Defining the association of TMEM106B variants among frontotemporal lobar degeneration patients with GRN mutations and C9orf72 repeat expansions.

Science.gov (United States)

Lattante, Serena; Le Ber, Isabelle; Galimberti, Daniela; Serpente, Maria; Rivaud-Péchoux, Sophie; Camuzat, Agnès; Clot, Fabienne; Fenoglio, Chiara; Scarpini, Elio; Brice, Alexis; Kabashi, Edor

2014-11-01

TMEM106B was identified as a risk factor for frontotemporal lobar degeneration (FTD) with TAR DNA-binding protein 43 kDa inclusions. It has been reported that variants in this gene are genetic modifiers of the disease and that this association is stronger in patients carrying a GRN mutation or a pathogenic expansion in chromosome 9 open reading frame 72 (C9orf72) gene. Here, we investigated the contribution of TMEM106B polymorphisms in cohorts of FTD and FTD with amyotrophic lateral sclerosis patients from France and Italy. Patients carrying the C9orf72 expansion (n = 145) and patients with GRN mutations (n = 76) were compared with a group of FTD patients (n = 384) negative for mutations and to a group of healthy controls (n = 552). In our cohorts, the presence of the C9orf72 expansion did not correlate with TMEM106B genotypes but the association was very strong in individuals with pathogenic GRN mutations (p = 9.54 × 10(-6)). Our data suggest that TMEM106B genotypes differ in FTD patient cohorts and strengthen the protective role of TMEM106B in GRN carriers. Further studies are needed to determine whether TMEM106B polymorphisms are associated with other genetic causes for FTD, including C9orf72 repeat expansions. Copyright © 2014 Elsevier Inc. All rights reserved.

New PDE-based methods for image enhancement using SOM and Bayesian inference in various discretization schemes

International Nuclear Information System (INIS)

Karras, D A; Mertzios, G B

2009-01-01

A novel approach is presented in this paper for improving anisotropic diffusion PDE models, based on the Perona–Malik equation. A solution is proposed from an engineering perspective to adaptively estimate the parameters of the regularizing function in this equation. The goal of such a new adaptive diffusion scheme is to better preserve edges when the anisotropic diffusion PDE models are applied to image enhancement tasks. The proposed adaptive parameter estimation in the anisotropic diffusion PDE model involves self-organizing maps and Bayesian inference to define edge probabilities accurately. The proposed modifications attempt to capture not only simple edges but also difficult textural edges and incorporate their probability in the anisotropic diffusion model. In the context of the application of PDE models to image processing such adaptive schemes are closely related to the discrete image representation problem and the investigation of more suitable discretization algorithms using constraints derived from image processing theory. The proposed adaptive anisotropic diffusion model illustrates these concepts when it is numerically approximated by various discretization schemes in a database of magnetic resonance images (MRI), where it is shown to be efficient in image filtering and restoration applications

Entropic Inference

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Caticha, Ariel

2011-03-01

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

Active inference and learning.

Science.gov (United States)

Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni

2016-09-01

This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Single-cell and coupled GRN models of cell patterning in the Arabidopsis thaliana root stem cell niche

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Alvarez-Buylla Elena R

2010-10-01

Full Text Available Abstract Background Recent experimental work has uncovered some of the genetic components required to maintain the Arabidopsis thaliana root stem cell niche (SCN and its structure. Two main pathways are involved. One pathway depends on the genes SHORTROOT and SCARECROW and the other depends on the PLETHORA genes, which have been proposed to constitute the auxin readouts. Recent evidence suggests that a regulatory circuit, composed of WOX5 and CLE40, also contributes to the SCN maintenance. Yet, we still do not understand how the niche is dynamically maintained and patterned or if the uncovered molecular components are sufficient to recover the observed gene expression configurations that characterize the cell types within the root SCN. Mathematical and computational tools have proven useful in understanding the dynamics of cell differentiation. Hence, to further explore root SCN patterning, we integrated available experimental data into dynamic Gene Regulatory Network (GRN models and addressed if these are sufficient to attain observed gene expression configurations in the root SCN in a robust and autonomous manner. Results We found that an SCN GRN model based only on experimental data did not reproduce the configurations observed within the root SCN. We developed several alternative GRN models that recover these expected stable gene configurations. Such models incorporate a few additional components and interactions in addition to those that have been uncovered. The recovered configurations are stable to perturbations, and the models are able to recover the observed gene expression profiles of almost all the mutants described so far. However, the robustness of the postulated GRNs is not as high as that of other previously studied networks. Conclusions These models are the first published approximations for a dynamic mechanism of the A. thaliana root SCN cellular pattering. Our model is useful to formally show that the data now available are not

Potential genetic modifiers of disease risk and age at onset in patients with frontotemporal lobar degeneration and GRN mutations: a genome-wide association study.

Science.gov (United States)

Pottier, Cyril; Zhou, Xiaolai; Perkerson, Ralph B; Baker, Matt; Jenkins, Gregory D; Serie, Daniel J; Ghidoni, Roberta; Benussi, Luisa; Binetti, Giuliano; López de Munain, Adolfo; Zulaica, Miren; Moreno, Fermin; Le Ber, Isabelle; Pasquier, Florence; Hannequin, Didier; Sánchez-Valle, Raquel; Antonell, Anna; Lladó, Albert; Parsons, Tammee M; Finch, NiCole A; Finger, Elizabeth C; Lippa, Carol F; Huey, Edward D; Neumann, Manuela; Heutink, Peter; Synofzik, Matthis; Wilke, Carlo; Rissman, Robert A; Slawek, Jaroslaw; Sitek, Emilia; Johannsen, Peter; Nielsen, Jørgen E; Ren, Yingxue; van Blitterswijk, Marka; DeJesus-Hernandez, Mariely; Christopher, Elizabeth; Murray, Melissa E; Bieniek, Kevin F; Evers, Bret M; Ferrari, Camilla; Rollinson, Sara; Richardson, Anna; Scarpini, Elio; Fumagalli, Giorgio G; Padovani, Alessandro; Hardy, John; Momeni, Parastoo; Ferrari, Raffaele; Frangipane, Francesca; Maletta, Raffaele; Anfossi, Maria; Gallo, Maura; Petrucelli, Leonard; Suh, EunRan; Lopez, Oscar L; Wong, Tsz H; van Rooij, Jeroen G J; Seelaar, Harro; Mead, Simon; Caselli, Richard J; Reiman, Eric M; Noel Sabbagh, Marwan; Kjolby, Mads; Nykjaer, Anders; Karydas, Anna M; Boxer, Adam L; Grinberg, Lea T; Grafman, Jordan; Spina, Salvatore; Oblak, Adrian; Mesulam, M-Marsel; Weintraub, Sandra; Geula, Changiz; Hodges, John R; Piguet, Olivier; Brooks, William S; Irwin, David J; Trojanowski, John Q; Lee, Edward B; Josephs, Keith A; Parisi, Joseph E; Ertekin-Taner, Nilüfer; Knopman, David S; Nacmias, Benedetta; Piaceri, Irene; Bagnoli, Silvia; Sorbi, Sandro; Gearing, Marla; Glass, Jonathan; Beach, Thomas G; Black, Sandra E; Masellis, Mario; Rogaeva, Ekaterina; Vonsattel, Jean-Paul; Honig, Lawrence S; Kofler, Julia; Bruni, Amalia C; Snowden, Julie; Mann, David; Pickering-Brown, Stuart; Diehl-Schmid, Janine; Winkelmann, Juliane; Galimberti, Daniela; Graff, Caroline; Öijerstedt, Linn; Troakes, Claire; Al-Sarraj, Safa; Cruchaga, Carlos; Cairns, Nigel J; Rohrer, Jonathan D; Halliday, Glenda M; Kwok, John B; van Swieten, John C; White, Charles L; Ghetti, Bernardino; Murell, Jill R; Mackenzie, Ian R A; Hsiung, Ging-Yuek R; Borroni, Barbara; Rossi, Giacomina; Tagliavini, Fabrizio; Wszolek, Zbigniew K; Petersen, Ronald C; Bigio, Eileen H; Grossman, Murray; Van Deerlin, Vivianna M; Seeley, William W; Miller, Bruce L; Graff-Radford, Neill R; Boeve, Bradley F; Dickson, Dennis W; Biernacka, Joanna M; Rademakers, Rosa

2018-06-01

Loss-of-function mutations in GRN cause frontotemporal lobar degeneration (FTLD). Patients with GRN mutations present with a uniform subtype of TAR DNA-binding protein 43 (TDP-43) pathology at autopsy (FTLD-TDP type A); however, age at onset and clinical presentation are variable, even within families. We aimed to identify potential genetic modifiers of disease onset and disease risk in GRN mutation carriers. The study was done in three stages: a discovery stage, a replication stage, and a meta-analysis of the discovery and replication data. In the discovery stage, genome-wide logistic and linear regression analyses were done to test the association of genetic variants with disease risk (case or control status) and age at onset in patients with a GRN mutation and controls free of neurodegenerative disorders. Suggestive loci (p<1 × 10 -5 ) were genotyped in a replication cohort of patients and controls, followed by a meta-analysis. The effect of genome-wide significant variants at the GFRA2 locus on expression of GFRA2 was assessed using mRNA expression studies in cerebellar tissue samples from the Mayo Clinic brain bank. The effect of the GFRA2 locus on progranulin concentrations was studied using previously generated ELISA-based expression data. Co-immunoprecipitation experiments in HEK293T cells were done to test for a direct interaction between GFRA2 and progranulin. Individuals were enrolled in the current study between Sept 16, 2014, and Oct 5, 2017. After quality control measures, statistical analyses in the discovery stage included 382 unrelated symptomatic GRN mutation carriers and 1146 controls free of neurodegenerative disorders collected from 34 research centres located in the USA, Canada, Australia, and Europe. In the replication stage, 210 patients (67 symptomatic GRN mutation carriers and 143 patients with FTLD without GRN mutations pathologically confirmed as FTLD-TDP type A) and 1798 controls free of neurodegenerative diseases were recruited

Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.

Science.gov (United States)

Xenitidis, P; Seimenis, I; Kakolyris, S; Adamopoulos, A

2017-08-07

High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time domain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. Copyright © 2017 Elsevier

Bayesian inference for hybrid discrete-continuous stochastic kinetic models

International Nuclear Information System (INIS)

Sherlock, Chris; Golightly, Andrew; Gillespie, Colin S

2014-01-01

We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump process (MJP), computational cost can be prohibitive for networks of realistic size and complexity. In this paper, we consider an inference scheme based on a novel hybrid simulator that classifies reactions as either ‘fast’ or ‘slow’ with fast reactions evolving as a continuous Markov process whilst the remaining slow reaction occurrences are modelled through a MJP with time-dependent hazards. A linear noise approximation (LNA) of fast reaction dynamics is employed and slow reaction events are captured by exploiting the ability to solve the stochastic differential equation driving the LNA. This simulation procedure is used as a proposal mechanism inside a particle MCMC scheme, thus allowing Bayesian inference for the model parameters. We apply the scheme to a simple application and compare the output with an existing hybrid approach and also a scheme for performing inference for the underlying discrete stochastic model. (paper)

A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data.

Science.gov (United States)

Baur, Brittany; Bozdag, Serdar

2015-04-01

One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.

Progranulin plasma levels predict the presence of GRN mutations in asymptomatic subjects and do not correlate with brain atrophy: Results from the GENFI study

NARCIS (Netherlands)

D. Galimberti (Daniela); Fumagalli, G.G. (Giorgio G.); C. Fenoglio (Chiara); Cioffi, S.M.G. (Sara M.G.); A. Arighi (Andrea); M. Serpente (Maria); B. Borroni (Barbara); A. Padovani (Alessandro); F. Tagliavini (Fabrizio); M. Masellis (Mario); M.C. Tartaglia (Maria Carmela); J.C. van Swieten (John); L.H.H. Meeter (Lieke H.H.); C. Graff (Caroline); A. De Mendonça (Alexandre); M. Bocchetta (Martina); J.D. Rohrer (Jonathan Daniel); Scarpini, E. (Elio)

2017-01-01

textabstractWe investigated whether progranulin plasma levels are predictors of the presence of progranulin gene (GRN) null mutations or of the development of symptoms in asymptomatic at risk members participating in the Genetic Frontotemporal Dementia Initiative, including 19 patients, 64

Isolation and Characterization of Two Lytic Bacteriophages, φSt2 and φGrn1; Phage Therapy Application for Biological Control of Vibrio alginolyticus in Aquaculture Live Feeds.

Directory of Open Access Journals (Sweden)

Panos G Kalatzis

Full Text Available Bacterial infections are a serious problem in aquaculture since they can result in massive mortalities in farmed fish and invertebrates. Vibriosis is one of the most common diseases in marine aquaculture hatcheries and its causative agents are bacteria of the genus Vibrio mostly entering larval rearing water through live feeds, such as Artemia and rotifers. The pathogenic Vibrio alginolyticus strain V1, isolated during a vibriosis outbreak in cultured seabream, Sparus aurata, was used as host to isolate and characterize the two novel bacteriophages φSt2 and φGrn1 for phage therapy application. In vitro cell lysis experiments were performed against the bacterial host V. alginolyticus strain V1 but also against 12 presumptive Vibrio strains originating from live prey Artemia salina cultures indicating the strong lytic efficacy of the 2 phages. In vivo administration of the phage cocktail, φSt2 and φGrn1, at MOI = 100 directly on live prey A. salina cultures, led to a 93% decrease of presumptive Vibrio population after 4 h of treatment. Current study suggests that administration of φSt2 and φGrn1 to live preys could selectively reduce Vibrio load in fish hatcheries. Innovative and environmental friendly solutions against bacterial diseases are more than necessary and phage therapy is one of them.

The anatomy of choice: active inference and agency

Directory of Open Access Journals (Sweden)

Karl eFriston

2013-09-01

Full Text Available This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behaviour. In particular, we consider prior beliefs that action minimises the Kullback-Leibler divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimises a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimising free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action – constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualises optimal decision theory and economic (utilitarian formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimisation, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria has a unique and Bayes-optimal solution – that minimises free energy. This sensitivity corresponds to the precision of beliefs about behaviour, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behaviour entails a representation of confidence about outcomes that are under an agent's control.

The anatomy of choice: active inference and agency.

Science.gov (United States)

Friston, Karl; Schwartenbeck, Philipp; Fitzgerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

Using polarimetric radar observations and probabilistic inference to develop the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS), a novel microphysical parameterization framework

Science.gov (United States)

van Lier-Walqui, M.; Morrison, H.; Kumjian, M. R.; Prat, O. P.

2016-12-01

Microphysical parameterization schemes have reached an impressive level of sophistication: numerous prognostic hydrometeor categories, and either size-resolved (bin) particle size distributions, or multiple prognostic moments of the size distribution. Yet, uncertainty in model representation of microphysical processes and the effects of microphysics on numerical simulation of weather has not shown a improvement commensurate with the advanced sophistication of these schemes. We posit that this may be caused by unconstrained assumptions of these schemes, such as ad-hoc parameter value choices and structural uncertainties (e.g. choice of a particular form for the size distribution). We present work on development and observational constraint of a novel microphysical parameterization approach, the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS), which seeks to address these sources of uncertainty. Our framework avoids unnecessary a priori assumptions, and instead relies on observations to provide probabilistic constraint of the scheme structure and sensitivities to environmental and microphysical conditions. We harness the rich microphysical information content of polarimetric radar observations to develop and constrain BOSS within a Bayesian inference framework using a Markov Chain Monte Carlo sampler (see Kumjian et al., this meeting for details on development of an associated polarimetric forward operator). Our work shows how knowledge of microphysical processes is provided by polarimetric radar observations of diverse weather conditions, and which processes remain highly uncertain, even after considering observations.

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment.

Science.gov (United States)

Yao, Yao; Storme, Veronique; Marchal, Kathleen; Van de Peer, Yves

2016-01-01

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

Science.gov (United States)

Yao, Yao; Storme, Veronique; Marchal, Kathleen

2016-01-01

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population. PMID:28028477

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

Directory of Open Access Journals (Sweden)

Yao Yao

2016-12-01

Full Text Available We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Hybrid Optical Inference Machines

Science.gov (United States)

1991-09-27

with labels. Now, events. a set of facts cal be generated in the dyadic form "u, R 1,2" Eichmann and Caulfield (19] consider the same type of and can...these enceding-schemes. These architectures are-based pri- 19. G. Eichmann and H. J. Caulfield, "Optical Learning (Inference)marily on optical inner

Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

Science.gov (United States)

Chen, Chi-Kan

2017-07-26

The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time series. We present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RE RNN /RE RMLP ), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RE RNN -RNN and RE RMLP -RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair genes. The unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RE RMLP using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RE RNN using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RE RMLP -RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The framework of two

Progranulin plasma levels predict the presence of GRN mutations in asymptomatic subjects and do not correlate with brain atrophy: results from the GENFI study.

Science.gov (United States)

Galimberti, Daniela; Fumagalli, Giorgio G; Fenoglio, Chiara; Cioffi, Sara M G; Arighi, Andrea; Serpente, Maria; Borroni, Barbara; Padovani, Alessandro; Tagliavini, Fabrizio; Masellis, Mario; Tartaglia, Maria Carmela; van Swieten, John; Meeter, Lieke; Graff, Caroline; de Mendonça, Alexandre; Bocchetta, Martina; Rohrer, Jonathan D; Scarpini, Elio

2018-02-01

We investigated whether progranulin plasma levels are predictors of the presence of progranulin gene (GRN) null mutations or of the development of symptoms in asymptomatic at risk members participating in the Genetic Frontotemporal Dementia Initiative, including 19 patients, 64 asymptomatic carriers, and 77 noncarriers. In addition, we evaluated a possible role of TMEM106B rs1990622 as a genetic modifier and correlated progranulin plasma levels and gray-matter atrophy. Plasma progranulin mean ± SD plasma levels in patients and asymptomatic carriers were significantly decreased compared with noncarriers (30.5 ± 13.0 and 27.7 ± 7.5 versus 99.6 ± 24.8 ng/mL, p 61.55 ng/mL, the test had a sensitivity of 98.8% and a specificity of 97.5% in predicting the presence of a mutation, independent of symptoms. No correlations were found between progranulin plasma levels and age, years from average age at onset in each family, or TMEM106B rs1990622 genotype (p > 0.05). Plasma progranulin levels did not correlate with brain atrophy. Plasma progranulin levels predict the presence of GRN null mutations independent of proximity to symptoms and brain atrophy. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Mathematical inference and control of molecular networks from perturbation experiments

Science.gov (United States)

Mohammed-Rasheed, Mohammed

One of the main challenges facing biologists and mathematicians in the post genomic era is to understand the behavior of molecular networks and harness this understanding into an educated intervention of the cell. The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks (GRNs) or molecular networks. GRNs can be viewed as dynamical systems with inherent properties and mechanisms, such as steady-state equilibriums and stability, that determine the behavior of the cell. The biological relevance of the mathematical concepts are important as they may predict the differentiation of a stem cell, the maintenance of a normal cell, the development of cancer and its aberrant behavior, and the design of drugs and response to therapy. Uncovering the underlying GRN structure from gene/protein expression data, e.g., microarrays or perturbation experiments, is called inference or reverse engineering of the molecular network. Because of the high cost and time consuming nature of biological experiments, the number of available measurements or experiments is very small compared to the number of molecules (genes, RNA and proteins). In addition, the observations are noisy, where the noise is due to the measurements imperfections as well as the inherent stochasticity of genetic expression levels. Intra-cellular activities and extra-cellular environmental attributes are also another source of variability. Thus, the inference of GRNs is, in general, an under-determined problem with a highly noisy set of observations. The ultimate goal of GRN inference and analysis is to be able to intervene within the network, in order to force it away from undesirable cellular states and into desirable ones. However, it remains a major challenge to design optimal intervention strategies

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems.

Science.gov (United States)

Salleh, Faridah Hani Mohamed; Zainudin, Suhaila; Arif, Shereena M

2017-01-01

Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

Directory of Open Access Journals (Sweden)

Faridah Hani Mohamed Salleh

2017-01-01

Full Text Available Gene regulatory network (GRN reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C as a direct interaction (A → C. Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.

Exact nonparametric inference for detection of nonlinear determinism

OpenAIRE

Luo, Xiaodong; Zhang, Jie; Small, Michael; Moroz, Irene

2005-01-01

We propose an exact nonparametric inference scheme for the detection of nonlinear determinism. The essential fact utilized in our scheme is that, for a linear stochastic process with jointly symmetric innovations, its ordinary least square (OLS) linear prediction error is symmetric about zero. Based on this viewpoint, a class of linear signed rank statistics, e.g. the Wilcoxon signed rank statistic, can be derived with the known null distributions from the prediction error. Thus one of the ad...

Improved Inference of Heteroscedastic Fixed Effects Models

Directory of Open Access Journals (Sweden)

Afshan Saeed

2016-12-01

Full Text Available Heteroscedasticity is a stern problem that distorts estimation and testing of panel data model (PDM. Arellano (1987 proposed the White (1980 estimator for PDM with heteroscedastic errors but it provides erroneous inference for the data sets including high leverage points. In this paper, our attempt is to improve heteroscedastic consistent covariance matrix estimator (HCCME for panel dataset with high leverage points. To draw robust inference for the PDM, our focus is to improve kernel bootstrap estimators, proposed by Racine and MacKinnon (2007. The Monte Carlo scheme is used for assertion of the results.

Surrogate based approaches to parameter inference in ocean models

KAUST Repository

Knio, Omar

2016-01-06

This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.

Surrogate based approaches to parameter inference in ocean models

KAUST Repository

Knio, Omar

2016-01-01

This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.

Bayesian inference for spatio-temporal spike-and-slab priors

DEFF Research Database (Denmark)

Andersen, Michael Riis; Vehtari, Aki; Winther, Ole

2017-01-01

a transformed Gaussian process on the spike-and-slab probabilities. An expectation propagation (EP) algorithm for posterior inference under the proposed model is derived. For large scale problems, the standard EP algorithm can be prohibitively slow. We therefore introduce three different approximation schemes...

Implementing and analyzing the multi-threaded LP-inference

Science.gov (United States)

Bolotova, S. Yu; Trofimenko, E. V.; Leschinskaya, M. V.

2018-03-01

The logical production equations provide new possibilities for the backward inference optimization in intelligent production-type systems. The strategy of a relevant backward inference is aimed at minimization of a number of queries to external information source (either to a database or an interactive user). The idea of the method is based on the computing of initial preimages set and searching for the true preimage. The execution of each stage can be organized independently and in parallel and the actual work at a given stage can also be distributed between parallel computers. This paper is devoted to the parallel algorithms of the relevant inference based on the advanced scheme of the parallel computations “pipeline” which allows to increase the degree of parallelism. The author also provides some details of the LP-structures implementation.

Children's schemes for anticipating the validity of nets for solids

Science.gov (United States)

Wright, Vince; Smith, Ken

2017-09-01

There is growing acknowledgement of the importance of spatial abilities to student achievement across a broad range of domains and disciplines. Nets are one way to connect three-dimensional shapes and their two-dimensional representations and are a common focus of geometry curricula. Thirty-four students at year 6 (upper primary school) were interviewed on two occasions about their anticipation of whether or not given nets for the cube- and square-based pyramid would fold to form the target solid. Vergnaud's ( Journal of Mathematical Behavior, 17(2), 167-181, 1998, Human Development, 52, 83-94, 2009) four characteristics of schemes were used as a theoretical lens to analyse the data. Successful schemes depended on the interaction of operational invariants, such as strategic choice of the base, rules for action, particularly rotation of shapes, and anticipations of composites of polygons in the net forming arrangements of faces in the solid. Inferences were rare. These data suggest that students need teacher support to make inferences, in order to create transferable schemes.

Inverse Ising inference with correlated samples

International Nuclear Information System (INIS)

Obermayer, Benedikt; Levine, Erel

2014-01-01

Correlations between two variables of a high-dimensional system can be indicative of an underlying interaction, but can also result from indirect effects. Inverse Ising inference is a method to distinguish one from the other. Essentially, the parameters of the least constrained statistical model are learned from the observed correlations such that direct interactions can be separated from indirect correlations. Among many other applications, this approach has been helpful for protein structure prediction, because residues which interact in the 3D structure often show correlated substitutions in a multiple sequence alignment. In this context, samples used for inference are not independent but share an evolutionary history on a phylogenetic tree. Here, we discuss the effects of correlations between samples on global inference. Such correlations could arise due to phylogeny but also via other slow dynamical processes. We present a simple analytical model to address the resulting inference biases, and develop an exact method accounting for background correlations in alignment data by combining phylogenetic modeling with an adaptive cluster expansion algorithm. We find that popular reweighting schemes are only marginally effective at removing phylogenetic bias, suggest a rescaling strategy that yields better results, and provide evidence that our conclusions carry over to the frequently used mean-field approach to the inverse Ising problem. (paper)

Dynamic droop scheme considering effect of intermittent renewable energy source

DEFF Research Database (Denmark)

Wang, Yanbo; Chen, Zhe; Deng, Fujin

2016-01-01

This paper presents a dynamic droop control scheme for islanded microgrids dominated by intermittent renewable energy sources, which is able to perform desirable power sharing in the presence of renewable energy source fluctuation. First, allowable maximum power points of wind generator and PV...... flexibility and effectiveness in the presence of the renewable energy sources fluctuation....... controller of each DG unit is activated through local logic variable inferred by wind speed and solar insolation information. Simulation results are given for validating the droop control scheme. The proposed dynamic droop scheme preserves the advantage of conventional droop control method, and provides...

Active Inference, Epistemic Value, and Vicarious Trial and Error

Science.gov (United States)

Pezzulo, Giovanni; Cartoni, Emilio; Rigoli, Francesco; io-Lopez, Léo; Friston, Karl

2016-01-01

Balancing habitual and deliberate forms of choice entails a comparison of their respective merits--the former being faster but inflexible, and the latter slower but more versatile. Here, we show that arbitration between these two forms of control can be derived from first principles within an Active Inference scheme. We illustrate our arguments…

Student Teachers’ Proof Schemes on Proof Tasks Involving Inequality: Deductive or Inductive?

Science.gov (United States)

Rosyidi, A. H.; Kohar, A. W.

2018-01-01

Exploring student teachers’ proof ability is crucial as it is important for improving the quality of their learning process and help their future students learn how to construct a proof. Hence, this study aims at exploring at the proof schemes of student teachers in the beginning of their studies. Data were collected from 130 proofs resulted by 65 Indonesian student teachers on two proof tasks involving algebraic inequality. To analyse, the proofs were classified into the refined proof schemes level proposed by Lee (2016) ranging from inductive, which only provides irrelevant inferences, to deductive proofs, which consider addressing formal representation. Findings present several examples of each of Lee’s level on the student teachers’ proofs spanning from irrelevant inferences, novice use of examples or logical reasoning, strategic use examples for reasoning, deductive inferences with major and minor logical coherence, and deductive proof with informal and formal representation. Besides, it was also found that more than half of the students’ proofs coded as inductive schemes, which does not meet the requirement for doing the proof for the proof tasks examined in this study. This study suggests teacher educators in teacher colleges to reform the curriculum regarding proof learning which can accommodate the improvement of student teachers’ proving ability from inductive to deductive proof as well from informal to formal proof.

Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

Science.gov (United States)

Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar

2017-08-01

Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

Dopamine, reward learning, and active inference.

Science.gov (United States)

FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

Dopamine, reward learning, and active inference

Directory of Open Access Journals (Sweden)

Thomas eFitzgerald

2015-11-01

Full Text Available Temporal difference learning models propose phasic dopamine signalling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behaviour. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

The confounding effect of population structure on bayesian skyline plot inferences of demographic history

DEFF Research Database (Denmark)

Heller, Rasmus; Chikhi, Lounes; Siegismund, Hans

2013-01-01

Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading...... when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences...... the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure...

A Self-adaptive Scope Allocation Scheme for Labeling Dynamic XML Documents

NARCIS (Netherlands)

Shen, Y.; Feng, L.; Shen, T.; Wang, B.

This paper proposes a self-adaptive scope allocation scheme for labeling dynamic XML documents. It is general, light-weight and can be built upon existing data retrieval mechanisms. Bayesian inference is used to compute the actual scope allocated for labeling a certain node based on both the prior

An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

Directory of Open Access Journals (Sweden)

Ali Safa Sadiq

2014-01-01

Full Text Available We propose an adaptive handover prediction (AHP scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Simple simulation of diffusion bridges with application to likelihood inference for diffusions

DEFF Research Database (Denmark)

Bladt, Mogens; Sørensen, Michael

2014-01-01

the accuracy and efficiency of the approximate method and compare it to exact simulation methods. In the study, our method provides a very good approximation to the distribution of a diffusion bridge for bridges that are likely to occur in applications to statistical inference. To illustrate the usefulness......With a view to statistical inference for discretely observed diffusion models, we propose simple methods of simulating diffusion bridges, approximately and exactly. Diffusion bridge simulation plays a fundamental role in likelihood and Bayesian inference for diffusion processes. First a simple......-dimensional diffusions and is applicable to all one-dimensional diffusion processes with finite speed-measure. One advantage of the new approach is that simple simulation methods like the Milstein scheme can be applied to bridge simulation. Another advantage over previous bridge simulation methods is that the proposed...

Active Inference and Learning in the Cerebellum.

Science.gov (United States)

Friston, Karl; Herreros, Ivan

2016-09-01

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

Structural and parameteric uncertainty quantification in cloud microphysics parameterization schemes

Science.gov (United States)

van Lier-Walqui, M.; Morrison, H.; Kumjian, M. R.; Prat, O. P.; Martinkus, C.

2017-12-01

Atmospheric model parameterization schemes employ approximations to represent the effects of unresolved processes. These approximations are a source of error in forecasts, caused in part by considerable uncertainty about the optimal value of parameters within each scheme -- parameteric uncertainty. Furthermore, there is uncertainty regarding the best choice of the overarching structure of the parameterization scheme -- structrual uncertainty. Parameter estimation can constrain the first, but may struggle with the second because structural choices are typically discrete. We address this problem in the context of cloud microphysics parameterization schemes by creating a flexible framework wherein structural and parametric uncertainties can be simultaneously constrained. Our scheme makes no assuptions about drop size distribution shape or the functional form of parametrized process rate terms. Instead, these uncertainties are constrained by observations using a Markov Chain Monte Carlo sampler within a Bayesian inference framework. Our scheme, the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS), has flexibility to predict various sets of prognostic drop size distribution moments as well as varying complexity of process rate formulations. We compare idealized probabilistic forecasts from versions of BOSS with varying levels of structural complexity. This work has applications in ensemble forecasts with model physics uncertainty, data assimilation, and cloud microphysics process studies.

A method of inferring k-infinity from reaction rate measurements in thermal reactor systems

International Nuclear Information System (INIS)

Newmarch, D.A.

1967-05-01

A scheme is described for inferring a value of k-infinity from reaction rate measurements. The method is devised with the METHUSELAH group structure in mind and was developed for the analysis of S.G.H.W. reactor experiments; the underlying principles, however, are general. (author)

Bayesian inference for Markov jump processes with informative observations.

Science.gov (United States)

Golightly, Andrew; Wilkinson, Darren J

2015-04-01

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

Inferring Pre-shock Acoustic Field From Post-shock Pitot Pressure Measurement

Science.gov (United States)

Wang, Jian-Xun; Zhang, Chao; Duan, Lian; Xiao, Heng; Virginia Tech Team; Missouri Univ of Sci; Tech Team

2017-11-01

Linear interaction analysis (LIA) and iterative ensemble Kalman method are used to convert post-shock Pitot pressure fluctuations to static pressure fluctuations in front of the shock. The LIA is used as the forward model for the transfer function associated with a homogeneous field of acoustic waves passing through a nominally normal shock wave. The iterative ensemble Kalman method is then employed to infer the spectrum of upstream acoustic waves based on the post-shock Pitot pressure measured at a single point. Several test cases with synthetic and real measurement data are used to demonstrate the merits of the proposed inference scheme. The study provides the basis for measuring tunnel freestream noise with intrusive probes in noisy supersonic wind tunnels.

Gauging Variational Inference

Energy Technology Data Exchange (ETDEWEB)

Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Ahn, Sungsoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Shin, Jinwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)

2017-05-25

Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where meanfield (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.

Quantum Enhanced Inference in Markov Logic Networks.

Science.gov (United States)

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

Science.gov (United States)

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.

Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient.

Science.gov (United States)

Mohamed Salleh, Faridah Hani; Arif, Shereena Mohd; Zainudin, Suhaila; Firdaus-Raih, Mohd

2015-12-01

A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5. Copyright © 2015 Elsevier Ltd. All rights reserved.

Surface radiant flux densities inferred from LAC and GAC AVHRR data

Science.gov (United States)

Berger, F.; Klaes, D.

To infer surface radiant flux densities from current (NOAA-AVHRR, ERS-1/2 ATSR) and future meteorological (Envisat AATSR, MSG, METOP) satellite data, the complex, modular analysis scheme SESAT (Strahlungs- und Energieflüsse aus Satellitendaten) could be developed (Berger, 2001). This scheme allows the determination of cloud types, optical and microphysical cloud properties as well as surface and TOA radiant flux densities. After testing of SESAT in Central Europe and the Baltic Sea catchment (more than 400scenes U including a detailed validation with various surface measurements) it could be applied to a large number of NOAA-16 AVHRR overpasses covering the globe.For the analysis, two different spatial resolutions U local area coverage (LAC) andwere considered. Therefore, all inferred results, like global area coverage (GAC) U cloud cover, cloud properties and radiant properties, could be intercompared. Specific emphasis could be made to the surface radiant flux densities (all radiative balance compoments), where results for different regions, like Southern America, Southern Africa, Northern America, Europe, and Indonesia, will be presented. Applying SESAT, energy flux densities, like latent and sensible heat flux densities could also be determined additionally. A statistical analysis of all results including a detailed discussion for the two spatial resolutions will close this study.

Protein-DNA binding dynamics predict transcriptional response to nutrients in archaea.

Science.gov (United States)

Todor, Horia; Sharma, Kriti; Pittman, Adrianne M C; Schmid, Amy K

2013-10-01

Organisms across all three domains of life use gene regulatory networks (GRNs) to integrate varied stimuli into coherent transcriptional responses to environmental pressures. However, inferring GRN topology and regulatory causality remains a central challenge in systems biology. Previous work characterized TrmB as a global metabolic transcription factor in archaeal extremophiles. However, it remains unclear how TrmB dynamically regulates its ∼100 metabolic enzyme-coding gene targets. Using a dynamic perturbation approach, we elucidate the topology of the TrmB metabolic GRN in the model archaeon Halobacterium salinarum. Clustering of dynamic gene expression patterns reveals that TrmB functions alone to regulate central metabolic enzyme-coding genes but cooperates with various regulators to control peripheral metabolic pathways. Using a dynamical model, we predict gene expression patterns for some TrmB-dependent promoters and infer secondary regulators for others. Our data suggest feed-forward gene regulatory topology for cobalamin biosynthesis. In contrast, purine biosynthesis appears to require TrmB-independent regulators. We conclude that TrmB is an important component for mediating metabolic modularity, integrating nutrient status and regulating gene expression dynamics alone and in concert with secondary regulators.

Outcome-Dependent Sampling Design and Inference for Cox's Proportional Hazards Model.

Science.gov (United States)

Yu, Jichang; Liu, Yanyan; Cai, Jianwen; Sandler, Dale P; Zhou, Haibo

2016-11-01

We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design. To account for the biased sampling scheme, we derive estimators from a weighted partial likelihood estimating equation. The proposed estimators for regression parameters are shown to be consistent and asymptotically normally distributed. A criteria that can be used to optimally implement the ODS design in practice is proposed and studied. The small sample performance of the proposed method is evaluated by simulation studies. The proposed design and inference procedure is shown to be statistically more powerful than existing alternative designs with the same sample sizes. We illustrate the proposed method with an existing real data from the Cancer Incidence and Mortality of Uranium Miners Study.

A perturbative study of two four-quark operators in finite volume renormalization schemes

CERN Document Server

Palombi, Filippo; Sint, S

2006-01-01

Starting from the QCD Schroedinger functional (SF), we define a family of renormalization schemes for two four-quark operators, which are, in the chiral limit, protected against mixing with other operators. With the appropriate flavour assignments these operators can be interpreted as part of either the $\\Delta F=1$ or $\\Delta F=2$ effective weak Hamiltonians. In view of lattice QCD with Wilson-type quarks, we focus on the parity odd components of the operators, since these are multiplicatively renormalized both on the lattice and in continuum schemes. We consider 9 different SF schemes and relate them to commonly used continuum schemes at one-loop order of perturbation theory. In this way the two-loop anomalous dimensions in the SF schemes can be inferred. As a by-product of our calculation we also obtain the one-loop cutoff effects in the step-scaling functions of the respective renormalization constants, for both O(a) improved and unimproved Wilson quarks. Our results will be needed in a separate study of ...

Fuzzy inference game approach to uncertainty in business decisions and market competitions.

Science.gov (United States)

Oderanti, Festus Oluseyi

2013-01-01

The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

Science.gov (United States)

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Post-model selection inference and model averaging

Directory of Open Access Journals (Sweden)

Georges Nguefack-Tsague

2011-07-01

Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.

Outcome-Dependent Sampling Design and Inference for Cox’s Proportional Hazards Model

Science.gov (United States)

Yu, Jichang; Liu, Yanyan; Cai, Jianwen; Sandler, Dale P.; Zhou, Haibo

2016-01-01

We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design. To account for the biased sampling scheme, we derive estimators from a weighted partial likelihood estimating equation. The proposed estimators for regression parameters are shown to be consistent and asymptotically normally distributed. A criteria that can be used to optimally implement the ODS design in practice is proposed and studied. The small sample performance of the proposed method is evaluated by simulation studies. The proposed design and inference procedure is shown to be statistically more powerful than existing alternative designs with the same sample sizes. We illustrate the proposed method with an existing real data from the Cancer Incidence and Mortality of Uranium Miners Study. PMID:28090134

Response of multiferroic composites inferred from a fast-Fourier-transform-based numerical scheme

International Nuclear Information System (INIS)

Brenner, Renald; Bravo-Castillero, Julián

2010-01-01

The effective response and the local fields within periodic magneto-electric multiferroic composites are investigated by means of a numerical scheme based on fast Fourier transforms. This computational framework relies on the iterative resolution of coupled series expansions for the magnetic, electric and strain fields. By using an augmented Lagrangian formulation, a simple and robust procedure which makes use of the uncoupled Green operators for the elastic, electrostatics and magnetostatics problems is proposed. Its accuracy is assessed in the cases of laminated and fibrous two-phase composites for which analytical solutions exist

Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica.

Science.gov (United States)

Trébulle, Pauline; Nicaud, Jean-Marc; Leplat, Christophe; Elati, Mohamed

2017-01-01

Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica . This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica . Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein-protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and (c) experimentally validates the impact of six of the nine regulators identified on lipid accumulation, with variations in lipid content from +43.2% to -31.2% on glucose or glycerol.

Time clustered sampling can inflate the inferred substitution rate in foot-and-mouth disease virus analyses

DEFF Research Database (Denmark)

Pedersen, Casper-Emil Tingskov; Frandsen, Peter; Wekesa, Sabenzia N.

2015-01-01

abundance of sequence data sampled under widely different schemes, an effort to keep results consistent and comparable is needed. This study emphasizes commonly disregarded problems in the inference of evolutionary rates in viral sequence data when sampling is unevenly distributed on a temporal scale...... through a study of the foot-and-mouth (FMD) disease virus serotypes SAT 1 and SAT 2. Our study shows that clustered temporal sampling in phylogenetic analyses of FMD viruses will strongly bias the inferences of substitution rates and tMRCA because the inferred rates in such data sets reflect a rate closer...... to the mutation rate rather than the substitution rate. Estimating evolutionary parameters from viral sequences should be performed with due consideration of the differences in short-term and longer-term evolutionary processes occurring within sets of temporally sampled viruses, and studies should carefully...

Additive operator-difference schemes splitting schemes

CERN Document Server

Vabishchevich, Petr N

2013-01-01

Applied mathematical modeling isconcerned with solving unsteady problems. This bookshows how toconstruct additive difference schemes to solve approximately unsteady multi-dimensional problems for PDEs. Two classes of schemes are highlighted: methods of splitting with respect to spatial variables (alternating direction methods) and schemes of splitting into physical processes. Also regionally additive schemes (domain decomposition methods)and unconditionally stable additive schemes of multi-component splitting are considered for evolutionary equations of first and second order as well as for sy

Progranulin haploinsufficiency causes biphasic social dominance abnormalities in the tube test.

Science.gov (United States)

Arrant, A E; Filiano, A J; Warmus, B A; Hall, A M; Roberson, E D

2016-07-01

Loss-of-function mutations in progranulin (GRN) are a major autosomal dominant cause of frontotemporal dementia (FTD), a neurodegenerative disorder in which social behavior is disrupted. Progranulin-insufficient mice, both Grn(+/-) and Grn(-/-) , are used as models of FTD due to GRN mutations, with Grn(+/-) mice mimicking the progranulin haploinsufficiency of FTD patients with GRN mutations. Grn(+/-) mice have increased social dominance in the tube test at 6 months of age, although this phenotype has not been reported in Grn(-/-) mice. In this study, we investigated how the tube test phenotype of progranulin-insufficient mice changes with age, determined its robustness under several testing conditions, and explored the associated cellular mechanisms. We observed biphasic social dominance abnormalities in Grn(+/-) mice: at 6-8 months, Grn(+/-) mice were more dominant than wild-type littermates, while after 9 months of age, Grn(+/-) mice were less dominant. In contrast, Grn(-/-) mice did not exhibit abnormal social dominance, suggesting that progranulin haploinsufficiency has distinct effects from complete progranulin deficiency. The biphasic tube test phenotype of Grn(+/-) mice was associated with abnormal cellular signaling and neuronal morphology in the amygdala and prefrontal cortex. At 6-9 months, Grn(+/-) mice exhibited increased mTORC2/Akt signaling in the amygdala and enhanced dendritic arbors in the basomedial amygdala, and at 9-16 months Grn(+/-) mice exhibited diminished basal dendritic arbors in the prelimbic cortex. These data show a progressive change in tube test dominance in Grn(+/-) mice and highlight potential underlying mechanisms by which progranulin insufficiency may disrupt social behavior. © 2016 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Clonorchis sinensis granulin: identification, immunolocalization, and function in promoting the metastasis of cholangiocarcinoma and hepatocellular carcinoma.

Science.gov (United States)

Wang, Caiqin; Lei, Huali; Tian, Yanli; Shang, Mei; Wu, Yinjuan; Li, Ye; Zhao, Lu; Shi, Mengchen; Tang, Xin; Chen, Tingjin; Lv, Zhiyue; Huang, Yan; Tang, Xiaoping; Yu, Xinbing; Li, Xuerong

2017-05-25

Long-term infections by Clonorchis sinensis are associated with cholangitis, cholecystitis, liver fibrosis, cirrhosis, and even liver cancer. Molecules from the worm play vital roles in disease progress. In the present study, we identified and explored molecular characterization of C. sinensis granulin (CsGRN), a growth factor-like protein from C. sinensis excretory/secretory products (CsESPs). The encoding sequence and conserved domains of CsGRN were identified and analysed by bioinformatics tools. Recombinant CsGRN (rCsGRN) protein was expressed in Escherichia coli BL21 (DE3). The localisation of CsGRN in adult worms and Balb/c mice infected with C. sinensis was investigated by immunofluorescence and immunohistochemistry, respectively. Stable CsGRN-overexpressed cell lines of hepatoma cells (PLC-GRN cells) and cholangiocarcinoma cells (RBE-GRN cells) were constructed by transfection of eukaryotic expression plasmid of pEGFP-C1-CsGRN. The effects on cell migration and invasion of CsGRN were assessed through the wound-healing assay and transwell assay. The levels of matrix metalloproteinase 2 and 9 (MMP2 and MMP9) in PLC-GRN or RBE-GRN cells were detected by real-time PCR (qRT-PCR). The levels of E-cadherin, vimentin, N-cadherin, zona occludens proteins (ZO-1), β-catenin, phosphorylated ERK (p-ERK) and phosphorylated AKT (p-AKT) were analysed by Western blotting. CsGRN, including the conserved GRN domains, was confirmed to be a member of the granulin family. CsGRN was identified as an ingredient of CsESPs. CsGRN was localised in the tegument and testes of the adult worm. Furthermore, it appeared in the cytoplasm of hepatocytes and biliary epithelium cells from infected Balb/c mouse. The enhancement of cell migration and invasion of PLC-GRN and RBE-GRN cells were observed. In addition, CsGRN upregulated the levels of vimentin, N-cadherin, β-catenin, MMP2 and MMP9, while it downregulated the level of ZO-1 in PLC-GRN/RBE-GRN cells. In total proteins of liver tissue

Reassessment of MLST schemes for Leptospira spp. typing worldwide.

Science.gov (United States)

Varni, Vanina; Ruybal, Paula; Lauthier, Juan José; Tomasini, Nicolás; Brihuega, Bibiana; Koval, Ariel; Caimi, Karina

2014-03-01

Leptospirosis is a neglected zoonosis of global importance. Several multilocus sequence typing (MLST) methods have been developed for Leptospira spp., the causative agent of leptospirosis. In this study we reassessed the most commonly used MLST schemes in a set of worldwide isolates, in order to select the loci that achieve the maximum power of discrimination for typing Leptospira spp. Global eBURST algorithm was used to detect clonal complexes among STs and phylogenetic relationships among concatenated and individual sequences were inferred through maximum likelihood (ML) analysis. The evaluation of 12 loci combined to type a subset of strains rendered 57 different STs. Seven of these loci were selected into a final scheme upon studying the number of alleles and polymorphisms, the typing efficiency, the discriminatory power and the ratio dN/dS per nucleotide site for each locus. This new 7-locus scheme was applied to a wider collection of worldwide strains. The ML tree constructed from concatenated sequences of the 7 loci identified 6 major clusters corresponding to 6 Leptospira species. Global eBURST established 8 CCs, which showed that genotypes were clearly related by geographic origin and host. ST52 and ST47, represented mostly by Argentinian isolates, grouped the higher number of isolates. These isolates were serotyped as serogroups Pomona and Icterohaemorrhagiae, showing a unidirectional correlation in which the isolates with the same ST belong to the same serogroup. In summary, this scheme combines the best loci from the most widely used MLST schemes for Leptospira spp. and supports worldwide strains classification. The Argentinian isolates exhibited congruence between allelic profile and serogroup, providing an alternative to serological methods. Published by Elsevier B.V.

A first generation numerical geomagnetic storm prediction scheme

International Nuclear Information System (INIS)

Akasofu, S.-I.; Fry, C.F.

1986-01-01

Because geomagnetic and auroral disturbances cause significant interference on many electrical systems, it is essential to develop a reliable geomagnetic and auroral storm prediction scheme. A first generation numerical prediction scheme has been developed. The scheme consists of two major computer codes which in turn consist of a large number of subroutine codes and of empirical relationships. First of all, when a solar flare occurs, six flare parameters are determined as the input data set for the first code which is devised to show the simulated propagation of solar wind disturbances in the heliosphere to a distance of 2 a.u. Thus, one can determine the relative location of the propagating disturbances with the Earth's position. The solar wind speed and the three interplanetary magnetic field (IMF) components are then computed as a function of time at the Earth's location or any other desired (space probe) locations. These quantities in turn become the input parameters for the second major code which computes first the power of the solar wind-magnetosphere dynamo as a function of time. The power thus obtained and the three IMF components can be used to compute or infer: the predicted geometry of the auroral oval; the cross-polar cap potential; the two geomagnetic indices AE and Dst; the total energy injection rate into the polar ionosphere; and the atmospheric temperature, etc. (author)

BayesCLUMPY: BAYESIAN INFERENCE WITH CLUMPY DUSTY TORUS MODELS

International Nuclear Information System (INIS)

Asensio Ramos, A.; Ramos Almeida, C.

2009-01-01

Our aim is to present a fast and general Bayesian inference framework based on the synergy between machine learning techniques and standard sampling methods and apply it to infer the physical properties of clumpy dusty torus using infrared photometric high spatial resolution observations of active galactic nuclei. We make use of the Metropolis-Hastings Markov Chain Monte Carlo algorithm for sampling the posterior distribution function. Such distribution results from combining all a priori knowledge about the parameters of the model and the information introduced by the observations. The main difficulty resides in the fact that the model used to explain the observations is computationally demanding and the sampling is very time consuming. For this reason, we apply a set of artificial neural networks that are used to approximate and interpolate a database of models. As a consequence, models not present in the original database can be computed ensuring continuity. We focus on the application of this solution scheme to the recently developed public database of clumpy dusty torus models. The machine learning scheme used in this paper allows us to generate any model from the database using only a factor of 10 -4 of the original size of the database and a factor of 10 -3 in computing time. The posterior distribution obtained for each model parameter allows us to investigate how the observations constrain the parameters and which ones remain partially or completely undetermined, providing statistically relevant confidence intervals. As an example, the application to the nuclear region of Centaurus A shows that the optical depth of the clouds, the total number of clouds, and the radial extent of the cloud distribution zone are well constrained using only six filters. The code is freely available from the authors.

Time Clustered Sampling Can Inflate the Inferred Substitution Rate in Foot-And-Mouth Disease Virus Analyses.

Science.gov (United States)

Pedersen, Casper-Emil T; Frandsen, Peter; Wekesa, Sabenzia N; Heller, Rasmus; Sangula, Abraham K; Wadsworth, Jemma; Knowles, Nick J; Muwanika, Vincent B; Siegismund, Hans R

2015-01-01

With the emergence of analytical software for the inference of viral evolution, a number of studies have focused on estimating important parameters such as the substitution rate and the time to the most recent common ancestor (tMRCA) for rapidly evolving viruses. Coupled with an increasing abundance of sequence data sampled under widely different schemes, an effort to keep results consistent and comparable is needed. This study emphasizes commonly disregarded problems in the inference of evolutionary rates in viral sequence data when sampling is unevenly distributed on a temporal scale through a study of the foot-and-mouth (FMD) disease virus serotypes SAT 1 and SAT 2. Our study shows that clustered temporal sampling in phylogenetic analyses of FMD viruses will strongly bias the inferences of substitution rates and tMRCA because the inferred rates in such data sets reflect a rate closer to the mutation rate rather than the substitution rate. Estimating evolutionary parameters from viral sequences should be performed with due consideration of the differences in short-term and longer-term evolutionary processes occurring within sets of temporally sampled viruses, and studies should carefully consider how samples are combined.

Characterization and in vitro evaluation of freeze-dried microparticles composed of granisetron-cyclodextrin complex and carboxymethylcellulose for intranasal delivery.

Science.gov (United States)

Cho, Hyun-Jong; Balakrishnan, Prabagar; Shim, Won-Sik; Chung, Suk-Jae; Shim, Chang-Koo; Kim, Dae-Duk

2010-11-15

The aim of this study was to prepare microparticles (MPs) of granisetron (GRN) in combination with hydroxypropyl-β-cyclodextrin (HP-β-CD) and sodium carboxymethylcellulose (CMC-Na) by the simple freeze-drying method for intranasal delivery. The composition of MPs was determined from the phase-solubility study of GRN in various CDs. Fourier transform infrared spectroscopy (FT-IR), powder X-ray diffraction (PXRD) analysis and differential scanning calorimetry (DSC) studies were performed to evaluate possible interactions between GRN and excipients. The results indicated the formation of inclusion complex between GRN and CD, and the conversion of drug into amorphous state. The in vitro release of GRN from MPs was determined in phosphate buffered saline (pH 6.4) at 37°C. Cytotoxicity of the MPs and in vitro permeation study were conducted by using primary human nasal epithelial (HNE) cells and their monolayer system cultured by air-liquid interface (ALI) method, respectively. The MPs showed significantly higher GRN release profile compared to pure GRN. Moreover, the prepared MPs showed significantly lower cytotoxicity and higher permeation profile than that of GRN powder (p<0.05). These results suggested that the MPs composed of GRN, HP-β-CD and CMC-Na represent a simple and new GRN intranasal delivery system as an alternative to the oral and intravenous administration of GRN. Copyright © 2010 Elsevier B.V. All rights reserved.

HYBRID SYSTEM BASED FUZZY-PID CONTROL SCHEMES FOR UNPREDICTABLE PROCESS

Directory of Open Access Journals (Sweden)

M.K. Tan

2011-07-01

Full Text Available In general, the primary aim of polymerization industry is to enhance the process operation in order to obtain high quality and purity product. However, a sudden and large amount of heat will be released rapidly during the mixing process of two reactants, i.e. phenol and formalin due to its exothermic behavior. The unpredictable heat will cause deviation of process temperature and hence affect the quality of the product. Therefore, it is vital to control the process temperature during the polymerization. In the modern industry, fuzzy logic is commonly used to auto-tune PID controller to control the process temperature. However, this method needs an experienced operator to fine tune the fuzzy membership function and universe of discourse via trial and error approach. Hence, the setting of fuzzy inference system might not be accurate due to the human errors. Besides that, control of the process can be challenging due to the rapid changes in the plant parameters which will increase the process complexity. This paper proposes an optimization scheme using hybrid of Q-learning (QL and genetic algorithm (GA to optimize the fuzzy membership function in order to allow the conventional fuzzy-PID controller to control the process temperature more effectively. The performances of the proposed optimization scheme are compared with the existing fuzzy-PID scheme. The results show that the proposed optimization scheme is able to control the process temperature more effectively even if disturbance is introduced.

More than one kind of inference: re-examining what's learned in feature inference and classification.

Science.gov (United States)

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

Perceptual inference.

Science.gov (United States)

Aggelopoulos, Nikolaos C

2015-08-01

Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. Copyright © 2015 Elsevier Ltd. All rights reserved.

SEMANTIC PATCH INFERENCE

DEFF Research Database (Denmark)

Andersen, Jesper

2009-01-01

Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set ...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....

Further attacks on Yeung-Mintzer fragile watermarking scheme

Science.gov (United States)

Fridrich, Jessica; Goljan, Miroslav; Memon, Nasir D.

2000-05-01

In this paper, we describe new and improved attacks on the authentication scheme previously proposed by Yeung and Mintzer. Previous attacks assumed that the binary watermark logo inserted in an image for the purposes of authentication was known. Here we remove that assumption and show how the scheme is still vulnerable, even if the binary logo is not known but the attacker has access to multiple images that have been watermarked with the same secret key and contain the same (but unknown) logo. We present two attacks. The first attack infers the secret watermark insertion function and the binary logo, given multiple images authenticated with the same key and containing the same logo. We show that a very good approximation to the logo and watermark insertion function can be constructed using as few as two images. With color images, one needs many more images, nevertheless the attack is still feasible. The second attack we present, which we call the 'collage-attack' is a variation of the Holliman-Memon counterfeiting attack. The proposed variation does not require knowledge of the watermark logo and produces counterfeits of superior quality by means of a suitable dithering process that we develop.

Multimodel inference and adaptive management

Science.gov (United States)

Rehme, S.E.; Powell, L.A.; Allen, Craig R.

2011-01-01

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

Adaptive neuro-fuzzy inference system based automatic generation control

Energy Technology Data Exchange (ETDEWEB)

Hosseini, S.H.; Etemadi, A.H. [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran)

2008-07-15

Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the proposed method is demonstrated via simulations. Compliance of the proposed method with NERC control performance standard is verified. (author)

On Converting Secret Sharing Scheme to Visual Secret Sharing Scheme

Directory of Open Access Journals (Sweden)

Wang Daoshun

2010-01-01

Full Text Available Abstract Traditional Secret Sharing (SS schemes reconstruct secret exactly the same as the original one but involve complex computation. Visual Secret Sharing (VSS schemes decode the secret without computation, but each share is m times as big as the original and the quality of the reconstructed secret image is reduced. Probabilistic visual secret sharing (Prob.VSS schemes for a binary image use only one subpixel to share the secret image; however the probability of white pixels in a white area is higher than that in a black area in the reconstructed secret image. SS schemes, VSS schemes, and Prob. VSS schemes have various construction methods and advantages. This paper first presents an approach to convert (transform a -SS scheme to a -VSS scheme for greyscale images. The generation of the shadow images (shares is based on Boolean XOR operation. The secret image can be reconstructed directly by performing Boolean OR operation, as in most conventional VSS schemes. Its pixel expansion is significantly smaller than that of VSS schemes. The quality of the reconstructed images, measured by average contrast, is the same as VSS schemes. Then a novel matrix-concatenation approach is used to extend the greyscale -SS scheme to a more general case of greyscale -VSS scheme.

Using a Novel Wireless-Networked Decentralized Control Scheme under Unpredictable Environmental Conditions.

Science.gov (United States)

Chang, Chung-Liang; Huang, Yi-Ming; Hong, Guo-Fong

2015-11-12

The direction of sunshine or the installation sites of environmental control facilities in the greenhouse result in different temperature and humidity levels in the various zones of the greenhouse, and thus, the production quality of crop is inconsistent. This study proposed a wireless-networked decentralized fuzzy control scheme to regulate the environmental parameters of various culture zones within a greenhouse. The proposed scheme can create different environmental conditions for cultivating different crops in various zones and achieve diversification or standardization of crop production. A star-type wireless sensor network is utilized to communicate with each sensing node, actuator node, and control node in various zones within the greenhouse. The fuzzy rule-based inference system is used to regulate the environmental parameters for temperature and humidity based on real-time data of plant growth response provided by a growth stage selector. The growth stage selector defines the control ranges of temperature and humidity of the various culture zones according to the leaf area of the plant, the number of leaves, and the cumulative amount of light. The experimental results show that the proposed scheme is stable and robust and provides basis for future greenhouse applications.

Using a Novel Wireless-Networked Decentralized Control Scheme under Unpredictable Environmental Conditions

Directory of Open Access Journals (Sweden)

Chung-Liang Chang

2015-11-01

Full Text Available The direction of sunshine or the installation sites of environmental control facilities in the greenhouse result in different temperature and humidity levels in the various zones of the greenhouse, and thus, the production quality of crop is inconsistent. This study proposed a wireless-networked decentralized fuzzy control scheme to regulate the environmental parameters of various culture zones within a greenhouse. The proposed scheme can create different environmental conditions for cultivating different crops in various zones and achieve diversification or standardization of crop production. A star-type wireless sensor network is utilized to communicate with each sensing node, actuator node, and control node in various zones within the greenhouse. The fuzzy rule-based inference system is used to regulate the environmental parameters for temperature and humidity based on real-time data of plant growth response provided by a growth stage selector. The growth stage selector defines the control ranges of temperature and humidity of the various culture zones according to the leaf area of the plant, the number of leaves, and the cumulative amount of light. The experimental results show that the proposed scheme is stable and robust and provides basis for future greenhouse applications.

Bayesian Inference on the Memory Parameter for Gamma-Modulated Regression Models

Directory of Open Access Journals (Sweden)

Plinio Andrade

2015-09-01

Full Text Available In this work, we propose a Bayesian methodology to make inferences for the memory parameter and other characteristics under non-standard assumptions for a class of stochastic processes. This class generalizes the Gamma-modulated process, with trajectories that exhibit long memory behavior, as well as decreasing variability as time increases. Different values of the memory parameter influence the speed of this decrease, making this heteroscedastic model very flexible. Its properties are used to implement an approximate Bayesian computation and MCMC scheme to obtain posterior estimates. We test and validate our method through simulations and real data from the big earthquake that occurred in 2010 in Chile.

Optimal inference with suboptimal models: Addiction and active Bayesian inference

Science.gov (United States)

Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

2015-01-01

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

DMAC-AN INTEGRATED ENCRYPTION SCHEME WITH RSA FOR AC TO OBSTRUCT INFERENCE ATTACKS

Directory of Open Access Journals (Sweden)

R. Jeeva

2012-12-01

Full Text Available The proposal of indistinguishable encryption in Randomized Arithmetic Coding(RAC doesn’t make the system efficient because it was not encrypting the messages it sends. It recomputes the cipher form of every messages it sends that increases not only the computational cost but also increases the response time.Floating point representation in cipher increases the difficulty in decryption side because of loss in precison.RAC doesn’t handle the inference attacks like Man-in-Middle attack,Third party attack etc. In our system, Dynamic Matrix Arithmetic Coding(DMAC using dynamic session matrix to encrypt the messages. The size of the matrix is deduced from the session key that contains ID of end users which proves the server authentication.Nonce values is represented as the public key of the opponents encrypted by the session key will be exchanged between the end users to provide mutual authentication. If the adversary try to compromise either server or end users,the other system won’t respond and the intrusion will be easily detected. we have increased the hacking complexity of AC by integrating with RSA upto 99%.

Inference rule and problem solving

Energy Technology Data Exchange (ETDEWEB)

Goto, S

1982-04-01

Intelligent information processing signifies an opportunity of having man's intellectual activity executed on the computer, in which inference, in place of ordinary calculation, is used as the basic operational mechanism for such an information processing. Many inference rules are derived from syllogisms in formal logic. The problem of programming this inference function is referred to as a problem solving. Although logically inference and problem-solving are in close relation, the calculation ability of current computers is on a low level for inferring. For clarifying the relation between inference and computers, nonmonotonic logic has been considered. The paper deals with the above topics. 16 references.

Knowledge and inference

CERN Document Server

Nagao, Makoto

1990-01-01

Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig

Geometric statistical inference

International Nuclear Information System (INIS)

Periwal, Vipul

1999-01-01

A reparametrization-covariant formulation of the inverse problem of probability is explicitly solved for finite sample sizes. The inferred distribution is explicitly continuous for finite sample size. A geometric solution of the statistical inference problem in higher dimensions is outlined

Calibrated birth-death phylogenetic time-tree priors for bayesian inference.

Science.gov (United States)

Heled, Joseph; Drummond, Alexei J

2015-05-01

Here we introduce a general class of multiple calibration birth-death tree priors for use in Bayesian phylogenetic inference. All tree priors in this class separate ancestral node heights into a set of "calibrated nodes" and "uncalibrated nodes" such that the marginal distribution of the calibrated nodes is user-specified whereas the density ratio of the birth-death prior is retained for trees with equal values for the calibrated nodes. We describe two formulations, one in which the calibration information informs the prior on ranked tree topologies, through the (conditional) prior, and the other which factorizes the prior on divergence times and ranked topologies, thus allowing uniform, or any arbitrary prior distribution on ranked topologies. Although the first of these formulations has some attractive properties, the algorithm we present for computing its prior density is computationally intensive. However, the second formulation is always faster and computationally efficient for up to six calibrations. We demonstrate the utility of the new class of multiple-calibration tree priors using both small simulations and a real-world analysis and compare the results to existing schemes. The two new calibrated tree priors described in this article offer greater flexibility and control of prior specification in calibrated time-tree inference and divergence time dating, and will remove the need for indirect approaches to the assessment of the combined effect of calibration densities and tree priors in Bayesian phylogenetic inference. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

A Multiobjective Fuzzy Inference System based Deployment Strategy for a Distributed Mobile Sensor Network

Directory of Open Access Journals (Sweden)

Amol P. Bhondekar

2010-03-01

Full Text Available Sensor deployment scheme highly governs the effectiveness of distributed wireless sensor network. Issues such as energy conservation and clustering make the deployment problem much more complex. A multiobjective Fuzzy Inference System based strategy for mobile sensor deployment is presented in this paper. This strategy gives a synergistic combination of energy capacity, clustering and peer-to-peer deployment. Performance of our strategy is evaluated in terms of coverage, uniformity, speed and clustering. Our algorithm is compared against a modified distributed self-spreading algorithm to exhibit better performance.

Goal inferences about robot behavior : goal inferences and human response behaviors

NARCIS (Netherlands)

Broers, H.A.T.; Ham, J.R.C.; Broeders, R.; De Silva, P.; Okada, M.

2014-01-01

This explorative research focused on the goal inferences human observers draw based on a robot's behavior, and the extent to which those inferences predict people's behavior in response to that robot. Results show that different robot behaviors cause different response behavior from people.

The Multivariate Generalised von Mises Distribution: Inference and Applications

DEFF Research Database (Denmark)

Navarro, Alexandre Khae Wu; Frellsen, Jes; Turner, Richard

2017-01-01

Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending some standard probabilistic modelling tools to the c......Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending some standard probabilistic modelling tools....... These models can leverage standard modelling tools (e.g. kernel functions and automatic relevance determination). Third, we show that the posterior distribution in these models is a mGvM distribution which enables development of an efficient variational free-energy scheme for performing approximate inference...... and approximate maximum-likelihood learning....

A novel lipid nanoemulsion system for improved permeation of granisetron.

Science.gov (United States)

Doh, Hea-Jeong; Jung, Yunjin; Balakrishnan, Prabagar; Cho, Hyun-Jong; Kim, Dae-Duk

2013-01-01

A new lipid nanoemulsion (LNE) system containing granisetron (GRN) was developed and its in vitro permeation-enhancing effect was evaluated using Caco-2 cell monolayers. Particle size, polydispersity index (PI) and stability of the prepared GRN-loaded LNE systems were also characterized. The mean diameters of prepared LNEs were around 50 nm with PI<0.2. Developed LNEs were stable at 4°C in the dark place over a period of 12 weeks. In vitro drug dissolution and cytotoxicity studies of GRN-loaded LNEs were performed. GRN-loaded LNEs exhibited significantly higher drug dissolution than GRN suspension at pH 6.8 for 2h (P<0.05). In vitro permeation study in Caco-2 cell monolayers showed that the LNEs significantly enhanced the drug permeation compared to GRN powder. The in vivo toxicity study in the rat jejunum revealed that the prepared GRN-loaded LNE was as safe as the commercial formulation (Kytril). These results suggest that LNE could be used as a potential oral liquid formulation of GRN for anti-emetic treatment on the post-operative and chemotherapeutic patients. Copyright © 2012 Elsevier B.V. All rights reserved.

Entropic Inference

OpenAIRE

Caticha, Ariel

2010-01-01

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEn...

Partial Tmem106b reduction does not correct abnormalities due to progranulin haploinsufficiency.

Science.gov (United States)

Arrant, Andrew E; Nicholson, Alexandra M; Zhou, Xiaolai; Rademakers, Rosa; Roberson, Erik D

2018-06-22

Loss of function mutations in progranulin (GRN) are a major cause of frontotemporal dementia (FTD). Progranulin is a secreted glycoprotein that localizes to lysosomes and is critical for proper lysosomal function. Heterozygous GRN mutation carriers develop FTD with TDP-43 pathology and exhibit signs of lysosomal dysfunction in the brain, with increased levels of lysosomal proteins and lipofuscin accumulation. Homozygous GRN mutation carriers develop neuronal ceroid lipofuscinosis (NCL), an earlier-onset lysosomal storage disorder caused by severe lysosomal dysfunction. Multiple genome-wide association studies have shown that risk of FTD in GRN mutation carriers is modified by polymorphisms in TMEM106B, which encodes a lysosomal membrane protein. Risk alleles of TMEM106B may increase TMEM106B levels through a variety of mechanisms. Brains from FTD patients with GRN mutations exhibit increased TMEM106B expression, and protective TMEM106B polymorphisms are associated with decreased TMEM106B expression. Together, these data raise the possibility that reduction of TMEM106B levels may protect against the pathogenic effects of progranulin haploinsufficiency. We crossed Tmem106b +/- mice with Grn +/- mice, which model the progranulin haploinsufficiency of GRN mutation carriers and develop age-dependent social deficits and lysosomal abnormalities in the brain. We tested whether partial Tmem106b reduction could normalize the social deficits and lysosomal abnormalities of Grn +/- mice. Partial reduction of Tmem106b levels did not correct the social deficits of Grn +/- mice. Tmem106b reduction also failed to normalize most lysosomal abnormalities of Grn +/- mice, except for β-glucuronidase activity, which was suppressed by Tmem106b reduction and increased by progranulin insufficiency. These data do not support the hypothesis that Tmem106b reduction protects against the pathogenic effects of progranulin haploinsufficiency, but do show that Tmem106b reduction normalizes some

Progranulin Gene Therapy Improves Lysosomal Dysfunction and Microglial Pathology Associated with Frontotemporal Dementia and Neuronal Ceroid Lipofuscinosis.

Science.gov (United States)

Arrant, Andrew E; Onyilo, Vincent C; Unger, Daniel E; Roberson, Erik D

2018-02-28

Loss-of-function mutations in progranulin, a lysosomal glycoprotein, cause neurodegenerative disease. Progranulin haploinsufficiency causes frontotemporal dementia (FTD) and complete progranulin deficiency causes CLN11 neuronal ceroid lipofuscinosis (NCL). Progranulin replacement is a rational therapeutic strategy for these disorders, but there are critical unresolved mechanistic questions about a progranulin gene therapy approach, including its potential to reverse existing pathology. Here, we address these issues using an AAV vector (AAV- Grn ) to deliver progranulin in Grn -/- mice (both male and female), which model aspects of NCL and FTD pathology, developing lysosomal dysfunction, lipofuscinosis, and microgliosis. We first tested whether AAV- Grn could improve preexisting pathology. Even with treatment after onset of pathology, AAV- Grn reduced lipofuscinosis in several brain regions of Grn -/- mice. AAV- Grn also reduced microgliosis in brain regions distant from the injection site. AAV-expressed progranulin was only detected in neurons, not in microglia, indicating that the microglial activation in progranulin deficiency can be improved by targeting neurons and thus may be driven at least in part by neuronal dysfunction. Even areas with sparse transduction and almost undetectable progranulin showed improvement, indicating that low-level replacement may be sufficiently effective. The beneficial effects of AAV- Grn did not require progranulin binding to sortilin. Finally, we tested whether AAV- Grn improved lysosomal function. AAV-derived progranulin was delivered to the lysosome, ameliorated the accumulation of LAMP-1 in Grn -/- mice, and corrected abnormal cathepsin D activity. These data shed light on progranulin biology and support progranulin-boosting therapies for NCL and FTD due to GRN mutations. SIGNIFICANCE STATEMENT Heterozygous loss-of-function progranulin ( GRN ) mutations cause frontotemporal dementia (FTD) and homozygous mutations cause neuronal

Active inference and epistemic value.

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

2015-01-01

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

Learning Convex Inference of Marginals

OpenAIRE

Domke, Justin

2012-01-01

Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process or the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main ...

Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors

International Nuclear Information System (INIS)

Lucka, Felix

2012-01-01

Sparsity has become a key concept for solving of high-dimensional inverse problems using variational regularization techniques. Recently, using similar sparsity-constraints in the Bayesian framework for inverse problems by encoding them in the prior distribution has attracted attention. Important questions about the relation between regularization theory and Bayesian inference still need to be addressed when using sparsity promoting inversion. A practical obstacle for these examinations is the lack of fast posterior sampling algorithms for sparse, high-dimensional Bayesian inversion. Accessing the full range of Bayesian inference methods requires being able to draw samples from the posterior probability distribution in a fast and efficient way. This is usually done using Markov chain Monte Carlo (MCMC) sampling algorithms. In this paper, we develop and examine a new implementation of a single component Gibbs MCMC sampler for sparse priors relying on L1-norms. We demonstrate that the efficiency of our Gibbs sampler increases when the level of sparsity or the dimension of the unknowns is increased. This property is contrary to the properties of the most commonly applied Metropolis–Hastings (MH) sampling schemes. We demonstrate that the efficiency of MH schemes for L1-type priors dramatically decreases when the level of sparsity or the dimension of the unknowns is increased. Practically, Bayesian inversion for L1-type priors using MH samplers is not feasible at all. As this is commonly believed to be an intrinsic feature of MCMC sampling, the performance of our Gibbs sampler also challenges common beliefs about the applicability of sample based Bayesian inference. (paper)

Probabilistic inductive inference: a survey

OpenAIRE

Ambainis, Andris

2001-01-01

Inductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since this simple paradigm has produced the most interesting (and most complex) results.

LAIT: a local ancestry inference toolkit.

Science.gov (United States)

Hui, Daniel; Fang, Zhou; Lin, Jerome; Duan, Qing; Li, Yun; Hu, Ming; Chen, Wei

2017-09-06

Inferring local ancestry in individuals of mixed ancestry has many applications, most notably in identifying disease-susceptible loci that vary among different ethnic groups. Many software packages are available for inferring local ancestry in admixed individuals. However, most of these existing software packages require specific formatted input files and generate output files in various types, yielding practical inconvenience. We developed a tool set, Local Ancestry Inference Toolkit (LAIT), which can convert standardized files into software-specific input file formats as well as standardize and summarize inference results for four popular local ancestry inference software: HAPMIX, LAMP, LAMP-LD, and ELAI. We tested LAIT using both simulated and real data sets and demonstrated that LAIT provides convenience to run multiple local ancestry inference software. In addition, we evaluated the performance of local ancestry software among different supported software packages, mainly focusing on inference accuracy and computational resources used. We provided a toolkit to facilitate the use of local ancestry inference software, especially for users with limited bioinformatics background.

Bayesian statistical inference

Directory of Open Access Journals (Sweden)

Bruno De Finetti

2017-04-01

Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.

Dissociation of frontotemporal dementia-related deficits and neuroinflammation in progranulin haploinsufficient mice.

Science.gov (United States)

Filiano, Anthony J; Martens, Lauren Herl; Young, Allen H; Warmus, Brian A; Zhou, Ping; Diaz-Ramirez, Grisell; Jiao, Jian; Zhang, Zhijun; Huang, Eric J; Gao, Fen-Biao; Farese, Robert V; Roberson, Erik D

2013-03-20

Frontotemporal dementia (FTD) is a neurodegenerative disease with hallmark deficits in social and emotional function. Heterozygous loss-of-function mutations in GRN, the progranulin gene, are a common genetic cause of the disorder, but the mechanisms by which progranulin haploinsufficiency causes neuronal dysfunction in FTD are unclear. Homozygous progranulin knock-out (Grn(-/-)) mice have been studied as a model of this disorder and show behavioral deficits and a neuroinflammatory phenotype with robust microglial activation. However, homozygous GRN mutations causing complete progranulin deficiency were recently shown to cause a different neurological disorder, neuronal ceroid lipofuscinosis, suggesting that the total absence of progranulin may have effects distinct from those of haploinsufficiency. Here, we studied progranulin heterozygous (Grn(+/-)) mice, which model progranulin haploinsufficiency. We found that Grn(+/-) mice developed age-dependent social and emotional deficits potentially relevant to FTD. However, unlike Grn(-/-) mice, behavioral deficits in Grn(+/-) mice occurred in the absence of gliosis or increased expression of tumor necrosis factor-α. Instead, we found neuronal abnormalities in the amygdala, an area of selective vulnerability in FTD, in Grn(+/-) mice. Our findings indicate that FTD-related deficits resulting from progranulin haploinsufficiency can develop in the absence of detectable gliosis and neuroinflammation, thereby dissociating microglial activation from functional deficits and suggesting an important effect of progranulin deficiency on neurons.

Is there a hierarchy of social inferences? The likelihood and speed of inferring intentionality, mind, and personality.

Science.gov (United States)

Malle, Bertram F; Holbrook, Jess

2012-04-01

People interpret behavior by making inferences about agents' intentionality, mind, and personality. Past research studied such inferences 1 at a time; in real life, people make these inferences simultaneously. The present studies therefore examined whether 4 major inferences (intentionality, desire, belief, and personality), elicited simultaneously in response to an observed behavior, might be ordered in a hierarchy of likelihood and speed. To achieve generalizability, the studies included a wide range of stimulus behaviors, presented them verbally and as dynamic videos, and assessed inferences both in a retrieval paradigm (measuring the likelihood and speed of accessing inferences immediately after they were made) and in an online processing paradigm (measuring the speed of forming inferences during behavior observation). Five studies provide evidence for a hierarchy of social inferences-from intentionality and desire to belief to personality-that is stable across verbal and visual presentations and that parallels the order found in developmental and primate research. (c) 2012 APA, all rights reserved.

INFERENCE BUILDING BLOCKS

Science.gov (United States)

2018-02-15

expressed a variety of inference techniques on discrete and continuous distributions: exact inference, importance sampling, Metropolis-Hastings (MH...without redoing any math or rewriting any code. And although our main goal is composable reuse, our performance is also good because we can use...control paths. • The Hakaru language can express mixtures of discrete and continuous distributions, but the current disintegration transformation

Practical Bayesian Inference

Science.gov (United States)

Bailer-Jones, Coryn A. L.

2017-04-01

Preface; 1. Probability basics; 2. Estimation and uncertainty; 3. Statistical models and inference; 4. Linear models, least squares, and maximum likelihood; 5. Parameter estimation: single parameter; 6. Parameter estimation: multiple parameters; 7. Approximating distributions; 8. Monte Carlo methods for inference; 9. Parameter estimation: Markov chain Monte Carlo; 10. Frequentist hypothesis testing; 11. Model comparison; 12. Dealing with more complicated problems; References; Index.

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

Science.gov (United States)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Reduced miR-659-3p levels correlate with progranulin increase in hypoxic conditions: implications for frontotemporal dementia.

Directory of Open Access Journals (Sweden)

Paola ePiscopo

2016-05-01

Full Text Available Progranulin (PGRN is a secreted protein expressed ubiquitously throughout the body, including the brain, where it localizes in neurons and activated microglia. Loss-of-function mutations in the GRN gene are an important cause of familial Frontotemporal Lobar Degeneration (FTLD. PGRN has a neurotrophic and anti-inflammatory activity, and it is neuroprotective in several injury conditions, such as oxygen or glucose deprivation, oxidative injury, and hypoxic stress. Indeed, we have previously demonstrated that hypoxia induces the up-regulation of GRN transcripts. Several studies have shown microRNAs involvement in hypoxia. Moreover, in FTLD patients with a genetic variant of GRN (rs5848, the reinforcement of miR-659-3p binding site has been suggested to be a risk factor. Here, we report that miR-659-3p interacts directly with GRN 3’UTR as shown by luciferase assay in HeLa cells and ELISA and Western Blot analysis in HeLa and Kelly cells. Moreover, we demonstrate the physical binding between GRN mRNA and miR-659-3p employing a miRNA capture-affinity technology in SK-N-BE and Kelly cells. In order to study miRNAs involvement in hypoxia-mediated up-regulation of GRN, we evaluated miR-659-3p levels in SK-N-BE cells after 24h of hypoxic treatment, finding them inversely correlated to GRN transcripts. Furthermore, we analyzed an animal model of asphyxia, finding that GRN mRNA levels increased at post-natal day (pnd 1 and pnd 4 in rat cortices subjected to asphyxia in comparison to control rats and miR-659-3p decreased at pnd 4 just when GRN reached the highest levels. Our results demonstrate the interaction between miR-659-3p and GRN transcript and the involvement of miR-659-3p in GRN up-regulation mediated by hypoxic/ischemic insults.

Association of TMEM106B gene polymorphism with age at onset in granulin mutation carriers and plasma granulin protein levels.

Science.gov (United States)

Cruchaga, Carlos; Graff, Caroline; Chiang, Huei-Hsin; Wang, Jun; Hinrichs, Anthony L; Spiegel, Noah; Bertelsen, Sarah; Mayo, Kevin; Norton, Joanne B; Morris, John C; Goate, Alison

2011-05-01

To test whether rs1990622 (TMEM106B) is associated with age at onset (AAO) in granulin (GRN) mutation carriers and with plasma GRN levels in mutation carriers and healthy, elderly individuals. Rs1990622 (TMEM106B) was identified as a risk factor for frontotemporal lobar degeneration with TAR DNA-binding protein inclusions (FTLD-TDP) in a recent genome-wide association. Rs1990622 was genotyped in GRN mutation carriers and tested for association with AAO using the Kaplan-Meier method and a Cox proportional hazards model. Alzheimer's Disease Research Center. Subjects  We analyzed 50 affected and unaffected GRN mutation carriers from 4 previously reported FTLD-TDP families (HDDD1, FD1, HDDD2, and the Karolinska family). The GRN plasma levels were also measured in 73 healthy, elderly individuals. Age at onset and GRN plasma levels. The risk allele of rs1990622 was associated with a mean decrease of the AAO of 13 years (P = 9.9 × 10(-7)) and with lower plasma GRN levels in both healthy older adults (P = 4 × 10(-4)) and GRN mutation carriers (P = .0027). Analysis of the HapMap database identified a nonsynonymous single-nucleotide polymorphism rs3173615 (T185S) in perfect linkage disequilibrium with rs1990622. The association of rs1990622 with AAO explains, in part, the wide range in the AAO of disease among GRN mutation carriers. We hypothesize that rs1990622 or another variant in linkage disequilibrium could act in a manner similar to APOE in Alzheimer disease, increasing risk for disease in the general population and modifying AAO in mutation carriers. Our results also suggest that genetic variation in TMEM106B may influence risk for FTLD-TDP by modulating secreted levels of GRN.

A relative variation-based method to unraveling gene regulatory networks.

Directory of Open Access Journals (Sweden)

Yali Wang

Full Text Available Gene regulatory network (GRN reconstruction is essential in understanding the functioning and pathology of a biological system. Extensive models and algorithms have been developed to unravel a GRN. The DREAM project aims to clarify both advantages and disadvantages of these methods from an application viewpoint. An interesting yet surprising observation is that compared with complicated methods like those based on nonlinear differential equations, etc., methods based on a simple statistics, such as the so-called Z-score, usually perform better. A fundamental problem with the Z-score, however, is that direct and indirect regulations can not be easily distinguished. To overcome this drawback, a relative expression level variation (RELV based GRN inference algorithm is suggested in this paper, which consists of three major steps. Firstly, on the basis of wild type and single gene knockout/knockdown experimental data, the magnitude of RELV of a gene is estimated. Secondly, probability for the existence of a direct regulation from a perturbed gene to a measured gene is estimated, which is further utilized to estimate whether a gene can be regulated by other genes. Finally, the normalized RELVs are modified to make genes with an estimated zero in-degree have smaller RELVs in magnitude than the other genes, which is used afterwards in queuing possibilities of the existence of direct regulations among genes and therefore leads to an estimate on the GRN topology. This method can in principle avoid the so-called cascade errors under certain situations. Computational results with the Size 100 sub-challenges of DREAM3 and DREAM4 show that, compared with the Z-score based method, prediction performances can be substantially improved, especially the AUPR specification. Moreover, it can even outperform the best team of both DREAM3 and DREAM4. Furthermore, the high precision of the obtained most reliable predictions shows that the suggested algorithm may be

Logical inference and evaluation

International Nuclear Information System (INIS)

Perey, F.G.

1981-01-01

Most methodologies of evaluation currently used are based upon the theory of statistical inference. It is generally perceived that this theory is not capable of dealing satisfactorily with what are called systematic errors. Theories of logical inference should be capable of treating all of the information available, including that not involving frequency data. A theory of logical inference is presented as an extension of deductive logic via the concept of plausibility and the application of group theory. Some conclusions, based upon the application of this theory to evaluation of data, are also given

Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi.

Science.gov (United States)

Donnarumma, Francesco; Maisto, Domenico; Pezzulo, Giovanni

2016-04-01

How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem

In vitro percutaneous absorption enhancement of granisetron by chemical penetration enhancers.

Science.gov (United States)

Zhao, Nanxi; Cun, Dongmei; Li, Wei; Ma, Xu; Sun, Lin; Xi, Honglei; Li, Li; Fang, Liang

2013-04-01

Granisetron (GRN), a potent antiemetic agent, is frequently used to prevent nausea and vomiting induced by cancer cytotoxic chemotherapy and radiation therapy. As part of our efforts to further modify the physicochemical properties of this market drug, with the ultimate goal to formulate a better dosage form for GRN, this work was carried out to improve its permeability in vitro. The permeation behavior of GRN in isopropyl myristate (IPM) was investigated across excised rabbit abdominal skin and the enhancing activities of three novel O-acylmenthol derivatives synthesized in our laboratory as well as five well-known chemical enhancers were evaluated. It was found that the steady-state flux of granisetron free base (GRN-B) was about 26-fold higher than that of granisetron hydrochloride (GRN-H). The novel enhancer, 2-isopropyl-5-methylcyclohexyl heptanoate (M-HEP), was observed to provide the most significant enhancement for the absorption of GRN-B. When incorporated in the donor solution with the optimal enhancer M-HEP, the steady-state flux of GRN-B increased from (196.44 ± 12.03) μg·cm⁻²·h⁻¹ to (1044.95 ± 71.99) μg·cm⁻²·h⁻¹ (P < 0.01). These findings indicated that the application of chemical enhancers was an effective approach to increase the percutaneous absorption of GRN in vitro.

Murine knockin model for progranulin-deficient frontotemporal dementia with nonsense-mediated mRNA decay.

Science.gov (United States)

Nguyen, Andrew D; Nguyen, Thi A; Zhang, Jiasheng; Devireddy, Swathi; Zhou, Ping; Karydas, Anna M; Xu, Xialian; Miller, Bruce L; Rigo, Frank; Ferguson, Shawn M; Huang, Eric J; Walther, Tobias C; Farese, Robert V

2018-03-20

Frontotemporal dementia (FTD) is the most common neurodegenerative disorder in individuals under age 60 and has no treatment or cure. Because many cases of FTD result from GRN nonsense mutations, an animal model for this type of mutation is highly desirable for understanding pathogenesis and testing therapies. Here, we generated and characterized Grn R493X knockin mice, which model the most common human GRN mutation, a premature stop codon at arginine 493 (R493X). Homozygous Grn R493X mice have markedly reduced Grn mRNA levels, lack detectable progranulin protein, and phenocopy Grn knockout mice, with CNS microgliosis, cytoplasmic TDP-43 accumulation, reduced synaptic density, lipofuscinosis, hyperinflammatory macrophages, excessive grooming behavior, and reduced survival. Inhibition of nonsense-mediated mRNA decay (NMD) by genetic, pharmacological, or antisense oligonucleotide-based approaches showed that NMD contributes to the reduced mRNA levels in Grn R493X mice and cell lines and in fibroblasts from patients containing the GRN R493X mutation. Moreover, the expressed truncated R493X mutant protein was functional in several assays in progranulin-deficient cells. Together, these findings establish a murine model for in vivo testing of NMD inhibition or other therapies as potential approaches for treating progranulin deficiency caused by the R493X mutation. Copyright © 2018 the Author(s). Published by PNAS.

A novel mutual information-based Boolean network inference method from time-series gene expression data.

Directory of Open Access Journals (Sweden)

Shohag Barman

Full Text Available Inferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately.In this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods.Taken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network.

Inference

DEFF Research Database (Denmark)

Møller, Jesper

(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the ......(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.......1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...

Lower complexity bounds for lifted inference

DEFF Research Database (Denmark)

Jaeger, Manfred

2015-01-01

instances of the model. Numerous approaches for such “lifted inference” techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show...... the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about...... that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing...

Exact analysis of Packet Reversed Packet Combining Scheme and Modified Packet Combining Scheme; and a combined scheme

International Nuclear Information System (INIS)

Bhunia, C.T.

2007-07-01

Packet combining scheme is a well defined simple error correction scheme for the detection and correction of errors at the receiver. Although it permits a higher throughput when compared to other basic ARQ protocols, packet combining (PC) scheme fails to correct errors when errors occur in the same bit locations of copies. In a previous work, a scheme known as Packet Reversed Packet Combining (PRPC) Scheme that will correct errors which occur at the same bit location of erroneous copies, was studied however PRPC does not handle a situation where a packet has more than 1 error bit. The Modified Packet Combining (MPC) Scheme that can correct double or higher bit errors was studied elsewhere. Both PRPC and MPC schemes are believed to offer higher throughput in previous studies, however neither adequate investigation nor exact analysis was done to substantiate this claim of higher throughput. In this work, an exact analysis of both PRPC and MPC is carried out and the results reported. A combined protocol (PRPC and MPC) is proposed and the analysis shows that it is capable of offering even higher throughput and better error correction capability at high bit error rate (BER) and larger packet size. (author)

A Dual-Field Sensing Scheme for a Guidance System for the Blind

Directory of Open Access Journals (Sweden)

Qing Lin

2016-05-01

Full Text Available An electronic guidance system is very helpful in improving blind people’s perceptions in a local environment. In our previous work “Lin, Q.; Han, Y. A Context-Aware-Based Audio Guidance System for Blind People Using a Multimodal Profile Model. Sensors 2014, 14, 18670–18700”, a context-aware guidance system using a combination of a laser scanner and a camera was proposed. By using a near-field graphical model, the proposed system could interpret a near-field scene in very high resolution. In this paper, our work is extended by adding a far-field graphical model. The integration of the near-field and the far-field models constitutes a dual-field sensing scheme. In the near-field range, reliable inference of the ground and object status is obtained by fusing range data and image data using the near-field graphical model. In the far-field range, which only the camera can cover, the far-field graphical model is proposed to interpret far-field image data based on appearance and spatial prototypes built using the near-field interpreted data. The dual-field sensing scheme provides a solution for the guidance systems to optimise their scene interpretation capability using simple sensor configurations. Experiments under various local conditions were conducted to show the efficiency of the proposed scheme in improving blind people’s perceptions in urban environments.

Variations on Bayesian Prediction and Inference

Science.gov (United States)

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

Directory of Open Access Journals (Sweden)

Xiaodong Cai

Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

Bayesian electron density inference from JET lithium beam emission spectra using Gaussian processes

Science.gov (United States)

Kwak, Sehyun; Svensson, J.; Brix, M.; Ghim, Y.-C.; Contributors, JET

2017-03-01

A Bayesian model to infer edge electron density profiles is developed for the JET lithium beam emission spectroscopy (Li-BES) system, measuring Li I (2p-2s) line radiation using 26 channels with  ∼1 cm spatial resolution and 10∼ 20 ms temporal resolution. The density profile is modelled using a Gaussian process prior, and the uncertainty of the density profile is calculated by a Markov Chain Monte Carlo (MCMC) scheme. From the spectra measured by the transmission grating spectrometer, the Li I line intensities are extracted, and modelled as a function of the plasma density by a multi-state model which describes the relevant processes between neutral lithium beam atoms and plasma particles. The spectral model fully takes into account interference filter and instrument effects, that are separately estimated, again using Gaussian processes. The line intensities are inferred based on a spectral model consistent with the measured spectra within their uncertainties, which includes photon statistics and electronic noise. Our newly developed method to infer JET edge electron density profiles has the following advantages in comparison to the conventional method: (i) providing full posterior distributions of edge density profiles, including their associated uncertainties, (ii) the available radial range for density profiles is increased to the full observation range (∼26 cm), (iii) an assumption of monotonic electron density profile is not necessary, (iv) the absolute calibration factor of the diagnostic system is automatically estimated overcoming the limitation of the conventional technique and allowing us to infer the electron density profiles for all pulses without preprocessing the data or an additional boundary condition, and (v) since the full spectrum is modelled, the procedure of modulating the beam to measure the background signal is only necessary for the case of overlapping of the Li I line with impurity lines.

Study of the shock ignition scheme in inertial confinement fusion

International Nuclear Information System (INIS)

Lafon, M.

2011-01-01

The Shock Ignition (SI) scheme is an alternative to classical ignition schemes in Inertial Confinement Fusion. Its singularity relies on the relaxation of constraints during the compression phase and fulfilment of ignition conditions by launching a short and intense laser pulse (∼500 ps, ∼300 TW) on the pre-assembled fuel at the end of the implosion.In this thesis, it has been established that the SI process leads to a non-isobaric fuel configuration at the ignition time thus modifying the ignition criteria of Deuterium-Tritium (DT) against the conventional schemes. A gain model has been developed and gain curves have been inferred and numerically validated. This hydrodynamical modeling has demonstrated that the SI process allows higher gain and lower ignition energy threshold than conventional ignition due to the high hot spot pressure at ignition time resulting from the ignitor shock propagation.The radiative hydrodynamic CHIC code developed at the CELIA laboratory has been used to determine parametric dependences describing the optimal conditions for target design leading to ignition. These numerical studies have enlightened the potential of SI with regards to saving up laser energy, obtain high gains but also to safety margins and ignition robustness.Finally, the results of the first SI experiments performed in spherical geometry on the OMEGA laser facility (NY, USA) are presented. An interpretation of the experimental data is proposed from mono and bidimensional hydrodynamic simulations. Then, different trails are explored to account for the differences observed between experimental and numerical data and alternative solutions to improve performances are suggested. (author) [fr

Adaptive Inference on General Graphical Models

OpenAIRE

Acar, Umut A.; Ihler, Alexander T.; Mettu, Ramgopal; Sumer, Ozgur

2012-01-01

Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional ...

Inferring modules from human protein interactome classes

Directory of Open Access Journals (Sweden)

Chaurasia Gautam

2010-07-01

Full Text Available Abstract Background The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features. Results We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules. Conclusions Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.

Finite Boltzmann schemes

NARCIS (Netherlands)

Sman, van der R.G.M.

2006-01-01

In the special case of relaxation parameter = 1 lattice Boltzmann schemes for (convection) diffusion and fluid flow are equivalent to finite difference/volume (FD) schemes, and are thus coined finite Boltzmann (FB) schemes. We show that the equivalence is inherent to the homology of the

Structural Inference in the Art of Violin Making.

Science.gov (United States)

Morse-Fortier, Leonard Joseph

The "secrets" of success of early Italian violins have long been sought. Among their many efforts to reproduce the results of Stradiveri, Guarneri, and Amati, luthiers have attempted to order and match natural resonant frequencies in the free violin plates. This tap-tone plate tuning technique is simply an eigenvalue extraction scheme. In the final stages of carving, the violin maker complements considerable intuitive knowledge of violin plate structure and of modal attributes with tap-tone frequency estimates to better understand plate structure and to inform decisions about plate carving and completeness. Examining the modal attributes of violin plates, this work develops and incorporates an impulse-response scheme for modal inference, measures resonant frequencies and modeshapes for a pair of violin plates, and presents modeshapes through a unique computer visualization scheme developed specifically for this purpose. The work explores, through simple examples questions of how plate modal attributes reflect underlying structure, and questions about the so -called evolution of modeshapes and frequencies through assembly of the violin. Separately, the work develops computer code for a carved, anisotropic, plate/shell finite element. Solutions are found to the static displacement and free-vibration eigenvalue problems for an orthotropic plate, and used to verify element accuracy. Finally, a violin back plate is modelled with full consideration of plate thickness and arching. Model estimates for modal attributes compare very well against experimentally acquired values. Finally, the modal synthesis technique is applied to predicting the modal attributes of the violin top plate with ribs attached from those of the top plate alone, and with an estimate of rib mass and stiffness. This last analysis serves to verify the modal synthesis method, and to quantify its limits of applicability in attempting to solve problems with severe structural modification. Conclusions

The inference from a single case: moral versus scientific inferences in implementing new biotechnologies.

Science.gov (United States)

Hofmann, B

2008-06-01

Are there similarities between scientific and moral inference? This is the key question in this article. It takes as its point of departure an instance of one person's story in the media changing both Norwegian public opinion and a brand-new Norwegian law prohibiting the use of saviour siblings. The case appears to falsify existing norms and to establish new ones. The analysis of this case reveals similarities in the modes of inference in science and morals, inasmuch as (a) a single case functions as a counter-example to an existing rule; (b) there is a common presupposition of stability, similarity and order, which makes it possible to reason from a few cases to a general rule; and (c) this makes it possible to hold things together and retain order. In science, these modes of inference are referred to as falsification, induction and consistency. In morals, they have a variety of other names. Hence, even without abandoning the fact-value divide, there appear to be similarities between inference in science and inference in morals, which may encourage communication across the boundaries between "the two cultures" and which are relevant to medical humanities.

A novel telomerase activator suppresses lung damage in a murine model of idiopathic pulmonary fibrosis.

Science.gov (United States)

Le Saux, Claude Jourdan; Davy, Philip; Brampton, Christopher; Ahuja, Seema S; Fauce, Steven; Shivshankar, Pooja; Nguyen, Hieu; Ramaseshan, Mahesh; Tressler, Robert; Pirot, Zhu; Harley, Calvin B; Allsopp, Richard

2013-01-01

The emergence of diseases associated with telomere dysfunction, including AIDS, aplastic anemia and pulmonary fibrosis, has bolstered interest in telomerase activators. We report identification of a new small molecule activator, GRN510, with activity ex vivo and in vivo. Using a novel mouse model, we tested the potential of GRN510 to limit fibrosis induced by bleomycin in mTERT heterozygous mice. Treatment with GRN510 at 10 mg/kg/day activated telomerase 2-4 fold both in hematopoietic progenitors ex vivo and in bone marrow and lung tissue in vivo, respectively. Telomerase activation was countered by co-treatment with Imetelstat (GRN163L), a potent telomerase inhibitor. In this model of bleomycin-induced fibrosis, treatment with GRN510 suppressed the development of fibrosis and accumulation of senescent cells in the lung via a mechanism dependent upon telomerase activation. Treatment of small airway epithelial cells (SAEC) or lung fibroblasts ex vivo with GRN510 revealed telomerase activating and replicative lifespan promoting effects only in the SAEC, suggesting that the mechanism accounting for the protective effects of GRN510 against induced lung fibrosis involves specific types of lung cells. Together, these results support the use of small molecule activators of telomerase in therapies to treat idiopathic pulmonary fibrosis.

Introductory statistical inference

CERN Document Server

Mukhopadhyay, Nitis

2014-01-01

This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of dist

Active inference, communication and hermeneutics.

Science.gov (United States)

Friston, Karl J; Frith, Christopher D

2015-07-01

Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Optimization methods for logical inference

CERN Document Server

Chandru, Vijay

2011-01-01

Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in

A Randomized Cross‐over Study of High‐dose Metoclopramide plus Dexamethasone versus Granisetron plus Dexamethasone in Patients Receiving Chemotherapy with High‐dose Cisplatin

Science.gov (United States)

Eguchi, Kenji; Shinkai, Tetsu; Tamura, Tomohide; Ohe, Yuichiro; Nisio, Masato; Kunikane, Hiroshi; Arioka, Hitoshi; Karato, Atsuya; Nakashima, Hajime; Sasaki, Yasutsuna; Tajima, Kinuko; Tada, Noriko; Saijo, Nagahiro

1994-01-01

We carried out a randomized, single‐blind, cross‐over trial to compare the antiemetic effect, for both acute and delayed emesis, of granisetron plus dexamethasone (GRN+Dx) with that of high‐dose metoclopramide plus dexamethasone (HDMP + Dx). Fifty‐four patients with primary or metastatic lung cancer, given single‐dose cisplatin (> 80 mg/m2) chemotherapy more than twice, were enrolled in this study. They were treated with both HDMP+Dx and GRN+Dx in two consecutive chemotherapy courses. On day 1, patients experienced a mean of 2.5 (SD=4.3) and 0,1 (SD = 0.4) episodes of vomiting in the HDMP+Dx and the GRN + Dx groups, respectively (P=0.0008). Complete response rate on day 1 was 45 and 90% in the HDMP+Dx and the GRN+Dx groups, respectively (P= 0.0001). Patients treated with GRN+Dx had a tendency to suffer more episodes of vomiting than the HDMP+Dx group on days 2–5, but it was not statistically significant. Twenty‐four patients (57%) preferred the GRN+Dx treatment and 14 patients (33%), HDMP + Dx. In the HDMP + Dx group, nine patients (21%) had an extrapyramidal reaction, and 5 patients (12%) had constipation that lasted for at least two days. In contrast, no patients had extrapyramidal reactions, and IS patients (43%) had constipation in the GRN+Dx group (P < 0.01). GRN+Dx was more effective than HDMP+Dx only in preventing the acute emesis induced by cisplatin. An effective treatment for delayed emesis is still needed. PMID:7829401

Inference in `poor` languages

Energy Technology Data Exchange (ETDEWEB)

Petrov, S.

1996-10-01

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

EI: A Program for Ecological Inference

Directory of Open Access Journals (Sweden)

Gary King

2004-09-01

Full Text Available The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997. Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological" data to infer discrete individual-level relationships of interest when individual-level data are not available. Ecological inferences are required in political science research when individual-level surveys are unavailable (e.g., local or comparative electoral politics, unreliable (racial politics, insufficient (political geography, or infeasible (political history. They are also required in numerous areas of ma jor significance in public policy (e.g., for applying the Voting Rights Act and other academic disciplines ranging from epidemiology and marketing to sociology and quantitative history.

Adaptive protection scheme

Directory of Open Access Journals (Sweden)

R. Sitharthan

2016-09-01

Full Text Available This paper aims at modelling an electronically coupled distributed energy resource with an adaptive protection scheme. The electronically coupled distributed energy resource is a microgrid framework formed by coupling the renewable energy source electronically. Further, the proposed adaptive protection scheme provides a suitable protection to the microgrid for various fault conditions irrespective of the operating mode of the microgrid: namely, grid connected mode and islanded mode. The outstanding aspect of the developed adaptive protection scheme is that it monitors the microgrid and instantly updates relay fault current according to the variations that occur in the system. The proposed adaptive protection scheme also employs auto reclosures, through which the proposed adaptive protection scheme recovers faster from the fault and thereby increases the consistency of the microgrid. The effectiveness of the proposed adaptive protection is studied through the time domain simulations carried out in the PSCAD⧹EMTDC software environment.

Comparative study of numerical schemes of TVD3, UNO3-ACM and optimized compact scheme

Science.gov (United States)

Lee, Duck-Joo; Hwang, Chang-Jeon; Ko, Duck-Kon; Kim, Jae-Wook

1995-01-01

Three different schemes are employed to solve the benchmark problem. The first one is a conventional TVD-MUSCL (Monotone Upwind Schemes for Conservation Laws) scheme. The second scheme is a UNO3-ACM (Uniformly Non-Oscillatory Artificial Compression Method) scheme. The third scheme is an optimized compact finite difference scheme modified by us: the 4th order Runge Kutta time stepping, the 4th order pentadiagonal compact spatial discretization with the maximum resolution characteristics. The problems of category 1 are solved by using the second (UNO3-ACM) and third (Optimized Compact) schemes. The problems of category 2 are solved by using the first (TVD3) and second (UNO3-ACM) schemes. The problem of category 5 is solved by using the first (TVD3) scheme. It can be concluded from the present calculations that the Optimized Compact scheme and the UN03-ACM show good resolutions for category 1 and category 2 respectively.

On the criticality of inferred models

Science.gov (United States)

Mastromatteo, Iacopo; Marsili, Matteo

2011-10-01

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

On the criticality of inferred models

International Nuclear Information System (INIS)

Mastromatteo, Iacopo; Marsili, Matteo

2011-01-01

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

An Inference Language for Imaging

DEFF Research Database (Denmark)

Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen

2014-01-01

We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framewor...

Inferring Molecular Processes Heterogeneity from Transcriptional Data.

Science.gov (United States)

Gogolewski, Krzysztof; Wronowska, Weronika; Lech, Agnieszka; Lesyng, Bogdan; Gambin, Anna

2017-01-01

RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs.

Inference

DEFF Research Database (Denmark)

Møller, Jesper

2010-01-01

Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...... (MCMC) techniques. Due to space limitations the focus is on spatial point processes....

Feature Inference Learning and Eyetracking

Science.gov (United States)

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

Multimodal FMRI resting-state functional connectivity in granulin mutations: the case of fronto-parietal dementia.

Directory of Open Access Journals (Sweden)

Enrico Premi

Full Text Available BACKGROUND: Monogenic dementias represent a great opportunity to trace disease progression from preclinical to symptomatic stages. Frontotemporal Dementia related to Granulin (GRN mutations presents a specific framework of brain damage, involving fronto-temporal regions and long inter-hemispheric white matter bundles. Multimodal resting-state functional MRI (rs-fMRI is a promising tool to carefully describe disease signature from the earliest disease phase. OBJECTIVE: To define local connectivity alterations in GRN related pathology moving from the presymptomatic (asymptomatic GRN mutation carriers to the clinical phase of the disease (GRN- related Frontotemporal Dementia. METHODS: Thirty-one GRN Thr272fs mutation carriers (14 patients with Frontotemporal Dementia and 17 asymptomatic carriers and 38 healthy controls were recruited. Local connectivity measures (Regional Homogeneity (ReHo, Fractional Amplitude of Low Frequency Fluctuation (fALFF and Degree Centrality (DC were computed, considering age and gender as nuisance variables as well as the influence of voxel-level gray matter atrophy. RESULTS: Asymptomatic GRN carriers had selective reduced ReHo in the left parietal region and increased ReHo in frontal regions compared to healthy controls. Considering Frontotemporal Dementia patients, all measures (ReHo, fALFF and DC were reduced in inferior parietal, frontal lobes and posterior cingulate cortex. Considering GRN mutation carriers, an inverse correlation with age in the posterior cingulate cortex, inferior parietal lobule and orbitofrontal cortex was found. CONCLUSIONS: GRN pathology is characterized by functional brain network alterations even decades before the clinical onset; they involve the parietal region primarily and then spread to the anterior regions of the brain, supporting the concept of molecular nexopathies.

Sensitivity to neurotoxic stress is not increased in progranulin-deficient mice.

Science.gov (United States)

Petkau, Terri L; Zhu, Shanshan; Lu, Ge; Fernando, Sarah; Cynader, Max; Leavitt, Blair R

2013-11-01

Loss-of-function mutations in the progranulin (GRN) gene are a common cause of autosomal dominant frontotemporal lobar degeneration, a fatal and progressive neurodegenerative disorder common in people less than 65 years of age. In the brain, progranulin is expressed in multiple regions at varying levels, and has been hypothesized to play a neuroprotective or neurotrophic role. Four neurotoxic agents were injected in vivo into constitutive progranulin knockout (Grn(-/-)) mice and their wild-type (Grn(+/+)) counterparts to assess neuronal sensitivity to toxic stress. Administration of 3-nitropropionic acid, quinolinic acid, kainic acid, and pilocarpine induced robust and measurable neuronal cell death in affected brain regions, but no differential cell death was observed between Grn(+/+) and Grn(-/-) mice. Thus, constitutive progranulin knockout mice do not have increased sensitivity to neuronal cell death induced by the acute chemical models of neuronal injury used in this study. Copyright © 2013. Published by Elsevier Inc.

Forward and backward inference in spatial cognition.

Directory of Open Access Journals (Sweden)

Will D Penny

Full Text Available This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.

A formal model of interpersonal inference

Directory of Open Access Journals (Sweden)

Michael eMoutoussis

2014-03-01

Full Text Available Introduction: We propose that active Bayesian inference – a general framework for decision-making – can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. Results: 1. Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to 'mentalising' in the psychological literature, is based upon the outcomes of interpersonal exchanges. 2. We show how some well-known social-psychological phenomena (e.g. self-serving biases can be explained in terms of active interpersonal inference. 3. Mentalising naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one’s own qualities and preferences. Conclusion: We inaugurate a Bayes optimal framework for modelling intersubject variability in mentalising during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalising is distorted.

Expression of the growth factor progranulin in endothelial cells influences growth and development of blood vessels: a novel mouse model.

Science.gov (United States)

Toh, Huishi; Cao, Mingju; Daniels, Eugene; Bateman, Andrew

2013-01-01

Progranulin is a secreted glycoprotein that regulates cell proliferation, migration and survival. It has roles in development, tumorigenesis, wound healing, neurodegeneration and inflammation. Endothelia in tumors, wounds and placenta express elevated levels of progranulin. In culture, progranulin activates endothelial proliferation and migration. This suggested that progranulin might regulate angiogenesis. It was, however, unclear how elevated endothelial progranulin levels influence vascular growth in vivo. To address this issue, we generated mice with progranulin expression targeted specifically to developing endothelial cells using a Tie2-promoter/enhancer construct. Three Tie2-Grn mouse lines were generated with varying Tie2-Grn copy number, and were called GrnLo, GrnMid, and GrnHi. All three lines showed increased mortality that correlates with Tie2-Grn copy number, with greatest mortality and lowest germline transmission in the GrnHi line. Death of the transgenic animals occurred around birth, and continued for three days after birth. Those that survived beyond day 3 survived into adulthood. Transgenic neonates that died showed vascular abnormalities of varying severity. Some exhibited bleeding into body cavities such as the pericardial space. Smaller localized hemorrhages were seen in many organs. Blood vessels were often dilated and thin-walled. To establish the development of these abnormalities, we examined mice at early (E10.5-14.5) and later (E15.5-17.5) developmental phases. Early events during vasculogenesis appear unaffected by Tie2-Grn as apparently normal primary vasculature had been established at E10.5. The earliest onset of vascular abnormality was at E15.5, with focal cerebral hemorrhage and enlarged vessels in various organs. Aberrant Tie2-Grn positive vessels showed thinning of the basement membrane and reduced investiture with mural cells. We conclude that progranulin promotes exaggerated vessel growth in vivo, with subsequent effects in

Expression of the growth factor progranulin in endothelial cells influences growth and development of blood vessels: a novel mouse model.

Directory of Open Access Journals (Sweden)

Huishi Toh

Full Text Available Progranulin is a secreted glycoprotein that regulates cell proliferation, migration and survival. It has roles in development, tumorigenesis, wound healing, neurodegeneration and inflammation. Endothelia in tumors, wounds and placenta express elevated levels of progranulin. In culture, progranulin activates endothelial proliferation and migration. This suggested that progranulin might regulate angiogenesis. It was, however, unclear how elevated endothelial progranulin levels influence vascular growth in vivo. To address this issue, we generated mice with progranulin expression targeted specifically to developing endothelial cells using a Tie2-promoter/enhancer construct. Three Tie2-Grn mouse lines were generated with varying Tie2-Grn copy number, and were called GrnLo, GrnMid, and GrnHi. All three lines showed increased mortality that correlates with Tie2-Grn copy number, with greatest mortality and lowest germline transmission in the GrnHi line. Death of the transgenic animals occurred around birth, and continued for three days after birth. Those that survived beyond day 3 survived into adulthood. Transgenic neonates that died showed vascular abnormalities of varying severity. Some exhibited bleeding into body cavities such as the pericardial space. Smaller localized hemorrhages were seen in many organs. Blood vessels were often dilated and thin-walled. To establish the development of these abnormalities, we examined mice at early (E10.5-14.5 and later (E15.5-17.5 developmental phases. Early events during vasculogenesis appear unaffected by Tie2-Grn as apparently normal primary vasculature had been established at E10.5. The earliest onset of vascular abnormality was at E15.5, with focal cerebral hemorrhage and enlarged vessels in various organs. Aberrant Tie2-Grn positive vessels showed thinning of the basement membrane and reduced investiture with mural cells. We conclude that progranulin promotes exaggerated vessel growth in vivo, with

Distributional Inference

NARCIS (Netherlands)

Kroese, A.H.; van der Meulen, E.A.; Poortema, Klaas; Schaafsma, W.

1995-01-01

The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is

PREFACE: ELC International Meeting on Inference, Computation, and Spin Glasses (ICSG2013)

Science.gov (United States)

Kabashima, Yoshiyuki; Hukushima, Koji; Inoue, Jun-ichi; Tanaka, Toshiyuki; Watanabe, Osamu

2013-12-01

The close relationship between probability-based inference and statistical mechanics of disordered systems has been noted for some time. This relationship has provided researchers with a theoretical foundation in various fields of information processing for analytical performance evaluation and construction of efficient algorithms based on message-passing or Monte Carlo sampling schemes. The ELC International Meeting on 'Inference, Computation, and Spin Glasses (ICSG2013)', was held in Sapporo 28-30 July 2013. The meeting was organized as a satellite meeting of STATPHYS25 in order to offer a forum where concerned researchers can assemble and exchange information on the latest results and newly established methodologies, and discuss future directions of the interdisciplinary studies between statistical mechanics and information sciences. Financial support from Grant-in-Aid for Scientific Research on Innovative Areas, MEXT, Japan 'Exploring the Limits of Computation (ELC)' is gratefully acknowledged. We are pleased to publish 23 papers contributed by invited speakers of ICSG2013 in this volume of Journal of Physics: Conference Series. We hope that this volume will promote further development of this highly vigorous interdisciplinary field between statistical mechanics and information/computer science. Editors and ICSG2013 Organizing Committee: Koji Hukushima Jun-ichi Inoue (Local Chair of ICSG2013) Yoshiyuki Kabashima (Editor-in-Chief) Toshiyuki Tanaka Osamu Watanabe (General Chair of ICSG2013)

Continuous Integrated Invariant Inference, Phase I

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National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...

Estimating uncertainty of inference for validation

Energy Technology Data Exchange (ETDEWEB)

Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM

2010-09-30

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

Phylogenetic relationships of Hemiptera inferred from mitochondrial and nuclear genes.

Science.gov (United States)

Song, Nan; Li, Hu; Cai, Wanzhi; Yan, Fengming; Wang, Jianyun; Song, Fan

2016-11-01

Here, we reconstructed the Hemiptera phylogeny based on the expanded mitochondrial protein-coding genes and the nuclear 18S rRNA gene, separately. The differential rates of change across lineages may associate with long-branch attraction (LBA) effect and result in conflicting estimates of phylogeny from different types of data. To reduce the potential effects of systematic biases on inferences of topology, various data coding schemes, site removal method, and different algorithms were utilized in phylogenetic reconstruction. We show that the outgroups Phthiraptera, Thysanoptera, and the ingroup Sternorrhyncha share similar base composition, and exhibit "long branches" relative to other hemipterans. Thus, the long-branch attraction between these groups is suspected to cause the failure of recovering Hemiptera under the homogeneous model. In contrast, a monophyletic Hemiptera is supported when heterogeneous model is utilized in the analysis. Although higher level phylogenetic relationships within Hemiptera remain to be answered, consensus between analyses is beginning to converge on a stable phylogeny.

TALE factors use two distinct functional modes to control an essential zebrafish gene expression program.

Science.gov (United States)

Ladam, Franck; Stanney, William; Donaldson, Ian J; Yildiz, Ozge; Bobola, Nicoletta; Sagerström, Charles G

2018-06-18

TALE factors are broadly expressed embryonically and known to function in complexes with transcription factors (TFs) like Hox proteins at gastrula/segmentation stages, but it is unclear if such generally expressed factors act by the same mechanism throughout embryogenesis. We identify a TALE-dependent gene regulatory network (GRN) required for anterior development and detect TALE occupancy associated with this GRN throughout embryogenesis. At blastula stages, we uncover a novel functional mode for TALE factors, where they occupy genomic DECA motifs with nearby NF-Y sites. We demonstrate that TALE and NF-Y form complexes and regulate chromatin state at genes of this GRN. At segmentation stages, GRN-associated TALE occupancy expands to include HEXA motifs near PBX:HOX sites. Hence, TALE factors control a key GRN, but utilize distinct DNA motifs and protein partners at different stages - a strategy that may also explain their oncogenic potential and may be employed by other broadly expressed TFs. © 2018, Ladam et al.

Quantum-Like Representation of Non-Bayesian Inference

Science.gov (United States)

Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

2013-01-01

This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

Bayesian Inference Methods for Sparse Channel Estimation

DEFF Research Database (Denmark)

Pedersen, Niels Lovmand

2013-01-01

This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

Statistical inference an integrated Bayesianlikelihood approach

CERN Document Server

Aitkin, Murray

2010-01-01

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre

Colour schemes

DEFF Research Database (Denmark)

van Leeuwen, Theo

2013-01-01

This chapter presents a framework for analysing colour schemes based on a parametric approach that includes not only hue, value and saturation, but also purity, transparency, luminosity, luminescence, lustre, modulation and differentiation.......This chapter presents a framework for analysing colour schemes based on a parametric approach that includes not only hue, value and saturation, but also purity, transparency, luminosity, luminescence, lustre, modulation and differentiation....

TMEM106B gene polymorphism is associated with age at onset in granulin mutation carriers and plasma granulin protein levels

Science.gov (United States)

Cruchaga, Carlos; Graff, Caroline; Chiang, Huei-Hsin; Wang, Jun; Hinrichs, Anthony L.; Spiegel, Noah; Bertelsen, Sarah; Mayo, Kevin; Norton, Joanne B.; Morris, John C.; Goate, Alison

2011-01-01

Objective A recent genome-wide association study for frontotemporal lobar degeneration with TAR DNA-binding protein inclusions (FTLD-TDP), identified rs1990622 (TMEM106B) as a risk factor for FTLD-TDP. In this study we tested whether rs1990622 is associated with age at onset (AAO) in granulin (GRN) mutation carriers and with plasma GRN levels in mutation carriers and healthy elderly individuals. Design Rs1990622 was genotyped in GRN mutation carriers and tested for association with AAO using the Kaplan-Meier and a Cox proportional hazards model. Subjects We analyzed 50 affected and unaffected GRN mutation carriers from four previously reported FTLD-TDP families (HDDD1, FD1, HDDD2 and the Karolinska family). GRN plasma levels were also measured in 73 healthy, elderly individuals. Results The risk allele of rs1990622 is associated with a mean decrease of the age at onset of thirteen years (p=9.9×10−7), with lower plasma granulin levels in both healthy older adults (p = 4×10−4) and GRN mutation carriers (p=0.0027). Analysis of the HAPMAP database identified a non-synonymous single nucleotide polymorphism, rs3173615 (T185S) in perfect linkage disequilibrium with rs1990622. Conclusions The association of rs1990622 with AAO explains, in part, the wide range in the age at onset of disease among GRN mutation carriers. We hypothesize that rs1990622 or another variant in linkage disequilibrium could act in a manner similar to APOE in Alzheimer’s disease, increasing risk for disease in the general population and modifying AAO in mutation carriers. Our results also suggest that genetic variation in TMEM106B may influence risk for FTLD-TDP by modulating secreted levels of GRN. PMID:21220649

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

Science.gov (United States)

Gao, Long; Uzun, Yasin; Gao, Peng; He, Bing; Ma, Xiaoke; Wang, Jiahui; Han, Shizhong; Tan, Kai

2018-02-16

Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.

Inference Attacks and Control on Database Structures

Directory of Open Access Journals (Sweden)

Muhamed Turkanovic

2015-02-01

Full Text Available Today’s databases store information with sensitivity levels that range from public to highly sensitive, hence ensuring confidentiality can be highly important, but also requires costly control. This paper focuses on the inference problem on different database structures. It presents possible treats on privacy with relation to the inference, and control methods for mitigating these treats. The paper shows that using only access control, without any inference control is inadequate, since these models are unable to protect against indirect data access. Furthermore, it covers new inference problems which rise from the dimensions of new technologies like XML, semantics, etc.

Type Inference with Inequalities

DEFF Research Database (Denmark)

Schwartzbach, Michael Ignatieff

1991-01-01

of (monotonic) inequalities on the types of variables and expressions. A general result about systems of inequalities over semilattices yields a solvable form. We distinguish between deciding typability (the existence of solutions) and type inference (the computation of a minimal solution). In our case, both......Type inference can be phrased as constraint-solving over types. We consider an implicitly typed language equipped with recursive types, multiple inheritance, 1st order parametric polymorphism, and assignments. Type correctness is expressed as satisfiability of a possibly infinite collection...

LevelScheme: A level scheme drawing and scientific figure preparation system for Mathematica

Science.gov (United States)

Caprio, M. A.

2005-09-01

LevelScheme is a scientific figure preparation system for Mathematica. The main emphasis is upon the construction of level schemes, or level energy diagrams, as used in nuclear, atomic, molecular, and hadronic physics. LevelScheme also provides a general infrastructure for the preparation of publication-quality figures, including support for multipanel and inset plotting, customizable tick mark generation, and various drawing and labeling tasks. Coupled with Mathematica's plotting functions and powerful programming language, LevelScheme provides a flexible system for the creation of figures combining diagrams, mathematical plots, and data plots. Program summaryTitle of program:LevelScheme Catalogue identifier:ADVZ Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADVZ Operating systems:Any which supports Mathematica; tested under Microsoft Windows XP, Macintosh OS X, and Linux Programming language used:Mathematica 4 Number of bytes in distributed program, including test and documentation:3 051 807 Distribution format:tar.gz Nature of problem:Creation of level scheme diagrams. Creation of publication-quality multipart figures incorporating diagrams and plots. Method of solution:A set of Mathematica packages has been developed, providing a library of level scheme drawing objects, tools for figure construction and labeling, and control code for producing the graphics.

Inference in models with adaptive learning

NARCIS (Netherlands)

Chevillon, G.; Massmann, M.; Mavroeidis, S.

2010-01-01

Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be

A dynamic discretization method for reliability inference in Dynamic Bayesian Networks

International Nuclear Information System (INIS)

Zhu, Jiandao; Collette, Matthew

2015-01-01

The material and modeling parameters that drive structural reliability analysis for marine structures are subject to a significant uncertainty. This is especially true when time-dependent degradation mechanisms such as structural fatigue cracking are considered. Through inspection and monitoring, information such as crack location and size can be obtained to improve these parameters and the corresponding reliability estimates. Dynamic Bayesian Networks (DBNs) are a powerful and flexible tool to model dynamic system behavior and update reliability and uncertainty analysis with life cycle data for problems such as fatigue cracking. However, a central challenge in using DBNs is the need to discretize certain types of continuous random variables to perform network inference while still accurately tracking low-probability failure events. Most existing discretization methods focus on getting the overall shape of the distribution correct, with less emphasis on the tail region. Therefore, a novel scheme is presented specifically to estimate the likelihood of low-probability failure events. The scheme is an iterative algorithm which dynamically partitions the discretization intervals at each iteration. Through applications to two stochastic crack-growth example problems, the algorithm is shown to be robust and accurate. Comparisons are presented between the proposed approach and existing methods for the discretization problem. - Highlights: • A dynamic discretization method is developed for low-probability events in DBNs. • The method is compared to existing approaches on two crack growth problems. • The method is shown to improve on existing methods for low-probability events

Packet reversed packet combining scheme

International Nuclear Information System (INIS)

Bhunia, C.T.

2006-07-01

The packet combining scheme is a well defined simple error correction scheme with erroneous copies at the receiver. It offers higher throughput combined with ARQ protocols in networks than that of basic ARQ protocols. But packet combining scheme fails to correct errors when the errors occur in the same bit locations of two erroneous copies. In the present work, we propose a scheme that will correct error if the errors occur at the same bit location of the erroneous copies. The proposed scheme when combined with ARQ protocol will offer higher throughput. (author)

Fiducial inference - A Neyman-Pearson interpretation

NARCIS (Netherlands)

Salome, D; VonderLinden, W; Dose,; Fischer, R; Preuss, R

1999-01-01

Fisher's fiducial argument is a tool for deriving inferences in the form of a probability distribution on the parameter space, not based on Bayes's Theorem. Lindley established that in exceptional situations fiducial inferences coincide with posterior distributions; in the other situations fiducial

Uncertainty in prediction and in inference

NARCIS (Netherlands)

Hilgevoord, J.; Uffink, J.

1991-01-01

The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close re-lationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in

Polynomial Chaos Surrogates for Bayesian Inference

KAUST Repository

Le Maitre, Olivier

2016-01-06

The Bayesian inference is a popular probabilistic method to solve inverse problems, such as the identification of field parameter in a PDE model. The inference rely on the Bayes rule to update the prior density of the sought field, from observations, and derive its posterior distribution. In most cases the posterior distribution has no explicit form and has to be sampled, for instance using a Markov-Chain Monte Carlo method. In practice the prior field parameter is decomposed and truncated (e.g. by means of Karhunen- Lo´eve decomposition) to recast the inference problem into the inference of a finite number of coordinates. Although proved effective in many situations, the Bayesian inference as sketched above faces several difficulties requiring improvements. First, sampling the posterior can be a extremely costly task as it requires multiple resolutions of the PDE model for different values of the field parameter. Second, when the observations are not very much informative, the inferred parameter field can highly depends on its prior which can be somehow arbitrary. These issues have motivated the introduction of reduced modeling or surrogates for the (approximate) determination of the parametrized PDE solution and hyperparameters in the description of the prior field. Our contribution focuses on recent developments in these two directions: the acceleration of the posterior sampling by means of Polynomial Chaos expansions and the efficient treatment of parametrized covariance functions for the prior field. We also discuss the possibility of making such approach adaptive to further improve its efficiency.

The zebrafish progranulin gene family and antisense transcripts

Directory of Open Access Journals (Sweden)

Baranowski David

2005-11-01

Full Text Available Abstract Background Progranulin is an epithelial tissue growth factor (also known as proepithelin, acrogranin and PC-cell-derived growth factor that has been implicated in development, wound healing and in the progression of many cancers. The single mammalian progranulin gene encodes a glycoprotein precursor consisting of seven and one half tandemly repeated non-identical copies of the cystine-rich granulin motif. A genome-wide duplication event hypothesized to have occurred at the base of the teleost radiation predicts that mammalian progranulin may be represented by two co-orthologues in zebrafish. Results The cDNAs encoding two zebrafish granulin precursors, progranulins-A and -B, were characterized and found to contain 10 and 9 copies of the granulin motif respectively. The cDNAs and genes encoding the two forms of granulin, progranulins-1 and -2, were also cloned and sequenced. Both latter peptides were found to be encoded by precursors with a simplified architecture consisting of one and one half copies of the granulin motif. A cDNA encoding a chimeric progranulin which likely arises through the mechanism of trans-splicing between grn1 and grn2 was also characterized. A non-coding RNA gene with antisense complementarity to both grn1 and grn2 was identified which may have functional implications with respect to gene dosage, as well as in restricting the formation of the chimeric form of progranulin. Chromosomal localization of the four progranulin (grn genes reveals syntenic conservation for grna only, suggesting that it is the true orthologue of mammalian grn. RT-PCR and whole-mount in situ hybridization analysis of zebrafish grns during development reveals that combined expression of grna and grnb, but not grn1 and grn2, recapitulate many of the expression patterns observed for the murine counterpart. This includes maternal deposition, widespread central nervous system distribution and specific localization within the epithelial

Interactive Instruction in Bayesian Inference

DEFF Research Database (Denmark)

Khan, Azam; Breslav, Simon; Hornbæk, Kasper

2018-01-01

An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....

Inferring Phylogenetic Networks Using PhyloNet.

Science.gov (United States)

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

Statistical inference for the additive hazards model under outcome-dependent sampling.

Science.gov (United States)

Yu, Jichang; Liu, Yanyan; Sandler, Dale P; Zhou, Haibo

2015-09-01

Cost-effective study design and proper inference procedures for data from such designs are always of particular interests to study investigators. In this article, we propose a biased sampling scheme, an outcome-dependent sampling (ODS) design for survival data with right censoring under the additive hazards model. We develop a weighted pseudo-score estimator for the regression parameters for the proposed design and derive the asymptotic properties of the proposed estimator. We also provide some suggestions for using the proposed method by evaluating the relative efficiency of the proposed method against simple random sampling design and derive the optimal allocation of the subsamples for the proposed design. Simulation studies show that the proposed ODS design is more powerful than other existing designs and the proposed estimator is more efficient than other estimators. We apply our method to analyze a cancer study conducted at NIEHS, the Cancer Incidence and Mortality of Uranium Miners Study, to study the risk of radon exposure to cancer.

Currents on Grassmann algebras

International Nuclear Information System (INIS)

Coquereaux, R.; Ragoucy, E.

1993-09-01

Currents are defined on a Grassmann algebra Gr(N) with N generators as distributions on its exterior algebra (using the symmetric wedge product). The currents are interpreted in terms of Z 2 -graded Hochschild cohomology and closed currents in terms of cyclic cocycles (they are particular multilinear forms on Gr(N)). An explicit construction of the vector space of closed currents of degree p on Gr(N) is given by using Berezin integration. (authors). 10 refs

A full quantum network scheme

International Nuclear Information System (INIS)

Ma Hai-Qiang; Wei Ke-Jin; Yang Jian-Hui; Li Rui-Xue; Zhu Wu

2014-01-01

We present a full quantum network scheme using a modified BB84 protocol. Unlike other quantum network schemes, it allows quantum keys to be distributed between two arbitrary users with the help of an intermediary detecting user. Moreover, it has good expansibility and prevents all potential attacks using loopholes in a detector, so it is more practical to apply. Because the fiber birefringence effects are automatically compensated, the scheme is distinctly stable in principle and in experiment. The simple components for every user make our scheme easier for many applications. The experimental results demonstrate the stability and feasibility of this scheme. (general)

Transmission usage cost allocation schemes

International Nuclear Information System (INIS)

Abou El Ela, A.A.; El-Sehiemy, R.A.

2009-01-01

This paper presents different suggested transmission usage cost allocation (TCA) schemes to the system individuals. Different independent system operator (ISO) visions are presented using the proportional rata and flow-based TCA methods. There are two proposed flow-based TCA schemes (FTCA). The first FTCA scheme generalizes the equivalent bilateral exchanges (EBE) concepts for lossy networks through two-stage procedure. The second FTCA scheme is based on the modified sensitivity factors (MSF). These factors are developed from the actual measurements of power flows in transmission lines and the power injections at different buses. The proposed schemes exhibit desirable apportioning properties and are easy to implement and understand. Case studies for different loading conditions are carried out to show the capability of the proposed schemes for solving the TCA problem. (author)

Large scale statistical inference of signaling pathways from RNAi and microarray data

Directory of Open Access Journals (Sweden)

Poustka Annemarie

2007-10-01

Full Text Available Abstract Background The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. Results In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. Conclusion Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-α pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.

Active Inference, homeostatic regulation and adaptive behavioural control.

Science.gov (United States)

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-11-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Generative Inferences Based on Learned Relations

Science.gov (United States)

Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.

2017-01-01

A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

Matroids and quantum-secret-sharing schemes

International Nuclear Information System (INIS)

Sarvepalli, Pradeep; Raussendorf, Robert

2010-01-01

A secret-sharing scheme is a cryptographic protocol to distribute a secret state in an encoded form among a group of players such that only authorized subsets of the players can reconstruct the secret. Classically, efficient secret-sharing schemes have been shown to be induced by matroids. Furthermore, access structures of such schemes can be characterized by an excluded minor relation. No such relations are known for quantum secret-sharing schemes. In this paper we take the first steps toward a matroidal characterization of quantum-secret-sharing schemes. In addition to providing a new perspective on quantum-secret-sharing schemes, this characterization has important benefits. While previous work has shown how to construct quantum-secret-sharing schemes for general access structures, these schemes are not claimed to be efficient. In this context the present results prove to be useful; they enable us to construct efficient quantum-secret-sharing schemes for many general access structures. More precisely, we show that an identically self-dual matroid that is representable over a finite field induces a pure-state quantum-secret-sharing scheme with information rate 1.

Dissociation of Frontotemporal Dementia–Related Deficits and Neuroinflammation in Progranulin Haploinsufficient Mice

Science.gov (United States)

Filiano, Anthony J.; Martens, Lauren Herl; Young, Allen H.; Warmus, Brian A.; Zhou, Ping; Diaz-Ramirez, Grisell; Jiao, Jian; Zhang, Zhijun; Huang, Eric J.; Gao, Fen-Biao; Farese, Robert V.; Roberson, Erik D.

2013-01-01

Frontotemporal dementia (FTD) is a neurodegenerative disease with hallmark deficits in social and emotional function. Heterozygous loss-of-function mutations in GRN, the progranulin gene, are a common genetic cause of the disorder, but the mechanisms by which progranulin haploinsufficiency causes neuronal dysfunction in FTD are unclear. Homozygous progranulin knockout (Grn−/−) mice have been studied as a model of this disorder and show behavioral deficits and a neuroinflammatory phenotype with robust microglial activation. However, homozygous GRN mutations causing complete progranulin deficiency were recently shown to cause a different neurological disorder, neuronal ceroid lipofuscinosis, suggesting that the total absence of progranulin may have effects distinct from those of haploinsufficiency. Here, we studied progranulin heterozygous (Grn+/−) mice, which model progranulin haploinsufficiency. We found that Grn+/− mice developed age-dependent social and emotional deficits potentially relevant to FTD. However, unlike Grn−/− mice, behavioral deficits in Grn+/− mice occurred in the absence of gliosis or increased expression of tumor necrosis factor–α. Instead, we found neuronal abnormalities in the amygdala, an area of selective vulnerability in FTD, in Grn+/− mice. Our findings indicate that FTD-related deficits due to progranulin haploinsufficiency can develop in the absence of detectable gliosis and neuroinflammation, thereby dissociating microglial activation from functional deficits and suggesting an important effect of progranulin deficiency on neurons. PMID:23516300

Parametric statistical inference basic theory and modern approaches

CERN Document Server

Zacks, Shelemyahu; Tsokos, C P

1981-01-01

Parametric Statistical Inference: Basic Theory and Modern Approaches presents the developments and modern trends in statistical inference to students who do not have advanced mathematical and statistical preparation. The topics discussed in the book are basic and common to many fields of statistical inference and thus serve as a jumping board for in-depth study. The book is organized into eight chapters. Chapter 1 provides an overview of how the theory of statistical inference is presented in subsequent chapters. Chapter 2 briefly discusses statistical distributions and their properties. Chapt

Optimal plasma progranulin cutoff value for predicting null progranulin mutations in neurodegenerative diseases: a multicenter Italian study.

Science.gov (United States)

Ghidoni, Roberta; Stoppani, Elena; Rossi, Giacomina; Piccoli, Elena; Albertini, Valentina; Paterlini, Anna; Glionna, Michela; Pegoiani, Eleonora; Agnati, Luigi F; Fenoglio, Chiara; Scarpini, Elio; Galimberti, Daniela; Morbin, Michela; Tagliavini, Fabrizio; Binetti, Giuliano; Benussi, Luisa

2012-01-01

Recently, attention was drawn to a role for progranulin in the central nervous system with the identification of mutations in the progranulin gene (GRN) as an important cause of frontotemporal lobar degeneration. GRN mutations are associated with a strong reduction of circulating progranulin and widely variable clinical phenotypes: thus, the dosage of plasma progranulin is a useful tool for a quick and inexpensive large-scale screening of carriers of GRN mutations. To establish the best cutoff threshold for normal versus abnormal levels of plasma progranulin. 309 cognitively healthy controls (25-87 years of age), 72 affected and unaffected GRN+ null mutation carriers (24-86 years of age), 3 affected GRN missense mutation carriers, 342 patients with neurodegenerative diseases and 293 subjects with mild cognitive impairment were enrolled at the Memory Clinic, IRCCS S. Giovanni di Dio-Fatebenefratelli, Brescia, Italy, and at the Alzheimer Unit, Ospedale Maggiore Policlinico and IRCCS Istituto Neurologico C. Besta, Milan, Italy. Plasma progranulin levels were measured using an ELISA kit (AdipoGen Inc., Seoul, Korea). Plasma progranulin did not correlate with age, gender or body mass index. We established a new plasma progranulin protein cutoff level of 61.55 ng/ml that identifies, with a specificity of 99.6% and a sensitivity of 95.8%, null mutation carriers among subjects attending to a memory clinic. Affected and unaffected GRN null mutation carriers did not differ in terms of circulating progranulin protein (p = 0.686). A significant disease anticipation was observed in GRN+ subjects with the lowest progranulin levels. We propose a new plasma progranulin protein cutoff level useful for clinical practice. Copyright © 2011 S. Karger AG, Basel.

Restoring neuronal progranulin reverses deficits in a mouse model of frontotemporal dementia.

Science.gov (United States)

Arrant, Andrew E; Filiano, Anthony J; Unger, Daniel E; Young, Allen H; Roberson, Erik D

2017-05-01

Loss-of-function mutations in progranulin (GRN), a secreted glycoprotein expressed by neurons and microglia, are a common autosomal dominant cause of frontotemporal dementia, a neurodegenerative disease commonly characterized by disrupted social and emotional behaviour. GRN mutations are thought to cause frontotemporal dementia through progranulin haploinsufficiency, therefore, boosting progranulin expression from the intact allele is a rational treatment strategy. However, this approach has not been tested in an animal model of frontotemporal dementia and it is unclear if boosting progranulin could correct pre-existing deficits. Here, we show that adeno-associated virus-driven expression of progranulin in the medial prefrontal cortex reverses social dominance deficits in Grn+/- mice, an animal model of frontotemporal dementia due to GRN mutations. Adeno-associated virus-progranulin also corrected lysosomal abnormalities in Grn+/- mice. The adeno-associated virus-progranulin vector only transduced neurons, suggesting that restoring neuronal progranulin is sufficient to correct deficits in Grn+/- mice. To further test the role of neuronal progranulin in the development of frontotemporal dementia-related deficits, we generated two neuronal progranulin-deficient mouse lines using CaMKII-Cre and Nestin-Cre. Measuring progranulin levels in these lines indicated that most brain progranulin is derived from neurons. Both neuronal progranulin-deficient lines developed social dominance deficits similar to those in global Grn+/- mice, showing that neuronal progranulin deficiency is sufficient to disrupt social behaviour. These data support the concept of progranulin-boosting therapies for frontotemporal dementia and highlight an important role for neuron-derived progranulin in maintaining normal social function. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Scheme Program Documentation Tools

DEFF Research Database (Denmark)

Nørmark, Kurt

2004-01-01

are separate and intended for different documentation purposes they are related to each other in several ways. Both tools are based on XML languages for tool setup and for documentation authoring. In addition, both tools rely on the LAML framework which---in a systematic way---makes an XML language available...... as named functions in Scheme. Finally, the Scheme Elucidator is able to integrate SchemeDoc resources as part of an internal documentation resource....

A Memory Efficient Network Encryption Scheme

Science.gov (United States)

El-Fotouh, Mohamed Abo; Diepold, Klaus

In this paper, we studied the two widely used encryption schemes in network applications. Shortcomings have been found in both schemes, as these schemes consume either more memory to gain high throughput or low memory with low throughput. The need has aroused for a scheme that has low memory requirements and in the same time possesses high speed, as the number of the internet users increases each day. We used the SSM model [1], to construct an encryption scheme based on the AES. The proposed scheme possesses high throughput together with low memory requirements.

Mobile sensing of point-source fugitive methane emissions using Bayesian inference: the determination of the likelihood function

Science.gov (United States)

Zhou, X.; Albertson, J. D.

2016-12-01

Natural gas is considered as a bridge fuel towards clean energy due to its potential lower greenhouse gas emission comparing with other fossil fuels. Despite numerous efforts, an efficient and cost-effective approach to monitor fugitive methane emissions along the natural gas production-supply chain has not been developed yet. Recently, mobile methane measurement has been introduced which applies a Bayesian approach to probabilistically infer methane emission rates and update estimates recursively when new measurements become available. However, the likelihood function, especially the error term which determines the shape of the estimate uncertainty, is not rigorously defined and evaluated with field data. To address this issue, we performed a series of near-source (using a specialized vehicle mounted with fast response methane analyzers and a GPS unit. Methane concentrations were measured at two different heights along mobile traversals downwind of the sources, and concurrent wind and temperature data are recorded by nearby 3-D sonic anemometers. With known methane release rates, the measurements were used to determine the functional form and the parameterization of the likelihood function in the Bayesian inference scheme under different meteorological conditions.

Variational inference & deep learning: A new synthesis

OpenAIRE

Kingma, D.P.

2017-01-01

In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.

Variational inference & deep learning : A new synthesis

NARCIS (Netherlands)

Kingma, D.P.

2017-01-01

In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.

Modified Aggressive Packet Combining Scheme

International Nuclear Information System (INIS)

Bhunia, C.T.

2010-06-01

In this letter, a few schemes are presented to improve the performance of aggressive packet combining scheme (APC). To combat error in computer/data communication networks, ARQ (Automatic Repeat Request) techniques are used. Several modifications to improve the performance of ARQ are suggested by recent research and are found in literature. The important modifications are majority packet combining scheme (MjPC proposed by Wicker), packet combining scheme (PC proposed by Chakraborty), modified packet combining scheme (MPC proposed by Bhunia), and packet reversed packet combining (PRPC proposed by Bhunia) scheme. These modifications are appropriate for improving throughput of conventional ARQ protocols. Leung proposed an idea of APC for error control in wireless networks with the basic objective of error control in uplink wireless data network. We suggest a few modifications of APC to improve its performance in terms of higher throughput, lower delay and higher error correction capability. (author)

Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment

DEFF Research Database (Denmark)

Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Cemgil, Ali Taylan

2010-01-01

a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment...

Ensemble stacking mitigates biases in inference of synaptic connectivity

Directory of Open Access Journals (Sweden)

Brendan Chambers

2018-03-01

Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures

Constraint Satisfaction Inference : Non-probabilistic Global Inference for Sequence Labelling

NARCIS (Netherlands)

Canisius, S.V.M.; van den Bosch, A.; Daelemans, W.; Basili, R.; Moschitti, A.

2006-01-01

We present a new method for performing sequence labelling based on the idea of using a machine-learning classifier to generate several possible output sequences, and then applying an inference procedure to select the best sequence among those. Most sequence labelling methods following a similar

Reasoning about Informal Statistical Inference: One Statistician's View

Science.gov (United States)

Rossman, Allan J.

2008-01-01

This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical…

Meta-learning framework applied in bioinformatics inference system design.

Science.gov (United States)

Arredondo, Tomás; Ormazábal, Wladimir

2015-01-01

This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

Bonus schemes and trading activity

NARCIS (Netherlands)

Pikulina, E.S.; Renneboog, L.D.R.; ter Horst, J.R.; Tobler, P.N.

2014-01-01

Little is known about how different bonus schemes affect traders' propensity to trade and which bonus schemes improve traders' performance. We study the effects of linear versus threshold bonus schemes on traders' behavior. Traders buy and sell shares in an experimental stock market on the basis of

Incorporating time-delays in S-System model for reverse engineering genetic networks.

Science.gov (United States)

Chowdhury, Ahsan Raja; Chetty, Madhu; Vinh, Nguyen Xuan

2013-06-18

In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions. In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization. The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in

Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

Energy Technology Data Exchange (ETDEWEB)

Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

2010-12-15

The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

Statistical inference and Aristotle's Rhetoric.

Science.gov (United States)

Macdonald, Ranald R

2004-11-01

Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical.

A Blind Adaptive Color Image Watermarking Scheme Based on Principal Component Analysis, Singular Value Decomposition and Human Visual System

Directory of Open Access Journals (Sweden)

M. Imran

2017-09-01

Full Text Available A blind adaptive color image watermarking scheme based on principal component analysis, singular value decomposition, and human visual system is proposed. The use of principal component analysis to decorrelate the three color channels of host image, improves the perceptual quality of watermarked image. Whereas, human visual system and fuzzy inference system helped to improve both imperceptibility and robustness by selecting adaptive scaling factor, so that, areas more prone to noise can be added with more information as compared to less prone areas. To achieve security, location of watermark embedding is kept secret and used as key at the time of watermark extraction, whereas, for capacity both singular values and vectors are involved in watermark embedding process. As a result, four contradictory requirements; imperceptibility, robustness, security and capacity are achieved as suggested by results. Both subjective and objective methods are acquired to examine the performance of proposed schemes. For subjective analysis the watermarked images and watermarks extracted from attacked watermarked images are shown. For objective analysis of proposed scheme in terms of imperceptibility, peak signal to noise ratio, structural similarity index, visual information fidelity and normalized color difference are used. Whereas, for objective analysis in terms of robustness, normalized correlation, bit error rate, normalized hamming distance and global authentication rate are used. Security is checked by using different keys to extract the watermark. The proposed schemes are compared with state-of-the-art watermarking techniques and found better performance as suggested by results.

Children's and adults' judgments of the certainty of deductive inferences, inductive inferences, and guesses.

Science.gov (United States)

Pillow, Bradford H; Pearson, Raeanne M; Hecht, Mary; Bremer, Amanda

2010-01-01

Children and adults rated their own certainty following inductive inferences, deductive inferences, and guesses. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Deductions were rated as more certain than strong inductions beginning in Grade 3, and fourth-grade children and adults differentiated strong inductions, weak inductions, and informed guesses from pure guesses. By Grade 3, participants also gave different types of explanations for their deductions and inductions. These results are discussed in relation to children's concepts of cognitive processes, logical reasoning, and epistemological development.

Bayesian inversion of a CRN depth profile to infer Quaternary erosion of the northwestern Campine Plateau (NE Belgium

Directory of Open Access Journals (Sweden)

E. Laloy

2017-07-01

Full Text Available The rate at which low-lying sandy areas in temperate regions, such as the Campine Plateau (NE Belgium, have been eroding during the Quaternary is a matter of debate. Current knowledge on the average pace of landscape evolution in the Campine area is largely based on geological inferences and modern analogies. We performed a Bayesian inversion of an in situ-produced 10Be concentration depth profile to infer the average long-term erosion rate together with two other parameters: the surface exposure age and the inherited 10Be concentration. Compared to the latest advances in probabilistic inversion of cosmogenic radionuclide (CRN data, our approach has the following two innovative components: it (1 uses Markov chain Monte Carlo (MCMC sampling and (2 accounts (under certain assumptions for the contribution of model errors to posterior uncertainty. To investigate to what extent our approach differs from the state of the art in practice, a comparison against the Bayesian inversion method implemented in the CRONUScalc program is made. Both approaches identify similar maximum a posteriori (MAP parameter values, but posterior parameter and predictive uncertainty derived using the method taken in CRONUScalc is moderately underestimated. A simple way for producing more consistent uncertainty estimates with the CRONUScalc-like method in the presence of model errors is therefore suggested. Our inferred erosion rate of 39 ± 8. 9 mm kyr−1 (1σ is relatively large in comparison with landforms that erode under comparable (paleo-climates elsewhere in the world. We evaluate this value in the light of the erodibility of the substrate and sudden base level lowering during the Middle Pleistocene. A denser sampling scheme of a two-nuclide concentration depth profile would allow for better inferred erosion rate resolution, and including more uncertain parameters in the MCMC inversion.

Bayesian inversion of a CRN depth profile to infer Quaternary erosion of the northwestern Campine Plateau (NE Belgium)

Science.gov (United States)

Laloy, Eric; Beerten, Koen; Vanacker, Veerle; Christl, Marcus; Rogiers, Bart; Wouters, Laurent

2017-07-01

The rate at which low-lying sandy areas in temperate regions, such as the Campine Plateau (NE Belgium), have been eroding during the Quaternary is a matter of debate. Current knowledge on the average pace of landscape evolution in the Campine area is largely based on geological inferences and modern analogies. We performed a Bayesian inversion of an in situ-produced 10Be concentration depth profile to infer the average long-term erosion rate together with two other parameters: the surface exposure age and the inherited 10Be concentration. Compared to the latest advances in probabilistic inversion of cosmogenic radionuclide (CRN) data, our approach has the following two innovative components: it (1) uses Markov chain Monte Carlo (MCMC) sampling and (2) accounts (under certain assumptions) for the contribution of model errors to posterior uncertainty. To investigate to what extent our approach differs from the state of the art in practice, a comparison against the Bayesian inversion method implemented in the CRONUScalc program is made. Both approaches identify similar maximum a posteriori (MAP) parameter values, but posterior parameter and predictive uncertainty derived using the method taken in CRONUScalc is moderately underestimated. A simple way for producing more consistent uncertainty estimates with the CRONUScalc-like method in the presence of model errors is therefore suggested. Our inferred erosion rate of 39 ± 8. 9 mm kyr-1 (1σ) is relatively large in comparison with landforms that erode under comparable (paleo-)climates elsewhere in the world. We evaluate this value in the light of the erodibility of the substrate and sudden base level lowering during the Middle Pleistocene. A denser sampling scheme of a two-nuclide concentration depth profile would allow for better inferred erosion rate resolution, and including more uncertain parameters in the MCMC inversion.

Deep Learning for Population Genetic Inference.

Science.gov (United States)

Sheehan, Sara; Song, Yun S

2016-03-01

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

Using Alien Coins to Test Whether Simple Inference Is Bayesian

Science.gov (United States)

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

2016-01-01

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

On Maximum Entropy and Inference

Directory of Open Access Journals (Sweden)

Luigi Gresele

2017-11-01

Full Text Available Maximum entropy is a powerful concept that entails a sharp separation between relevant and irrelevant variables. It is typically invoked in inference, once an assumption is made on what the relevant variables are, in order to estimate a model from data, that affords predictions on all other (dependent variables. Conversely, maximum entropy can be invoked to retrieve the relevant variables (sufficient statistics directly from the data, once a model is identified by Bayesian model selection. We explore this approach in the case of spin models with interactions of arbitrary order, and we discuss how relevant interactions can be inferred. In this perspective, the dimensionality of the inference problem is not set by the number of parameters in the model, but by the frequency distribution of the data. We illustrate the method showing its ability to recover the correct model in a few prototype cases and discuss its application on a real dataset.

Compiling Relational Bayesian Networks for Exact Inference

DEFF Research Database (Denmark)

Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan

2004-01-01

We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...

MiR-145 mediates zebrafish hepatic outgrowth through progranulin A signaling.

Directory of Open Access Journals (Sweden)

Ya-Wen Li

Full Text Available MicroRNAs (miRs are mRNA-regulatory molecules that fine-tune gene expression and modulate both processes of development and tumorigenesis. Our previous studies identified progranulin A (GrnA as a growth factor which induces zebrafish hepatic outgrowth through MET signaling. We also found that miR-145 is one of potential fine-tuning regulators of GrnA involved in embryonic hepatic outgrowth. The low level of miR-145 seen in hepatocarinogenesis has been shown to promote pathological liver growth. However, little is known about the regulatory mechanism of miR-145 in embryonic liver development. In this study, we demonstrate a significant decrease in miR-145 expression during hepatogenesis. We modulate miR-145 expression in zebrafish embryos by injection with a miR-145 mimic or a miR-145 hairpin inhibitor. Altered embryonic liver outgrowth is observed in response to miR-145 expression modulation. We also confirm a critical role of miR-145 in hepatic outgrowth by using whole-mount in situ hybridization. Loss of miR-145 expression in embryos results in hepatic cell proliferation, and vice versa. Furthermore, we demonstrate that GrnA is a target of miR-145 and GrnA-induced MET signaling is also regulated by miR-145 as determined by luciferase reporter assay and gene expression analysis, respectively. In addition, co-injection of GrnA mRNA with miR-145 mimic or MO-GrnA with miR-145 inhibitor restores the liver defects caused by dysregulation of miR-145 expression. In conclusion, our findings suggest an important role of miR-145 in regulating GrnA-dependent hepatic outgrowth in zebrafish embryonic development.

Progranulin mutation causes frontotemporal dementia in the Swedish Karolinska family.

Science.gov (United States)

Chiang, Huei-Hsin; Rosvall, Lina; Brohede, Jesper; Axelman, Karin; Björk, Behnosh F; Nennesmo, Inger; Robins, Tiina; Graff, Caroline

2008-11-01

Frontotemporal dementia (FTD) is a neurodegenerative disease characterized by cognitive impairment, language dysfunction, and/or changes in personality. Recently it has been shown that progranulin (GRN) mutations can cause FTD as well as other neurodegenerative phenotypes. DNA from 30 family members, of whom seven were diagnosed with FTD, in the Karolinska family was available for GRN sequencing. Fibroblast cell mRNA from one affected family member and six control individuals was available for relative quantitative real-time polymerase chain reaction to investigate the effect of the mutation. Furthermore, the cDNA of an affected individual was sequenced. Clinical and neuropathologic findings of a previously undescribed family branch are presented. A frameshift mutation in GRN (g.102delC) was detected in all affected family members and absent in four unaffected family members older than 70 years. Real-time polymerase chain reaction data showed an approximately 50% reduction of GRN fibroblast mRNA in an affected individual. The mutated mRNA transcripts were undetectable by cDNA sequencing. Segregation and RNA analyses showed that the g.102delC mutation, previously reported, causes FTD in the Karolinska family. Our findings add further support to the significance of GRN in FTD etiology and the presence of modifying genes, which emphasize the need for further studies into the mechanisms of clinical heterogeneity. However, the results already call for attention to the complexity of predictive genetic testing of GRN mutations.

Circulating progranulin as a biomarker for neurodegenerative diseases.

Science.gov (United States)

Ghidoni, Roberta; Paterlini, Anna; Benussi, Luisa

2012-01-01

Progranulin is a growth factor involved in the regulation of multiple processes including tumorigenesis, wound repair, development, and inflammation. The recent discovery that mutations in the gene encoding for progranulin (GRN) cause frontotemporal lobar degeneration (FTLD), and other neurodegenerative diseases leading to dementia, has brought renewed interest in progranulin and its functions in the central nervous system. GRN null mutations cause protein haploinsufficiency, leading to a significant decrease in progranulin levels that can be detected in plasma, serum and cerebrospinal fluid (CSF) of mutation carriers. The dosage of circulating progranulin sped up the identification of GRN mutations thus favoring genotype-phenotype correlation studies. Researchers demonstrated that, in GRN null mutation carriers, the shortage of progranulin invariably precedes clinical symptoms and thus mutation carriers are "captured" regardless of their disease status. GRN is a particularly appealing gene for drug targeting, in the way that boosting its expression may be beneficial for mutation carriers, preventing or delaying the onset of GRN-related neurodegenerative diseases. Physiological regulation of progranulin expression level is only partially known. Progranulin expression reflects mutation status and, intriguingly, its levels can be modulated by some additional factor (i.e. genetic background; drugs). Thus, factors increasing the production and secretion of progranulin from the normal gene are promising potential therapeutic avenues. In conclusion, peripheral progranulin is a nonintrusive highly accurate biomarker for early identification of mutation carriers and for monitoring future treatments that might boost the level of this protein.

Causal inference in economics and marketing.

Science.gov (United States)

Varian, Hal R

2016-07-05

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

CSR schemes in agribusiness

DEFF Research Database (Denmark)

Pötz, Katharina Anna; Haas, Rainer; Balzarova, Michaela

2013-01-01

of schemes that can be categorized on focus areas, scales, mechanisms, origins, types and commitment levels. Research limitations/implications – The findings contribute to conceptual and empirical research on existing models to compare and analyse CSR standards. Sampling technique and depth of analysis limit......Purpose – The rise of CSR followed a demand for CSR standards and guidelines. In a sector already characterized by a large number of standards, the authors seek to ask what CSR schemes apply to agribusiness, and how they can be systematically compared and analysed. Design....../methodology/approach – Following a deductive-inductive approach the authors develop a model to compare and analyse CSR schemes based on existing studies and on coding qualitative data on 216 CSR schemes. Findings – The authors confirm that CSR standards and guidelines have entered agribusiness and identify a complex landscape...

Uncertainty in prediction and in inference

International Nuclear Information System (INIS)

Hilgevoord, J.; Uffink, J.

1991-01-01

The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close relationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in inference can be obtained by means of the so-called statistical distance between probability distributions. When applied to quantum mechanics, this distance leads to a measure of the distinguishability of quantum states, which essentially is the absolute value of the matrix element between the states. The importance of this result to the quantum mechanical uncertainty principle is noted. The second part of the paper provides a derivation of the statistical distance on the basis of the so-called method of support

Threshold Signature Schemes Application

Directory of Open Access Journals (Sweden)

Anastasiya Victorovna Beresneva

2015-10-01

Full Text Available This work is devoted to an investigation of threshold signature schemes. The systematization of the threshold signature schemes was done, cryptographic constructions based on interpolation Lagrange polynomial, elliptic curves and bilinear pairings were examined. Different methods of generation and verification of threshold signatures were explored, the availability of practical usage of threshold schemes in mobile agents, Internet banking and e-currency was shown. The topics of further investigation were given and it could reduce a level of counterfeit electronic documents signed by a group of users.

A Spatial Domain Quantum Watermarking Scheme

International Nuclear Information System (INIS)

Wei Zhan-Hong; Chen Xiu-Bo; Niu Xin-Xin; Yang Yi-Xian; Xu Shu-Jiang

2016-01-01

This paper presents a spatial domain quantum watermarking scheme. For a quantum watermarking scheme, a feasible quantum circuit is a key to achieve it. This paper gives a feasible quantum circuit for the presented scheme. In order to give the quantum circuit, a new quantum multi-control rotation gate, which can be achieved with quantum basic gates, is designed. With this quantum circuit, our scheme can arbitrarily control the embedding position of watermark images on carrier images with the aid of auxiliary qubits. Besides reversely acting the given quantum circuit, the paper gives another watermark extracting algorithm based on quantum measurements. Moreover, this paper also gives a new quantum image scrambling method and its quantum circuit. Differ from other quantum watermarking schemes, all given quantum circuits can be implemented with basic quantum gates. Moreover, the scheme is a spatial domain watermarking scheme, and is not based on any transform algorithm on quantum images. Meanwhile, it can make sure the watermark be secure even though the watermark has been found. With the given quantum circuit, this paper implements simulation experiments for the presented scheme. The experimental result shows that the scheme does well in the visual quality and the embedding capacity. (paper)

Nonparametric predictive inference in statistical process control

NARCIS (Netherlands)

Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

2000-01-01

New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on

Compiling Relational Bayesian Networks for Exact Inference

DEFF Research Database (Denmark)

Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark

2006-01-01

We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...

Making inference from wildlife collision data: inferring predator absence from prey strikes

Directory of Open Access Journals (Sweden)

Peter Caley

2017-02-01

Full Text Available Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.

Making inference from wildlife collision data: inferring predator absence from prey strikes.

Science.gov (United States)

Caley, Peter; Hosack, Geoffrey R; Barry, Simon C

2017-01-01

Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.

Causal inference in biology networks with integrated belief propagation.

Science.gov (United States)

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

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

Labeling schemes for bounded degree graphs

DEFF Research Database (Denmark)

Adjiashvili, David; Rotbart, Noy Galil

2014-01-01

We investigate adjacency labeling schemes for graphs of bounded degree Δ = O(1). In particular, we present an optimal (up to an additive constant) log n + O(1) adjacency labeling scheme for bounded degree trees. The latter scheme is derived from a labeling scheme for bounded degree outerplanar...... graphs. Our results complement a similar bound recently obtained for bounded depth trees [Fraigniaud and Korman, SODA 2010], and may provide new insights for closing the long standing gap for adjacency in trees [Alstrup and Rauhe, FOCS 2002]. We also provide improved labeling schemes for bounded degree...

Efficient Bayesian inference for ARFIMA processes

Science.gov (United States)

Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.

2015-03-01

Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more flexible Autoregressive Fractional Integrated Moving Average (ARFIMA) model which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LRD, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g. short memory effects) can be integrated over in order to focus on long memory parameters, and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data, with favorable comparison to the standard estimators.

Multiresolution signal decomposition schemes

NARCIS (Netherlands)

J. Goutsias (John); H.J.A.M. Heijmans (Henk)

1998-01-01

textabstract[PNA-R9810] Interest in multiresolution techniques for signal processing and analysis is increasing steadily. An important instance of such a technique is the so-called pyramid decomposition scheme. This report proposes a general axiomatic pyramid decomposition scheme for signal analysis

Deep Learning for Population Genetic Inference.

Directory of Open Access Journals (Sweden)

Sara Sheehan

2016-03-01

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

Deep Learning for Population Genetic Inference

Science.gov (United States)

Sheehan, Sara; Song, Yun S.

2016-01-01

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

Tabled Execution in Scheme

Energy Technology Data Exchange (ETDEWEB)

Willcock, J J; Lumsdaine, A; Quinlan, D J

2008-08-19

Tabled execution is a generalization of memorization developed by the logic programming community. It not only saves results from tabled predicates, but also stores the set of currently active calls to them; tabled execution can thus provide meaningful semantics for programs that seemingly contain infinite recursions with the same arguments. In logic programming, tabled execution is used for many purposes, both for improving the efficiency of programs, and making tasks simpler and more direct to express than with normal logic programs. However, tabled execution is only infrequently applied in mainstream functional languages such as Scheme. We demonstrate an elegant implementation of tabled execution in Scheme, using a mix of continuation-passing style and mutable data. We also show the use of tabled execution in Scheme for a problem in formal language and automata theory, demonstrating that tabled execution can be a valuable tool for Scheme users.

Optimal Face-Iris Multimodal Fusion Scheme

Directory of Open Access Journals (Sweden)

Omid Sharifi

2016-06-01

Full Text Available Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA, a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.

A Bayesian Network Schema for Lessening Database Inference

National Research Council Canada - National Science Library

Chang, LiWu; Moskowitz, Ira S

2001-01-01

.... The authors introduce a formal schema for database inference analysis, based upon a Bayesian network structure, which identifies critical parameters involved in the inference problem and represents...

Type Inference for Session Types in the Pi-Calculus

DEFF Research Database (Denmark)

Graversen, Eva Fajstrup; Harbo, Jacob Buchreitz; Huttel, Hans

2014-01-01

In this paper we present a direct algorithm for session type inference for the π-calculus. Type inference for session types has previously been achieved by either imposing limitations and restriction on the π-calculus, or by reducing the type inference problem to that for linear types. Our approach...

Explanatory Preferences Shape Learning and Inference.

Science.gov (United States)

Lombrozo, Tania

2016-10-01

Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference. Copyright © 2016 Elsevier Ltd. All rights reserved.

Grammatical inference algorithms, routines and applications

CERN Document Server

Wieczorek, Wojciech

2017-01-01

This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>.

BagReg: Protein inference through machine learning.

Science.gov (United States)

Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou

2015-08-01

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Ensemble stacking mitigates biases in inference of synaptic connectivity.

Science.gov (United States)

Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

2018-01-01

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

Stochastic processes inference theory

CERN Document Server

Rao, Malempati M

2014-01-01

This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.

Russell and Humean Inferences

Directory of Open Access Journals (Sweden)

João Paulo Monteiro

2001-12-01

Full Text Available Russell's The Problems of Philosophy tries to establish a new theory of induction, at the same time that Hume is there accused of an irrational/ scepticism about induction". But a careful analysis of the theory of knowledge explicitly acknowledged by Hume reveals that, contrary to the standard interpretation in the XXth century, possibly influenced by Russell, Hume deals exclusively with causal inference (which he never classifies as "causal induction", although now we are entitled to do so, never with inductive inference in general, mainly generalizations about sensible qualities of objects ( whether, e.g., "all crows are black" or not is not among Hume's concerns. Russell's theories are thus only false alternatives to Hume's, in (1912 or in his (1948.

Efficient algorithms for conditional independence inference

Czech Academy of Sciences Publication Activity Database

Bouckaert, R.; Hemmecke, R.; Lindner, S.; Studený, Milan

2010-01-01

Roč. 11, č. 1 (2010), s. 3453-3479 ISSN 1532-4435 R&D Projects: GA ČR GA201/08/0539; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : conditional independence inference * linear programming approach Subject RIV: BA - General Mathematics Impact factor: 2.949, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-efficient algorithms for conditional independence inference.pdf

State-Space Inference and Learning with Gaussian Processes

OpenAIRE

Turner, R; Deisenroth, MP; Rasmussen, CE

2010-01-01

18.10.13 KB. Ok to add author version to spiral, authors hold copyright. State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. C...

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

Science.gov (United States)

Chen, Daizhuo; Fraiberger, Samuel P; Moakler, Robert; Provost, Foster

2017-09-01

Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from "Likes" on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the "cloaking device"-a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users.

Sortilin-Mediated Endocytosis Determines Levels of the Fronto-Temporal Dementia Protein, Progranulin

DEFF Research Database (Denmark)

Hu, Fenghua; Padukkavidana, Thihan; Vægter, Christian Bjerggaard

2010-01-01

The most common inherited form of Fronto-Temporal Lobar Degeneration (FTLD) known stems from Progranulin (GRN) mutation, and exhibits TDP-43 plus ubiquitin protein aggregates in brain. Despite the causative role of GRN haploinsufficiency in FTLD-TDP, the neurobiology of this secreted glycoprotein...

Multiuser switched diversity scheduling schemes

KAUST Repository

Shaqfeh, Mohammad; Alnuweiri, Hussein M.; Alouini, Mohamed-Slim

2012-01-01

Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions. © 2012 IEEE.

Short-Term Saved Leave Scheme

CERN Multimedia

2007-01-01

As announced at the meeting of the Standing Concertation Committee (SCC) on 26 June 2007 and in http://Bulletin No. 28/2007, the existing Saved Leave Scheme will be discontinued as of 31 December 2007. Staff participating in the Scheme will shortly receive a contract amendment stipulating the end of financial contributions compensated by save leave. Leave already accumulated on saved leave accounts can continue to be taken in accordance with the rules applicable to the current scheme. A new system of saved leave will enter into force on 1 January 2008 and will be the subject of a new implementation procedure entitled "Short-term saved leave scheme" dated 1 January 2008. At its meeting on 4 December 2007, the SCC agreed to recommend the Director-General to approve this procedure, which can be consulted on the HR Department’s website at the following address: https://cern.ch/hr-services/services-Ben/sls_shortterm.asp All staff wishing to participate in the new scheme a...

Short-Term Saved Leave Scheme

CERN Multimedia

HR Department

2007-01-01

As announced at the meeting of the Standing Concertation Committee (SCC) on 26 June 2007 and in http://Bulletin No. 28/2007, the existing Saved Leave Scheme will be discontinued as of 31 December 2007. Staff participating in the Scheme will shortly receive a contract amendment stipulating the end of financial contributions compensated by save leave. Leave already accumulated on saved leave accounts can continue to be taken in accordance with the rules applicable to the current scheme. A new system of saved leave will enter into force on 1 January 2008 and will be the subject of a new im-plementation procedure entitled "Short-term saved leave scheme" dated 1 January 2008. At its meeting on 4 December 2007, the SCC agreed to recommend the Director-General to approve this procedure, which can be consulted on the HR Department’s website at the following address: https://cern.ch/hr-services/services-Ben/sls_shortterm.asp All staff wishing to participate in the new scheme ...

Multiuser switched diversity scheduling schemes

KAUST Repository

Shaqfeh, Mohammad

2012-09-01

Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions. © 2012 IEEE.

Fused Regression for Multi-source Gene Regulatory Network Inference.

Directory of Open Access Journals (Sweden)

Kari Y Lam

2016-12-01

Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

Numerical schemes for explosion hazards

International Nuclear Information System (INIS)

Therme, Nicolas

2015-01-01

In nuclear facilities, internal or external explosions can cause confinement breaches and radioactive materials release in the environment. Hence, modeling such phenomena is crucial for safety matters. Blast waves resulting from explosions are modeled by the system of Euler equations for compressible flows, whereas Navier-Stokes equations with reactive source terms and level set techniques are used to simulate the propagation of flame front during the deflagration phase. The purpose of this thesis is to contribute to the creation of efficient numerical schemes to solve these complex models. The work presented here focuses on two major aspects: first, the development of consistent schemes for the Euler equations, then the buildup of reliable schemes for the front propagation. In both cases, explicit in time schemes are used, but we also introduce a pressure correction scheme for the Euler equations. Staggered discretization is used in space. It is based on the internal energy formulation of the Euler system, which insures its positivity and avoids tedious discretization of the total energy over staggered grids. A discrete kinetic energy balance is derived from the scheme and a source term is added in the discrete internal energy balance equation to preserve the exact total energy balance at the limit. High order methods of MUSCL type are used in the discrete convective operators, based solely on material velocity. They lead to positivity of density and internal energy under CFL conditions. This ensures that the total energy cannot grow and we can furthermore derive a discrete entropy inequality. Under stability assumptions of the discrete L8 and BV norms of the scheme's solutions one can prove that a sequence of converging discrete solutions necessarily converges towards the weak solution of the Euler system. Besides it satisfies a weak entropy inequality at the limit. Concerning the front propagation, we transform the flame front evolution equation (the so called

HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR

International Nuclear Information System (INIS)

Schneider, Michael D.; Dawson, William A.; Hogg, David W.; Marshall, Philip J.; Bard, Deborah J.; Meyers, Joshua; Lang, Dustin

2015-01-01

Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics

Bayesian structural inference for hidden processes

Science.gov (United States)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Compact Spreader Schemes

Energy Technology Data Exchange (ETDEWEB)

Placidi, M.; Jung, J. -Y.; Ratti, A.; Sun, C.

2014-07-25

This paper describes beam distribution schemes adopting a novel implementation based on low amplitude vertical deflections combined with horizontal ones generated by Lambertson-type septum magnets. This scheme offers substantial compactness in the longitudinal layouts of the beam lines and increased flexibility for beam delivery of multiple beam lines on a shot-to-shot basis. Fast kickers (FK) or transverse electric field RF Deflectors (RFD) provide the low amplitude deflections. Initially proposed at the Stanford Linear Accelerator Center (SLAC) as tools for beam diagnostics and more recently adopted for multiline beam pattern schemes, RFDs offer repetition capabilities and a likely better amplitude reproducibility when compared to FKs, which, in turn, offer more modest financial involvements both in construction and operation. Both solutions represent an ideal approach for the design of compact beam distribution systems resulting in space and cost savings while preserving flexibility and beam quality.

The Impact of Disablers on Predictive Inference

Science.gov (United States)

Cummins, Denise Dellarosa

2014-01-01

People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive…

Automatic physical inference with information maximizing neural networks

Science.gov (United States)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

Inference as Prediction

Science.gov (United States)

Watson, Jane

2007-01-01

Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…

Effects of Exercise on Progranulin Levels and Gliosis in Progranulin-Insufficient Mice.

Science.gov (United States)

Arrant, Andrew E; Patel, Aashka R; Roberson, Erik D

2015-01-01

Loss-of-function mutations in progranulin ( GRN ) are one of the most common genetic causes of frontotemporal dementia (FTD), a progressive, fatal neurodegenerative disorder with no available disease-modifying treatments. Through haploinsufficiency, these mutations reduce levels of progranulin, a protein that has neurotrophic and anti-inflammatory effects. Increasing progranulin expression from the intact allele is therefore a potential approach for treating individuals with GRN mutations. Based on the well-known effects of physical exercise on other neurotrophic factors, we hypothesized that exercise might increase brain progranulin levels. We tested this hypothesis in progranulin heterozygous ( Grn + / - ) mice, which model progranulin haploinsufficiency. We housed wild-type and progranulin-insufficient mice in standard cages or cages with exercise wheels for 4 or 7.5 weeks, and then measured brain and plasma progranulin levels. Although exercise modestly increased progranulin in very young (2-month-old) wild-type mice, this effect was limited to the hippocampus. Exercise did not increase brain progranulin mRNA or protein in multiple regions, nor did it increase plasma progranulin, in 4- to 8-month-old wild-type or Grn + / - mice, across multiple experiments and under conditions that increased hippocampal BDNF and neurogenesis. Grn - / - mice were included in the study to test for progranulin-independent benefits of exercise on gliosis. Exercise attenuated cortical microgliosis in 8-month-old Grn - / - mice, consistent with a progranulin-independent, anti-inflammatory effect of exercise. These results suggest that exercise may have some modest, nonspecific benefits for FTD patients with progranulin mutations, but do not support exercise as a strategy to raise progranulin levels.

Conditional loss of progranulin in neurons is not sufficient to cause neuronal ceroid lipofuscinosis-like neuropathology in mice.

Science.gov (United States)

Petkau, Terri L; Blanco, Jake; Leavitt, Blair R

2017-10-01

Progranulin deficiency due to heterozygous null mutations in the GRN gene is a common cause of familial frontotemporal lobar degeneration (FTLD), while homozygous loss-of-function GRN mutations cause neuronal ceroid lipofuscinosis (NCL). Aged progranulin-knockout mice display highly exaggerated lipofuscinosis, microgliosis, and astrogliosis, as well as mild cell loss in specific brain regions. Progranulin is a secreted glycoprotein expressed in both neurons and microglia, but not astrocytes, in the brain. We generated conditional progranulin-knockout mice that lack progranulin in nestin-expressing cells (Nes-cKO mice), which include most neurons as well as astrocytes. We confirmed near complete knockout of progranulin in neurons in Nes-cKO mice, while microglial progranulin levels remained similar to that of wild-type animals. Overall brain progranulin levels were reduced by about 50% in Nes-cKO, and no Grn was detected in primary Nes-cKO neurons. Nes-cKO mice aged to 12months did not display any increase in lipofuscin deposition, microgliosis, or astrogliosis in the four brain regions examined, though increases were observed for most of these measures in Grn-null animals. We conclude that neuron-specific loss of progranulin is not sufficient to cause similar neuropathological changes to those seen in constitutive Grn-null animals. Our results suggest that increased lipofuscinosis and gliosis in Grn-null animals are not caused by intrinsic progranulin deficiency in neurons, and that microglia-derived progranulin may be sufficient to maintain neuronal health and homeostasis in the brain. Copyright © 2017 Elsevier Inc. All rights reserved.

Problem solving and inference mechanisms

Energy Technology Data Exchange (ETDEWEB)

Furukawa, K; Nakajima, R; Yonezawa, A; Goto, S; Aoyama, A

1982-01-01

The heart of the fifth generation computer will be powerful mechanisms for problem solving and inference. A deduction-oriented language is to be designed, which will form the core of the whole computing system. The language is based on predicate logic with the extended features of structuring facilities, meta structures and relational data base interfaces. Parallel computation mechanisms and specialized hardware architectures are being investigated to make possible efficient realization of the language features. The project includes research into an intelligent programming system, a knowledge representation language and system, and a meta inference system to be built on the core. 30 references.

Quantum signature scheme for known quantum messages

International Nuclear Information System (INIS)

Kim, Taewan; Lee, Hyang-Sook

2015-01-01

When we want to sign a quantum message that we create, we can use arbitrated quantum signature schemes which are possible to sign for not only known quantum messages but also unknown quantum messages. However, since the arbitrated quantum signature schemes need the help of a trusted arbitrator in each verification of the signature, it is known that the schemes are not convenient in practical use. If we consider only known quantum messages such as the above situation, there can exist a quantum signature scheme with more efficient structure. In this paper, we present a new quantum signature scheme for known quantum messages without the help of an arbitrator. Differing from arbitrated quantum signature schemes based on the quantum one-time pad with the symmetric key, since our scheme is based on quantum public-key cryptosystems, the validity of the signature can be verified by a receiver without the help of an arbitrator. Moreover, we show that our scheme provides the functions of quantum message integrity, user authentication and non-repudiation of the origin as in digital signature schemes. (paper)

Two-level schemes for the advection equation

Science.gov (United States)

Vabishchevich, Petr N.

2018-06-01

The advection equation is the basis for mathematical models of continuum mechanics. In the approximate solution of nonstationary problems it is necessary to inherit main properties of the conservatism and monotonicity of the solution. In this paper, the advection equation is written in the symmetric form, where the advection operator is the half-sum of advection operators in conservative (divergent) and non-conservative (characteristic) forms. The advection operator is skew-symmetric. Standard finite element approximations in space are used. The standard explicit two-level scheme for the advection equation is absolutely unstable. New conditionally stable regularized schemes are constructed, on the basis of the general theory of stability (well-posedness) of operator-difference schemes, the stability conditions of the explicit Lax-Wendroff scheme are established. Unconditionally stable and conservative schemes are implicit schemes of the second (Crank-Nicolson scheme) and fourth order. The conditionally stable implicit Lax-Wendroff scheme is constructed. The accuracy of the investigated explicit and implicit two-level schemes for an approximate solution of the advection equation is illustrated by the numerical results of a model two-dimensional problem.

Elements of Causal Inference: Foundations and Learning Algorithms

DEFF Research Database (Denmark)

Peters, Jonas Martin; Janzing, Dominik; Schölkopf, Bernhard

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning......A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning...

Optimal Sales Schemes for Network Goods

DEFF Research Database (Denmark)

Parakhonyak, Alexei; Vikander, Nick

consumers simultaneously, serve them all sequentially, or employ any intermediate scheme. We show that the optimal sales scheme is purely sequential, where each consumer observes all previous sales before choosing whether to buy himself. A sequential scheme maximizes the amount of information available...

Bayesian methods for hackers probabilistic programming and Bayesian inference

CERN Document Server

Davidson-Pilon, Cameron

2016-01-01

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...

A magnet lattice for a tau-charm factory suitable for both standard scheme and monochromatization scheme

International Nuclear Information System (INIS)

Beloshitsky, P.

1992-06-01

A versatile magnet lattice for a tau-charm factory is considered in this report. The main feature of this lattice is the possibility to use it for both standard flat beam scheme and beam monochromatization scheme. The detailed description of the lattice is given. The restrictions following the compatibility of both schemes are discussed

THROUGHPUT ANALYSIS OF EXTENDED ARQ SCHEMES

African Journals Online (AJOL)

PUBLICATIONS1

ABSTRACT. Various Automatic Repeat Request (ARQ) schemes have been used to combat errors that befall in- formation transmitted in digital communication systems. Such schemes include simple ARQ, mixed mode ARQ and Hybrid ARQ (HARQ). In this study we introduce extended ARQ schemes and derive.

Causal inference in econometrics

CERN Document Server

Kreinovich, Vladik; Sriboonchitta, Songsak

2016-01-01

This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

Wild-type bone marrow transplant partially reverses neuroinflammation in progranulin-deficient mice.

Science.gov (United States)

Yang, Yue; Aloi, Macarena S; Cudaback, Eiron; Josephsen, Samuel R; Rice, Samantha J; Jorstad, Nikolas L; Keene, C Dirk; Montine, Thomas J

2014-11-01

Frontotemporal dementia (FTD) is a neurodegenerative disease with devastating changes in behavioral performance and social function. Mutations in the progranulin gene (GRN) are one of the most common causes of inherited FTD due to reduced progranulin expression or activity, including in brain where it is expressed primarily by neurons and microglia. Thus, efforts aimed at enhancing progranulin levels might be a promising therapeutic strategy. Bone marrow (BM)-derived cells are able to engraft in the brain and adopt a microglial phenotype under myeloablative irradiation conditioning. This ability makes BM-derived cells a potential cellular vehicle for transferring therapeutic molecules to the central nervous system. Here, we utilized BM cells from Grn(+/+) (wild type or wt) mice labeled with green fluorescence protein for delivery of progranulin to progranulin-deficient (Grn(-/-)) mice. Our results showed that wt bone marrow transplantation (BMT) partially reconstituted progranulin in the periphery and in cerebral cortex of Grn(-/-) mice. We demonstrated a pro-inflammatory effect in vivo and in ex vivo preparations of cerebral cortex of Grn(-/-) mice that was partially to fully reversed 5 months after BMT. Our findings suggest that BMT can be administered as a stem cell-based approach to prevent or to treat neurodegenerative diseases.

Ponzi scheme diffusion in complex networks

Science.gov (United States)

Zhu, Anding; Fu, Peihua; Zhang, Qinghe; Chen, Zhenyue

2017-08-01

Ponzi schemes taking the form of Internet-based financial schemes have been negatively affecting China's economy for the last two years. Because there is currently a lack of modeling research on Ponzi scheme diffusion within social networks yet, we develop a potential-investor-divestor (PID) model to investigate the diffusion dynamics of Ponzi scheme in both homogeneous and inhomogeneous networks. Our simulation study of artificial and real Facebook social networks shows that the structure of investor networks does indeed affect the characteristics of dynamics. Both the average degree of distribution and the power-law degree of distribution will reduce the spreading critical threshold and will speed up the rate of diffusion. A high speed of diffusion is the key to alleviating the interest burden and improving the financial outcomes for the Ponzi scheme operator. The zero-crossing point of fund flux function we introduce proves to be a feasible index for reflecting the fast-worsening situation of fiscal instability and predicting the forthcoming collapse. The faster the scheme diffuses, the higher a peak it will reach and the sooner it will collapse. We should keep a vigilant eye on the harm of Ponzi scheme diffusion through modern social networks.

The Performance-based Funding Scheme of Universities

Directory of Open Access Journals (Sweden)

Juha KETTUNEN

2016-05-01

Full Text Available The purpose of this study is to analyse the effectiveness of the performance-based funding scheme of the Finnish universities that was adopted at the beginning of 2013. The political decision-makers expect that the funding scheme will create incentives for the universities to improve performance, but these funding schemes have largely failed in many other countries, primarily because public funding is only a small share of the total funding of universities. This study is interesting because Finnish universities have no tuition fees, unlike in many other countries, and the state allocates funding based on the objectives achieved. The empirical evidence of the graduation rates indicates that graduation rates increased when a new scheme was adopted, especially among male students, who have more room for improvement than female students. The new performance-based funding scheme allocates the funding according to the output-based indicators and limits the scope of strategic planning and the autonomy of the university. The performance-based funding scheme is transformed to the strategy map of the balanced scorecard. The new funding scheme steers universities in many respects but leaves the research and teaching skills to the discretion of the universities. The new scheme has also diminished the importance of the performance agreements between the university and the Ministry. The scheme increases the incentives for universities to improve the processes and structures in order to attain as much public funding as possible. It is optimal for the central administration of the university to allocate resources to faculties and other organisational units following the criteria of the performance-based funding scheme. The new funding scheme has made the universities compete with each other, because the total funding to the universities is allocated to each university according to the funding scheme. There is a tendency that the funding schemes are occasionally

Probability and Statistical Inference

OpenAIRE

Prosper, Harrison B.

2006-01-01

These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.

Fuzzy logic controller using different inference methods

International Nuclear Information System (INIS)

Liu, Z.; De Keyser, R.

1994-01-01

In this paper the design of fuzzy controllers by using different inference methods is introduced. Configuration of the fuzzy controllers includes a general rule-base which is a collection of fuzzy PI or PD rules, the triangular fuzzy data model and a centre of gravity defuzzification algorithm. The generalized modus ponens (GMP) is used with the minimum operator of the triangular norm. Under the sup-min inference rule, six fuzzy implication operators are employed to calculate the fuzzy look-up tables for each rule base. The performance is tested in simulated systems with MATLAB/SIMULINK. Results show the effects of using the fuzzy controllers with different inference methods and applied to different test processes

An algebra-based method for inferring gene regulatory networks.

Science.gov (United States)

Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

2014-03-26

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

A Classification Scheme for Literary Characters

Directory of Open Access Journals (Sweden)

Matthew Berry

2017-10-01

Full Text Available There is no established classification scheme for literary characters in narrative theory short of generic categories like protagonist vs. antagonist or round vs. flat. This is so despite the ubiquity of stock characters that recur across media, cultures, and historical time periods. We present here a proposal of a systematic psychological scheme for classifying characters from the literary and dramatic fields based on a modification of the Thomas-Kilmann (TK Conflict Mode Instrument used in applied studies of personality. The TK scheme classifies personality along the two orthogonal dimensions of assertiveness and cooperativeness. To examine the validity of a modified version of this scheme, we had 142 participants provide personality ratings for 40 characters using two of the Big Five personality traits as well as assertiveness and cooperativeness from the TK scheme. The results showed that assertiveness and cooperativeness were orthogonal dimensions, thereby supporting the validity of using a modified version of TK’s two-dimensional scheme for classifying characters.

How can conceptual schemes change teaching?

Science.gov (United States)

Wickman, Per-Olof

2012-03-01

Lundqvist, Almqvist and Östman describe a teacher's manner of teaching and the possible consequences it may have for students' meaning making. In doing this the article examines a teacher's classroom practice by systematizing the teacher's transactions with the students in terms of certain conceptual schemes, namely the epistemological moves, educational philosophies and the selective traditions of this practice. In connection to their study one may ask how conceptual schemes could change teaching. This article examines how the relationship of the conceptual schemes produced by educational researchers to educational praxis has developed from the middle of the last century to today. The relationship is described as having been transformed in three steps: (1) teacher deficit and social engineering, where conceptual schemes are little acknowledged, (2) reflecting practitioners, where conceptual schemes are mangled through teacher practice to aid the choices of already knowledgeable teachers, and (3) the mangling of the conceptual schemes by researchers through practice with the purpose of revising theory.

Statistical inference based on divergence measures

CERN Document Server

Pardo, Leandro

2005-01-01

The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, prese...

Active inference, sensory attenuation and illusions.

Science.gov (United States)

Brown, Harriet; Adams, Rick A; Parees, Isabel; Edwards, Mark; Friston, Karl

2013-11-01

Active inference provides a simple and neurobiologically plausible account of how action and perception are coupled in producing (Bayes) optimal behaviour. This can be seen most easily as minimising prediction error: we can either change our predictions to explain sensory input through perception. Alternatively, we can actively change sensory input to fulfil our predictions. In active inference, this action is mediated by classical reflex arcs that minimise proprioceptive prediction error created by descending proprioceptive predictions. However, this creates a conflict between action and perception; in that, self-generated movements require predictions to override the sensory evidence that one is not actually moving. However, ignoring sensory evidence means that externally generated sensations will not be perceived. Conversely, attending to (proprioceptive and somatosensory) sensations enables the detection of externally generated events but precludes generation of actions. This conflict can be resolved by attenuating the precision of sensory evidence during movement or, equivalently, attending away from the consequences of self-made acts. We propose that this Bayes optimal withdrawal of precise sensory evidence during movement is the cause of psychophysical sensory attenuation. Furthermore, it explains the force-matching illusion and reproduces empirical results almost exactly. Finally, if attenuation is removed, the force-matching illusion disappears and false (delusional) inferences about agency emerge. This is important, given the negative correlation between sensory attenuation and delusional beliefs in normal subjects--and the reduction in the magnitude of the illusion in schizophrenia. Active inference therefore links the neuromodulatory optimisation of precision to sensory attenuation and illusory phenomena during the attribution of agency in normal subjects. It also provides a functional account of deficits in syndromes characterised by false inference

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

Science.gov (United States)

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Contingency inferences driven by base rates: Valid by sampling

Directory of Open Access Journals (Sweden)

Florian Kutzner

2011-04-01

Full Text Available Fiedler et al. (2009, reviewed evidence for the utilization of a contingency inference strategy termed pseudocontingencies (PCs. In PCs, the more frequent levels (and, by implication, the less frequent levels are assumed to be associated. PCs have been obtained using a wide range of task settings and dependent measures. Yet, the readiness with which decision makers rely on PCs is poorly understood. A computer simulation explored two potential sources of subjective validity of PCs. First, PCs are shown to perform above chance level when the task is to infer the sign of moderate to strong population contingencies from a sample of observations. Second, contingency inferences based on PCs and inferences based on cell frequencies are shown to partially agree across samples. Intriguingly, this criterion and convergent validity are by-products of random sampling error, highlighting the inductive nature of contingency inferences.

Reinforcement and inference in cross-situational word learning.

Science.gov (United States)

Tilles, Paulo F C; Fontanari, José F

2013-01-01

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

Data-driven inference for the spatial scan statistic.

Science.gov (United States)

Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C

2011-08-02

Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

An Arbitrated Quantum Signature Scheme without Entanglement*

International Nuclear Information System (INIS)

Li Hui-Ran; Luo Ming-Xing; Peng Dai-Yuan; Wang Xiao-Jun

2017-01-01

Several quantum signature schemes are recently proposed to realize secure signatures of quantum or classical messages. Arbitrated quantum signature as one nontrivial scheme has attracted great interests because of its usefulness and efficiency. Unfortunately, previous schemes cannot against Trojan horse attack and DoS attack and lack of the unforgeability and the non-repudiation. In this paper, we propose an improved arbitrated quantum signature to address these secure issues with the honesty arbitrator. Our scheme takes use of qubit states not entanglements. More importantly, the qubit scheme can achieve the unforgeability and the non-repudiation. Our scheme is also secure for other known quantum attacks . (paper)

Breeding schemes in reindeer husbandry

Directory of Open Access Journals (Sweden)

Lars Rönnegård

2003-04-01

Full Text Available The objective of the paper was to investigate annual genetic gain from selection (G, and the influence of selection on the inbreeding effective population size (Ne, for different possible breeding schemes within a reindeer herding district. The breeding schemes were analysed for different proportions of the population within a herding district included in the selection programme. Two different breeding schemes were analysed: an open nucleus scheme where males mix and mate between owner flocks, and a closed nucleus scheme where the males in non-selected owner flocks are culled to maximise G in the whole population. The theory of expected long-term genetic contributions was used and maternal effects were included in the analyses. Realistic parameter values were used for the population, modelled with 5000 reindeer in the population and a sex ratio of 14 adult females per male. The standard deviation of calf weights was 4.1 kg. Four different situations were explored and the results showed: 1. When the population was randomly culled, Ne equalled 2400. 2. When the whole population was selected on calf weights, Ne equalled 1700 and the total annual genetic gain (direct + maternal in calf weight was 0.42 kg. 3. For the open nucleus scheme, G increased monotonically from 0 to 0.42 kg as the proportion of the population included in the selection programme increased from 0 to 1.0, and Ne decreased correspondingly from 2400 to 1700. 4. In the closed nucleus scheme the lowest value of Ne was 1300. For a given proportion of the population included in the selection programme, the difference in G between a closed nucleus scheme and an open one was up to 0.13 kg. We conclude that for mass selection based on calf weights in herding districts with 2000 animals or more, there are no risks of inbreeding effects caused by selection.

Quantum Secure Communication Scheme with W State

International Nuclear Information System (INIS)

Wang Jian; Zhang Quan; Tang Chaojng

2007-01-01

We present a quantum secure communication scheme using three-qubit W state. It is unnecessary for the present scheme to use alternative measurement or Bell basis measurement. Compared with the quantum secure direct communication scheme proposed by Cao et al. [H.J. Cao and H.S. Song, Chin. Phys. Lett. 23 (2006) 290], in our scheme, the detection probability for an eavesdropper's attack increases from 8.3% to 25%. We also show that our scheme is secure for a noise quantum channel.

Eight challenges in phylodynamic inference

Directory of Open Access Journals (Sweden)

Simon D.W. Frost

2015-03-01

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

Optimum RA reactor fuelling scheme

International Nuclear Information System (INIS)

Strugar, P.; Nikolic, V.

1965-10-01

Ideal reactor refueling scheme can be achieved only by continuous fuel elements movement in the core, which is not possible, and thus approximations are applied. One of the possible approximations is discontinuous movement of fuel elements groups in radial direction. This enables higher burnup especially if axial exchange is possible. Analysis of refueling schemes in the RA reactor core and schemes with mixing the fresh and used fuel elements show that 30% higher burnup can be achieved by applying mixing, and even 40% if reactivity due to decrease in experimental space is taken into account. Up to now, mean burnup of 4400 MWd/t has been achieved, and the proposed fueling scheme with reduction of experimental space could achieve mean burnup of 6300 MWd/t which means about 25 Mwd/t per fuel channel [sr

Human Inferences about Sequences: A Minimal Transition Probability Model.

Directory of Open Access Journals (Sweden)

Florent Meyniel

2016-12-01

Full Text Available The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.

Making Type Inference Practical

DEFF Research Database (Denmark)

Schwartzbach, Michael Ignatieff; Oxhøj, Nicholas; Palsberg, Jens

1992-01-01

We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding. The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as List, in a useful way. Abo......, the complexity has been dramatically improved, from exponential time to low polynomial time. The implementation uses the techniques of incremental graph construction and constraint template instantiation to avoid representing intermediate results, doing superfluous work, and recomputing type information....... Experiments indicate that the implementation type checks as much as 100 lines pr. second. This results in a mature product, on which a number of tools can be based, for example a safety tool, an image compression tool, a code optimization tool, and an annotation tool. This may make type inference for object...

Student’s scheme in solving mathematics problems

Science.gov (United States)

Setyaningsih, Nining; Juniati, Dwi; Suwarsono

2018-03-01

The purpose of this study was to investigate students’ scheme in solving mathematics problems. Scheme are data structures for representing the concepts stored in memory. In this study, we used it in solving mathematics problems, especially ratio and proportion topics. Scheme is related to problem solving that assumes that a system is developed in the human mind by acquiring a structure in which problem solving procedures are integrated with some concepts. The data were collected by interview and students’ written works. The results of this study revealed are students’ scheme in solving the problem of ratio and proportion as follows: (1) the content scheme, where students can describe the selected components of the problem according to their prior knowledge, (2) the formal scheme, where students can explain in construct a mental model based on components that have been selected from the problem and can use existing schemes to build planning steps, create something that will be used to solve problems and (3) the language scheme, where students can identify terms, or symbols of the components of the problem.Therefore, by using the different strategies to solve the problems, the students’ scheme in solving the ratio and proportion problems will also differ.

Examples in parametric inference with R

CERN Document Server

Dixit, Ulhas Jayram

2016-01-01

This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory cou...

Causal Effect Inference with Deep Latent-Variable Models

NARCIS (Netherlands)

Louizos, C; Shalit, U.; Mooij, J.; Sontag, D.; Zemel, R.; Welling, M.

2017-01-01

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of

Causal inference in survival analysis using pseudo-observations

DEFF Research Database (Denmark)

Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

2017-01-01

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs ...

hybrid modulation scheme fo rid modulation scheme fo dulation

African Journals Online (AJOL)

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control technique is done through simulations and ex control technique .... HYBRID MODULATION SCHEME FOR CASCADED H-BRIDGE INVERTER CELLS. C. I. Odeh ..... and OR operations. Referring to ... MATLAB/SIMULINK environment.

Towards Symbolic Encryption Schemes

DEFF Research Database (Denmark)

Ahmed, Naveed; Jensen, Christian D.; Zenner, Erik

2012-01-01

, namely an authenticated encryption scheme that is secure under chosen ciphertext attack. Therefore, many reasonable encryption schemes, such as AES in the CBC or CFB mode, are not among the implementation options. In this paper, we report new attacks on CBC and CFB based implementations of the well......Symbolic encryption, in the style of Dolev-Yao models, is ubiquitous in formal security models. In its common use, encryption on a whole message is specified as a single monolithic block. From a cryptographic perspective, however, this may require a resource-intensive cryptographic algorithm......-known Needham-Schroeder and Denning-Sacco protocols. To avoid such problems, we advocate the use of refined notions of symbolic encryption that have natural correspondence to standard cryptographic encryption schemes....

Statistical Inference at Work: Statistical Process Control as an Example

Science.gov (United States)

Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia

2008-01-01

To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…

On quantum statistical inference

NARCIS (Netherlands)

Barndorff-Nielsen, O.E.; Gill, R.D.; Jupp, P.E.

2003-01-01

Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, developments in the theory of quantum measurements have

Statistical inference

CERN Document Server

Rohatgi, Vijay K

2003-01-01

Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Contents: 1. Introduction. 2. Probability Model. 3. Probability Distributions. 4. Introduction to Statistical Inference. 5. More on Mathematical Expectation. 6. Some Discrete Models. 7. Some Continuous Models. 8. Functions of Random Variables and Random Vectors. 9. Large-Sample Theory. 10. General Meth

Setting aside transactions from pyramid schemes as impeachable ...

African Journals Online (AJOL)

These schemes, which are often referred to as pyramid or Ponzi schemes, are unsustainable operations and give rise to problems in the law of insolvency. Investors in these schemes are often left empty-handed upon the scheme's eventual collapse and insolvency. Investors who received pay-outs from the scheme find ...

The Impact of Contextual Clue Selection on Inference

Directory of Open Access Journals (Sweden)

Leila Barati

2010-05-01

Full Text Available Linguistic information can be conveyed in the form of speech and written text, but it is the content of the message that is ultimately essential for higher-level processes in language comprehension, such as making inferences and associations between text information and knowledge about the world. Linguistically, inference is the shovel that allows receivers to dig meaning out from the text with selecting different embedded contextual clues. Naturally, people with different world experiences infer similar contextual situations differently. Lack of contextual knowledge of the target language can present an obstacle to comprehension (Anderson & Lynch, 2003. This paper tries to investigate how true contextual clue selection from the text can influence listener’s inference. In the present study 60 male and female teenagers (13-19 and 60 male and female young adults (20-26 were selected randomly based on Oxford Placement Test (OPT. During the study two fiction and two non-fiction passages were read to the participants in the experimental and control groups respectively and they were given scores according to Lexile’s Score (LS[1] based on their correct inference and logical thinking ability. In general the results show that participants’ clue selection based on their personal schematic references and background knowledge differ between teenagers and young adults and influence inference and listening comprehension. [1]- This is a framework for reading and listening which matches the appropriate score to each text based on degree of difficulty of text and each text was given a Lexile score from zero to four.

Renormalization scheme-invariant perturbation theory

International Nuclear Information System (INIS)

Dhar, A.

1983-01-01

A complete solution to the problem of the renormalization scheme dependence of perturbative approximants to physical quantities is presented. An equation is derived which determines any physical quantity implicitly as a function of only scheme independent variables. (orig.)

Nonlinear secret image sharing scheme.

Science.gov (United States)

Shin, Sang-Ho; Lee, Gil-Je; Yoo, Kee-Young

2014-01-01

Over the past decade, most of secret image sharing schemes have been proposed by using Shamir's technique. It is based on a linear combination polynomial arithmetic. Although Shamir's technique based secret image sharing schemes are efficient and scalable for various environments, there exists a security threat such as Tompa-Woll attack. Renvall and Ding proposed a new secret sharing technique based on nonlinear combination polynomial arithmetic in order to solve this threat. It is hard to apply to the secret image sharing. In this paper, we propose a (t, n)-threshold nonlinear secret image sharing scheme with steganography concept. In order to achieve a suitable and secure secret image sharing scheme, we adapt a modified LSB embedding technique with XOR Boolean algebra operation, define a new variable m, and change a range of prime p in sharing procedure. In order to evaluate efficiency and security of proposed scheme, we use the embedding capacity and PSNR. As a result of it, average value of PSNR and embedding capacity are 44.78 (dB) and 1.74t⌈log2 m⌉ bit-per-pixel (bpp), respectively.

Inferring Demographic History Using Two-Locus Statistics.

Science.gov (United States)

Ragsdale, Aaron P; Gutenkunst, Ryan N

2017-06-01

Population demographic history may be learned from contemporary genetic variation data. Methods based on aggregating the statistics of many single loci into an allele frequency spectrum (AFS) have proven powerful, but such methods ignore potentially informative patterns of linkage disequilibrium (LD) between neighboring loci. To leverage such patterns, we developed a composite-likelihood framework for inferring demographic history from aggregated statistics of pairs of loci. Using this framework, we show that two-locus statistics are more sensitive to demographic history than single-locus statistics such as the AFS. In particular, two-locus statistics escape the notorious confounding of depth and duration of a bottleneck, and they provide a means to estimate effective population size based on the recombination rather than mutation rate. We applied our approach to a Zambian population of Drosophila melanogaster Notably, using both single- and two-locus statistics, we inferred a substantially lower ancestral effective population size than previous works and did not infer a bottleneck history. Together, our results demonstrate the broad potential for two-locus statistics to enable powerful population genetic inference. Copyright © 2017 by the Genetics Society of America.

Statistical Inference on the Canadian Middle Class

Directory of Open Access Journals (Sweden)

Russell Davidson

2018-03-01

Full Text Available Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for inference on these shares, by means of deriving expressions for their asymptotic variances of sample estimates, and the covariance of the estimates. Asymptotic inference can be undertaken based on asymptotic normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an illustration, samples of individual earnings drawn from Canadian census data are used to test various hypotheses about the middle-class shares, and confidence intervals for them are computed. It is found that, for the earlier censuses, sample sizes are large enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap fails on account of a strange phenomenon whereby many presumably different incomes in the data are rounded to one and the same value. Another difference between the centuries is the appearance of heavy right-hand tails in the income distributions of both men and women.

The importance of learning when making inferences

Directory of Open Access Journals (Sweden)

Jorg Rieskamp

2008-03-01

Full Text Available The assumption that people possess a repertoire of strategies to solve the inference problems they face has been made repeatedly. The experimental findings of two previous studies on strategy selection are reexamined from a learning perspective, which argues that people learn to select strategies for making probabilistic inferences. This learning process is modeled with the strategy selection learning (SSL theory, which assumes that people develop subjective expectancies for the strategies they have. They select strategies proportional to their expectancies, which are updated on the basis of experience. For the study by Newell, Weston, and Shanks (2003 it can be shown that people did not anticipate the success of a strategy from the beginning of the experiment. Instead, the behavior observed at the end of the experiment was the result of a learning process that can be described by the SSL theory. For the second study, by Br"oder and Schiffer (2006, the SSL theory is able to provide an explanation for why participants only slowly adapted to new environments in a dynamic inference situation. The reanalysis of the previous studies illustrates the importance of learning for probabilistic inferences.

Good governance for pension schemes

CERN Document Server

Thornton, Paul

2011-01-01

Regulatory and market developments have transformed the way in which UK private sector pension schemes operate. This has increased demands on trustees and advisors and the trusteeship governance model must evolve in order to remain fit for purpose. This volume brings together leading practitioners to provide an overview of what today constitutes good governance for pension schemes, from both a legal and a practical perspective. It provides the reader with an appreciation of the distinctive characteristics of UK occupational pension schemes, how they sit within the capital markets and their social and fiduciary responsibilities. Providing a holistic analysis of pension risk, both from the trustee and the corporate perspective, the essays cover the crucial role of the employer covenant, financing and investment risk, developments in longevity risk hedging and insurance de-risking, and best practice scheme administration.

Bayesian inference of substrate properties from film behavior

International Nuclear Information System (INIS)

Aggarwal, R; Demkowicz, M J; Marzouk, Y M

2015-01-01

We demonstrate that by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred with quantified uncertainty using Bayesian methods. We carry out this demonstration on an illustrative film/substrate model where the substrate is a Gaussian random field and the film is a two-component mixture that obeys the Cahn–Hilliard equation. We construct a stochastic reduced order model to describe the film/substrate interaction and use it to infer substrate properties from film behavior. This quantitative inference strategy may be adapted to other film/substrate systems. (paper)

Brain Imaging, Forward Inference, and Theories of Reasoning

Science.gov (United States)

Heit, Evan

2015-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities. PMID:25620926

Brain imaging, forward inference, and theories of reasoning.

Science.gov (United States)

Heit, Evan

2014-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities.

Data-driven inference for the spatial scan statistic

Directory of Open Access Journals (Sweden)

Duczmal Luiz H

2011-08-01

Full Text Available Abstract Background Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. Results A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. Conclusions A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

Symmetric weak ternary quantum homomorphic encryption schemes

Science.gov (United States)

Wang, Yuqi; She, Kun; Luo, Qingbin; Yang, Fan; Zhao, Chao

2016-03-01

Based on a ternary quantum logic circuit, four symmetric weak ternary quantum homomorphic encryption (QHE) schemes were proposed. First, for a one-qutrit rotation gate, a QHE scheme was constructed. Second, in view of the synthesis of a general 3 × 3 unitary transformation, another one-qutrit QHE scheme was proposed. Third, according to the one-qutrit scheme, the two-qutrit QHE scheme about generalized controlled X (GCX(m,n)) gate was constructed and further generalized to the n-qutrit unitary matrix case. Finally, the security of these schemes was analyzed in two respects. It can be concluded that the attacker can correctly guess the encryption key with a maximum probability pk = 1/33n, thus it can better protect the privacy of users’ data. Moreover, these schemes can be well integrated into the future quantum remote server architecture, and thus the computational security of the users’ private quantum information can be well protected in a distributed computing environment.

Labelling schemes: From a consumer perspective

DEFF Research Database (Denmark)

Juhl, Hans Jørn; Stacey, Julia

2000-01-01

Labelling of food products attracts a lot of political attention these days. As a result of a number of food scandals, most European countries have acknowledged the need for more information and better protection of consumers. Labelling schemes are one way of informing and guiding consumers....... However, initiatives in relation to labelling schemes seldom take their point of departure in consumers' needs and expectations; and in many cases, the schemes are defined by the institutions guaranteeing the label. It is therefore interesting to study how consumers actually value labelling schemes....... A recent MAPP study has investigated the value consumers attach the Government-controlled labels 'Ø-mærket' and 'Den Blå Lup' and the private supermarket label 'Mesterhakket' when they purchase minced meat. The results reveal four consumer segments that use labelling schemes for food products very...

Analysis of central and upwind compact schemes

International Nuclear Information System (INIS)

Sengupta, T.K.; Ganeriwal, G.; De, S.

2003-01-01

Central and upwind compact schemes for spatial discretization have been analyzed with respect to accuracy in spectral space, numerical stability and dispersion relation preservation. A von Neumann matrix spectral analysis is developed here to analyze spatial discretization schemes for any explicit and implicit schemes to investigate the full domain simultaneously. This allows one to evaluate various boundary closures and their effects on the domain interior. The same method can be used for stability analysis performed for the semi-discrete initial boundary value problems (IBVP). This analysis tells one about the stability for every resolved length scale. Some well-known compact schemes that were found to be G-K-S and time stable are shown here to be unstable for selective length scales by this analysis. This is attributed to boundary closure and we suggest special boundary treatment to remove this shortcoming. To demonstrate the asymptotic stability of the resultant schemes, numerical solution of the wave equation is compared with analytical solution. Furthermore, some of these schemes are used to solve two-dimensional Navier-Stokes equation and a computational acoustic problem to check their ability to solve problems for long time. It is found that those schemes, that were found unstable for the wave equation, are unsuitable for solving incompressible Navier-Stokes equation. In contrast, the proposed compact schemes with improved boundary closure and an explicit higher-order upwind scheme produced correct results. The numerical solution for the acoustic problem is compared with the exact solution and the quality of the match shows that the used compact scheme has the requisite DRP property

Statistical inference an integrated approach

CERN Document Server

Migon, Helio S; Louzada, Francisco

2014-01-01

Introduction Information The concept of probability Assessing subjective probabilities An example Linear algebra and probability Notation Outline of the bookElements of Inference Common statistical modelsLikelihood-based functions Bayes theorem Exchangeability Sufficiency and exponential family Parameter elimination Prior Distribution Entirely subjective specification Specification through functional forms Conjugacy with the exponential family Non-informative priors Hierarchical priors Estimation Introduction to decision theoryBayesian point estimation Classical point estimation Empirical Bayes estimation Comparison of estimators Interval estimation Estimation in the Normal model Approximating Methods The general problem of inference Optimization techniquesAsymptotic theory Other analytical approximations Numerical integration methods Simulation methods Hypothesis Testing Introduction Classical hypothesis testingBayesian hypothesis testing Hypothesis testing and confidence intervalsAsymptotic tests Prediction...

Statistical learning and selective inference.

Science.gov (United States)

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Principal scheme of preventive maintenance support system for nuclear power plants

International Nuclear Information System (INIS)

Nishiyama, Takuya; Terano, Takao; Yokoo, Takeshi; Shinohara, Yasushi

1985-01-01

It is of great importance to turn lessons learned from abnormal event experiences to advantage as well as taking apriori actions for prevention of abnormal events in nuclear power plants. From this point of view, a consultation system, named as Preventive Maintenance Support System (PMSS), which is to possess accumulated knowledges drawn from the past abnormal events in nuclear power plants infer occurrences, factors and developments of abnormal events and recommend preventive countermeasures on the basis of the knowledges, has been proposed. This report presents the principal scheme of PMSS. To begin with, following to the discussion of the purpose and use of PMSS, the fundamental functions PMSS should perform are specified. They are (1) event factors analyzation, (2) event prediction (event development estimation and analogous event estimation), (3) event significance evaluation and (4) preventive countermeasures presentation. In the next place, it is asserted that such a system should be constructed as a knowledge engineering one. Then, the R and D subjects and related schedule for PMSS are set up. (author)

A Novel Iris Segmentation Scheme

Directory of Open Access Journals (Sweden)

Chen-Chung Liu

2014-01-01

Full Text Available One of the key steps in the iris recognition system is the accurate iris segmentation from its surrounding noises including pupil, sclera, eyelashes, and eyebrows of a captured eye-image. This paper presents a novel iris segmentation scheme which utilizes the orientation matching transform to outline the outer and inner iris boundaries initially. It then employs Delogne-Kåsa circle fitting (instead of the traditional Hough transform to further eliminate the outlier points to extract a more precise iris area from an eye-image. In the extracted iris region, the proposed scheme further utilizes the differences in the intensity and positional characteristics of the iris, eyelid, and eyelashes to detect and delete these noises. The scheme is then applied on iris image database, UBIRIS.v1. The experimental results show that the presented scheme provides a more effective and efficient iris segmentation than other conventional methods.

Analysis of Program Obfuscation Schemes with Variable Encoding Technique

Science.gov (United States)

Fukushima, Kazuhide; Kiyomoto, Shinsaku; Tanaka, Toshiaki; Sakurai, Kouichi

Program analysis techniques have improved steadily over the past several decades, and software obfuscation schemes have come to be used in many commercial programs. A software obfuscation scheme transforms an original program or a binary file into an obfuscated program that is more complicated and difficult to analyze, while preserving its functionality. However, the security of obfuscation schemes has not been properly evaluated. In this paper, we analyze obfuscation schemes in order to clarify the advantages of our scheme, the XOR-encoding scheme. First, we more clearly define five types of attack models that we defined previously, and define quantitative resistance to these attacks. Then, we compare the security, functionality and efficiency of three obfuscation schemes with encoding variables: (1) Sato et al.'s scheme with linear transformation, (2) our previous scheme with affine transformation, and (3) the XOR-encoding scheme. We show that the XOR-encoding scheme is superior with regard to the following two points: (1) the XOR-encoding scheme is more secure against a data-dependency attack and a brute force attack than our previous scheme, and is as secure against an information-collecting attack and an inverse transformation attack as our previous scheme, (2) the XOR-encoding scheme does not restrict the calculable ranges of programs and the loss of efficiency is less than in our previous scheme.

Efficient multiparty quantum-secret-sharing schemes

International Nuclear Information System (INIS)

Xiao Li; Deng Fuguo; Long Guilu; Pan Jianwei

2004-01-01

In this work, we generalize the quantum-secret-sharing scheme of Hillery, Buzek, and Berthiaume [Phys. Rev. A 59, 1829 (1999)] into arbitrary multiparties. Explicit expressions for the shared secret bit is given. It is shown that in the Hillery-Buzek-Berthiaume quantum-secret-sharing scheme the secret information is shared in the parity of binary strings formed by the measured outcomes of the participants. In addition, we have increased the efficiency of the quantum-secret-sharing scheme by generalizing two techniques from quantum key distribution. The favored-measuring-basis quantum-secret-sharing scheme is developed from the Lo-Chau-Ardehali technique [H. K. Lo, H. F. Chau, and M. Ardehali, e-print quant-ph/0011056] where all the participants choose their measuring-basis asymmetrically, and the measuring-basis-encrypted quantum-secret-sharing scheme is developed from the Hwang-Koh-Han technique [W. Y. Hwang, I. G. Koh, and Y. D. Han, Phys. Lett. A 244, 489 (1998)] where all participants choose their measuring basis according to a control key. Both schemes are asymptotically 100% in efficiency, hence nearly all the Greenberger-Horne-Zeilinger states in a quantum-secret-sharing process are used to generate shared secret information

Alzheimer neuropathology without frontotemporal lobar degeneration hallmarks (TAR DNA-binding protein 43 inclusions) in missense progranulin mutation Cys139Arg.

Science.gov (United States)

Redaelli, Veronica; Rossi, Giacomina; Maderna, Emanuela; Kovacs, Gabor G; Piccoli, Elena; Caroppo, Paola; Cacciatore, Francesca; Spinello, Sonia; Grisoli, Marina; Sozzi, Giuliano; Salmaggi, Andrea; Tagliavini, Fabrizio; Giaccone, Giorgio

2018-01-01

Null mutations in progranulin gene (GRN) reduce the progranulin production resulting in haploinsufficiency and are tightly associated with tau-negative frontotemporal lobar degeneration with TAR DNA-binding protein 43-positive inclusions (FTLD-TDP). Missense mutations of GRN were also identified, but their effects are not completely clear, in particular unanswered is the question of what neuropathology they elicit, also considering that their occurrence has been reported in patients with typical clinical features of Alzheimer disease. They describe two fraternal twins carrying the missense GRN Cys139Arg mutation affected by late-onset dementia and we report the neuropathological study of one of them. Both patients were examined by neuroimaging, neuropsychological assessment and genetic analysis of GRN and other genes associated with dementia. The brain of one was obtained at autopsy and examined neuropathologically. One sister presented clinical and MRI features leading to the diagnosis of Alzheimer disease. The other underwent autopsy and the brain showed neuropathological hallmarks of Alzheimer disease with abundant Aβ-amyloid deposition and Braak stage V of neurofibrillary pathology, in the absence of the hallmark lesions of FTLD-TDP. Their findings may contribute to better clarify the role of progranulin in neurodegenerative diseases indicating that some GRN mutations, in particular missense ones, may act as strong risk factor for Alzheimer disease rather than induce FTLD-TDP. © 2016 International Society of Neuropathology.

Losing protein in the brain: the case of progranulin.

Science.gov (United States)

Ghidoni, Roberta; Paterlini, Anna; Albertini, Valentina; Binetti, Giuliano; Benussi, Luisa

2012-10-02

It is well known that progranulin protein is involved in wound repair, inflammation, and tumor formation. The wedding between progranulin and brain was celebrated in 2006 with the involvement of progranulin gene (GRN) in Frontotemporal lobar degeneration (FTLD), the most common form of early-onset dementia: up to date, 75 mutations have been detected in FTLD patients as well as in patients with widely variable clinical phenotypes. All pathogenic GRN mutations identified thus far cause the disease through a uniform mechanism, i.e. loss of functional progranulin or haploinsufficiency. Studies on GRN knockout mice suggest that progranulin-related neurodegenerative diseases may result from lifetime depletion of neurotrophic support together with cumulative damage in association with dysregulated inflammation, thus highlighting possible new molecular targets for GRN-related FTLD treatment. Recently, the dosage of plasma progranulin has been proposed as a useful tool for a quick and inexpensive large-scale screening of affected and unaffected carriers of GRN mutations. Before it is systematically translated into clinical practice and, more importantly, included into diagnostic criteria for dementias, further standardization of plasma progranulin test and harmonization of its use are required. Once a specific treatment becomes available for these pathologies, this test - being applicable on large scale - will represent an important step towards personalized healthcare. This article is part of a Special Issue entitled: Brain Integration. Copyright © 2012 Elsevier B.V. All rights reserved.

Gamma spectrometry; level schemes

International Nuclear Information System (INIS)

Blachot, J.; Bocquet, J.P.; Monnand, E.; Schussler, F.

1977-01-01

The research presented dealt with: a new beta emitter, isomer of 131 Sn; the 136 I levels fed through the radioactive decay of 136 Te (20.9s); the A=145 chain (β decay of Ba, La and Ce, and level schemes for 145 La, 145 Ce, 145 Pr); the A=47 chain (La and Ce, β decay, and the level schemes of 147 Ce and 147 Pr) [fr

Assessing an ensemble Kalman filter inference of Manning's n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC

Science.gov (United States)

Siripatana, Adil; Mayo, Talea; Sraj, Ihab; Knio, Omar; Dawson, Clint; Le Maitre, Olivier; Hoteit, Ibrahim

2017-08-01

Bayesian estimation/inversion is commonly used to quantify and reduce modeling uncertainties in coastal ocean model, especially in the framework of parameter estimation. Based on Bayes rule, the posterior probability distribution function (pdf) of the estimated quantities is obtained conditioned on available data. It can be computed either directly, using a Markov chain Monte Carlo (MCMC) approach, or by sequentially processing the data following a data assimilation approach, which is heavily exploited in large dimensional state estimation problems. The advantage of data assimilation schemes over MCMC-type methods arises from the ability to algorithmically accommodate a large number of uncertain quantities without significant increase in the computational requirements. However, only approximate estimates are generally obtained by this approach due to the restricted Gaussian prior and noise assumptions that are generally imposed in these methods. This contribution aims at evaluating the effectiveness of utilizing an ensemble Kalman-based data assimilation method for parameter estimation of a coastal ocean model against an MCMC polynomial chaos (PC)-based scheme. We focus on quantifying the uncertainties of a coastal ocean ADvanced CIRCulation (ADCIRC) model with respect to the Manning's n coefficients. Based on a realistic framework of observation system simulation experiments (OSSEs), we apply an ensemble Kalman filter and the MCMC method employing a surrogate of ADCIRC constructed by a non-intrusive PC expansion for evaluating the likelihood, and test both approaches under identical scenarios. We study the sensitivity of the estimated posteriors with respect to the parameters of the inference methods, including ensemble size, inflation factor, and PC order. A full analysis of both methods, in the context of coastal ocean model, suggests that an ensemble Kalman filter with appropriate ensemble size and well-tuned inflation provides reliable mean estimates and

Coordinated renewable energy support schemes

DEFF Research Database (Denmark)

Morthorst, P.E.; Jensen, S.G.

2006-01-01

. The first example covers countries with regional power markets that also regionalise their support schemes, the second countries with separate national power markets that regionalise their support schemes. The main findings indicate that the almost ideal situation exists if the region prior to regionalising...

Object-Oriented Type Inference

DEFF Research Database (Denmark)

Schwartzbach, Michael Ignatieff; Palsberg, Jens

1991-01-01

We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op...

Asynchronous Channel-Hopping Scheme under Jamming Attacks

Directory of Open Access Journals (Sweden)

Yongchul Kim

2018-01-01

Full Text Available Cognitive radio networks (CRNs are considered an attractive technology to mitigate inefficiency in the usage of licensed spectrum. CRNs allow the secondary users (SUs to access the unused licensed spectrum and use a blind rendezvous process to establish communication links between SUs. In particular, quorum-based channel-hopping (CH schemes have been studied recently to provide guaranteed blind rendezvous in decentralized CRNs without using global time synchronization. However, these schemes remain vulnerable to jamming attacks. In this paper, we first analyze the limitations of quorum-based rendezvous schemes called asynchronous channel hopping (ACH. Then, we introduce a novel sequence sensing jamming attack (SSJA model in which a sophisticated jammer can dramatically reduce the rendezvous success rates of ACH schemes. In addition, we propose a fast and robust asynchronous rendezvous scheme (FRARS that can significantly enhance robustness under jamming attacks. Our numerical results demonstrate that the performance of the proposed scheme vastly outperforms the ACH scheme when there are security concerns about a sequence sensing jammer.

The Probabilistic Convolution Tree: Efficient Exact Bayesian Inference for Faster LC-MS/MS Protein Inference

Science.gov (United States)

Serang, Oliver

2014-01-01

Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234

Reward inference by primate prefrontal and striatal neurons.

Science.gov (United States)

Pan, Xiaochuan; Fan, Hongwei; Sawa, Kosuke; Tsuda, Ichiro; Tsukada, Minoru; Sakagami, Masamichi

2014-01-22

The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus-reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning.

A fast resonance interference treatment scheme with subgroup method

International Nuclear Information System (INIS)

Cao, L.; He, Q.; Wu, H.; Zu, T.; Shen, W.

2015-01-01

A fast Resonance Interference Factor (RIF) scheme is proposed to treat the resonance interference effects between different resonance nuclides. This scheme utilizes the conventional subgroup method to evaluate the self-shielded cross sections of the dominant resonance nuclide in the heterogeneous system and the hyper-fine energy group method to represent the resonance interference effects in a simplified homogeneous model. In this paper, the newly implemented scheme is compared to the background iteration scheme, the Resonance Nuclide Group (RNG) scheme and the conventional RIF scheme. The numerical results show that the errors of the effective self-shielded cross sections are significantly reduced by the fast RIF scheme compared with the background iteration scheme and the RNG scheme. Besides, the fast RIF scheme consumes less computation time than the conventional RIF schemes. The speed-up ratio is ~4.5 for MOX pin cell problems. (author)

Bootstrap inference when using multiple imputation.

Science.gov (United States)

Schomaker, Michael; Heumann, Christian

2018-04-16

Many modern estimators require bootstrapping to calculate confidence intervals because either no analytic standard error is available or the distribution of the parameter of interest is nonsymmetric. It remains however unclear how to obtain valid bootstrap inference when dealing with multiple imputation to address missing data. We present 4 methods that are intuitively appealing, easy to implement, and combine bootstrap estimation with multiple imputation. We show that 3 of the 4 approaches yield valid inference, but that the performance of the methods varies with respect to the number of imputed data sets and the extent of missingness. Simulation studies reveal the behavior of our approaches in finite samples. A topical analysis from HIV treatment research, which determines the optimal timing of antiretroviral treatment initiation in young children, demonstrates the practical implications of the 4 methods in a sophisticated and realistic setting. This analysis suffers from missing data and uses the g-formula for inference, a method for which no standard errors are available. Copyright © 2018 John Wiley & Sons, Ltd.

Arbitrated quantum signature scheme with message recovery

International Nuclear Information System (INIS)

Lee, Hwayean; Hong, Changho; Kim, Hyunsang; Lim, Jongin; Yang, Hyung Jin

2004-01-01

Two quantum signature schemes with message recovery relying on the availability of an arbitrator are proposed. One scheme uses a public board and the other does not. However both schemes provide confidentiality of the message and a higher efficiency in transmission

CANONICAL BACKWARD DIFFERENTIATION SCHEMES FOR ...

African Journals Online (AJOL)

This paper describes a new nonlinear backward differentiation schemes for the numerical solution of nonlinear initial value problems of first order ordinary differential equations. The schemes are based on rational interpolation obtained from canonical polynomials. They are A-stable. The test problems show that they give ...

Evolutionary inference via the Poisson Indel Process.

Science.gov (United States)

Bouchard-Côté, Alexandre; Jordan, Michael I

2013-01-22

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

Solving the Sea-Level Equation in an Explicit Time Differencing Scheme

Science.gov (United States)

Klemann, V.; Hagedoorn, J. M.; Thomas, M.

2016-12-01

In preparation of coupling the solid-earth to an ice-sheet compartment in an earth-system model, the dependency of initial topography on the ice-sheet history and viscosity structure has to be analysed. In this study, we discuss this dependency and how it influences the reconstruction of former sea level during a glacial cycle. The modelling is based on the VILMA code in which the field equations are solved in the time domain applying an explicit time-differencing scheme. The sea-level equation is solved simultaneously in the same explicit scheme as the viscoleastic field equations (Hagedoorn et al., 2007). With the assumption of only small changes, we neglect the iterative solution at each time step as suggested by e.g. Kendall et al. (2005). Nevertheless, the prediction of the initial paleo topography in case of moving coastlines remains to be iterated by repeated integration of the whole load history. The sensitivity study sketched at the beginning is accordingly motivated by the question if the iteration of the paleo topography can be replaced by a predefined one. This study is part of the German paleoclimate modelling initiative PalMod. Lit:Hagedoorn JM, Wolf D, Martinec Z, 2007. An estimate of global mean sea-level rise inferred from tide-gauge measurements using glacial-isostatic models consistent with the relative sea-level record. Pure appl. Geophys. 164: 791-818, doi:10.1007/s00024-007-0186-7Kendall RA, Mitrovica JX, Milne GA, 2005. On post-glacial sea level - II. Numerical formulation and comparative reesults on spherically symmetric models. Geophys. J. Int., 161: 679-706, doi:10.1111/j.365-246.X.2005.02553.x

A simple angular transmit diversity scheme using a single RF frontend for PSK modulation schemes

DEFF Research Database (Denmark)

Alrabadi, Osama Nafeth Saleem; Papadias, Constantinos B.; Kalis, Antonis

2009-01-01

array (SPA) with a single transceiver, and an array area of 0.0625 square wavelengths. The scheme which requires no channel state information (CSI) at the transmitter, provides mainly a diversity gain to combat against multipath fading. The performance/capacity of the proposed diversity scheme...

Evaluating statistical cloud schemes

OpenAIRE

Grützun, Verena; Quaas, Johannes; Morcrette , Cyril J.; Ament, Felix

2015-01-01

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based re...

System Support for Forensic Inference

Science.gov (United States)

Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan

Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.

Geostatistical inference using crosshole ground-penetrating radar

DEFF Research Database (Denmark)

Looms, Majken C; Hansen, Thomas Mejer; Cordua, Knud Skou

2010-01-01

of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance...... reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisturecontent measurements, obtained gravimetrically from samples collected at the field site....

Bayesian Inference for Functional Dynamics Exploring in fMRI Data

Directory of Open Access Journals (Sweden)

Xuan Guo

2016-01-01

Full Text Available This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM, Bayesian Connectivity Change Point Model (BCCPM, and Dynamic Bayesian Variable Partition Model (DBVPM, and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

LDPC-PPM Coding Scheme for Optical Communication

Science.gov (United States)

Barsoum, Maged; Moision, Bruce; Divsalar, Dariush; Fitz, Michael

2009-01-01

In a proposed coding-and-modulation/demodulation-and-decoding scheme for a free-space optical communication system, an error-correcting code of the low-density parity-check (LDPC) type would be concatenated with a modulation code that consists of a mapping of bits to pulse-position-modulation (PPM) symbols. Hence, the scheme is denoted LDPC-PPM. This scheme could be considered a competitor of a related prior scheme in which an outer convolutional error-correcting code is concatenated with an interleaving operation, a bit-accumulation operation, and a PPM inner code. Both the prior and present schemes can be characterized as serially concatenated pulse-position modulation (SCPPM) coding schemes. Figure 1 represents a free-space optical communication system based on either the present LDPC-PPM scheme or the prior SCPPM scheme. At the transmitting terminal, the original data (u) are processed by an encoder into blocks of bits (a), and the encoded data are mapped to PPM of an optical signal (c). For the purpose of design and analysis, the optical channel in which the PPM signal propagates is modeled as a Poisson point process. At the receiving terminal, the arriving optical signal (y) is demodulated to obtain an estimate (a^) of the coded data, which is then processed by a decoder to obtain an estimate (u^) of the original data.

The Evolution of the Secreted Regulatory Protein Progranulin.

Science.gov (United States)

Palfree, Roger G E; Bennett, Hugh P J; Bateman, Andrew

2015-01-01

Progranulin is a secreted growth factor that is active in tumorigenesis, wound repair, and inflammation. Haploinsufficiency of the human progranulin gene, GRN, causes frontotemporal dementia. Progranulins are composed of chains of cysteine-rich granulin modules. Modules may be released from progranulin by proteolysis as 6kDa granulin polypeptides. Both intact progranulin and some of the granulin polypeptides are biologically active. The granulin module occurs in certain plant proteases and progranulins are present in early diverging metazoan clades such as the sponges, indicating their ancient evolutionary origin. There is only one Grn gene in mammalian genomes. More gene-rich Grn families occur in teleost fish with between 3 and 6 members per species including short-form Grns that have no tetrapod counterparts. Our goals are to elucidate progranulin and granulin module evolution by investigating (i): the origins of metazoan progranulins (ii): the evolutionary relationships between the single Grn of tetrapods and the multiple Grn genes of fish (iii): the evolution of granulin module architectures of vertebrate progranulins (iv): the conservation of mammalian granulin polypeptide sequences and how the conserved granulin amino acid sequences map to the known three dimensional structures of granulin modules. We report that progranulin-like proteins are present in unicellular eukaryotes that are closely related to metazoa suggesting that progranulin is among the earliest extracellular regulatory proteins still employed by multicellular animals. From the genomes of the elephant shark and coelacanth we identified contemporary representatives of a precursor for short-from Grn genes of ray-finned fish that is lost in tetrapods. In vertebrate Grns pathways of exon duplication resulted in a conserved module architecture at the amino-terminus that is frequently accompanied by an unusual pattern of tandem nearly identical module repeats near the carboxyl-terminus. Polypeptide

Multidimensional flux-limited advection schemes

International Nuclear Information System (INIS)

Thuburn, J.

1996-01-01

A general method for building multidimensional shape preserving advection schemes using flux limiters is presented. The method works for advected passive scalars in either compressible or incompressible flow and on arbitrary grids. With a minor modification it can be applied to the equation for fluid density. Schemes using the simplest form of the flux limiter can cause distortion of the advected profile, particularly sideways spreading, depending on the orientation of the flow relative to the grid. This is partly because the simple limiter is too restrictive. However, some straightforward refinements lead to a shape-preserving scheme that gives satisfactory results, with negligible grid-flow angle-dependent distortion

Tightly Secure Signatures From Lossy Identification Schemes

OpenAIRE

Abdalla , Michel; Fouque , Pierre-Alain; Lyubashevsky , Vadim; Tibouchi , Mehdi

2015-01-01

International audience; In this paper, we present three digital signature schemes with tight security reductions in the random oracle model. Our first signature scheme is a particularly efficient version of the short exponent discrete log-based scheme of Girault et al. (J Cryptol 19(4):463–487, 2006). Our scheme has a tight reduction to the decisional short discrete logarithm problem, while still maintaining the non-tight reduction to the computational version of the problem upon which the or...

Scheme of energy utilities

International Nuclear Information System (INIS)

2002-04-01

This scheme defines the objectives relative to the renewable energies and the rational use of the energy in the framework of the national energy policy. It evaluates the needs and the potentialities of the regions and preconizes the actions between the government and the territorial organizations. The document is presented in four parts: the situation, the stakes and forecasts; the possible actions for new measures; the scheme management and the regional contributions analysis. (A.L.B.)

Error forecasting schemes of error correction at receiver

International Nuclear Information System (INIS)

Bhunia, C.T.

2007-08-01

To combat error in computer communication networks, ARQ (Automatic Repeat Request) techniques are used. Recently Chakraborty has proposed a simple technique called the packet combining scheme in which error is corrected at the receiver from the erroneous copies. Packet Combining (PC) scheme fails: (i) when bit error locations in erroneous copies are the same and (ii) when multiple bit errors occur. Both these have been addressed recently by two schemes known as Packet Reversed Packet Combining (PRPC) Scheme, and Modified Packet Combining (MPC) Scheme respectively. In the letter, two error forecasting correction schemes are reported, which in combination with PRPC offer higher throughput. (author)

Working memory supports inference learning just like classification learning.

Science.gov (United States)

Craig, Stewart; Lewandowsky, Stephan

2013-08-01

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.

Statistical inference for stochastic processes

National Research Council Canada - National Science Library

Basawa, Ishwar V; Prakasa Rao, B. L. S

1980-01-01

The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects...

CONSTRUCTING A FLEXIBLE LIKELIHOOD FUNCTION FOR SPECTROSCOPIC INFERENCE

International Nuclear Information System (INIS)

Czekala, Ian; Andrews, Sean M.; Mandel, Kaisey S.; Green, Gregory M.; Hogg, David W.

2015-01-01

We present a modular, extensible likelihood framework for spectroscopic inference based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. For the high signal-to-noise data with large spectral range that is commonly employed in stellar astrophysics, that covariant structure can lead to dramatically underestimated parameter uncertainties (and, in some cases, biases). We construct a likelihood function that accounts for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. This framework specifically addresses the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic/molecular databases or opacity prescriptions) by developing a novel local covariance kernel formalism that identifies and self-consistently downweights pathological spectral line “outliers.” By fitting many spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic spectral libraries. An open-source software implementation of this approach is available at http://iancze.github.io/Starfish, including a sophisticated probabilistic scheme for spectral interpolation when using model libraries that are sparsely sampled in the stellar parameters. We demonstrate some salient features of the framework by fitting the high-resolution V-band spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate-resolution K-band spectrum of Gliese 51, an M5 field dwarf

Estimating plume dispersion: a comparison of several sigma schemes

International Nuclear Information System (INIS)

Irwin, J.S.

1983-01-01

The lateral and vertical Gaussian plume dispersion parameters are estimated and compared with field tracer data collected at 11 sites. The dispersion parameter schemes used in this analysis include Cramer's scheme, suggested for tall stack dispersion estimates, Draxler's scheme, suggested for elevated and surface releases, Pasquill's scheme, suggested for interim use in dispersion estimates, and the Pasquill--Gifford scheme using Turner's technique for assigning stability categories. The schemes suggested by Cramer, Draxler and Pasquill estimate the dispersion parameters using onsite measurements of the vertical and lateral wind-velocity variances at the effective release height. The performances of these schemes in estimating the dispersion parameters are compared with that of the Pasquill--Gifford scheme, using the Prairie Grass and Karlsruhe data. For these two experiments, the estimates of the dispersion parameters using Draxler's scheme correlate better with the measurements than did estimates using the Pasquill--Gifford scheme. Comparison of the dispersion parameter estimates with the measurement suggests that Draxler's scheme for characterizing the dispersion results in the smallest mean fractional error in the estimated dispersion parameters and the smallest variance of the fractional errors

Inference of Large Phylogenies Using Neighbour-Joining

DEFF Research Database (Denmark)

Simonsen, Martin; Mailund, Thomas; Pedersen, Christian Nørgaard Storm

2011-01-01

The neighbour-joining method is a widely used method for phylogenetic reconstruction which scales to thousands of taxa. However, advances in sequencing technology have made data sets with more than 10,000 related taxa widely available. Inference of such large phylogenies takes hours or days using...... the Neighbour-Joining method on a normal desktop computer because of the O(n^3) running time. RapidNJ is a search heuristic which reduce the running time of the Neighbour-Joining method significantly but at the cost of an increased memory consumption making inference of large phylogenies infeasible. We present...... two extensions for RapidNJ which reduce the memory requirements and \\makebox{allows} phylogenies with more than 50,000 taxa to be inferred efficiently on a desktop computer. Furthermore, an improved version of the search heuristic is presented which reduces the running time of RapidNJ on many data...

An authentication scheme for secure access to healthcare services.

Science.gov (United States)

Khan, Muhammad Khurram; Kumari, Saru

2013-08-01

Last few decades have witnessed boom in the development of information and communication technologies. Health-sector has also been benefitted with this advancement. To ensure secure access to healthcare services some user authentication mechanisms have been proposed. In 2012, Wei et al. proposed a user authentication scheme for telecare medical information system (TMIS). Recently, Zhu pointed out offline password guessing attack on Wei et al.'s scheme and proposed an improved scheme. In this article, we analyze both of these schemes for their effectiveness in TMIS. We show that Wei et al.'s scheme and its improvement proposed by Zhu fail to achieve some important characteristics necessary for secure user authentication. We find that security problems of Wei et al.'s scheme stick with Zhu's scheme; like undetectable online password guessing attack, inefficacy of password change phase, traceability of user's stolen/lost smart card and denial-of-service threat. We also identify that Wei et al.'s scheme lacks forward secrecy and Zhu's scheme lacks session key between user and healthcare server. We therefore propose an authentication scheme for TMIS with forward secrecy which preserves the confidentiality of air messages even if master secret key of healthcare server is compromised. Our scheme retains advantages of Wei et al.'s scheme and Zhu's scheme, and offers additional security. The security analysis and comparison results show the enhanced suitability of our scheme for TMIS.

Statistical causal inferences and their applications in public health research

CERN Document Server

Wu, Pan; Chen, Ding-Geng

2016-01-01

This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.

Cost-based droop scheme for DC microgrid

DEFF Research Database (Denmark)

Nutkani, Inam Ullah; Wang, Peng; Loh, Poh Chiang

2014-01-01

voltage level, less on optimized operation and control of generation sources. The latter theme is perused in this paper, where cost-based droop scheme is proposed for distributed generators (DGs) in DC microgrids. Unlike traditional proportional power sharing based droop scheme, the proposed scheme......-connected operation. Most importantly, the proposed scheme can reduce overall total generation cost in DC microgrids without centralized controller and communication links. The performance of the proposed scheme has been verified under different load conditions.......DC microgrids are gaining interest due to higher efficiencies of DC distribution compared with AC. The benefits of DC systems have been widely researched for data centers, IT facilities and residential applications. The research focus, however, has been more on system architecture and optimal...

Resonance ionization scheme development for europium

Energy Technology Data Exchange (ETDEWEB)

Chrysalidis, K., E-mail: katerina.chrysalidis@cern.ch; Goodacre, T. Day; Fedosseev, V. N.; Marsh, B. A. [CERN (Switzerland); Naubereit, P. [Johannes Gutenberg-Universität, Institiut für Physik (Germany); Rothe, S.; Seiffert, C. [CERN (Switzerland); Kron, T.; Wendt, K. [Johannes Gutenberg-Universität, Institiut für Physik (Germany)

2017-11-15

Odd-parity autoionizing states of europium have been investigated by resonance ionization spectroscopy via two-step, two-resonance excitations. The aim of this work was to establish ionization schemes specifically suited for europium ion beam production using the ISOLDE Resonance Ionization Laser Ion Source (RILIS). 13 new RILIS-compatible ionization schemes are proposed. The scheme development was the first application of the Photo Ionization Spectroscopy Apparatus (PISA) which has recently been integrated into the RILIS setup.

Secure RAID Schemes for Distributed Storage

OpenAIRE

Huang, Wentao; Bruck, Jehoshua

2016-01-01

We propose secure RAID, i.e., low-complexity schemes to store information in a distributed manner that is resilient to node failures and resistant to node eavesdropping. We generalize the concept of systematic encoding to secure RAID and show that systematic schemes have significant advantages in the efficiencies of encoding, decoding and random access. For the practical high rate regime, we construct three XOR-based systematic secure RAID schemes with optimal or almost optimal encoding and ...

Assessing an ensemble Kalman filter inference of Manning’s n coefficient of an idealized tidal inlet against a polynomial chaos-based MCMC

KAUST Repository

Siripatana, Adil

2017-06-08

Bayesian estimation/inversion is commonly used to quantify and reduce modeling uncertainties in coastal ocean model, especially in the framework of parameter estimation. Based on Bayes rule, the posterior probability distribution function (pdf) of the estimated quantities is obtained conditioned on available data. It can be computed either directly, using a Markov chain Monte Carlo (MCMC) approach, or by sequentially processing the data following a data assimilation approach, which is heavily exploited in large dimensional state estimation problems. The advantage of data assimilation schemes over MCMC-type methods arises from the ability to algorithmically accommodate a large number of uncertain quantities without significant increase in the computational requirements. However, only approximate estimates are generally obtained by this approach due to the restricted Gaussian prior and noise assumptions that are generally imposed in these methods. This contribution aims at evaluating the effectiveness of utilizing an ensemble Kalman-based data assimilation method for parameter estimation of a coastal ocean model against an MCMC polynomial chaos (PC)-based scheme. We focus on quantifying the uncertainties of a coastal ocean ADvanced CIRCulation (ADCIRC) model with respect to the Manning’s n coefficients. Based on a realistic framework of observation system simulation experiments (OSSEs), we apply an ensemble Kalman filter and the MCMC method employing a surrogate of ADCIRC constructed by a non-intrusive PC expansion for evaluating the likelihood, and test both approaches under identical scenarios. We study the sensitivity of the estimated posteriors with respect to the parameters of the inference methods, including ensemble size, inflation factor, and PC order. A full analysis of both methods, in the context of coastal ocean model, suggests that an ensemble Kalman filter with appropriate ensemble size and well-tuned inflation provides reliable mean estimates and

Universal Darwinism As a Process of Bayesian Inference.

Science.gov (United States)

Campbell, John O

2016-01-01

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

Wireless Broadband Access and Accounting Schemes

Institute of Scientific and Technical Information of China (English)

无

2003-01-01

In this paper, we propose two wireless broadband access and accounting schemes. In both schemes, the accounting system adopts RADIUS protocol, but the access system adopts SSH and SSL protocols respectively.

sick: The Spectroscopic Inference Crank

Science.gov (United States)

Casey, Andrew R.

2016-03-01

There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

SICK: THE SPECTROSCOPIC INFERENCE CRANK

Energy Technology Data Exchange (ETDEWEB)

Casey, Andrew R., E-mail: arc@ast.cam.ac.uk [Institute of Astronomy, University of Cambridge, Madingley Road, Cambdridge, CB3 0HA (United Kingdom)

2016-03-15

There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

SICK: THE SPECTROSCOPIC INFERENCE CRANK

International Nuclear Information System (INIS)

Casey, Andrew R.

2016-01-01

There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

Security analysis and improvements of arbitrated quantum signature schemes

International Nuclear Information System (INIS)

Zou Xiangfu; Qiu Daowen

2010-01-01

A digital signature is a mathematical scheme for demonstrating the authenticity of a digital message or document. For signing quantum messages, some arbitrated quantum signature (AQS) schemes have been proposed. It was claimed that these AQS schemes could guarantee unconditional security. However, we show that they can be repudiated by the receiver Bob. To conquer this shortcoming, we construct an AQS scheme using a public board. The AQS scheme not only avoids being disavowed by the receiver but also preserves all merits in the existing schemes. Furthermore, we discover that entanglement is not necessary while all these existing AQS schemes depend on entanglement. Therefore, we present another AQS scheme without utilizing entangled states in the signing phase and the verifying phase. This scheme has three advantages: it does not utilize entangled states and it preserves all merits in the existing schemes; the signature can avoid being disavowed by the receiver; and it provides a higher efficiency in transmission and reduces the complexity of implementation.

On principles of inductive inference

OpenAIRE

Kostecki, Ryszard Paweł

2011-01-01

We propose an intersubjective epistemic approach to foundations of probability theory and statistical inference, based on relative entropy and category theory, and aimed to bypass the mathematical and conceptual problems of existing foundational approaches.

Model averaging, optimal inference and habit formation

Directory of Open Access Journals (Sweden)

Thomas H B FitzGerald

2014-06-01

Full Text Available Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour.

Capacity-achieving CPM schemes

OpenAIRE

Perotti, Alberto; Tarable, Alberto; Benedetto, Sergio; Montorsi, Guido

2008-01-01

The pragmatic approach to coded continuous-phase modulation (CPM) is proposed as a capacity-achieving low-complexity alternative to the serially-concatenated CPM (SC-CPM) coding scheme. In this paper, we first perform a selection of the best spectrally-efficient CPM modulations to be embedded into SC-CPM schemes. Then, we consider the pragmatic capacity (a.k.a. BICM capacity) of CPM modulations and optimize it through a careful design of the mapping between input bits and CPM waveforms. The s...

Improvement of a Quantum Proxy Blind Signature Scheme

Science.gov (United States)

Zhang, Jia-Lei; Zhang, Jian-Zhong; Xie, Shu-Cui

2018-06-01

Improvement of a quantum proxy blind signature scheme is proposed in this paper. Six-qubit entangled state functions as quantum channel. In our scheme, a trust party Trent is introduced so as to avoid David's dishonest behavior. The receiver David verifies the signature with the help of Trent in our scheme. The scheme uses the physical characteristics of quantum mechanics to implement message blinding, delegation, signature and verification. Security analysis proves that our scheme has the properties of undeniability, unforgeability, anonymity and can resist some common attacks.

A group signature scheme based on quantum teleportation

International Nuclear Information System (INIS)

Wen Xiaojun; Tian Yuan; Ji Liping; Niu Xiamu

2010-01-01

In this paper, we present a group signature scheme using quantum teleportation. Different from classical group signature and current quantum signature schemes, which could only deliver either group signature or unconditional security, our scheme guarantees both by adopting quantum key preparation, quantum encryption algorithm and quantum teleportation. Security analysis proved that our scheme has the characteristics of group signature, non-counterfeit, non-disavowal, blindness and traceability. Our quantum group signature scheme has a foreseeable application in the e-payment system, e-government, e-business, etc.

A group signature scheme based on quantum teleportation

Energy Technology Data Exchange (ETDEWEB)

Wen Xiaojun; Tian Yuan; Ji Liping; Niu Xiamu, E-mail: wxjun36@gmail.co [Information Countermeasure Technique Research Institute, Harbin Institute of Technology, Harbin 150001 (China)

2010-05-01

In this paper, we present a group signature scheme using quantum teleportation. Different from classical group signature and current quantum signature schemes, which could only deliver either group signature or unconditional security, our scheme guarantees both by adopting quantum key preparation, quantum encryption algorithm and quantum teleportation. Security analysis proved that our scheme has the characteristics of group signature, non-counterfeit, non-disavowal, blindness and traceability. Our quantum group signature scheme has a foreseeable application in the e-payment system, e-government, e-business, etc.

Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages.

Science.gov (United States)

Peterman, William; Brocato, Emily R; Semlitsch, Raymond D; Eggert, Lori S

2016-01-01

In population or landscape genetics studies, an unbiased sampling scheme is essential for generating accurate results, but logistics may lead to deviations from the sample design. Such deviations may come in the form of sampling multiple life stages. Presently, it is largely unknown what effect sampling different life stages can have on population or landscape genetic inference, or how mixing life stages can affect the parameters being measured. Additionally, the removal of siblings from a data set is considered best-practice, but direct comparisons of inferences made with and without siblings are limited. In this study, we sampled embryos, larvae, and adult Ambystoma maculatum from five ponds in Missouri, and analyzed them at 15 microsatellite loci. We calculated allelic richness, heterozygosity and effective population sizes for each life stage at each pond and tested for genetic differentiation (F ST and D C ) and isolation-by-distance (IBD) among ponds. We tested for differences in each of these measures between life stages, and in a pooled population of all life stages. All calculations were done with and without sibling pairs to assess the effect of sibling removal. We also assessed the effect of reducing the number of microsatellites used to make inference. No statistically significant differences were found among ponds or life stages for any of the population genetic measures, but patterns of IBD differed among life stages. There was significant IBD when using adult samples, but tests using embryos, larvae, or a combination of the three life stages were not significant. We found that increasing the ratio of larval or embryo samples in the analysis of genetic distance weakened the IBD relationship, and when using D C , the IBD was no longer significant when larvae and embryos exceeded 60% of the population sample. Further, power to detect an IBD relationship was reduced when fewer microsatellites were used in the analysis.

Reducing bias in population and landscape genetic inferences: the effects of sampling related individuals and multiple life stages

Directory of Open Access Journals (Sweden)

William Peterman

2016-03-01

Full Text Available In population or landscape genetics studies, an unbiased sampling scheme is essential for generating accurate results, but logistics may lead to deviations from the sample design. Such deviations may come in the form of sampling multiple life stages. Presently, it is largely unknown what effect sampling different life stages can have on population or landscape genetic inference, or how mixing life stages can affect the parameters being measured. Additionally, the removal of siblings from a data set is considered best-practice, but direct comparisons of inferences made with and without siblings are limited. In this study, we sampled embryos, larvae, and adult Ambystoma maculatum from five ponds in Missouri, and analyzed them at 15 microsatellite loci. We calculated allelic richness, heterozygosity and effective population sizes for each life stage at each pond and tested for genetic differentiation (FST and DC and isolation-by-distance (IBD among ponds. We tested for differences in each of these measures between life stages, and in a pooled population of all life stages. All calculations were done with and without sibling pairs to assess the effect of sibling removal. We also assessed the effect of reducing the number of microsatellites used to make inference. No statistically significant differences were found among ponds or life stages for any of the population genetic measures, but patterns of IBD differed among life stages. There was significant IBD when using adult samples, but tests using embryos, larvae, or a combination of the three life stages were not significant. We found that increasing the ratio of larval or embryo samples in the analysis of genetic distance weakened the IBD relationship, and when using DC, the IBD was no longer significant when larvae and embryos exceeded 60% of the population sample. Further, power to detect an IBD relationship was reduced when fewer microsatellites were used in the analysis.

A new access scheme in OFDMA systems

Institute of Scientific and Technical Information of China (English)

GU Xue-lin; YAN Wei; TIAN Hui; ZHANG Ping

2006-01-01

This article presents a dynamic random access scheme for orthogonal frequency division multiple access (OFDMA) systems. The key features of the proposed scheme are:it is a combination of both the distributed and the centralized schemes, it can accommodate several delay sensitivity classes,and it can adjust the number of random access channels in a media access control (MAC) frame and the access probability according to the outcome of Mobile Terminals access attempts in previous MAC frames. For floating populated packet-based networks, the proposed scheme possibly leads to high average user satisfaction.

Adaptive transmission schemes for MISO spectrum sharing systems

KAUST Repository

Bouida, Zied

2013-06-01

We propose three adaptive transmission techniques aiming to maximize the capacity of a multiple-input-single-output (MISO) secondary system under the scenario of an underlay cognitive radio network. In the first scheme, namely the best antenna selection (BAS) scheme, the antenna maximizing the capacity of the secondary link is used for transmission. We then propose an orthogonal space time bloc code (OSTBC) transmission scheme using the Alamouti scheme with transmit antenna selection (TAS), namely the TAS/STBC scheme. The performance improvement offered by this scheme comes at the expense of an increased complexity and delay when compared to the BAS scheme. As a compromise between these schemes, we propose a hybrid scheme using BAS when only one antenna verifies the interference condition and TAS/STBC when two or more antennas are illegible for communication. We first derive closed-form expressions of the statistics of the received signal-to-interference-and-noise ratio (SINR) at the secondary receiver (SR). These results are then used to analyze the performance of the proposed techniques in terms of the average spectral efficiency, the average number of transmit antennas, and the average bit error rate (BER). This performance is then illustrated via selected numerical examples. © 2013 IEEE.

Bootstrapping phylogenies inferred from rearrangement data

Directory of Open Access Journals (Sweden)

Lin Yu

2012-08-01

Full Text Available Abstract Background Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. Results We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Conclusions Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its

Bootstrapping phylogenies inferred from rearrangement data.

Science.gov (United States)

Lin, Yu; Rajan, Vaibhav; Moret, Bernard Me

2012-08-29

Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its support values follow a similar scale and its receiver

Classification versus inference learning contrasted with real-world categories.

Science.gov (United States)

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

Statistical inference via fiducial methods

OpenAIRE

Salomé, Diemer

1998-01-01

In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary

Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific

KAUST Repository

Sraj, Ihab

2016-08-26

The authors present a polynomial chaos (PC)-based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference of the uncertain parameters is based on a Markov chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal time scales in addition to the data quality, and filters for the effects of parameter perturbations over those as a result of changes in the wind. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, a surrogate model for the test statistic using the PC method is built. Because of the noise in the model predictions, a basis-pursuit-denoising (BPDN) compressed sensing approach is employed to determine the PC coefficients of a representative surrogate model. The PC surrogate is then used to evaluate the test statistic in the MCMC step for sampling the posterior of the uncertain parameters. Results of the posteriors indicate good agreement with the default values for two parameters of the KPP model, namely the critical bulk and gradient Richardson numbers; while the posteriors of the remaining parameters were barely informative. © 2016 American Meteorological Society.

Scheme-Independent Predictions in QCD: Commensurate Scale Relations and Physical Renormalization Schemes

International Nuclear Information System (INIS)

Brodsky, Stanley J.

1998-01-01

Commensurate scale relations are perturbative QCD predictions which relate observable to observable at fixed relative scale, such as the ''generalized Crewther relation'', which connects the Bjorken and Gross-Llewellyn Smith deep inelastic scattering sum rules to measurements of the e + e - annihilation cross section. All non-conformal effects are absorbed by fixing the ratio of the respective momentum transfer and energy scales. In the case of fixed-point theories, commensurate scale relations relate both the ratio of couplings and the ratio of scales as the fixed point is approached. The relations between the observables are independent of the choice of intermediate renormalization scheme or other theoretical conventions. Commensurate scale relations also provide an extension of the standard minimal subtraction scheme, which is analytic in the quark masses, has non-ambiguous scale-setting properties, and inherits the physical properties of the effective charge α V (Q 2 ) defined from the heavy quark potential. The application of the analytic scheme to the calculation of quark-mass-dependent QCD corrections to the Z width is also reviewed

Quantum attack-resistent certificateless multi-receiver signcryption scheme.

Directory of Open Access Journals (Sweden)

Huixian Li

Full Text Available The existing certificateless signcryption schemes were designed mainly based on the traditional public key cryptography, in which the security relies on the hard problems, such as factor decomposition and discrete logarithm. However, these problems will be easily solved by the quantum computing. So the existing certificateless signcryption schemes are vulnerable to the quantum attack. Multivariate public key cryptography (MPKC, which can resist the quantum attack, is one of the alternative solutions to guarantee the security of communications in the post-quantum age. Motivated by these concerns, we proposed a new construction of the certificateless multi-receiver signcryption scheme (CLMSC based on MPKC. The new scheme inherits the security of MPKC, which can withstand the quantum attack. Multivariate quadratic polynomial operations, which have lower computation complexity than bilinear pairing operations, are employed in signcrypting a message for a certain number of receivers in our scheme. Security analysis shows that our scheme is a secure MPKC-based scheme. We proved its security under the hardness of the Multivariate Quadratic (MQ problem and its unforgeability under the Isomorphism of Polynomials (IP assumption in the random oracle model. The analysis results show that our scheme also has the security properties of non-repudiation, perfect forward secrecy, perfect backward secrecy and public verifiability. Compared with the existing schemes in terms of computation complexity and ciphertext length, our scheme is more efficient, which makes it suitable for terminals with low computation capacity like smart cards.

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

Directory of Open Access Journals (Sweden)

Meyer Patrick

2007-01-01

Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

Directory of Open Access Journals (Sweden)

Patrick E. Meyer

2007-06-01

Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

Directory of Open Access Journals (Sweden)

Gonzalo A Ruz

2014-01-01

Full Text Available BACKGROUND: Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle. RESULTS: Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G1 fixed point was found for deterministic updating schemes. CONCLUSIONS: In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the

Birkhoffian Symplectic Scheme for a Quantum System

International Nuclear Information System (INIS)

Su Hongling

2010-01-01

In this paper, a classical system of ordinary differential equations is built to describe a kind of n-dimensional quantum systems. The absorption spectrum and the density of the states for the system are defined from the points of quantum view and classical view. From the Birkhoffian form of the equations, a Birkhoffian symplectic scheme is derived for solving n-dimensional equations by using the generating function method. Besides the Birkhoffian structure-preserving, the new scheme is proven to preserve the discrete local energy conservation law of the system with zero vector f. Some numerical experiments for a 3-dimensional example show that the new scheme can simulate the general Birkhoffian system better than the implicit midpoint scheme, which is well known to be symplectic scheme for Hamiltonian system. (general)

Autonomous droop scheme with reduced generation cost

DEFF Research Database (Denmark)

Nutkani, Inam Ullah; Loh, Poh Chiang; Blaabjerg, Frede

2013-01-01

Droop scheme has been widely applied to the control of Distributed Generators (DGs) in microgrids for proportional power sharing based on their ratings. For standalone microgrid, where centralized management system is not viable, the proportional power sharing based droop might not suit well since...... DGs are usually of different types unlike synchronous generators. This paper presents an autonomous droop scheme that takes into consideration the operating cost, efficiency and emission penalty of each DG since all these factors directly or indirectly contributes to the Total Generation Cost (TGC......) of the overall microgrid. Comparing it with the traditional scheme, the proposed scheme has retained its simplicity, which certainly is a feature preferred by the industry. The overall performance of the proposed scheme has been verified through simulation and experiment....

Enhanced arbitrated quantum signature scheme using Bell states

International Nuclear Information System (INIS)

Wang Chao; Liu Jian-Wei; Shang Tao

2014-01-01

We investigate the existing arbitrated quantum signature schemes as well as their cryptanalysis, including intercept-resend attack and denial-of-service attack. By exploring the loopholes of these schemes, a malicious signatory may successfully disavow signed messages, or the receiver may actively negate the signature from the signatory without being detected. By modifying the existing schemes, we develop counter-measures to these attacks using Bell states. The newly proposed scheme puts forward the security of arbitrated quantum signature. Furthermore, several valuable topics are also presented for further research of the quantum signature scheme

IMAGINE: Interstellar MAGnetic field INference Engine

Science.gov (United States)

Steininger, Theo

2018-03-01

IMAGINE (Interstellar MAGnetic field INference Engine) performs inference on generic parametric models of the Galaxy. The modular open source framework uses highly optimized tools and technology such as the MultiNest sampler (ascl:1109.006) and the information field theory framework NIFTy (ascl:1302.013) to create an instance of the Milky Way based on a set of parameters for physical observables, using Bayesian statistics to judge the mismatch between measured data and model prediction. The flexibility of the IMAGINE framework allows for simple refitting for newly available data sets and makes state-of-the-art Bayesian methods easily accessible particularly for random components of the Galactic magnetic field.

Decoupling schemes for the SSC Collider

International Nuclear Information System (INIS)

Cai, Y.; Bourianoff, G.; Cole, B.; Meinke, R.; Peterson, J.; Pilat, F.; Stampke, S.; Syphers, M.; Talman, R.

1993-05-01

A decoupling system is designed for the SSC Collider. This system can accommodate three decoupling schemes by using 44 skew quadrupoles in the different configurations. Several decoupling schemes are studied and compared in this paper

Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways.

Science.gov (United States)

Wexler, Eric M; Rosen, Ezra; Lu, Daning; Osborn, Gregory E; Martin, Elizabeth; Raybould, Helen; Geschwind, Daniel H

2011-10-04

Wnt proteins are critical to mammalian brain development and function. The canonical Wnt signaling pathway involves the stabilization and nuclear translocation of β-catenin; however, Wnt also signals through alternative, noncanonical pathways. To gain a systems-level, genome-wide view of Wnt signaling, we analyzed Wnt1-stimulated changes in gene expression by transcriptional microarray analysis in cultured human neural progenitor (hNP) cells at multiple time points over a 72-hour time course. We observed a widespread oscillatory-like pattern of changes in gene expression, involving components of both the canonical and the noncanonical Wnt signaling pathways. A higher-order, systems-level analysis that combined independent component analysis, waveform analysis, and mutual information-based network construction revealed effects on pathways related to cell death and neurodegenerative disease. Wnt effectors were tightly clustered with presenilin1 (PSEN1) and granulin (GRN), which cause dominantly inherited forms of Alzheimer's disease and frontotemporal dementia (FTD), respectively. We further explored a potential link between Wnt1 and GRN and found that Wnt1 decreased GRN expression by hNPs. Conversely, GRN knockdown increased WNT1 expression, demonstrating that Wnt and GRN reciprocally regulate each other. Finally, we provided in vivo validation of the in vitro findings by analyzing gene expression data from individuals with FTD. These unbiased and genome-wide analyses provide evidence for a connection between Wnt signaling and the transcriptional regulation of neurodegenerative disease genes.

Amygdala TDP-43 Pathology in Frontotemporal Lobar Degeneration and Motor Neuron Disease.

Science.gov (United States)

Takeda, Takahiro; Seilhean, Danielle; Le Ber, Isabelle; Millecamps, Stéphanie; Sazdovitch, Véronique; Kitagawa, Kazuo; Uchihara, Toshiki; Duyckaerts, Charles

2017-09-01

TDP-43-positive inclusions are present in the amygdala in frontotemporal lobar degeneration (FTLD) and motor neuron disease (MND) including amyotrophic lateral sclerosis. Behavioral abnormalities, one of the chief symptoms of FTLD, could be, at least partly, related to amygdala pathology. We examined TDP-43 inclusions in the amygdala of patients with sporadic FTLD/MND (sFTLD/MND), FTLD/MND with mutation of the C9ORF72 (FTLD/MND-C9) and FTLD with mutation of the progranulin (FTLD-GRN). TDP-43 inclusions were common in each one of these subtypes, which can otherwise be distinguished on topographical and genetic grounds. Conventional and immunological stainings were performed and we quantified the numerical density of inclusions on a regional basis. TDP-43 inclusions in amygdala could be seen in 10 out of 26 sFTLD/MND cases, 5 out of 9 FTLD/MND-C9 cases, and all 4 FTLD-GRN cases. Their numerical density was lower in FTLD/MND-C9 than in sFTLD/MND and FTLD-GRN. TDP-43 inclusions were more numerous in the ventral region of the basolateral nucleus group in all subtypes. This contrast was apparent in sporadic and C9-mutated FTLD/MND, while it was less evident in FTLD-GRN. Such differences in subregional involvement of amygdala may be related to the region-specific neuronal connections that are differentially affected in FTLD/MND and FTLD-GRN. © 2017 American Association of Neuropathologists, Inc. All rights reserved.

Time-and-ID-Based Proxy Reencryption Scheme

Directory of Open Access Journals (Sweden)

Kambombo Mtonga

2014-01-01

Full Text Available Time- and ID-based proxy reencryption scheme is proposed in this paper in which a type-based proxy reencryption enables the delegator to implement fine-grained policies with one key pair without any additional trust on the proxy. However, in some applications, the time within which the data was sampled or collected is very critical. In such applications, for example, healthcare and criminal investigations, the delegatee may be interested in only some of the messages with some types sampled within some time bound instead of the entire subset. Hence, in order to carter for such situations, in this paper, we propose a time-and-identity-based proxy reencryption scheme that takes into account the time within which the data was collected as a factor to consider when categorizing data in addition to its type. Our scheme is based on Boneh and Boyen identity-based scheme (BB-IBE and Matsuo’s proxy reencryption scheme for identity-based encryption (IBE to IBE. We prove that our scheme is semantically secure in the standard model.

Cancelable remote quantum fingerprint templates protection scheme

International Nuclear Information System (INIS)

Liao Qin; Guo Ying; Huang Duan

2017-01-01

With the increasing popularity of fingerprint identification technology, its security and privacy have been paid much attention. Only the security and privacy of biological information are insured, the biological technology can be better accepted and used by the public. In this paper, we propose a novel quantum bit (qbit)-based scheme to solve the security and privacy problem existing in the traditional fingerprint identification system. By exploiting the properties of quantm mechanics, our proposed scheme, cancelable remote quantum fingerprint templates protection scheme, can achieve the unconditional security guaranteed in an information-theoretical sense. Moreover, this novel quantum scheme can invalidate most of the attacks aimed at the fingerprint identification system. In addition, the proposed scheme is applicable to the requirement of remote communication with no need to worry about its security and privacy during the transmission. This is an absolute advantage when comparing with other traditional methods. Security analysis shows that the proposed scheme can effectively ensure the communication security and the privacy of users’ information for the fingerprint identification. (paper)

Inferring epidemic network topology from surveillance data.

Directory of Open Access Journals (Sweden)

Xiang Wan

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

A Learning Algorithm for Multimodal Grammar Inference.

Science.gov (United States)

D'Ulizia, A; Ferri, F; Grifoni, P

2011-12-01

The high costs of development and maintenance of multimodal grammars in integrating and understanding input in multimodal interfaces lead to the investigation of novel algorithmic solutions in automating grammar generation and in updating processes. Many algorithms for context-free grammar inference have been developed in the natural language processing literature. An extension of these algorithms toward the inference of multimodal grammars is necessary for multimodal input processing. In this paper, we propose a novel grammar inference mechanism that allows us to learn a multimodal grammar from its positive samples of multimodal sentences. The algorithm first generates the multimodal grammar that is able to parse the positive samples of sentences and, afterward, makes use of two learning operators and the minimum description length metrics in improving the grammar description and in avoiding the over-generalization problem. The experimental results highlight the acceptable performances of the algorithm proposed in this paper since it has a very high probability of parsing valid sentences.

Bayesian Inference of High-Dimensional Dynamical Ocean Models

Science.gov (United States)

Lin, J.; Lermusiaux, P. F. J.; Lolla, S. V. T.; Gupta, A.; Haley, P. J., Jr.

2015-12-01

This presentation addresses a holistic set of challenges in high-dimension ocean Bayesian nonlinear estimation: i) predict the probability distribution functions (pdfs) of large nonlinear dynamical systems using stochastic partial differential equations (PDEs); ii) assimilate data using Bayes' law with these pdfs; iii) predict the future data that optimally reduce uncertainties; and (iv) rank the known and learn the new model formulations themselves. Overall, we allow the joint inference of the state, equations, geometry, boundary conditions and initial conditions of dynamical models. Examples are provided for time-dependent fluid and ocean flows, including cavity, double-gyre and Strait flows with jets and eddies. The Bayesian model inference, based on limited observations, is illustrated first by the estimation of obstacle shapes and positions in fluid flows. Next, the Bayesian inference of biogeochemical reaction equations and of their states and parameters is presented, illustrating how PDE-based machine learning can rigorously guide the selection and discovery of complex ecosystem models. Finally, the inference of multiscale bottom gravity current dynamics is illustrated, motivated in part by classic overflows and dense water formation sites and their relevance to climate monitoring and dynamics. This is joint work with our MSEAS group at MIT.

A New Adaptive Hungarian Mating Scheme in Genetic Algorithms

Directory of Open Access Journals (Sweden)

Chanju Jung

2016-01-01

Full Text Available In genetic algorithms, selection or mating scheme is one of the important operations. In this paper, we suggest an adaptive mating scheme using previously suggested Hungarian mating schemes. Hungarian mating schemes consist of maximizing the sum of mating distances, minimizing the sum, and random matching. We propose an algorithm to elect one of these Hungarian mating schemes. Every mated pair of solutions has to vote for the next generation mating scheme. The distance between parents and the distance between parent and offspring are considered when they vote. Well-known combinatorial optimization problems, the traveling salesperson problem, and the graph bisection problem are used for the test bed of our method. Our adaptive strategy showed better results than not only pure and previous hybrid schemes but also existing distance-based mating schemes.

A Network Inference Workflow Applied to Virulence-Related Processes in Salmonella typhimurium

Energy Technology Data Exchange (ETDEWEB)

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

2009-04-20

Inference of the structure of mRNA transcriptional regulatory networks, protein regulatory or interaction networks, and protein activation/inactivation-based signal transduction networks are critical tasks in systems biology. In this article we discuss a workflow for the reconstruction of parts of the transcriptional regulatory network of the pathogenic bacterium Salmonella typhimurium based on the information contained in sets of microarray gene expression data now available for that organism, and describe our results obtained by following this workflow. The primary tool is one of the network inference algorithms deployed in the Software Environment for BIological Network Inference (SEBINI). Specifically, we selected the algorithm called Context Likelihood of Relatedness (CLR), which uses the mutual information contained in the gene expression data to infer regulatory connections. The associated analysis pipeline automatically stores the inferred edges from the CLR runs within SEBINI and, upon request, transfers the inferred edges into either Cytoscape or the plug-in Collective Analysis of Biological of Biological Interaction Networks (CABIN) tool for further post-analysis of the inferred regulatory edges. The following article presents the outcome of this workflow, as well as the protocols followed for microarray data collection, data cleansing, and network inference. Our analysis revealed several interesting interactions, functional groups, metabolic pathways, and regulons in S. typhimurium.

Training Inference Making Skills Using a Situation Model Approach Improves Reading Comprehension

Directory of Open Access Journals (Sweden)

Lisanne eBos

2016-02-01

Full Text Available This study aimed to enhance third and fourth graders’ text comprehension at the situation model level. Therefore, we tested a reading strategy training developed to target inference making skills, which are widely considered to be pivotal to situation model construction. The training was grounded in contemporary literature on situation model-based inference making and addressed the source (text-based versus knowledge-based, type (necessary versus unnecessary for (re-establishing coherence, and depth of an inference (making single lexical inferences versus combining multiple lexical inferences, as well as the type of searching strategy (forward versus backward. Results indicated that, compared to a control group (n = 51, children who followed the experimental training (n = 67 improved their inference making skills supportive to situation model construction. Importantly, our training also resulted in increased levels of general reading comprehension and motivation. In sum, this study showed that a ‘level of text representation’-approach can provide a useful framework to teach inference making skills to third and fourth graders.

Robust Demographic Inference from Genomic and SNP Data

Science.gov (United States)

Excoffier, Laurent; Dupanloup, Isabelle; Huerta-Sánchez, Emilia; Sousa, Vitor C.; Foll, Matthieu

2013-01-01

We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets. PMID:24204310

Universal Darwinism as a process of Bayesian inference

Directory of Open Access Journals (Sweden)

John Oberon Campbell

2016-06-01

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

Behavior Intention Derivation of Android Malware Using Ontology Inference

Directory of Open Access Journals (Sweden)

Jian Jiao

2018-01-01

Full Text Available Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.

Genealogical and evolutionary inference with the human Y chromosome.

Science.gov (United States)

Stumpf, M P; Goldstein, D B

2001-03-02

Population genetics has emerged as a powerful tool for unraveling human history. In addition to the study of mitochondrial and autosomal DNA, attention has recently focused on Y-chromosome variation. Ambiguities and inaccuracies in data analysis, however, pose an important obstacle to further development of the field. Here we review the methods available for genealogical inference using Y-chromosome data. Approaches can be divided into those that do and those that do not use an explicit population model in genealogical inference. We describe the strengths and weaknesses of these model-based and model-free approaches, as well as difficulties associated with the mutation process that affect both methods. In the case of genealogical inference using microsatellite loci, we use coalescent simulations to show that relatively simple generalizations of the mutation process can greatly increase the accuracy of genealogical inference. Because model-free and model-based approaches have different biases and limitations, we conclude that there is considerable benefit in the continued use of both types of approaches.

SDG multiple fault diagnosis by real-time inverse inference

International Nuclear Information System (INIS)

Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng

2005-01-01

In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency

SDG multiple fault diagnosis by real-time inverse inference

Energy Technology Data Exchange (ETDEWEB)

Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng

2005-02-01

In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency.

Quantum Communication Scheme Using Non-symmetric Quantum Channel

International Nuclear Information System (INIS)

Cao Haijing; Chen Zhonghua; Song Heshan

2008-01-01

A theoretical quantum communication scheme based on entanglement swapping and superdense coding is proposed with a 3-dimensional Bell state and 2-dimensional Bell state function as quantum channel. quantum key distribution and quantum secure direct communication can be simultaneously accomplished in the scheme. The scheme is secure and has high source capacity. At last, we generalize the quantum communication scheme to d-dimensional quantum channel

Functional networks inference from rule-based machine learning models.

Science.gov (United States)

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

A universal encoding scheme for MIMO transmission using a single active element for PSK modulation schemes

DEFF Research Database (Denmark)

Alrabadi, Osama; Papadias, C.B.; Kalis, A.

2009-01-01

A universal scheme for encoding multiple symbol streams using a single driven element (and consequently a single radio frequency (RF) frontend) surrounded by parasitic elements (PE) loaded with variable reactive loads, is proposed in this paper. The proposed scheme is based on creating a MIMO sys...

Causal inference based on counterfactuals

Directory of Open Access Journals (Sweden)

Höfler M

2005-09-01

Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

TVD schemes in one and two space dimensions

International Nuclear Information System (INIS)

Leveque, R.J.; Goodman, J.B.; New York Univ., NY)

1985-01-01

The recent development of schemes which are second order accurate in smooth regions has made it possible to overcome certain difficulties which used to arise in numerical computations of discontinuous solutions of conservation laws. The present investigation is concerned with scalar conservation laws, taking into account the employment of total variation diminishing (TVD) schemes. The concept of a TVD scheme was introduced by Harten et al. (1976). Harten et al. first constructed schemes which are simultaneously TVD and second order accurate on smooth solutions. In the present paper, a summary is provided of recently conducted work in this area. Attention is given to TVD schemes in two space dimensions, a second order accurate TVD scheme in one dimension, and the entropy condition and spreading of rarefaction waves. 19 references

Schemes for fibre-based entanglement generation in the telecom band

International Nuclear Information System (INIS)

Chen, Jun; Lee, Kim Fook; Li Xiaoying; Voss, Paul L; Kumar, Prem

2007-01-01

We investigate schemes for generating polarization-entangled photon pairs in standard optical fibres. The advantages of a double-loop scheme are explored through comparison with two other schemes, namely, the Sagnac-loop scheme and the counter-propagating scheme. Experimental measurements with the double-loop scheme verify the predicted advantages

Tradable schemes

NARCIS (Netherlands)

J.K. Hoogland (Jiri); C.D.D. Neumann

2000-01-01

textabstractIn this article we present a new approach to the numerical valuation of derivative securities. The method is based on our previous work where we formulated the theory of pricing in terms of tradables. The basic idea is to fit a finite difference scheme to exact solutions of the pricing

Finite-volume scheme for anisotropic diffusion

Energy Technology Data Exchange (ETDEWEB)

Es, Bram van, E-mail: bramiozo@gmail.com [Centrum Wiskunde & Informatica, P.O. Box 94079, 1090GB Amsterdam (Netherlands); FOM Institute DIFFER, Dutch Institute for Fundamental Energy Research, The Netherlands" 1 (Netherlands); Koren, Barry [Eindhoven University of Technology (Netherlands); Blank, Hugo J. de [FOM Institute DIFFER, Dutch Institute for Fundamental Energy Research, The Netherlands" 1 (Netherlands)

2016-02-01

In this paper, we apply a special finite-volume scheme, limited to smooth temperature distributions and Cartesian grids, to test the importance of connectivity of the finite volumes. The area of application is nuclear fusion plasma with field line aligned temperature gradients and extreme anisotropy. We apply the scheme to the anisotropic heat-conduction equation, and compare its results with those of existing finite-volume schemes for anisotropic diffusion. Also, we introduce a general model adaptation of the steady diffusion equation for extremely anisotropic diffusion problems with closed field lines.

Computing with high-resolution upwind schemes for hyperbolic equations

International Nuclear Information System (INIS)

Chakravarthy, S.R.; Osher, S.; California Univ., Los Angeles)

1985-01-01

Computational aspects of modern high-resolution upwind finite-difference schemes for hyperbolic systems of conservation laws are examined. An operational unification is demonstrated for constructing a wide class of flux-difference-split and flux-split schemes based on the design principles underlying total variation diminishing (TVD) schemes. Consideration is also given to TVD scheme design by preprocessing, the extension of preprocessing and postprocessing approaches to general control volumes, the removal of expansion shocks and glitches, relaxation methods for implicit TVD schemes, and a new family of high-accuracy TVD schemes. 21 references

Mixed ultrasoft/norm-conserved pseudopotential scheme

DEFF Research Database (Denmark)

Stokbro, Kurt

1996-01-01

A variant of the Vanderbilt ultrasoft pseudopotential scheme, where the norm conservation is released for only one or a few angular channels, is presented. Within this scheme some difficulties of the truly ultrasoft pseudopotentials are overcome without sacrificing the pseudopotential softness. (...

New practicable Siberian Snake schemes

International Nuclear Information System (INIS)

Steffen, K.

1983-07-01

Siberian Snake schemes can be inserted in ring accelerators for making the spin tune almost independent of energy. Two such schemes are here suggested which lend particularly well to practical application over a wide energy range. Being composed of horizontal and vertical bending magnets, the proposed snakes are designed to have a small maximum beam excursion in one plane. By applying in this plane a bending correction that varies with energy, they can be operated at fixed geometry in the other plane where most of the bending occurs, thus avoiding complicated magnet motion or excessively large magnet apertures that would otherwise be needed for large energy variations. The first of the proposed schemes employs a pair of standard-type Siberian Snakes, i.e. of the usual 1st and 2nd kind which rotate the spin about the longitudinal and the transverse horizontal axis, respectively. The second scheme employs a pair of novel-type snakes which rotate the spin about either one of the horizontal axes that are at 45 0 to the beam direction. In obvious reference to these axes, they are called left-pointed and right-pointed snakes. (orig.)

International Conference on Trends and Perspectives in Linear Statistical Inference

CERN Document Server

Rosen, Dietrich

2018-01-01

This volume features selected contributions on a variety of topics related to linear statistical inference. The peer-reviewed papers from the International Conference on Trends and Perspectives in Linear Statistical Inference (LinStat 2016) held in Istanbul, Turkey, 22-25 August 2016, cover topics in both theoretical and applied statistics, such as linear models, high-dimensional statistics, computational statistics, the design of experiments, and multivariate analysis. The book is intended for statisticians, Ph.D. students, and professionals who are interested in statistical inference. .

Packaging design as communicator of product attributes: Effects on consumers’ attribute inferences

NARCIS (Netherlands)

van Ooijen, I.

2016-01-01

This dissertation will focus on two types of attribute inferences that result from packaging design cues. First, the effects of product packaging design on quality related inferences are investigated. Second, the effects of product packaging design on healthiness related inferences are examined (See

Fast and scalable inference of multi-sample cancer lineages.

KAUST Repository

Popic, Victoria; Salari, Raheleh; Hajirasouliha, Iman; Kashef-Haghighi, Dorna; West, Robert B; Batzoglou, Serafim

2015-01-01

Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .

Fast and scalable inference of multi-sample cancer lineages.

KAUST Repository

Popic, Victoria

2015-05-06

Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .

Design of a modified adaptive neuro fuzzy inference system classifier for medical diagnosis of Pima Indians Diabetes

Science.gov (United States)

Sagir, Abdu Masanawa; Sathasivam, Saratha

2017-08-01

Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most of the diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system with Modified Levenberg-Marquardt algorithm using analytical derivation scheme for computation of Jacobian matrix. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about presence or absence of a disease. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent system was tested with Pima Indian Diabetes dataset obtained from the University of California at Irvine's (UCI) machine learning repository. The proposed method's performance was evaluated based on training and test datasets. In addition, an attempt was done to specify the effectiveness of the performance measuring total accuracy, sensitivity and specificity. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system

Science.gov (United States)

Nourmohammadi, Hossein; Keighobadi, Jafar

2018-01-01

Due to weak stand-alone accuracy as well as poor run-to-run stability of micro-electro mechanical system (MEMS)-based inertial sensors, special approaches are required to integrate low-cost strap-down inertial navigation system (SINS) with global positioning system (GPS), particularly in long-term applications. This paper aims to enhance long-term performance of conventional SINS/GPS navigation systems using a fuzzy adaptive integration scheme. The main concept behind the proposed adaptive integration is the good performance of attitude-heading reference system (AHRS) in low-accelerated motions and its degradation in maneuvered or accelerated motions. Depending on vehicle maneuvers, gravity-based attitude angles can be intelligently utilized to improve orientation estimation in the SINS. Knowledge-based fuzzy inference system is developed for decision-making between the AHRS and the SINS according to vehicle maneuvering conditions. Inertial measurements are the main input data of the fuzzy system to determine the maneuvering level during the vehicle motions. Accordingly, appropriate weighting coefficients are produced to combine the SINS/GPS and the AHRS, efficiently. The assessment of the proposed integrated navigation system is conducted via real data in airborne tests.

Inferring causality from noisy time series data

DEFF Research Database (Denmark)

Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian

2016-01-01

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...

Robust and Efficient Authentication Scheme for Session Initiation Protocol

Directory of Open Access Journals (Sweden)

Yanrong Lu

2015-01-01

Full Text Available The session initiation protocol (SIP is a powerful application-layer protocol which is used as a signaling one for establishing, modifying, and terminating sessions among participants. Authentication is becoming an increasingly crucial issue when a user asks to access SIP services. Hitherto, many authentication schemes have been proposed to enhance the security of SIP. In 2014, Arshad and Nikooghadam proposed an enhanced authentication and key agreement scheme for SIP and claimed that their scheme could withstand various attacks. However, in this paper, we show that Arshad and Nikooghadam’s authentication scheme is still susceptible to key-compromise impersonation and trace attacks and does not provide proper mutual authentication. To conquer the flaws, we propose a secure and efficient ECC-based authentication scheme for SIP. Through the informal and formal security analyses, we demonstrate that our scheme is resilient to possible known attacks including the attacks found in Arshad et al.’s scheme. In addition, the performance analysis shows that our scheme has similar or better efficiency in comparison with other existing ECC-based authentication schemes for SIP.

Certificateless Key-Insulated Generalized Signcryption Scheme without Bilinear Pairings

Directory of Open Access Journals (Sweden)

Caixue Zhou

2017-01-01

Full Text Available Generalized signcryption (GSC can be applied as an encryption scheme, a signature scheme, or a signcryption scheme with only one algorithm and one key pair. A key-insulated mechanism can resolve the private key exposure problem. To ensure the security of cloud storage, we introduce the key-insulated mechanism into GSC and propose a concrete scheme without bilinear pairings in the certificateless cryptosystem setting. We provide a formal definition and a security model of certificateless key-insulated GSC. Then, we prove that our scheme is confidential under the computational Diffie-Hellman (CDH assumption and unforgeable under the elliptic curve discrete logarithm (EC-DL assumption. Our scheme also supports both random-access key update and secure key update. Finally, we evaluate the efficiency of our scheme and demonstrate that it is highly efficient. Thus, our scheme is more suitable for users who communicate with the cloud using mobile devices.

Anonymous Credential Schemes with Encrypted Attributes

NARCIS (Netherlands)

Guajardo Merchan, J.; Mennink, B.; Schoenmakers, B.

2011-01-01

In anonymous credential schemes, users obtain credentials on certain attributes from an issuer, and later show these credentials to a relying party anonymously and without fully disclosing the attributes. In this paper, we introduce the notion of (anonymous) credential schemes with encrypted

Simple Numerical Schemes for the Korteweg-deVries Equation

International Nuclear Information System (INIS)

McKinstrie, C. J.; Kozlov, M.V.

2000-01-01

Two numerical schemes, which simulate the propagation of dispersive non-linear waves, are described. The first is a split-step Fourier scheme for the Korteweg-de Vries (KdV) equation. The second is a finite-difference scheme for the modified KdV equation. The stability and accuracy of both schemes are discussed. These simple schemes can be used to study a wide variety of physical processes that involve dispersive nonlinear waves

Simple Numerical Schemes for the Korteweg-deVries Equation

Energy Technology Data Exchange (ETDEWEB)

C. J. McKinstrie; M. V. Kozlov

2000-12-01

Two numerical schemes, which simulate the propagation of dispersive non-linear waves, are described. The first is a split-step Fourier scheme for the Korteweg-de Vries (KdV) equation. The second is a finite-difference scheme for the modified KdV equation. The stability and accuracy of both schemes are discussed. These simple schemes can be used to study a wide variety of physical processes that involve dispersive nonlinear waves.

Performance comparison of renewable incentive schemes using optimal control

International Nuclear Information System (INIS)

Oak, Neeraj; Lawson, Daniel; Champneys, Alan

2014-01-01

Many governments worldwide have instituted incentive schemes for renewable electricity producers in order to meet carbon emissions targets. These schemes aim to boost investment and hence growth in renewable energy industries. This paper examines four such schemes: premium feed-in tariffs, fixed feed-in tariffs, feed-in tariffs with contract for difference and the renewable obligations scheme. A generalised mathematical model of industry growth is presented and fitted with data from the UK onshore wind industry. The model responds to subsidy from each of the four incentive schemes. A utility or ‘fitness’ function that maximises installed capacity at some fixed time in the future while minimising total cost of subsidy is postulated. Using this function, the optimal strategy for provision and timing of subsidy for each scheme is calculated. Finally, a comparison of the performance of each scheme, given that they use their optimal control strategy, is presented. This model indicates that the premium feed-in tariff and renewable obligation scheme produce the joint best results. - Highlights: • Stochastic differential equation model of renewable energy industry growth and prices, using UK onshore wind data 1992–2010. • Cost of production reduces as cumulative installed capacity of wind energy increases, consistent with the theory of learning. • Studies the effect of subsidy using feed-in tariff schemes, and the ‘renewable obligations’ scheme. • We determine the optimal timing and quantity of subsidy required to maximise industry growth and minimise costs. • The premium feed-in tariff scheme and the renewable obligations scheme produce the best results under optimal control

A rational function based scheme for solving advection equation

International Nuclear Information System (INIS)

Xiao, Feng; Yabe, Takashi.

1995-07-01

A numerical scheme for solving advection equations is presented. The scheme is derived from a rational interpolation function. Some properties of the scheme with respect to convex-concave preserving and monotone preserving are discussed. We find that the scheme is attractive in surpressinging overshoots and undershoots even in the vicinities of discontinuity. The scheme can also be easily swicthed as the CIP (Cubic interpolated Pseudo-Particle) method to get a third-order accuracy in smooth region. Numbers of numerical tests are carried out to show the non-oscillatory and less diffusive nature of the scheme. (author)

Making Inferences in Adulthood: Falling Leaves Mean It's Fall.

Science.gov (United States)

Zandi, Taher; Gregory, Monica E.

1988-01-01

Assessed age differences in making inferences from prose. Older adults correctly answered mean of 10 questions related to implicit information and 8 related to explicit information. Young adults answered mean of 7 implicit and 12 explicit information questions. In spite of poorer recall of factual details, older subjects made inferences to greater…

Mixed normal inference on multicointegration

NARCIS (Netherlands)

Boswijk, H.P.

2009-01-01

Asymptotic likelihood analysis of cointegration in I(2) models, see Johansen (1997, 2006), Boswijk (2000) and Paruolo (2000), has shown that inference on most parameters is mixed normal, implying hypothesis test statistics with an asymptotic 2 null distribution. The asymptotic distribution of the

Algebraic K-theory of generalized schemes

DEFF Research Database (Denmark)

Anevski, Stella Victoria Desiree

and geometry over the field with one element. It also permits the construction of important Arakelov theoretical objects, such as the completion \\Spec Z of Spec Z. In this thesis, we prove a projective bundle theorem for the eld with one element and compute the Chow rings of the generalized schemes Sp\\ec ZN......Nikolai Durov has developed a generalization of conventional scheme theory in which commutative algebraic monads replace commutative unital rings as the basic algebraic objects. The resulting geometry is expressive enough to encompass conventional scheme theory, tropical algebraic geometry......, appearing in the construction of \\Spec Z....

A modified symplectic PRK scheme for seismic wave modeling

Science.gov (United States)

Liu, Shaolin; Yang, Dinghui; Ma, Jian

2017-02-01

A new scheme for the temporal discretization of the seismic wave equation is constructed based on symplectic geometric theory and a modified strategy. The ordinary differential equation in terms of time, which is obtained after spatial discretization via the spectral-element method, is transformed into a Hamiltonian system. A symplectic partitioned Runge-Kutta (PRK) scheme is used to solve the Hamiltonian system. A term related to the multiplication of the spatial discretization operator with the seismic wave velocity vector is added into the symplectic PRK scheme to create a modified symplectic PRK scheme. The symplectic coefficients of the new scheme are determined via Taylor series expansion. The positive coefficients of the scheme indicate that its long-term computational capability is more powerful than that of conventional symplectic schemes. An exhaustive theoretical analysis reveals that the new scheme is highly stable and has low numerical dispersion. The results of three numerical experiments demonstrate the high efficiency of this method for seismic wave modeling.

Baselines and test data for cross-lingual inference

DEFF Research Database (Denmark)

Agic, Zeljko; Schluter, Natalie

2018-01-01

The recent years have seen a revival of interest in textual entailment, sparked by i) the emergence of powerful deep neural network learners for natural language processing and ii) the timely development of large-scale evaluation datasets such as SNLI. Recast as natural language inference......, the problem now amounts to detecting the relation between pairs of statements: they either contradict or entail one another, or they are mutually neutral. Current research in natural language inference is effectively exclusive to English. In this paper, we propose to advance the research in SNLI-style natural...... language inference toward multilingual evaluation. To that end, we provide test data for four major languages: Arabic, French, Spanish, and Russian. We experiment with a set of baselines. Our systems are based on cross-lingual word embeddings and machine translation. While our best system scores an average...

Bayesian inference with ecological applications

CERN Document Server

Link, William A

2009-01-01

This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...

Nonparametric Bayesian inference in biostatistics

CERN Document Server

Müller, Peter

2015-01-01

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...

Finite Difference Schemes as Algebraic Correspondences between Layers

Science.gov (United States)

Malykh, Mikhail; Sevastianov, Leonid

2018-02-01

For some differential equations, especially for Riccati equation, new finite difference schemes are suggested. These schemes define protective correspondences between the layers. Calculation using these schemes can be extended to the area beyond movable singularities of exact solution without any error accumulation.

Financial incentive schemes in primary care

Directory of Open Access Journals (Sweden)

Gillam S

2015-09-01

Full Text Available Stephen Gillam Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK Abstract: Pay-for-performance (P4P schemes have become increasingly common in primary care, and this article reviews their impact. It is based primarily on existing systematic reviews. The evidence suggests that P4P schemes can change health professionals' behavior and improve recorded disease management of those clinical processes that are incentivized. P4P may narrow inequalities in performance comparing deprived with nondeprived areas. However, such schemes have unintended consequences. Whether P4P improves the patient experience, the outcomes of care or population health is less clear. These practical uncertainties mirror the ethical concerns of many clinicians that a reductionist approach to managing markers of chronic disease runs counter to the humanitarian values of family practice. The variation in P4P schemes between countries reflects different historical and organizational contexts. With so much uncertainty regarding the effects of P4P, policy makers are well advised to proceed carefully with the implementation of such schemes until and unless clearer evidence for their cost–benefit emerges. Keywords: financial incentives, pay for performance, quality improvement, primary care

Intracranial EEG correlates of implicit relational inference within the hippocampus.

Science.gov (United States)

Reber, T P; Do Lam, A T A; Axmacher, N; Elger, C E; Helmstaedter, C; Henke, K; Fell, J

2016-01-01

Drawing inferences from past experiences enables adaptive behavior in future situations. Inference has been shown to depend on hippocampal processes. Usually, inference is considered a deliberate and effortful mental act which happens during retrieval, and requires the focus of our awareness. Recent fMRI studies hint at the possibility that some forms of hippocampus-dependent inference can also occur during encoding and possibly also outside of awareness. Here, we sought to further explore the feasibility of hippocampal implicit inference, and specifically address the temporal evolution of implicit inference using intracranial EEG. Presurgical epilepsy patients with hippocampal depth electrodes viewed a sequence of word pairs, and judged the semantic fit between two words in each pair. Some of the word pairs entailed a common word (e.g., "winter-red," "red-cat") such that an indirect relation was established in following word pairs (e.g., "winter-cat"). The behavioral results suggested that drawing inference implicitly from past experience is feasible because indirect relations seemed to foster "fit" judgments while the absence of indirect relations fostered "do not fit" judgments, even though the participants were unaware of the indirect relations. A event-related potential (ERP) difference emerging 400 ms post-stimulus was evident in the hippocampus during encoding, suggesting that indirect relations were already established automatically during encoding of the overlapping word pairs. Further ERP differences emerged later post-stimulus (1,500 ms), were modulated by the participants' responses and were evident during encoding and test. Furthermore, response-locked ERP effects were evident at test. These ERP effects could hence be a correlate of the interaction of implicit memory with decision-making. Together, the data map out a time-course in which the hippocampus automatically integrates memories from discrete but related episodes to implicitly influence future

Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

Science.gov (United States)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Lundquist, Jessica D.

2015-10-01

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.

Feature inference with uncertain categorization: Re-assessing Anderson's rational model.

Science.gov (United States)

Konovalova, Elizaveta; Le Mens, Gaël

2017-09-18

A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson's model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson's model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson's model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.

Integrating distributed Bayesian inference and reinforcement learning for sensor management

NARCIS (Netherlands)

Grappiolo, C.; Whiteson, S.; Pavlin, G.; Bakker, B.

2009-01-01

This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically

Towards the ultimate variance-conserving convection scheme

International Nuclear Information System (INIS)

Os, J.J.A.M. van; Uittenbogaard, R.E.

2004-01-01

In the past various arguments have been used for applying kinetic energy-conserving advection schemes in numerical simulations of incompressible fluid flows. One argument is obeying the programmed dissipation by viscous stresses or by sub-grid stresses in Direct Numerical Simulation and Large Eddy Simulation, see e.g. [Phys. Fluids A 3 (7) (1991) 1766]. Another argument is that, according to e.g. [J. Comput. Phys. 6 (1970) 392; 1 (1966) 119], energy-conserving convection schemes are more stable i.e. by prohibiting a spurious blow-up of volume-integrated energy in a closed volume without external energy sources. In the above-mentioned references it is stated that nonlinear instability is due to spatial truncation rather than to time truncation and therefore these papers are mainly concerned with the spatial integration. In this paper we demonstrate that discretized temporal integration of a spatially variance-conserving convection scheme can induce non-energy conserving solutions. In this paper the conservation of the variance of a scalar property is taken as a simple model for the conservation of kinetic energy. In addition, the derivation and testing of a variance-conserving scheme allows for a clear definition of kinetic energy-conserving advection schemes for solving the Navier-Stokes equations. Consequently, we first derive and test a strictly variance-conserving space-time discretization for the convection term in the convection-diffusion equation. Our starting point is the variance-conserving spatial discretization of the convection operator presented by Piacsek and Williams [J. Comput. Phys. 6 (1970) 392]. In terms of its conservation properties, our variance-conserving scheme is compared to other spatially variance-conserving schemes as well as with the non-variance-conserving schemes applied in our shallow-water solver, see e.g. [Direct and Large-eddy Simulation Workshop IV, ERCOFTAC Series, Kluwer Academic Publishers, 2001, pp. 409-287

Reliability of dose volume constraint inference from clinical data

Science.gov (United States)

Lutz, C. M.; Møller, D. S.; Hoffmann, L.; Knap, M. M.; Alber, M.

2017-04-01

Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an ‘ideal’ cohort was generated where the most predictive model was equal to the postulated model. A bootstrap and a Cohort Replication Monte Carlo (CoRepMC) approach were applied to create 1000 equally sized populations each. The cohorts were then analyzed to establish inference frequency distributions. This was applied to nine scenarios for cohort sizes of 102 (1), 500 (2) to 2000 (3) patients (by sampling with replacement) and three postulated DVHP models. The Bootstrap was repeated for a ‘non-ideal’ cohort, where the most predictive model did not coincide with the postulated model. The Bootstrap produced chaotic results for all models of cohort size 1 for both the ideal and non-ideal cohorts. For cohort size 2 and 3, the distributions for all populations were more concentrated around the postulated DVHP. For the CoRepMC, the inference frequency increased with cohort size and incidence rate. Correct inference rates  >85 % were only achieved by cohorts with more than 500 patients. Both Bootstrap and CoRepMC indicate that inference of the correct or approximate DVHP for typical cohort sizes is highly uncertain. CoRepMC results were less spurious than Bootstrap results, demonstrating the large influence that randomness in dose-response has on the statistical analysis.

Generalization of binary tensor product schemes depends upon four parameters

International Nuclear Information System (INIS)

Bashir, R.; Bari, M.; Mustafa, G.

2018-01-01

This article deals with general formulae of parametric and non parametric bivariate subdivision scheme with four parameters. By assigning specific values to those parameters we get some special cases of existing tensor product schemes as well as a new proposed scheme. The behavior of schemes produced by the general formulae is interpolating, approximating and relaxed. Approximating bivariate subdivision schemes produce some other surfaces as compared to interpolating bivariate subdivision schemes. Polynomial reproduction and polynomial generation are desirable properties of subdivision schemes. Capability of polynomial reproduction and polynomial generation is strongly connected with smoothness, sum rules, convergence and approximation order. We also calculate the polynomial generation and polynomial reproduction of 9-point bivariate approximating subdivision scheme. Comparison of polynomial reproduction, polynomial generation and continuity of existing and proposed schemes has also been established. Some numerical examples are also presented to show the behavior of bivariate schemes. (author)

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

Directory of Open Access Journals (Sweden)

Vipin Narang

Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

Progranulin genetic polymorphisms influence progression of disability and relapse recovery in multiple sclerosis.

Science.gov (United States)

Vercellino, Marco; Fenoglio, Chiara; Galimberti, Daniela; Mattioda, Alessandra; Chiavazza, Carlotta; Binello, Eleonora; Pinessi, Lorenzo; Giobbe, Dario; Scarpini, Elio; Cavalla, Paola

2016-07-01

Progranulin (GRN) is a multifunctional protein involved in inflammation and repair, and also a neurotrophic factor critical for neuronal survival. Progranulin is strongly expressed in multiple sclerosis (MS) brains by macrophages and microglia. In this study we evaluated GRN genetic variability in 400 MS patients, in correlation with clinical variables such as disease severity and relapse recovery. We also evaluated serum progranulin levels in the different groups of GRN variants carriers. We found that incomplete recovery after a relapse is correlated with an increased frequency of the rs9897526 A allele (odds ratio (OR) 4.367, p = 0.005). A more severe disease course (Multiple Sclerosis Severity Score > 5) is correlated with an increased frequency of the rs9897526 A allele (OR 1.886, p = 0.002) and of the rs5848 T allele (OR 1.580, p = 0.019). Carriers of the variants associated with a more severe disease course (rs9897526 A, rs5848 T) have significantly lower levels of circulating progranulin (80.5 ± 9.1 ng/mL vs. 165.7 ng/mL, p = 0.01). GRN genetic polymorphisms likely influence disease course and relapse recovery in MS. © The Author(s), 2015.

PROGRANULIN MUTATIONS AFFECTS BRAIN OSCILLATORY ACTIVITY IN FRONTO-TEMPORAL DEMENTIA

Directory of Open Access Journals (Sweden)

Davide Vito Moretti

2016-02-01

Full Text Available Background: mild cognitive impairment (MCI is a clinical stage indicating a prodromal phase of dementia. This practical concept could be used also for fronto-temporal dementia (FTD. Progranulin (PGRN has been recently recognized as a useful diagnostic biomarker for fronto-temporal lobe degeneration (FTLD due to GRN null mutations. Electroencephalography (EEG is a reliable tool in detecting brain networks changes. The working hypothesis of the present study is that EEG oscillations could detect different modifications among FTLD stages (FTD-MCI versus overt FTD as well as differences between GRN mutation carriers versus non carriers in patients with overt FTD. Methods: EEG in all patients and PGRN dosage in patients with a clear FTD were detected. The cognitive state has been investigated through mini mental state examination (MMSE. Results: MCI-FTD showed a significant lower spectral power in both alpha and theta oscillations as compared to overt FTD. GRN mutations carriers affected by FTLD show an increase in high alpha and decrease in theta oscillations as compared to non-carriers.Conclusion: EEG frequency rhythms are sensible to different stage of FTD and could detect changes in brain oscillatory activity affected by GRN mutations

BioTapestry now provides a web application and improved drawing and layout tools.

Science.gov (United States)

Paquette, Suzanne M; Leinonen, Kalle; Longabaugh, William J R

2016-01-01

Gene regulatory networks (GRNs) control embryonic development, and to understand this process in depth, researchers need to have a detailed understanding of both the network architecture and its dynamic evolution over time and space. Interactive visualization tools better enable researchers to conceptualize, understand, and share GRN models. BioTapestry is an established application designed to fill this role, and recent enhancements released in Versions 6 and 7 have targeted two major facets of the program. First, we introduced significant improvements for network drawing and automatic layout that have now made it much easier for the user to create larger, more organized network drawings. Second, we revised the program architecture so it could continue to support the current Java desktop Editor program, while introducing a new BioTapestry GRN Viewer that runs as a JavaScript web application in a browser. We have deployed a number of GRN models using this new web application. These improvements will ensure that BioTapestry remains viable as a research tool in the face of the continuing evolution of web technologies, and as our understanding of GRN models grows.

Inference in {open_quotes}poor{close_quotes} languages

Energy Technology Data Exchange (ETDEWEB)

Petrov, S. [Oak Ridge National Lab., TN (United States)

1996-12-31

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

Inference of beliefs and emotions in patients with Alzheimer's disease.

Science.gov (United States)

Zaitchik, Deborah; Koff, Elissa; Brownell, Hiram; Winner, Ellen; Albert, Marilyn

2006-01-01

The present study compared 20 patients with mild to moderate Alzheimer's disease with 20 older controls (ages 69-94 years) on their ability to make inferences about emotions and beliefs in others. Six tasks tested their ability to make 1st-order and 2nd-order inferences as well as to offer explanations and moral evaluations of human action by appeal to emotions and beliefs. Results showed that the ability to infer emotions and beliefs in 1st-order tasks remains largely intact in patients with mild to moderate Alzheimer's. Patients were able to use mental states in the prediction, explanation, and moral evaluation of behavior. Impairment on 2nd-order tasks involving inference of mental states was equivalent to impairment on control tasks, suggesting that patients' difficulty is secondary to their cognitive impairments. ((c) 2006 APA, all rights reserved).

Inference method using bayesian network for diagnosis of pulmonary nodules

International Nuclear Information System (INIS)

Kawagishi, Masami; Iizuka, Yoshio; Yamamoto, Hiroyuki; Yakami, Masahiro; Kubo, Takeshi; Fujimoto, Koji; Togashi, Kaori

2010-01-01

This report describes the improvements of a naive Bayes model that infers the diagnosis of pulmonary nodules in chest CT images based on the findings obtained when a radiologist interprets the CT images. We have previously introduced an inference model using a naive Bayes classifier and have reported its clinical value based on evaluation using clinical data. In the present report, we introduce the following improvements to the original inference model: the selection of findings based on correlations and the generation of a model using only these findings, and the introduction of classifiers that integrate several simple classifiers each of which is specialized for specific diagnosis. These improvements were found to increase the inference accuracy by 10.4% (p<.01) as compared to the original model in 100 cases (222 nodules) based on leave-one-out evaluation. (author)

Generative inference for cultural evolution.

Science.gov (United States)

Kandler, Anne; Powell, Adam

2018-04-05

One of the major challenges in cultural evolution is to understand why and how various forms of social learning are used in human populations, both now and in the past. To date, much of the theoretical work on social learning has been done in isolation of data, and consequently many insights focus on revealing the learning processes or the distributions of cultural variants that are expected to have evolved in human populations. In population genetics, recent methodological advances have allowed a greater understanding of the explicit demographic and/or selection mechanisms that underlie observed allele frequency distributions across the globe, and their change through time. In particular, generative frameworks-often using coalescent-based simulation coupled with approximate Bayesian computation (ABC)-have provided robust inferences on the human past, with no reliance on a priori assumptions of equilibrium. Here, we demonstrate the applicability and utility of generative inference approaches to the field of cultural evolution. The framework advocated here uses observed population-level frequency data directly to establish the likely presence or absence of particular hypothesized learning strategies. In this context, we discuss the problem of equifinality and argue that, in the light of sparse cultural data and the multiplicity of possible social learning processes, the exclusion of those processes inconsistent with the observed data might be the most instructive outcome. Finally, we summarize the findings of generative inference approaches applied to a number of case studies.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'. © 2018 The Author(s).

Signature Schemes Secure against Hard-to-Invert Leakage

DEFF Research Database (Denmark)

Faust, Sebastian; Hazay, Carmit; Nielsen, Jesper Buus

2012-01-01

of the secret key. As a second contribution, we construct a signature scheme that achieves security for random messages assuming that the adversary is given a polynomial-time hard to invert function. Here, polynomial-hardness is required even when given the entire public-key – so called weak auxiliary input......-theoretically reveal the entire secret key. In this work, we propose the first constructions of digital signature schemes that are secure in the auxiliary input model. Our main contribution is a digital signature scheme that is secure against chosen message attacks when given an exponentially hard-to-invert function...... security. We show that such signature schemes readily give us auxiliary input secure identification schemes...

Inferring Domain Plans in Question-Answering

National Research Council Canada - National Science Library

Pollack, Martha E

1986-01-01

The importance of plan inference in models of conversation has been widely noted in the computational-linguistics literature, and its incorporation in question-answering systems has enabled a range...

ONU Power Saving Scheme for EPON System

Science.gov (United States)

Mukai, Hiroaki; Tano, Fumihiko; Tanaka, Masaki; Kozaki, Seiji; Yamanaka, Hideaki

PON (Passive Optical Network) achieves FTTH (Fiber To The Home) economically, by sharing an optical fiber among plural subscribers. Recently, global climate change has been recognized as a serious near term problem. Power saving techniques for electronic devices are important. In PON system, the ONU (Optical Network Unit) power saving scheme has been studied and defined in XG-PON. In this paper, we propose an ONU power saving scheme for EPON. Then, we present an analysis of the power reduction effect and the data transmission delay caused by the ONU power saving scheme. According to the analysis, we propose an efficient provisioning method for the ONU power saving scheme which is applicable to both of XG-PON and EPON.

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

DEFF Research Database (Denmark)

Brouwer, Thomas; Frellsen, Jes; Liò, Pietro

2017-01-01

In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri......-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real...

A survey of Strong Convergent Schemes for the Simulation of ...

African Journals Online (AJOL)

We considered strong convergent stochastic schemes for the simulation of stochastic differential equations. The stochastic Taylor's expansion, which is the main tool used for the derivation of strong convergent schemes; the Euler Maruyama, Milstein scheme, stochastic multistep schemes, Implicit and Explicit schemes were ...

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

Science.gov (United States)

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

Role of Utility and Inference in the Evolution of Functional Information

Science.gov (United States)

Sharov, Alexei A.

2009-01-01

Functional information means an encoded network of functions in living organisms from molecular signaling pathways to an organism’s behavior. It is represented by two components: code and an interpretation system, which together form a self-sustaining semantic closure. Semantic closure allows some freedom between components because small variations of the code are still interpretable. The interpretation system consists of inference rules that control the correspondence between the code and the function (phenotype) and determines the shape of the fitness landscape. The utility factor operates at multiple time scales: short-term selection drives evolution towards higher survival and reproduction rate within a given fitness landscape, and long-term selection favors those fitness landscapes that support adaptability and lead to evolutionary expansion of certain lineages. Inference rules make short-term selection possible by shaping the fitness landscape and defining possible directions of evolution, but they are under control of the long-term selection of lineages. Communication normally occurs within a set of agents with compatible interpretation systems, which I call communication system. Functional information cannot be directly transferred between communication systems with incompatible inference rules. Each biological species is a genetic communication system that carries unique functional information together with inference rules that determine evolutionary directions and constraints. This view of the relation between utility and inference can resolve the conflict between realism/positivism and pragmatism. Realism overemphasizes the role of inference in evolution of human knowledge because it assumes that logic is embedded in reality. Pragmatism substitutes usefulness for truth and therefore ignores the advantage of inference. The proposed concept of evolutionary pragmatism rejects the idea that logic is embedded in reality; instead, inference rules are

l1- and l2-Norm Joint Regularization Based Sparse Signal Reconstruction Scheme

Directory of Open Access Journals (Sweden)

Chanzi Liu

2016-01-01

Full Text Available Many problems in signal processing and statistical inference involve finding sparse solution to some underdetermined linear system of equations. This is also the application condition of compressive sensing (CS which can find the sparse solution from the measurements far less than the original signal. In this paper, we propose l1- and l2-norm joint regularization based reconstruction framework to approach the original l0-norm based sparseness-inducing constrained sparse signal reconstruction problem. Firstly, it is shown that, by employing the simple conjugate gradient algorithm, the new formulation provides an effective framework to deduce the solution as the original sparse signal reconstruction problem with l0-norm regularization item. Secondly, the upper reconstruction error limit is presented for the proposed sparse signal reconstruction framework, and it is unveiled that a smaller reconstruction error than l1-norm relaxation approaches can be realized by using the proposed scheme in most cases. Finally, simulation results are presented to validate the proposed sparse signal reconstruction approach.

A Fuzzy Commitment Scheme with McEliece's Cipher

Directory of Open Access Journals (Sweden)

Deo Brat Ojha

2010-04-01

Full Text Available In this paper an attempt has been made to explain a fuzzy commitment scheme with McEliece scheme. The efficiency and security of this cryptosystem is comparatively better than any other cryptosystem. This scheme is one of the interesting candidates for post quantum cryptography. Hence our interest to deal with this system with fuzzy commitment scheme. The concept itself is illustrated with the help of a simple situation and the validation of mathematical experimental verification is provided.

Feasible Teleportation Schemes with Five-Atom Entangled State

Institute of Scientific and Technical Information of China (English)

XUE Zheng-Yuan; YI You-Min; CAO Zhuo-Liang

2006-01-01

Teleportation schemes with a five-atom entangled state are investigated. In the teleportation scheme Bell state measurements (BSMs) are difficult for physical realization, so we investigate another strategy using separate measurements instead of BSM based on cavity quantum electrodynamics techniques. The scheme of two-atom entangled state teleportation is a controlled and probabilistic one. For the teleportation of the three-atom entangled state, the scheme is a probabilistic one. The fidelity and the probability of the successful teleportation are also obtained.

Inference for shared-frailty survival models with left-truncated data

NARCIS (Netherlands)

van den Berg, G.J.; Drepper, B.

2016-01-01

Shared-frailty survival models specify that systematic unobserved determinants of duration outcomes are identical within groups of individuals. We consider random-effects likelihood-based statistical inference if the duration data are subject to left-truncation. Such inference with left-truncated

Homogenization scheme for acoustic metamaterials

KAUST Repository

Yang, Min

2014-02-26

We present a homogenization scheme for acoustic metamaterials that is based on reproducing the lowest orders of scattering amplitudes from a finite volume of metamaterials. This approach is noted to differ significantly from that of coherent potential approximation, which is based on adjusting the effective-medium parameters to minimize scatterings in the long-wavelength limit. With the aid of metamaterials’ eigenstates, the effective parameters, such as mass density and elastic modulus can be obtained by matching the surface responses of a metamaterial\\'s structural unit cell with a piece of homogenized material. From the Green\\'s theorem applied to the exterior domain problem, matching the surface responses is noted to be the same as reproducing the scattering amplitudes. We verify our scheme by applying it to three different examples: a layered lattice, a two-dimensional hexagonal lattice, and a decorated-membrane system. It is shown that the predicted characteristics and wave fields agree almost exactly with numerical simulations and experiments and the scheme\\'s validity is constrained by the number of dominant surface multipoles instead of the usual long-wavelength assumption. In particular, the validity extends to the full band in one dimension and to regimes near the boundaries of the Brillouin zone in two dimensions.

A hybrid pi control scheme for airship hovering

International Nuclear Information System (INIS)

Ashraf, Z.; Choudhry, M.A.; Hanif, A.

2012-01-01

Airship provides us many attractive applications in aerospace industry including transportation of heavy payloads, tourism, emergency management, communication, hover and vision based applications. Hovering control of airship has many utilizations in different engineering fields. However, it is a difficult problem to sustain the hover condition maintaining controllability. So far, different solutions have been proposed in literature but most of them are difficult in analysis and implementation. In this paper, we have presented a simple and efficient scheme to design a multi input multi output hybrid PI control scheme for airship. It can maintain stability of the plant by rejecting disturbance inputs to ensure robustness. A control scheme based on feedback theory is proposed that uses principles of optimality with integral action for hovering applications. Simulations are carried out in MTALAB for examining the proposed control scheme for hovering in different wind conditions. Comparison of the technique with an existing scheme is performed, describing the effectiveness of control scheme. (author)

Privacy Preserving Mapping Schemes Supporting Comparison

NARCIS (Netherlands)

Tang, Qiang

2010-01-01

To cater to the privacy requirements in cloud computing, we introduce a new primitive, namely Privacy Preserving Mapping (PPM) schemes supporting comparison. An PPM scheme enables a user to map data items into images in such a way that, with a set of images, any entity can determine the <, =, >

Consolidation of the health insurance scheme

CERN Document Server

Association du personnel

2009-01-01

In the last issue of Echo, we highlighted CERN’s obligation to guarantee a social security scheme for all employees, pensioners and their families. In that issue we talked about the first component: pensions. This time we shall discuss the other component: the CERN Health Insurance Scheme (CHIS).

Progranulin gene variation affects serum progranulin levels differently in Danish bipolar individuals compared with healthy controls.

Science.gov (United States)

Buttenschøn, Henriette N; Nielsen, Marit N; Thotakura, Gangadaar; Lee, Chris W; Nykjær, Anders; Mors, Ole; Glerup, Simon

2017-06-01

The identification of peripheral biomarkers for bipolar disorder is of great importance and has the potential to improve diagnosis, treatment and prognosis. Recent studies have reported lower plasma progranulin levels in bipolar individuals compared with controls and association with single nucleotide polymorphisms (SNPs) within the progranulin gene (GRN). In the present study, we investigated the effect of GRN and sortilin (SORT1) gene variation on serum progranulin levels in bipolar individuals and controls. In a Danish cohort of individuals with bipolar disorder and controls, we analysed the serum progranulin level (nbipolar=80, ncontrols=76) and five SNPs located within GRN and two SNPs near the SORT1 gene encoding sortilin, a progranulin scavenger receptor known to affect circulating progranulin levels (nbipolar=166, ncontrols=186). We observed no significant difference in the serum progranulin level between cases and controls and none of the analysed SNPs located within GRN or close to SORT1 were associated with bipolar disorder. Crude and adjusted (adjusted for case-control status, sex and age) linear regression analyses showed no effect of any SNPs on the serum progranulin level. However, we observed that the mean serum progranulin level in cases and controls is affected differently depending on the genotypes of two SNPs within GRN (rs2879096 and rs4792938). The sample size is relatively small and detailed information on medication and polarity of the disorder is not available. No correction for multiple testing was performed. Our study suggests that the potential of progranulin as a biomarker for bipolar disorder is genotype dependent.

A numerical scheme for the generalized Burgers–Huxley equation

Directory of Open Access Journals (Sweden)

Brajesh K. Singh

2016-10-01

Full Text Available In this article, a numerical solution of generalized Burgers–Huxley (gBH equation is approximated by using a new scheme: modified cubic B-spline differential quadrature method (MCB-DQM. The scheme is based on differential quadrature method in which the weighting coefficients are obtained by using modified cubic B-splines as a set of basis functions. This scheme reduces the equation into a system of first-order ordinary differential equation (ODE which is solved by adopting SSP-RK43 scheme. Further, it is shown that the proposed scheme is stable. The efficiency of the proposed method is illustrated by four numerical experiments, which confirm that obtained results are in good agreement with earlier studies. This scheme is an easy, economical and efficient technique for finding numerical solutions for various kinds of (nonlinear physical models as compared to the earlier schemes.

Study of Streptococcus thermophilus population on a world-wide and historical collection by a new MLST scheme.

Science.gov (United States)

Delorme, Christine; Legravet, Nicolas; Jamet, Emmanuel; Hoarau, Caroline; Alexandre, Bolotin; El-Sharoud, Walid M; Darwish, Mohamed S; Renault, Pierre

2017-02-02

We analyzed 178 Streptococcus thermophilus strains isolated from diverse products, from around the world, over a 60-year period with a new multilocus sequence typing (MLST) scheme. This collection included isolates from two traditional cheese-making sites with different starter-use practices, in sampling campaigns carried out over a three years period. The nucleotide diversity of the S. thermophilus population was limited, but 116 sequence types (ST) were identified. Phylogenetic analysis of the concatenated sequences of the six housekeeping genes revealed the existence of groups confirmed by eBURST analysis. Deeper analyses performed on 25 strains by CRISPR and whole-genome analysis showed that phylogenies obtained by MLST and whole-genome analysis were in agreement but differed from that inferred by CRISPR analysis. Strains isolated from traditional products could cluster in specific groups indicating their origin, but also be mixed in groups containing industrial starter strains. In the traditional cheese-making sites, we found that S. thermophilus persisted on dairy equipment, but that occasionally added starter strains may become dominant. It underlined the impact of starter use that may reshape S. thermophilus populations including in traditional products. This new MLST scheme thus provides a framework for analyses of S. thermophilus populations and the management of its biodiversity. Copyright © 2016 Elsevier B.V. All rights reserved.

Parametric inference for biological sequence analysis.

Science.gov (United States)

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Inference of neuronal network spike dynamics and topology from calcium imaging data

Directory of Open Access Journals (Sweden)

Henry eLütcke

2013-12-01

Full Text Available Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP occurrence ('spike trains' from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.

Cortical hierarchies perform Bayesian causal inference in multisensory perception.