resolving pronoun reference that consists of two components. First, a core language network in peri-Sylvian cortex supports syntactic and semantic resources for interpreting pronoun meaning in sentences. Second, a frontal-parietal network that supports strategic decision-making is recruited to support probabilistic and risk-related components of resolving a pronoun's referent. In an fMRI study of healthy young adults, we observed activation of left inferior frontal and superior temporal cortex, consistent with a language network. We also observed activation of brain regions not associated with traditional language areas. By manipulating the context of the pronoun, we were able to demonstrate recruitment of dorsolateral prefrontal cortex during probabilistic evaluation of a pronoun's reference, and orbital frontal activation when a pronoun must adopt a risky referent. Together, these findings are consistent with a two-component model for resolving a pronoun's reference that includes neuroanatomic regions supporting core linguistic and decision-making mechanisms. Copyright © 2012 Elsevier Ltd. All rights reserved.

Sensitivity Analysis for Probabilistic Neural Network Structure Reduction.

PubMed

Kowalski, Piotr A; Kusy, Maciej

2018-05-01

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function. Therefore, the smoothing parameter is separately calculated for each dimension by means of the plug-in method. The classification qualities of the reduced and full structure PNN are compared. Furthermore, we evaluate the performance of PNN, for which global sensitivity analysis (GSA) and the common reduction methods are applied, both in the input layer and the pattern layer. The models are tested on the classification problems of eight repository data sets. A 10-fold cross validation procedure is used to determine the prediction ability of the networks. Based on the obtained results, it is shown that the LSA can be used as an alternative PNN reduction approach.

Final Report. Analysis and Reduction of Complex Networks Under Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marzouk, Youssef M.; Coles, T.; Spantini, A.

2013-09-30

The project was a collaborative effort among MIT, Sandia National Laboratories (local PI Dr. Habib Najm), the University of Southern California (local PI Prof. Roger Ghanem), and The Johns Hopkins University (local PI Prof. Omar Knio, now at Duke University). Our focus was the analysis and reduction of large-scale dynamical systems emerging from networks of interacting components. Such networks underlie myriad natural and engineered systems. Examples important to DOE include chemical models of energy conversion processes, and elements of national infrastructure—e.g., electric power grids. Time scales in chemical systems span orders of magnitude, while infrastructure networks feature both local andmore » long-distance connectivity, with associated clusters of time scales. These systems also blend continuous and discrete behavior; examples include saturation phenomena in surface chemistry and catalysis, and switching in electrical networks. Reducing size and stiffness is essential to tractable and predictive simulation of these systems. Computational singular perturbation (CSP) has been effectively used to identify and decouple dynamics at disparate time scales in chemical systems, allowing reduction of model complexity and stiffness. In realistic settings, however, model reduction must contend with uncertainties, which are often greatest in large-scale systems most in need of reduction. Uncertainty is not limited to parameters; one must also address structural uncertainties—e.g., whether a link is present in a network—and the impact of random perturbations, e.g., fluctuating loads or sources. Research under this project developed new methods for the analysis and reduction of complex multiscale networks under uncertainty, by combining computational singular perturbation (CSP) with probabilistic uncertainty quantification. CSP yields asymptotic approximations of reduceddimensionality “slow manifolds” on which a multiscale dynamical system evolves. Introducing uncertainty in this context raised fundamentally new issues, e.g., how is the topology of slow manifolds transformed by parametric uncertainty? How to construct dynamical models on these uncertain manifolds? To address these questions, we used stochastic spectral polynomial chaos (PC) methods to reformulate uncertain network models and analyzed them using CSP in probabilistic terms. Finding uncertain manifolds involved the solution of stochastic eigenvalue problems, facilitated by projection onto PC bases. These problems motivated us to explore the spectral properties stochastic Galerkin systems. We also introduced novel methods for rank-reduction in stochastic eigensystems—transformations of a uncertain dynamical system that lead to lower storage and solution complexity. These technical accomplishments are detailed below. This report focuses on the MIT portion of the joint project.« less

Bayesian network models for error detection in radiotherapy plans

NASA Astrophysics Data System (ADS)

Kalet, Alan M.; Gennari, John H.; Ford, Eric C.; Phillips, Mark H.

2015-04-01

The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network’s conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

NASA Technical Reports Server (NTRS)

Luxhoj, J. T.; Ancel, E.; Green, L. L.; Shih, A. T.; Jones, S. M.; Reveley, M. S.

2014-01-01

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Toward the Probabilistic Forecasting of High-latitude GPS Phase Scintillation

NASA Technical Reports Server (NTRS)

Prikryl, P.; Jayachandran, P.T.; Mushini, S. C.; Richardson, I. G.

2012-01-01

The phase scintillation index was obtained from L1 GPS data collected with the Canadian High Arctic Ionospheric Network (CHAIN) during years of extended solar minimum 2008-2010. Phase scintillation occurs predominantly on the dayside in the cusp and in the nightside auroral oval. We set forth a probabilistic forecast method of phase scintillation in the cusp based on the arrival time of either solar wind corotating interaction regions (CIRs) or interplanetary coronal mass ejections (ICMEs). CIRs on the leading edge of high-speed streams (HSS) from coronal holes are known to cause recurrent geomagnetic and ionospheric disturbances that can be forecast one or several solar rotations in advance. Superposed epoch analysis of phase scintillation occurrence showed a sharp increase in scintillation occurrence just after the arrival of high-speed solar wind and a peak associated with weak to moderate CMEs during the solar minimum. Cumulative probability distribution functions for the phase scintillation occurrence in the cusp are obtained from statistical data for days before and after CIR and ICME arrivals. The probability curves are also specified for low and high (below and above median) values of various solar wind plasma parameters. The initial results are used to demonstrate a forecasting technique on two example periods of CIRs and ICMEs.

Probabilities and predictions: modeling the development of scientific problem-solving skills.

PubMed

Stevens, Ron; Johnson, David F; Soller, Amy

2005-01-01

The IMMEX (Interactive Multi-Media Exercises) Web-based problem set platform enables the online delivery of complex, multimedia simulations, the rapid collection of student performance data, and has already been used in several genetic simulations. The next step is the use of these data to understand and improve student learning in a formative manner. This article describes the development of probabilistic models of undergraduate student problem solving in molecular genetics that detailed the spectrum of strategies students used when problem solving, and how the strategic approaches evolved with experience. The actions of 776 university sophomore biology majors from three molecular biology lecture courses were recorded and analyzed. Each of six simulations were first grouped by artificial neural network clustering to provide individual performance measures, and then sequences of these performances were probabilistically modeled by hidden Markov modeling to provide measures of progress. The models showed that students with different initial problem-solving abilities choose different strategies. Initial and final strategies varied across different sections of the same course and were not strongly correlated with other achievement measures. In contrast to previous studies, we observed no significant gender differences. We suggest that instructor interventions based on early student performances with these simulations may assist students to recognize effective and efficient problem-solving strategies and enhance learning.

Inference for Continuous-Time Probabilistic Programming

DTIC Science & Technology

2017-12-01

Parzen window density estimator to jointly model the inter-camera travel time intervals, locations of exit/entrances, and velocities of ob- jects...asked to travel across the scene multiple times . Even in such a scenario they formed groups and made social interactions, which Fig. 7: Topology of...INFERENCE FOR CONTINUOUS- TIME PROBABILISTIC PROGRAMMING UNIVERSITY OF CALIFORNIA AT RIVERSIDE DECEMBER 2017 FINAL TECHNICAL REPORT APPROVED FOR

Probabilistic risk models for multiple disturbances: an example of forest insects and wildfires

Treesearch

Haiganoush K. Preisler; Alan A. Ager; Jane L. Hayes

2010-01-01

Building probabilistic risk models for highly random forest disturbances like wildfire and forest insect outbreaks is a challenging. Modeling the interactions among natural disturbances is even more difficult. In the case of wildfire and forest insects, we looked at the probability of a large fire given an insect outbreak and also the incidence of insect outbreaks...

MC EMiNEM maps the interaction landscape of the Mediator.

PubMed

Niederberger, Theresa; Etzold, Stefanie; Lidschreiber, Michael; Maier, Kerstin C; Martin, Dietmar E; Fröhlich, Holger; Cramer, Patrick; Tresch, Achim

2012-01-01

The Mediator is a highly conserved, large multiprotein complex that is involved essentially in the regulation of eukaryotic mRNA transcription. It acts as a general transcription factor by integrating regulatory signals from gene-specific activators or repressors to the RNA Polymerase II. The internal network of interactions between Mediator subunits that conveys these signals is largely unknown. Here, we introduce MC EMiNEM, a novel method for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription. MC EMiNEM is based on Nested Effects Models (NEMs), a class of probabilistic graphical models that extends the idea of hierarchical clustering. It combines mode-hopping Monte Carlo (MC) sampling with an Expectation-Maximization (EM) algorithm for NEMs to increase sensitivity compared to existing methods. A meta-analysis of four Mediator perturbation studies in Saccharomyces cerevisiae, three of which are unpublished, provides new insight into the Mediator signaling network. In addition to the known modular organization of the Mediator subunits, MC EMiNEM reveals a hierarchical ordering of its internal information flow, which is putatively transmitted through structural changes within the complex. We identify the N-terminus of Med7 as a peripheral entity, entailing only local structural changes upon perturbation, while the C-terminus of Med7 and Med19 appear to play a central role. MC EMiNEM associates Mediator subunits to most directly affected genes, which, in conjunction with gene set enrichment analysis, allows us to construct an interaction map of Mediator subunits and transcription factors.

Formalizing Probabilistic Safety Claims

NASA Technical Reports Server (NTRS)

Herencia-Zapana, Heber; Hagen, George E.; Narkawicz, Anthony J.

2011-01-01

A safety claim for a system is a statement that the system, which is subject to hazardous conditions, satisfies a given set of properties. Following work by John Rushby and Bev Littlewood, this paper presents a mathematical framework that can be used to state and formally prove probabilistic safety claims. It also enables hazardous conditions, their uncertainties, and their interactions to be integrated into the safety claim. This framework provides a formal description of the probabilistic composition of an arbitrary number of hazardous conditions and their effects on system behavior. An example is given of a probabilistic safety claim for a conflict detection algorithm for aircraft in a 2D airspace. The motivation for developing this mathematical framework is that it can be used in an automated theorem prover to formally verify safety claims.

Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation

PubMed Central

Murabito, Ettore; Verma, Malkhey; Bekker, Martijn; Bellomo, Domenico; Westerhoff, Hans V.; Teusink, Bas; Steuer, Ralf

2014-01-01

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models. PMID:25268481

Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children.

PubMed

Wen, Hongwei; Liu, Yue; Rekik, Islem; Wang, Shengpei; Zhang, Jishui; Zhang, Yue; Peng, Yun; He, Huiguang

2017-08-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age- and gender-matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network-based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988-4008, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Parallel and Distributed Systems for Probabilistic Reasoning

DTIC Science & Technology

2012-12-01

work at CMU I had the opportunity to work with Andreas Krause on Gaussian process models for signal quality estimation in wireless sensor networks ...we reviewed the natural parallelization of the belief propagation algorithm using the synchronous schedule and demonstrated both theoretically and...problem is that the power-law sparsity structure, commonly found in graphs derived from natural phenomena (e.g., social networks and the web

Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

ERIC Educational Resources Information Center

Mason, Cindi; Twomey, Janet; Wright, David; Whitman, Lawrence

2018-01-01

As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm…

Efficient Effects-Based Military Planning Final Report

DTIC Science & Technology

2010-11-13

using probabilistic infer- ence methods,” in Proc. 8th Annu. Conf. Uncertainty Artificial Intelli - gence (UAI), Stanford, CA. San Mateo, CA: Morgan...Imprecise Probabilities, the 24th Conference on Uncertainty in Artificial Intelligence (UAI), 2008. 7. Yan Tong and Qiang Ji, Learning Bayesian Networks...Bayesian Networks using Constraints Cassio P. de Campos cassiopc@acm.org Dalle Molle Institute for Artificial Intelligence Galleria 2, Manno 6928

Solving graph data issues using a layered architecture approach with applications to web spam detection.

PubMed

Scarselli, Franco; Tsoi, Ah Chung; Hagenbuchner, Markus; Noi, Lucia Di

2013-12-01

This paper proposes the combination of two state-of-the-art algorithms for processing graph input data, viz., the probabilistic mapping graph self organizing map, an unsupervised learning approach, and the graph neural network, a supervised learning approach. We organize these two algorithms in a cascade architecture containing a probabilistic mapping graph self organizing map, and a graph neural network. We show that this combined approach helps us to limit the long-term dependency problem that exists when training the graph neural network resulting in an overall improvement in performance. This is demonstrated in an application to a benchmark problem requiring the detection of spam in a relatively large set of web sites. It is found that the proposed method produces results which reach the state of the art when compared with some of the best results obtained by others using quite different approaches. A particular strength of our method is its applicability towards any input domain which can be represented as a graph. Copyright © 2013 Elsevier Ltd. All rights reserved.

Synchronization Analysis of Master-Slave Probabilistic Boolean Networks.

PubMed

Lu, Jianquan; Zhong, Jie; Li, Lulu; Ho, Daniel W C; Cao, Jinde

2015-08-28

In this paper, we analyze the synchronization problem of master-slave probabilistic Boolean networks (PBNs). The master Boolean network (BN) is a deterministic BN, while the slave BN is determined by a series of possible logical functions with certain probability at each discrete time point. In this paper, we firstly define the synchronization of master-slave PBNs with probability one, and then we investigate synchronization with probability one. By resorting to new approach called semi-tensor product (STP), the master-slave PBNs are expressed in equivalent algebraic forms. Based on the algebraic form, some necessary and sufficient criteria are derived to guarantee synchronization with probability one. Further, we study the synchronization of master-slave PBNs in probability. Synchronization in probability implies that for any initial states, the master BN can be synchronized by the slave BN with certain probability, while synchronization with probability one implies that master BN can be synchronized by the slave BN with probability one. Based on the equivalent algebraic form, some efficient conditions are derived to guarantee synchronization in probability. Finally, several numerical examples are presented to show the effectiveness of the main results.

Processing Pathways in Mental Arithmetic—Evidence from Probabilistic Fiber Tracking

PubMed Central

Glauche, Volkmar; Weiller, Cornelius; Willmes, Klaus

2013-01-01

Numerical cognition is a case of multi-modular and distributed cerebral processing. So far neither the anatomo-functional connections between the cortex areas involved nor their integration into established frameworks such as the differentiation between dorsal and ventral processing streams have been specified. The current study addressed this issue combining a re-analysis of previously published fMRI data with probabilistic fiber tracking data from an independent sample. We aimed at differentiating neural correlates and connectivity for relatively easy and more difficult addition problems in healthy adults and their association with either rather verbally mediated fact retrieval or magnitude manipulations, respectively. The present data suggest that magnitude- and fact retrieval-related processing seem to be subserved by two largely separate networks, both of them comprising dorsal and ventral connections. Importantly, these networks not only differ in localization of activation but also in the connections between the cortical areas involved. However, it has to be noted that even though seemingly distinct anatomically, these networks operate as a functionally integrated circuit for mental calculation as revealed by a parametric analysis of brain activation. PMID:23383194

A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems.

PubMed

Raman, M R Gauthama; Somu, Nivethitha; Kirthivasan, Kannan; Sriram, V S Shankar

2017-08-01

Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on Artificial Neural Network (ANN) to improve the performance of the IDSs. However, there exists a tradeoff with respect to the stability of ANN architecture and the detection rate for less frequent attacks. This paper presents a novel approach based on Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN) to address the classification problem in IDS. The Helly property of Hypergraph was exploited for the identification of the optimal feature subset and the arithmetic residue of the optimal feature subset was used to train the PNN. The performance of HG AR-PNN was evaluated using KDD CUP 1999 intrusion dataset. Experimental results prove the dominance of HG AR-PNN classifier over the existing classifiers with respect to the stability and improved detection rate for less frequent attacks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Synchronization Analysis of Master-Slave Probabilistic Boolean Networks

PubMed Central

Lu, Jianquan; Zhong, Jie; Li, Lulu; Ho, Daniel W. C.; Cao, Jinde

2015-01-01

In this paper, we analyze the synchronization problem of master-slave probabilistic Boolean networks (PBNs). The master Boolean network (BN) is a deterministic BN, while the slave BN is determined by a series of possible logical functions with certain probability at each discrete time point. In this paper, we firstly define the synchronization of master-slave PBNs with probability one, and then we investigate synchronization with probability one. By resorting to new approach called semi-tensor product (STP), the master-slave PBNs are expressed in equivalent algebraic forms. Based on the algebraic form, some necessary and sufficient criteria are derived to guarantee synchronization with probability one. Further, we study the synchronization of master-slave PBNs in probability. Synchronization in probability implies that for any initial states, the master BN can be synchronized by the slave BN with certain probability, while synchronization with probability one implies that master BN can be synchronized by the slave BN with probability one. Based on the equivalent algebraic form, some efficient conditions are derived to guarantee synchronization in probability. Finally, several numerical examples are presented to show the effectiveness of the main results. PMID:26315380

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a stochastic generator of synthetic data was developed. Synthetic basin characteristics were randomised, keeping the statistical properties of observed physical and climatic variables in the homogeneous region. The synthetic flood quantiles were stochastically generated taking the regression equation as basis. The learnt Bayesian network was validated by the reliability diagram, the Brier Score and the ROC diagram, which are common measures used in the validation of probabilistic forecasts. Summarising, the flood quantile estimations through Bayesian networks supply information about the prediction uncertainty as a probability distribution function of discharges is given as result. Therefore, the Bayesian network model has application as a decision support for water resources and planning management.

Approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays: a robust stability problem.

PubMed

Pandiselvi, S; Raja, R; Cao, Jinde; Rajchakit, G; Ahmad, Bashir

2018-01-01

This work predominantly labels the problem of approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays. Here we design a linear estimator in such a way that the absorption of mRNA and protein can be approximated via known measurement outputs. By utilizing a Lyapunov-Krasovskii functional and some stochastic analysis execution, we obtain the stability formula of the estimation error systems in the structure of linear matrix inequalities under which the estimation error dynamics is robustly exponentially stable. Further, the obtained conditions (in the form of LMIs) can be effortlessly solved by some available software packages. Moreover, the specific expression of the desired estimator is also shown in the main section. Finally, two mathematical illustrative examples are accorded to show the advantage of the proposed conceptual results.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pichara, Karim; Protopapas, Pavlos

We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilizes sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model, we use three catalogs with missing data (SAGE, Two Micron All Sky Survey, and UBVI) and one complete catalog (MACHO). We examine howmore » classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches, and at what computational cost. Integrating these catalogs with missing data, we find that classification of variable objects improves by a few percent and by 15% for quasar detection while keeping the computational cost the same.« less

Dynamic sensing model for accurate delectability of environmental phenomena using event wireless sensor network

NASA Astrophysics Data System (ADS)

Missif, Lial Raja; Kadhum, Mohammad M.

2017-09-01

Wireless Sensor Network (WSN) has been widely used for monitoring where sensors are deployed to operate independently to sense abnormal phenomena. Most of the proposed environmental monitoring systems are designed based on a predetermined sensing range which does not reflect the sensor reliability, event characteristics, and the environment conditions. Measuring of the capability of a sensor node to accurately detect an event within a sensing field is of great important for monitoring applications. This paper presents an efficient mechanism for even detection based on probabilistic sensing model. Different models have been presented theoretically in this paper to examine their adaptability and applicability to the real environment applications. The numerical results of the experimental evaluation have showed that the probabilistic sensing model provides accurate observation and delectability of an event, and it can be utilized for different environment scenarios.

Review of methods for developing regional probabilistic risk assessments, part 2: modeling invasive plant, insect, and pathogen species

Treesearch

P. B. Woodbury; D. A. Weinstein

2010-01-01

We reviewed probabilistic regional risk assessment methodologies to identify the methods that are currently in use and are capable of estimating threats to ecosystems from fire and fuels, invasive species, and their interactions with stressors. In a companion chapter, we highlight methods useful for evaluating risks from fire. In this chapter, we highlight methods...

Functional mechanisms of probabilistic inference in feature- and space-based attentional systems.

PubMed

Dombert, Pascasie L; Kuhns, Anna; Mengotti, Paola; Fink, Gereon R; Vossel, Simone

2016-11-15

Humans flexibly attend to features or locations and these processes are influenced by the probability of sensory events. We combined computational modeling of response times with fMRI to compare the functional correlates of (re-)orienting, and the modulation by probabilistic inference in spatial and feature-based attention systems. Twenty-four volunteers performed two task versions with spatial or color cues. Percentage of cue validity changed unpredictably. A hierarchical Bayesian model was used to derive trial-wise estimates of probability-dependent attention, entering the fMRI analysis as parametric regressors. Attentional orienting activated a dorsal frontoparietal network in both tasks, without significant parametric modulation. Spatially invalid trials activated a bilateral frontoparietal network and the precuneus, while invalid feature trials activated the left intraparietal sulcus (IPS). Probability-dependent attention modulated activity in the precuneus, left posterior IPS, middle occipital gyrus, and right temporoparietal junction for spatial attention, and in the left anterior IPS for feature-based and spatial attention. These findings provide novel insights into the generality and specificity of the functional basis of attentional control. They suggest that probabilistic inference can distinctively affect each attentional subsystem, but that there is an overlap in the left IPS, which responds to both spatial and feature-based expectancy violations. Copyright © 2016 Elsevier Inc. All rights reserved.

Assessment of uncertainty in discrete fracture network modeling using probabilistic distribution method.

PubMed

Wei, Yaqiang; Dong, Yanhui; Yeh, Tian-Chyi J; Li, Xiao; Wang, Liheng; Zha, Yuanyuan

2017-11-01

There have been widespread concerns about solute transport problems in fractured media, e.g. the disposal of high-level radioactive waste in geological fractured rocks. Numerical simulation of particle tracking is gradually being employed to address these issues. Traditional predictions of radioactive waste transport using discrete fracture network (DFN) models often consider one particular realization of the fracture distribution based on fracture statistic features. This significantly underestimates the uncertainty of the risk of radioactive waste deposit evaluation. To adequately assess the uncertainty during the DFN modeling in a potential site for the disposal of high-level radioactive waste, this paper utilized the probabilistic distribution method (PDM). The method was applied to evaluate the risk of nuclear waste deposit in Beishan, China. Moreover, the impact of the number of realizations on the simulation results was analyzed. In particular, the differences between the modeling results of one realization and multiple realizations were demonstrated. Probabilistic distributions of 20 realizations at different times were also obtained. The results showed that the employed PDM can be used to describe the ranges of the contaminant particle transport. The high-possibility contaminated areas near the release point were more concentrated than the farther areas after 5E6 days, which was 25,400 m 2 .

A Practical Probabilistic Graphical Modeling Tool for Weighing ...

EPA Pesticide Factsheets

Past weight-of-evidence frameworks for adverse ecological effects have provided soft-scoring procedures for judgments based on the quality and measured attributes of evidence. Here, we provide a flexible probabilistic structure for weighing and integrating lines of evidence for ecological risk determinations. Probabilistic approaches can provide both a quantitative weighing of lines of evidence and methods for evaluating risk and uncertainty. The current modeling structure wasdeveloped for propagating uncertainties in measured endpoints and their influence on the plausibility of adverse effects. To illustrate the approach, we apply the model framework to the sediment quality triad using example lines of evidence for sediment chemistry measurements, bioassay results, and in situ infauna diversity of benthic communities using a simplified hypothetical case study. We then combine the three lines evidence and evaluate sensitivity to the input parameters, and show how uncertainties are propagated and how additional information can be incorporated to rapidly update the probability of impacts. The developed network model can be expanded to accommodate additional lines of evidence, variables and states of importance, and different types of uncertainties in the lines of evidence including spatial and temporal as well as measurement errors. We provide a flexible Bayesian network structure for weighing and integrating lines of evidence for ecological risk determinations

GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data.

PubMed

Mifsud, Borbala; Martincorena, Inigo; Darbo, Elodie; Sugar, Robert; Schoenfelder, Stefan; Fraser, Peter; Luscombe, Nicholas M

2017-01-01

Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html).

Finding a planet's heartbeat: surprising results from patient Mars

NASA Astrophysics Data System (ADS)

Stamenkovic, Vlada; Ward, Lewis; Fischer, Woodward; Russell, Michael J.

2016-10-01

We explore, from a 3D time-dependent perspective, the evolution of oxidizing and reducing planetary niches and how they form a planetary-scale redox network - from a planet's deep interior to its atmosphere. Such redox networks are similar to the circulatory system of animals, but instead of pressure gradients redox gradients drive the flow of electrons and create hotspots for nutrients and metabolic activity.Using time-dependent geodynamic and atmospheric models, we compute for Mars the time-dependent 3D distribution of 1) hydrogen- and methane-rich reducing subsurface environments, driven by serpentinization and radiolysis of water, and 2) oxygen-rich oases as a product of atmosphere-brine interactions governed by climate and surface chemistry.This is only a first step towards our greater goal to globally model the evolution of local redox environments through time for rocky planets. However, already now our preliminary results show where on Mars oxidizing and reducing oases might have existed and might still exist today. This opens the window to search for extinct and extant life on Mars from a probabilistic global 3D perspective.

Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins.

PubMed

Hu, Pingzhao; Janga, Sarath Chandra; Babu, Mohan; Díaz-Mejía, J Javier; Butland, Gareth; Yang, Wenhong; Pogoutse, Oxana; Guo, Xinghua; Phanse, Sadhna; Wong, Peter; Chandran, Shamanta; Christopoulos, Constantine; Nazarians-Armavil, Anaies; Nasseri, Negin Karimi; Musso, Gabriel; Ali, Mehrab; Nazemof, Nazila; Eroukova, Veronika; Golshani, Ashkan; Paccanaro, Alberto; Greenblatt, Jack F; Moreno-Hagelsieb, Gabriel; Emili, Andrew

2009-04-28

One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans' biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a "systems-wide" functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Sheng, Yehua; Zhang, Ka; Ye, Chun; Liang, Cheng; Li, Jian

2008-04-01

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

Photometric redshift estimation via deep learning. Generalized and pre-classification-less, image based, fully probabilistic redshifts

NASA Astrophysics Data System (ADS)

D'Isanto, A.; Polsterer, K. L.

2018-01-01

Context. The need to analyze the available large synoptic multi-band surveys drives the development of new data-analysis methods. Photometric redshift estimation is one field of application where such new methods improved the results, substantially. Up to now, the vast majority of applied redshift estimation methods have utilized photometric features. Aims: We aim to develop a method to derive probabilistic photometric redshift directly from multi-band imaging data, rendering pre-classification of objects and feature extraction obsolete. Methods: A modified version of a deep convolutional network was combined with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) were applied as performance criteria. We have adopted a feature based random forest and a plain mixture density network to compare performances on experiments with data from SDSS (DR9). Results: We show that the proposed method is able to predict redshift PDFs independently from the type of source, for example galaxies, quasars or stars. Thereby the prediction performance is better than both presented reference methods and is comparable to results from the literature. Conclusions: The presented method is extremely general and allows us to solve of any kind of probabilistic regression problems based on imaging data, for example estimating metallicity or star formation rate of galaxies. This kind of methodology is tremendously important for the next generation of surveys.

Design of robust reliable control for T-S fuzzy Markovian jumping delayed neutral type neural networks with probabilistic actuator faults and leakage delays: An event-triggered communication scheme.

PubMed

Syed Ali, M; Vadivel, R; Saravanakumar, R

2018-06-01

This study examines the problem of robust reliable control for Takagi-Sugeno (T-S) fuzzy Markovian jumping delayed neural networks with probabilistic actuator faults and leakage terms. An event-triggered communication scheme. First, the randomly occurring actuator faults and their failures rates are governed by two sets of unrelated random variables satisfying certain probabilistic failures of every actuator, new type of distribution based event triggered fault model is proposed, which utilize the effect of transmission delay. Second, Takagi-Sugeno (T-S) fuzzy model is adopted for the neural networks and the randomness of actuators failures is modeled in a Markov jump model framework. Third, to guarantee the considered closed-loop system is exponential mean square stable with a prescribed reliable control performance, a Markov jump event-triggered scheme is designed in this paper, which is the main purpose of our study. Fourth, by constructing appropriate Lyapunov-Krasovskii functional, employing Newton-Leibniz formulation and integral inequalities, several delay-dependent criteria for the solvability of the addressed problem are derived. The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones, among them one example was supported by real-life application of the benchmark problem. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.

PubMed

Sesen, M Berkan; Peake, Michael D; Banares-Alcantara, Rene; Tse, Donald; Kadir, Timor; Stanley, Roz; Gleeson, Fergus; Brady, Michael

2014-09-06

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

NASA Technical Reports Server (NTRS)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks.

PubMed

Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon

2015-08-11

Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.

Probabilistic modeling of discourse-aware sentence processing.

PubMed

Dubey, Amit; Keller, Frank; Sturt, Patrick

2013-07-01

Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co-reference. The novel combination of probabilistic syntactic components with co-reference classifiers permits them to more closely mimic human behavior than existing models. The first model uses a deep model of linguistics, based in part on probabilistic logic, allowing it to make qualitative predictions on experimental data; the second model uses shallow processing to make quantitative predictions on a broad-coverage reading-time corpus. Copyright © 2013 Cognitive Science Society, Inc.

Systems and methods for modeling and analyzing networks

DOEpatents

Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W

2013-10-29

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Stochastic Characterization of Communication Network Latency for Wide Area Grid Control Applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ameme, Dan Selorm Kwami; Guttromson, Ross

This report characterizes communications network latency under various network topologies and qualities of service (QoS). The characterizations are probabilistic in nature, allowing deeper analysis of stability for Internet Protocol (IP) based feedback control systems used in grid applications. The work involves the use of Raspberry Pi computers as a proxy for a controlled resource, and an ns-3 network simulator on a Linux server to create an experimental platform (testbed) that can be used to model wide-area grid control network communications in smart grid. Modbus protocol is used for information transport, and Routing Information Protocol is used for dynamic route selectionmore » within the simulated network.« less

Multi-disciplinary coupling for integrated design of propulsion systems

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Singhal, S. N.

1993-01-01

Effective computational simulation procedures are described for modeling the inherent multi-disciplinary interactions for determining the true response of propulsion systems. Results are presented for propulsion system responses including multi-discipline coupling effects via (1) coupled multi-discipline tailoring, (2) an integrated system of multidisciplinary simulators, (3) coupled material-behavior/fabrication-process tailoring, (4) sensitivities using a probabilistic simulator, and (5) coupled materials/structures/fracture/probabilistic behavior simulator. The results show that the best designs can be determined if the analysis/tailoring methods account for the multi-disciplinary coupling effects. The coupling across disciplines can be used to develop an integrated interactive multi-discipline numerical propulsion system simulator.

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.

PubMed

Borchani, Hanen; Bielza, Concha; Toro, Carlos; Larrañaga, Pedro

2013-03-01

Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries. Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input instance in the data set has to be assigned simultaneously to multiple output class variables that are not necessarily binary. In this paper, we introduce a new method, named MB-MBC, for learning MBCs from data by determining the Markov blanket around each class variable using the HITON algorithm. Our method is applied to both reverse transcriptase and protease data sets obtained from the Stanford HIV-1 database. Regarding the prediction of antiretroviral combination therapies, the experimental study shows promising results in terms of classification accuracy compared with state-of-the-art MBC learning algorithms. For reverse transcriptase inhibitors, we get 71% and 11% in mean and global accuracy, respectively; while for protease inhibitors, we get more than 84% and 31% in mean and global accuracy, respectively. In addition, the analysis of MBC graphical structures lets us gain insight into both known and novel interactions between reverse transcriptase and protease inhibitors and their respective resistance mutations. MB-MBC algorithm is a valuable tool to analyze the HIV-1 reverse transcriptase and protease inhibitors prediction problem and to discover interactions within and between these two classes of inhibitors. Copyright © 2012 Elsevier B.V. All rights reserved.

Interactive Model Visualization for NET-VISA

NASA Astrophysics Data System (ADS)

Kuzma, H. A.; Arora, N. S.

2013-12-01

NET-VISA is a probabilistic system developed for seismic network processing of data measured on the International Monitoring System (IMS) of the Comprehensive nuclear Test Ban Treaty Organization (CTBTO). NET-VISA is composed of a Generative Model (GM) and an Inference Algorithm (IA). The GM is an explicit mathematical description of the relationships between various factors in seismic network analysis. Some of the relationships inside the GM are deterministic and some are statistical. Statistical relationships are described by probability distributions, the exact parameters of which (such as mean and standard deviation) are found by training NET-VISA using recent data. The IA uses the GM to evaluate the probability of various events and associations, searching for the seismic bulletin which has the highest overall probability and is consistent with a given set of measured arrivals. An Interactive Model Visualization tool (IMV) has been developed which makes 'peeking into' the GM simple and intuitive through a web-based interfaced. For example, it is now possible to access the probability distributions for attributes of events and arrivals such as the detection rate for each station for each of 14 phases. It also clarifies the assumptions and prior knowledge that are incorporated into NET-VISA's event determination. When NET-VISA is retrained, the IMV will be a visual tool for quality control both as a means of testing that the training has been accomplished correctly and that the IMS network has not changed unexpectedly. A preview of the IMV will be shown at this poster presentation. Homepage for the IMV IMV shows current model file and reference image.

Adaptive Probabilistic Protocols for Advanced Networks/Assuring the Integrity of Highly Decentralized Communications Systems

DTIC Science & Technology

2005-03-01

to obtain a protocol customized to the needs of a specific setting, under control of an automated theorem proving system that can guarantee...new “compositional” method for protocol design and implementation, in which small microprotocols are combined to obtain a protocol customized to the...and Network Centric Enterprise (NCES) visions. This final report documents a wide range of contributions and technology transitions, including: A

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Don't Fear Optimality: Sampling for Probabilistic-Logic Sequence Models

NASA Astrophysics Data System (ADS)

Thon, Ingo

One of the current challenges in artificial intelligence is modeling dynamic environments that change due to the actions or activities undertaken by people or agents. The task of inferring hidden states, e.g. the activities or intentions of people, based on observations is called filtering. Standard probabilistic models such as Dynamic Bayesian Networks are able to solve this task efficiently using approximative methods such as particle filters. However, these models do not support logical or relational representations. The key contribution of this paper is the upgrade of a particle filter algorithm for use with a probabilistic logical representation through the definition of a proposal distribution. The performance of the algorithm depends largely on how well this distribution fits the target distribution. We adopt the idea of logical compilation into Binary Decision Diagrams for sampling. This allows us to use the optimal proposal distribution which is normally prohibitively slow.

Fast, noise-free memory for photon synchronization at room temperature.

PubMed

Finkelstein, Ran; Poem, Eilon; Michel, Ohad; Lahad, Ohr; Firstenberg, Ofer

2018-01-01

Future quantum photonic networks require coherent optical memories for synchronizing quantum sources and gates of probabilistic nature. We demonstrate a fast ladder memory (FLAME) mapping the optical field onto the superposition between electronic orbitals of rubidium vapor. Using a ladder-level system of orbital transitions with nearly degenerate frequencies simultaneously enables high bandwidth, low noise, and long memory lifetime. We store and retrieve 1.7-ns-long pulses, containing 0.5 photons on average, and observe short-time external efficiency of 25%, memory lifetime (1/ e ) of 86 ns, and below 10 -4 added noise photons. Consequently, coupling this memory to a probabilistic source would enhance the on-demand photon generation probability by a factor of 12, the highest number yet reported for a noise-free, room temperature memory. This paves the way toward the controlled production of large quantum states of light from probabilistic photon sources.

Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making

PubMed Central

Drugowitsch, Jan; Pouget, Alexandre

2012-01-01

Optimal binary perceptual decision making requires accumulation of evidence in the form of a probability distribution that specifies the probability of the choices being correct given the evidence so far. Reward rates can then be maximized by stopping the accumulation when the confidence about either option reaches a threshold. Behavioral and neuronal evidence suggests that humans and animals follow such a probabilitistic decision strategy, although its neural implementation has yet to be fully characterized. Here we show that that diffusion decision models and attractor network models provide an approximation to the optimal strategy only under certain circumstances. In particular, neither model type is sufficiently flexible to encode the reliability of both the momentary and the accumulated evidence, which is a pre-requisite to accumulate evidence of time-varying reliability. Probabilistic population codes, in contrast, can encode these quantities and, as a consequence, have the potential to implement the optimal strategy accurately. PMID:22884815

Implementation of single-photon quantum routing and decoupling using a nitrogen-vacancy center and a whispering-gallery-mode resonator-waveguide system.

PubMed

Cao, Cong; Duan, Yu-Wen; Chen, Xi; Zhang, Ru; Wang, Tie-Jun; Wang, Chuan

2017-07-24

Quantum router is a key element needed for the construction of future complex quantum networks. However, quantum routing with photons, and its inverse, quantum decoupling, are difficult to implement as photons do not interact, or interact very weakly in nonlinear media. In this paper, we investigate the possibility of implementing photonic quantum routing based on effects in cavity quantum electrodynamics, and present a scheme for single-photon quantum routing controlled by the other photon using a hybrid system consisting of a single nitrogen-vacancy (NV) center coupled with a whispering-gallery-mode resonator-waveguide structure. Different from the cases in which classical information is used to control the path of quantum signals, both the control and signal photons are quantum in our implementation. Compared with the probabilistic quantum routing protocols based on linear optics, our scheme is deterministic and also scalable to multiple photons. We also present a scheme for single-photon quantum decoupling from an initial state with polarization and spatial-mode encoding, which can implement an inverse operation to the quantum routing. We discuss the feasibility of our schemes by considering current or near-future techniques, and show that both the schemes can operate effectively in the bad-cavity regime. We believe that the schemes could be key building blocks for future complex quantum networks and large-scale quantum information processing.

Influence diagrams as oil spill decision science tools

EPA Science Inventory

Making inferences on risks to ecosystem services (ES) from ecological crises can be more reliably handled using decision science tools. Influence diagrams (IDs) are probabilistic networks that explicitly represent the decisions related to a problem and evidence of their influence...

Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains.

PubMed

Ron, Gil; Globerson, Yuval; Moran, Dror; Kaplan, Tommy

2017-12-21

Proximity-ligation methods such as Hi-C allow us to map physical DNA-DNA interactions along the genome, and reveal its organization into topologically associating domains (TADs). As the Hi-C data accumulate, computational methods were developed for identifying domain borders in multiple cell types and organisms. Here, we present PSYCHIC, a computational approach for analyzing Hi-C data and identifying promoter-enhancer interactions. We use a unified probabilistic model to segment the genome into domains, which we then merge hierarchically and fit using a local background model, allowing us to identify over-represented DNA-DNA interactions across the genome. By analyzing the published Hi-C data sets in human and mouse, we identify hundreds of thousands of putative enhancers and their target genes, and compile an extensive genome-wide catalog of gene regulation in human and mouse. As we show, our predictions are highly enriched for ChIP-seq and DNA accessibility data, evolutionary conservation, eQTLs and other DNA-DNA interaction data.

Structural factoring approach for analyzing stochastic networks

NASA Technical Reports Server (NTRS)

Hayhurst, Kelly J.; Shier, Douglas R.

1991-01-01

The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

Hidden Markov models and neural networks for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

Probabilistic simulation of the human factor in structural reliability

NASA Technical Reports Server (NTRS)

Chamis, C. C.

1993-01-01

A formal approach is described in an attempt to computationally simulate the probable ranges of uncertainties of the human factor in structural probabilistic assessments. A multi-factor interaction equation (MFIE) model has been adopted for this purpose. Human factors such as marital status, professional status, home life, job satisfaction, work load and health, are considered to demonstrate the concept. Parametric studies in conjunction with judgment are used to select reasonable values for the participating factors (primitive variables). Suitability of the MFIE in the subsequently probabilistic sensitivity studies are performed to assess the validity of the whole approach. Results obtained show that the uncertainties for no error range from five to thirty percent for the most optimistic case.

Probabilistic simulation of the human factor in structural reliability

NASA Astrophysics Data System (ADS)

Chamis, Christos C.; Singhal, Surendra N.

1994-09-01

The formal approach described herein computationally simulates the probable ranges of uncertainties for the human factor in probabilistic assessments of structural reliability. Human factors such as marital status, professional status, home life, job satisfaction, work load, and health are studied by using a multifactor interaction equation (MFIE) model to demonstrate the approach. Parametric studies in conjunction with judgment are used to select reasonable values for the participating factors (primitive variables). Subsequently performed probabilistic sensitivity studies assess the suitability of the MFIE as well as the validity of the whole approach. Results show that uncertainties range from 5 to 30 percent for the most optimistic case, assuming 100 percent for no error (perfect performance).

Probabilistic Simulation of the Human Factor in Structural Reliability

NASA Technical Reports Server (NTRS)

Chamis, Christos C.; Singhal, Surendra N.

1994-01-01

The formal approach described herein computationally simulates the probable ranges of uncertainties for the human factor in probabilistic assessments of structural reliability. Human factors such as marital status, professional status, home life, job satisfaction, work load, and health are studied by using a multifactor interaction equation (MFIE) model to demonstrate the approach. Parametric studies in conjunction with judgment are used to select reasonable values for the participating factors (primitive variables). Subsequently performed probabilistic sensitivity studies assess the suitability of the MFIE as well as the validity of the whole approach. Results show that uncertainties range from 5 to 30 percent for the most optimistic case, assuming 100 percent for no error (perfect performance).

MC EMiNEM Maps the Interaction Landscape of the Mediator

PubMed Central

Niederberger, Theresa; Etzold, Stefanie; Lidschreiber, Michael; Maier, Kerstin C.; Martin, Dietmar E.; Fröhlich, Holger; Cramer, Patrick; Tresch, Achim

2012-01-01

The Mediator is a highly conserved, large multiprotein complex that is involved essentially in the regulation of eukaryotic mRNA transcription. It acts as a general transcription factor by integrating regulatory signals from gene-specific activators or repressors to the RNA Polymerase II. The internal network of interactions between Mediator subunits that conveys these signals is largely unknown. Here, we introduce MC EMiNEM, a novel method for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription. MC EMiNEM is based on Nested Effects Models (NEMs), a class of probabilistic graphical models that extends the idea of hierarchical clustering. It combines mode-hopping Monte Carlo (MC) sampling with an Expectation-Maximization (EM) algorithm for NEMs to increase sensitivity compared to existing methods. A meta-analysis of four Mediator perturbation studies in Saccharomyces cerevisiae, three of which are unpublished, provides new insight into the Mediator signaling network. In addition to the known modular organization of the Mediator subunits, MC EMiNEM reveals a hierarchical ordering of its internal information flow, which is putatively transmitted through structural changes within the complex. We identify the N-terminus of Med7 as a peripheral entity, entailing only local structural changes upon perturbation, while the C-terminus of Med7 and Med19 appear to play a central role. MC EMiNEM associates Mediator subunits to most directly affected genes, which, in conjunction with gene set enrichment analysis, allows us to construct an interaction map of Mediator subunits and transcription factors. PMID:22737066

Greater Philadelphia Bioinformatics Alliance (GPBA) 3rd Annual Retreat 2005

DTIC Science & Technology

2005-11-01

Using Probabilistic Network Reliability. Genome Res. 14:1170-1175 [27] Batagelj , V. and Mrvar , A. (1998). Pajek: Program for large network analysis...and Neural Computation, Division of Informatics, Centre for Cognitive Science, University of Edinburgh, Scotland, April 1996 . www.anc.ed.ac.uk/-mjo...research center in the College of Engineering, and one of the foremost academic research centers in its field. From 1996 to 1998 he was the Founding

Automation on the generation of genome-scale metabolic models.

PubMed

Reyes, R; Gamermann, D; Montagud, A; Fuente, D; Triana, J; Urchueguía, J F; de Córdoba, P Fernández

2012-12-01

Nowadays, the reconstruction of genome-scale metabolic models is a nonautomatized and interactive process based on decision making. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze, and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic, and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. This work presents the automation of a methodology for the reconstruction of genome-scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome-scale metabolic model of a photosynthetic organism, Synechocystis sp. PCC6803. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models, like connectivity and average shortest mean path of the different models, have been compared and analyzed.

On the Latent Variable Interpretation in Sum-Product Networks.

PubMed

Peharz, Robert; Gens, Robert; Pernkopf, Franz; Domingos, Pedro

2017-10-01

One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.

Alterations of resting state networks and structural connectivity in relation to the prefrontal and anterior cingulate cortices in late prematurity.

PubMed

Degnan, Andrew J; Wisnowski, Jessica L; Choi, SoYoung; Ceschin, Rafael; Bhushan, Chitresh; Leahy, Richard M; Corby, Patricia; Schmithorst, Vincent J; Panigrahy, Ashok

2015-01-07

Late preterm birth is increasingly recognized as a risk factor for cognitive and social deficits. The prefrontal cortex is particularly vulnerable to injury in late prematurity because of its protracted development and extensive cortical connections. Our study examined children born late preterm without access to advanced postnatal care to assess structural and functional connectivity related to the prefrontal cortex. Thirty-eight preadolescents [19 born late preterm (34-36 /7 weeks gestational age) and 19 at term] were recruited from a developing community in Brazil. Participants underwent neuropsychological testing. Individuals underwent three-dimensional T1-weighted, diffusion-weighted, and resting state functional MRI. Probabilistic tractography and functional connectivity analyses were carried out using unilateral seeds combining the medial prefrontal cortex and the anterior cingulate cortex. Late preterm children showed increased functional connectivity within regions of the default mode, salience, and central-executive networks from both right and left frontal cortex seeds. Decreased functional connectivity was observed within the right parahippocampal region from left frontal seeding. Probabilistic tractography showed a pattern of decreased streamlines in frontal white matter pathways and the corpus callosum, but also increased streamlines in the left orbitofrontal white matter and the right frontal white matter when seeded from the right. Late preterm children and term control children scored similarly on neuropsychological testing. Prefrontal cortical connectivity is altered in late prematurity, with hyperconnectivity observed in key resting state networks in the absence of neuropsychological deficits. Abnormal structural connectivity indicated by probabilistic tractography suggests subtle changes in white matter development, implying disruption of normal maturation during the late gestational period.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty.

PubMed

Mihaljević, Bojan; Bielza, Concha; Benavides-Piccione, Ruth; DeFelipe, Javier; Larrañaga, Pedro

2014-01-01

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Global Infrasound Association Based on Probabilistic Clutter Categorization

NASA Astrophysics Data System (ADS)

Arora, N. S.; Mialle, P.

2015-12-01

The IDC collects waveforms from a global network of infrasound sensors maintained by the IMS, and automatically detects signal onsets and associates them to form event hypotheses. However, a large number of signal onsets are due to local clutter sources such as microbaroms (from standing waves in the oceans), 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 NET-VISA [2], a Bayesian approach to network processing. The resulting model is a fusion of seismic, hydro-acoustic and infrasound processing built on a unified probabilistic framework. Notes: The attached figure shows all the unassociated arrivals detected at IMS station I09BR for 2012 distributed by azimuth and center frequency. (The title displays the bandwidth of the kernel density estimate along the azimuth and frequency dimensions).This plot shows multiple micro-barom sources as well as other sources of infrasound clutter. A diverse clutter-field such as this one is quite common for most IMS infrasound stations, and it highlights the dangers of forming events without due consideration of this source of noise. References: [1] Infrasound categorization Towards a statistics-based approach. J. Vergoz, P. Gaillard, A. Le Pichon, N. Brachet, and L. Ceranna. ITW 2011 [2] NET-VISA: Network Processing Vertically Integrated Seismic Analysis. N. S. Arora, S. Russell, and E. Sudderth. BSSA 2013.

Detecting event-related changes of multivariate phase coupling in dynamic brain networks.

PubMed

Canolty, Ryan T; Cadieu, Charles F; Koepsell, Kilian; Ganguly, Karunesh; Knight, Robert T; Carmena, Jose M

2012-04-01

Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171-189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474-480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506-515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110-113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107-3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194-208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

NASA Astrophysics Data System (ADS)

Laloy, Eric; Hérault, Romain; Lee, John; Jacques, Diederik; Linde, Niklas

2017-12-01

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200-500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.

Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Logic cycle

NASA Astrophysics Data System (ADS)

Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Zhu, Ma; Wang, Xiaochen

2017-04-01

Intelligent diagnosis system are applied to fault diagnosis in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In the knowledge expression of spacecraft fault diagnosis, feedback among variables is frequently encountered, which may cause directed cyclic graphs (DCGs). Probabilistic graphical models (PGMs) such as bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning, but BN does not allow DCGs. In this paper, DUGG is applied to fault diagnosis in spacecraft: introducing the inference algorithm for the DUCG to deal with feedback. Now, DUCG has been tested in 16 typical faults with 100% diagnosis accuracy.

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

PubMed Central

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-01-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

A probabilistic approach to identify putative drug targets in biochemical networks.

PubMed

Murabito, Ettore; Smallbone, Kieran; Swinton, Jonathan; Westerhoff, Hans V; Steuer, Ralf

2011-06-06

Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity. © 2010 The Royal Society

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization.

PubMed

Yang, Jihong; Li, Zheng; Fan, Xiaohui; Cheng, Yiyu

2014-09-22

The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks

PubMed Central

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-01-01

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance. PMID:28471387

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks.

PubMed

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-05-04

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.

Data-driven Modeling of Metal-oxide Sensors with Dynamic Bayesian Networks

NASA Astrophysics Data System (ADS)

Gosangi, Rakesh; Gutierrez-Osuna, Ricardo

2011-09-01

We present a data-driven probabilistic framework to model the transient response of MOX sensors modulated with a sequence of voltage steps. Analytical models of MOX sensors are usually built based on the physico-chemical properties of the sensing materials. Although building these models provides an insight into the sensor behavior, they also require a thorough understanding of the underlying operating principles. Here we propose a data-driven approach to characterize the dynamical relationship between sensor inputs and outputs. Namely, we use dynamic Bayesian networks (DBNs), probabilistic models that represent temporal relations between a set of random variables. We identify a set of control variables that influence the sensor responses, create a graphical representation that captures the causal relations between these variables, and finally train the model with experimental data. We validated the approach on experimental data in terms of predictive accuracy and classification performance. Our results show that DBNs can accurately predict the dynamic response of MOX sensors, as well as capture the discriminatory information present in the sensor transients.

Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks.

PubMed

Dura-Bernal, Salvador; Garreau, Guillaume; Georgiou, Julius; Andreou, Andreas G; Denham, Susan L; Wennekers, Thomas

2013-10-01

The ability to recognize the behavior of individuals is of great interest in the general field of safety (e.g. building security, crowd control, transport analysis, independent living for the elderly). Here we report a new real-time acoustic system for human action and behavior recognition that integrates passive audio and active micro-Doppler sonar signatures over multiple time scales. The system architecture is based on a six-layer convolutional neural network, trained and evaluated using a dataset of 10 subjects performing seven different behaviors. Probabilistic combination of system output through time for each modality separately yields 94% (passive audio) and 91% (micro-Doppler sonar) correct behavior classification; probabilistic multimodal integration increases classification performance to 98%. This study supports the efficacy of micro-Doppler sonar systems in characterizing human actions, which can then be efficiently classified using ConvNets. It also demonstrates that the integration of multiple sources of acoustic information can significantly improve the system's performance.

Filtering for networked control systems with single/multiple measurement packets subject to multiple-step measurement delays and multiple packet dropouts

NASA Astrophysics Data System (ADS)

Moayedi, Maryam; Foo, Yung Kuan; Chai Soh, Yeng

2011-03-01

The minimum-variance filtering problem in networked control systems, where both random measurement transmission delays and packet dropouts may occur, is investigated in this article. Instead of following the many existing results that solve the problem by using probabilistic approaches based on the probabilities of the uncertainties occurring between the sensor and the filter, we propose a non-probabilistic approach by time-stamping the measurement packets. Both single-measurement and multiple measurement packets are studied. We also consider the case of burst arrivals, where more than one packet may arrive between the receiver's previous and current sampling times; the scenario where the control input is non-zero and subject to delays and packet dropouts is examined as well. It is shown that, in such a situation, the optimal state estimate would generally be dependent on the possible control input. Simulations are presented to demonstrate the performance of the various proposed filters.

Distinction of cervical cancer biopsies by use of infrared microspectroscopy and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Podshyvalov, A.; Sahu, R. K.; Mark, S.; Kantarovich, K.; Guterman, H.; Goldstein, J.; Jagannathan, R.; Argov, S.; Mordechai, S.

2005-06-01

Fourier-transform infrared spectroscopy has shown alterations of spectral characteristics of cells and tissues as a result of carcinogenesis. The research reported here focuses on the diagnosis of cancer in formalin-fixed biopsied tissue for which immunochemistry is not possible and when PAP-smear results are to be confirmed. The data from two groups of patients (a control group and a group of patients diagnosed with cervical cancer) were analyzed. It was found that the glucose/phosphate ratio decreases (by 23-49%) and the RNA/DNA ratio increases (by 38-150%) in carcinogenic compared with normal tissue. Fourier-transform microspectroscopy was used to examine these tissues. This type of study in larger populations may help to set standards or classes with which to use treated biopsied tissue to predict the possibility of cancer. Probabilistic neural networks and statistical tests as parts of these biopsies predict the possibility of cancer with a high degree of accuracy (>95%).

Applying knowledge engineering and representation methods to improve support vector machine and multivariate probabilistic neural network CAD performance

NASA Astrophysics Data System (ADS)

Land, Walker H., Jr.; Anderson, Frances; Smith, Tom; Fahlbusch, Stephen; Choma, Robert; Wong, Lut

2005-04-01

Achieving consistent and correct database cases is crucial to the correct evaluation of any computer-assisted diagnostic (CAD) paradigm. This paper describes the application of artificial intelligence (AI), knowledge engineering (KE) and knowledge representation (KR) to a data set of ~2500 cases from six separate hospitals, with the objective of removing/reducing inconsistent outlier data. Several support vector machine (SVM) kernels were used to measure diagnostic performance of the original and a "cleaned" data set. Specifically, KE and ER principles were applied to the two data sets which were re-examined with respect to the environment and agents. One data set was found to contain 25 non-characterizable sets. The other data set contained 180 non-characterizable sets. CAD system performance was measured with both the original and "cleaned" data sets using two SVM kernels as well as a multivariate probabilistic neural network (PNN). Results demonstrated: (i) a 10% average improvement in overall Az and (ii) approximately a 50% average improvement in partial Az.

Probabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherence.

PubMed

Sankari, Ziad; Adeli, Hojjat

2011-04-15

Recently, the authors presented an EEG (electroencephalogram) coherence study of the Alzheimer's disease (AD) and found statistically significant differences between AD and control groups. In this paper a probabilistic neural network (PNN) model is presented for classification of AD and healthy controls using features extracted in coherence and wavelet coherence studies on cortical connectivity in AD. The model is verified using EEGs obtained from 20 AD probable patients and 7 healthy/control subjects based on a standard 10-20 electrode configuration on the scalp. It is shown that extracting features from EEG sub-bands using coherence, as a measure of cortical connectivity, can discriminate AD patients from healthy controls effectively when a mixed band classification model is applied. For the data set used a classification accuracy of 100% is achieved using the conventional coherence and a spread parameter of the Gaussian function in a particular range found in this research. Copyright © 2011 Elsevier B.V. All rights reserved.

A comparison of the weights-of-evidence method and probabilistic neural networks

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

1999-01-01

The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of −1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.

Using a Bayesian network to clarify areas requiring research in a host-pathogen system.

PubMed

Bower, D S; Mengersen, K; Alford, R A; Schwarzkopf, L

2017-12-01

Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease-driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host-pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious-disease systems. © 2017 Society for Conservation Biology.

A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.

PubMed

Liao, Stephen Shaoyi; Wang, Huai Qing; Li, Qiu Dan; Liu, Wei Yi

2006-06-01

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.

Gender differences in the association of perceived social support and social network with self-rated health status among older adults: a population-based study in Brazil

PubMed Central

2013-01-01

Background Older adults are more likely to live alone, because they may have been predeceased by their spouse and friends. Social interaction could also be reduced in this age group due by limited mobility caused by chronic conditions. Therefore, aging is frequently accompanied by reduced social support, which might affect health status. Little is known about the role of gender in the relationship between social support and health in older adults. Hence, the present study tests the hypothesis that gender differences exist in the relationship between perceived social support, social network, and self-rated health (SRH) among older adults. Methods A cross-sectional study using two-stage probabilistic sampling recruited 3,649 individuals aged 60 years and above. Data were collected during the national influenza vaccination campaign in Rio de Janeiro, Brazil, in 2006. Individual interviews collected information on SRH, perceived social support, social network, and other covariates. Multivariate logistic regression analyses using nested models were conducted separately for males and females. Independent variables were organised into six blocks: (1) perceived social support and social network, (2) age group, (3) socioeconomic characteristics, (4) health-related behaviours, (5) use of health care services, (6) functional status measures and somatic health problems. Results Older men who did not participate in group activities were more likely to report poor SRH compared to those who did, (OR = 1.63; 95% CI = 1.16–2.30). Low perceived social support predicted the probability of poor SRH in women (OR = 1.64; 95% CI = 1.16–2.34). Poor SRH was associated with low age, low income, not working, poor functional capacity, and depression in both men and women. More somatic health problems were associated with poor SRH in women. Conclusions The association between social interactions and SRH varies between genders. Low social network involvement is associated with poor SRH in older men, whereas low perceived social support is associated with poor SRH in older women. The hypothesis that the relationship of perceived social support and social networks to SRH differs according to gender has been confirmed. PMID:24229389

Gender differences in the association of perceived social support and social network with self-rated health status among older adults: a population-based study in Brazil.

PubMed

Caetano, Silvana C; Silva, Cosme M F P; Vettore, Mario V

2013-11-15

Older adults are more likely to live alone, because they may have been predeceased by their spouse and friends. Social interaction could also be reduced in this age group due by limited mobility caused by chronic conditions. Therefore, aging is frequently accompanied by reduced social support, which might affect health status. Little is known about the role of gender in the relationship between social support and health in older adults. Hence, the present study tests the hypothesis that gender differences exist in the relationship between perceived social support, social network, and self-rated health (SRH) among older adults. A cross-sectional study using two-stage probabilistic sampling recruited 3,649 individuals aged 60 years and above. Data were collected during the national influenza vaccination campaign in Rio de Janeiro, Brazil, in 2006. Individual interviews collected information on SRH, perceived social support, social network, and other covariates. Multivariate logistic regression analyses using nested models were conducted separately for males and females. Independent variables were organised into six blocks: (1) perceived social support and social network, (2) age group, (3) socioeconomic characteristics, (4) health-related behaviours, (5) use of health care services, (6) functional status measures and somatic health problems. Older men who did not participate in group activities were more likely to report poor SRH compared to those who did, (OR = 1.63; 95% CI = 1.16-2.30). Low perceived social support predicted the probability of poor SRH in women (OR = 1.64; 95% CI = 1.16-2.34). Poor SRH was associated with low age, low income, not working, poor functional capacity, and depression in both men and women. More somatic health problems were associated with poor SRH in women. The association between social interactions and SRH varies between genders. Low social network involvement is associated with poor SRH in older men, whereas low perceived social support is associated with poor SRH in older women. The hypothesis that the relationship of perceived social support and social networks to SRH differs according to gender has been confirmed.

The probabilistic neural network architecture for high speed classification of remotely sensed imagery

NASA Technical Reports Server (NTRS)

Chettri, Samir R.; Cromp, Robert F.

1993-01-01

In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy.

Models and simulation of 3D neuronal dendritic trees using Bayesian networks.

PubMed

López-Cruz, Pedro L; Bielza, Concha; Larrañaga, Pedro; Benavides-Piccione, Ruth; DeFelipe, Javier

2011-12-01

Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and their probability distributions used in a machine learning algorithm to induce the model from the data. A simulation algorithm is also proposed to obtain new dendrites by sampling values from Bayesian networks. The main advantage of this approach is that it takes into account and automatically locates the relationships between variables in the data instead of using predefined dependencies. Therefore, the methodology can be applied to any neuronal class while at the same time exploiting class-specific properties. Also, a Bayesian network was defined for each part of the dendrite, allowing the relationships to change in the different sections and to model heterogeneous developmental factors or spatial influences. Several univariate statistical tests and a novel multivariate test based on Kullback-Leibler divergence estimation confirmed that virtual dendrites were similar to real ones. The analyses of the models showed relationships that conform to current neuroanatomical knowledge and support model correctness. At the same time, studying the relationships in the models can help to identify new interactions between variables related to dendritic morphology.

Computational simulation of coupled material degradation processes for probabilistic lifetime strength of aerospace materials

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.

1992-01-01

The research included ongoing development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primative variables. These primative variable may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above described constitutive equation using actual experimental materials data together with linear regression of that data, thereby predicting values for the empirical material constraints for each effect or primative variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from the open literature for materials typically of interest to those studying aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Sangkyu, E-mail: sangkyu.lee@mail.mcgill.ca; Ybarra, Norma; Jeyaseelan, Krishinima

2015-05-15

Purpose: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose–volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems’ biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. Methods: The authors studied 54 nonsmall-cellmore » lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller–Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes’ rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls. Results: RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0.77), mean heart dose (0.69), and a pre-to-midtreatment change in ACE (0.66). When RP prediction was made only with pretreatment information, the AUC ranged from 0.76 to 0.81 depending on the ensemble size. Bootstrap validation of graph features in the ensemble quantified confidence of association between variables in the graphs where ten interactions were statistically significant. Conclusions: The presented BN methodology provides the flexibility to model hierarchical interactions between RP covariates, which is applied to probabilistic inference on RP. The authors’ preliminary results demonstrate that such framework combined with an ensemble method can possibly improve prediction of RP under real-life clinical circumstances such as missing data or treatment plan adaptation.« less

Probabilistic Methods for Structural Reliability and Risk

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2010-01-01

A probabilistic method is used to evaluate the structural reliability and risk of select metallic and composite structures. The method is a multiscale, multifunctional and it is based on the most elemental level. A multifactor interaction model is used to describe the material properties which are subsequently evaluated probabilistically. The metallic structure is a two rotor aircraft engine, while the composite structures consist of laminated plies (multiscale) and the properties of each ply are the multifunctional representation. The structural component is modeled by finite element. The solution method for structural responses is obtained by an updated simulation scheme. The results show that the risk for the two rotor engine is about 0.0001 and the composite built-up structure is also 0.0001.

Probabilistic Methods for Structural Reliability and Risk

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A probabilistic method is used to evaluate the structural reliability and risk of select metallic and composite structures. The method is a multiscale, multifunctional and it is based on the most elemental level. A multi-factor interaction model is used to describe the material properties which are subsequently evaluated probabilistically. The metallic structure is a two rotor aircraft engine, while the composite structures consist of laminated plies (multiscale) and the properties of each ply are the multifunctional representation. The structural component is modeled by finite element. The solution method for structural responses is obtained by an updated simulation scheme. The results show that the risk for the two rotor engine is about 0.0001 and the composite built-up structure is also 0.0001.

InFlo: a novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer.

PubMed

Dimitrova, N; Nagaraj, A B; Razi, A; Singh, S; Kamalakaran, S; Banerjee, N; Joseph, P; Mankovich, A; Mittal, P; DiFeo, A; Varadan, V

2017-04-27

Characterizing the complex interplay of cellular processes in cancer would enable the discovery of key mechanisms underlying its development and progression. Published approaches to decipher driver mechanisms do not explicitly model tissue-specific changes in pathway networks and the regulatory disruptions related to genomic aberrations in cancers. We therefore developed InFlo, a novel systems biology approach for characterizing complex biological processes using a unique multidimensional framework integrating transcriptomic, genomic and/or epigenomic profiles for any given cancer sample. We show that InFlo robustly characterizes tissue-specific differences in activities of signalling networks on a genome scale using unique probabilistic models of molecular interactions on a per-sample basis. Using large-scale multi-omics cancer datasets, we show that InFlo exhibits higher sensitivity and specificity in detecting pathway networks associated with specific disease states when compared to published pathway network modelling approaches. Furthermore, InFlo's ability to infer the activity of unmeasured signalling network components was also validated using orthogonal gene expression signatures. We then evaluated multi-omics profiles of primary high-grade serous ovarian cancer tumours (N=357) to delineate mechanisms underlying resistance to frontline platinum-based chemotherapy. InFlo was the only algorithm to identify hyperactivation of the cAMP-CREB1 axis as a key mechanism associated with resistance to platinum-based therapy, a finding that we subsequently experimentally validated. We confirmed that inhibition of CREB1 phosphorylation potently sensitized resistant cells to platinum therapy and was effective in killing ovarian cancer stem cells that contribute to both platinum-resistance and tumour recurrence. Thus, we propose InFlo to be a scalable and widely applicable and robust integrative network modelling framework for the discovery of evidence-based biomarkers and therapeutic targets.

Emergence, evolution and scaling of online social networks.

PubMed

Wang, Le-Zhi; Huang, Zi-Gang; Rong, Zhi-Hai; Wang, Xiao-Fan; Lai, Ying-Cheng

2014-01-01

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

An intercomparison of artificial intelligence approaches for polar scene identification

NASA Technical Reports Server (NTRS)

Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

1993-01-01

The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

A Comprehensive Probabilistic Tsunami Hazard Assessment: Multiple Sources and Short-Term Interactions

NASA Astrophysics Data System (ADS)

Anita, G.; Selva, J.; Laura, S.

2011-12-01

We develop a comprehensive and total probabilistic tsunami hazard assessment (TotPTHA), in which many different possible source types concur to the definition of the total tsunami hazard at given target sites. In a multi-hazard and multi-risk perspective, such an innovative approach allows, in principle, to consider all possible tsunamigenic sources, from seismic events, to slides, asteroids, volcanic eruptions, etc. In this respect, we also formally introduce and discuss the treatment of interaction/cascade effects in the TotPTHA analysis. We demonstrate how external triggering events may induce significant temporary variations in the tsunami hazard. Because of this, such effects should always be considered, at least in short-term applications, to obtain unbiased analyses. Finally, we prove the feasibility of the TotPTHA and of the treatment of interaction/cascade effects by applying this methodology to an ideal region with realistic characteristics (Neverland).

Metrics for Labeled Markov Systems

NASA Technical Reports Server (NTRS)

Desharnais, Josee; Jagadeesan, Radha; Gupta, Vineet; Panangaden, Prakash

1999-01-01

Partial Labeled Markov Chains are simultaneously generalizations of process algebra and of traditional Markov chains. They provide a foundation for interacting discrete probabilistic systems, the interaction being synchronization on labels as in process algebra. Existing notions of process equivalence are too sensitive to the exact probabilities of various transitions. This paper addresses contextual reasoning principles for reasoning about more robust notions of "approximate" equivalence between concurrent interacting probabilistic systems. The present results indicate that:We develop a family of metrics between partial labeled Markov chains to formalize the notion of distance between processes. We show that processes at distance zero are bisimilar. We describe a decision procedure to compute the distance between two processes. We show that reasoning about approximate equivalence can be done compositionally by showing that process combinators do not increase distance. We introduce an asymptotic metric to capture asymptotic properties of Markov chains; and show that parallel composition does not increase asymptotic distance.

Globally conditioned Granger causality in brain–brain and brain–heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study

PubMed Central

Passamonti, Luca; Wald, Lawrence L.; Barbieri, Riccardo

2016-01-01

The causal, directed interactions between brain regions at rest (brain–brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain–heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain–brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain–brain and brain–heart interactions reflecting central modulation of ANS outflow. PMID:27044985

Dynamic instability of cooperation due to diverse activity patterns in evolutionary social dilemmas

NASA Astrophysics Data System (ADS)

Xia, Cheng-Yi; Meloni, Sandro; Perc, Matjaž; Moreno, Yamir

2015-03-01

Individuals might abstain from participating in an instance of an evolutionary game for various reasons, ranging from lack of interest to risk aversion. In order to understand the consequences of such diverse activity patterns on the evolution of cooperation, we study a weak prisoner's dilemma where each player's participation is probabilistic rather than certain. Players that do not participate get a null payoff and are unable to replicate. We show that inactivity introduces cascading failures of cooperation, which are particularly severe on scale-free networks with frequently inactive hubs. The drops in the fraction of cooperators are sudden, while the spatiotemporal reorganization of compact cooperative clusters, and thus the recovery, takes time. Nevertheless, if the activity of players is directly proportional to their degree, or if the interaction network is not strongly heterogeneous, the overall evolution of cooperation is not impaired. This is because inactivity negatively affects the potency of low-degree defectors, who are hence unable to utilize on their inherent evolutionary advantage. Between cascading failures, the fraction of cooperators is therefore higher than usual, which lastly balances out the asymmetric dynamic instabilities that emerge due to intermittent blackouts of cooperative hubs.

Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

A Bayesian Approach to Interactive Retrieval

ERIC Educational Resources Information Center

Tague, Jean M.

1973-01-01

A probabilistic model for interactive retrieval is presented. Bayesian statistical decision theory principles are applied: use of prior and sample information about the relationship of document descriptions to query relevance; maximization of expected value of a utility function, to the problem of optimally restructuring search strategies in an…

What do we gain with Probabilistic Flood Loss Models?

NASA Astrophysics Data System (ADS)

Schroeter, K.; Kreibich, H.; Vogel, K.; Merz, B.; Lüdtke, S.

2015-12-01

The reliability of flood loss models is a prerequisite for their practical usefulness. Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks and traditional stage damage functions which are cast in a probabilistic framework. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005, 2006 and 2013 in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The reliability of the probabilistic predictions within validation runs decreases only slightly and achieves a very good coverage of observations within the predictive interval. Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

A Tool Chain for the V and V of NASA Cryogenic Fuel Loading Health Management

DTIC Science & Technology

2014-10-02

Here, K. (2011). Formal testing for separation assurance. Ann. Math. Artif . Intell., 63(1), 5–30. Goodrich, C., Narasimhan, S., Daigle, M...Probabilistic Reasoning in Intelligent Sys- tems: Networks of plausible inference Morgan Kauf- mann: . Reed, E., Schumann, J., & Mengshoel, O. (2011

Learning Orthographic Structure with Sequential Generative Neural Networks

ERIC Educational Resources Information Center

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-01-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

Perspectives of Probabilistic Inferences: Reinforcement Learning and an Adaptive Network Compared

ERIC Educational Resources Information Center

Rieskamp, Jorg

2006-01-01

The assumption that people possess a strategy repertoire for inferences has been raised repeatedly. The strategy selection learning theory specifies how people select strategies from this repertoire. The theory assumes that individuals select strategies proportional to their subjective expectations of how well the strategies solve particular…

Minimizing risks from spilled oil to ecosystem services using influence diagrams: The Deepwater Horizon spill response

EPA Science Inventory

Making inferences on risks to ecosystem services (ES) from ecological crises may be improved using decision science tools. Influence diagrams (IDs) are probabilistic networks that explicitly represent the decisions related to a problem and evidence of their influence on desired o...

Summing up the noise in gene networks

NASA Astrophysics Data System (ADS)

Paulsson, Johan

2004-01-01

Random fluctuations in genetic networks are inevitable as chemical reactions are probabilistic and many genes, RNAs and proteins are present in low numbers per cell. Such `noise' affects all life processes and has recently been measured using green fluorescent protein (GFP). Two studies show that negative feedback suppresses noise, and three others identify the sources of noise in gene expression. Here I critically analyse these studies and present a simple equation that unifies and extends both the mathematical and biological perspectives.

Bayesian networks and statistical analysis application to analyze the diagnostic test accuracy

NASA Astrophysics Data System (ADS)

Orzechowski, P.; Makal, Jaroslaw; Onisko, A.

2005-02-01

The computer aided BPH diagnosis system based on Bayesian network is described in the paper. First result are compared to a given statistical method. Different statistical methods are used successfully in medicine for years. However, the undoubted advantages of probabilistic methods make them useful in application in newly created systems which are frequent in medicine, but do not have full and competent knowledge. The article presents advantages of the computer aided BPH diagnosis system in clinical practice for urologists.

Reduced activation in ventral striatum and ventral tegmental area during probabilistic decision-making in schizophrenia.

PubMed

Rausch, Franziska; Mier, Daniela; Eifler, Sarah; Esslinger, Christine; Schilling, Claudia; Schirmbeck, Frederike; Englisch, Susanne; Meyer-Lindenberg, Andreas; Kirsch, Peter; Zink, Mathias

2014-07-01

Patients with schizophrenia suffer from deficits in monitoring and controlling their own thoughts. Within these so-called metacognitive impairments, alterations in probabilistic reasoning might be one cognitive phenomenon disposing to delusions. However, so far little is known about alterations in associated brain functionality. A previously established task for functional magnetic resonance imaging (fMRI), which requires a probabilistic decision after a variable amount of stimuli, was applied to 23 schizophrenia patients and 28 healthy controls matched for age, gender and educational levels. We compared activation patterns during decision-making under conditions of certainty versus uncertainty and evaluated the process of final decision-making in ventral striatum (VS) and ventral tegmental area (VTA). We replicated a pre-described extended cortical activation pattern during probabilistic reasoning. During final decision-making, activations in several fronto- and parietocortical areas, as well as in VS and VTA became apparent. In both of these regions schizophrenia patients showed a significantly reduced activation. These results further define the network underlying probabilistic decision-making. The observed hypo-activation in regions commonly associated with dopaminergic neurotransmission fits into current concepts of disrupted prediction error signaling in schizophrenia and suggests functional links to reward anticipation. Forthcoming studies with patients at risk for psychosis and drug-naive first episode patients are necessary to elucidate the development of these findings over time and the interplay with associated clinical symptoms. Copyright © 2014 Elsevier B.V. All rights reserved.

Stationary and structural control in gene regulatory networks: basic concepts

NASA Astrophysics Data System (ADS)

Dougherty, Edward R.; Pal, Ranadip; Qian, Xiaoning; Bittner, Michael L.; Datta, Aniruddha

2010-01-01

A major reason for constructing gene regulatory networks is to use them as models for determining therapeutic intervention strategies by deriving ways of altering their long-run dynamics in such a way as to reduce the likelihood of entering undesirable states. In general, two paradigms have been taken for gene network intervention: (1) stationary external control is based on optimally altering the status of a control gene (or genes) over time to drive network dynamics; and (2) structural intervention involves an optimal one-time change of the network structure (wiring) to beneficially alter the long-run behaviour of the network. These intervention approaches have mainly been developed within the context of the probabilistic Boolean network model for gene regulation. This article reviews both types of intervention and applies them to reducing the metastatic competence of cells via intervention in a melanoma-related network.

Topological bifurcations in a model society of reasonable contrarians

NASA Astrophysics Data System (ADS)

Bagnoli, Franco; Rechtman, Raúl

2013-12-01

People are often divided into conformists and contrarians, the former tending to align to the majority opinion in their neighborhood and the latter tending to disagree with that majority. In practice, however, the contrarian tendency is rarely followed when there is an overwhelming majority with a given opinion, which denotes a social norm. Such reasonable contrarian behavior is often considered a mark of independent thought and can be a useful strategy in financial markets. We present the opinion dynamics of a society of reasonable contrarian agents. The model is a cellular automaton of Ising type, with antiferromagnetic pair interactions modeling contrarianism and plaquette terms modeling social norms. We introduce the entropy of the collective variable as a way of comparing deterministic (mean-field) and probabilistic (simulations) bifurcation diagrams. In the mean-field approximation the model exhibits bifurcations and a chaotic phase, interpreted as coherent oscillations of the whole society. However, in a one-dimensional spatial arrangement one observes incoherent oscillations and a constant average. In simulations on Watts-Strogatz networks with a small-world effect the mean-field behavior is recovered, with a bifurcation diagram that resembles the mean-field one but where the rewiring probability is used as the control parameter. Similar bifurcation diagrams are found for scale-free networks, and we are able to compute an effective connectivity for such networks.

Topological bifurcations in a model society of reasonable contrarians.

PubMed

Bagnoli, Franco; Rechtman, Raúl

2013-12-01

People are often divided into conformists and contrarians, the former tending to align to the majority opinion in their neighborhood and the latter tending to disagree with that majority. In practice, however, the contrarian tendency is rarely followed when there is an overwhelming majority with a given opinion, which denotes a social norm. Such reasonable contrarian behavior is often considered a mark of independent thought and can be a useful strategy in financial markets. We present the opinion dynamics of a society of reasonable contrarian agents. The model is a cellular automaton of Ising type, with antiferromagnetic pair interactions modeling contrarianism and plaquette terms modeling social norms. We introduce the entropy of the collective variable as a way of comparing deterministic (mean-field) and probabilistic (simulations) bifurcation diagrams. In the mean-field approximation the model exhibits bifurcations and a chaotic phase, interpreted as coherent oscillations of the whole society. However, in a one-dimensional spatial arrangement one observes incoherent oscillations and a constant average. In simulations on Watts-Strogatz networks with a small-world effect the mean-field behavior is recovered, with a bifurcation diagram that resembles the mean-field one but where the rewiring probability is used as the control parameter. Similar bifurcation diagrams are found for scale-free networks, and we are able to compute an effective connectivity for such networks.

Capturing Ecosystem Services, Stakeholders' Preferences and Trade-Offs in Coastal Aquaculture Decisions: A Bayesian Belief Network Application

PubMed Central

Schmitt, Laetitia Helene Marie; Brugere, Cecile

2013-01-01

Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12) and primary farm data on the economics of shrimp farming (n = 20) were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development. PMID:24155876

Information processing by networks of quantum decision makers

NASA Astrophysics Data System (ADS)

Yukalov, V. I.; Yukalova, E. P.; Sornette, D.

2018-02-01

We suggest a model of a multi-agent society of decision makers taking decisions being based on two criteria, one is the utility of the prospects and the other is the attractiveness of the considered prospects. The model is the generalization of quantum decision theory, developed earlier for single decision makers realizing one-step decisions, in two principal aspects. First, several decision makers are considered simultaneously, who interact with each other through information exchange. Second, a multistep procedure is treated, when the agents exchange information many times. Several decision makers exchanging information and forming their judgment, using quantum rules, form a kind of a quantum information network, where collective decisions develop in time as a result of information exchange. In addition to characterizing collective decisions that arise in human societies, such networks can describe dynamical processes occurring in artificial quantum intelligence composed of several parts or in a cluster of quantum computers. The practical usage of the theory is illustrated on the dynamic disjunction effect for which three quantitative predictions are made: (i) the probabilistic behavior of decision makers at the initial stage of the process is described; (ii) the decrease of the difference between the initial prospect probabilities and the related utility factors is proved; (iii) the existence of a common consensus after multiple exchange of information is predicted. The predicted numerical values are in very good agreement with empirical data.

A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

NASA Astrophysics Data System (ADS)

Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

2017-04-01

The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of land-use/land-cover changes and river regulation on network-scale connectivity.

Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder.

PubMed

Cao, Qingjiu; Shu, Ni; An, Li; Wang, Peng; Sun, Li; Xia, Ming-Rui; Wang, Jin-Hui; Gong, Gao-Lang; Zang, Yu-Feng; Wang, Yu-Feng; He, Yong

2013-06-26

Attention-deficit/hyperactivity disorder (ADHD), which is characterized by core symptoms of inattention and hyperactivity/impulsivity, is one of the most common neurodevelopmental disorders of childhood. Neuroimaging studies have suggested that these behavioral disturbances are associated with abnormal functional connectivity among brain regions. However, the alterations in the structural connections that underlie these behavioral and functional deficits remain poorly understood. Here, we used diffusion magnetic resonance imaging and probabilistic tractography method to examine whole-brain white matter (WM) structural connectivity in 30 drug-naive boys with ADHD and 30 healthy controls. The WM networks of the human brain were constructed by estimating inter-regional connectivity probability. The topological properties of the resultant networks (e.g., small-world and network efficiency) were then analyzed using graph theoretical approaches. Nonparametric permutation tests were applied for between-group comparisons of these graphic metrics. We found that both the ADHD and control groups showed an efficient small-world organization in the whole-brain WM networks, suggesting a balance between structurally segregated and integrated connectivity patterns. However, relative to controls, patients with ADHD exhibited decreased global efficiency and increased shortest path length, with the most pronounced efficiency decreases in the left parietal, frontal, and occipital cortices. Intriguingly, the ADHD group showed decreased structural connectivity in the prefrontal-dominant circuitry and increased connectivity in the orbitofrontal-striatal circuitry, and these changes significantly correlated with the inattention and hyperactivity/impulsivity symptoms, respectively. The present study shows disrupted topological organization of large-scale WM networks in ADHD, extending our understanding of how structural disruptions of neuronal circuits underlie behavioral disturbances in patients with ADHD.

A probabilistic approach for shallow rainfall-triggered landslide modeling at basin scale. A case study in the Luquillo Forest, Puerto Rico

NASA Astrophysics Data System (ADS)

Dialynas, Y. G.; Arnone, E.; Noto, L. V.; Bras, R. L.

2013-12-01

Slope stability depends on geotechnical and hydrological factors that exhibit wide natural spatial variability, yet sufficient measurements of the related parameters are rarely available over entire study areas. The uncertainty associated with the inability to fully characterize hydrologic behavior has an impact on any attempt to model landslide hazards. This work suggests a way to systematically account for this uncertainty in coupled distributed hydrological-stability models for shallow landslide hazard assessment. A probabilistic approach for the prediction of rainfall-triggered landslide occurrence at basin scale was implemented in an existing distributed eco-hydrological and landslide model, tRIBS-VEGGIE -landslide (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator - VEGetation Generator for Interactive Evolution). More precisely, we upgraded tRIBS-VEGGIE- landslide to assess the likelihood of shallow landslides by accounting for uncertainty related to geotechnical and hydrological factors that directly affect slope stability. Natural variability of geotechnical soil characteristics was considered by randomizing soil cohesion and friction angle. Hydrological uncertainty related to the estimation of matric suction was taken into account by considering soil retention parameters as correlated random variables. The probability of failure is estimated through an assumed theoretical Factor of Safety (FS) distribution, conditioned on soil moisture content. At each cell, the temporally variant FS statistics are approximated by the First Order Second Moment (FOSM) method, as a function of parameters statistical properties. The model was applied on the Rio Mameyes Basin, located in the Luquillo Experimental Forest in Puerto Rico, where previous landslide analyses have been carried out. At each time step, model outputs include the probability of landslide occurrence across the basin, and the most probable depth of failure at each soil column. The use of the proposed probabilistic approach for shallow landslide prediction is able to reveal and quantify landslide risk at slopes assessed as stable by simpler deterministic methods.

Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

NASA Astrophysics Data System (ADS)

Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

2014-05-01

Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of preventive measures (e.g. state resources allocation for wildfire prevention and preparedness) and assist recuperation plans of damaged areas.

A Probabilistic Approach for Real-Time Volcano Surveillance

NASA Astrophysics Data System (ADS)

Cannavo, F.; Cannata, A.; Cassisi, C.; Di Grazia, G.; Maronno, P.; Montalto, P.; Prestifilippo, M.; Privitera, E.; Gambino, S.; Coltelli, M.

2016-12-01

Continuous evaluation of the state of potentially dangerous volcanos plays a key role for civil protection purposes. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the coupling of highly non-linear and complex volcanic dynamic processes leads to measurable effects that can show a large variety of different behaviors. Moreover, due to intrinsic uncertainties and possible failures in some recorded data, the volcano state needs to be expressed in probabilistic terms, thus making the fast volcano state assessment sometimes impracticable for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, we present a probabilistic graphical model to estimate automatically the ongoing volcano state from all the available different kind of measurements. The model consists of a Bayesian network able to represent a set of variables and their conditional dependencies via a directed acyclic graph. The model variables are both the measurements and the possible states of the volcano through the time. The model output is an estimation of the probability distribution of the feasible volcano states. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision making purposes.

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution Mass Spectrometry.

PubMed

Woldegebriel, Michael; Derks, Eduard

2017-01-17

In this work, a novel probabilistic untargeted feature detection algorithm for liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) using artificial neural network (ANN) is presented. The feature detection process is approached as a pattern recognition problem, and thus, ANN was utilized as an efficient feature recognition tool. Unlike most existing feature detection algorithms, with this approach, any suspected chromatographic profile (i.e., shape of a peak) can easily be incorporated by training the network, avoiding the need to perform computationally expensive regression methods with specific mathematical models. In addition, with this method, we have shown that the high-resolution raw data can be fully utilized without applying any arbitrary thresholds or data reduction, therefore improving the sensitivity of the method for compound identification purposes. Furthermore, opposed to existing deterministic (binary) approaches, this method rather estimates the probability of a feature being present/absent at a given point of interest, thus giving chance for all data points to be propagated down the data analysis pipeline, weighed with their probability. The algorithm was tested with data sets generated from spiked samples in forensic and food safety context and has shown promising results by detecting features for all compounds in a computationally reasonable time.

Towards a multilevel cognitive probabilistic representation of space

NASA Astrophysics Data System (ADS)

Tapus, Adriana; Vasudevan, Shrihari; Siegwart, Roland

2005-03-01

This paper addresses the problem of perception and representation of space for a mobile agent. A probabilistic hierarchical framework is suggested as a solution to this problem. The method proposed is a combination of probabilistic belief with "Object Graph Models" (OGM). The world is viewed from a topological optic, in terms of objects and relationships between them. The hierarchical representation that we propose permits an efficient and reliable modeling of the information that the mobile agent would perceive from its environment. The integration of both navigational and interactional capabilities through efficient representation is also addressed. Experiments on a set of images taken from the real world that validate the approach are reported. This framework draws on the general understanding of human cognition and perception and contributes towards the overall efforts to build cognitive robot companions.

Simulation of probabilistic wind loads and building analysis

NASA Technical Reports Server (NTRS)

Shah, Ashwin R.; Chamis, Christos C.

1991-01-01

Probabilistic wind loads likely to occur on a structure during its design life are predicted. Described here is a suitable multifactor interactive equation (MFIE) model and its use in the Composite Load Spectra (CLS) computer program to simulate the wind pressure cumulative distribution functions on four sides of a building. The simulated probabilistic wind pressure load was applied to a building frame, and cumulative distribution functions of sway displacements and reliability against overturning were obtained using NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), a stochastic finite element computer code. The geometry of the building and the properties of building members were also considered as random in the NESSUS analysis. The uncertainties of wind pressure, building geometry, and member section property were qualified in terms of their respective sensitivities on the structural response.

Integrated Campaign Probabilistic Cost, Schedule, Performance, and Value for Program Office Support

NASA Technical Reports Server (NTRS)

Cornelius, David; Sasamoto, Washito; Daugherty, Kevin; Deacon, Shaun

2012-01-01

This paper describes an integrated assessment tool developed at NASA Langley Research Center that incorporates probabilistic analysis of life cycle cost, schedule, launch performance, on-orbit performance, and value across a series of planned space-based missions, or campaign. Originally designed as an aid in planning the execution of missions to accomplish the National Research Council 2007 Earth Science Decadal Survey, it utilizes Monte Carlo simulation of a series of space missions for assessment of resource requirements and expected return on investment. Interactions between simulated missions are incorporated, such as competition for launch site manifest, to capture unexpected and non-linear system behaviors. A novel value model is utilized to provide an assessment of the probabilistic return on investment. A demonstration case is discussed to illustrate the tool utility.

Probabilistic grammatical model for helix‐helix contact site classification

PubMed Central

2013-01-01

Background Hidden Markov Models power many state‐of‐the‐art tools in the field of protein bioinformatics. While excelling in their tasks, these methods of protein analysis do not convey directly information on medium‐ and long‐range residue‐residue interactions. This requires an expressive power of at least context‐free grammars. However, application of more powerful grammar formalisms to protein analysis has been surprisingly limited. Results In this work, we present a probabilistic grammatical framework for problem‐specific protein languages and apply it to classification of transmembrane helix‐helix pairs configurations. The core of the model consists of a probabilistic context‐free grammar, automatically inferred by a genetic algorithm from only a generic set of expert‐based rules and positive training samples. The model was applied to produce sequence based descriptors of four classes of transmembrane helix‐helix contact site configurations. The highest performance of the classifiers reached AUCROC of 0.70. The analysis of grammar parse trees revealed the ability of representing structural features of helix‐helix contact sites. Conclusions We demonstrated that our probabilistic context‐free framework for analysis of protein sequences outperforms the state of the art in the task of helix‐helix contact site classification. However, this is achieved without necessarily requiring modeling long range dependencies between interacting residues. A significant feature of our approach is that grammar rules and parse trees are human‐readable. Thus they could provide biologically meaningful information for molecular biologists. PMID:24350601

Modeling Array Stations in SIG-VISA

NASA Astrophysics Data System (ADS)

Ding, N.; Moore, D.; Russell, S.

2013-12-01

We add support for array stations to SIG-VISA, a system for nuclear monitoring using probabilistic inference on seismic signals. Array stations comprise a large portion of the IMS network; they can provide increased sensitivity and more accurate directional information compared to single-component stations. Our existing model assumed that signals were independent at each station, which is false when lots of stations are close together, as in an array. The new model removes that assumption by jointly modeling signals across array elements. This is done by extending our existing Gaussian process (GP) regression models, also known as kriging, from a 3-dimensional single-component space of events to a 6-dimensional space of station-event pairs. For each array and each event attribute (including coda decay, coda height, amplitude transfer and travel time), we model the joint distribution across array elements using a Gaussian process that learns the correlation lengthscale across the array, thereby incorporating information of array stations into the probabilistic inference framework. To evaluate the effectiveness of our model, we perform ';probabilistic beamforming' on new events using our GP model, i.e., we compute the event azimuth having highest posterior probability under the model, conditioned on the signals at array elements. We compare the results from our probabilistic inference model to the beamforming currently performed by IMS station processing.

Impact assessment of extreme storm events using a Bayesian network

USGS Publications Warehouse

den Heijer, C.(Kees); Knipping, Dirk T.J.A.; Plant, Nathaniel G.; van Thiel de Vries, Jaap S. M.; Baart, Fedor; van Gelder, Pieter H. A. J. M.

2012-01-01

This paper describes an investigation on the usefulness of Bayesian Networks in the safety assessment of dune coasts. A network has been created that predicts the erosion volume based on hydraulic boundary conditions and a number of cross-shore profile indicators. Field measurement data along a large part of the Dutch coast has been used to train the network. Corresponding storm impact on the dunes was calculated with an empirical dune erosion model named duros+. Comparison between the Bayesian Network predictions and the original duros+ results, here considered as observations, results in a skill up to 0.88, provided that the training data covers the range of predictions. Hence, the predictions from a deterministic model (duros+) can be captured in a probabilistic model (Bayesian Network) such that both the process knowledge and uncertainties can be included in impact and vulnerability assessments.

Minimizing risks from spilled oil to ecosystem services using influence diagrams: the Deepwater Horizon spill response.

PubMed

Carriger, John F; Barron, Mace G

2011-09-15

Decision science tools can be used in evaluating response options and making inferences on risks to ecosystem services (ES) from ecological disasters. Influence diagrams (IDs) are probabilistic networks that explicitly represent the decisions related to a problem and their influence on desired or undesired outcomes. To examine how IDs might be useful in probabilistic risk management for spill response efforts, an ID was constructed to display the potential interactions between exposure events and the trade-offs between costs and ES impacts from spilled oil and response decisions in the DWH spill event. Quantitative knowledge was not formally incorporated but an ID platform for doing this was examined. Probabilities were assigned for conditional relationships in the ID and scenarios examining the impact of different response actions on components of spilled oil were investigated in hypothetical scenarios. Given the structure of the ID, potential knowledge gaps included understanding of the movement of oil, the ecological risk of different spill-related stressors to key receptors (e.g., endangered species, fisheries), and the need for stakeholder valuation of the ES benefits that could be impacted by a spill. Framing the Deepwater Horizon problem domain in an ID conceptualized important variables and relationships that could be optimally accounted for in preparing and managing responses in future spills. These features of the developed IDs may assist in better investigating the uncertainty, costs, and the trade-offs if large-scale, deep ocean spills were to occur again.

BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods

NASA Astrophysics Data System (ADS)

Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.

2017-12-01

Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made through Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. With the ability to cope with incomplete information and use expert knowledge, as well as inherently providing quantitative uncertainty information, it is shown that loss models based on BNs are superior to deterministic approaches for pluvial flood risk assessment.

Use of a probabilistic neural network to reduce costs of selecting construction rock

USGS Publications Warehouse

Singer, Donald A.; Bliss, James D.

2003-01-01

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.

Topology Counts: Force Distributions in Circular Spring Networks.

PubMed

Heidemann, Knut M; Sageman-Furnas, Andrew O; Sharma, Abhinav; Rehfeldt, Florian; Schmidt, Christoph F; Wardetzky, Max

2018-02-09

Filamentous polymer networks govern the mechanical properties of many biological materials. Force distributions within these networks are typically highly inhomogeneous, and, although the importance of force distributions for structural properties is well recognized, they are far from being understood quantitatively. Using a combination of probabilistic and graph-theoretical techniques, we derive force distributions in a model system consisting of ensembles of random linear spring networks on a circle. We show that characteristic quantities, such as the mean and variance of the force supported by individual springs, can be derived explicitly in terms of only two parameters: (i) average connectivity and (ii) number of nodes. Our analysis shows that a classical mean-field approach fails to capture these characteristic quantities correctly. In contrast, we demonstrate that network topology is a crucial determinant of force distributions in an elastic spring network. Our results for 1D linear spring networks readily generalize to arbitrary dimensions.

On investigating social dynamics in tactical opportunistic mobile networks

NASA Astrophysics Data System (ADS)

Gao, Wei; Li, Yong

2014-06-01

The efficiency of military mobile network operations at the tactical edge is challenging due to the practical Disconnected, Intermittent, and Limited (DIL) environments at the tactical edge which make it hard to maintain persistent end-to-end wireless network connectivity. Opportunistic mobile networks are hence devised to depict such tactical networking scenarios. Social relations among warfighters in tactical opportunistic mobile networks are implicitly represented by their opportunistic contacts via short-range radios, but were inappropriately considered as stationary over time by the conventional wisdom. In this paper, we develop analytical models to probabilistically investigate the temporal dynamics of this social relationship, which is critical to efficient mobile communication in the battlespace. We propose to formulate such dynamics by developing various sociological metrics, including centrality and community, with respect to the opportunistic mobile network contexts. These metrics investigate social dynamics based on the experimentally validated skewness of users' transient contact distributions over time.

First-order design of geodetic networks using the simulated annealing method

NASA Astrophysics Data System (ADS)

Berné, J. L.; Baselga, S.

2004-09-01

The general problem of the optimal design for a geodetic network subject to any extrinsic factors, namely the first-order design problem, can be dealt with as a numeric optimization problem. The classic theory of this problem and the optimization methods are revised. Then the innovative use of the simulated annealing method, which has been successfully applied in other fields, is presented for this classical geodetic problem. This method, belonging to iterative heuristic techniques in operational research, uses a thermodynamical analogy to crystalline networks to offer a solution that converges probabilistically to the global optimum. Basic formulation and some examples are studied.

Fuzzy probabilistic design of water distribution networks

NASA Astrophysics Data System (ADS)

Fu, Guangtao; Kapelan, Zoran

2011-05-01

The primary aim of this paper is to present a fuzzy probabilistic approach for optimal design and rehabilitation of water distribution systems, combining aleatoric and epistemic uncertainties in a unified framework. The randomness and imprecision in future water consumption are characterized using fuzzy random variables whose realizations are not real but fuzzy numbers, and the nodal head requirements are represented by fuzzy sets, reflecting the imprecision in customers' requirements. The optimal design problem is formulated as a two-objective optimization problem, with minimization of total design cost and maximization of system performance as objectives. The system performance is measured by the fuzzy random reliability, defined as the probability that the fuzzy head requirements are satisfied across all network nodes. The satisfactory degree is represented by necessity measure or belief measure in the sense of the Dempster-Shafer theory of evidence. An efficient algorithm is proposed, within a Monte Carlo procedure, to calculate the fuzzy random system reliability and is effectively combined with the nondominated sorting genetic algorithm II (NSGAII) to derive the Pareto optimal design solutions. The newly proposed methodology is demonstrated with two case studies: the New York tunnels network and Hanoi network. The results from both cases indicate that the new methodology can effectively accommodate and handle various aleatoric and epistemic uncertainty sources arising from the design process and can provide optimal design solutions that are not only cost-effective but also have higher reliability to cope with severe future uncertainties.

Probabilistic Perception, Empathy, and Dynamic Homeostasis: Insights in Autism Spectrum Disorders and Conduct Disorders

PubMed Central

Guilé, Jean Marc

2013-01-01

Homeostasis is not a permanent and stable state but instead results from conflicting forces. Therefore, infants have to engage in dynamic exchanges with their environment, in biological, cognitive, and affective domains. Empathy is an adaptive response to these environmental challenges, which contributes to reaching proper dynamic homeostasis and development. Empathy relies on implicit interactive processes, namely probabilistic perception and synchrony, which will be reviewed in the article. If typically-developed neonates are fully equipped to automatically and synchronously interact with their human environment, conduct disorders (CD) and autism spectrum disorders (ASD) present with impairments in empathetic communication, e.g., emotional arousal and facial emotion processing. In addition sensorimotor resonance is lacking in ASD, and emotional concern and semantic empathy are impaired in CD with Callous-Unemotional traits. PMID:24479115

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

ERIC Educational Resources Information Center

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Minimizing Expected Maximum Risk from Cyber-Attacks with Probabilistic Attack Success

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhuiyan, Tanveer H.; Nandi, Apurba; Medal, Hugh

The goal of our work is to enhance network security by generating partial cut-sets, which are a subset of edges that remove paths from initially vulnerable nodes (initial security conditions) to goal nodes (critical assets), on an attack graph given costs for cutting an edge and a limited overall budget.

Population viability assessment of salmonids by using probabilistic networks

Treesearch

Danny C. Lee; Bruce E. Rieman

1997-01-01

Public agencies are being asked to quantitatively assess the impact of land management activities on sensitive populations of salmonids. To aid in these assessments, we developed a Bayesian viability assessment procedure (BayVAM) to help characterize land use risks to salmonids in the Pacific Northwest. This procedure incorporates a hybrid approach to viability...

Probabilistic Multi-Person Tracking Using Dynamic Bayes Networks

NASA Astrophysics Data System (ADS)

Klinger, T.; Rottensteiner, F.; Heipke, C.

2015-08-01

Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available benchmark. The results confirm that our approach is well suited for tracking pedestrians over long distances while at the same time achieving comparatively good geometric accuracy.

Towards dropout training for convolutional neural networks.

PubMed

Wu, Haibing; Gu, Xiaodong

2015-11-01

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems

PubMed Central

Reafee, Waleed; Salim, Naomie; Khan, Atif

2016-01-01

The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy. PMID:27152663

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

PubMed

Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

2018-01-24

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

Infering and Calibrating Triadic Closure in a Dynamic Network

NASA Astrophysics Data System (ADS)

Mantzaris, Alexander V.; Higham, Desmond J.

In the social sciences, the hypothesis of triadic closure contends that new links in a social contact network arise preferentially between those who currently share neighbours. Here, in a proof-of-principle study, we show how to calibrate a recently proposed evolving network model to time-dependent connectivity data. The probabilistic edge birth rate in the model contains a triadic closure term, so we are also able to assess statistically the evidence for this effect. The approach is shown to work on data generated synthetically from the model. We then apply this methodology to some real, large-scale data that records the build up of connections in a business-related social networking site, and find evidence for triadic closure.

Cellular automata models for diffusion of information and highway traffic flow

NASA Astrophysics Data System (ADS)

Fuks, Henryk

In the first part of this work we study a family of deterministic models for highway traffic flow which generalize cellular automaton rule 184. This family is parameterized by the speed limit m and another parameter k that represents degree of 'anticipatory driving'. We compare two driving strategies with identical maximum throughput: 'conservative' driving with high speed limit and 'anticipatory' driving with low speed limit. Those two strategies are evaluated in terms of accident probability. We also discuss fundamental diagrams of generalized traffic rules and examine limitations of maximum achievable throughput. Possible modifications of the model are considered. For rule 184, we present exact calculations of the order parameter in a transition from the moving phase to the jammed phase using the method of preimage counting, and use this result to construct a solution to the density classification problem. In the second part we propose a probabilistic cellular automaton model for the spread of innovations, rumors, news, etc., in a social system. We start from simple deterministic models, for which exact expressions for the density of adopters are derived. For a more realistic model, based on probabilistic cellular automata, we study the influence of a range of interaction R on the shape of the adoption curve. When the probability of adoption is proportional to the local density of adopters, and individuals can drop the innovation with some probability p, the system exhibits a second order phase transition. Critical line separating regions of parameter space in which asymptotic density of adopters is positive from the region where it is equal to zero converges toward the mean-field line when the range of the interaction increases. In a region between R=1 critical line and the mean-field line asymptotic density of adopters depends on R, becoming zero if R is too small (smaller than some critical value). This result demonstrates the importance of connectivity in diffusion of information. We also define a new class of automata networks which incorporates non-local interactions, and discuss its applicability in modeling of diffusion of innovations.

Probabilistic performance assessment of complex energy process systems - The case of a self-sustained sanitation system.

PubMed

Kolios, Athanasios; Jiang, Ying; Somorin, Tosin; Sowale, Ayodeji; Anastasopoulou, Aikaterini; Anthony, Edward J; Fidalgo, Beatriz; Parker, Alison; McAdam, Ewan; Williams, Leon; Collins, Matt; Tyrrel, Sean

2018-05-01

A probabilistic modelling approach was developed and applied to investigate the energy and environmental performance of an innovative sanitation system, the "Nano-membrane Toilet" (NMT). The system treats human excreta via an advanced energy and water recovery island with the aim of addressing current and future sanitation demands. Due to the complex design and inherent characteristics of the system's input material, there are a number of stochastic variables which may significantly affect the system's performance. The non-intrusive probabilistic approach adopted in this study combines a finite number of deterministic thermodynamic process simulations with an artificial neural network (ANN) approximation model and Monte Carlo simulations (MCS) to assess the effect of system uncertainties on the predicted performance of the NMT system. The joint probability distributions of the process performance indicators suggest a Stirling Engine (SE) power output in the range of 61.5-73 W with a high confidence interval (CI) of 95%. In addition, there is high probability (with 95% CI) that the NMT system can achieve positive net power output between 15.8 and 35 W. A sensitivity study reveals the system power performance is mostly affected by SE heater temperature. Investigation into the environmental performance of the NMT design, including water recovery and CO 2 /NO x emissions, suggests significant environmental benefits compared to conventional systems. Results of the probabilistic analysis can better inform future improvements on the system design and operational strategy and this probabilistic assessment framework can also be applied to similar complex engineering systems.

Altered Structural and Functional Connectivity in Late Preterm Preadolescence: An Anatomic Seed-Based Study of Resting State Networks Related to the Posteromedial and Lateral Parietal Cortex.

PubMed

Degnan, Andrew J; Wisnowski, Jessica L; Choi, SoYoung; Ceschin, Rafael; Bhushan, Chitresh; Leahy, Richard M; Corby, Patricia; Schmithorst, Vincent J; Panigrahy, Ashok

2015-01-01

Late preterm birth confers increased risk of developmental delay, academic difficulties and social deficits. The late third trimester may represent a critical period of development of neural networks including the default mode network (DMN), which is essential to normal cognition. Our objective is to identify functional and structural connectivity differences in the posteromedial cortex related to late preterm birth. Thirty-eight preadolescents (ages 9-13; 19 born in the late preterm period (≥32 weeks gestational age) and 19 at term) without access to advanced neonatal care were recruited from a low socioeconomic status community in Brazil. Participants underwent neurocognitive testing, 3-dimensional T1-weighted imaging, diffusion-weighted imaging and resting state functional MRI (RS-fMRI). Seed-based probabilistic diffusion tractography and RS-fMRI analyses were performed using unilateral seeds within the posterior DMN (posterior cingulate cortex, precuneus) and lateral parietal DMN (superior marginal and angular gyri). Late preterm children demonstrated increased functional connectivity within the posterior default mode networks and increased anti-correlation with the central-executive network when seeded from the posteromedial cortex (PMC). Key differences were demonstrated between PMC components with increased anti-correlation with the salience network seen only with posterior cingulate cortex seeding but not with precuneus seeding. Probabilistic tractography showed increased streamlines within the right inferior longitudinal fasciculus and inferior fronto-occipital fasciculus within late preterm children while decreased intrahemispheric streamlines were also observed. No significant differences in neurocognitive testing were demonstrated between groups. Late preterm preadolescence is associated with altered functional connectivity from the PMC and lateral parietal cortex to known distributed functional cortical networks despite no significant executive neurocognitive differences. Selective increased structural connectivity was observed in the setting of decreased posterior interhemispheric connections. Future work is needed to determine if these findings represent a compensatory adaptation employing alternate neural circuitry or could reflect subtle pathology resulting in emotional processing deficits not seen with neurocognitive testing.

Discounting of Monetary Rewards that are Both Delayed and Probabilistic: Delay and Probability Combine Multiplicatively, not Additively

PubMed Central

Vanderveldt, Ariana; Green, Leonard; Myerson, Joel

2014-01-01

The value of an outcome is affected both by the delay until its receipt (delay discounting) and by the likelihood of its receipt (probability discounting). Despite being well-described by the same hyperboloid function, delay and probability discounting involve fundamentally different processes, as revealed, for example, by the differential effects of reward amount. Previous research has focused on the discounting of delayed and probabilistic rewards separately, with little research examining more complex situations in which rewards are both delayed and probabilistic. In two experiments, participants made choices between smaller rewards that were both immediate and certain and larger rewards that were both delayed and probabilistic. Analyses revealed significant interactions between delay and probability factors inconsistent with an additive model. In contrast, a hyperboloid discounting model in which delay and probability were combined multiplicatively provided an excellent fit to the data. These results suggest that the hyperboloid is a good descriptor of decision making in complicated monetary choice situations like those people encounter in everyday life. PMID:24933696

Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference

PubMed Central

Ehrenfeld, Stephan; Herbort, Oliver; Butz, Martin V.

2013-01-01

This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control. PMID:24191151

Causal Inference and Explaining Away in a Spiking Network

PubMed Central

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Causal Inference and Explaining Away in a Spiking Network.

PubMed

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-12-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.

Rethinking the learning of belief network probabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Musick, R.

Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rotemore » learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neutral networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.« less

An Evaluation of the Interactive Query Expansion in an Online Library Catalogue with a Graphical User Interface.

ERIC Educational Resources Information Center

Hancock-Beaulieu, Micheline; And Others

1995-01-01

An online library catalog was used to evaluate an interactive query expansion facility based on relevance feedback for the Okapi, probabilistic, term weighting, retrieval system. A graphical user interface allowed searchers to select candidate terms extracted from relevant retrieved items to reformulate queries. Results suggested that the…

Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

PubMed Central

Hwang, Sohyun; Rhee, Seung Y; Marcotte, Edward M; Lee, Insuk

2012-01-01

AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests. PMID:21886106

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting.

PubMed

Alomar, Miquel L; Canals, Vincent; Perez-Mora, Nicolas; Martínez-Moll, Víctor; Rosselló, Josep L

2016-01-01

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting.

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

PubMed Central

Alomar, Miquel L.; Canals, Vincent; Perez-Mora, Nicolas; Martínez-Moll, Víctor; Rosselló, Josep L.

2016-01-01

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting. PMID:26880876

Exact computation of the maximum-entropy potential of spiking neural-network models.

PubMed

Cofré, R; Cessac, B

2014-05-01

Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in neural networks is a central question in computational neuroscience. The maximum-entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. However, in spite of good performance in terms of prediction, the fitting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuromimetic models) provide a probabilistic mapping between the stimulus, network architecture, and spike patterns in terms of conditional probabilities. In this paper we build an exact analytical mapping between neuromimetic and maximum-entropy models.

Assessing State Nuclear Weapons Proliferation: Using Bayesian Network Analysis of Social Factors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coles, Garill A.; Brothers, Alan J.; Olson, Jarrod

A Bayesian network (BN) model of social factors can support proliferation assessments by estimating the likelihood that a state will pursue a nuclear weapon. Social factors including political, economic, nuclear capability, security, and national identity and psychology factors may play as important a role in whether a State pursues nuclear weapons as more physical factors. This paper will show how using Bayesian reasoning on a generic case of a would-be proliferator State can be used to combine evidence that supports proliferation assessment. Theories and analysis by political scientists can be leveraged in a quantitative and transparent way to indicate proliferationmore » risk. BN models facilitate diagnosis and inference in a probabilistic environment by using a network of nodes and acyclic directed arcs between the nodes whose connections, or absence of, indicate probabilistic relevance, or independence. We propose a BN model that would use information from both traditional safeguards and the strengthened safeguards associated with the Additional Protocol to indicate countries with a high risk of proliferating nuclear weapons. This model could be used in a variety of applications such a prioritization tool and as a component of state safeguards evaluations. This paper will discuss the benefits of BN reasoning, the development of Pacific Northwest National Laboratory’s (PNNL) BN state proliferation model and how it could be employed as an analytical tool.« less

Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes.

PubMed

Gonzalez-Redin, Julen; Luque, Sandra; Poggio, Laura; Smith, Ron; Gimona, Alessandro

2016-01-01

An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human-landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests - biodiversity value for conservation and timber production potential - with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas. Copyright © 2015 Elsevier Inc. All rights reserved.

A computational framework for modeling targets as complex adaptive systems

NASA Astrophysics Data System (ADS)

Santos, Eugene; Santos, Eunice E.; Korah, John; Murugappan, Vairavan; Subramanian, Suresh

2017-05-01

Modeling large military targets is a challenge as they can be complex systems encompassing myriad combinations of human, technological, and social elements that interact, leading to complex behaviors. Moreover, such targets have multiple components and structures, extending across multiple spatial and temporal scales, and are in a state of change, either in response to events in the environment or changes within the system. Complex adaptive system (CAS) theory can help in capturing the dynamism, interactions, and more importantly various emergent behaviors, displayed by the targets. However, a key stumbling block is incorporating information from various intelligence, surveillance and reconnaissance (ISR) sources, while dealing with the inherent uncertainty, incompleteness and time criticality of real world information. To overcome these challenges, we present a probabilistic reasoning network based framework called complex adaptive Bayesian Knowledge Base (caBKB). caBKB is a rigorous, overarching and axiomatic framework that models two key processes, namely information aggregation and information composition. While information aggregation deals with the union, merger and concatenation of information and takes into account issues such as source reliability and information inconsistencies, information composition focuses on combining information components where such components may have well defined operations. Since caBKBs can explicitly model the relationships between information pieces at various scales, it provides unique capabilities such as the ability to de-aggregate and de-compose information for detailed analysis. Using a scenario from the Network Centric Operations (NCO) domain, we will describe how our framework can be used for modeling targets with a focus on methodologies for quantifying NCO performance metrics.

An autocatalytic network model for stock markets

NASA Astrophysics Data System (ADS)

Caetano, Marco Antonio Leonel; Yoneyama, Takashi

2015-02-01

The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.

Probabilistic Analysis of Hierarchical Cluster Protocols for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Kaj, Ingemar

Wireless sensor networks are designed to extract data from the deployment environment and combine sensing, data processing and wireless communication to provide useful information for the network users. Hundreds or thousands of small embedded units, which operate under low-energy supply and with limited access to central network control, rely on interconnecting protocols to coordinate data aggregation and transmission. Energy efficiency is crucial and it has been proposed that cluster based and distributed architectures such as LEACH are particularly suitable. We analyse the random cluster hierarchy in this protocol and provide a solution for low-energy and limited-loss optimization. Moreover, we extend these results to a multi-level version of LEACH, where clusters of nodes again self-organize to form clusters of clusters, and so on.

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system components, part 2

NASA Technical Reports Server (NTRS)

1991-01-01

The technical effort and computer code enhancements performed during the sixth year of the Probabilistic Structural Analysis Methods program are summarized. Various capabilities are described to probabilistically combine structural response and structural resistance to compute component reliability. A library of structural resistance models is implemented in the Numerical Evaluations of Stochastic Structures Under Stress (NESSUS) code that included fatigue, fracture, creep, multi-factor interaction, and other important effects. In addition, a user interface was developed for user-defined resistance models. An accurate and efficient reliability method was developed and was successfully implemented in the NESSUS code to compute component reliability based on user-selected response and resistance models. A risk module was developed to compute component risk with respect to cost, performance, or user-defined criteria. The new component risk assessment capabilities were validated and demonstrated using several examples. Various supporting methodologies were also developed in support of component risk assessment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coleman, Justin; Slaughter, Andrew; Veeraraghavan, Swetha

Multi-hazard Analysis for STOchastic time-DOmaiN phenomena (MASTODON) is a finite element application that aims at analyzing the response of 3-D soil-structure systems to natural and man-made hazards such as earthquakes, floods and fire. MASTODON currently focuses on the simulation of seismic events and has the capability to perform extensive ‘source-to-site’ simulations including earthquake fault rupture, nonlinear wave propagation and nonlinear soil-structure interaction (NLSSI) analysis. MASTODON is being developed to be a dynamic probabilistic risk assessment framework that enables analysts to not only perform deterministic analyses, but also easily perform probabilistic or stochastic simulations for the purpose of risk assessment.

Predicting Coupled Ocean-Atmosphere Modes with a Climate Modeling Hierarchy -- Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michael Ghil, UCLA; Andrew W. Robertson, IRI, Columbia Univ.; Sergey Kravtsov, U. of Wisconsin, Milwaukee

The goal of the project was to determine midlatitude climate predictability associated with tropical-extratropical interactions on interannual-to-interdecadal time scales. Our strategy was to develop and test a hierarchy of climate models, bringing together large GCM-based climate models with simple fluid-dynamical coupled ocean-ice-atmosphere models, through the use of advanced probabilistic network (PN) models. PN models were used to develop a new diagnostic methodology for analyzing coupled ocean-atmosphere interactions in large climate simulations made with the NCAR Parallel Climate Model (PCM), and to make these tools user-friendly and available to other researchers. We focused on interactions between the tropics and extratropics throughmore » atmospheric teleconnections (the Hadley cell, Rossby waves and nonlinear circulation regimes) over both the North Atlantic and North Pacific, and the ocean’s thermohaline circulation (THC) in the Atlantic. We tested the hypothesis that variations in the strength of the THC alter sea surface temperatures in the tropical Atlantic, and that the latter influence the atmosphere in high latitudes through an atmospheric teleconnection, feeding back onto the THC. The PN model framework was used to mediate between the understanding gained with simplified primitive equations models and multi-century simulations made with the PCM. The project team is interdisciplinary and built on an existing synergy between atmospheric and ocean scientists at UCLA, computer scientists at UCI, and climate researchers at the IRI.« less

Potential microRNA-mediated oncogenic intercellular communication revealed by pan-cancer analysis

NASA Astrophysics Data System (ADS)

Li, Yue; Zhang, Zhaolei

2014-11-01

Carcinogenesis consists of oncogenesis and metastasis, and intriguingly microRNAs (miRNAs) are involved in both processes. Although aberrant miRNA activities are prevalent in diverse tumor types, the exact mechanisms for how they regulate cancerous processes are not always clear. To this end, we performed a large-scale pan-cancer analysis via a novel probabilistic approach to infer recurrent miRNA-target interactions implicated in 12 cancer types using data from The Cancer Genome Atlas. We discovered ~20,000 recurrent miRNA regulations, which are enriched for cancer-related miRNAs/genes. Notably, miRNA 200 family (miR-200/141/429) is among the most prominent miRNA regulators, which is known to be involved in metastasis. Importantly, the recurrent miRNA regulatory network is not only enriched for cancer pathways but also for extracellular matrix (ECM) organization and ECM-receptor interactions. The results suggest an intriguing cancer mechanism involving miRNA-mediated cell-to-cell communication, which possibly involves delivery of tumorigenic miRNA messengers to adjacent cells via exosomes. Finally, survival analysis revealed 414 recurrent-prognostic associations, where both gene and miRNA involved in each interaction conferred significant prognostic power in one or more cancer types. Together, our comprehensive pan-cancer analysis provided not only biological insights into metastasis but also brought to bear the clinical relevance of the proposed recurrent miRNA-gene associations.

Modeling the resilience of critical infrastructure: the role of network dependencies.

PubMed

Guidotti, Roberto; Chmielewski, Hana; Unnikrishnan, Vipin; Gardoni, Paolo; McAllister, Therese; van de Lindt, John

2016-01-01

Water and wastewater network, electric power network, transportation network, communication network, and information technology network are among the critical infrastructure in our communities; their disruption during and after hazard events greatly affects communities' well-being, economic security, social welfare, and public health. In addition, a disruption in one network may cause disruption to other networks and lead to their reduced functionality. This paper presents a unified theoretical methodology for the modeling of dependent/interdependent infrastructure networks and incorporates it in a six-step probabilistic procedure to assess their resilience. Both the methodology and the procedure are general, can be applied to any infrastructure network and hazard, and can model different types of dependencies between networks. As an illustration, the paper models the direct effects of seismic events on the functionality of a potable water distribution network and the cascading effects of the damage of the electric power network (EPN) on the potable water distribution network (WN). The results quantify the loss of functionality and delay in the recovery process due to dependency of the WN on the EPN. The results show the importance of capturing the dependency between networks in modeling the resilience of critical infrastructure.

Modeling the resilience of critical infrastructure: the role of network dependencies

PubMed Central

Guidotti, Roberto; Chmielewski, Hana; Unnikrishnan, Vipin; Gardoni, Paolo; McAllister, Therese; van de Lindt, John

2017-01-01

Water and wastewater network, electric power network, transportation network, communication network, and information technology network are among the critical infrastructure in our communities; their disruption during and after hazard events greatly affects communities’ well-being, economic security, social welfare, and public health. In addition, a disruption in one network may cause disruption to other networks and lead to their reduced functionality. This paper presents a unified theoretical methodology for the modeling of dependent/interdependent infrastructure networks and incorporates it in a six-step probabilistic procedure to assess their resilience. Both the methodology and the procedure are general, can be applied to any infrastructure network and hazard, and can model different types of dependencies between networks. As an illustration, the paper models the direct effects of seismic events on the functionality of a potable water distribution network and the cascading effects of the damage of the electric power network (EPN) on the potable water distribution network (WN). The results quantify the loss of functionality and delay in the recovery process due to dependency of the WN on the EPN. The results show the importance of capturing the dependency between networks in modeling the resilience of critical infrastructure. PMID:28825037

Perspective: Stochastic magnetic devices for cognitive computing

NASA Astrophysics Data System (ADS)

Roy, Kaushik; Sengupta, Abhronil; Shim, Yong

2018-06-01

Stochastic switching of nanomagnets can potentially enable probabilistic cognitive hardware consisting of noisy neural and synaptic components. Furthermore, computational paradigms inspired from the Ising computing model require stochasticity for achieving near-optimality in solutions to various types of combinatorial optimization problems such as the Graph Coloring Problem or the Travelling Salesman Problem. Achieving optimal solutions in such problems are computationally exhaustive and requires natural annealing to arrive at the near-optimal solutions. Stochastic switching of devices also finds use in applications involving Deep Belief Networks and Bayesian Inference. In this article, we provide a multi-disciplinary perspective across the stack of devices, circuits, and algorithms to illustrate how the stochastic switching dynamics of spintronic devices in the presence of thermal noise can provide a direct mapping to the computational units of such probabilistic intelligent systems.

Faithful teleportation of multi-particle states involving multi spatially remote agents via probabilistic channels

NASA Astrophysics Data System (ADS)

Jiang, Min; Li, Hui; Zhang, Zeng-ke; Zeng, Jia

2011-02-01

We present an approach to faithfully teleport an unknown quantum state of entangled particles in a multi-particle system involving multi spatially remote agents via probabilistic channels. In our scheme, the integrity of an entangled multi-particle state can be maintained even when the construction of a faithful channel fails. Furthermore, in a quantum teleportation network, there are generally multi spatially remote agents which play the role of relay nodes between a sender and a distant receiver. Hence, we propose two schemes for directly and indirectly constructing a faithful channel between the sender and the distant receiver with the assistance of relay agents, respectively. Our results show that the required auxiliary particle resources, local operations and classical communications are considerably reduced for the present purpose.

DAMS: A Model to Assess Domino Effects by Using Agent-Based Modeling and Simulation.

PubMed

Zhang, Laobing; Landucci, Gabriele; Reniers, Genserik; Khakzad, Nima; Zhou, Jianfeng

2017-12-19

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases. © 2017 Society for Risk Analysis.

A Probabilistic Graphical Model to Detect Chromosomal Domains

NASA Astrophysics Data System (ADS)

Heermann, Dieter; Hofmann, Andreas; Weber, Eva

To understand the nature of a cell, one needs to understand the structure of its genome. For this purpose, experimental techniques such as Hi-C detecting chromosomal contacts are used to probe the three-dimensional genomic structure. These experiments yield topological information, consistently showing a hierarchical subdivision of the genome into self-interacting domains across many organisms. Current methods for detecting these domains using the Hi-C contact matrix, i.e. a doubly-stochastic matrix, are mostly based on the assumption that the domains are distinct, thus non-overlapping. For overcoming this simplification and for being able to unravel a possible nested domain structure, we developed a probabilistic graphical model that makes no a priori assumptions on the domain structure. Within this approach, the Hi-C contact matrix is analyzed using an Ising like probabilistic graphical model whose coupling constant is proportional to each lattice point (entry in the contact matrix). The results show clear boundaries between identified domains and the background. These domain boundaries are dependent on the coupling constant, so that one matrix yields several clusters of different sizes, which show the self-interaction of the genome on different scales. This work was supported by a Grant from the International Human Frontier Science Program Organization (RGP0014/2014).

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep and thermal fatigue

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.; Trimble, Greg A.

1992-01-01

This report presents the results of a fourth year effort of a research program, conducted for NASA-LeRC by the University of Texas at San Antonio (UTSA). The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subject to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 has been analyzed using the developed methodology.

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep, and thermal fatigue

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.; Trimble, Greg A.

1992-01-01

The results of a fourth year effort of a research program conducted for NASA-LeRC by The University of Texas at San Antonio (UTSA) are presented. The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue, or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation was randomized and is included in the computer program, PROMISC. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Analysis of the French insurance market exposure to floods: a stochastic model combining river overflow and surface runoff

NASA Astrophysics Data System (ADS)

Moncoulon, D.; Labat, D.; Ardon, J.; Onfroy, T.; Leblois, E.; Poulard, C.; Aji, S.; Rémy, A.; Quantin, A.

2013-07-01

The analysis of flood exposure at a national scale for the French insurance market must combine the generation of a probabilistic event set of all possible but not yet occurred flood situations with hazard and damage modeling. In this study, hazard and damage models are calibrated on a 1995-2012 historical event set, both for hazard results (river flow, flooded areas) and loss estimations. Thus, uncertainties in the deterministic estimation of a single event loss are known before simulating a probabilistic event set. To take into account at least 90% of the insured flood losses, the probabilistic event set must combine the river overflow (small and large catchments) with the surface runoff due to heavy rainfall, on the slopes of the watershed. Indeed, internal studies of CCR claim database has shown that approximately 45% of the insured flood losses are located inside the floodplains and 45% outside. 10% other percent are due to seasurge floods and groundwater rise. In this approach, two independent probabilistic methods are combined to create a single flood loss distribution: generation of fictive river flows based on the historical records of the river gauge network and generation of fictive rain fields on small catchments, calibrated on the 1958-2010 Météo-France rain database SAFRAN. All the events in the probabilistic event sets are simulated with the deterministic model. This hazard and damage distribution is used to simulate the flood losses at the national scale for an insurance company (MACIF) and to generate flood areas associated with hazard return periods. The flood maps concern river overflow and surface water runoff. Validation of these maps is conducted by comparison with the address located claim data on a small catchment (downstream Argens).

New strong motion network in Georgia: basis for specifying seismic hazard

NASA Astrophysics Data System (ADS)

Kvavadze, N.; Tsereteli, N. S.

2017-12-01

Risk created by hazardous natural events is closely related to sustainable development of the society. Global observations have confirmed tendency of growing losses resulting from natural disasters, one of the most dangerous and destructive if which are earthquakes. Georgia is located in seismically active region. So, it is imperative to evaluate probabilistic seismic hazard and seismic risk with proper accuracy. National network of Georgia includes 35 station all of which are seismometers. There are significant gaps in strong motion recordings, which essential for seismic hazard assessment. To gather more accelerometer recordings, we have built a strong motion network distributed on the territory of Georgia. The network includes 6 stations for now, with Basalt 4x datalogger and strong motion sensor Episensor ES-T. For each site, Vs30 and soil resonance frequencies have been measured. Since all but one station (Tabakhmelam near Tbilisi), are located far from power and internet lines special system was created for instrument operation. Solar power is used to supply the system with electricity and GSM/LTE modems for internet access. VPN tunnel was set up using Raspberry pi, for two-way communication with stations. Tabakhmela station is located on grounds of Ionosphere Observatory, TSU and is used as a hub for the network. This location also includes a broadband seismometer and VLF electromagnetic waves observation antenna, for possible earthquake precursor studies. On server, located in Tabakhmela, the continues data is collected from all the stations, for later use. The recordings later will be used in different seismological and engineering problems, namely selecting and creating GMPE model for Caucasus, for probabilistic seismic hazard and seismic risk evaluation. These stations are a start and in the future expansion of strong motion network is planned. Along with this, electromagnetic wave observations will continue and additional antennas will be implemented with strong motion sensors and possible earthquake precursors will be studied using complex methods of observation and data analysis.

Evaluating coastal landscape response to sea-level rise in the northeastern United States: approach and methods

USGS Publications Warehouse

Lentz, Erika E.; Stippa, Sawyer R.; Thieler, E. Robert; Plant, Nathaniel G.; Gesch, Dean B.; Horton, Radley M.

2014-02-13

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

Evaluating Coastal Landscape Response to Sea-Level Rise in the Northeastern United States - Approach and Methods

NASA Technical Reports Server (NTRS)

Lentz, Erika E.; Stippa, Sawyer R.; Thieler, E. Robert; Plant, Nathaniel G.; Gesch, Dean B.; Horton, Radley M.

2015-01-01

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

Organization and scaling in water supply networks

NASA Astrophysics Data System (ADS)

Cheng, Likwan; Karney, Bryan W.

2017-12-01

Public water supply is one of the society's most vital resources and most costly infrastructures. Traditional concepts of these networks capture their engineering identity as isolated, deterministic hydraulic units, but overlook their physics identity as related entities in a probabilistic, geographic ensemble, characterized by size organization and property scaling. Although discoveries of allometric scaling in natural supply networks (organisms and rivers) raised the prospect for similar findings in anthropogenic supplies, so far such a finding has not been reported in public water or related civic resource supplies. Examining an empirical ensemble of large number and wide size range, we show that water supply networks possess self-organized size abundance and theory-explained allometric scaling in spatial, infrastructural, and resource- and emission-flow properties. These discoveries establish scaling physics for water supply networks and may lead to novel applications in resource- and jurisdiction-scale water governance.

On the holistic approach in cellular and cancer biology: nonlinearity, complexity, and quasi-determinism of the dynamic cellular network.

PubMed

Waliszewski, P; Molski, M; Konarski, J

1998-06-01

A keystone of the molecular reductionist approach to cellular biology is a specific deductive strategy relating genotype to phenotype-two distinct categories. This relationship is based on the assumption that the intermediary cellular network of actively transcribed genes and their regulatory elements is deterministic (i.e., a link between expression of a gene and a phenotypic trait can always be identified, and evolution of the network in time is predetermined). However, experimental data suggest that the relationship between genotype and phenotype is nonbijective (i.e., a gene can contribute to the emergence of more than just one phenotypic trait or a phenotypic trait can be determined by expression of several genes). This implies nonlinearity (i.e., lack of the proportional relationship between input and the outcome), complexity (i.e. emergence of the hierarchical network of multiple cross-interacting elements that is sensitive to initial conditions, possesses multiple equilibria, organizes spontaneously into different morphological patterns, and is controlled in dispersed rather than centralized manner), and quasi-determinism (i.e., coexistence of deterministic and nondeterministic events) of the network. Nonlinearity within the space of the cellular molecular events underlies the existence of a fractal structure within a number of metabolic processes, and patterns of tissue growth, which is measured experimentally as a fractal dimension. Because of its complexity, the same phenotype can be associated with a number of alternative sequences of cellular events. Moreover, the primary cause initiating phenotypic evolution of cells such as malignant transformation can be favored probabilistically, but not identified unequivocally. Thermodynamic fluctuations of energy rather than gene mutations, the material traits of the fluctuations alter both the molecular and informational structure of the network. Then, the interplay between deterministic chaos, complexity, self-organization, and natural selection drives formation of malignant phenotype. This concept offers a novel perspective for investigation of tumorigenesis without invalidating current molecular findings. The essay integrates the ideas of the sciences of complexity in a biological context.

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

PubMed Central

Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

2015-01-01

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. PMID:26714277

SCAP: a new methodology for safety management based on feedback from credible accident-probabilistic fault tree analysis system.

PubMed

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.

Dynamic Bayesian Networks as a Probabilistic Metamodel for Combat Simulations

DTIC Science & Technology

2014-09-18

test is commonly used for large data sets and is the method of comparison presented in Section 5.5. 4.3.3 Kullback - Leibler Divergence Goodness of Fit ...methods exist that might improve the results. A goodness of fit test using the Kullback - Leibler Divergence was proposed in the first paper, but still... Kullback - Leibler Divergence Goodness of Fit Test . . .

A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network

PubMed Central

Xu, Jing; Wang, Zhongbin; Tan, Chao; Si, Lei; Liu, Xinhua

2015-01-01

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. PMID:26528985

Effects of delay and probability combinations on discounting in humans

PubMed Central

Cox, David J.; Dallery, Jesse

2017-01-01

To determine discount rates, researchers typically adjust the amount of an immediate or certain option relative to a delayed or uncertain option. Because this adjusting amount method can be relatively time consuming, researchers have developed more efficient procedures. One such procedure is a 5-trial adjusting delay procedure, which measures the delay at which an amount of money loses half of its value (e.g., $1000 is valued at $500 with a 10-year delay to its receipt). Experiment 1 (n = 212) used 5-trial adjusting delay or probability tasks to measure delay discounting of losses, probabilistic gains, and probabilistic losses. Experiment 2 (n = 98) assessed combined probabilistic and delayed alternatives. In both experiments, we compared results from 5-trial adjusting delay or probability tasks to traditional adjusting amount procedures. Results suggest both procedures produced similar rates of probability and delay discounting in six out of seven comparisons. A magnitude effect consistent with previous research was observed for probabilistic gains and losses, but not for delayed losses. Results also suggest that delay and probability interact to determine the value of money. Five-trial methods may allow researchers to assess discounting more efficiently as well as study more complex choice scenarios. PMID:27498073

Reliability of a Parallel Pipe Network

NASA Technical Reports Server (NTRS)

Herrera, Edgar; Chamis, Christopher (Technical Monitor)

2001-01-01

The goal of this NASA-funded research is to advance research and education objectives in theoretical and computational probabilistic structural analysis, reliability, and life prediction methods for improved aerospace and aircraft propulsion system components. Reliability methods are used to quantify response uncertainties due to inherent uncertainties in design variables. In this report, several reliability methods are applied to a parallel pipe network. The observed responses are the head delivered by a main pump and the head values of two parallel lines at certain flow rates. The probability that the flow rates in the lines will be less than their specified minimums will be discussed.

Network approach for decision making under risk—How do we choose among probabilistic options with the same expected value?

PubMed Central

Chen, Yi-Shin

2018-01-01

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing ‘goal’ and ‘time’ factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight. PMID:29702665

Network approach for decision making under risk-How do we choose among probabilistic options with the same expected value?

PubMed

Pan, Wei; Chen, Yi-Shin

2018-01-01

Conventional decision theory suggests that under risk, people choose option(s) by maximizing the expected utility. However, theories deal ambiguously with different options that have the same expected utility. A network approach is proposed by introducing 'goal' and 'time' factors to reduce the ambiguity in strategies for calculating the time-dependent probability of reaching a goal. As such, a mathematical foundation that explains the irrational behavior of choosing an option with a lower expected utility is revealed, which could imply that humans possess rationality in foresight.

Intervention in gene regulatory networks with maximal phenotype alteration.

PubMed

Yousefi, Mohammadmahdi R; Dougherty, Edward R

2013-07-15

A basic issue for translational genomics is to model gene interaction via gene regulatory networks (GRNs) and thereby provide an informatics environment to study the effects of intervention (say, via drugs) and to derive effective intervention strategies. Taking the view that the phenotype is characterized by the long-run behavior (steady-state distribution) of the network, we desire interventions to optimally move the probability mass from undesirable to desirable states Heretofore, two external control approaches have been taken to shift the steady-state mass of a GRN: (i) use a user-defined cost function for which desirable shift of the steady-state mass is a by-product and (ii) use heuristics to design a greedy algorithm. Neither approach provides an optimal control policy relative to long-run behavior. We use a linear programming approach to optimally shift the steady-state mass from undesirable to desirable states, i.e. optimization is directly based on the amount of shift and therefore must outperform previously proposed methods. Moreover, the same basic linear programming structure is used for both unconstrained and constrained optimization, where in the latter case, constraints on the optimization limit the amount of mass that may be shifted to 'ambiguous' states, these being states that are not directly undesirable relative to the pathology of interest but which bear some perceived risk. We apply the method to probabilistic Boolean networks, but the theory applies to any Markovian GRN. Supplementary materials, including the simulation results, MATLAB source code and description of suboptimal methods are available at http://gsp.tamu.edu/Publications/supplementary/yousefi13b. edward@ece.tamu.edu Supplementary data are available at Bioinformatics online.

AnRAD: A Neuromorphic Anomaly Detection Framework for Massive Concurrent Data Streams.

PubMed

Chen, Qiuwen; Luley, Ryan; Wu, Qing; Bishop, Morgan; Linderman, Richard W; Qiu, Qinru

2018-05-01

The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic. In this paper, we propose anomaly recognition and detection (AnRAD), a bioinspired detection framework that performs probabilistic inferences. We analyze the feature dependency and develop a self-structuring method that learns an efficient confabulation network using unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly detection approaches, our method provides competitive detection quality. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementations of the detection algorithm on the graphic processing unit and the Xeon Phi coprocessor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor. The framework provides real-time service to concurrent data streams within diversified knowledge contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle behavior detection, the framework is able to monitor up to 16000 vehicles (data streams) and their interactions in real time with a single commodity coprocessor, and uses less than 0.2 ms for one testing subject. Finally, the detection network is ported to our spiking neural network simulator to show the potential of adapting to the emerging neuromorphic architectures.

Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

NASA Astrophysics Data System (ADS)

Clark, John W.; Rafelski, Johann; Winston, Jeffrey V.

1985-07-01

Aspects of brain function are examined in terms of a nonlinear dynamical system of highly interconnected neuron-like binary decision elements. The model neurons operate synchronously in discrete time, according to deterministic or probabilistic equations of motion. Plasticity of the nervous system, which underlies such cognitive collective phenomena as adaptive development, learning, and memory, is represented by temporal modification of interneuronal connection strengths depending on momentary or recent neural activity. A formal basis is presented for the construction of local plasticity algorithms, or connection-modification routines, spanning a large class. To build an intuitive understanding of the behavior of discrete-time network models, extensive computer simulations have been carried out (a) for nets with fixed, quasirandom connectivity and (b) for nets with connections that evolve under one or another choice of plasticity algorithm. From the former experiments, insights are gained concerning the spontaneous emergence of order in the form of cyclic modes of neuronal activity. In the course of the latter experiments, a simple plasticity routine (“brainwashing,” or “anti-learning”) was identified which, applied to nets with initially quasirandom connectivity, creates model networks which provide more felicitous starting points for computer experiments on the engramming of content-addressable memories and on learning more generally. The potential relevance of this algorithm to developmental neurobiology and to sleep states is discussed. The model considered is at the same time a synthesis of earlier synchronous neural-network models and an elaboration upon them; accordingly, the present article offers both a focused review of the dynamical properties of such systems and a selection of new findings derived from computer simulation.

Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing.

PubMed

Ciullo, Valentina; Vecchio, Daniela; Gili, Tommaso; Spalletta, Gianfranco; Piras, Federica

2018-01-01

The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition) or onset (temporal condition) were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation. Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between explicit and implicit temporal orienting processes was considered at the long interval, we found that explicit processes were related to centrality measures of the bilateral inferior parietal lobule. Degree centrality of the same region in the left hemisphere covaried with behavioral measures indexing the process of attentional re-orienting. These results represent a crucial step forward the ordinary predictive processing description, as we identified the patterns of connectivity characterizing the brain organization associated with the ability to generate and update temporal expectancies in case of contextual violations.

Evaluation of power system security and development of transmission pricing method

NASA Astrophysics Data System (ADS)

Kim, Hyungchul

The electric power utility industry is presently undergoing a change towards the deregulated environment. This has resulted in unbundling of generation, transmission and distribution services. The introduction of competition into unbundled electricity services may lead system operation closer to its security boundaries resulting in smaller operating safety margins. The competitive environment is expected to lead to lower price rates for customers and higher efficiency for power suppliers in the long run. Under this deregulated environment, security assessment and pricing of transmission services have become important issues in power systems. This dissertation provides new methods for power system security assessment and transmission pricing. In power system security assessment, the following issues are discussed (1) The description of probabilistic methods for power system security assessment; (2) The computation time of simulation methods; (3) on-line security assessment for operation. A probabilistic method using Monte-Carlo simulation is proposed for power system security assessment. This method takes into account dynamic and static effects corresponding to contingencies. Two different Kohonen networks, Self-Organizing Maps and Learning Vector Quantization, are employed to speed up the probabilistic method. The combination of Kohonen networks and Monte-Carlo simulation can reduce computation time in comparison with straight Monte-Carlo simulation. A technique for security assessment employing Bayes classifier is also proposed. This method can be useful for system operators to make security decisions during on-line power system operation. This dissertation also suggests an approach for allocating transmission transaction costs based on reliability benefits in transmission services. The proposed method shows the transmission transaction cost of reliability benefits when transmission line capacities are considered. The ratio between allocation by transmission line capacity-use and allocation by reliability benefits is computed using the probability of system failure.

A Probabilistic Risk Mitigation Model for Cyber-Attacks to PMU Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mousavian, Seyedamirabbas; Valenzuela, Jorge; Wang, Jianhui

The power grid is becoming more dependent on information and communication technologies. Complex networks of advanced sensors such as phasor measurement units (PMUs) are used to collect real time data to improve the observability of the power system. Recent studies have shown that the power grid has significant cyber vulnerabilities which could increase when PMUs are used extensively. Therefore, recognizing and responding to vulnerabilities are critical to the security of the power grid. This paper proposes a risk mitigation model for optimal response to cyber-attacks to PMU networks. We model the optimal response action as a mixed integer linear programmingmore » (MILP) problem to prevent propagation of the cyber-attacks and maintain the observability of the power system.« less

Risk analysis of Safety Service Patrol (SSP) systems in Virginia.

PubMed

Dickey, Brett D; Santos, Joost R

2011-12-01

The transportation infrastructure is a vital backbone of any regional economy as it supports workforce mobility, tourism, and a host of socioeconomic activities. In this article, we specifically examine the incident management function of the transportation infrastructure. In many metropolitan regions, incident management is handled primarily by safety service patrols (SSPs), which monitor and resolve roadway incidents. In Virginia, SSP allocation across highway networks is based typically on average vehicle speeds and incident volumes. This article implements a probabilistic network model that partitions "business as usual" traffic flow with extreme-event scenarios. Results of simulated network scenarios reveal that flexible SSP configurations can improve incident resolution times relative to predetermined SSP assignments. © 2011 Society for Risk Analysis.

Bayesian network modelling of upper gastrointestinal bleeding

NASA Astrophysics Data System (ADS)

Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri

2013-09-01

Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.

UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization.

PubMed

Choo, Jaegul; Lee, Changhyun; Reddy, Chandan K; Park, Haesun

2013-12-01

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.

A random spatial network model based on elementary postulates

USGS Publications Warehouse

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

[Early warning on measles through the neural networks].

PubMed

Yu, Bin; Ding, Chun; Wei, Shan-bo; Chen, Bang-hua; Liu, Pu-lin; Luo, Tong-yong; Wang, Jia-gang; Pan, Zhi-wei; Lu, Jun-an

2011-01-01

To discuss the effects on early warning of measles, using the neural networks. Based on the available data through monthly and weekly reports on measles from January 1986 to August 2006 in Wuhan city. The modal was developed using the neural networks to predict and analyze the prevalence and incidence of measles. When the dynamic time series modal was established with back propagation (BP) networks consisting of two layers, if p was assigned as 9, the convergence speed was acceptable and the correlation coefficient was equal to 0.85. It was more acceptable for monthly forecasting the specific value, but better for weekly forecasting the classification under probabilistic neural networks (PNN). When data was big enough to serve the purpose, it seemed more feasible for early warning using the two-layer BP networks. However, when data was not enough, then PNN could be used for the purpose of prediction. This method seemed feasible to be used in the system for early warning.

Percolation of networks with directed dependency links

NASA Astrophysics Data System (ADS)

Niu, Dunbiao; Yuan, Xin; Du, Minhui; Stanley, H. Eugene; Hu, Yanqing

2016-04-01

The self-consistent probabilistic approach has proven itself powerful in studying the percolation behavior of interdependent or multiplex networks without tracking the percolation process through each cascading step. In order to understand how directed dependency links impact criticality, we employ this approach to study the percolation properties of networks with both undirected connectivity links and directed dependency links. We find that when a random network with a given degree distribution undergoes a second-order phase transition, the critical point and the unstable regime surrounding the second-order phase transition regime are determined by the proportion of nodes that do not depend on any other nodes. Moreover, we also find that the triple point and the boundary between first- and second-order transitions are determined by the proportion of nodes that depend on no more than one node. This implies that it is maybe general for multiplex network systems, some important properties of phase transitions can be determined only by a few parameters. We illustrate our findings using Erdős-Rényi networks.

a Generic Probabilistic Model and a Hierarchical Solution for Sensor Localization in Noisy and Restricted Conditions

NASA Astrophysics Data System (ADS)

Ji, S.; Yuan, X.

2016-06-01

A generic probabilistic model, under fundamental Bayes' rule and Markov assumption, is introduced to integrate the process of mobile platform localization with optical sensors. And based on it, three relative independent solutions, bundle adjustment, Kalman filtering and particle filtering are deduced under different and additional restrictions. We want to prove that first, Kalman filtering, may be a better initial-value supplier for bundle adjustment than traditional relative orientation in irregular strips and networks or failed tie-point extraction. Second, in high noisy conditions, particle filtering can act as a bridge for gap binding when a large number of gross errors fail a Kalman filtering or a bundle adjustment. Third, both filtering methods, which help reduce the error propagation and eliminate gross errors, guarantee a global and static bundle adjustment, who requires the strictest initial values and control conditions. The main innovation is about the integrated processing of stochastic errors and gross errors in sensor observations, and the integration of the three most used solutions, bundle adjustment, Kalman filtering and particle filtering into a generic probabilistic localization model. The tests in noisy and restricted situations are designed and examined to prove them.

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-01-01

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm. PMID:28587084

Predictability of short-range forecasting: a multimodel approach

NASA Astrophysics Data System (ADS)

García-Moya, Jose-Antonio; Callado, Alfons; Escribà, Pau; Santos, Carlos; Santos-Muñoz, Daniel; Simarro, Juan

2011-05-01

Numerical weather prediction (NWP) models (including mesoscale) have limitations when it comes to dealing with severe weather events because extreme weather is highly unpredictable, even in the short range. A probabilistic forecast based on an ensemble of slightly different model runs may help to address this issue. Among other ensemble techniques, Multimodel ensemble prediction systems (EPSs) are proving to be useful for adding probabilistic value to mesoscale deterministic models. A Multimodel Short Range Ensemble Prediction System (SREPS) focused on forecasting the weather up to 72 h has been developed at the Spanish Meteorological Service (AEMET). The system uses five different limited area models (LAMs), namely HIRLAM (HIRLAM Consortium), HRM (DWD), the UM (UKMO), MM5 (PSU/NCAR) and COSMO (COSMO Consortium). These models run with initial and boundary conditions provided by five different global deterministic models, namely IFS (ECMWF), UM (UKMO), GME (DWD), GFS (NCEP) and CMC (MSC). AEMET-SREPS (AE) validation on the large-scale flow, using ECMWF analysis, shows a consistent and slightly underdispersive system. For surface parameters, the system shows high skill forecasting binary events. 24-h precipitation probabilistic forecasts are verified using an up-scaling grid of observations from European high-resolution precipitation networks, and compared with ECMWF-EPS (EC).

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors.

PubMed

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-05-25

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ -connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

Fragment Impact Toolkit (FIT)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shevitz, Daniel Wolf; Key, Brian P.; Garcia, Daniel B.

2017-09-05

The Fragment Impact Toolkit (FIT) is a software package used for probabilistic consequence evaluation of fragmenting sources. The typical use case for FIT is to simulate an exploding shell and evaluate the consequence on nearby objects. FIT is written in the programming language Python and is designed as a collection of interacting software modules. Each module has a function that interacts with the other modules to produce desired results.

Quantum Graphical Models and Belief Propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leifer, M.S.; Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo Ont., N2L 2Y5; Poulin, D.

Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large numbers of random variables. This paper presents a generalization of these concepts and methods to the quantum case, based on the idea that quantum theory can be thought of as a noncommutative, operator-valued, generalization of classical probability theory. Some novel characterizations of quantum conditional independence are derived, and definitions of Quantum n-Bifactor Networks, Markov Networks, Factor Graphs and Bayesian Networks are proposed. The structure of Quantum Markovmore » Networks is investigated and some partial characterization results are obtained, along the lines of the Hammersley-Clifford theorem. A Quantum Belief Propagation algorithm is presented and is shown to converge on 1-Bifactor Networks and Markov Networks when the underlying graph is a tree. The use of Quantum Belief Propagation as a heuristic algorithm in cases where it is not known to converge is discussed. Applications to decoding quantum error correcting codes and to the simulation of many-body quantum systems are described.« less

Bayesian networks of age estimation and classification based on dental evidence: A study on the third molar mineralization.

PubMed

Sironi, Emanuele; Pinchi, Vilma; Pradella, Francesco; Focardi, Martina; Bozza, Silvia; Taroni, Franco

2018-04-01

Not only does the Bayesian approach offer a rational and logical environment for evidence evaluation in a forensic framework, but it also allows scientists to coherently deal with uncertainty related to a collection of multiple items of evidence, due to its flexible nature. Such flexibility might come at the expense of elevated computational complexity, which can be handled by using specific probabilistic graphical tools, namely Bayesian networks. In the current work, such probabilistic tools are used for evaluating dental evidence related to the development of third molars. A set of relevant properties characterizing the graphical models are discussed and Bayesian networks are implemented to deal with the inferential process laying beyond the estimation procedure, as well as to provide age estimates. Such properties include operationality, flexibility, coherence, transparence and sensitivity. A data sample composed of Italian subjects was employed for the analysis; results were in agreement with previous studies in terms of point estimate and age classification. The influence of the prior probability elicitation in terms of Bayesian estimate and classifies was also analyzed. Findings also supported the opportunity to take into consideration multiple teeth in the evaluative procedure, since it can be shown this results in an increased robustness towards the prior probability elicitation process, as well as in more favorable outcomes from a forensic perspective. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Cross hole GPR traveltime inversion using a fast and accurate neural network as a forward model

NASA Astrophysics Data System (ADS)

Mejer Hansen, Thomas

2017-04-01

Probabilistic formulated inverse problems can be solved using Monte Carlo based sampling methods. In principle both advanced prior information, such as based on geostatistics, and complex non-linear forward physical models can be considered. However, in practice these methods can be associated with huge computational costs that in practice limit their application. This is not least due to the computational requirements related to solving the forward problem, where the physical response of some earth model has to be evaluated. Here, it is suggested to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. This will introduce a modeling error, that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem. We demonstrate the methodology for first arrival travel time inversion of cross hole ground-penetrating radar (GPR) data. An accurate forward model, based on 2D full-waveform modeling followed by automatic travel time picking, is replaced by a fast neural network. This provides a sampling algorithm three orders of magnitude faster than using the full forward model, and considerably faster, and more accurate, than commonly used approximate forward models. The methodology has the potential to dramatically change the complexity of the types of inverse problems that can be solved using non-linear Monte Carlo sampling techniques.

Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities.

PubMed

Kim, Kee-Hoon; Cho, Sung-Bae

2017-12-11

Recently, recognizing a user's daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user's obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the "Five W's", and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54-14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing.

Analysis of feature selection with Probabilistic Neural Network (PNN) to classify sources influencing indoor air quality

NASA Astrophysics Data System (ADS)

Saad, S. M.; Shakaff, A. Y. M.; Saad, A. R. M.; Yusof, A. M.; Andrew, A. M.; Zakaria, A.; Adom, A. H.

2017-03-01

There are various sources influencing indoor air quality (IAQ) which could emit dangerous gases such as carbon monoxide (CO), carbon dioxide (CO2), ozone (O3) and particulate matter. These gases are usually safe for us to breathe in if they are emitted in safe quantity but if the amount of these gases exceeded the safe level, they might be hazardous to human being especially children and people with asthmatic problem. Therefore, a smart indoor air quality monitoring system (IAQMS) is needed that able to tell the occupants about which sources that trigger the indoor air pollution. In this project, an IAQMS that able to classify sources influencing IAQ has been developed. This IAQMS applies a classification method based on Probabilistic Neural Network (PNN). It is used to classify the sources of indoor air pollution based on five conditions: ambient air, human activity, presence of chemical products, presence of food and beverage, and presence of fragrance. In order to get good and best classification accuracy, an analysis of several feature selection based on data pre-processing method is done to discriminate among the sources. The output from each data pre-processing method has been used as the input for the neural network. The result shows that PNN analysis with the data pre-processing method give good classification accuracy of 99.89% and able to classify the sources influencing IAQ high classification rate.

Scalable Probabilistic Inference for Global Seismic Monitoring

NASA Astrophysics Data System (ADS)

Arora, N. S.; Dear, T.; Russell, S.

2011-12-01

We describe a probabilistic generative model for seismic events, their transmission through the earth, and their detection (or mis-detection) at seismic stations. We also describe an inference algorithm that constructs the most probable event bulletin explaining the observed set of detections. The model and inference are called NET-VISA (network processing vertically integrated seismic analysis) and is designed to replace the current automated network processing at the IDC, the SEL3 bulletin. Our results (attached table) demonstrate that NET-VISA significantly outperforms SEL3 by reducing the missed events from 30.3% down to 12.5%. The difference is even more dramatic for smaller magnitude events. NET-VISA has no difficulty in locating nuclear explosions as well. The attached figure demonstrates the location predicted by NET-VISA versus other bulletins for the second DPRK event. Further evaluation on dense regional networks demonstrates that NET-VISA finds many events missed in the LEB bulletin, which is produced by the human analysts. Large aftershock sequences, as produced by the 2004 December Sumatra earthquake and the 2011 March Tohoku earthquake, can pose a significant load for automated processing, often delaying the IDC bulletins by weeks or months. Indeed these sequences can overload the serial NET-VISA inference as well. We describe an enhancement to NET-VISA to make it multi-threaded, and hence take full advantage of the processing power of multi-core and -cpu machines. Our experiments show that the new inference algorithm is able to achieve 80% efficiency in parallel speedup.

Hardware friendly probabilistic spiking neural network with long-term and short-term plasticity.

PubMed

Hsieh, Hung-Yi; Tang, Kea-Tiong

2013-12-01

This paper proposes a probabilistic spiking neural network (PSNN) with unimodal weight distribution, possessing long- and short-term plasticity. The proposed algorithm is derived by both the arithmetic gradient decent calculation and bioinspired algorithms. The algorithm is benchmarked by the Iris and Wisconsin breast cancer (WBC) data sets. The network features fast convergence speed and high accuracy. In the experiment, the PSNN took not more than 40 epochs for convergence. The average testing accuracy for Iris and WBC data is 96.7% and 97.2%, respectively. To test the usefulness of the PSNN to real world application, the PSNN was also tested with the odor data, which was collected by our self-developed electronic nose (e-nose). Compared with the algorithm (K-nearest neighbor) that has the highest classification accuracy in the e-nose for the same odor data, the classification accuracy of the PSNN is only 1.3% less but the memory requirement can be reduced at least 40%. All the experiments suggest that the PSNN is hardware friendly. First, it requires only nine-bits weight resolution for training and testing. Second, the PSNN can learn complex data sets with a little number of neurons that in turn reduce the cost of VLSI implementation. In addition, the algorithm is insensitive to synaptic noise and the parameter variation induced by the VLSI fabrication. Therefore, the algorithm can be implemented by either software or hardware, making it suitable for wider application.

Probabilistic prediction of barrier-island response to hurricanes

USGS Publications Warehouse

Plant, Nathaniel G.; Stockdon, Hilary F.

2012-01-01

Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

On problems of analyzing aerodynamic properties of blunted rotary bodies with small random surface distortions under supersonic and hypersonic flows

NASA Astrophysics Data System (ADS)

Degtyar, V. G.; Kalashnikov, S. T.; Mokin, Yu. A.

2017-10-01

The paper considers problems of analyzing aerodynamic properties (ADP) of reenetry vehicles (RV) as blunted rotary bodies with small random surface distortions. The interactions of math simulation of surface distortions, selection of tools for predicting ADPs of shaped bodies, evaluation of different-type ADP variations and their adaptation for dynamic problems are analyzed. The possibilities of deterministic and probabilistic approaches to evaluation of ADP variations are considered. The practical value of the probabilistic approach is demonstrated. The examples of extremal deterministic evaluations of ADP variations for a sphere and a sharp cone are given.

Multi-disciplinary coupling effects for integrated design of propulsion systems

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Singhal, S. N.

1993-01-01

Effective computational simulation procedures are described for modeling the inherent multi-disciplinary interactions which govern the accurate response of propulsion systems. Results are presented for propulsion system responses including multi-disciplinary coupling effects using coupled multi-discipline thermal, structural, and acoustic tailoring; an integrated system of multi-disciplinary simulators; coupled material behavior/fabrication process tailoring; sensitivities using a probabilistic simulator; and coupled materials, structures, fracture, and probabilistic behavior simulator. The results demonstrate that superior designs can be achieved if the analysis/tailoring methods account for the multi-disciplinary coupling effects. The coupling across disciplines can be used to develop an integrated coupled multi-discipline numerical propulsion system simulator.

The neural dynamics of song syntax in songbirds

NASA Astrophysics Data System (ADS)

Jin, Dezhe

2010-03-01

Songbird is ``the hydrogen atom'' of the neuroscience of complex, learned vocalizations such as human speech. Songs of Bengalese finch consist of sequences of syllables. While syllables are temporally stereotypical, syllable sequences can vary and follow complex, probabilistic syntactic rules, which are rudimentarily similar to grammars in human language. Songbird brain is accessible to experimental probes, and is understood well enough to construct biologically constrained, predictive computational models. In this talk, I will discuss the structure and dynamics of neural networks underlying the stereotypy of the birdsong syllables and the flexibility of syllable sequences. Recent experiments and computational models suggest that a syllable is encoded in a chain network of projection neurons in premotor nucleus HVC (proper name). Precisely timed spikes propagate along the chain, driving vocalization of the syllable through downstream nuclei. Through a computational model, I show that that variable syllable sequences can be generated through spike propagations in a network in HVC in which the syllable-encoding chain networks are connected into a branching chain pattern. The neurons mutually inhibit each other through the inhibitory HVC interneurons, and are driven by external inputs from nuclei upstream of HVC. At a branching point that connects the final group of a chain to the first groups of several chains, the spike activity selects one branch to continue the propagation. The selection is probabilistic, and is due to the winner-take-all mechanism mediated by the inhibition and noise. The model predicts that the syllable sequences statistically follow partially observable Markov models. Experimental results supporting this and other predictions of the model will be presented. We suggest that the syntax of birdsong syllable sequences is embedded in the connection patterns of HVC projection neurons.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125

A Bayesian network to predict coastal vulnerability to sea level rise

USGS Publications Warehouse

Gutierrez, B.T.; Plant, N.G.; Thieler, E.R.

2011-01-01

Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems contributes to large uncertainties in predicting long-term sea level rise impacts. Here we explore and demonstrate the capabilities of a Bayesian network (BN) to predict long-term shoreline change associated with sea level rise and make quantitative assessments of prediction uncertainty. A BN is used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN is used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates. Results demonstrate that the probability of shoreline retreat increases with higher rates of sea level rise. Where more specific information is included, the probability of shoreline change increases in a number of cases, indicating more confident predictions. A hindcast evaluation of the BN indicates that the network correctly predicts 71% of the cases. Evaluation of the results using Brier skill and log likelihood ratio scores indicates that the network provides shoreline change predictions that are better than the prior probability. Shoreline change outcomes indicating stability (-1 1 m/yr) was not well predicted. We find that BNs can assimilate important factors contributing to coastal change in response to sea level rise and can make quantitative, probabilistic predictions that can be applied to coastal management decisions. Copyright ?? 2011 by the American Geophysical Union.

Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

NASA Astrophysics Data System (ADS)

Laloy, Eric; Hérault, Romain; Jacques, Diederik; Linde, Niklas

2018-01-01

Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2-D and 3-D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2-D and 3-D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2-D steady state flow and 3-D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN-based inversion. For the 2-D case, the inversion rapidly explores the posterior model distribution. For the 3-D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.

Altered Structural and Functional Connectivity in Late Preterm Preadolescence: An Anatomic Seed-Based Study of Resting State Networks Related to the Posteromedial and Lateral Parietal Cortex

PubMed Central

Degnan, Andrew J.; Wisnowski, Jessica L.; Choi, SoYoung; Ceschin, Rafael; Bhushan, Chitresh; Leahy, Richard M.; Corby, Patricia; Schmithorst, Vincent J.; Panigrahy, Ashok

2015-01-01

Objective Late preterm birth confers increased risk of developmental delay, academic difficulties and social deficits. The late third trimester may represent a critical period of development of neural networks including the default mode network (DMN), which is essential to normal cognition. Our objective is to identify functional and structural connectivity differences in the posteromedial cortex related to late preterm birth. Methods Thirty-eight preadolescents (ages 9–13; 19 born in the late preterm period (≥32 weeks gestational age) and 19 at term) without access to advanced neonatal care were recruited from a low socioeconomic status community in Brazil. Participants underwent neurocognitive testing, 3-dimensional T1-weighted imaging, diffusion-weighted imaging and resting state functional MRI (RS-fMRI). Seed-based probabilistic diffusion tractography and RS-fMRI analyses were performed using unilateral seeds within the posterior DMN (posterior cingulate cortex, precuneus) and lateral parietal DMN (superior marginal and angular gyri). Results Late preterm children demonstrated increased functional connectivity within the posterior default mode networks and increased anti-correlation with the central-executive network when seeded from the posteromedial cortex (PMC). Key differences were demonstrated between PMC components with increased anti-correlation with the salience network seen only with posterior cingulate cortex seeding but not with precuneus seeding. Probabilistic tractography showed increased streamlines within the right inferior longitudinal fasciculus and inferior fronto-occipital fasciculus within late preterm children while decreased intrahemispheric streamlines were also observed. No significant differences in neurocognitive testing were demonstrated between groups. Conclusion Late preterm preadolescence is associated with altered functional connectivity from the PMC and lateral parietal cortex to known distributed functional cortical networks despite no significant executive neurocognitive differences. Selective increased structural connectivity was observed in the setting of decreased posterior interhemispheric connections. Future work is needed to determine if these findings represent a compensatory adaptation employing alternate neural circuitry or could reflect subtle pathology resulting in emotional processing deficits not seen with neurocognitive testing. PMID:26098888

Background for Joint Systems Aspects of AIR 6000

DTIC Science & Technology

2000-04-01

Checkland’s Soft Systems Methodology [7, 8,9]. The analytical techniques that are proposed for joint systems work are based on calculating probability...Supporting Global Interests 21 DSTO-CR-0155 SLMP Structural Life Management Plan SOW Stand-Off Weapon SSM Soft Systems Methodology UAV Uninhabited Aerial... Systems Methodology in Action, John Wiley & Sons, Chichester, 1990. [101 Pearl, Judea, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible

A prior-based integrative framework for functional transcriptional regulatory network inference

PubMed Central

Siahpirani, Alireza F.

2017-01-01

Abstract Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization. PMID:27794550

From degree-correlated to payoff-correlated activity for an optimal resolution of social dilemmas

NASA Astrophysics Data System (ADS)

Aleta, Alberto; Meloni, Sandro; Perc, Matjaž; Moreno, Yamir

2016-12-01

An active participation of players in evolutionary games depends on several factors, ranging from personal stakes to the properties of the interaction network. Diverse activity patterns thus have to be taken into account when studying the evolution of cooperation in social dilemmas. Here we study the weak prisoner's dilemma game, where the activity of each player is determined in a probabilistic manner either by its degree or by its payoff. While degree-correlated activity introduces cascading failures of cooperation that are particularly severe on scale-free networks with frequently inactive hubs, payoff-correlated activity provides a more nuanced activity profile, which ultimately hinders systemic breakdowns of cooperation. To determine optimal conditions for the evolution of cooperation, we introduce an exponential decay to payoff-correlated activity that determines how fast the activity of a player returns to its default state. We show that there exists an intermediate decay rate at which the resolution of the social dilemma is optimal. This can be explained by the emerging activity patterns of players, where the inactivity of hubs is compensated effectively by the increased activity of average-degree players, who through their collective influence in the network sustain a higher level of cooperation. The sudden drops in the fraction of cooperators observed with degree-correlated activity therefore vanish, and so does the need for the lengthy spatiotemporal reorganization of compact cooperative clusters. The absence of such asymmetric dynamic instabilities thus leads to an optimal resolution of social dilemmas, especially when the conditions for the evolution of cooperation are strongly adverse.

Early Correlated Network Activity in the Hippocampus: Its Putative Role in Shaping Neuronal Circuits.

PubMed

Griguoli, Marilena; Cherubini, Enrico

2017-01-01

Synchronized neuronal activity occurring at different developmental stages in various brain structures represents a hallmark of developmental circuits. This activity, which differs in its specific patterns among animal species may play a crucial role in de novo formation and in shaping neuronal networks. In the rodent hippocampus in vitro , the so-called giant depolarizing potentials (GDPs) constitute a primordial form of neuronal synchrony preceding more organized forms of activity such as oscillations in the theta and gamma frequency range. GDPs are generated at the network level by the interaction of the neurotransmitters glutamate and GABA which, immediately after birth, exert both a depolarizing and excitatory action on their targets. GDPs are triggered by GABAergic interneurons, which in virtue of their extensive axonal branching operate as functional hubs to synchronize large ensembles of cells. Intrinsic bursting activity, driven by a persistent sodium conductance and facilitated by the low expression of Kv7.2 and Kv7.3 channel subunits, responsible for I M , exerts a permissive role in GDP generation. Here, we discuss how GDPs are generated in a probabilistic way when neuronal excitability within a local circuit reaches a certain threshold and how GDP-associated calcium transients act as coincident detectors for enhancing synaptic strength at emerging GABAergic and glutamatergic synapses. We discuss the possible in vivo correlate of this activity. Finally, we debate recent data showing how, in several animal models of neuropsychiatric disorders including autism, a GDPs dysfunction is associated to morphological alterations of neuronal circuits and behavioral deficits reminiscent of those observed in patients.

Unsteady Probabilistic Analysis of a Gas Turbine System

NASA Technical Reports Server (NTRS)

Brown, Marilyn

2003-01-01

In this work, we have considered an annular cascade configuration subjected to unsteady inflow conditions. The unsteady response calculation has been implemented into the time marching CFD code, MSUTURBO. The computed steady state results for the pressure distribution demonstrated good agreement with experimental data. We have computed results for the amplitudes of the unsteady pressure over the blade surfaces. With the increase in gas turbine engine structural complexity and performance over the past 50 years, structural engineers have created an array of safety nets to ensure against component failures in turbine engines. In order to reduce what is now considered to be excessive conservatism and yet maintain the same adequate margins of safety, there is a pressing need to explore methods of incorporating probabilistic design procedures into engine development. Probabilistic methods combine and prioritize the statistical distributions of each design variable, generate an interactive distribution and offer the designer a quantified relationship between robustness, endurance and performance. The designer can therefore iterate between weight reduction, life increase, engine size reduction, speed increase etc.

Probabilistic Material Strength Degradation Model for Inconel 718 Components Subjected to High Temperature, High-Cycle and Low-Cycle Mechanical Fatigue, Creep and Thermal Fatigue Effects

NASA Technical Reports Server (NTRS)

Bast, Callie C.; Boyce, Lola

1995-01-01

The development of methodology for a probabilistic material strength degradation is described. The probabilistic model, in the form of a postulated randomized multifactor equation, provides for quantification of uncertainty in the lifetime material strength of aerospace propulsion system components subjected to a number of diverse random effects. This model is embodied in the computer program entitled PROMISS, which can include up to eighteen different effects. Presently, the model includes five effects that typically reduce lifetime strength: high temperature, high-cycle mechanical fatigue, low-cycle mechanical fatigue, creep and thermal fatigue. Results, in the form of cumulative distribution functions, illustrated the sensitivity of lifetime strength to any current value of an effect. In addition, verification studies comparing predictions of high-cycle mechanical fatigue and high temperature effects with experiments are presented. Results from this limited verification study strongly supported that material degradation can be represented by randomized multifactor interaction models.

Mean and modal ϵ in the deaggregation of probabilistic ground motion

USGS Publications Warehouse

Harmsen, Stephen C.

2001-01-01

Mean and modal ϵ exhibit a wide variation geographically for any specified PE. Modal ϵ for the 2% in 50 yr PE exceeds 2 near the most active western California faults, is less than –1 near some less active faults of the western United States (principally in the Basin and Range), and may be less than 0 in areal fault zones of the central and eastern United States (CEUS). This geographic variation is useful for comparing probabilistic ground motions with ground motions from scenario earthquakes on dominating faults, often used in seismic-resistant provisions of building codes. An interactive seismic-hazard deaggregation menu item has been added to the USGS probabilistic seismic-hazard analysis Web site, http://geohazards.cr.usgs.gov/eq/, allowing visitors to compute mean and modal distance, magnitude, and ϵ corresponding to ground motions having mean return times from 250 to 5000 yr for any site in the United States.

Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample.

PubMed

Le, Quang A; Doctor, Jason N

2011-05-01

As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain quality-adjusted life years has become a solution when health utilities are not directly available. However, current mapping methods are limited due to their predictive validity, reliability, and/or other methodological issues. We employ probability theory together with a graphical model, called a Bayesian network, to convert health-profile measures into preference-based measures and to compare the results to those estimated with current mapping methods. A sample of 19,678 adults who completed both the 12-item Short Form Health Survey (SF-12v2) and EuroQoL 5D (EQ-5D) questionnaires from the 2003 Medical Expenditure Panel Survey was split into training and validation sets. Bayesian networks were constructed to explore the probabilistic relationships between each EQ-5D domain and 12 items of the SF-12v2. The EQ-5D utility scores were estimated on the basis of the predicted probability of each response level of the 5 EQ-5D domains obtained from the Bayesian inference process using the following methods: Monte Carlo simulation, expected utility, and most-likely probability. Results were then compared with current mapping methods including multinomial logistic regression, ordinary least squares, and censored least absolute deviations. The Bayesian networks consistently outperformed other mapping models in the overall sample (mean absolute error=0.077, mean square error=0.013, and R overall=0.802), in different age groups, number of chronic conditions, and ranges of the EQ-5D index. Bayesian networks provide a new robust and natural approach to map health status responses into health utility measures for health economic evaluations.

A Probabilistic Approach to Network Event Formation from Pre-Processed Waveform Data

NASA Astrophysics Data System (ADS)

Kohl, B. C.; Given, J.

2017-12-01

The current state of the art for seismic event detection still largely depends on signal detection at individual sensor stations, including picking accurate arrivals times and correctly identifying phases, and relying on fusion algorithms to associate individual signal detections to form event hypotheses. But increasing computational capability has enabled progress toward the objective of fully utilizing body-wave recordings in an integrated manner to detect events without the necessity of previously recorded ground truth events. In 2011-2012 Leidos (then SAIC) operated a seismic network to monitor activity associated with geothermal field operations in western Nevada. We developed a new association approach for detecting and quantifying events by probabilistically combining pre-processed waveform data to deal with noisy data and clutter at local distance ranges. The ProbDet algorithm maps continuous waveform data into continuous conditional probability traces using a source model (e.g. Brune earthquake or Mueller-Murphy explosion) to map frequency content and an attenuation model to map amplitudes. Event detection and classification is accomplished by combining the conditional probabilities from the entire network using a Bayesian formulation. This approach was successful in producing a high-Pd, low-Pfa automated bulletin for a local network and preliminary tests with regional and teleseismic data show that it has promise for global seismic and nuclear monitoring applications. The approach highlights several features that we believe are essential to achieving low-threshold automated event detection: Minimizes the utilization of individual seismic phase detections - in traditional techniques, errors in signal detection, timing, feature measurement and initial phase ID compound and propagate into errors in event formation, Has a formalized framework that utilizes information from non-detecting stations, Has a formalized framework that utilizes source information, in particular the spectral characteristics of events of interest, Is entirely model-based, i.e. does not rely on a priori's - particularly important for nuclear monitoring, Does not rely on individualized signal detection thresholds - it's the network solution that matters.

Common Randomness Principles of Secrecy

ERIC Educational Resources Information Center

Tyagi, Himanshu

2013-01-01

This dissertation concerns the secure processing of distributed data by multiple terminals, using interactive public communication among themselves, in order to accomplish a given computational task. In the setting of a probabilistic multiterminal source model in which several terminals observe correlated random signals, we analyze secure…

Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Mengshoel, Ole

2008-01-01

Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

Free-Energy-Based Design Policy for Robust Network Control against Environmental Fluctuation.

PubMed

Iwai, Takuya; Kominami, Daichi; Murata, Masayuki; Yomo, Tetsuya

2015-01-01

Bioinspired network control is a promising approach for realizing robust network controls. It relies on a probabilistic mechanism composed of positive and negative feedback that allows the system to eventually stabilize on the best solution. When the best solution fails due to environmental fluctuation, the system cannot keep its function until the system finds another solution again. To prevent the temporal loss of the function, the system should prepare some solution candidates and stochastically select available one from them. However, most bioinspired network controls are not designed with this issue in mind. In this paper, we propose a thermodynamics-based design policy that allows systems to retain an appropriate degree of randomness depending on the degree of environmental fluctuation, which prepares the system for the occurrence of environmental fluctuation. Furthermore, we verify the design policy by using an attractor selection model-based multipath routing to run simulation experiments.

Learning Bayesian Networks from Correlated Data

NASA Astrophysics Data System (ADS)

Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

2016-05-01

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

PubMed

Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming

2015-01-01

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

An elementary quantum network using robust nuclear spin qubits in diamond

NASA Astrophysics Data System (ADS)

Kalb, Norbert; Reiserer, Andreas; Humphreys, Peter; Blok, Machiel; van Bemmelen, Koen; Twitchen, Daniel; Markham, Matthew; Taminiau, Tim; Hanson, Ronald

Quantum registers containing multiple robust qubits can form the nodes of future quantum networks for computation and communication. Information storage within such nodes must be resilient to any type of local operation. Here we demonstrate multiple robust memories by employing five nuclear spins adjacent to a nitrogen-vacancy defect centre in diamond. We characterize the storage of quantum superpositions and their resilience to entangling attempts with the electron spin of the defect centre. The storage fidelity is found to be limited by the probabilistic electron spin reset after failed entangling attempts. Control over multiple memories is then utilized to encode states in decoherence protected subspaces with increased robustness. Furthermore we demonstrate memory control in two optically linked network nodes and characterize the storage capabilities of both memories in terms of the process fidelity with the identity. These results pave the way towards multi-qubit quantum algorithms in a remote network setting.

Tracing information flow on a global scale using Internet chain-letter data

PubMed Central

Liben-Nowell, David; Kleinberg, Jon

2008-01-01

Although information, news, and opinions continuously circulate in the worldwide social network, the actual mechanics of how any single piece of information spreads on a global scale have been a mystery. Here, we trace such information-spreading processes at a person-by-person level using methods to reconstruct the propagation of massively circulated Internet chain letters. We find that rather than fanning out widely, reaching many people in very few steps according to “small-world” principles, the progress of these chain letters proceeds in a narrow but very deep tree-like pattern, continuing for several hundred steps. This suggests a new and more complex picture for the spread of information through a social network. We describe a probabilistic model based on network clustering and asynchronous response times that produces trees with this characteristic structure on social-network data. PMID:18353985

Damage and Loss Estimation for Natural Gas Networks: The Case of Istanbul

NASA Astrophysics Data System (ADS)

Çaktı, Eser; Hancılar, Ufuk; Şeşetyan, Karin; Bıyıkoǧlu, Hikmet; Şafak, Erdal

2017-04-01

Natural gas networks are one of the major lifeline systems to support human, urban and industrial activities. The continuity of gas supply is critical for almost all functions of modern life. Under natural phenomena such as earthquakes and landslides the damages to the system elements may lead to explosions and fires compromising human life and damaging physical environment. Furthermore, the disruption in the gas supply puts human activities at risk and also results in economical losses. This study is concerned with the performance of one of the largest natural gas distribution systems in the world. Physical damages to Istanbul's natural gas network are estimated under the most recent probabilistic earthquake hazard models available, as well as under simulated ground motions from physics based models. Several vulnerability functions are used in modelling damages to system elements. A first-order assessment of monetary losses to Istanbul's natural gas distribution network is also attempted.

On the capacity of ternary Hebbian networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1991-01-01

Networks of ternary neurons storing random vectors over the set -1,0,1 by the so-called Hebbian rule are considered. It is shown that the maximal number of stored patterns that are equilibrium states of the network with probability tending to one as N tends to infinity is at least on the order of (N exp 2-1/alpha)/K, where N is the number of neurons, K is the number of nonzero elements in a pattern, and t = alpha x K, alpha between 1/2 and 1, is the threshold in the neuron function. While, for small K, this bound is similar to that obtained for fully connected binary networks, the number of interneural connections required in the ternary case is considerably smaller. Similar bounds, incorporating error probabilities, are shown to guarantee, in the same probabilistic sense, the correction of errors in the nonzero elements and in the location of these elements.

BoolNet--an R package for generation, reconstruction and analysis of Boolean networks.

PubMed

Müssel, Christoph; Hopfensitz, Martin; Kestler, Hans A

2010-05-15

As the study of information processing in living cells moves from individual pathways to complex regulatory networks, mathematical models and simulation become indispensable tools for analyzing the complex behavior of such networks and can provide deep insights into the functioning of cells. The dynamics of gene expression, for example, can be modeled with Boolean networks (BNs). These are mathematical models of low complexity, but have the advantage of being able to capture essential properties of gene-regulatory networks. However, current implementations of BNs only focus on different sub-aspects of this model and do not allow for a seamless integration into existing preprocessing pipelines. BoolNet efficiently integrates methods for synchronous, asynchronous and probabilistic BNs. This includes reconstructing networks from time series, generating random networks, robustness analysis via perturbation, Markov chain simulations, and identification and visualization of attractors. The package BoolNet is freely available from the R project at http://cran.r-project.org/ or http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/boolnet/ under Artistic License 2.0. hans.kestler@uni-ulm.de Supplementary data are available at Bioinformatics online.

Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states.

PubMed

Kohn, Lucas; Tschirsich, Ferdinand; Keck, Maximilian; Plenio, Martin B; Tamascelli, Dario; Montangero, Simone

2018-01-01

We provide evidence that randomized low-rank factorization is a powerful tool for the determination of the ground-state properties of low-dimensional lattice Hamiltonians through tensor network techniques. In particular, we show that randomized matrix factorization outperforms truncated singular value decomposition based on state-of-the-art deterministic routines in time-evolving block decimation (TEBD)- and density matrix renormalization group (DMRG)-style simulations, even when the system under study gets close to a phase transition: We report linear speedups in the bond or local dimension of up to 24 times in quasi-two-dimensional cylindrical systems.

Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Goroch, A. K.; Rabindra, P.; Rangaraj, N.; Navar, M. S.

1992-01-01

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.

Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states

NASA Astrophysics Data System (ADS)

Kohn, Lucas; Tschirsich, Ferdinand; Keck, Maximilian; Plenio, Martin B.; Tamascelli, Dario; Montangero, Simone

2018-01-01

We provide evidence that randomized low-rank factorization is a powerful tool for the determination of the ground-state properties of low-dimensional lattice Hamiltonians through tensor network techniques. In particular, we show that randomized matrix factorization outperforms truncated singular value decomposition based on state-of-the-art deterministic routines in time-evolving block decimation (TEBD)- and density matrix renormalization group (DMRG)-style simulations, even when the system under study gets close to a phase transition: We report linear speedups in the bond or local dimension of up to 24 times in quasi-two-dimensional cylindrical systems.

Identification of water quality management policy of watershed system with multiple uncertain interactions using a multi-level-factorial risk-inference-based possibilistic-probabilistic programming approach.

PubMed

Liu, Jing; Li, Yongping; Huang, Guohe; Fu, Haiyan; Zhang, Junlong; Cheng, Guanhui

2017-06-01

In this study, a multi-level-factorial risk-inference-based possibilistic-probabilistic programming (MRPP) method is proposed for supporting water quality management under multiple uncertainties. The MRPP method can handle uncertainties expressed as fuzzy-random-boundary intervals, probability distributions, and interval numbers, and analyze the effects of uncertainties as well as their interactions on modeling outputs. It is applied to plan water quality management in the Xiangxihe watershed. Results reveal that a lower probability of satisfying the objective function (θ) as well as a higher probability of violating environmental constraints (q i ) would correspond to a higher system benefit with an increased risk of violating system feasibility. Chemical plants are the major contributors to biological oxygen demand (BOD) and total phosphorus (TP) discharges; total nitrogen (TN) would be mainly discharged by crop farming. It is also discovered that optimistic decision makers should pay more attention to the interactions between chemical plant and water supply, while decision makers who possess a risk-averse attitude would focus on the interactive effect of q i and benefit of water supply. The findings can help enhance the model's applicability and identify a suitable water quality management policy for environmental sustainability according to the practical situations.

A probabilistic method for determining the volume fraction of pre-embedded capsules in self-healing materials

NASA Astrophysics Data System (ADS)

Lv, Zhong; Chen, Huisu

2014-10-01

Autonomous healing of cracks using pre-embedded capsules containing healing agent is becoming a promising approach to restore the strength of damaged structures. In addition to the material properties, the size and volume fraction of capsules influence crack healing in the matrix. Understanding the crack and capsule interaction is critical in the development and design of structures made of self-healing materials. Assuming that the pre-embedded capsules are randomly dispersed we theoretically model flat ellipsoidal crack interaction with capsules and determine the probability of a crack intersecting the pre-embedded capsules i.e. the self-healing probability. We also develop a probabilistic model of a crack simultaneously meeting with capsules and catalyst carriers in two-component self-healing system matrix. Using a risk-based healing approach, we determine the volume fraction and size of the pre-embedded capsules that are required to achieve a certain self-healing probability. To understand the effect of the shape of the capsules on self-healing we theoretically modeled crack interaction with spherical and cylindrical capsules. We compared the results of our theoretical model with Monte-Carlo simulations of crack interaction with capsules. The formulae presented in this paper will provide guidelines for engineers working with self-healing structures in material selection and sustenance.

Effects of delay and probability combinations on discounting in humans.

PubMed

Cox, David J; Dallery, Jesse

2016-10-01

To determine discount rates, researchers typically adjust the amount of an immediate or certain option relative to a delayed or uncertain option. Because this adjusting amount method can be relatively time consuming, researchers have developed more efficient procedures. One such procedure is a 5-trial adjusting delay procedure, which measures the delay at which an amount of money loses half of its value (e.g., $1000 is valued at $500 with a 10-year delay to its receipt). Experiment 1 (n=212) used 5-trial adjusting delay or probability tasks to measure delay discounting of losses, probabilistic gains, and probabilistic losses. Experiment 2 (n=98) assessed combined probabilistic and delayed alternatives. In both experiments, we compared results from 5-trial adjusting delay or probability tasks to traditional adjusting amount procedures. Results suggest both procedures produced similar rates of probability and delay discounting in six out of seven comparisons. A magnitude effect consistent with previous research was observed for probabilistic gains and losses, but not for delayed losses. Results also suggest that delay and probability interact to determine the value of money. Five-trial methods may allow researchers to assess discounting more efficiently as well as study more complex choice scenarios. Copyright © 2016 Elsevier B.V. All rights reserved.

Using a probabilistic approach in an ecological risk assessment simulation tool: test case for depleted uranium (DU).

PubMed

Fan, Ming; Thongsri, Tepwitoon; Axe, Lisa; Tyson, Trevor A

2005-06-01

A probabilistic approach was applied in an ecological risk assessment (ERA) to characterize risk and address uncertainty employing Monte Carlo simulations for assessing parameter and risk probabilistic distributions. This simulation tool (ERA) includes a Window's based interface, an interactive and modifiable database management system (DBMS) that addresses a food web at trophic levels, and a comprehensive evaluation of exposure pathways. To illustrate this model, ecological risks from depleted uranium (DU) exposure at the US Army Yuma Proving Ground (YPG) and Aberdeen Proving Ground (APG) were assessed and characterized. Probabilistic distributions showed that at YPG, a reduction in plant root weight is considered likely to occur (98% likelihood) from exposure to DU; for most terrestrial animals, likelihood for adverse reproduction effects ranges from 0.1% to 44%. However, for the lesser long-nosed bat, the effects are expected to occur (>99% likelihood) through the reduction in size and weight of offspring. Based on available DU data for the firing range at APG, DU uptake will not likely affect survival of aquatic plants and animals (<0.1% likelihood). Based on field and laboratory studies conducted at APG and YPG on pocket mice, kangaroo rat, white-throated woodrat, deer, and milfoil, body burden concentrations observed fall into the distributions simulated at both sites.

Capturing changes in flood risk with Bayesian approaches for flood damage assessment

NASA Astrophysics Data System (ADS)

Vogel, Kristin; Schröter, Kai; Kreibich, Heidi; Thieken, Annegret; Müller, Meike; Sieg, Tobias; Laudan, Jonas; Kienzler, Sarah; Weise, Laura; Merz, Bruno; Scherbaum, Frank

2016-04-01

Flood risk is a function of hazard as well as of exposure and vulnerability. All three components are under change over space and time and have to be considered for reliable damage estimations and risk analyses, since this is the basis for an efficient, adaptable risk management. Hitherto, models for estimating flood damage are comparatively simple and cannot sufficiently account for changing conditions. The Bayesian network approach allows for a multivariate modeling of complex systems without relying on expert knowledge about physical constraints. In a Bayesian network each model component is considered to be a random variable. The way of interactions between those variables can be learned from observations or be defined by expert knowledge. Even a combination of both is possible. Moreover, the probabilistic framework captures uncertainties related to the prediction and provides a probability distribution for the damage instead of a point estimate. The graphical representation of Bayesian networks helps to study the change of probabilities for changing circumstances and may thus simplify the communication between scientists and public authorities. In the framework of the DFG-Research Training Group "NatRiskChange" we aim to develop Bayesian networks for flood damage and vulnerability assessments of residential buildings and companies under changing conditions. A Bayesian network learned from data, collected over the last 15 years in flooded regions in the Elbe and Danube catchments (Germany), reveals the impact of many variables like building characteristics, precaution and warning situation on flood damage to residential buildings. While the handling of incomplete and hybrid (discrete mixed with continuous) data are the most challenging issues in the study on residential buildings, a similar study, that focuses on the vulnerability of small to medium sized companies, bears new challenges. Relying on a much smaller data set for the determination of the model parameters, overly complex models should be avoided. A so called Markov Blanket approach aims at the identification of the most relevant factors and constructs a Bayesian network based on those findings. With our approach we want to exploit a major advantage of Bayesian networks which is their ability to consider dependencies not only pairwise, but to capture the joint effects and interactions of driving forces. Hence, the flood damage network does not only show the impact of precaution on the building damage separately, but also reveals the mutual effects of precaution and the quality of warning for a variety of flood settings. Thus, it allows for a consideration of changing conditions and different courses of action and forms a novel and valuable tool for decision support. This study is funded by the Deutsche Forschungsgemeinschaft (DFG) within the research training program GRK 2043/1 "NatRiskChange - Natural hazards and risks in a changing world" at the University of Potsdam.

Recursive Bayesian recurrent neural networks for time-series modeling.

PubMed

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.