Binary neutron star merger rate via the luminosity function of short gamma-ray bursts

NASA Astrophysics Data System (ADS)

Paul, Debdutta

2018-04-01

The luminosity function of short Gamma Ray Bursts (GRBs) is modelled by using the available catalogue data of all short GRBs (sGRBs) detected till October, 2017. The luminosities are estimated via the `pseudo-redshifts' obtained from the `Yonetoku correlation', assuming a standard delay distribution between the cosmic star formation rate and the production rate of their progenitors. While the simple powerlaw is ruled out to high confidence, the data is fit well both by exponential cutoff powerlaw and broken powerlaw models. Using the derived parameters of these models along with conservative values in the jet opening angles seen from afterglow observations, the true rate of short GRBs are derived. Assuming a short GRB is produced from each binary neutron star merger (BNSM), the rate of gravitational wave (GW) detections from these mergers are derived for the past, present and future configurations of the GW detector networks. Stringent lower limits of 1.87yr-1 for the aLIGO-VIRGO, and 3.11yr-1 for the upcoming aLIGO-VIRGO-KAGRA-LIGO/India configurations are thus derived for the BNSM rate at 68% confidence. The BNSM rates calculated from this work and that independently inferred from the observation of the only confirmed BNSM observed till date, are shown to have a mild tension; however the scenario that all BNSMs produce sGRBs cannot be ruled out.

Binary neutron star merger rate via the luminosity function of short gamma-ray bursts

NASA Astrophysics Data System (ADS)

Paul, Debdutta

2018-07-01

The luminosity function of short gamma ray bursts (GRBs) is modelled by using the available catalogue data of all short GRBs (sGRBs) detected till 2017 October. The luminosities are estimated via the `pseudo-redshifts' obtained from the `Yonetoku correlation', assuming a standard delay distribution between the cosmic star formation rate and the production rate of their progenitors. While the simple power law is ruled out to high confidence, the data is fit well both by exponential cutoff power law and broken power law models. Using the derived parameters of these models along with conservative values in the jet opening angles seen from afterglow observations, the true rate of sGRBs is derived. Assuming a sGRB is produced from each binary neutron star merger (BNSM), the rate of gravitational wave (GW) detections from these mergers are derived for the past, present, and future configurations of the GW detector networks. Stringent lower limits of 1.87 { yr^{-1}} for the aLIGO-VIRGO, and 3.11 { yr^{-1}} for the upcoming aLIGO-VIRGO-KAGRA-LIGO/India configurations are thus derived for the BNSM rate at 68 per cent confidence. The BNSM rates calculated from this work and that independently inferred from the observation of the only confirmed BNSM observed till date are shown to have a mild tension; however, the scenario that all BNSMs produce sGRBs cannot be ruled out.

Common-Sense Rule Inference

NASA Astrophysics Data System (ADS)

Lombardi, Ilaria; Console, Luca

In the paper we show how rule-based inference can be made more flexible by exploiting semantic information associated with the concepts involved in the rules. We introduce flexible forms of common sense reasoning in which whenever no rule applies to a given situation, the inference engine can fire rules that apply to more general or to similar situations. This can be obtained by defining new forms of match between rules and the facts in the working memory and new forms of conflict resolution. We claim that in this way we can overcome some of the brittleness problems that are common in rule-based systems.

Inferences about Supernova Physics from Gravitational-Wave Measurements: GW151226 Spin Misalignment as an Indicator of Strong Black-Hole Natal Kicks

NASA Astrophysics Data System (ADS)

O'Shaughnessy, Richard; Gerosa, Davide; Wysocki, Daniel

2017-07-01

The inferred parameters of the binary black hole GW151226 are consistent with nonzero spin for the most massive black hole, misaligned from the binary's orbital angular momentum. If the black holes formed through isolated binary evolution from an initially aligned binary star, this misalignment would then arise from a natal kick imparted to the first-born black hole at its birth during stellar collapse. We use simple kinematic arguments to constrain the characteristic magnitude of this kick, and find that a natal kick vk≳50 km /s must be imparted to the black hole at birth to produce misalignments consistent with GW151226. Such large natal kicks exceed those adopted by default in most of the current supernova and binary evolution models.

Inferences about Supernova Physics from Gravitational-Wave Measurements: GW151226 Spin Misalignment as an Indicator of Strong Black-Hole Natal Kicks.

PubMed

O'Shaughnessy, Richard; Gerosa, Davide; Wysocki, Daniel

2017-07-07

The inferred parameters of the binary black hole GW151226 are consistent with nonzero spin for the most massive black hole, misaligned from the binary's orbital angular momentum. If the black holes formed through isolated binary evolution from an initially aligned binary star, this misalignment would then arise from a natal kick imparted to the first-born black hole at its birth during stellar collapse. We use simple kinematic arguments to constrain the characteristic magnitude of this kick, and find that a natal kick v_{k}≳50  km/s must be imparted to the black hole at birth to produce misalignments consistent with GW151226. Such large natal kicks exceed those adopted by default in most of the current supernova and binary evolution models.

The role of familiarity in binary choice inferences.

PubMed

Honda, Hidehito; Abe, Keiga; Matsuka, Toshihiko; Yamagishi, Kimihiko

2011-07-01

In research on the recognition heuristic (Goldstein & Gigerenzer, Psychological Review, 109, 75-90, 2002), knowledge of recognized objects has been categorized as "recognized" or "unrecognized" without regard to the degree of familiarity of the recognized object. In the present article, we propose a new inference model--familiarity-based inference. We hypothesize that when subjective knowledge levels (familiarity) of recognized objects differ, the degree of familiarity of recognized objects will influence inferences. Specifically, people are predicted to infer that the more familiar object in a pair of two objects has a higher criterion value on the to-be-judged dimension. In two experiments, using a binary choice task, we examined inferences about populations in a pair of two cities. Results support predictions of familiarity-based inference. Participants inferred that the more familiar city in a pair was more populous. Statistical modeling showed that individual differences in familiarity-based inference lie in the sensitivity to differences in familiarity. In addition, we found that familiarity-based inference can be generally regarded as an ecologically rational inference. Furthermore, when cue knowledge about the inference criterion was available, participants made inferences based on the cue knowledge about population instead of familiarity. Implications of the role of familiarity in psychological processes are discussed.

Bayesian probability estimates are not necessary to make choices satisfying Bayes' rule in elementary situations.

PubMed

Domurat, Artur; Kowalczuk, Olga; Idzikowska, Katarzyna; Borzymowska, Zuzanna; Nowak-Przygodzka, Marta

2015-01-01

This paper has two aims. First, we investigate how often people make choices conforming to Bayes' rule when natural sampling is applied. Second, we show that using Bayes' rule is not necessary to make choices satisfying Bayes' rule. Simpler methods, even fallacious heuristics, might prescribe correct choices reasonably often under specific circumstances. We considered elementary situations with binary sets of hypotheses and data. We adopted an ecological approach and prepared two-stage computer tasks resembling natural sampling. Probabilistic relations were inferred from a set of pictures, followed by a choice which was made to maximize the chance of a preferred outcome. Use of Bayes' rule was deduced indirectly from choices. Study 1 used a stratified sample of N = 60 participants equally distributed with regard to gender and type of education (humanities vs. pure sciences). Choices satisfying Bayes' rule were dominant. To investigate ways of making choices more directly, we replicated Study 1, adding a task with a verbal report. In Study 2 (N = 76) choices conforming to Bayes' rule dominated again. However, the verbal reports revealed use of a new, non-inverse rule, which always renders correct choices, but is easier than Bayes' rule to apply. It does not require inversion of conditions [transforming P(H) and P(D|H) into P(H|D)] when computing chances. Study 3 examined the efficiency of three fallacious heuristics (pre-Bayesian, representativeness, and evidence-only) in producing choices concordant with Bayes' rule. Computer-simulated scenarios revealed that the heuristics produced correct choices reasonably often under specific base rates and likelihood ratios. Summing up we conclude that natural sampling results in most choices conforming to Bayes' rule. However, people tend to replace Bayes' rule with simpler methods, and even use of fallacious heuristics may be satisfactorily efficient.

Bayesian probability estimates are not necessary to make choices satisfying Bayes’ rule in elementary situations

PubMed Central

Domurat, Artur; Kowalczuk, Olga; Idzikowska, Katarzyna; Borzymowska, Zuzanna; Nowak-Przygodzka, Marta

2015-01-01

This paper has two aims. First, we investigate how often people make choices conforming to Bayes’ rule when natural sampling is applied. Second, we show that using Bayes’ rule is not necessary to make choices satisfying Bayes’ rule. Simpler methods, even fallacious heuristics, might prescribe correct choices reasonably often under specific circumstances. We considered elementary situations with binary sets of hypotheses and data. We adopted an ecological approach and prepared two-stage computer tasks resembling natural sampling. Probabilistic relations were inferred from a set of pictures, followed by a choice which was made to maximize the chance of a preferred outcome. Use of Bayes’ rule was deduced indirectly from choices. Study 1 used a stratified sample of N = 60 participants equally distributed with regard to gender and type of education (humanities vs. pure sciences). Choices satisfying Bayes’ rule were dominant. To investigate ways of making choices more directly, we replicated Study 1, adding a task with a verbal report. In Study 2 (N = 76) choices conforming to Bayes’ rule dominated again. However, the verbal reports revealed use of a new, non-inverse rule, which always renders correct choices, but is easier than Bayes’ rule to apply. It does not require inversion of conditions [transforming P(H) and P(D|H) into P(H|D)] when computing chances. Study 3 examined the efficiency of three fallacious heuristics (pre-Bayesian, representativeness, and evidence-only) in producing choices concordant with Bayes’ rule. Computer-simulated scenarios revealed that the heuristics produced correct choices reasonably often under specific base rates and likelihood ratios. Summing up we conclude that natural sampling results in most choices conforming to Bayes’ rule. However, people tend to replace Bayes’ rule with simpler methods, and even use of fallacious heuristics may be satisfactorily efficient. PMID:26347676

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

NASA Technical Reports Server (NTRS)

Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

1998-01-01

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Modelling Chemical Reasoning to Predict and Invent Reactions.

PubMed

Segler, Marwin H S; Waller, Mark P

2017-05-02

The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bayesian inference for joint modelling of longitudinal continuous, binary and ordinal events.

PubMed

Li, Qiuju; Pan, Jianxin; Belcher, John

2016-12-01

In medical studies, repeated measurements of continuous, binary and ordinal outcomes are routinely collected from the same patient. Instead of modelling each outcome separately, in this study we propose to jointly model the trivariate longitudinal responses, so as to take account of the inherent association between the different outcomes and thus improve statistical inferences. This work is motivated by a large cohort study in the North West of England, involving trivariate responses from each patient: Body Mass Index, Depression (Yes/No) ascertained with cut-off score not less than 8 at the Hospital Anxiety and Depression Scale, and Pain Interference generated from the Medical Outcomes Study 36-item short-form health survey with values returned on an ordinal scale 1-5. There are some well-established methods for combined continuous and binary, or even continuous and ordinal responses, but little work was done on the joint analysis of continuous, binary and ordinal responses. We propose conditional joint random-effects models, which take into account the inherent association between the continuous, binary and ordinal outcomes. Bayesian analysis methods are used to make statistical inferences. Simulation studies show that, by jointly modelling the trivariate outcomes, standard deviations of the estimates of parameters in the models are smaller and much more stable, leading to more efficient parameter estimates and reliable statistical inferences. In the real data analysis, the proposed joint analysis yields a much smaller deviance information criterion value than the separate analysis, and shows other good statistical properties too. © The Author(s) 2014.

Algorithm Optimally Orders Forward-Chaining Inference Rules

NASA Technical Reports Server (NTRS)

James, Mark

2008-01-01

People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.

PubMed

Wright, Adam; Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

How to select combination operators for fuzzy expert systems using CRI

NASA Technical Reports Server (NTRS)

Turksen, I. B.; Tian, Y.

1992-01-01

A method to select combination operators for fuzzy expert systems using the Compositional Rule of Inference (CRI) is proposed. First, fuzzy inference processes based on CRI are classified into three categories in terms of their inference results: the Expansion Type Inference, the Reduction Type Inference, and Other Type Inferences. Further, implication operators under Sup-T composition are classified as the Expansion Type Operator, the Reduction Type Operator, and the Other Type Operators. Finally, the combination of rules or their consequences is investigated for inference processes based on CRI.

On the inherent competition between valid and spurious inductive inferences in Boolean data

NASA Astrophysics Data System (ADS)

Andrecut, M.

Inductive inference is the process of extracting general rules from specific observations. This problem also arises in the analysis of biological networks, such as genetic regulatory networks, where the interactions are complex and the observations are incomplete. A typical task in these problems is to extract general interaction rules as combinations of Boolean covariates, that explain a measured response variable. The inductive inference process can be considered as an incompletely specified Boolean function synthesis problem. This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules. Using random Boolean data as a null model, here we attempt to measure the competition between valid and spurious inductive inference rules from a given data set. We formulate two greedy search algorithms, which synthesize a given Boolean response variable in a sparse disjunct normal form, and respectively a sparse generalized algebraic normal form of the variables from the observation data, and we evaluate numerically their performance.

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record

PubMed Central

Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Background Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. Objective To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. Study design and methods We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100 000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100 000 records to assess its accuracy. Results Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100 000 randomly selected patients showed high sensitivity (range: 62.8–100.0%) and positive predictive value (range: 79.8–99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. Conclusion We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts. PMID:21613643

Natural language processing and inference rules as strategies for updating problem list in an electronic health record.

PubMed

Plazzotta, Fernando; Otero, Carlos; Luna, Daniel; de Quiros, Fernan Gonzalez Bernaldo

2013-01-01

Physicians do not always keep the problem list accurate, complete and updated. To analyze natural language processing (NLP) techniques and inference rules as strategies to maintain completeness and accuracy of the problem list in EHRs. Non systematic literature review in PubMed, in the last 10 years. Strategies to maintain the EHRs problem list were analyzed in two ways: inputting and removing problems from the problem list. NLP and inference rules have acceptable performance for inputting problems into the problem list. No studies using these techniques for removing problems were published Conclusion: Both tools, NLP and inference rules have had acceptable results as tools for maintain the completeness and accuracy of the problem list.

Inferred Eccentricity and Period Distributions of Kepler Eclipsing Binaries

NASA Astrophysics Data System (ADS)

Prsa, Andrej; Matijevic, G.

2014-01-01

Determining the underlying eccentricity and orbital period distributions from an observed sample of eclipsing binary stars is not a trivial task. Shen and Turner (2008) have shown that the commonly used maximum likelihood estimators are biased to larger eccentricities and they do not describe the underlying distribution correctly; orbital periods suffer from a similar bias. Hogg, Myers and Bovy (2010) proposed a hierarchical probabilistic method for inferring the true eccentricity distribution of exoplanet orbits that uses the likelihood functions for individual star eccentricities. The authors show that proper inference outperforms the simple histogramming of the best-fit eccentricity values. We apply this method to the complete sample of eclipsing binary stars observed by the Kepler mission (Prsa et al. 2011) to derive the unbiased underlying eccentricity and orbital period distributions. These distributions can be used for the studies of multiple star formation, dynamical evolution, and they can serve as a drop-in replacement to prior, ad-hoc distributions used in the exoplanet field for determining false positive occurrence rates.

Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation

NASA Astrophysics Data System (ADS)

Arotaritei, D.; Rotariu, C.

2015-09-01

In this paper we present a novel method to develop an atrial fibrillation (AF) based on statistical descriptors and hybrid neuro-fuzzy and crisp system. The inference of system produce rules of type if-then-else that care extracted to construct a binary decision system: normal of atrial fibrillation. We use TPR (Turning Point Ratio), SE (Shannon Entropy) and RMSSD (Root Mean Square of Successive Differences) along with a new descriptor, Teager- Kaiser energy, in order to improve the accuracy of detection. The descriptors are calculated over a sliding window that produce very large number of vectors (massive dataset) used by classifier. The length of window is a crisp descriptor meanwhile the rest of descriptors are interval-valued type. The parameters of hybrid system are adapted using Genetic Algorithm (GA) algorithm with fitness single objective target: highest values for sensibility and sensitivity. The rules are extracted and they are part of the decision system. The proposed method was tested using the Physionet MIT-BIH Atrial Fibrillation Database and the experimental results revealed a good accuracy of AF detection in terms of sensitivity and specificity (above 90%).

The Multi-Intelligence Tools Suite - Supporting Research and Development in Information and Knowledge Exploitation

DTIC Science & Technology

2011-06-01

to build a membership fact. The atom definition also defines the precise order of the pieces. Each argument has a label (D) and a type ( E ). The...list of ato argument). Figure 2 shows the inference rule editor. B. Name E . Rule Premises F. Rule Conclusions Figure 2. Inference rule editor One...created using this specific rule. one premise in the rule premises list ( E ), which represents a list of fact conditions that need to be found in the fact

Spectroscopic obit for the eclipsing binary IQ Persei

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

Young, A.

1975-10-01

Spectroscopic orbital elements are derived for the eclipsing binary IQ Per. Faint secondary lines are detected, and a mass ratio and individual masses are inferred. The components are found to be on the main sequence, and the system is detached. (auth)

Constraints on the Dynamical Environments of Supermassive Black-Hole Binaries Using Pulsar-Timing Arrays.

PubMed

Taylor, Stephen R; Simon, Joseph; Sampson, Laura

2017-05-05

We introduce a technique for gravitational-wave analysis, where Gaussian process regression is used to emulate the strain spectrum of a stochastic background by training on population-synthesis simulations. This leads to direct Bayesian inference on astrophysical parameters. For pulsar timing arrays specifically, we interpolate over the parameter space of supermassive black-hole binary environments, including three-body stellar scattering, and evolving orbital eccentricity. We illustrate our approach on mock data, and assess the prospects for inference with data similar to the NANOGrav 9-yr data release.

Role of Utility and Inference in the Evolution of Functional Information

PubMed Central

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 constructed within each communication system to represent reality and they evolve towards higher adaptability on a long time scale. PMID:20160960

Semi-empirical correlation for binary interaction parameters of the Peng-Robinson equation of state with the van der Waals mixing rules for the prediction of high-pressure vapor-liquid equilibrium.

PubMed

Fateen, Seif-Eddeen K; Khalil, Menna M; Elnabawy, Ahmed O

2013-03-01

Peng-Robinson equation of state is widely used with the classical van der Waals mixing rules to predict vapor liquid equilibria for systems containing hydrocarbons and related compounds. This model requires good values of the binary interaction parameter kij . In this work, we developed a semi-empirical correlation for kij partly based on the Huron-Vidal mixing rules. We obtained values for the adjustable parameters of the developed formula for over 60 binary systems and over 10 categories of components. The predictions of the new equation system were slightly better than the constant-kij model in most cases, except for 10 systems whose predictions were considerably improved with the new correlation.

Preschoolers can infer general rules governing fantastical events in fiction.

PubMed

Van de Vondervoort, Julia W; Friedman, Ori

2014-05-01

Young children are frequently exposed to fantastic fiction. How do they make sense of the unrealistic and impossible events that occur in such fiction? Although children could view such events as isolated episodes, the present experiments suggest that children use such events to infer general fantasy rules. In 2 experiments, 2- to 4-year-olds were shown scenarios in which 2 animals behaved unrealistically (N = 78 in Experiment 1, N = 94 in Experiment 2). When asked to predict how other animals in the fiction would behave, children predicted novel behaviors consistent with the nature of the fiction. These findings suggest that preschoolers can infer the general rules that govern the events and entities in fantastic fiction and can use these rules to predict what events will happen in the fiction. The findings also provide evidence that children may infer fantasy rules at a more superordinate level than the basic level. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Targeted numerical simulations of binary black holes for GW170104

NASA Astrophysics Data System (ADS)

Healy, J.; Lange, J.; O'Shaughnessy, R.; Lousto, C. O.; Campanelli, M.; Williamson, A. R.; Zlochower, Y.; Calderón Bustillo, J.; Clark, J. A.; Evans, C.; Ferguson, D.; Ghonge, S.; Jani, K.; Khamesra, B.; Laguna, P.; Shoemaker, D. M.; Boyle, M.; García, A.; Hemberger, D. A.; Kidder, L. E.; Kumar, P.; Lovelace, G.; Pfeiffer, H. P.; Scheel, M. A.; Teukolsky, S. A.

2018-03-01

In response to LIGO's observation of GW170104, we performed a series of full numerical simulations of binary black holes, each designed to replicate likely realizations of its dynamics and radiation. These simulations have been performed at multiple resolutions and with two independent techniques to solve Einstein's equations. For the nonprecessing and precessing simulations, we demonstrate the two techniques agree mode by mode, at a precision substantially in excess of statistical uncertainties in current LIGO's observations. Conversely, we demonstrate our full numerical solutions contain information which is not accurately captured with the approximate phenomenological models commonly used to infer compact binary parameters. To quantify the impact of these differences on parameter inference for GW170104 specifically, we compare the predictions of our simulations and these approximate models to LIGO's observations of GW170104.

Improving Predictions of Multiple Binary Models in ILP

PubMed Central

2014-01-01

Despite the success of ILP systems in learning first-order rules from small number of examples and complexly structured data in various domains, they struggle in dealing with multiclass problems. In most cases they boil down a multiclass problem into multiple black-box binary problems following the one-versus-one or one-versus-rest binarisation techniques and learn a theory for each one. When evaluating the learned theories of multiple class problems in one-versus-rest paradigm particularly, there is a bias caused by the default rule toward the negative classes leading to an unrealistic high performance beside the lack of prediction integrity between the theories. Here we discuss the problem of using one-versus-rest binarisation technique when it comes to evaluating multiclass data and propose several methods to remedy this problem. We also illustrate the methods and highlight their link to binary tree and Formal Concept Analysis (FCA). Our methods allow learning of a simple, consistent, and reliable multiclass theory by combining the rules of the multiple one-versus-rest theories into one rule list or rule set theory. Empirical evaluation over a number of data sets shows that our proposed methods produce coherent and accurate rule models from the rules learned by the ILP system of Aleph. PMID:24696657

Structural difference rule for amorphous alloy formation by ion mixing

NASA Technical Reports Server (NTRS)

Liu, B.-X.; Johnson, W. L.; Nicolet, M.A.; Lau, S. S.

1983-01-01

A rule is formulated which establishes a sufficient condition that an amorphous binary alloy will be formed by ion mixing of multilayered samples when the two constituent metals are of different crystalline structure, regardless of their atomic sizes and electronegativities. The rule is supported by the experimental results obtained on six selected binary metal systems, as well as by the previous data reported in the literature. The amorphization mechanism is discussed in terms of the competition between two different structures resulting in frustration of the crystallization process.

BINARY ASTROMETRIC MICROLENSING WITH GAIA

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

Sajadian, Sedighe, E-mail: sajadian@ipm.ir; Department of Physics, Sharif University of Technology, P.O. Box 11155-9161, Tehran

2015-04-15

We investigate whether or not Gaia can specify the binary fractions of massive stellar populations in the Galactic disk through astrometric microlensing. Furthermore, we study whether or not some information about their mass distributions can be inferred via this method. In this regard, we simulate the binary astrometric microlensing events due to massive stellar populations according to the Gaia observing strategy by considering (i) stellar-mass black holes, (ii) neutron stars, (iii) white dwarfs, and (iv) main-sequence stars as microlenses. The Gaia efficiency for detecting the binary signatures in binary astrometric microlensing events is ∼10%–20%. By calculating the optical depth duemore » to the mentioned stellar populations, the numbers of the binary astrometric microlensing events being observed with Gaia with detectable binary signatures, for the binary fraction of about 0.1, are estimated to be 6, 11, 77, and 1316, respectively. Consequently, Gaia can potentially specify the binary fractions of these massive stellar populations. However, the binary fraction of black holes measured with this method has a large uncertainty owing to a low number of the estimated events. Knowing the binary fractions in massive stellar populations helps with studying the gravitational waves. Moreover, we investigate the number of massive microlenses for which Gaia specifies masses through astrometric microlensing of single lenses toward the Galactic bulge. The resulting efficiencies of measuring the mass of mentioned populations are 9.8%, 2.9%, 1.2%, and 0.8%, respectively. The numbers of their astrometric microlensing events being observed in the Gaia era in which the lens mass can be inferred with the relative error less than 0.5 toward the Galactic bulge are estimated as 45, 34, 76, and 786, respectively. Hence, Gaia potentially gives us some information about the mass distribution of these massive stellar populations.« less

On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies.

PubMed

Kashyap, Vipul; Morales, Alfredo; Hongsermeier, Tonya

2006-01-01

We present an approach and architecture for implementing scalable and maintainable clinical decision support at the Partners HealthCare System. The architecture integrates a business rules engine that executes declarative if-then rules stored in a rule-base referencing objects and methods in a business object model. The rules engine executes object methods by invoking services implemented on the clinical data repository. Specialized inferences that support classification of data and instances into classes are identified and an approach to implement these inferences using an OWL based ontology engine is presented. Alternative representations of these specialized inferences as if-then rules or OWL axioms are explored and their impact on the scalability and maintenance of the system is presented. Architectural alternatives for integration of clinical decision support functionality with the invoking application and the underlying clinical data repository; and their associated trade-offs are discussed and presented.

Ultraviolet observations of alpha Aurigae from Copernicus

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

Dupree, A. K.

1975-08-01

Emission lines of L$alpha$ (1215.67 A) and O vi (1031.94 A) were detected in the spectroscopic binary $alpha$ Aur (Capella) with the Princeton experiment on Copernicus. Temperatures of the emitting regions are inferred to be in excess of 3times10$sup 5$ K. The temperature and emission measure are consistent with atmosphere is expanding with velocities approx.20 to 100 km s$sup -1$. Such expansion can lead to material within the binary system. The density of interstellar hydrogen inferred from absorption of stellar L$alpha$ appears to be approx.0.01 hydrogen atoms cm$sup -3$.

Hierarchy-associated semantic-rule inference framework for classifying indoor scenes

NASA Astrophysics Data System (ADS)

Yu, Dan; Liu, Peng; Ye, Zhipeng; Tang, Xianglong; Zhao, Wei

2016-03-01

Typically, the initial task of classifying indoor scenes is challenging, because the spatial layout and decoration of a scene can vary considerably. Recent efforts at classifying object relationships commonly depend on the results of scene annotation and predefined rules, making classification inflexible. Furthermore, annotation results are easily affected by external factors. Inspired by human cognition, a scene-classification framework was proposed using the empirically based annotation (EBA) and a match-over rule-based (MRB) inference system. The semantic hierarchy of images is exploited by EBA to construct rules empirically for MRB classification. The problem of scene classification is divided into low-level annotation and high-level inference from a macro perspective. Low-level annotation involves detecting the semantic hierarchy and annotating the scene with a deformable-parts model and a bag-of-visual-words model. In high-level inference, hierarchical rules are extracted to train the decision tree for classification. The categories of testing samples are generated from the parts to the whole. Compared with traditional classification strategies, the proposed semantic hierarchy and corresponding rules reduce the effect of a variable background and improve the classification performance. The proposed framework was evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness.

Bayesian performance metrics of binary sensors in homeland security applications

NASA Astrophysics Data System (ADS)

Jannson, Tomasz P.; Forrester, Thomas C.

2008-04-01

Bayesian performance metrics, based on such parameters, as: prior probability, probability of detection (or, accuracy), false alarm rate, and positive predictive value, characterizes the performance of binary sensors; i.e., sensors that have only binary response: true target/false target. Such binary sensors, very common in Homeland Security, produce an alarm that can be true, or false. They include: X-ray airport inspection, IED inspections, product quality control, cancer medical diagnosis, part of ATR, and many others. In this paper, we analyze direct and inverse conditional probabilities in the context of Bayesian inference and binary sensors, using X-ray luggage inspection statistical results as a guideline.

Self-organization in a system of binary strings with spatial interactions

NASA Astrophysics Data System (ADS)

Banzhaf, W.; Dittrich, P.; Eller, B.

1999-01-01

We consider an artificial reaction system whose components are binary strings. Upon encounter, two binary strings produce a third string which competes for storage space with the originators. String types or species can only survive when produced in sufficient numbers. Spatial interactions through introduction of a topology and rules for distance-dependent reactions are discussed. We observe various kinds of survival strategies of binary strings.

Bayesian inference for unidirectional misclassification of a binary response trait.

PubMed

Xia, Michelle; Gustafson, Paul

2018-03-15

When assessing association between a binary trait and some covariates, the binary response may be subject to unidirectional misclassification. Unidirectional misclassification can occur when revealing a particular level of the trait is associated with a type of cost, such as a social desirability or financial cost. The feasibility of addressing misclassification is commonly obscured by model identification issues. The current paper attempts to study the efficacy of inference when the binary response variable is subject to unidirectional misclassification. From a theoretical perspective, we demonstrate that the key model parameters possess identifiability, except for the case with a single binary covariate. From a practical standpoint, the logistic model with quantitative covariates can be weakly identified, in the sense that the Fisher information matrix may be near singular. This can make learning some parameters difficult under certain parameter settings, even with quite large samples. In other cases, the stronger identification enables the model to provide more effective adjustment for unidirectional misclassification. An extension to the Poisson approximation of the binomial model reveals the identifiability of the Poisson and zero-inflated Poisson models. For fully identified models, the proposed method adjusts for misclassification based on learning from data. For binary models where there is difficulty in identification, the method is useful for sensitivity analyses on the potential impact from unidirectional misclassification. Copyright © 2017 John Wiley & Sons, Ltd.

Java implementation of Class Association Rule algorithms

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

Tamura, Makio

2007-08-30

Java implementation of three Class Association Rule mining algorithms, NETCAR, CARapriori, and clustering based rule mining. NETCAR algorithm is a novel algorithm developed by Makio Tamura. The algorithm is discussed in a paper: UCRL-JRNL-232466-DRAFT, and would be published in a peer review scientific journal. The software is used to extract combinations of genes relevant with a phenotype from a phylogenetic profile and a phenotype profile. The phylogenetic profiles is represented by a binary matrix and a phenotype profile is represented by a binary vector. The present application of this software will be in genome analysis, however, it could be appliedmore » more generally.« less

Theory of mind broad and narrow: reasoning about social exchange engages ToM areas, precautionary reasoning does not.

PubMed

Ermer, Elsa; Guerin, Scott A; Cosmides, Leda; Tooby, John; Miller, Michael B

2006-01-01

Baron-Cohen (1995) proposed that the theory of mind (ToM) inference system evolved to promote strategic social interaction. Social exchange--a form of co-operation for mutual benefit--involves strategic social interaction and requires ToM inferences about the contents of other individuals' mental states, especially their desires, goals, and intentions. There are behavioral and neuropsychological dissociations between reasoning about social exchange and reasoning about equivalent problems tapping other, more general content domains. It has therefore been proposed that social exchange behavior is regulated by social contract algorithms: a domain-specific inference system that is functionally specialized for reasoning about social exchange. We report an fMRI study using the Wason selection task that provides further support for this hypothesis. Precautionary rules share so many properties with social exchange rules--they are conditional, deontic, and involve subjective utilities--that most reasoning theories claim they are processed by the same neurocomputational machinery. Nevertheless, neuroimaging shows that reasoning about social exchange activates brain areas not activated by reasoning about precautionary rules, and vice versa. As predicted, neural correlates of ToM (anterior and posterior temporal cortex) were activated when subjects interpreted social exchange rules, but not precautionary rules (where ToM inferences are unnecessary). We argue that the interaction between ToM and social contract algorithms can be reciprocal: social contract algorithms requires ToM inferences, but their functional logic also allows ToM inferences to be made. By considering interactions between ToM in the narrower sense (belief-desire reasoning) and all the social inference systems that create the logic of human social interaction--ones that enable as well as use inferences about the content of mental states--a broader conception of ToM may emerge: a computational model embodying a Theory of Human Nature (ToHN).

An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

PubMed

Liang, Ying; Liao, Bo; Zhu, Wen

2017-01-01

Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

On Inference Rules of Logic-Based Information Retrieval Systems.

ERIC Educational Resources Information Center

Chen, Patrick Shicheng

1994-01-01

Discussion of relevance and the needs of the users in information retrieval focuses on a deductive object-oriented approach and suggests eight inference rules for the deduction. Highlights include characteristics of a deductive object-oriented system, database and data modeling language, implementation, and user interface. (Contains 24…

Protoplanetary disk evolution and stellar parameters of T Tauri binaries in Chamaeleon I

NASA Astrophysics Data System (ADS)

Daemgen, S.; Petr-Gotzens, M. G.; Correia, S.; Teixeira, P. S.; Brandner, W.; Kley, W.; Zinnecker, H.

2013-06-01

Aims: This study aims to determine the impact of stellar binary companions on the lifetime and evolution of circumstellar disks in the Chamaeleon I (Cha I) star-forming region by measuring the frequency and strength of accretion and circumstellar dust signatures around the individual components of T Tauri binary stars. Methods: We used high-angular resolution adaptive optics JHKsL' -band photometry and 1.5-2.5 μm spectroscopy of 19 visual binary and 7 triple stars in Cha I - including one newly discovered tertiary component - with separations between ~25 and ~1000 AU. The data allowed us to infer stellar component masses and ages and, from the detection of near-infrared excess emission and the strength of Brackett-γ emission, the presence of ongoing accretion and hot circumstellar dust of the individual stellar components of each binary. Results: Of all the stellar components in close binaries with separations of 25-100 AU, 10+15-5% show signs of accretion. This is less than half of the accretor fraction found in wider binaries, which itself appears significantly reduced (~44%) compared with previous measurements of single stars in Cha I. Hot dust was found around 50+30-15% of the target components, a value that is indistinguishable from that of Cha I single stars. Only the closest binaries (<25 AU) were inferred to have a significantly reduced fraction (≲25%) of components that harbor hot dust. Accretors were exclusively found in binary systems with unequal component masses Msecondary/Mprimary < 0.8, implying that the detected accelerated disk dispersal is a function of mass-ratio. This agrees with the finding that only one accreting secondary star was found, which is also the weakest accretor in the sample. Conclusions: The results imply that disk dispersal is more accelerated the stronger the dynamical disk truncation, i.e., the smaller the inferred radius of the disk. Nonetheless, the overall measured mass accretion rates appear to be independent of the cluster environment or the existence of stellar companions at any separation ≳25 AU, because they agree well with observations from our previous binary study in the Orion Nebula cluster and with studies of single stars in these and other star-forming regions. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere, Chile. ESO Data ID: 086.C-0762.Tables 2, 4, and Appendix A are available in electronic form at http://www.aanda.org

Spectral analysis of a family of binary inflation rules

NASA Astrophysics Data System (ADS)

Baake, Michael; Grimm, Uwe; Mañibo, Neil

2018-01-01

The family of primitive binary substitutions defined by 1 \\mapsto 0 \\mapsto 0 1^m with m\\in N is investigated. The spectral type of the corresponding diffraction measure is analysed for its geometric realisation with prototiles (intervals) of natural length. Apart from the well-known Fibonacci inflation (m=1 ), the inflation rules either have integer inflation factors, but non-constant length, or are of non-Pisot type. We show that all of them have singular diffraction, either of pure point type or essentially singular continuous.

A Test by Any Other Name: P Values, Bayes Factors, and Statistical Inference.

PubMed

Stern, Hal S

2016-01-01

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks.

PubMed

Zhang, Haitao; Wu, Chenxue; Chen, Zewei; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

PubMed Central

Wu, Chenxue; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules. PMID:28767687

The architecture of the hierarchical triple star KOI 928 from eclipse timing variations seen in Kepler photometry

DOE PAGES

Steffen, J. H.; Quinn, S. N.; Borucki, W. J.; ...

2011-10-01

We present a hierarchical triple star system (KIC 9140402) where a low mass eclipsing binary orbits a more massive third star. The orbital period of the binary (4.98829 Days) is determined by the eclipse times seen in photometry from NASA's Kepler spacecraft. The periodically changing tidal field, due to the eccentric orbit of the binary about the tertiary, causes a change in the orbital period of the binary. The resulting eclipse timing variations provide insight into the dynamics and architecture of this system and allow the inference of the total mass of the binary (0.424±0.017M circle-dot) and the orbital parametersmore » of the binary about the central star.« less

Ultraviolet observations of alpha Aurigae from Copernicus

NASA Technical Reports Server (NTRS)

Dupree, A. K.

1975-01-01

Emission lines of L-alpha (1215.67 A) and O VI (1031.94 A) were detected in the spectroscopic binary alpha Aur (Capella) with the Princeton experiment on Copernicus. Temperatures of the emitting regions are inferred to be in excess of 300,000 K. The temperature and emission measure are consistent with a variable source of soft X-rays. If the emission is attributed to the primary star (G5 III), the atmosphere is expanding with velocities of about 20-100 km/s. Such expansion can lead to material within the binary system. The density of interstellar hydrogen inferred from absorption of stellar L-alpha appears to be approximately 0.01 hydrogen atoms per cu cm.

Inferring Metadata for a Semantic Web Peer-to-Peer Environment

ERIC Educational Resources Information Center

Brase, Jan; Painter, Mark

2004-01-01

Learning Objects Metadata (LOM) aims at describing educational resources in order to allow better reusability and retrieval. In this article we show how additional inference rules allows us to derive additional metadata from existing ones. Additionally, using these rules as integrity constraints helps us to define the constraints on LOM elements,…

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

PubMed Central

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

PubMed

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

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

PubMed

Orhan, A Emin; Ma, Wei Ji

2017-07-26

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

Learning a Markov Logic network for supervised gene regulatory network inference

PubMed Central

2013-01-01

Background Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. Results We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate “regulates”, starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions. Conclusions The numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge. PMID:24028533

Learning a Markov Logic network for supervised gene regulatory network inference.

PubMed

Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

2013-09-12

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a pairwise SVM while providing relevant insights on the predictions. The numerical studies show that MLN achieves very good predictive performance while opening the door to some interpretability of the decisions. Besides the ability to suggest new regulations, such an approach allows to cross-validate experimental data with existing knowledge.

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

NASA Astrophysics Data System (ADS)

Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Causal inference in survival analysis using pseudo-observations.

PubMed

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

2017-07-30

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 to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Preschoolers Can Infer General Rules Governing Fantastical Events in Fiction

ERIC Educational Resources Information Center

Van de Vondervoort, Julia W.; Friedman, Ori

2014-01-01

Young children are frequently exposed to fantastic fiction. How do they make sense of the unrealistic and impossible events that occur in such fiction? Although children could view such events as isolated episodes, the present experiments suggest that children use such events to infer general fantasy rules. In 2 experiments, 2-to 4-year-olds were…

Testing Models of Stellar Structure and Evolution I. Comparison with Detached Eclipsing Binaries

NASA Astrophysics Data System (ADS)

del Burgo, C.; Allende Prieto, C.

2018-05-01

We present the results of an analysis aimed at testing the accuracy and precision of the PARSEC v1.2S library of stellar evolution models, combined with a Bayesian approach, to infer stellar parameters. We mainly employ the online DEBCat catalogue by Southworth, a compilation of detached eclipsing binary systems with published measurements of masses and radii to ˜ 2 per cent precision. We select a sample of 318 binary components, with masses between 0.10 and 14.5 solar units, and distances between 1.3 pc and ˜ 8 kpc for Galactic objects and ˜ 44-68 kpc for the extragalactic ones. The Bayesian analysis applied takes on input effective temperature, radius, and [Fe/H], and their uncertainties, returning theoretical predictions for other stellar parameters. From the comparison with dynamical masses, we conclude inferred masses are precisely derived for stars on the main-sequence and in the core-helium-burning phase, with respective uncertainties of 4 per cent and 7 per cent, on average. Subgiants and red giants masses are predicted within 14 per cent, and early asymptotic giant branch stars within 24 per cent. These results are helpful to further improve the models, in particular for advanced evolutionary stages for which our understanding is limited. We obtain distances and ages for the binary systems and compare them, whenever possible, with precise literature estimates, finding excellent agreement. We discuss evolutionary effects and the challenges associated with the inference of stellar ages from evolutionary models. We also provide useful polynomial fittings to theoretical zero-age main-sequence relations.

Confusing Binaries: The Role of Stellar Binaries in Biasing Disk Properties in the Galactic Center

NASA Astrophysics Data System (ADS)

Naoz, Smadar; Ghez, Andrea M.; Hees, Aurelien; Do, Tuan; Witzel, Gunther; Lu, Jessica R.

2018-02-01

The population of young stars near the supermassive black hole (SMBH) in the Galactic Center (GC) has presented an unexpected challenge to theories of star formation. Kinematic measurements of these stars have revealed a stellar disk structure (with an apparent 20% disk membership) that has provided important clues regarding the origin of these mysterious young stars. However, many of the apparent disk properties are difficult to explain, including the low disk membership fraction and the high eccentricities given the youth of this population. Thus far, all efforts to derive the properties of this disk have made the simplifying assumption that stars at the GC are single stars. Nevertheless, stellar binaries are prevalent in our Galaxy, and recent investigations suggested that they may also be abundant in the Galactic Center. Here, we show that binaries in the disk can largely alter the apparent orbital properties of the disk. The motion of binary members around each other adds a velocity component, which can be comparable to the magnitude of the velocity around the SMBH in the GC. Thus, neglecting the contribution of binaries can significantly vary the inferred stars’ orbital properties. While the disk orientation is unaffected, the apparent disk’s 2D width will be increased to about 11.°2, similar to the observed width. For a population of stars orbiting the SMBH with zero eccentricity, unaccounted for binaries will create a wide apparent eccentricity distribution with an average of 0.23. This is consistent with the observed average eccentricity of the stars’ in the disk. We suggest that this high eccentricity value, which poses a theoretical challenge, may be an artifact of binary stars. Finally, our results suggest that the actual disk membership might be significantly higher than the one inferred by observations that ignore the contribution of binaries, alleviating another theoretical challenge.

Testing general relativity using gravitational wave signals from the inspiral, merger and ringdown of binary black holes

NASA Astrophysics Data System (ADS)

Ghosh, Abhirup; Johnson-McDaniel, Nathan K.; Ghosh, Archisman; Kant Mishra, Chandra; Ajith, Parameswaran; Del Pozzo, Walter; Berry, Christopher P. L.; Nielsen, Alex B.; London, Lionel

2018-01-01

Advanced LIGO’s recent observations of gravitational waves (GWs) from merging binary black holes have opened up a unique laboratory to test general relativity (GR) in the highly relativistic regime. One of the tests used to establish the consistency of the first LIGO event with a binary black hole merger predicted by GR was the inspiral-merger-ringdown consistency test. This involves inferring the mass and spin of the remnant black hole from the inspiral (low-frequency) part of the observed signal and checking for the consistency of the inferred parameters with the same estimated from the post-inspiral (high-frequency) part of the signal. Based on the observed rate of binary black hole mergers, we expect the advanced GW observatories to observe hundreds of binary black hole mergers every year when operating at their design sensitivities, most of them with modest signal to noise ratios (SNRs). Anticipating such observations, this paper shows how constraints from a large number of events with modest SNRs can be combined to produce strong constraints on deviations from GR. Using kludge modified GR waveforms, we demonstrate how this test could identify certain types of deviations from GR if such deviations are present in the signal waveforms. We also study the robustness of this test against reasonable variations of a variety of different analysis parameters.

Astrophysical Implications of the Binary Black-hole Merger GW150914

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allocca, A.; Altin, P. A.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Babak, S.; Bacon, P.; Bader, M. K. M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Baune, C.; Bavigadda, V.; Bazzan, M.; Behnke, B.; Bejger, M.; Belczynski, C.; Bell, A. S.; Bell, C. J.; Berger, B. K.; Bergman, J.; Bergmann, G.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Birney, R.; Biscans, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Bodiya, T. P.; Boer, M.; Bogaert, G.; Bogan, C.; Bohe, A.; Bojtos, P.; Bond, C.; Bondu, F.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brown, N. M.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T.; Calloni, E.; Camp, J. B.; Cannon, K. C.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C.; Cerboni Baiardi, L.; Cerretani, G.; Cesarini, E.; Chakraborty, R.; Chalermsongsak, T.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Chen, H. Y.; Chen, Y.; Cheng, C.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, S.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C. G.; Cominsky, L.; Constancio, M., Jr.; Conte, A.; Conti, L.; Cook, D.; Corbitt, T. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Craig, K.; Creighton, J. D. E.; Cripe, J.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Darman, N. S.; Dattilo, V.; Dave, I.; Daveloza, H. P.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; DeBra, D.; Debreczeni, G.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dereli, H.; Dergachev, V.; DeRosa, R.; DeRosa, R. T.; DeSalvo, R.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Dojcinoski, G.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Douglas, R.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Engels, W.; Essick, R. C.; Etzel, T.; Evans, M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Fang, Q.; Farinon, S.; Farr, B.; Farr, W. M.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M.; Fournier, J.-D.; Franco, S.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fricke, T. T.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H. A. G.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gatto, A.; Gaur, G.; Gehrels, N.; Gemme, G.; Gendre, B.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gordon, N. A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Hall, B. R.; Hall, E. D.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Hofman, D.; Hollitt, S. E.; Holt, K.; Holz, D. E.; Hopkins, P.; Hosken, D. J.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huang, S.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Idrisy, A.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J.-M.; Isi, M.; Islas, G.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jang, H.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; K, Haris; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karki, S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kawazoe, F.; Kéfélian, F.; Kehl, M. S.; Keitel, D.; Kelley, D. B.; Kells, W.; Kennedy, R.; Key, J. S.; Khalaidovski, A.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, C.; Kim, J.; Kim, K.; Kim, Nam-Gyu; Kim, Namjun; Kim, Y.-M.; King, E. J.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Koehlenbeck, S. M.; Kokeyama, K.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Kringel, V.; Krishnan, B.; Królak, A.; Krueger, C.; Kuehn, G.; Kumar, P.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lange, J.; Lantz, B.; Lasky, P. D.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, K.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Levine, B. M.; Li, T. G. F.; Libson, A.; Littenberg, T. B.; Lockerbie, N. A.; Logue, J.; Lombardi, A. L.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lück, H.; Lundgren, A. P.; Luo, J.; Lynch, R.; Ma, Y.; MacDonald, T.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Magee, R. M.; Mageswaran, M.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandel, I.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martynov, D. V.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mendoza-Gandara, D.; Mercer, R. A.; Merilh, E.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B. C.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Murphy, D. J.; Murray, P. G.; Mytidis, A.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Necula, V.; Nedkova, K.; Nelemans, G.; Neri, M.; Neunzert, A.; Newton, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E. N.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; O'Shaughnessy, R.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Patrick, Z.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perreca, A.; Phelps, M.; Piccinni, O.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poggiani, R.; Popolizio, P.; Post, A.; Powell, J.; Prasad, J.; Predoi, V.; Premachandra, S. S.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prix, R.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Ricci, F.; Riles, K.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sanchez, E. J.; Sandberg, V.; Sandeen, B.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Schilling, R.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sentenac, D.; Sequino, V.; Sergeev, A.; Serna, G.; Setyawati, Y.; Sevigny, A.; Shaddock, D. A.; Shah, S.; Shahriar, M. S.; Shaltev, M.; Shao, Z.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sigg, D.; Silva, A. D.; Simakov, D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, J. R.; Smith, N. D.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stevenson, S. P.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strauss, N. A.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tápai, M.; Tarabrin, S. P.; Taracchini, A.; Taylor, R.; Theeg, T.; Thirugnanasambandam, M. P.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Tomlinson, C.; Tonelli, M.; Torres, C. V.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Tringali, M. C.; Trozzo, L.; Tse, M.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; van den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, X.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; White, D. J.; Whiting, B. F.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Worden, J.; Wright, J. L.; Wu, G.; Yablon, J.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yap, M. J.; Yu, H.; Yvert, M.; Zadrożny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, F.; Zhang, L.; Zhang, M.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, X. J.; Zucker, M. E.; Zuraw, S. E.; and; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration

2016-02-01

The discovery of the gravitational-wave (GW) source GW150914 with the Advanced LIGO detectors provides the first observational evidence for the existence of binary black hole (BH) systems that inspiral and merge within the age of the universe. Such BH mergers have been predicted in two main types of formation models, involving isolated binaries in galactic fields or dynamical interactions in young and old dense stellar environments. The measured masses robustly demonstrate that relatively “heavy” BHs (≳ 25 {M}⊙ ) can form in nature. This discovery implies relatively weak massive-star winds and thus the formation of GW150914 in an environment with a metallicity lower than about 1/2 of the solar value. The rate of binary-BH (BBH) mergers inferred from the observation of GW150914 is consistent with the higher end of rate predictions (≳ 1 Gpc-3 yr-1) from both types of formation models. The low measured redshift (z≃ 0.1) of GW150914 and the low inferred metallicity of the stellar progenitor imply either BBH formation in a low-mass galaxy in the local universe and a prompt merger, or formation at high redshift with a time delay between formation and merger of several Gyr. This discovery motivates further studies of binary-BH formation astrophysics. It also has implications for future detections and studies by Advanced LIGO and Advanced Virgo, and GW detectors in space.

Astrophysical Implications of the Binary Black Hole Merger GW150914

NASA Technical Reports Server (NTRS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.;

Theory of magnetic cataclysmic binary X-ray sources

NASA Technical Reports Server (NTRS)

Lamb, Don Q.

1988-01-01

The theory of magnetic cataclysmic binary X-ray sources is reviewed. The physics of the accretion torque for disk and for stream accretion is described, and the magnetic field strengths of DQ Her stars inferred from their spin behavior and of AM Her stars from direct measurement are discussed. The implications of disk and stream accretion for the geometry of the emission region and for the X-ray pulse profiles are considered. The physicl properties of the X-ray emission region and the expected infrared, optical, soft X-ray, and hard X-ray spectra are described. The orientations of the magnetic moment in AM Her stars inferred from the circular and linear polarization of the optical light and the optical light curve are commented on.

A Logical Framework for Service Migration Based Survivability

DTIC Science & Technology

2016-06-24

platforms; Service Migration Strategy Fuzzy Inference System Knowledge Base Fuzzy rules representing domain expert knowledge about implications of...service migration strategy. Our approach uses expert knowledge as linguistic reasoning rules and takes service programs damage assessment, service...programs complexity, and available network capability as input. The fuzzy inference system includes four components as shown in Figure 5: (1) a knowledge

Inferring duplications, losses, transfers and incomplete lineage sorting with nonbinary species trees.

PubMed

Stolzer, Maureen; Lai, Han; Xu, Minli; Sathaye, Deepa; Vernot, Benjamin; Durand, Dannie

2012-09-15

Gene duplication (D), transfer (T), loss (L) and incomplete lineage sorting (I) are crucial to the evolution of gene families and the emergence of novel functions. The history of these events can be inferred via comparison of gene and species trees, a process called reconciliation, yet current reconciliation algorithms model only a subset of these evolutionary processes. We present an algorithm to reconcile a binary gene tree with a nonbinary species tree under a DTLI parsimony criterion. This is the first reconciliation algorithm to capture all four evolutionary processes driving tree incongruence and the first to reconcile non-binary species trees with a transfer model. Our algorithm infers all optimal solutions and reports complete, temporally feasible event histories, giving the gene and species lineages in which each event occurred. It is fixed-parameter tractable, with polytime complexity when the maximum species outdegree is fixed. Application of our algorithms to prokaryotic and eukaryotic data show that use of an incomplete event model has substantial impact on the events inferred and resulting biological conclusions. Our algorithms have been implemented in Notung, a freely available phylogenetic reconciliation software package, available at http://www.cs.cmu.edu/~durand/Notung. mstolzer@andrew.cmu.edu.

Truncation of the Accretion Disk at One-third of the Eddington Limit in the Neutron Star Low-mass X-Ray Binary Aquila X-1

NASA Astrophysics Data System (ADS)

Ludlam, R. M.; Miller, J. M.; Degenaar, N.; Sanna, A.; Cackett, E. M.; Altamirano, D.; King, A. L.

2017-10-01

We perform a reflection study on a new observation of the neutron star (NS) low-mass X-ray binary Aquila X-1 taken with NuSTAR during the 2016 August outburst and compare with the 2014 July outburst. The source was captured at ˜32% L Edd, which is over four times more luminous than the previous observation during the 2014 outburst. Both observations exhibit a broadened Fe line profile. Through reflection modeling, we determine that the inner disk is truncated {R}{in,2016}={11}-1+2 {R}g (where R g = GM/c 2) and {R}{in,2014}=14+/- 2 {R}g (errors quoted at the 90% confidence level). Fiducial NS parameters (M NS = 1.4 M ⊙, R NS = 10 km) give a stellar radius of R NS = 4.85 R g ; our measurements rule out a disk extending to that radius at more than the 6σ level of confidence. We are able to place an upper limit on the magnetic field strength of B ≤ 3.0-4.5 × 109 G at the magnetic poles, assuming that the disk is truncated at the magnetospheric radius in each case. This is consistent with previous estimates of the magnetic field strength for Aquila X-1. However, if the magnetosphere is not responsible for truncating the disk prior to the NS surface, we estimate a boundary layer with a maximum extent of {R}{BL,2016}˜ 10 {R}g and {R}{BL,2014}˜ 6 {R}g. Additionally, we compare the magnetic field strength inferred from the Fe line profile of Aquila X-1 and other NS low-mass X-ray binaries to known accreting millisecond X-ray pulsars.

The very faint hard state of the persistent neutron star X-ray binary SLX 1737-282 near the Galactic Centre

NASA Astrophysics Data System (ADS)

Armas Padilla, M.; Ponti, G.; De Marco, B.; Muñoz-Darias, T.; Haberl, F.

2018-01-01

We report on a detailed study of the spectral and temporal properties of the neutron star low-mass X-ray binary SLX 1737-282, which is located only ∼1° away from Sgr A*. The system is expected to have a short orbital period, even within the ultracompact regime, given its persistent nature at low X-ray luminosities and the long duration thermonuclear burst that it has displayed. We have analysed a Suzaku (18 ks) observation and an XMM-Newton (39 ks) observation taken 7 yr apart. We infer (0.5-10 keV) X-ray luminosities in the range of 3-6 × 1035ergs-1, in agreement with previous findings. The spectra are well described by a relatively cool (kTbb = 0.5 keV) blackbody component plus a Comptonized emission component with Γ ∼ 1.5-1.7. These values are consistent with the source being in a faint hard state, as confirmed by the ∼20 per cent fractional root-mean-square amplitude of the fast variability (0.1-7 Hz) inferred from the XMM-Newton data. The electron temperature of the corona is ≳7 keV for the Suzaku observation, but it is measured to be as low as ∼2 keV in the XMM-Newton data at higher flux. The latter is significantly lower than expected for systems in the hard state. We searched for X-ray pulsations and imposed an upper limit to their semi-amplitude of 2 per cent (0.001-7 Hz). Finally, we investigated the origin of the low-frequency variability emission present in the XMM-Newton data and ruled out an absorption dip origin. This constraint the orbital inclination of the system to ≲65° unless the orbital period is longer than 11 h (i.e. the length of the XMM-Newton observation).

Bi-lobed Shape of Comet 67P from a Collapsed Binary

NASA Astrophysics Data System (ADS)

Nesvorný, David; Parker, Joel; Vokrouhlický, David

2018-06-01

The Rosetta spacecraft observations revealed that the nucleus of comet 67P/Churyumov–Gerasimenko consists of two similarly sized lobes connected by a narrow neck. Here, we evaluate the possibility that 67P is a collapsed binary. We assume that the progenitor of 67P was a binary and consider various physical mechanisms that could have brought the binary components together, including small-scale impacts and gravitational encounters with planets. We find that 67P could be a primordial body (i.e., not a collisional fragment) if the outer planetesimal disk lasted ≲10 Myr before it was dispersed by migrating Neptune. The probability of binary collapse by impact is ≃30% for tightly bound binaries. Most km-class binaries become collisionally dissolved. Roughly 10% of the surviving binaries later evolve to become contact binaries during the disk dispersal, when bodies suffer gravitational encounters with Neptune. Overall, the processes described in this work do not seem to be efficient enough to explain the large fraction (∼67%) of bi-lobed cometary nuclei inferred from spacecraft imaging.

Measuring the Binary Black Hole Mass Spectrum with an Astrophysically Motivated Parameterization

NASA Astrophysics Data System (ADS)

Talbot, Colm; Thrane, Eric

2018-04-01

Gravitational-wave detections have revealed a previously unknown population of stellar mass black holes with masses above 20 M ⊙. These observations provide a new way to test models of stellar evolution for massive stars. By considering the astrophysical processes likely to determine the shape of the binary black hole mass spectrum, we construct a parameterized model to capture key spectral features that relate gravitational-wave data to theoretical stellar astrophysics. In particular, we model the signature of pulsational pair-instability supernovae, which are expected to cause all stars with initial mass 100 M ⊙ ≲ M ≲ 150 M ⊙ to form ∼40 M ⊙ black holes. This would cause a cutoff in the black hole mass spectrum along with an excess of black holes near 40 M ⊙. We carry out a simulated data study to illustrate some of the stellar physics that can be inferred using gravitational-wave measurements of binary black holes and demonstrate several such inferences that might be made in the near future. First, we measure the minimum and maximum stellar black hole mass. Second, we infer the presence of a peak due to pair-instability supernovae. Third, we measure the distribution of black hole mass ratios. Finally, we show how inadequate models of the black hole mass spectrum lead to biased estimates of the merger rate and the amplitude of the stochastic gravitational-wave background.

Experiments on neural network architectures for fuzzy logic

NASA Technical Reports Server (NTRS)

Keller, James M.

1991-01-01

The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

Experimental evidence for excess entropy discontinuities in glass-forming solutions.

PubMed

Lienhard, Daniel M; Zobrist, Bernhard; Zuend, Andreas; Krieger, Ulrich K; Peter, Thomas

2012-02-21

Glass transition temperatures T(g) are investigated in aqueous binary and multi-component solutions consisting of citric acid, calcium nitrate (Ca(NO(3))(2)), malonic acid, raffinose, and ammonium bisulfate (NH(4)HSO(4)) using a differential scanning calorimeter. Based on measured glass transition temperatures of binary aqueous mixtures and fitted binary coefficients, the T(g) of multi-component systems can be predicted using mixing rules. However, the experimentally observed T(g) in multi-component solutions show considerable deviations from two theoretical approaches considered. The deviations from these predictions are explained in terms of the molar excess mixing entropy difference between the supercooled liquid and glassy state at T(g). The multi-component mixtures involve contributions to these excess mixing entropies that the mixing rules do not take into account. © 2012 American Institute of Physics

Likelihoods for fixed rank nomination networks

PubMed Central

HOFF, PETER; FOSDICK, BAILEY; VOLFOVSKY, ALEX; STOVEL, KATHERINE

2014-01-01

Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of completely observed binary networks. Statistical analyses of FRN data with such models ignore the censored and ranked nature of the data and could potentially result in misleading statistical inference. To investigate this possibility, we compare Bayesian parameter estimates obtained from a likelihood for complete binary networks with those obtained from likelihoods that are derived from the FRN scheme, and therefore accommodate the ranked and censored nature of the data. We show analytically and via simulation that the binary likelihood can provide misleading inference, particularly for certain model parameters that relate network ties to characteristics of individuals and pairs of individuals. We also compare these different likelihoods in a data analysis of several adolescent social networks. For some of these networks, the parameter estimates from the binary and FRN likelihoods lead to different conclusions, indicating the importance of analyzing FRN data with a method that accounts for the FRN survey design. PMID:25110586

Possibility of determining cosmological parameters from measurements of gravitational waves emitted by coalescing, compact binaries

NASA Astrophysics Data System (ADS)

Marković, Dragoljub

1993-11-01

We explore the feasibility of using LIGO and/or VIRGO gravitational-wave measurements of coalescing, neutron-star-neutron-star (NS-NS) binaries and black-hole-neutron-star (BH-NS) binaries at cosmological distances to determine the cosmological parameters of our Universe. From the observed gravitational waveforms one can infer, as direct observables, the luminosity distance D of the source and the binary's two ``redshifted masses,'' M'1≡M1(1+z) and M'2≡M2(1+z), where Mi are the actual masses and z≡Δλ/λ is the binary's cosmological redshift. Assuming that the NS mass spectrum is sharply peaked about 1.4Msolar, as binary pulsar and x-ray source observations suggest, the redshift can be estimated as z=M'NS/1.4Msolar-1. The actual distance-redshift relation D(z) for our Universe is strongly dependent on its cosmological parameters [the Hubble constant H0, or h0≡H0/100 km s-1Mpc-1, the mean mass density ρm, or density parameter Ω0≡(8π/3H20)ρm, and the cosmological constant, Λ, or λ0≡Λ/(3H20)], so by a statistical study of (necessarily noisy) measurements of D and z for a large number of binaries, one can deduce the cosmological parameters. The various noise sources that will plague such a cosmological study are discussed and estimated, and the accuracies of the inferred parameters are determined as functions of the detectors' noise characteristics, the number of binaries observed, and the neutron-star mass spectrum. The dominant source of error is the detectors' intrinsic noise, though stochastic gravitational lensing of the waves by intervening matter might significantly influence the inferred cosmological constant λ0, when the detectors reach ``advanced'' stages of development. The estimated errors of parameters inferred from BH-NS measurements can be described by the following rough analytic fits: Δh0/h0~=0.02(N/h0)(τR)-1/2 (for N/h0<~2), where N is the detector's noise level (strain/Hz) in units of the ``advanced LIGO'' noise level, R is the event rate in units of the best-estimate value, 100 yr-1 Gpc-3, and τ is the observation time in years. In a ``high density'' universe (Ω0=1, λ0=0) ΔΩ0~=0.3(N/h0)2(τR)-1/2, Δλ0~=0.4(N/h0)1.5(τR)-1/2, for N/h0<~1. In a ``low density'' universe (Ω0=0.2, λ0=0), ΔΩ0~=0.5(N/h0)3(τR)-1/2, Δλ0~=0.7(N/h0)2.5(τR)-1/2, also for N/h0<~1. These formulas indicate that, if event rates are those currently estimated (~3 per year out to 200 Mpc), then when the planned LIGO and/or VIRGO detectors get to be about as sensitive as the so-called ``advanced detector level'' (presumably in the early 2000s), interesting cosmological measurements can begin.

Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets.

PubMed

Park, Inho; Lee, Kwang H; Lee, Doheon

2010-06-15

Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology. Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/~ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material. Supplementary data are available at Bioinformatics online.

Causal inference, probability theory, and graphical insights.

PubMed

Baker, Stuart G

2013-11-10

Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design. Published 2013. This article is a US Government work and is in the public domain in the USA.

The development of causal reasoning.

PubMed

Kuhn, Deanna

2012-05-01

How do inference rules for causal learning themselves change developmentally? A model of the development of causal reasoning must address this question, as well as specify the inference rules. Here, the evidence for developmental changes in processes of causal reasoning is reviewed, with the distinction made between diagnostic causal inference and causal prediction. Also addressed is the paradox of a causal reasoning literature that highlights the competencies of young children and the proneness to error among adults. WIREs Cogn Sci 2012, 3:327-335. doi: 10.1002/wcs.1160 For further resources related to this article, please visit the WIREs website. Copyright © 2012 John Wiley & Sons, Ltd.

Effective Bayesian Transfer Learning

DTIC Science & Technology

2010-03-01

reasonable value of k , defined by the task B training set size. Transfer Regret 1 Regret = 100 * G AB B No Transfer With Transfer AB...a. REPORT U b . ABSTRACT U c. THIS PAGE U 19b. TELEPHONE NUMBER (Include area code) N/A Standard Form 298 (Rev. 8-98) Prescribed...rule set given the prior and developed staged approximate inference strategy, in which data from observed tasks 1 to k are used to infer general rule

Accuracy of inference on the physics of binary evolution from gravitational-wave observations

NASA Astrophysics Data System (ADS)

Barrett, Jim W.; Gaebel, Sebastian M.; Neijssel, Coenraad J.; Vigna-Gómez, Alejandro; Stevenson, Simon; Berry, Christopher P. L.; Farr, Will M.; Mandel, Ilya

2018-04-01

The properties of the population of merging binary black holes encode some of the uncertain physics underlying the evolution of massive stars in binaries. The binary black hole merger rate and chirp-mass distribution are being measured by ground-based gravitational-wave detectors. We consider isolated binary evolution, and explore how accurately the physical model can be constrained with such observations by applying the Fisher information matrix to the merging black hole population simulated with the rapid binary-population synthesis code COMPAS. We investigate variations in four COMPAS parameters: common-envelope efficiency, kick-velocity dispersion, and mass-loss rates during the luminous blue variable and Wolf-Rayet stellar-evolutionary phases. We find that ˜1000 observations would constrain these model parameters to a fractional accuracy of a few per cent. Given the empirically determined binary black hole merger rate, we can expect gravitational-wave observations alone to place strong constraints on the physics of stellar and binary evolution within a few years. Our approach can be extended to use other observational data sets; combining observations at different evolutionary stages will lead to a better understanding of stellar and binary physics.

Accuracy of inference on the physics of binary evolution from gravitational-wave observations

NASA Astrophysics Data System (ADS)

Barrett, Jim W.; Gaebel, Sebastian M.; Neijssel, Coenraad J.; Vigna-Gómez, Alejandro; Stevenson, Simon; Berry, Christopher P. L.; Farr, Will M.; Mandel, Ilya

2018-07-01

The properties of the population of merging binary black holes encode some of the uncertain physics underlying the evolution of massive stars in binaries. The binary black hole merger rate and chirp-mass distribution are being measured by ground-based gravitational-wave detectors. We consider isolated binary evolution, and explore how accurately the physical model can be constrained with such observations by applying the Fisher information matrix to the merging black hole population simulated with the rapid binary-population synthesis code COMPAS. We investigate variations in four COMPAS parameters: common-envelope efficiency, kick-velocity dispersion and mass-loss rates during the luminous blue variable, and Wolf-Rayet stellar-evolutionary phases. We find that ˜1000 observations would constrain these model parameters to a fractional accuracy of a few per cent. Given the empirically determined binary black hole merger rate, we can expect gravitational-wave observations alone to place strong constraints on the physics of stellar and binary evolution within a few years. Our approach can be extended to use other observational data sets; combining observations at different evolutionary stages will lead to a better understanding of stellar and binary physics.

The binary protein-protein interaction landscape of Escherichia coli

PubMed Central

Rajagopala, Seesandra V.; Vlasblom, James; Arnold, Roland; Franca-Koh, Jonathan; Pakala, Suman B.; Phanse, Sadhna; Ceol, Arnaud; Häuser, Roman; Siszler, Gabriella; Wuchty, Stefan; Emili, Andrew; Babu, Mohan; Aloy, Patrick; Pieper, Rembert; Uetz, Peter

2014-01-01

Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (~70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, approximately doubling the number of known binary PPIs in E. coli. Integration of binary PPIs and genetic interactions revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that could be mapped within multi-protein complexes were informative regarding internal topology and indicated that interactions within complexes are significantly more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily significant model microbe. PMID:24561554

Is black-hole ringdown a memory of its progenitor?

PubMed

Kamaretsos, Ioannis; Hannam, Mark; Sathyaprakash, B S

2012-10-05

We perform an extensive numerical study of coalescing black-hole binaries to understand the gravitational-wave spectrum of quasinormal modes excited in the merged black hole. Remarkably, we find that the masses and spins of the progenitor are clearly encoded in the mode spectrum of the ringdown signal. Some of the mode amplitudes carry the signature of the binary's mass ratio, while others depend critically on the spins. Simulations of precessing binaries suggest that our results carry over to generic systems. Using Bayesian inference, we demonstrate that it is possible to accurately measure the mass ratio and a proper combination of spins even when the binary is itself invisible to a detector. Using a mapping of the binary masses and spins to the final black-hole spin allows us to further extract the spin components of the progenitor. Our results could have tremendous implications for gravitational astronomy by facilitating novel tests of general relativity using merging black holes.

First detections of gravitational waves emitted from binary black hole mergers

NASA Astrophysics Data System (ADS)

Reitze, D. H.

2017-11-01

The LIGO Scientific Collaboration and the Virgo Collaboration carried out the inaugural ‘O1’ observing run from September 12, 2015 through January 19, 2016 using the newly commissioned Advanced LIGO interferometers located in Hanford,WAand Livingston, LA. During theO1 run and the O2 run currently underway, three definitive detections of gravitational waves have occurred, each produced during the mergers of binary stellar mass black holes. A fourth candidate gravitational-wave event was identified, also likely produced from a binary black hole merger. The detected gravitational waveforms allow for the inference of the intrinsic astrophysical parameters of the merging binary systems, as well as the resulting black hole produced by the mergers. The first detect detections of gravitational waves confirm the existence of binary black hole systems and have profound implications for astrophysics using gravitational waves as a new and powerful probe of the universe.

Infrared photometry of the dwarf nova V2051 Ophiuchi - I. The mass-donor star and the distance

NASA Astrophysics Data System (ADS)

Wojcikiewicz, Eduardo; Baptista, Raymundo; Ribeiro, Tiago

2018-04-01

We report the analysis of time series of infrared JHKs photometry of the dwarf nova V2051 Oph in quiescence. We modelled the ellipsoidal variations caused by the distorted mass-donor star to infer its JHKs fluxes. From its infrared colours, we estimate a spectral type of M(8.0 ± 1.5) and an equivalent blackbody temperature of TBB = (2700 ± 270) K. We used the Barnes & Evans relation to infer a photometric parallax distance of dBE = (102 ± 16) pc to the binary. At this short distance, the corresponding accretion disc temperatures in outburst are too low to be explained by the disc-instability model for dwarf nova outbursts, underscoring a previous suggestion that the outbursts of this binary are powered by mass-transfer bursts.

Digging Deeper: Observing Primordial Gravitational Waves below the Binary-Black-Hole-Produced Stochastic Background.

PubMed

Regimbau, T; Evans, M; Christensen, N; Katsavounidis, E; Sathyaprakash, B; Vitale, S

2017-04-14

The merger rate of black hole binaries inferred from the detections in the first Advanced LIGO science run implies that a stochastic background produced by a cosmological population of mergers will likely mask the primordial gravitational wave background. Here we demonstrate that the next generation of ground-based detectors, such as the Einstein Telescope and Cosmic Explorer, will be able to observe binary black hole mergers throughout the Universe with sufficient efficiency that the confusion background can potentially be subtracted to observe the primordial background at the level of Ω_{GW}≃10^{-13} after 5 years of observation.

X-ray Source Populations in Old Open Clusters - Collinder 261

NASA Astrophysics Data System (ADS)

Vats, Smriti

2014-11-01

We are carrying out an X-ray survey of old open clusters (OCs) with the Chandra X-ray Observatory. Single old stars emit very faint X-rays, making X-rays produced by mass transfer in CVs, or by rapid rotation of the stars in tidally-locked, detached binaries detectable, without contamination from single stars. By comparing properties of interacting binaries in different environments, we aim to study binary evolution, and how dynamical encounters with other cluster members affect it. Collinder (Cr) 261 is an old OC(~7Gyr), with one of the richest populations inferred, of close binary populations and blue stragglers of all OCs. We will present the first results, detailing the X-ray population of Cr 261, in conjugation with other OCs, and in comparison with populations in globular clusters.

Constraining Engine Paradigms of Pre-Planetary Nebulae Using Kinematic Properties of their Outflows

NASA Astrophysics Data System (ADS)

Blackman, E.

2014-04-01

Binary interactions and accretion plausibly conspire to produce the ubiquitous collimated outflows from planetary nebulae (PN) and their presumed pre-planetary nebulae (PPN) progenitors. But which accretion engines are viable? The difficulty in observationally resolving the engines warrants indirect constraints. I discuss how momentum outflow data for PPN can be used to determine the minimum required accretion rate for presumed main sequence (MS) or white dwarf (WD) accretors by comparing to several example accretion rates inferred from published models. While the main goal is to show the method in anticipation of more data and better theoretical constraints, taking the present results at face value already rule out modes of accretion: Bondi-Hoyle Lyttleton (BHL) wind accretion and wind Roche lobe overflow (M-WRLOF, based on Mira parameters) are too feeble for all 19/19 objects for a MS accretor. For a WD accretor, BHL is ruled out for 18/19 objects and M-WRLOF for 15/19 objects. Roche lobe overflow from the primary can accommodate 7/19 objects but only common envelope evolution accretion modes seem to be able to accommodate all 19 objects. Sub-Eddington rates for a MS accretor are acceptable but 8/19 would require super-Eddington rates for a WD. I also briefly discuss a possible anti-correlation between age and maximum observed outflow speed, and the role of magnetic fields.

Automatic inference of indexing rules for MEDLINE

PubMed Central

Névéol, Aurélie; Shooshan, Sonya E; Claveau, Vincent

2008-01-01

Background: Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. Methods: In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Results: Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. Conclusion: We expect the sets of ILP rules obtained in this experiment to be integrated into MTI. PMID:19025687

Automatic inference of indexing rules for MEDLINE.

PubMed

Névéol, Aurélie; Shooshan, Sonya E; Claveau, Vincent

2008-11-19

Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.

Radio crickets: chirping jets from black hole binaries entering their gravitational wave inspiral

NASA Astrophysics Data System (ADS)

Kulkarni, Girish; Loeb, Abraham

2016-03-01

We study a novel electromagnetic signature of supermassive black hole (BH) binaries whose inspiral starts being dominated by gravitational wave (GW) emission. Recent simulations suggest that the binary's member BHs can continue to accrete gas from the circumbinary accretion disc in this phase of the binary's evolution, all the way until coalescence. If one of the binary members produces a radio jet as a result of accretion, the jet precesses along a biconical surface due to the binary's orbital motion. When the binary enters the GW phase of its evolution, the opening angle widens, the jet exhibits milliarcsecond-scale wiggles, and the conical surface of jet precession is twisted due to apparent superluminal motion. The rapidly increasing orbital velocity of the binary gives the jet an appearance of a `chirp'. This helical chirping morphology of the jet can be used to infer the binary parameters. For binaries with mass 107-1010 M⊙ at redshifts z < 0.5, monitoring these features in current and archival data will place a lower limit on sources that could be detected by Evolved Laser Interferometer Space Antenna and Pulsar Timing Arrays. In the future, microarcsecond interferometry with the Square Kilometre Array will increase the potential usefulness of this technique.

Generic comparison of protein inference engines.

PubMed

Claassen, Manfred; Reiter, Lukas; Hengartner, Michael O; Buhmann, Joachim M; Aebersold, Ruedi

2012-04-01

Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context.

The GW170817/GRB 170817A/AT 2017gfo Association: Some Implications for Physics and Astrophysics

NASA Astrophysics Data System (ADS)

Wang, Hao; Zhang, Fu-Wen; Wang, Yuan-Zhu; Shen, Zhao-Qiang; Liang, Yun-Feng; Li, Xiang; Liao, Neng-Hui; Jin, Zhi-Ping; Yuan, Qiang; Zou, Yuan-Chuan; Fan, Yi-Zhong; Wei, Da-Ming

2017-12-01

On 2017 August 17, a gravitational-wave event (GW170817) and an associated short gamma-ray burst (GRB 170817A) from a binary neutron star merger had been detected. The follow-up optical/infrared observations also identified the macronova/kilonova emission (AT 2017gfo). In this work, we discuss some implications of the remarkable GW170817/GRB 170817A/AT 2017gfo association. We show that the ∼1.7 s time delay between the gravitational-wave (GW) and GRB signals imposes very tight constraints on the superluminal movement of gravitational waves (i.e., the relative departure of GW velocity from the speed of light is ≤slant 4.3× {10}-16) or the possible violation of the weak equivalence principle (i.e., the difference of the gamma-ray and GW trajectories in the gravitational field of the galaxy and the local universe should be within a factor of ∼ 3.4× {10}-9). The so-called Dark Matter Emulators and a class of contender models for cosmic acceleration (“Covariant Galileon”) are ruled out as well. The successful identification of lanthanide elements in the macronova/kilonova spectrum also excludes the possibility that the progenitors of GRB 170817A are a binary strange star system. The high neutron star merger rate (inferred from both the local sGRB data and the gravitational-wave data) together with the significant ejected mass strongly suggest that such mergers are the prime sites of heavy r-process nucleosynthesis.

Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves

NASA Astrophysics Data System (ADS)

Chang, Ya-Ting; Chang, Li-Chiu; Chang, Fi-John

2005-04-01

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network-based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M-5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input-output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M-5 curves in real-time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved.

Inferring the Limit Behavior of Some Elementary Cellular Automata

NASA Astrophysics Data System (ADS)

Ruivo, Eurico L. P.; de Oliveira, Pedro P. B.

Cellular automata locally define dynamical systems, discrete in space, time and in the state variables, capable of displaying arbitrarily complex global emergent behavior. One core question in the study of cellular automata refers to their limit behavior, that is, to the global dynamical features in an infinite time evolution. Previous works have shown that for finite time evolutions, the dynamics of one-dimensional cellular automata can be described by regular languages and, therefore, by finite automata. Such studies have shown the existence of growth patterns in the evolution of such finite automata for some elementary cellular automata rules and also inferred the limit behavior of such rules based upon the growth patterns; however, the results on the limit behavior were obtained manually, by direct inspection of the structures that arise during the time evolution. Here we present the formalization of an automatic method to compute such structures. Based on this, the rules of the elementary cellular automata space were classified according to the existence of a growth pattern in their finite automata. Also, we present a method to infer the limit graph of some elementary cellular automata rules, derived from the analysis of the regular expressions that describe their behavior in finite time. Finally, we analyze some attractors of two rules for which we could not compute the whole limit set.

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

PubMed

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.

Numerical Relativity Simulations of Compact Binary Populations in Dense Stellar Environments

NASA Astrophysics Data System (ADS)

Glennon, Derek Ray; Huerta, Eliu; Allen, Gabrielle; Haas, Roland; Seidel, Edward; NCSA Gravity Group

2018-01-01

We present a catalog of numerical relativity simulations that describe binary black hole mergers on eccentric orbits. These simulations have been obtained with the open source, Einstein Toolkit numerical relativity software, using the Blue Waters supercomputer. We use this catalog to quantify observables, such as the mass and spin of black holes formed by binary black hole mergers, as a function of eccentricity. This study is the first of its kind in the literature to quantify these astrophysical observables for binary black hole mergers with mass-ratios q<6, and eccentricities e<0.2. This study is an important step in understanding the properties of eccentric binary black hole mergers, and informs the use of gravitational wave observations to confirm or rule out the existence of compact binary populations in dense stellar environments.

Learning and inference in a nonequilibrium Ising model with hidden nodes.

PubMed

Dunn, Benjamin; Roudi, Yasser

2013-02-01

We study inference and reconstruction of couplings in a partially observed kinetic Ising model. With hidden spins, calculating the likelihood of a sequence of observed spin configurations requires performing a trace over the configurations of the hidden ones. This, as we show, can be represented as a path integral. Using this representation, we demonstrate that systematic approximate inference and learning rules can be derived using dynamical mean-field theory. Although naive mean-field theory leads to an unstable learning rule, taking into account Gaussian corrections allows learning the couplings involving hidden nodes. It also improves learning of the couplings between the observed nodes compared to when hidden nodes are ignored.

Fuzzy logic

NASA Technical Reports Server (NTRS)

Zadeh, Lofti A.

1988-01-01

The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.

Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes

NASA Astrophysics Data System (ADS)

Ianni, Giovambattista; Krennwallner, Thomas; Martello, Alessandra; Polleres, Axel

RDF Schema (RDFS) as a lightweight ontology language is gaining popularity and, consequently, tools for scalable RDFS inference and querying are needed. SPARQL has become recently a W3C standard for querying RDF data, but it mostly provides means for querying simple RDF graphs only, whereas querying with respect to RDFS or other entailment regimes is left outside the current specification. In this paper, we show that SPARQL faces certain unwanted ramifications when querying ontologies in conjunction with RDF datasets that comprise multiple named graphs, and we provide an extension for SPARQL that remedies these effects. Moreover, since RDFS inference has a close relationship with logic rules, we generalize our approach to select a custom ruleset for specifying inferences to be taken into account in a SPARQL query. We show that our extensions are technically feasible by providing benchmark results for RDFS querying in our prototype system GiaBATA, which uses Datalog coupled with a persistent Relational Database as a back-end for implementing SPARQL with dynamic rule-based inference. By employing different optimization techniques like magic set rewriting our system remains competitive with state-of-the-art RDFS querying systems.

Bayesian truthing as experimental verification of C4ISR sensors

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Forrester, Thomas; Romanov, Volodymyr; Wang, Wenjian; Nielsen, Thomas; Kostrzewski, Andrew

2015-05-01

In this paper, the general methodology for experimental verification/validation of C4ISR and other sensors' performance, is presented, based on Bayesian inference, in general, and binary sensors, in particular. This methodology, called Bayesian Truthing, defines Performance Metrics for binary sensors in: physics, optics, electronics, medicine, law enforcement, C3ISR, QC, ATR (Automatic Target Recognition), terrorism related events, and many others. For Bayesian Truthing, the sensing medium itself is not what is truly important; it is how the decision process is affected.

Rational and Boundedly Rational Behavior in a Binary Choice Sender-Receiver Game

ERIC Educational Resources Information Center

Landi, Massimiliano; Colucci, Domenico

2008-01-01

The authors investigate the strategic rationale behind the message sent by Osama bin Laden on the eve of the 2004 U.S. Presidential elections. They model this situation as a signaling game in which a population of receivers takes a binary choice, the outcome is decided by majority rule, sender and receivers have conflicting interests, and there is…

The fidelity of Kepler eclipsing binary parameters inferred by the neural network

NASA Astrophysics Data System (ADS)

Holanda, N.; da Silva, J. R. P.

2018-04-01

This work aims to test the fidelity and efficiency of obtaining automatic orbital elements of eclipsing binary systems, from light curves using neural network models. We selected a random sample with 78 systems, from over 1400 eclipsing binary detached obtained from the Kepler Eclipsing Binaries Catalog, processed using the neural network approach. The orbital parameters of the sample systems were measured applying the traditional method of light curve adjustment with uncertainties calculated by the bootstrap method, employing the JKTEBOP code. These estimated parameters were compared with those obtained by the neural network approach for the same systems. The results reveal a good agreement between techniques for the sum of the fractional radii and moderate agreement for e cos ω and e sin ω, but orbital inclination is clearly underestimated in neural network tests.

The fidelity of Kepler eclipsing binary parameters inferred by the neural network

NASA Astrophysics Data System (ADS)

Holanda, N.; da Silva, J. R. P.

2018-07-01

This work aims to test the fidelity and efficiency of obtaining automatic orbital elements of eclipsing binary systems, from light curves using neural network models. We selected a random sample with 78 systems, from over 1400 detached eclipsing binaries obtained from the Kepler Eclipsing Binaries Catalog, processed using the neural network approach. The orbital parameters of the sample systems were measured applying the traditional method of light-curve adjustment with uncertainties calculated by the bootstrap method, employing the JKTEBOP code. These estimated parameters were compared with those obtained by the neural network approach for the same systems. The results reveal a good agreement between techniques for the sum of the fractional radii and moderate agreement for e cosω and e sinω, but orbital inclination is clearly underestimated in neural network tests.

Adult Age Differences in Categorization and Multiple-Cue Judgment

ERIC Educational Resources Information Center

Mata, Rui; von Helversen, Bettina; Karlsson, Linnea; Cupper, Lutz

2012-01-01

We often need to infer unknown properties of objects from observable ones, just like detectives must infer guilt from observable clues and behavior. But how do inferential processes change with age? We examined young and older adults' reliance on rule-based and similarity-based processes in an inference task that can be considered either a…

Refining the Relationships among Historical Figures by Implementing Inference Rules in SWRL

NASA Astrophysics Data System (ADS)

Fajrin Ariyani, Nurul; Saralita, Madis; Sarwosri; Sarno, Riyanarto

2018-03-01

The biography of historical figures is often fascinating to be known. Everything about their character, work, invention, and personal life sometimes are presented in their biography. The social and family relationships among historical figures also put into concern, especially for political figures, heroes, kings or persons who have ever been ruled a monarchy in their past. Some biographies can be found in Wikipedia as articles. Most of the social and family relationship contents of these figures are not completely depicted due to a various article’s contributors and sources. Fortunately, the missing relatives of a person might reside in the other figures’ biography in different pages. Each Wikipedia article has metadata which represents its essential information of content. By processing the metadata obtained from DBpedia and composing the inferencing rules (in the form of ontology) to identify the relationships content, it can generate several new inferred facts that complement the existing relationships. This work proposes a methodology for finding missing relationships among historical figures using inference rules in an ontology. As a result, our method can present new facts about the relationships that absent in the existing Wikipedia articles.

A new approximate sum rule for bulk alloy properties

NASA Technical Reports Server (NTRS)

Bozzolo, Guillermo; Ferrante, John

1991-01-01

A new, approximate sum rule is introduced for determining bulk properties of multicomponent systems, in terms of the pure components properties. This expression is applied for the study of lattice parameters, cohesive energies, and bulk moduli of binary alloys. The correct experimental trends (i.e., departure from average values) are predicted in all cases.

Plausible inference: A multi-valued logic for problem solving

NASA Technical Reports Server (NTRS)

Friedman, L.

1979-01-01

A new logic is developed which permits continuously variable strength of belief in the truth of assertions. Four inference rules result, with formal logic as a limiting case. Quantification of belief is defined. Propagation of belief to linked assertions results from dependency-based techniques of truth maintenance so that local consistency is achieved or contradiction discovered in problem solving. Rules for combining, confirming, or disconfirming beliefs are given, and several heuristics are suggested that apply to revising already formed beliefs in the light of new evidence. The strength of belief that results in such revisions based on conflicting evidence are a highly subjective phenomenon. Certain quantification rules appear to reflect an orderliness in the subjectivity. Several examples of reasoning by plausible inference are given, including a legal example and one from robot learning. Propagation of belief takes place in directions forbidden in formal logic and this results in conclusions becoming possible for a given set of assertions that are not reachable by formal logic.

An Ada inference engine for expert systems

NASA Technical Reports Server (NTRS)

Lavallee, David B.

1986-01-01

The purpose is to investigate the feasibility of using Ada for rule-based expert systems with real-time performance requirements. This includes exploring the Ada features which give improved performance to expert systems as well as optimizing the tradeoffs or workarounds that the use of Ada may require. A prototype inference engine was built using Ada, and rule firing rates in excess of 500 per second were demonstrated on a single MC68000 processor. The knowledge base uses a directed acyclic graph to represent production lines. The graph allows the use of AND, OR, and NOT logical operators. The inference engine uses a combination of both forward and backward chaining in order to reach goals as quickly as possible. Future efforts will include additional investigation of multiprocessing to improve performance and creating a user interface allowing rule input in an Ada-like syntax. Investigation of multitasking and alternate knowledge base representations will help to analyze some of the performance issues as they relate to larger problems.

TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions.

PubMed

Heinrich, Mattias P; Blendowski, Max; Oktay, Ozan

2018-05-30

Deep convolutional neural networks (DCNN) are currently ubiquitous in medical imaging. While their versatility and high-quality results for common image analysis tasks including segmentation, localisation and prediction is astonishing, the large representational power comes at the cost of highly demanding computational effort. This limits their practical applications for image-guided interventions and diagnostic (point-of-care) support using mobile devices without graphics processing units (GPU). We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions. Our solution enables the removal of the expensive floating-point matrix multiplications throughout any convolutional neural network and replaces them by energy- and time-preserving binary operators and population counts. We evaluate our approach for the segmentation of the pancreas in CT. Here, our ternary approximation within a fully convolutional network leads to more than 90% memory reductions and high accuracy (without any post-processing) with a Dice overlap of 71.0% that comes close to the one obtained when using networks with high-precision weights and activations. We further provide a concept for sub-second inference without GPUs and demonstrate significant improvements in comparison with binary quantisation and without our proposed ternary hyperbolic tangent continuation. We present a key enabling technique for highly efficient DCNN inference without GPUs that will help to bring the advances of deep learning to practical clinical applications. It has also great promise for improving accuracies in large-scale medical data retrieval.

Subjective randomness as statistical inference.

PubMed

Griffiths, Thomas L; Daniels, Dylan; Austerweil, Joseph L; Tenenbaum, Joshua B

2018-06-01

Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences - which have been the focus of much of the previous work on subjective randomness - but also to binary matrices and spatial clustering. Copyright © 2018 Elsevier Inc. All rights reserved.

Neutron Skins and Neutron Stars in the Multimessenger Era

NASA Astrophysics Data System (ADS)

Fattoyev, F. J.; Piekarewicz, J.; Horowitz, C. J.

2018-04-01

The historical first detection of a binary neutron star merger by the LIGO-Virgo Collaboration [B. P. Abbott et al., Phys. Rev. Lett. 119, 161101 (2017), 10.1103/PhysRevLett.119.161101] is providing fundamental new insights into the astrophysical site for the r process and on the nature of dense matter. A set of realistic models of the equation of state (EOS) that yield an accurate description of the properties of finite nuclei, support neutron stars of two solar masses, and provide a Lorentz covariant extrapolation to dense matter are used to confront its predictions against tidal polarizabilities extracted from the gravitational-wave data. Given the sensitivity of the gravitational-wave signal to the underlying EOS, limits on the tidal polarizability inferred from the observation translate into constraints on the neutron-star radius. Based on these constraints, models that predict a stiff symmetry energy, and thus large stellar radii, can be ruled out. Indeed, we deduce an upper limit on the radius of a 1.4 M⊙ neutron star of R⋆1.4<13.76 km . Given the sensitivity of the neutron-skin thickness of Pb 208 to the symmetry energy, albeit at a lower density, we infer a corresponding upper limit of about Rskin208≲0.25 fm . However, if the upcoming PREX-II experiment measures a significantly thicker skin, this may be evidence of a softening of the symmetry energy at high densities—likely indicative of a phase transition in the interior of neutron stars.

Optimal Recall from Bounded Metaplastic Synapses: Predicting Functional Adaptations in Hippocampal Area CA3

PubMed Central

Savin, Cristina; Dayan, Peter; Lengyel, Máté

2014-01-01

A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown. Here, we develop a theoretical framework for memory storage and recall with bounded synapses. We formulate the recall of a previously stored pattern from a noisy recall cue and limited-capacity (and therefore lossy) synapses as a probabilistic inference problem, and derive neural dynamics that implement approximate inference algorithms to solve this problem efficiently. In particular, for binary synapses with metaplastic states, we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that are completely constrained by the synaptic plasticity rule, and the statistics of the stored patterns and of the recall cue. Our theory organises into a coherent framework a wide range of existing data about the regulation of excitability, feedback inhibition, and network oscillations in area CA3, and makes novel and directly testable predictions that can guide future experiments. PMID:24586137

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

PubMed

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015 Cognitive Science Society, Inc.

Improved Constraints on the Disk around MWC 349A from the 23 m LBTI

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

Sallum, S.; Eisner, J. A.; Hinz, P. M.

2017-07-20

We present new spatially resolved observations of MWC 349A from the Large Binocular Telescope Interferometer (LBTI), a 23 m baseline interferometer made up of two, co-mounted 8 m telescopes. MWC 349A is a B[e] star with an unknown evolutionary state. Proposed scenarios range from a young stellar object, to a B[e] supergiant, to a tight binary system. Radio continuum and recombination line observations of this source revealed a sub-arcsecond bipolar outflow surrounding an ∼100 mas circumstellar disk. Follow-up infrared studies detected the disk, and suggested that it may have skew and an inner clearing. Our new infrared interferometric observations, whichmore » have more than twice the resolution of previously published data sets, support the presence of both skew and a compact infrared excess. They rule out inner clearings with radii greater than ∼14 mas. We show the improvements in disk parameter constraints provided by LBTI, and discuss the inferred disk parameters in the context of the posited evolutionary states for MWC 349A.« less

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

PubMed Central

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks. PMID:28932180

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.

PubMed

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Afrough, M.; Agarwal, B.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allen, G.; Allocca, A.; Altin, P. A.; Amato, A.; Ananyeva, A.; Anderson, S. B.; Anderson, W. G.; Angelova, S. V.; Antier, S.; Appert, S.; Arai, K.; Araya, M. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Atallah, D. V.; Aufmuth, P.; Aulbert, C.; AultONeal, K.; Austin, C.; Avila-Alvarez, A.; Babak, S.; Bacon, P.; Bader, M. K. M.; Bae, S.; Bailes, M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Banagiri, S.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barkett, K.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Barthelmy, S. D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Bawaj, M.; Bayley, J. C.; Bazzan, M.; Bécsy, B.; Beer, C.; Bejger, M.; Belahcene, I.; Bell, A. S.; Berger, B. K.; Bergmann, G.; Bernuzzi, S.; Bero, J. J.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Billman, C. R.; Birch, J.; Birney, R.; Birnholtz, O.; Biscans, S.; Biscoveanu, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blackman, J.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Bode, N.; Boer, M.; Bogaert, G.; Bohe, A.; Bondu, F.; Bonilla, E.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bossie, K.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Broida, J. E.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brunett, S.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cabero, M.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T. A.; Calloni, E.; Camp, J. B.; Canepa, M.; Canizares, P.; Cannon, K. C.; Cao, H.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Carney, M. F.; Carullo, G.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Cerdá-Durán, P.; Cerretani, G.; Cesarini, E.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chase, E.; Chassande-Mottin, E.; Chatterjee, D.; Chatziioannou, K.; Cheeseboro, B. D.; Chen, H. Y.; Chen, X.; Chen, Y.; Cheng, H.-P.; Chia, H.; Chincarini, A.; Chiummo, A.; Chmiel, T.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, A. J. K.; Chua, S.; Chung, A. K. W.; Chung, S.; Ciani, G.; Ciolfi, R.; Cirelli, C. E.; Cirone, A.; Clara, F.; Clark, J. A.; Clearwater, P.; Cleva, F.; Cocchieri, C.; Coccia, E.; Cohadon, P.-F.; Cohen, D.; Colla, A.; Collette, C. G.; Cominsky, L. R.; Constancio, M.; Conti, L.; Cooper, S. J.; Corban, P.; Corbitt, T. R.; Cordero-Carrión, I.; Corley, K. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Covas, P. B.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Creighton, J. D. E.; Creighton, T. D.; Cripe, J.; Crowder, S. G.; Cullen, T. J.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Dálya, G.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dasgupta, A.; Da Silva Costa, C. F.; Dattilo, V.; Dave, I.; Davier, M.; Davis, D.; Daw, E. J.; Day, B.; De, S.; DeBra, D.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Demos, N.; Denker, T.; Dent, T.; De Pietri, R.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; De Rossi, C.; DeSalvo, R.; de Varona, O.; Devenson, J.; Dhurandhar, S.; Díaz, M. C.; Dietrich, T.; Di Fiore, L.; Di Giovanni, M.; Di Girolamo, T.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Renzo, F.; Doctor, Z.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorrington, I.; Douglas, R.; Dovale Álvarez, M.; Downes, T. P.; Drago, M.; Dreissigacker, C.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dudi, R.; Dupej, P.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Eisenstein, R. A.; Essick, R. C.; Estevez, D.; Etienne, Z. B.; Etzel, T.; Evans, M.; Evans, T. M.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Farinon, S.; Farr, B.; Farr, W. M.; Fauchon-Jones, E. J.; Favata, M.; Fays, M.; Fee, C.; Fehrmann, H.; Feicht, J.; Fejer, M. M.; Fernandez-Galiana, A.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Finstad, D.; Fiori, I.; Fiorucci, D.; Fishbach, M.; Fisher, R. P.; Fitz-Axen, M.; Flaminio, R.; Fletcher, M.; Fong, H.; Font, J. A.; Forsyth, P. W. F.; Forsyth, S. S.; Fournier, J.-D.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fries, E. M.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H.; Gadre, B. U.; Gaebel, S. M.; Gair, J. R.; Gammaitoni, L.; Ganija, M. R.; Gaonkar, S. G.; Garcia-Quiros, C.; Garufi, F.; Gateley, B.; Gaudio, S.; Gaur, G.; Gayathri, V.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; George, D.; George, J.; Gergely, L.; Germain, V.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glover, L.; Goetz, E.; Goetz, R.; Gomes, S.; Goncharov, B.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Grado, A.; Graef, C.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Gretarsson, E. M.; Groot, P.; Grote, H.; Grunewald, S.; Gruning, P.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Halim, O.; Hall, B. R.; Hall, E. D.; Hamilton, E. Z.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hannuksela, O. A.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Haster, C.-J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hinderer, T.; Ho, W. C. G.; Hoak, D.; Hofman, D.; Holt, K.; Holz, D. E.; Hopkins, P.; Horst, C.; Hough, J.; Houston, E. A.; Howell, E. J.; Hreibi, A.; Hu, Y. M.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Indik, N.; Inta, R.; Intini, G.; Isa, H. N.; Isac, J.-M.; Isi, M.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Johnson-McDaniel, N. K.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Junker, J.; Kalaghatgi, C. V.; Kalogera, V.; Kamai, B.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kapadia, S. J.; Karki, S.; Karvinen, K. S.; Kasprzack, M.; Kastaun, W.; Katolik, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kawabe, K.; Kéfélian, F.; Keitel, D.; Kemball, A. J.; Kennedy, R.; Kent, C.; Key, J. S.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, Chunglee; Kim, J. C.; Kim, K.; Kim, W.; Kim, W. S.; Kim, Y.-M.; Kimbrell, S. J.; King, E. J.; King, P. J.; Kinley-Hanlon, M.; Kirchhoff, R.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Knowles, T. D.; Koch, P.; Koehlenbeck, S. M.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Krämer, C.; Kringel, V.; Krishnan, B.; Królak, A.; Kuehn, G.; Kumar, P.; Kumar, R.; Kumar, S.; Kuo, L.; Kutynia, A.; Kwang, S.; Lackey, B. D.; Lai, K. H.; Landry, M.; Lang, R. N.; Lange, J.; Lantz, B.; Lanza, R. K.; Larson, S. L.; Lartaux-Vollard, A.; Lasky, P. D.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, H. W.; Lee, K.; Lehmann, J.; Lenon, A.; Leon, E.; Leonardi, M.; Leroy, N.; Letendre, N.; Levin, Y.; Li, T. G. F.; Linker, S. D.; Littenberg, T. B.; Liu, J.; Liu, X.; Lo, R. K. L.; Lockerbie, N. A.; London, L. T.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lousto, C. O.; Lovelace, G.; Lück, H.; Lumaca, D.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Macas, R.; Macfoy, S.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña Hernandez, I.; Magaña-Sandoval, F.; Magaña Zertuche, L.; Magee, R. M.; Majorana, E.; Maksimovic, I.; Man, N.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markakis, C.; Markosyan, A. S.; Markowitz, A.; Maros, E.; Marquina, A.; Marsh, P.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martynov, D. V.; Marx, J. N.; Mason, K.; Massera, E.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Mastrogiovanni, S.; Matas, A.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McCuller, L.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McNeill, L.; McRae, T.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Mehmet, M.; Meidam, J.; Mejuto-Villa, E.; Melatos, A.; Mendell, G.; Mercer, R. A.; Merilh, E. L.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Metzdorff, R.; Meyers, P. M.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, A. L.; Miller, B. B.; Miller, J.; Millhouse, M.; Milovich-Goff, M. C.; Minazzoli, O.; Minenkov, Y.; Ming, J.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moffa, D.; Moggi, A.; Mogushi, K.; Mohan, M.; Mohapatra, S. R. P.; Molina, I.; Montani, M.; Moore, C. J.; Moraru, D.; Moreno, G.; Morisaki, S.; Morriss, S. R.; Mours, B.; Mow-Lowry, C. M.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Muñiz, E. A.; Muratore, M.; Murray, P. G.; Nagar, A.; Napier, K.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Neilson, J.; Nelemans, G.; Nelson, T. J. N.; Nery, M.; Neunzert, A.; Nevin, L.; Newport, J. M.; Newton, G.; Ng, K. K. Y.; Nguyen, P.; Nguyen, T. T.; Nichols, D.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Noack, A.; Nocera, F.; Nolting, D.; North, C.; Nuttall, L. K.; Oberling, J.; O'Dea, G. D.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Okada, M. A.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; Ormiston, R.; Ortega, L. F.; O'Shaughnessy, R.; Ossokine, S.; Ottaway, D. J.; Overmier, H.; Owen, B. J.; Pace, A. E.; Page, J.; Page, M. A.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, Howard; Pan, Huang-Wei; Pang, B.; Pang, P. T. H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Parida, A.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patil, M.; Patricelli, B.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perez, C. J.; Perreca, A.; Perri, L. M.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O. J.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pirello, M.; Pitkin, M.; Poe, M.; Poggiani, R.; Popolizio, P.; Porter, E. K.; Post, A.; Powell, J.; Prasad, J.; Pratt, J. W. W.; Pratten, G.; Predoi, V.; Prestegard, T.; Prijatelj, M.; Principe, M.; Privitera, S.; Prix, R.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rajan, C.; Rajbhandari, B.; Rakhmanov, M.; Ramirez, K. E.; Ramos-Buades, A.; Rapagnani, P.; Raymond, V.; Razzano, M.; Read, J.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Ren, W.; Reyes, S. D.; Ricci, F.; Ricker, P. M.; Rieger, S.; Riles, K.; Rizzo, M.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romel, C. L.; Romie, J. H.; Rosińska, D.; Ross, M. P.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Rutins, G.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Sakellariadou, M.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sampson, L. M.; Sanchez, E. J.; Sanchez, L. E.; Sanchis-Gual, N.; Sandberg, V.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Scheel, M.; Scheuer, J.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schulte, B. W.; Schutz, B. F.; Schwalbe, S. G.; Scott, J.; Scott, S. M.; Seidel, E.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Shaddock, D. A.; Shaffer, T. J.; Shah, A. A.; Shahriar, M. S.; Shaner, M. B.; Shao, L.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sieniawska, M.; Sigg, D.; Silva, A. D.; Singer, L. P.; Singh, A.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, B.; Smith, J. R.; Smith, R. J. E.; Somala, S.; Son, E. J.; Sonnenberg, J. A.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Spencer, A. P.; Srivastava, A. K.; Staats, K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stevenson, S. P.; Stone, R.; Stops, D. J.; Strain, K. A.; Stratta, G.; Strigin, S. E.; Strunk, A.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sunil, S.; Suresh, J.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Tait, S. C.; Talbot, C.; Talukder, D.; Tanner, D. B.; Tápai, M.; Taracchini, A.; Tasson, J. D.; Taylor, J. A.; Taylor, R.; Tewari, S. V.; Theeg, T.; Thies, F.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Toland, K.; Tonelli, M.; Tornasi, Z.; Torres-Forné, A.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trinastic, J.; Tringali, M. C.; Trozzo, L.; Tsang, K. W.; Tse, M.; Tso, R.; Tsukada, L.; Tsuna, D.; Tuyenbayev, D.; Ueno, K.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; Vallisneri, M.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Varma, V.; Vass, S.; Vasúth, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Venugopalan, G.; Verkindt, D.; Vetrano, F.; Viceré, A.; Viets, A. D.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walet, R.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, J. Z.; Wang, W. H.; Wang, Y. F.; Ward, R. L.; Warner, J.; Was, M.; Watchi, J.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Wessel, E. K.; Weßels, P.; Westerweck, J.; Westphal, T.; Wette, K.; Whelan, J. T.; Whitcomb, S. E.; Whiting, B. F.; Whittle, C.; Wilken, D.; Williams, D.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Woehler, J.; Wofford, J.; Wong, K. W. K.; Worden, J.; Wright, J. L.; Wu, D. S.; Wysocki, D. M.; Xiao, S.; Yamamoto, H.; Yancey, C. C.; Yang, L.; Yap, M. J.; Yazback, M.; Yu, Hang; Yu, Haocun; Yvert, M.; ZadroŻny, A.; Zanolin, M.; Zelenova, T.; Zendri, J.-P.; Zevin, M.; Zhang, L.; Zhang, M.; Zhang, T.; Zhang, Y.-H.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, S. J.; Zhu, X. J.; Zimmerman, A. B.; Zucker, M. E.; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration

2017-10-01

On August 17, 2017 at 12∶41:04 UTC the Advanced LIGO and Advanced Virgo gravitational-wave detectors made their first observation of a binary neutron star inspiral. The signal, GW170817, was detected with a combined signal-to-noise ratio of 32.4 and a false-alarm-rate estimate of less than one per 8.0 ×104 years . We infer the component masses of the binary to be between 0.86 and 2.26 M⊙ , in agreement with masses of known neutron stars. Restricting the component spins to the range inferred in binary neutron stars, we find the component masses to be in the range 1.17 - 1.60 M⊙ , with the total mass of the system 2.7 4-0.01+0.04M⊙ . The source was localized within a sky region of 28 deg2 (90% probability) and had a luminosity distance of 4 0-14+8 Mpc , the closest and most precisely localized gravitational-wave signal yet. The association with the γ -ray burst GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ -ray bursts. Subsequent identification of transient counterparts across the electromagnetic spectrum in the same location further supports the interpretation of this event as a neutron star merger. This unprecedented joint gravitational and electromagnetic observation provides insight into astrophysics, dense matter, gravitation, and cosmology.

Shapes and binary fractions of Jovian Trojans and Hildas through NEOWISE

NASA Astrophysics Data System (ADS)

Sonnett, S.; Mainzer, A.; Grav, T.; Bauer, J.; Masiero, J.; Stevenson, R.; Nugent, C.

2014-07-01

Jovian Trojans (hereafter, Trojans) and Hildas are indicative of planetary migration patterns since their capture and physical state must be explained by dynamical evolution models. Early models of minimal planetary migration necessitate that Trojans were dynamically captured from the giant planet region (e.g., Marzari & Scholl 1998). The Nice model instead suggests that Trojans were injected from the outer solar system during a period of significant giant planet migration (e.g., Morbidelli et al. 2005). A more recent version of the Nice model suggests that asymmetric scatterings and collisions would have taken place, producing dissimilar L4 and L5 clouds (Nesvorny et al. 2013). Each of these formation scenarios predicts a different origin and/or collisional evolution for Trojans, which can be inferred from rotation properties. Namely, the physical shape as a function of size helps determine the degree of collisional processing (Farinella et al. 1992). Also, the binary fraction as a function of separation between the two components can be used to determine the dominant binary formation mechanism and thus helps characterize the dynamical environment (e.g., Kern & Elliot 2006). Rotational variation usually corresponds to elongated shapes, but high amplitudes (> 0.9 magnitudes; Sheppard & Jewitt 2004) can only be explained by close or contact binaries. Therefore, rotational lightcurves can be used to infer both shape and the presence of a close companion. Motivated by the need for more observational constraints on solar system formation models and a poor understanding of the rotation properties and binary fraction of Trojans and Hildas, we are studying their rotational lightcurve amplitudes using infrared photometry from NEOWISE (Mainzer et al. 2011; Grav et al. 2011) in order to determine debiased rotational lightcurve amplitude distributions for various Trojan subpopulations and for Trojans compared to Hildas. Preliminary amplitude distributions show a large fraction of potential close or contact binaries (having Δ m > 0.9). These distributions can be used to constrain the collisional and dynamical history of solar system formation models.

An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data.

PubMed

Mugzach, Omri; Peleg, Mor; Bagley, Steven C; Guter, Stephen J; Cook, Edwin H; Altman, Russ B

2015-08-01

Our goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects' Autism Diagnostic Interview-Revised (ADI-R) assessment data. Knowledge regarding diagnostic instruments, ASD phenotypes and risk factors was added to augment an existing autism ontology via Ontology Web Language class definitions and semantic web rules. We developed a custom Protégé plugin for enumerating combinatorial OWL axioms to support the many-to-many relations of ADI-R items to diagnostic categories in the DSM. We utilized a reasoner to infer whether 2642 subjects, whose data was obtained from the Simons Foundation Autism Research Initiative, meet DSM-IV-TR (DSM-IV) and DSM-5 diagnostic criteria based on their ADI-R data. We extended the ontology by adding 443 classes and 632 rules that represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. Applying the rules on the data set showed that the method produced accurate results: the true positive and true negative rates for inferring autistic disorder diagnosis according to DSM-IV criteria were 1 and 0.065, respectively; the true positive rate for inferring ASD based on DSM-5 criteria was 0.94. The ontology allows automatic inference of subjects' disease phenotypes and diagnosis with high accuracy. The ontology may benefit future studies by serving as a knowledge base for ASD. In addition, by adding knowledge of related NDDs, commonalities and differences in manifestations and risk factors could be automatically inferred, contributing to the understanding of ASD pathophysiology. Copyright © 2015 Elsevier Inc. All rights reserved.

Hilbertian sine as an absolute measure of Bayesian inference in ISR, homeland security, medicine, and defense

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Wang, Wenjian; Hodelin, Juan; Forrester, Thomas; Romanov, Volodymyr; Kostrzewski, Andrew

2016-05-01

In this paper, Bayesian Binary Sensing (BBS) is discussed as an effective tool for Bayesian Inference (BI) evaluation in interdisciplinary areas such as ISR (and, C3I), Homeland Security, QC, medicine, defense, and many others. In particular, Hilbertian Sine (HS) as an absolute measure of BI, is introduced, while avoiding relativity of decision threshold identification, as in the case of traditional measures of BI, related to false positives and false negatives.

Knowledge requirements for automated inference of medical textbook markup.

PubMed Central

Berrios, D. C.; Kehler, A.; Fagan, L. M.

1999-01-01

Indexing medical text in journals or textbooks requires a tremendous amount of resources. We tested two algorithms for automatically indexing nouns, noun-modifiers, and noun phrases, and inferring selected binary relations between UMLS concepts in a textbook of infectious disease. Sixty-six percent of nouns and noun-modifiers and 81% of noun phrases were correctly matched to UMLS concepts. Semantic relations were identified with 100% specificity and 94% sensitivity. For some medical sub-domains, these algorithms could permit expeditious generation of more complex indexing. PMID:10566445

Exact Algorithms for Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees.

PubMed

Kordi, Misagh; Bansal, Mukul S

2017-06-01

Duplication-Transfer-Loss (DTL) reconciliation is a powerful method for studying gene family evolution in the presence of horizontal gene transfer. DTL reconciliation seeks to reconcile gene trees with species trees by postulating speciation, duplication, transfer, and loss events. Efficient algorithms exist for finding optimal DTL reconciliations when the gene tree is binary. In practice, however, gene trees are often non-binary due to uncertainty in the gene tree topologies, and DTL reconciliation with non-binary gene trees is known to be NP-hard. In this paper, we present the first exact algorithms for DTL reconciliation with non-binary gene trees. Specifically, we (i) show that the DTL reconciliation problem for non-binary gene trees is fixed-parameter tractable in the maximum degree of the gene tree, (ii) present an exponential-time, but in-practice efficient, algorithm to track and enumerate all optimal binary resolutions of a non-binary input gene tree, and (iii) apply our algorithms to a large empirical data set of over 4700 gene trees from 100 species to study the impact of gene tree uncertainty on DTL-reconciliation and to demonstrate the applicability and utility of our algorithms. The new techniques and algorithms introduced in this paper will help biologists avoid incorrect evolutionary inferences caused by gene tree uncertainty.

Inference in fuzzy rule bases with conflicting evidence

NASA Technical Reports Server (NTRS)

Koczy, Laszlo T.

1992-01-01

Inference based on fuzzy 'If ... then' rules has played a very important role since when Zadeh proposed the Compositional Rule of Inference and, especially, since the first successful application presented by Mamdani. From the mid-1980's when the 'fuzzy boom' started in Japan, numerous industrial applications appeared, all using simplified techniques because of the high levels of computational complexity. Another feature is that antecedents in the rules are distributed densely in the input space, so the conclusion can be calculated by some weighted combination of the consequents of the matching (fired) rules. The CRI works in the following way: If R is a rule and A* is an observation, the conclusion is computed by B* = R o A* (o stands for the max-min composition). Algorithms implementing this idea directly have an exponential time complexity (maybe the problem is NP-hard) as the rules are relations in X x Y, a k1 x k2 dimensional space, if X is k1, Y is k2 dimensional. The simplified techniques usually decompose the relation into k1 projections in X(sub i) and measure in some way the degree of similarity between observation and antecedent by some parameter of the overlapping. These parameters are aggregated to a single value in (0,1) which is applied as a resulting weight for the given rule. The projections of rules in dimensions Y(sub i) are weighted by these aggregated values and then they are combined in order to obtain a resulting conclusion separately in every dimension. This method is unapplicable with sparse bases as there is no guarantee that an arbitrary observation matches with any of the antecedents. Then, the degree of similarity is 0 and all consequents are weighted by 0. Some considerations for such a situation are summarized in the next sections.

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

1991-01-01

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

Inferring Binary and Trinary Stellar Populations in Photometric and Astrometric Surveys

NASA Astrophysics Data System (ADS)

Widmark, Axel; Leistedt, Boris; Hogg, David W.

2018-04-01

Multiple stellar systems are ubiquitous in the Milky Way but are often unresolved and seen as single objects in spectroscopic, photometric, and astrometric surveys. However, modeling them is essential for developing a full understanding of large surveys such as Gaia and connecting them to stellar and Galactic models. In this paper, we address this problem by jointly fitting the Gaia and Two Micron All Sky Survey photometric and astrometric data using a data-driven Bayesian hierarchical model that includes populations of binary and trinary systems. This allows us to classify observations into singles, binaries, and trinaries, in a robust and efficient manner, without resorting to external models. We are able to identify multiple systems and, in some cases, make strong predictions for the properties of their unresolved stars. We will be able to compare such predictions with Gaia Data Release 4, which will contain astrometric identification and analysis of binary systems.

Mesoscopic model for binary fluids

NASA Astrophysics Data System (ADS)

Echeverria, C.; Tucci, K.; Alvarez-Llamoza, O.; Orozco-Guillén, E. E.; Morales, M.; Cosenza, M. G.

2017-10-01

We propose a model for studying binary fluids based on the mesoscopic molecular simulation technique known as multiparticle collision, where the space and state variables are continuous, and time is discrete. We include a repulsion rule to simulate segregation processes that does not require calculation of the interaction forces between particles, so binary fluids can be described on a mesoscopic scale. The model is conceptually simple and computationally efficient; it maintains Galilean invariance and conserves the mass and energy in the system at the micro- and macro-scale, whereas momentum is conserved globally. For a wide range of temperatures and densities, the model yields results in good agreement with the known properties of binary fluids, such as the density profile, interface width, phase separation, and phase growth. We also apply the model to the study of binary fluids in crowded environments with consistent results.

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

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

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

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

A Theory of Electrical Conductivity of Pseudo-Binary Equivalent Molten Salt

NASA Astrophysics Data System (ADS)

Matsunaga, Shigeki; Koishi, Takahiro; Tamaki, Shigeru

2008-02-01

Many years ago, Sundheim proposed the "universal golden rule" by experiments, i.e. the ratio of the partial ionic conductivities in molten binary salt is equal to the inverse mass ratio of each ions, σ+/σ- = m-/m-. In the previous works, we have proved this relation by the theory using Langevin equation, and by molecular dynamics simulations (MD). In this study, the pseudo binary molten salt NaCl-KCl system is investigated in the same theoretical framework as previous works as the serial work in molten salts. The MD results are also reported in connection with the theoretical analysis.

DISTINGUISHING COMPACT BINARY POPULATION SYNTHESIS MODELS USING GRAVITATIONAL WAVE OBSERVATIONS OF COALESCING BINARY BLACK HOLES

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

Stevenson, Simon; Ohme, Frank; Fairhurst, Stephen, E-mail: simon.stevenson@ligo.org

2015-09-01

The coalescence of compact binaries containing neutron stars or black holes is one of the most promising signals for advanced ground-based laser interferometer gravitational-wave (GW) detectors, with the first direct detections expected over the next few years. The rate of binary coalescences and the distribution of component masses is highly uncertain, and population synthesis models predict a wide range of plausible values. Poorly constrained parameters in population synthesis models correspond to poorly understood astrophysics at various stages in the evolution of massive binary stars, the progenitors of binary neutron star and binary black hole systems. These include effects such asmore » supernova kick velocities, parameters governing the energetics of common envelope evolution and the strength of stellar winds. Observing multiple binary black hole systems through GWs will allow us to infer details of the astrophysical mechanisms that lead to their formation. Here we simulate GW observations from a series of population synthesis models including the effects of known selection biases, measurement errors and cosmology. We compare the predictions arising from different models and show that we will be able to distinguish between them with observations (or the lack of them) from the early runs of the advanced LIGO and Virgo detectors. This will allow us to narrow down the large parameter space for binary evolution models.« less

Hunting For Wild Brown Dwarf Companions To White Dwarfs In UKIDSS And SDSS

NASA Astrophysics Data System (ADS)

Day-Jones, Avril; Pinfield, D. J.; Jones, H. R. A.; Napiwotzki, R.; Burningham, B.; Jenkins, J. S.; UKIDSS Cool Dwarf Science Working Group

2008-03-01

We present findings from our search of the latest releases of SDSS and UKIDSS LAS for very widely separated white dwarf - ultracool dwarf binaries. Ultracool dwarfs found in such binary systems could be used as benchmark objects, whose properties, such as age and distance can be inferred indirectly from the white dwarf primary (with no need to refer to atmospheric models) and can provide a test bed for theoretical models, they can therefore be used observationally pin down how physical properties affect ultracool dwarf spectra.

Causal analysis of ordinal treatments and binary outcomes under truncation by death.

PubMed

Wang, Linbo; Richardson, Thomas S; Zhou, Xiao-Hua

2017-06-01

It is common that in multi-arm randomized trials, the outcome of interest is "truncated by death," meaning that it is only observed or well-defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal treatments and binary outcomes in presence of truncation by death. We illustrate via examples the appropriateness of our assumptions in different scientific contexts.

Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems

NASA Technical Reports Server (NTRS)

Hinchey, Mike; Rash, James; Erickson, John; Gracanin, Denis; Rouff, Chris

2010-01-01

Mathematically sound techniques are used to view a knowledge-based system (KBS) as a set of processes executing in parallel and being enabled in response to specific rules being fired. The set of processes can be manipulated, examined, analyzed, and used in a simulation. The tool that embodies this technology may warn developers of errors in their rules, but may also highlight rules (or sets of rules) in the system that are underspecified (or overspecified) and need to be corrected for the KBS to operate as intended. The rules embodied in a KBS specify the allowed situations, events, and/or results of the system they describe. In that sense, they provide a very abstract specification of a system. The system is implemented through the combination of the system specification together with an appropriate inference engine, independent of the algorithm used in that inference engine. Viewing the rule base as a major component of the specification, and choosing an appropriate specification notation to represent it, reveals how additional power can be derived from an approach to the knowledge-base system that involves analysis, simulation, and verification. This innovative approach requires no special knowledge of the rules, and allows a general approach where standardized analysis, verification, simulation, and model checking techniques can be applied to the KBS.

Estimation of tool wear length in finish milling using a fuzzy inference algorithm

NASA Astrophysics Data System (ADS)

Ko, Tae Jo; Cho, Dong Woo

1993-10-01

The geometric accuracy and surface roughness are mainly affected by the flank wear at the minor cutting edge in finish machining. A fuzzy estimator obtained by a fuzzy inference algorithm with a max-min composition rule to evaluate the minor flank wear length in finish milling is introduced. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration of the spindle housing. Linguistic rules for fuzzy estimation are constructed using these features, and then fuzzy inferences are carried out with test data sets under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length, and its mean error is less than 12%.

DNA biosensors that reason.

PubMed

Sainz de Murieta, Iñaki; Rodríguez-Patón, Alfonso

2012-08-01

Despite the many designs of devices operating with the DNA strand displacement, surprisingly none is explicitly devoted to the implementation of logical deductions. The present article introduces a new model of biosensor device that uses nucleic acid strands to encode simple rules such as "IF DNA_strand(1) is present THEN disease(A)" or "IF DNA_strand(1) AND DNA_strand(2) are present THEN disease(B)". Taking advantage of the strand displacement operation, our model makes these simple rules interact with input signals (either DNA or any type of RNA) to generate an output signal (in the form of nucleotide strands). This output signal represents a diagnosis, which either can be measured using FRET techniques, cascaded as the input of another logical deduction with different rules, or even be a drug that is administered in response to a set of symptoms. The encoding introduces an implicit error cancellation mechanism, which increases the system scalability enabling longer inference cascades with a bounded and controllable signal-noise relation. It also allows the same rule to be used in forward inference or backward inference, providing the option of validly outputting negated propositions (e.g. "diagnosis A excluded"). The models presented in this paper can be used to implement smart logical DNA devices that perform genetic diagnosis in vitro. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Induction of belief decision trees from data

NASA Astrophysics Data System (ADS)

AbuDahab, Khalil; Xu, Dong-ling; Keane, John

2012-09-01

In this paper, a method for acquiring belief rule-bases by inductive inference from data is described and evaluated. Existing methods extract traditional rules inductively from data, with consequents that are believed to be either 100% true or 100% false. Belief rules can capture uncertain or incomplete knowledge using uncertain belief degrees in consequents. Instead of using singled-value consequents, each belief rule deals with a set of collectively exhaustive and mutually exclusive consequents. The proposed method extracts belief rules from data which contain uncertain or incomplete knowledge.

Inference of cancer-specific gene regulatory networks using soft computing rules.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Cellular automata rule characterization and classification using texture descriptors

NASA Astrophysics Data System (ADS)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.

GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral.

PubMed

Abbott, B P; Abbott, R; Abbott, T D; Acernese, F; Ackley, K; Adams, C; Adams, T; Addesso, P; Adhikari, R X; Adya, V B; Affeldt, C; Afrough, M; Agarwal, B; Agathos, M; Agatsuma, K; Aggarwal, N; Aguiar, O D; Aiello, L; Ain, A; Ajith, P; Allen, B; Allen, G; Allocca, A; Altin, P A; Amato, A; Ananyeva, A; Anderson, S B; Anderson, W G; Angelova, S V; Antier, S; Appert, S; Arai, K; Araya, M C; Areeda, J S; Arnaud, N; Arun, K G; Ascenzi, S; Ashton, G; Ast, M; Aston, S M; Astone, P; Atallah, D V; Aufmuth, P; Aulbert, C; AultONeal, K; Austin, C; Avila-Alvarez, A; Babak, S; Bacon, P; Bader, M K M; Bae, S; Bailes, M; Baker, P T; Baldaccini, F; Ballardin, G; Ballmer, S W; Banagiri, S; Barayoga, J C; Barclay, S E; Barish, B C; Barker, D; Barkett, K; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barta, D; Barthelmy, S D; Bartlett, J; Bartos, I; Bassiri, R; Basti, A; Batch, J C; Bawaj, M; Bayley, J C; Bazzan, M; Bécsy, B; Beer, C; Bejger, M; Belahcene, I; Bell, A S; Berger, B K; Bergmann, G; Bernuzzi, S; Bero, J J; Berry, C P L; Bersanetti, D; Bertolini, A; Betzwieser, J; Bhagwat, S; Bhandare, R; Bilenko, I A; Billingsley, G; Billman, C R; Birch, J; Birney, R; Birnholtz, O; Biscans, S; Biscoveanu, S; Bisht, A; Bitossi, M; Biwer, C; Bizouard, M A; Blackburn, J K; Blackman, J; Blair, C D; Blair, D G; Blair, R M; Bloemen, S; Bock, O; Bode, N; Boer, M; Bogaert, G; Bohe, A; Bondu, F; Bonilla, E; Bonnand, R; Boom, B A; Bork, R; Boschi, V; Bose, S; Bossie, K; Bouffanais, Y; Bozzi, A; Bradaschia, C; Brady, P R; Branchesi, M; Brau, J E; Briant, T; Brillet, A; Brinkmann, M; Brisson, V; Brockill, P; Broida, J E; Brooks, A F; Brown, D A; Brown, D D; Brunett, S; Buchanan, C C; Buikema, A; Bulik, T; Bulten, H J; Buonanno, A; Buskulic, D; Buy, C; Byer, R L; Cabero, M; Cadonati, L; Cagnoli, G; Cahillane, C; Calderón Bustillo, J; Callister, T A; Calloni, E; Camp, J B; Canepa, M; Canizares, P; Cannon, K C; Cao, H; Cao, J; Capano, C D; Capocasa, E; Carbognani, F; Caride, S; Carney, M F; Carullo, G; Casanueva Diaz, J; Casentini, C; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Cella, G; Cepeda, C B; Cerdá-Durán, P; Cerretani, G; Cesarini, E; Chamberlin, S J; Chan, M; Chao, S; Charlton, P; Chase, E; Chassande-Mottin, E; Chatterjee, D; Chatziioannou, K; Cheeseboro, B D; Chen, H Y; Chen, X; Chen, Y; Cheng, H-P; Chia, H; Chincarini, A; Chiummo, A; Chmiel, T; Cho, H S; Cho, M; Chow, J H; Christensen, N; Chu, Q; Chua, A J K; Chua, S; Chung, A K W; Chung, S; Ciani, G; Ciolfi, R; Cirelli, C E; Cirone, A; Clara, F; Clark, J A; Clearwater, P; Cleva, F; Cocchieri, C; Coccia, E; Cohadon, P-F; Cohen, D; Colla, A; Collette, C G; Cominsky, L R; Constancio, M; Conti, L; Cooper, S J; Corban, P; Corbitt, T R; Cordero-Carrión, I; Corley, K R; Cornish, N; Corsi, A; Cortese, S; Costa, C A; Coughlin, M W; Coughlin, S B; Coulon, J-P; Countryman, S T; Couvares, P; Covas, P B; Cowan, E E; Coward, D M; Cowart, M J; Coyne, D C; Coyne, R; Creighton, J D E; Creighton, T D; Cripe, J; Crowder, S G; Cullen, T J; Cumming, A; Cunningham, L; Cuoco, E; Dal Canton, T; Dálya, G; Danilishin, S L; D'Antonio, S; Danzmann, K; Dasgupta, A; Da Silva Costa, C F; Dattilo, V; Dave, I; Davier, M; Davis, D; Daw, E J; Day, B; De, S; DeBra, D; Degallaix, J; De Laurentis, M; Deléglise, S; Del Pozzo, W; Demos, N; Denker, T; Dent, T; De Pietri, R; Dergachev, V; De Rosa, R; DeRosa, R T; De Rossi, C; DeSalvo, R; de Varona, O; Devenson, J; Dhurandhar, S; Díaz, M C; Dietrich, T; Di Fiore, L; Di Giovanni, M; Di Girolamo, T; Di Lieto, A; Di Pace, S; Di Palma, I; Di Renzo, F; Doctor, Z; Dolique, V; Donovan, F; Dooley, K L; Doravari, S; Dorrington, I; Douglas, R; Dovale Álvarez, M; Downes, T P; Drago, M; Dreissigacker, C; Driggers, J C; Du, Z; Ducrot, M; Dudi, R; Dupej, P; Dwyer, S E; Edo, T B; Edwards, M C; Effler, A; Eggenstein, H-B; Ehrens, P; Eichholz, J; Eikenberry, S S; Eisenstein, R A; Essick, R C; Estevez, D; Etienne, Z B; Etzel, T; Evans, M; Evans, T M; Factourovich, M; Fafone, V; Fair, H; Fairhurst, S; Fan, X; Farinon, S; Farr, B; Farr, W M; Fauchon-Jones, E J; Favata, M; Fays, M; Fee, C; Fehrmann, H; Feicht, J; Fejer, M M; Fernandez-Galiana, A; Ferrante, I; Ferreira, E C; Ferrini, F; Fidecaro, F; Finstad, D; Fiori, I; Fiorucci, D; Fishbach, M; Fisher, R P; Fitz-Axen, M; Flaminio, R; Fletcher, M; Fong, H; Font, J A; Forsyth, P W F; Forsyth, S S; Fournier, J-D; Frasca, S; Frasconi, F; Frei, Z; Freise, A; Frey, R; Frey, V; Fries, E M; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gabbard, H; Gadre, B U; Gaebel, S M; Gair, J R; Gammaitoni, L; Ganija, M R; Gaonkar, S G; Garcia-Quiros, C; Garufi, F; Gateley, B; Gaudio, S; Gaur, G; Gayathri, V; Gehrels, N; Gemme, G; Genin, E; Gennai, A; George, D; George, J; Gergely, L; Germain, V; Ghonge, S; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gill, K; Glover, L; Goetz, E; Goetz, R; Gomes, S; Goncharov, B; González, G; Gonzalez Castro, J M; Gopakumar, A; Gorodetsky, M L; Gossan, S E; Gosselin, M; Gouaty, R; Grado, A; Graef, C; Granata, M; 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Ju, L; Junker, J; Kalaghatgi, C V; Kalogera, V; Kamai, B; Kandhasamy, S; Kang, G; Kanner, J B; Kapadia, S J; Karki, S; Karvinen, K S; Kasprzack, M; Kastaun, W; Katolik, M; Katsavounidis, E; Katzman, W; Kaufer, S; Kawabe, K; Kéfélian, F; Keitel, D; Kemball, A J; Kennedy, R; Kent, C; Key, J S; Khalili, F Y; Khan, I; Khan, S; Khan, Z; Khazanov, E A; Kijbunchoo, N; Kim, Chunglee; Kim, J C; Kim, K; Kim, W; Kim, W S; Kim, Y-M; Kimbrell, S J; King, E J; King, P J; Kinley-Hanlon, M; Kirchhoff, R; Kissel, J S; Kleybolte, L; Klimenko, S; Knowles, T D; Koch, P; Koehlenbeck, S M; Koley, S; Kondrashov, V; Kontos, A; Korobko, M; Korth, W Z; Kowalska, I; Kozak, D B; Krämer, C; Kringel, V; Krishnan, B; Królak, A; Kuehn, G; Kumar, P; Kumar, R; Kumar, S; Kuo, L; Kutynia, A; Kwang, S; Lackey, B D; Lai, K H; Landry, M; Lang, R N; Lange, J; Lantz, B; Lanza, R K; Larson, S L; Lartaux-Vollard, A; Lasky, P D; Laxen, M; Lazzarini, A; Lazzaro, C; Leaci, P; Leavey, S; Lee, C H; Lee, H K; Lee, H M; Lee, H W; Lee, K; Lehmann, J; Lenon, A; Leon, E; Leonardi, M; Leroy, N; Letendre, N; Levin, Y; Li, T G F; Linker, S D; Littenberg, T B; Liu, J; Liu, X; Lo, R K L; Lockerbie, N A; London, L T; Lord, J E; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J D; Lousto, C O; Lovelace, G; Lück, H; Lumaca, D; Lundgren, A P; Lynch, R; Ma, Y; Macas, R; Macfoy, S; Machenschalk, B; MacInnis, M; Macleod, D M; Magaña Hernandez, I; Magaña-Sandoval, F; Magaña Zertuche, L; Magee, R M; Majorana, E; Maksimovic, I; Man, N; Mandic, V; Mangano, V; Mansell, G L; Manske, M; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markakis, C; Markosyan, A S; Markowitz, A; Maros, E; Marquina, A; Marsh, P; Martelli, F; Martellini, L; Martin, I W; Martin, R M; Martynov, D V; Marx, J N; Mason, K; Massera, E; Masserot, A; Massinger, T J; Masso-Reid, M; Mastrogiovanni, S; Matas, A; Matichard, F; Matone, L; Mavalvala, N; Mazumder, N; McCarthy, R; McClelland, D E; McCormick, S; McCuller, L; McGuire, S C; McIntyre, G; McIver, J; McManus, D J; McNeill, L; McRae, T; McWilliams, S T; Meacher, D; Meadors, G D; Mehmet, M; Meidam, J; Mejuto-Villa, E; Melatos, A; Mendell, G; Mercer, R A; Merilh, E L; Merzougui, M; Meshkov, S; Messenger, C; Messick, C; Metzdorff, R; Meyers, P M; Miao, H; Michel, C; Middleton, H; Mikhailov, E E; Milano, L; Miller, A L; Miller, B B; Miller, J; Millhouse, M; Milovich-Goff, M C; Minazzoli, O; Minenkov, Y; Ming, J; Mishra, C; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moffa, D; Moggi, A; Mogushi, K; Mohan, M; Mohapatra, S R P; Molina, I; Montani, M; Moore, C J; Moraru, D; Moreno, G; Morisaki, S; Morriss, S R; Mours, B; Mow-Lowry, C M; Mueller, G; Muir, A W; Mukherjee, Arunava; Mukherjee, D; Mukherjee, S; Mukund, N; Mullavey, A; Munch, J; Muñiz, E A; Muratore, M; Murray, P G; Nagar, A; Napier, K; Nardecchia, I; Naticchioni, L; Nayak, R K; Neilson, J; Nelemans, G; Nelson, T J N; Nery, M; Neunzert, A; Nevin, L; Newport, J M; Newton, G; Ng, K K Y; Nguyen, P; Nguyen, T T; Nichols, D; Nielsen, A B; Nissanke, S; Nitz, A; Noack, A; Nocera, F; Nolting, D; North, C; Nuttall, L K; Oberling, J; O'Dea, G D; Ogin, G H; Oh, J J; Oh, S H; Ohme, F; Okada, M A; Oliver, M; Oppermann, P; Oram, Richard J; O'Reilly, B; Ormiston, R; Ortega, L F; O'Shaughnessy, R; Ossokine, S; Ottaway, D J; Overmier, H; Owen, B J; Pace, A E; Page, J; Page, M A; Pai, A; Pai, S A; Palamos, J R; Palashov, O; Palomba, C; Pal-Singh, A; Pan, Howard; Pan, Huang-Wei; Pang, B; Pang, P T H; Pankow, C; Pannarale, F; Pant, B C; Paoletti, F; Paoli, A; Papa, M A; Parida, A; Parker, W; Pascucci, D; Pasqualetti, A; Passaquieti, R; Passuello, D; Patil, M; Patricelli, B; Pearlstone, B L; Pedraza, M; Pedurand, R; Pekowsky, L; Pele, A; Penn, S; Perez, C J; Perreca, A; Perri, L M; Pfeiffer, H P; Phelps, M; Piccinni, O J; Pichot, M; Piergiovanni, F; Pierro, V; Pillant, G; Pinard, L; Pinto, I M; Pirello, M; Pitkin, M; Poe, M; Poggiani, R; Popolizio, P; Porter, E K; Post, A; Powell, J; Prasad, J; Pratt, J W W; Pratten, G; Predoi, V; Prestegard, T; Prijatelj, M; Principe, M; Privitera, S; Prix, R; Prodi, G A; Prokhorov, L G; Puncken, O; Punturo, M; Puppo, P; Pürrer, M; Qi, H; Quetschke, V; Quintero, E A; Quitzow-James, R; Raab, F J; Rabeling, D S; Radkins, H; Raffai, P; Raja, S; Rajan, C; Rajbhandari, B; Rakhmanov, M; Ramirez, K E; Ramos-Buades, A; Rapagnani, P; Raymond, V; Razzano, M; Read, J; Regimbau, T; Rei, L; Reid, S; Reitze, D H; Ren, W; Reyes, S D; Ricci, F; Ricker, P M; Rieger, S; Riles, K; Rizzo, M; Robertson, N A; Robie, R; Robinet, F; Rocchi, A; Rolland, L; Rollins, J G; Roma, V J; Romano, J D; Romano, R; Romel, C L; Romie, J H; Rosińska, D; Ross, M P; Rowan, S; Rüdiger, A; Ruggi, P; Rutins, G; Ryan, K; Sachdev, S; Sadecki, T; Sadeghian, L; Sakellariadou, M; Salconi, L; Saleem, M; Salemi, F; Samajdar, A; Sammut, L; Sampson, L M; Sanchez, E J; Sanchez, L E; Sanchis-Gual, N; Sandberg, V; Sanders, J R; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Sauter, O; Savage, R L; Sawadsky, A; Schale, P; Scheel, M; Scheuer, J; Schmidt, J; Schmidt, P; Schnabel, R; Schofield, R M S; Schönbeck, A; Schreiber, E; Schuette, D; Schulte, B W; Schutz, B F; Schwalbe, S G; Scott, J; Scott, S M; Seidel, E; Sellers, D; Sengupta, A S; Sentenac, D; Sequino, V; Sergeev, A; Shaddock, D A; Shaffer, T J; Shah, A A; Shahriar, M S; Shaner, M B; Shao, L; Shapiro, B; Shawhan, P; Sheperd, A; Shoemaker, D H; Shoemaker, D M; Siellez, K; Siemens, X; Sieniawska, M; Sigg, D; Silva, A D; Singer, L P; Singh, A; Singhal, A; Sintes, A M; Slagmolen, B J J; Smith, B; Smith, J R; Smith, R J E; Somala, S; Son, E J; Sonnenberg, J A; Sorazu, B; Sorrentino, F; Souradeep, T; Spencer, A P; Srivastava, A K; Staats, K; Staley, A; Steinke, M; Steinlechner, J; Steinlechner, S; Steinmeyer, D; Stevenson, S P; Stone, R; Stops, D J; Strain, K A; Stratta, G; Strigin, S E; Strunk, A; Sturani, R; Stuver, A L; Summerscales, T Z; Sun, L; Sunil, S; Suresh, J; Sutton, P J; Swinkels, B L; Szczepańczyk, M J; Tacca, M; Tait, S C; Talbot, C; Talukder, D; Tanner, D B; Tápai, M; Taracchini, A; Tasson, J D; Taylor, J A; Taylor, R; Tewari, S V; Theeg, T; Thies, F; Thomas, E G; Thomas, M; Thomas, P; Thorne, K A; Thorne, K S; Thrane, E; Tiwari, S; Tiwari, V; Tokmakov, K V; Toland, K; Tonelli, M; Tornasi, Z; Torres-Forné, A; Torrie, C I; Töyrä, D; Travasso, F; Traylor, G; Trinastic, J; Tringali, M C; Trozzo, L; Tsang, K W; Tse, M; Tso, R; Tsukada, L; Tsuna, D; Tuyenbayev, D; Ueno, K; Ugolini, D; Unnikrishnan, C S; Urban, A L; Usman, S A; Vahlbruch, H; Vajente, G; Valdes, G; Vallisneri, M; van Bakel, N; van Beuzekom, M; van den Brand, J F J; Van Den Broeck, C; Vander-Hyde, D C; van der Schaaf, L; van Heijningen, J V; van Veggel, A A; Vardaro, M; Varma, V; Vass, S; Vasúth, M; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Venugopalan, G; Verkindt, D; Vetrano, F; Viceré, A; Viets, A D; Vinciguerra, S; Vine, D J; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Vyatchanin, S P; Wade, A R; Wade, L E; Wade, M; Walet, R; Walker, M; Wallace, L; Walsh, S; Wang, G; Wang, H; Wang, J Z; Wang, W H; Wang, Y F; Ward, R L; Warner, J; Was, M; Watchi, J; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Wen, L; Wessel, E K; Weßels, P; Westerweck, J; Westphal, T; Wette, K; Whelan, J T; Whitcomb, S E; Whiting, B F; Whittle, C; Wilken, D; Williams, D; Williams, R D; Williamson, A R; Willis, J L; Willke, B; Wimmer, M H; Winkler, W; Wipf, C C; Wittel, H; Woan, G; Woehler, J; Wofford, J; Wong, K W K; Worden, J; Wright, J L; Wu, D S; Wysocki, D M; Xiao, S; Yamamoto, H; Yancey, C C; Yang, L; Yap, M J; Yazback, M; Yu, Hang; Yu, Haocun; Yvert, M; Zadrożny, A; Zanolin, M; Zelenova, T; Zendri, J-P; Zevin, M; Zhang, L; Zhang, M; Zhang, T; Zhang, Y-H; Zhao, C; Zhou, M; Zhou, Z; Zhu, S J; Zhu, X J; Zimmerman, A B; Zucker, M E; Zweizig, J

2017-10-20

On August 17, 2017 at 12∶41:04 UTC the Advanced LIGO and Advanced Virgo gravitational-wave detectors made their first observation of a binary neutron star inspiral. The signal, GW170817, was detected with a combined signal-to-noise ratio of 32.4 and a false-alarm-rate estimate of less than one per 8.0×10^{4}  years. We infer the component masses of the binary to be between 0.86 and 2.26  M_{⊙}, in agreement with masses of known neutron stars. Restricting the component spins to the range inferred in binary neutron stars, we find the component masses to be in the range 1.17-1.60  M_{⊙}, with the total mass of the system 2.74_{-0.01}^{+0.04}M_{⊙}. The source was localized within a sky region of 28  deg^{2} (90% probability) and had a luminosity distance of 40_{-14}^{+8}  Mpc, the closest and most precisely localized gravitational-wave signal yet. The association with the γ-ray burst GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts. Subsequent identification of transient counterparts across the electromagnetic spectrum in the same location further supports the interpretation of this event as a neutron star merger. This unprecedented joint gravitational and electromagnetic observation provides insight into astrophysics, dense matter, gravitation, and cosmology.

High energy radiation precursors to the collapse of black holes binaries based on resonating plasma modes

NASA Astrophysics Data System (ADS)

Coppi, B.

2018-05-01

The presence of well organized plasma structures around binary systems of collapsed objects [1,2] (black holes and neutron stars) is proposed in which processes can develop [3] leading to high energy electromagnetic radiation emission immediately before the binary collapse. The formulated theoretical model supporting this argument shows that resonating plasma collective modes can be excited in the relevant magnetized plasma structure. Accordingly, the collapse of the binary approaches, with the loss of angular momentum by emission of gravitational waves [2], the resonance conditions with vertically standing plasma density and magnetic field oscillations are met. Then, secondary plasma modes propagating along the magnetic field are envisioned to be sustained with mode-particle interactions producing the particle populations responsible for the observable electromagnetic radiation emission. Weak evidence for a precursor to the binary collapse reported in Ref. [2], has been offered by the Agile X-γ-ray observatory [4] while the August 17 (2017) event, identified first by the LIGO-Virgo detection of gravitational waves and featuring the inferred collapse of a neutron star binary, improves the evidence of such a precursor. A new set of experimental observations is needed to reassess the presented theory.

An Investigation and Interpretation of Selected Topics in Uncertainty Reasoning

DTIC Science & Technology

1989-12-01

Characterizing seconditry uncertainty as spurious evidence and including it in the inference process , It was shown that probability ratio graphs are a...in the inference process has great impact on the computational complexity of an Inference process . viii An Investigation and Interpretation of...Systems," he outlines a five step process that incorporates Blyeslan reasoning in the development of the expert system rule base: 1. A group of

The impact of category structure and training methodology on learning and generalizing within-category representations.

PubMed

Ell, Shawn W; Smith, David B; Peralta, Gabriela; Hélie, Sébastien

2017-08-01

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.

DISCOVERY OF A HIGHLY UNEQUAL-MASS BINARY T DWARF WITH KECK LASER GUIDE STAR ADAPTIVE OPTICS: A COEVALITY TEST OF SUBSTELLAR THEORETICAL MODELS AND EFFECTIVE TEMPERATURES

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

Liu, Michael C.; Dupuy, Trent J.; Leggett, S. K., E-mail: mliu@ifa.hawaii.ed

Highly unequal-mass ratio binaries are rare among field brown dwarfs, with the mass ratio distribution of the known census described by q {sup (4.9{+-}0.7)}. However, such systems enable a unique test of the joint accuracy of evolutionary and atmospheric models, under the constraint of coevality for the individual components (the 'isochrone test'). We carry out this test using two of the most extreme field substellar binaries currently known, the T1 + T6 {epsilon} Ind Bab binary and a newly discovered 0.''14 T2.0 + T7.5 binary, 2MASS J12095613-1004008AB, identified with Keck laser guide star adaptive optics. The latter is the mostmore » extreme tight binary resolved to date (q {approx} 0.5). Based on the locations of the binary components on the Hertzsprung-Russell (H-R) diagram, current models successfully indicate that these two systems are coeval, with internal age differences of log(age) = -0.8 {+-} 1.3(-1.0{sup +1.2}{sub -1.3}) dex and 0.5{sup +0.4}{sub -0.3}(0.3{sup +0.3}{sub -0.4}) dex for 2MASS J1209-1004AB and {epsilon} Ind Bab, respectively, as inferred from the Lyon (Tucson) models. However, the total mass of {epsilon} Ind Bab derived from the H-R diagram ({approx} 80 M{sub Jup} using the Lyon models) is strongly discrepant with the reported dynamical mass. This problem, which is independent of the assumed age of the {epsilon} Ind Bab system, can be explained by a {approx} 50-100 K systematic error in the model atmosphere fitting, indicating slightly warmer temperatures for both components; bringing the mass determinations from the H-R diagram and the visual orbit into consistency leads to an inferred age of {approx} 6 Gyr for {epsilon} Ind Bab, older than previously assumed. Overall, the two T dwarf binaries studied here, along with recent results from T dwarfs in age and mass benchmark systems, yield evidence for small ({approx}100 K) errors in the evolutionary models and/or model atmospheres, but not significantly larger. Future parallax, resolved spectroscopy, and dynamical mass measurements for 2MASS J1209-1004AB will enable a more stringent application of the isochrone test. Finally, the binary nature of this object reduces its utility as the primary T3 near-IR spectral typing standard; we suggest SDSS J1206+2813 as a replacement.« less

On Some Assumptions of the Null Hypothesis Statistical Testing

ERIC Educational Resources Information Center

Patriota, Alexandre Galvão

2017-01-01

Bayesian and classical statistical approaches are based on different types of logical principles. In order to avoid mistaken inferences and misguided interpretations, the practitioner must respect the inference rules embedded into each statistical method. Ignoring these principles leads to the paradoxical conclusions that the hypothesis…

Genie Inference Engine Rule Writer’s Guide.

DTIC Science & Technology

1987-08-01

33 APPENDIX D. Animal Bootstrap File.............................................................. 39...APPENDIX E. Sample Run of Animal Identification Expert System.......................... 43 APPENDIX F. Numeric Test Knowledge Base...and other data s.tructures stored in the knowledge base (KB), queries the user for input, and draws conclusions. Genie (GENeric Inference Engine) is

Development of the Diagnostic Expert System for Tea Processing

NASA Astrophysics Data System (ADS)

Yoshitomi, Hitoshi; Yamaguchi, Yuichi

A diagnostic expert system for tea processing which can presume the cause of the defect of the processed tea was developed to contribute to the improvement of tea processing. This system that consists of some programs can be used through the Internet. The inference engine, the core of the system adopts production system which is well used on artificial intelligence, and is coded by Prolog as the artificial intelligence oriented language. At present, 176 rules for inference have been registered on this system. The system will be able to presume better if more rules are added to the system.

TWO STARS TWO WAYS: CONFIRMING A MICROLENSING BINARY LENS SOLUTION WITH A SPECTROSCOPIC MEASUREMENT OF THE ORBIT

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

Yee, Jennifer C.; Johnson, John Asher; Eastman, Jason

Light curves of microlensing events involving stellar binaries and planetary systems can provide information about the orbital elements of the system due to orbital modulations of the caustic structure. Accurately measuring the orbit in either the stellar or planetary case requires detailed modeling of subtle deviations in the light curve. At the same time, the natural, Cartesian parameterization of a microlensing binary is partially degenerate with the microlens parallax. Hence, it is desirable to perform independent tests of the predictions of microlens orbit models using radial velocity (RV) time series of the lens binary system. To this end, we presentmore » 3.5 years of RV monitoring of the binary lens system OGLE-2009-BLG-020 L, for which Skowron et al. constrained all internal parameters of the 200–700 day orbit. Our RV measurements reveal an orbit that is consistent with the predictions of the microlens light curve analysis, thereby providing the first confirmation of orbital elements inferred from microlensing events.« less

The PyCBC search for compact binary mergers in the second run of Advanced LIGO

NASA Astrophysics Data System (ADS)

Dal Canton, Tito; PyCBC Team

2017-01-01

The PyCBC software implements a matched-filter search for gravitational-wave signals associated with mergers of compact binaries. During the first observing run of Advanced LIGO, it played a fundamental role in the discovery of the binary-black-hole merger signals GW150914, GW151226 and LVT151012. In preparation for Advanced LIGO's second run, PyCBC has been modified with the goal of increasing the sensitivity of the search, reducing its computational cost and expanding the explored parameter space. The ability to report signals with a latency of tens of seconds and to perform inference on the parameters of the detected signals has also been introduced. I will give an overview of PyCBC and present the new features and their impact.

A transient radio jet in an erupting dwarf nova.

PubMed

Körding, Elmar; Rupen, Michael; Knigge, Christian; Fender, Rob; Dhawan, Vivek; Templeton, Matthew; Muxlow, Tom

2008-06-06

Astrophysical jets seem to occur in nearly all types of accreting objects, from supermassive black holes to young stellar objects. On the basis of x-ray binaries, a unified scenario describing the disc/jet coupling has evolved and been extended to many accreting objects. The only major exceptions are thought to be cataclysmic variables: Dwarf novae, weakly accreting white dwarfs, show similar outburst behavior to x-ray binaries, but no jet has yet been detected. Here we present radio observations of a dwarf nova in outburst showing variable flat-spectrum radio emission that is best explained as synchrotron emission originating in a transient jet. Both the inferred jet power and the relation to the outburst cycle are analogous to those seen in x-ray binaries, suggesting that the disc/jet coupling mechanism is ubiquitous.

A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

PubMed

Lin, Chin-Teng; Wu, Rui-Cheng; Chang, Jyh-Yeong; Liang, Sheng-Fu

2004-02-01

In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules

PubMed Central

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

2015-01-01

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods. PMID:25938136

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules.

PubMed

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods.

Knowledge representation for fuzzy inference aided medical image interpretation.

PubMed

Gal, Norbert; Stoicu-Tivadar, Vasile

2012-01-01

Knowledge defines how an automated system transforms data into information. This paper suggests a representation method of medical imaging knowledge using fuzzy inference systems coded in XML files. The imaging knowledge incorporates features of the investigated objects in linguistic form and inference rules that can transform the linguistic data into information about a possible diagnosis. A fuzzy inference system is used to model the vagueness of the linguistic medical imaging terms. XML files are used to facilitate easy manipulation and deployment of the knowledge into the imaging software. Preliminary results are presented.

Entropic Inference

NASA Astrophysics Data System (ADS)

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.

Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training

ERIC Educational Resources Information Center

Baschera, Gian-Marco; Gross, Markus

2010-01-01

We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…

Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control

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

Delgoshaei, Parastoo; Austin, Mark A.; Pertzborn, Amanda J.

State-of-the-art building simulation control methods incorporate physical constraints into their mathematical models, but omit implicit constraints associated with policies of operation and dependency relationships among rules representing those constraints. To overcome these shortcomings, there is a recent trend in enabling the control strategies with inference-based rule checking capabilities. One solution is to exploit semantic web technologies in building simulation control. Such approaches provide the tools for semantic modeling of domains, and the ability to deduce new information based on the models through use of Description Logic (DL). In a step toward enabling this capability, this paper presents a cross-disciplinary data-drivenmore » control strategy for building energy management simulation that integrates semantic modeling and formal rule checking mechanisms into a Model Predictive Control (MPC) formulation. The results show that MPC provides superior levels of performance when initial conditions and inputs are derived from inference-based rules.« less

Bayesian performance metrics and small system integration in recent homeland security and defense applications

NASA Astrophysics Data System (ADS)

Jannson, Tomasz; Kostrzewski, Andrew; Patton, Edward; Pradhan, Ranjit; Shih, Min-Yi; Walter, Kevin; Savant, Gajendra; Shie, Rick; Forrester, Thomas

2010-04-01

In this paper, Bayesian inference is applied to performance metrics definition of the important class of recent Homeland Security and defense systems called binary sensors, including both (internal) system performance and (external) CONOPS. The medical analogy is used to define the PPV (Positive Predictive Value), the basic Bayesian metrics parameter of the binary sensors. Also, Small System Integration (SSI) is discussed in the context of recent Homeland Security and defense applications, emphasizing a highly multi-technological approach, within the broad range of clusters ("nexus") of electronics, optics, X-ray physics, γ-ray physics, and other disciplines.

The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

PubMed

Gálvez, Jorge A; Pappas, Janine M; Ahumada, Luis; Martin, John N; Simpao, Allan F; Rehman, Mohamed A; Witmer, Char

2017-10-01

Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural language processing (NLP) tools to radiologists' reports. We validated an NLP tool, Reveal NLP (Health Fidelity Inc, San Mateo, CA) and inference rules engine's performance in identifying reports with deep venous thrombosis using a curated set of ultrasound reports. We then configured the NLP tool to scan all available radiology reports on a daily basis for studies that met criteria for VTE between July 1, 2015, and March 31, 2016. The NLP tool and inference rules engine correctly identified 140 out of 144 reports with positive DVT findings and 98 out of 106 negative reports in the validation set. The tool's sensitivity was 97.2% (95% CI 93-99.2%), specificity was 92.5% (95% CI 85.7-96.7%). Subsequently, the NLP tool and inference rules engine processed 6373 radiology reports from 3371 hospital encounters. The NLP tool and inference rules engine identified 178 positive reports and 3193 negative reports with a sensitivity of 82.9% (95% CI 74.8-89.2) and specificity of 97.5% (95% CI 96.9-98). The system functions well as a safety net to screen patients for HA-VTE on a daily basis and offers value as an automated, redundant system. To our knowledge, this is the first pediatric study to apply NLP technology in a prospective manner for HA-VTE identification.

78 FR 59994 - Self-Regulatory Organizations; The Options Clearing Corporation; Order Approving Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-30

... to avoid any erroneous inference that those are the only provisions of OCC's By-Laws and Rules that... its By-Laws and Rules as well. III. Discussion Section 19(b)(2)(C) of the Act \\5\\ directs the... of participants or among participants in the use of the clearing agency. \\5\\ 15 U.S.C. 78s(b)(2)(C...

Determining rules for closing customer service centers: A public utility company's fuzzy decision

NASA Technical Reports Server (NTRS)

Dekorvin, Andre; Shipley, Margaret F.; Lea, Robert N.

1992-01-01

In the present work, we consider the general problem of knowledge acquisition under uncertainty. Simply stated, the problem reduces to the following: how can we capture the knowledge of an expert when the expert is unable to clearly formulate how he or she arrives at a decision? A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision may have been made. Unique to our work is the fuzzy set representation of the conditions or attributes upon which the expert may possibly base his fuzzy decision. From our examples, we infer certain and possible fuzzy rules for closing a customer service center and illustrate the importance of having the decision closely relate to the conditions under consideration.

An Algebraic Approach to the Study and Optimization of the Set of Rules of a Conditional Rewrite System

NASA Astrophysics Data System (ADS)

Makhortov, S. D.

2018-03-01

An algebraic system containing the semantics of a set of rules of the conditional equational theory (or the conditional term rewriting system) is introduced. The following basic questions are considered for the given model: existence of logical closure, structure of logical closure, possibility of equivalent transformations, and construction of logical reduction. The obtained results can be applied to the analysis and automatic optimization of the corresponding set of rules. The basis for the given research is the theory of lattices and binary relations.

Photometric studies of two solar type marginal contact binaries in the Small Magellanic Cloud

NASA Astrophysics Data System (ADS)

Shanti Priya, Devarapalli; Rukmini, Jagirdar

2018-04-01

Using the Optical Gravitational Lensing Experiment catalogue, two contact binaries were studied using data in the V and I bands. The photometric solutions for the V and I bands are presented for two contact binaries OGLE 003835.24-735413.2 (V1) and OGLE 004619.65-725056.2 (V2) in Small Maglellanic Cloud. The presented light curves are analyzed using the Wilson-Devinney code. The results show that the variables are in good thermal and marginal geometrical contact with features like the O’Connell effect in V1. The absolute dimensions are estimated and its dynamical evolution is inferred. They tend to be solar type marginal contact binaries. The 3.6-m Devasthal Optical Telescope and the 4.0-m International Liquid Mirror Telescope of the Aryabhatta Research Institute of Observational Sciences (ARIES, Nainithal) can facilitate the continuous monitoring of such kind of objects which will help in finding the reasons behind their period changes and their impact on the evolution of the clusters.

A recurrent self-organizing neural fuzzy inference network.

PubMed

Juang, C F; Lin, C T

1999-01-01

A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

Is awareness necessary for true inference?

PubMed

Leo, Peter D; Greene, Anthony J

2008-09-01

In transitive inference, participants learn a set of context-dependent discriminations that can be organized into a hierarchy that supports inference. Several studies show that inference occurs with or without task awareness. However, some studies assert that without awareness, performance is attributable to pseudoinference. By this account, inference-like performance is achieved by differential stimulus weighting according to the stimuli's proximity to the end items of the hierarchy. We implement an inference task that cannot be based on differential stimulus weighting. The design itself rules out pseudoinference strategies. Success on the task without evidence of deliberative strategies would therefore suggest that true inference can be achieved implicitly. We found that accurate performance on the inference task was not dependent on explicit awareness. The finding is consistent with a growing body of evidence that indicates that forms of learning and memory supporting inference and flexibility do not necessarily depend on task awareness.

Oxytocin conditions trait-based rule adherence

PubMed Central

De Dreu, Carsten K.W.

2017-01-01

Abstract Rules, whether in the form of norms, taboos or laws, regulate and coordinate human life. Some rules, however, are arbitrary and adhering to them can be personally costly. Rigidly sticking to such rules can be considered maladaptive. Here, we test whether, at the neurobiological level, (mal)adaptive rule adherence is reduced by oxytocin—a hypothalamic neuropeptide that biases the biobehavioural approach-avoidance system. Participants (N = 139) self-administered oxytocin or placebo intranasally, and reported their need for structure and approach-avoidance sensitivity. Next, participants made binary decisions and were given an arbitrary rule that demanded to forgo financial benefits. Under oxytocin, participants violated the rule more often, especially when they had high need for structure and high approach sensitivity. Possibly, oxytocin dampens the need for a highly structured environment and enables individuals to flexibly trade-off internal desires against external restrictions. Implications for the treatment of clinical disorders marked by maladaptive rule adherence are discussed. PMID:27664999

High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections.

PubMed

Cocco, S; Monasson, R; Sessak, V

2011-05-01

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that maximum likelihood inference is deeply related to principal component analysis when the amplitude of the pattern components ξ is negligible compared to √N. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in ξ/√N. We stress the need to generalize the Hopfield model and include both attractive and repulsive patterns in order to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size N and of the amplitude ξ. The inference approach is illustrated on synthetic and biological data.

Linguistic Summarization of Video for Fall Detection Using Voxel Person and Fuzzy Logic

PubMed Central

Anderson, Derek; Luke, Robert H.; Keller, James M.; Skubic, Marjorie; Rantz, Marilyn; Aud, Myra

2009-01-01

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel person’s states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. PMID:20046216

Evaluation of fuzzy inference systems using fuzzy least squares

NASA Technical Reports Server (NTRS)

Barone, Joseph M.

1992-01-01

Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.

48 CFR 6101.21 - Hearing procedures [Rule 21].

Code of Federal Regulations, 2011 CFR

2011-10-01

... determination of the amount of recovery, if any, for other proceedings. (5) Before the hearing begins, the Board... the record the inferences it draws from the witness's refusal to testify under oath or affirmation... and, in the event of continued refusal, the Board may state for the record the inferences it draws...

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics

PubMed Central

2017-01-01

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration. PMID:29065473

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics.

PubMed

Sotelo Monge, Marco Antonio; Maestre Vidal, Jorge; García Villalba, Luis Javier

2017-10-21

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration.

Oscillating red giants in eclipsing binary systems: empirical reference value for asteroseismic scaling relation

NASA Astrophysics Data System (ADS)

Themeßl, N.; Hekker, S.; Southworth, J.; Beck, P. G.; Pavlovski, K.; Tkachenko, A.; Angelou, G. C.; Ball, W. H.; Barban, C.; Corsaro, E.; Elsworth, Y.; Handberg, R.; Kallinger, T.

2018-05-01

The internal structures and properties of oscillating red-giant stars can be accurately inferred through their global oscillation modes (asteroseismology). Based on 1460 days of Kepler observations we perform a thorough asteroseismic study to probe the stellar parameters and evolutionary stages of three red giants in eclipsing binary systems. We present the first detailed analysis of individual oscillation modes of the red-giant components of KIC 8410637, KIC 5640750 and KIC 9540226. We obtain estimates of their asteroseismic masses, radii, mean densities and logarithmic surface gravities by using the asteroseismic scaling relations as well as grid-based modelling. As these red giants are in double-lined eclipsing binaries, it is possible to derive their independent dynamical masses and radii from the orbital solution and compare it with the seismically inferred values. For KIC 5640750 we compute the first spectroscopic orbit based on both components of this system. We use high-resolution spectroscopic data and light curves of the three systems to determine up-to-date values of the dynamical stellar parameters. With our comprehensive set of stellar parameters we explore consistencies between binary analysis and asteroseismic methods, and test the reliability of the well-known scaling relations. For the three red giants under study, we find agreement between dynamical and asteroseismic stellar parameters in cases where the asteroseismic methods account for metallicity, temperature and mass dependence as well as surface effects. We are able to attain agreement from the scaling laws in all three systems if we use Δνref, emp = 130.8 ± 0.9 μHz instead of the usual solar reference value.

Serendipitous discovery of an irregular and a semi-regular type variable in the field of BY Draconis

NASA Astrophysics Data System (ADS)

Messina, S.; Marino, G.; Rodonò, M.; Cutispoto, G.

2000-12-01

We present new evidence of the optical variability of two red giant stars: HD 172468 and HK Dra, based on photometric and spectroscopic observations. These stars had been included as check stars in our photometric monitoring program of BY Dra and turned out to be variable. HD 172468, whereas almost constant for most of the time, suddenly started to drop in brightness to such a low level to become undetectable. We suspect that such an abrupt event may be an ``obscurational'' minimum, that is typical of eruptive RCB stars, or may be due to the variable extinction by circumstellar dust in a young Orion type object. HK Dra, already known as an irregular variable, is characterised by periodic flux modulation with season-to-season changes of the photometric period, as inferred from a periodogram analysis. It also shows changes of the light curve peak-to-peak amplitude and shape. Such a behaviour in giant stars is commonly found among semi-regular giants (SR) at the Asymptotic Giant Branch (AGB). Our radial velocity measurements rule out that HK Dra may be a close binary system.

Binary Black Hole Mergers in the First Advanced LIGO Observing Run

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allocca, A.; Altin, P. A.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Babak, S.; Bacon, P.; Bader, M. K. M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Baune, C.; Bavigadda, V.; Bazzan, M.; Bejger, M.; Bell, A. S.; Berger, B. K.; Bergmann, G.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Birney, R.; Birnholtz, O.; Biscans, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Boer, M.; Bogaert, G.; Bogan, C.; Bohe, A.; Bond, C.; Bondu, F.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Broida, J. E.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brown, N. M.; Brunett, S.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cabero, M.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T.; Calloni, E.; Camp, J. B.; Cannon, K. C.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Cerboni Baiardi, L.; Cerretani, G.; Cesarini, E.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Cheeseboro, B. D.; Chen, H. Y.; Chen, Y.; Cheng, C.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, S.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C. G.; Cominsky, L.; Constancio, M.; Conte, A.; Conti, L.; Cook, D.; Corbitt, T. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Craig, K.; Creighton, J. D. E.; Cripe, J.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Darman, N. S.; Dasgupta, A.; Da Silva Costa, C. F.; Dattilo, V.; Dave, I.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; De, S.; DeBra, D.; Debreczeni, G.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Devine, R. C.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Girolamo, T.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Douglas, R.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Ducrot, M.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Engels, W.; Essick, R. C.; Etzel, T.; Evans, M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Fang, Q.; Farinon, S.; Farr, B.; Farr, W. M.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Fenyvesi, E.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M.; Fong, H.; Fournier, J.-D.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H. A. G.; Gaebel, S.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gaur, G.; Gehrels, N.; Gemme, G.; Geng, P.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gordon, N. A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Grado, A.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Hall, B. R.; Hall, E. D.; Hamilton, H.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Henry, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hofman, D.; Holt, K.; Holz, D. E.; Hopkins, P.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huang, S.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J.-M.; Isi, M.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jang, H.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jian, L.; Jiménez-Forteza, F.; Johnson, W. W.; Johnson-McDaniel, N. K.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; K, Haris; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kapadia, S. J.; Karki, S.; Karvinen, K. S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kéfélian, F.; Kehl, M. S.; Keitel, D.; Kelley, D. B.; Kells, W.; Kennedy, R.; Key, J. S.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, Chi-Woong; Kim, Chunglee; Kim, J.; Kim, K.; Kim, N.; Kim, W.; Kim, Y.-M.; Kimbrell, S. J.; King, E. J.; King, P. J.; Kissel, J. S.; Klein, B.; Kleybolte, L.; Klimenko, S.; Koehlenbeck, S. M.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Kringel, V.; Krishnan, B.; Królak, A.; Krueger, C.; Kuehn, G.; Kumar, P.; Kumar, R.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lange, J.; Lantz, B.; Lasky, P. D.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, K.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Lewis, J. B.; Li, T. G. F.; Libson, A.; Littenberg, T. B.; Lockerbie, N. A.; Lombardi, A. L.; London, L. T.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lousto, C.; Lück, H.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Magaña Zertuche, L.; Magee, R. M.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandel, I.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martynov, D. V.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Mastrogiovanni, S.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McRae, T.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mercer, R. A.; Merilh, E. L.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Metzdorff, R.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, A. L.; Miller, A.; Miller, B. B.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B. C.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Murphy, D. J.; Murray, P. G.; Mytidis, A.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Nedkova, K.; Nelemans, G.; Nelson, T. J. N.; Neri, M.; Neunzert, A.; Newton, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E. N.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; O'Shaughnessy, R.; Ottaway, D. J.; Overmier, H.; Owen, B. J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pan, Y.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Patrick, Z.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perreca, A.; Perri, L. M.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O. J.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poe, M.; Poggiani, R.; Popolizio, P.; Porter, E.; Post, A.; Powell, J.; Prasad, J.; Predoi, V.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prix, R.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Qiu, S.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rajan, C.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Ricci, F.; Riles, K.; Rizzo, M.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Sakellariadou, M.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sanchez, E. J.; Sandberg, V.; Sandeen, B.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O. E. S.; Savage, R. L.; Sawadsky, A.; Schale, P.; Schilling, R.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Setyawati, Y.; Shaddock, D. A.; Shaffer, T.; Shahriar, M. S.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sieniawska, M.; Sigg, D.; Silva, A. D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, J. R.; Smith, N. D.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stevenson, S.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strauss, N. A.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sunil, S.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tápai, M.; Tarabrin, S. P.; Taracchini, A.; Taylor, R.; Theeg, T.; Thirugnanasambandam, M. P.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Toland, K.; Tomlinson, C.; Tonelli, M.; Tornasi, Z.; Torres, C. V.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Tringali, M. C.; Trozzo, L.; Tse, M.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; Vallisneri, M.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D. V.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, X.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; Whitcomb, S. E.; Whiting, B. F.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Woehler, J.; Worden, J.; Wright, J. L.; Wu, D. S.; Wu, G.; Yablon, J.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yu, H.; Yvert, M.; ZadroŻny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, L.; Zhang, M.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, X. J.; Zucker, M. E.; Zuraw, S. E.; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration

2016-10-01

The first observational run of the Advanced LIGO detectors, from September 12, 2015 to January 19, 2016, saw the first detections of gravitational waves from binary black hole mergers. In this paper, we present full results from a search for binary black hole merger signals with total masses up to 100 M⊙ and detailed implications from our observations of these systems. Our search, based on general-relativistic models of gravitational-wave signals from binary black hole systems, unambiguously identified two signals, GW150914 and GW151226, with a significance of greater than 5 σ over the observing period. It also identified a third possible signal, LVT151012, with substantially lower significance and with an 87% probability of being of astrophysical origin. We provide detailed estimates of the parameters of the observed systems. Both GW150914 and GW151226 provide an unprecedented opportunity to study the two-body motion of a compact-object binary in the large velocity, highly nonlinear regime. We do not observe any deviations from general relativity, and we place improved empirical bounds on several high-order post-Newtonian coefficients. From our observations, we infer stellar-mass binary black hole merger rates lying in the range 9 - 240 Gpc-3 yr-1 . These observations are beginning to inform astrophysical predictions of binary black hole formation rates and indicate that future observing runs of the Advanced detector network will yield many more gravitational-wave detections.

32 CFR 776.29 - Imputed disqualification: General rule.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 5 2011-07-01 2011-07-01 false Imputed disqualification: General rule. 776.29... inferences, deductions, or working presumptions that reasonably may be made about the way in which covered... interests of another. When such independence is lacking or unlikely, representation cannot be zealous. (5...

26 CFR 1.279-1 - General rule; purpose.

Code of Federal Regulations, 2010 CFR

2010-04-01

... (5), 279(f), or 279(i) are present. However, no inference should be drawn from the rules of section... respect to its corporate acquisition indebtedness to the extent such interest exceeds $5 million. However, the $5 million limitation is reduced by the amount of interest paid or incurred on obligations issued...

32 CFR 776.29 - Imputed disqualification: General rule.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 5 2010-07-01 2010-07-01 false Imputed disqualification: General rule. 776.29... inferences, deductions, or working presumptions that reasonably may be made about the way in which covered... interests of another. When such independence is lacking or unlikely, representation cannot be zealous. (5...

Automatic Diagnosis of Fetal Heart Rate: Comparison of Different Methodological Approaches

DTIC Science & Technology

2001-10-25

Apgar score). Each recording lasted at least 30 minutes and it contained both the cardiographic series and the toco trace. We focused on four...inference rules automatically generated by the learning procedure showed that n° Rules can be manually reduced to 37 without deteriorating so much the

Willpower and Personal Rules.

ERIC Educational Resources Information Center

Benabou, Roland; Tirole, Jean

2004-01-01

We develop a theory of internal commitments or "personal rules" based on self-reputation over one's willpower, which transforms lapses into precedents that undermine future self-restraint. The foundation for this mechanism is the imperfect recall of past motives and feelings, leading people to draw inferences from their past actions. The degree of…

A Rational Analysis of Rule-Based Concept Learning

ERIC Educational Resources Information Center

Goodman, Noah D.; Tenenbaum, Joshua B.; Feldman, Jacob; Griffiths, Thomas L.

2008-01-01

This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…

Are Scientific Analogies Metaphors?

DTIC Science & Technology

1981-02-01

attraction is certainly familiar, but its rules are unfortunately unclear; so this analogy does not tell the student precisely what to map from the...seriously. The rules of analogical mappings are such that, unless there is a principled reason to exempt a given predicate, it must be mapped if it belongs...contributes to their richness, for no possible mapping need be ruled out. Mutually contradictory inferences can co-exist. These analogies derive much

Inside the black box: starting to uncover the underlying decision rules used in one-by-one expert assessment of occupational exposure in case-control studies

PubMed Central

Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Yu, Kai; Shortreed, Susan M.; Pronk, Anjoeka; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Silverman, Debra T.; Friesen, Melissa C.

2014-01-01

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets. Results The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies. PMID:23155187

X-Ray source populations in old open clusters: Collinder 261

NASA Astrophysics Data System (ADS)

Vats, Smriti; van den Berg, Maureen; Wijnands, Rudy

2014-09-01

We are carrying out an X-ray survey of old open clusters with the Chandra X-ray Observatory. Single old stars, being slow rotators, are very faint in X-rays (L_X < 1×10^27 erg/s). Hence, X-rays produced by mass transfer in cataclysmic variables (CVs) or by rapid rotation of the stars in tidally locked, detached binaries (active binaries; ABs) can be detected, without contamination from single stars. By comparing the properties of various types of interacting binaries in different environments (the Galactic field, old open clusters, globular clusters), we aim to study binary evolution and how it may be affected by dynamical encounters with other cluster stars. Stellar clusters are good targets to study binaries, as age, distance, chemical composition, are well constrained. Collinder (Cr) 261 is an old open cluster (age ~ 7 Gyr), with one of the richest populations inferred of close binaries and blue stragglers of all open clusters and is therefore an obvious target to study the products of close encounters in open clusters. We will present the first results of this study, detailing the low-luminosity X-ray population of Cr 261, in conjunction with other open clusters in our survey (NGC 188, Berkeley 17, NGC 6253, M67, NGC 6791) and in comparison with populations in globular clusters.

CLIPS: A tool for corn disease diagnostic system and an aid to neural network for automated knowledge acquisition

NASA Technical Reports Server (NTRS)

Wu, Cathy; Taylor, Pam; Whitson, George; Smith, Cathy

1990-01-01

This paper describes the building of a corn disease diagnostic expert system using CLIPS, and the development of a neural expert system using the fact representation method of CLIPS for automated knowledge acquisition. The CLIPS corn expert system diagnoses 21 diseases from 52 symptoms and signs with certainty factors. CLIPS has several unique features. It allows the facts in rules to be broken down to object-attribute-value (OAV) triples, allows rule-grouping, and fires rules based on pattern-matching. These features combined with the chained inference engine result to a natural user query system and speedy execution. In order to develop a method for automated knowledge acquisition, an Artificial Neural Expert System (ANES) is developed by a direct mapping from the CLIPS system. The ANES corn expert system uses the same OAV triples in the CLIPS system for its facts. The LHS and RHS facts of the CLIPS rules are mapped into the input and output layers of the ANES, respectively; and the inference engine of the rules is imbedded in the hidden layer. The fact representation by OAC triples gives a natural grouping of the rules. These features allow the ANES system to automate rule-generation, and make it efficient to execute and easy to expand for a large and complex domain.

Phase equilibria of CFC alternative refrigerant mixtures: Binary systems of isobutane + 1,1,1,2-tetrafluoroethane, + 1,1-difluoroethane, and + difluoromethane

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

Lim, J.S.; Park, J.Y.; Lee, B.G.

1999-12-01

Isothermal vapor-liquid equilibria were measured in the binary systems 1,1,1,2-tetrafluoroethane + isobutane at 303.2 and 323.2 K, 1,1-difluoroethane + isobutane at 303.2, 313.2, 323.2, and 333.2 K, and difluoromethane + isobutane at 301.8 and 321.8 K in a circulation-type equilibrium apparatus. The experimental data were well correlated with the Peng-Robinson equation of state using the Wong and Sandler mixing rules.

Soret motion in non-ionic binary molecular mixtures

NASA Astrophysics Data System (ADS)

Leroyer, Yves; Würger, Alois

2011-08-01

We study the Soret coefficient of binary molecular mixtures with dispersion forces. Relying on standard transport theory for liquids, we derive explicit expressions for the thermophoretic mobility and the Soret coefficient. Their sign depends on composition, the size ratio of the two species, and the ratio of Hamaker constants. Our results account for several features observed in experiment, such as a linear variation with the composition; they confirm the general rule that small molecules migrate to the warm, and large ones to the cold.

A Very Bright, Very Hot, and Very Long Flaring Event from the M Dwarf Binary System DG CVn

NASA Technical Reports Server (NTRS)

Osten, Rachel A.; Kowalski, Adam; Drake, Stephen; Krimm, Hans; Page, Kim; Gazeas, Kosmas; Page, Mathew; Miguel, Enrique De; Novak, Rudolf; Gehrels, Cornelis

2016-01-01

On 2014 April 23, the Swift satellite responded to a hard X-ray transient detected by its Burst Alert Telescope, which turned out to be a stellar flare from a nearby, young M dwarf binary DG CVn. We utilize observations at X-ray, UV, optical, and radio wavelengths to infer the properties of two large flares. The X-ray spectrum of the primary outburst can be described over the 0.3100 kiloelectron volts bandpass by either a single very high-temperature plasma or a nonthermal thick-target bremsstrahlung model, and we rule out the nonthermal model based on energetic grounds. The temperatures were the highest seen spectroscopically in a stellar flare, at T(sub x) of 290 megakelvin. The first event was followed by a comparably energetic event almost a day later. We constrain the photospheric area involved in each of the two flares to be greater than 10(exp 20) sq cm, and find evidence from flux ratios in the second event of contributions to the white light flare emission in addition to the usual hot, T approximately 10(exp 4) K blackbody emission seen in the impulsive phase of flares. The radiated energy in X-rays and white light reveal these events to be the two most energetic X-ray flares observed from an M dwarf, with X-ray radiated energies in the 0.3-10 kiloelectron volts bandpass of 4 x 10(exp 35) and 9 x 10(exp 35) erg, and optical flare energies at E(sub V) of 2.8 x 10(exp 34) and 5.2 x 10(exp 34) erg, respectively. The results presented here should be integrated into updated modeling of the astrophysical impact of large stellar flares on close-in exoplanetary atmospheres.

The Lore of the Hair

NASA Astrophysics Data System (ADS)

Yunes, Nicolas; Yagi, Kent; Stein, Leo

2016-03-01

Stars can be hairy beasts, especially in theories that go beyond Einstein's. In the latter, a scalar field can be sourced and anchored to a neutron star, and if the later is in a binary system, the scalar field will emit dipole radiation. This radiation removes energy from the binary, forcing the orbit to adiabatically decay much more rapidly than due to the emission of gravitational waves as predicted in General Relativity. The detailed radio observation of binary pulsars has constrained the orbital decay of compact binaries stringently, so much so that theories that predict neutron stars with scalar hair are believed to be essentially ruled out. In this talk I will explain why this ``lore'' is actually incorrect, providing a counter-example in which scalar hair is sourced by neutron stars, yet dipole radiation is absent. I will then describe what binary systems need to be observed to constrain such theories with future astrophysical observations. I acknowledge support from NSF CAREER Grant PHY-1250636.

Refractive Index Mixing Rules and Excess Infrared Spectra of Binary Mixtures.

PubMed

Baranović, Goran

2017-05-01

Three refractive index mixing rules, Arago-Biot, Lorentz-Lorenz, and Newton, are generalized to complex refractive index and used to define infrared (IR) spectra of the corresponding ideal liquid mixtures. Using the measured optical constants n and k for acetonitrile-water mixtures (Bertie and Lan, 1997) the excess absorbances, A E  =  A obs  -  A ideal , are calculated. Relying upon the well-established properties of the acetonitrile-water mixtures, the interpretation of the excess absorbances is established that is essentially based on the understanding of a liquid as a set of oscillators. The set depends on the composition of the mixture and comprises oscillators as present in the pure components and oscillators perturbed by hydrogen bonding between unlike molecules. The main features of an excess spectrum can be established assuming chemical equilibria among various oscillators. The most informative parts of the spectrum of a yet unstudied binary system can well be observed and even qualitatively explained from the excess absorbance provided: first, a detailed vibrational study of the components has been done; and, second, it is well understood what actually is subtracted from A obs . As examples, the binary mixtures of ethynylbenzene and tetrachloroethylene and 2-ethynylpyridine and tetrachloroethylene are considered.

Determining rules for closing customer service centers: A public utility company's fuzzy decision

NASA Technical Reports Server (NTRS)

Dekorvin, Andre; Shipley, Margaret F.

1992-01-01

In the present work, we consider the general problem of knowledge acquisition under uncertainty. A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision was made. Unique to this work is the fuzzy set representation of the conditions or attributes upon which the decision make may base his fuzzy set decision. From our examples, we infer certain and possible rules containing fuzzy terms. It should be stressed that the procedure determines how closely the expert follows the conditions under consideration in making his decision. We offer two examples pertaining to the possible decision to close a customer service center of a public utility company. In the first example, the decision maker does not follow too closely the conditions. In the second example, the conditions are much more relevant to the decision of the expert.

Binary versus non-binary information in real time series: empirical results and maximum-entropy matrix models

NASA Astrophysics Data System (ADS)

Almog, Assaf; Garlaschelli, Diego

2014-09-01

The dynamics of complex systems, from financial markets to the brain, can be monitored in terms of multiple time series of activity of the constituent units, such as stocks or neurons, respectively. While the main focus of time series analysis is on the magnitude of temporal increments, a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. In this paper we provide further evidence of this by showing strong nonlinear relations between binary and non-binary properties of financial time series. These relations are a novel quantification of the fact that extreme price increments occur more often when most stocks move in the same direction. We then introduce an information-theoretic approach to the analysis of the binary signature of single and multiple time series. Through the definition of maximum-entropy ensembles of binary matrices and their mapping to spin models in statistical physics, we quantify the information encoded into the simplest binary properties of real time series and identify the most informative property given a set of measurements. Our formalism is able to accurately replicate, and mathematically characterize, the observed binary/non-binary relations. We also obtain a phase diagram allowing us to identify, based only on the instantaneous aggregate return of a set of multiple time series, a regime where the so-called ‘market mode’ has an optimal interpretation in terms of collective (endogenous) effects, a regime where it is parsimoniously explained by pure noise, and a regime where it can be regarded as a combination of endogenous and exogenous factors. Our approach allows us to connect spin models, simple stochastic processes, and ensembles of time series inferred from partial information.

Astronomy in Denver: Spectropolarimetric Observations of 5 Wolf-Rayet Binary Stars with SALT/RSS

NASA Astrophysics Data System (ADS)

Fullard, Andrew; Ansary, Zyed; Azancot Luchtan, Daniel; Gallegos, Hunter; Luepker, Martin; Hoffman, Jennifer L.; Nordsieck, Kenneth H.; SALT observation team

2018-06-01

Mass loss from massive stars is an important yet poorly understood factor in shaping their evolution. Wolf-Rayet (WR) stars are of particular interest due to their stellar winds, which create large regions of circumstellar material (CSM). They are also supernova and possible gamma-ray burst (GRB) progenitors. Like other massive stars, WR stars often occur in binaries, where interaction can affect their mass loss rates and provide the rapid rotation thought to be required for GRB production. The diagnostic tool of spectropolarimetry, along with the potentially eclipsing nature of a binary system, helps us to better characterize the CSM created by the stars’ colliding winds. Thus, we can determine mass loss rates and infer rapid rotation. We present spectropolarimetric results for five WR+O eclipsing binary systems, obtained with the Robert Stobie Spectrograph at the South African Large Telescope, between April 2017 and April 2018. The data allow us to map both continuum and emission line polarization variations with phase, which constrains where different CSM components scatter light in the systems. We discuss our initial findings and interpretations of the polarimetric variability in each binary system, and compare the systems.

Mediation Analysis with Multiple Mediators

PubMed Central

VanderWeele, T.J.; Vansteelandt, S.

2014-01-01

Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators. PMID:25580377

Mediation Analysis with Multiple Mediators.

PubMed

VanderWeele, T J; Vansteelandt, S

2014-01-01

Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators.

Dynamical and Physical Properties of 65803 Didymos, the AIDA Mission Target

NASA Astrophysics Data System (ADS)

Campo Bagatin, A.; Richardson, D. C.; Tsiganis, K.; Cheng, A. F.; Michel, P.

2017-09-01

The near-Earth asteroid (NEA) 65803 Didymos is a binary system and is the target of the proposed Asteroid Impact & Deflection Assessment (AIDA) mission, which combines an orbiter (Asteroid Impact Mission, AIM, or the reduced-scope AIM Deflection Demonstration, AIM-D2) [1, 2] and a kinetic impactor experiment (Double Asteroid Redirection Test, DART) planned to impact the secondary of the Didymos binary system in October, 2022 [3]. The Dynamical and Physical Properties of Didymos Working Group supports the AIDA mission by addressing questions related to understanding the dynamical state of the system and inferring the physical properties of the components

A study of accretion discs around rapidly rotating neutron stars in general relativity and its applications to four low mass X-ray binaries

NASA Astrophysics Data System (ADS)

Bhattacharyya, Sudip

2002-02-01

We calculate the accretion disc temperature profiles, disc luminosities and boundary layer luminosities for rapidly rotating neutron stars considering the full effect of general relativity. We compare the theoretical values of these quantities with their values inferred from EXOSAT data for four low mass X-ray binary sources: XB 1820-30, GX 17+2, GX 9+1 and GX 349+2 and constrain the values of several properties of these sources. According to our calculations, the neutron stars in GX 9+1 and GX 349+2 are rapidly rotating and stiffer equations of state are unfavoured.

46 CFR 355.3 - Criteria to be applied in support of stock data in affidavit.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Affidavit as those observed for the primary corporation. If, on the other hand, the “fair inference rule” is... the veracity of the statutory statements made in the Affidavit (paragraph 5) may be relied upon by the Maritime Administration. (b) When applying the fair inference rule (where there are more than 30...

46 CFR 355.3 - Criteria to be applied in support of stock data in affidavit.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Affidavit as those observed for the primary corporation. If, on the other hand, the “fair inference rule” is... the veracity of the statutory statements made in the Affidavit (paragraph 5) may be relied upon by the Maritime Administration. (b) When applying the fair inference rule (where there are more than 30...

Real Rules of Inference

DTIC Science & Technology

1986-01-01

the AAAI Workshop on Uncertainty and Probability in Artificial Intelligence , 1985. [McC771 McCarthy, J. "Epistemological Problems of Aritificial ...NUMBER OF PAGES Artificial Intelligence , Data Fusion, Inference, Probability, 30 Philosophy, Inheritance Hierachies, Default Reasoning ia.PRCECODE I...prominent philosophers Glymour and Thomason even applaud the uninhibited steps: Artificial Intelligence has done us the service not only of reminding us

Semantic Web Research Trends and Directions

DTIC Science & Technology

2006-01-01

workflow templates. Workflow templates are used for various different tasks such as en- coding business rules in a B2B application, specifying domain...recently suggest that rules are desirable in this space, both in terms of their expressivity, and in some cases, due to their attractive computational...of OWL documents. However, in most cases, a more attractive solution is to simply write a rule that captures the inference needed, as it is reusable

Heuristic rule for binary superlattice coassembly: mixed plastic mesophases of hard polyhedral nanoparticles.

PubMed

Khadilkar, Mihir R; Escobedo, Fernando A

2014-10-17

Sought-after ordered structures of mixtures of hard anisotropic nanoparticles can often be thermodynamically unfavorable due to the components' geometric incompatibility to densely pack into regular lattices. A simple compatibilization rule is identified wherein the particle sizes are chosen such that the order-disorder transition pressures of the pure components match (and the entropies of the ordered phases are similar). Using this rule with representative polyhedra from the truncated-cube family that form pure-component plastic crystals, Monte Carlo simulations show the formation of plastic-solid solutions for all compositions and for a wide range of volume fractions.

An Expert-System Engine With Operative Probabilities

NASA Technical Reports Server (NTRS)

Orlando, N. E.; Palmer, M. T.; Wallace, R. S.

1986-01-01

Program enables proof-of-concepts tests of expert systems under development. AESOP is rule-based inference engine for expert system, which makes decisions about particular situation given user-supplied hypotheses, rules, and answers to questions drawn from rules. If knowledge base containing hypotheses and rules governing environment is available to AESOP, almost any situation within that environment resolved by answering questions asked by AESOP. Questions answered with YES, NO, MAYBE, DON'T KNOW, DON'T CARE, or with probability factor ranging from 0 to 10. AESOP written in Franz LISP for interactive execution.

Empirical comparison study of approximate methods for structure selection in binary graphical models.

PubMed

Viallon, Vivian; Banerjee, Onureena; Jougla, Eric; Rey, Grégoire; Coste, Joel

2014-03-01

Looking for associations among multiple variables is a topical issue in statistics due to the increasing amount of data encountered in biology, medicine, and many other domains involving statistical applications. Graphical models have recently gained popularity for this purpose in the statistical literature. In the binary case, however, exact inference is generally very slow or even intractable because of the form of the so-called log-partition function. In this paper, we review various approximate methods for structure selection in binary graphical models that have recently been proposed in the literature and compare them through an extensive simulation study. We also propose a modification of one existing method, that is shown to achieve good performance and to be generally very fast. We conclude with an application in which we search for associations among causes of death recorded on French death certificates. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

PubMed Central

Knijnenburg, Theo A.; Klau, Gunnar W.; Iorio, Francesco; Garnett, Mathew J.; McDermott, Ultan; Shmulevich, Ilya; Wessels, Lodewyk F. A.

2016-01-01

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present ‘Logic Optimization for Binary Input to Continuous Output’ (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models. PMID:27876821

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy.

PubMed

Knijnenburg, Theo A; Klau, Gunnar W; Iorio, Francesco; Garnett, Mathew J; McDermott, Ultan; Shmulevich, Ilya; Wessels, Lodewyk F A

2016-11-23

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present 'Logic Optimization for Binary Input to Continuous Output' (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.

Solving puzzles of GW150914 by primordial black holes

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

Blinnikov, S.; Dolgov, A.; Porayko, N.K.

The black hole binary properties inferred from the LIGO gravitational wave signal GW150914 posed several serious problems. The high masses and low effective spin of black hole binary can be explained if they are primordial (PBH) rather than the products of the stellar binary evolution. Such PBH properties are postulated ad hoc but not derived from fundamental theory. We show that the necessary features of PBHs naturally follow from the slightly modified Affleck-Dine (AD) mechanism of baryogenesis. The log-normal distribution of PBHs, predicted within the AD paradigm, is adjusted to provide an abundant population of low-spin stellar mass black holes.more » The same distribution gives a sufficient number of quickly growing seeds of supermassive black holes observed at high redshifts and may comprise an appreciable fraction of Dark Matter which does not contradict any existing observational limits. Testable predictions of this scenario are discussed.« less

Investigations into the thermal non-equilibrium of W UMa-type contact binaries

NASA Astrophysics Data System (ADS)

Xiong, Xiao; Liu, Liang; Qian, Sheng-Bang

2018-05-01

Traditionally, some physical details (e.g., magnetic braking, energy transfer, angular momentum loss, etc.) have to be taken into consideration during investigations into the evolution of contact binaries. However, the real evolutionary processes which usually contain several of these physical mechanisms are very complicated as a result of strong interaction between components. To avoid dealing with these factors, a linear relationship is applied to the temperatures of components. It is found that the higher the mass ratio (M 2/M 1) of a contact system, the weaker the deviation from thermal equilibrium. On this basis, a variation trend of fill-out factor (f) changing with mass ratio can be inferred, which is consistent with observations. Moreover, if we stick to this point of view, it should be natural that the number of semi-detached binaries in the predicted broken-contact phase of relaxation oscillations is less than the number in the contact phase.

Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Afrough, M.; Agarwal, B.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allen, G.; Allocca, A.; Altin, P. A.; Amato, A.; Ananyeva, A.; Anderson, S. B.; Anderson, W. G.; Angelova, S. V.; Antier, S.; Appert, S.; Arai, K.; Araya, M. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Atallah, D. V.; Aufmuth, P.; Aulbert, C.; AultONeal, K.; Austin, C.; Avila-Alvarez, A.; Babak, S.; Bacon, P.; Bader, M. K. M.; Bae, S.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Banagiri, S.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barkett, K.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Bawaj, M.; Bayley, J. C.; Bazzan, M.; Bécsy, B.; Beer, C.; Bejger, M.; Belahcene, I.; Bell, A. S.; Bergmann, G.; Bernuzzi, S.; Bero, J. J.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Billman, C. R.; Birch, J.; Birney, R.; Birnholtz, O.; Biscans, S.; Biscoveanu, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blackman, J.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Bode, N.; Boer, M.; Bogaert, G.; Bohe, A.; Bondu, F.; Bonilla, E.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bossie, K.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Broida, J. E.; Brooks, A. F.; Brown, D. D.; Brunett, S.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cabero, M.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T. A.; Calloni, E.; Camp, J. B.; Canepa, M.; Canizares, P.; Cannon, K. C.; Cao, H.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Carney, M. F.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Cerdá-Durán, P.; Cerretani, G.; Cesarini, E.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chase, E.; Chassande-Mottin, E.; Chatterjee, D.; Chatziioannou, K.; Cheeseboro, B. D.; Chen, H. Y.; Chen, X.; Chen, Y.; Cheng, H.-P.; Chia, H.; Chincarini, A.; Chiummo, A.; Chmiel, T.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, A. J. K.; Chua, S.; Chung, A. K. W.; Chung, S.; Ciani, G.; Ciolfi, R.; Cirelli, C. E.; Cirone, A.; Clara, F.; Clark, J. A.; Clearwater, P.; Cleva, F.; Cocchieri, C.; Coccia, E.; Cohadon, P.-F.; Cohen, D.; Colla, A.; Collette, C. G.; Cominsky, L. R.; Constancio, M., Jr.; Conti, L.; Cooper, S. J.; Corban, P.; Corbitt, T. R.; Cordero-Carrión, I.; Corley, K. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Covas, P. B.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Creighton, J. D. E.; Creighton, T. D.; Cripe, J.; Crowder, S. G.; Cullen, T. J.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Dálya, G.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dasgupta, A.; Da Silva Costa, C. F.; Dattilo, V.; Dave, I.; Davier, M.; Davis, D.; Daw, E. J.; Day, B.; De, S.; DeBra, D.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Demos, N.; Denker, T.; Dent, T.; De Pietri, R.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; De Rossi, C.; DeSalvo, R.; de Varona, O.; Devenson, J.; Dhurandhar, S.; Díaz, M. C.; Dietrich, T.; Di Fiore, L.; Di Giovanni, M.; Di Girolamo, T.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Renzo, F.; Doctor, Z.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorrington, I.; Douglas, R.; Dovale Álvarez, M.; Downes, T. P.; Drago, M.; Dreissigacker, C.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dupej, P.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Eisenstein, R. A.; Essick, R. C.; Estevez, D.; Etienne, Z. B.; Etzel, T.; Evans, M.; Evans, T. M.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Farinon, S.; Farr, B.; Farr, W. M.; Fauchon-Jones, E. J.; Favata, M.; Fays, M.; Fee, C.; Fehrmann, H.; Feicht, J.; Fejer, M. M.; Fernandez-Galiana, A.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Finstad, D.; Fiori, I.; Fiorucci, D.; Fishbach, M.; Fisher, R. P.; Fitz-Axen, M.; Flaminio, R.; Fletcher, M.; Fong, H.; Font, J. A.; Forsyth, P. W. F.; Forsyth, S. S.; Fournier, J.-D.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fries, E. M.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H.; Gadre, B. U.; Gaebel, S. M.; Gair, J. R.; Gammaitoni, L.; Ganija, M. R.; Gaonkar, S. G.; Garcia-Quiros, C.; Garufi, F.; Gateley, B.; Gaudio, S.; Gaur, G.; Gayathri, V.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; George, D.; George, J.; Gergely, L.; Germain, V.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glover, L.; Goetz, E.; Goetz, R.; Gomes, S.; Goncharov, B.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Grado, A.; Graef, C.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Gretarsson, E. M.; Groot, P.; Grote, H.; Grunewald, S.; Gruning, P.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Halim, O.; Hall, B. R.; Hall, E. D.; Hamilton, E. Z.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hannuksela, O. A.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Haster, C.-J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hinderer, T.; Hoak, D.; Hofman, D.; Holt, K.; Holz, D. E.; Hopkins, P.; Horst, C.; Hough, J.; Houston, E. A.; Howell, E. J.; Hreibi, A.; Hu, Y. M.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Indik, N.; Inta, R.; Intini, G.; Isa, H. N.; Isac, J.-M.; Isi, M.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Johnson-McDaniel, N. K.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Junker, J.; Kalaghatgi, C. V.; Kalogera, V.; Kamai, B.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kapadia, S. J.; Karki, S.; Karvinen, K. S.; Kasprzack, M.; Kastaun, W.; Katolik, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kawabe, K.; Kawaguchi, K.; Kéfélian, F.; Keitel, D.; Kemball, A. J.; Kennedy, R.; Kent, C.; Key, J. S.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, Chunglee; Kim, J. C.; Kim, K.; Kim, W.; Kim, W. S.; Kim, Y.-M.; Kimbrell, S. J.; King, E. J.; King, P. J.; Kinley-Hanlon, M.; Kirchhoff, R.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Knowles, T. D.; Koch, P.; Koehlenbeck, S. M.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Krämer, C.; Kringel, V.; Królak, A.; Kuehn, G.; Kumar, P.; Kumar, R.; Kumar, S.; Kuo, L.; Kutynia, A.; Kwang, S.; Lackey, B. D.; Lai, K. H.; Landry, M.; Lang, R. N.; Lange, J.; Lantz, B.; Lanza, R. K.; Larson, S. L.; Lartaux-Vollard, A.; Lasky, P. D.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, H. W.; Lee, K.; Lehmann, J.; Lenon, A.; Leonardi, M.; Leroy, N.; Letendre, N.; Levin, Y.; Li, T. G. F.; Linker, S. D.; Littenberg, T. B.; Liu, J.; Liu, X.; Lo, R. K. L.; Lockerbie, N. A.; London, L. T.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lousto, C. O.; Lovelace, G.; Lück, H.; Lumaca, D.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Macas, R.; Macfoy, S.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña Hernandez, I.; Magaña-Sandoval, F.; Magaña Zertuche, L.; Magee, R. M.; Majorana, E.; Maksimovic, I.; Man, N.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markakis, C.; Markosyan, A. S.; Markowitz, A.; Maros, E.; Marquina, A.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martynov, D. V.; Mason, K.; Massera, E.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Mastrogiovanni, S.; Matas, A.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McCuller, L.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McNeill, L.; McRae, T.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Mehmet, M.; Meidam, J.; Mejuto-Villa, E.; Melatos, A.; Mendell, G.; Mercer, R. A.; Merilh, E. L.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Metzdorff, R.; Meyers, P. M.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, A. L.; Miller, B. B.; Miller, J.; Millhouse, M.; Milovich-Goff, M. C.; Minazzoli, O.; Minenkov, Y.; Ming, J.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moffa, D.; Moggi, A.; Mogushi, K.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mours, B.; Mow-Lowry, C. M.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Muñiz, E. A.; Muratore, M.; Murray, P. G.; Napier, K.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Neilson, J.; Nelemans, G.; Nelson, T. J. N.; Nery, M.; Neunzert, A.; Nevin, L.; Newport, J. M.; Newton, G.; Ng, K. K. Y.; Nguyen, T. T.; Nichols, D.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Noack, A.; Nocera, F.; Nolting, D.; North, C.; Nuttall, L. K.; Oberling, J.; O'Dea, G. D.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Okada, M. A.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; Ormiston, R.; Ortega, L. F.; O'Shaughnessy, R.; Ossokine, S.; Ottaway, D. J.; Overmier, H.; Owen, B. J.; Pace, A. E.; Page, J.; Page, M. A.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, Howard; Pan, Huang-Wei; Pang, B.; Pang, P. T. H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Parida, A.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patil, M.; Patricelli, B.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perez, C. J.; Perreca, A.; Perri, L. M.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O. J.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pirello, M.; Pitkin, M.; Poe, M.; Poggiani, R.; Popolizio, P.; Porter, E. K.; Post, A.; Powell, J.; Prasad, J.; Pratt, J. W. W.; Pratten, G.; Predoi, V.; Prestegard, T.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rajan, C.; Rajbhandari, B.; Rakhmanov, M.; Ramirez, K. E.; Ramos-Buades, A.; Rapagnani, P.; Raymond, V.; Razzano, M.; Read, J.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Ren, W.; Reyes, S. D.; Ricci, F.; Ricker, P. M.; Rieger, S.; Riles, K.; Rizzo, M.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, R.; Romel, C. L.; Romie, J. H.; Rosińska, D.; Ross, M. P.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Rutins, G.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Sakellariadou, M.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sampson, L. M.; Sanchez, E. J.; Sanchez, L. E.; Sanchis-Gual, N.; Sandberg, V.; Sanders, J. R.; Sassolas, B.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Scheel, M.; Scheuer, J.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schulte, B. W.; Schutz, B. F.; Schwalbe, S. G.; Scott, J.; Scott, S. M.; Seidel, E.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Shaddock, D. A.; Shaffer, T. J.; Shah, A. A.; Shahriar, M. S.; Shaner, M. B.; Shao, L.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sieniawska, M.; Sigg, D.; Silva, A. D.; Singer, L. P.; Singh, A.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, B.; Smith, J. R.; Smith, R. J. E.; Somala, S.; Son, E. J.; Sonnenberg, J. A.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Spencer, A. P.; Srivastava, A. K.; Staats, K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stevenson, S. P.; Stone, R.; Stops, D. J.; Strain, K. A.; Stratta, G.; Strigin, S. E.; Strunk, A.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sunil, S.; Suresh, J.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Tait, S. C.; Talbot, C.; Talukder, D.; Tanner, D. B.; Tápai, M.; Taracchini, A.; Tasson, J. D.; Taylor, J. A.; Taylor, R.; Tewari, S. V.; Theeg, T.; Thies, F.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Toland, K.; Tonelli, M.; Tornasi, Z.; Torres-Forné, A.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trinastic, J.; Tringali, M. C.; Trozzo, L.; Tsang, K. W.; Tse, M.; Tso, R.; Tsukada, L.; Tsuna, D.; Tuyenbayev, D.; Ueno, K.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Varma, V.; Vass, S.; Vasúth, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Venugopalan, G.; Verkindt, D.; Vetrano, F.; Viceré, A.; Viets, A. D.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walet, R.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, J. Z.; Wang, W. H.; Wang, Y. F.; Ward, R. L.; Warner, J.; Was, M.; Watchi, J.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Wessel, E. K.; Weßels, P.; Westerweck, J.; Westphal, T.; Wette, K.; Whelan, J. T.; Whiting, B. F.; Whittle, C.; Wilken, D.; Williams, D.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Woehler, J.; Wofford, J.; Wong, K. W. K.; Worden, J.; Wright, J. L.; Wu, D. S.; Wysocki, D. M.; Xiao, S.; Yamamoto, H.; Yancey, C. C.; Yang, L.; Yap, M. J.; Yazback, M.; Yu, Hang; Yu, Haocun; Yvert, M.; Zadrożny, A.; Zanolin, M.; Zelenova, T.; Zendri, J.-P.; Zevin, M.; Zhang, L.; Zhang, M.; Zhang, T.; Zhang, Y.-H.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, S. J.; Zhu, X. J.; Zimmerman, A. B.; Zucker, M. E.; Zweizig, J.; (LIGO Scientific Collaboration; Virgo Collaboration

2017-12-01

The source of the gravitational-wave (GW) signal GW170817, very likely a binary neutron star merger, was also observed electromagnetically, providing the first multi-messenger observations of this type. The two-week-long electromagnetic (EM) counterpart had a signature indicative of an r-process-induced optical transient known as a kilonova. This Letter examines how the mass of the dynamical ejecta can be estimated without a direct electromagnetic observation of the kilonova, using GW measurements and a phenomenological model calibrated to numerical simulations of mergers with dynamical ejecta. Specifically, we apply the model to the binary masses inferred from the GW measurements, and use the resulting mass of the dynamical ejecta to estimate its contribution (without the effects of wind ejecta) to the corresponding kilonova light curves from various models. The distributions of dynamical ejecta mass range between {M}{ej}={10}-3-{10}-2 {M}⊙ for various equations of state, assuming that the neutron stars are rotating slowly. In addition, we use our estimates of the dynamical ejecta mass and the neutron star merger rates inferred from GW170817 to constrain the contribution of events like this to the r-process element abundance in the Galaxy when ejecta mass from post-merger winds is neglected. We find that if ≳10% of the matter dynamically ejected from binary neutron star (BNS) mergers is converted to r-process elements, GW170817-like BNS mergers could fully account for the amount of r-process material observed in the Milky Way.

Compact Binary Progenitors of Short Gamma-Ray Bursts

NASA Technical Reports Server (NTRS)

Giacomazzo, Bruno; Perna, Rosalba; Rezzolla, Luciano; Troja, Eleonora; Lazzati, Davide

2013-01-01

In recent years, detailed observations and accurate numerical simulations have provided support to the idea that mergers of compact binaries containing either two neutron stars (NSs) or an NS and a black hole (BH) may constitute the central engine of short gamma-ray bursts (SGRBs). The merger of such compact binaries is expected to lead to the production of a spinning BH surrounded by an accreting torus. Several mechanisms can extract energy from this system and power the SGRBs. Here we connect observations and numerical simulations of compact binary mergers, and use the current sample of SGRBs with measured energies to constrain the mass of their powering tori. By comparing the masses of the tori with the results of fully general-relativistic simulations, we are able to infer the properties of the binary progenitors that yield SGRBs. By assuming a constant efficiency in converting torus mass into jet energy epsilon(sub jet) = 10%, we find that most of the tori have masses smaller than 0.01 Solar M, favoring "high-mass" binary NSs mergers, i.e., binaries with total masses approx >1.5 the maximum mass of an isolated NS. This has important consequences for the gravitational wave signals that may be detected in association with SGRBs, since "high-mass" systems do not form a long-lived hypermassive NS after the merger. While NS-BH systems cannot be excluded to be the engine of at least some of the SGRBs, the BH would need to have an initial spin of approx. 0.9 or higher.

Biases in Planet Occurrence Caused by Unresolved Binaries in Transit Surveys

NASA Astrophysics Data System (ADS)

Bouma, L. G.; Masuda, Kento; Winn, Joshua N.

2018-06-01

Wide-field surveys for transiting planets, such as the NASA Kepler and TESS missions, are usually conducted without knowing which stars have binary companions. Unresolved and unrecognized binaries give rise to systematic errors in planet occurrence rates, including misclassified planets and mistakes in completeness corrections. The individual errors can have different signs, making it difficult to anticipate the net effect on inferred occurrence rates. Here, we use simplified models of signal-to-noise limited transit surveys to try and clarify the situation. We derive a formula for the apparent occurrence rate density measured by an observer who falsely assumes all stars are single. The formula depends on the binary fraction, the mass function of the secondary stars, and the true occurrence of planets around primaries, secondaries, and single stars. It also takes into account the Malmquist bias by which binaries are over-represented in flux-limited samples. Application of the formula to an idealized Kepler-like survey shows that for planets larger than 2 R ⊕, the net systematic error is of order 5%. In particular, unrecognized binaries are unlikely to be the reason for the apparent discrepancies between hot-Jupiter occurrence rates measured in different surveys. For smaller planets the errors are potentially larger: the occurrence of Earth-sized planets could be overestimated by as much as 50%. We also show that whenever high-resolution imaging reveals a transit host star to be a binary, the planet is usually more likely to orbit the primary star than the secondary star.

Binary Black Hole Mergers in the First Advanced LIGO Observing Run

NASA Technical Reports Server (NTRS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, F.; Camp, J. B.;

Natural frequencies facilitate diagnostic inferences of managers

PubMed Central

Hoffrage, Ulrich; Hafenbrädl, Sebastian; Bouquet, Cyril

2015-01-01

In Bayesian inference tasks, information about base rates as well as hit rate and false-alarm rate needs to be integrated according to Bayes’ rule after the result of a diagnostic test became known. Numerous studies have found that presenting information in a Bayesian inference task in terms of natural frequencies leads to better performance compared to variants with information presented in terms of probabilities or percentages. Natural frequencies are the tallies in a natural sample in which hit rate and false-alarm rate are not normalized with respect to base rates. The present research replicates the beneficial effect of natural frequencies with four tasks from the domain of management, and with management students as well as experienced executives as participants. The percentage of Bayesian responses was almost twice as high when information was presented in natural frequencies compared to a presentation in terms of percentages. In contrast to most tasks previously studied, the majority of numerical responses were lower than the Bayesian solutions. Having heard of Bayes’ rule prior to the study did not affect Bayesian performance. An implication of our work is that textbooks explaining Bayes’ rule should teach how to represent information in terms of natural frequencies instead of how to plug probabilities or percentages into a formula. PMID:26157397

Evidence for a maximum mass cut-off in the neutron star mass distribution and constraints on the equation of state

NASA Astrophysics Data System (ADS)

Alsing, Justin; Silva, Hector O.; Berti, Emanuele

2018-04-01

We infer the mass distribution of neutron stars in binary systems using a flexible Gaussian mixture model and use Bayesian model selection to explore evidence for multi-modality and a sharp cut-off in the mass distribution. We find overwhelming evidence for a bimodal distribution, in agreement with previous literature, and report for the first time positive evidence for a sharp cut-off at a maximum neutron star mass. We measure the maximum mass to be 2.0M⊙ < mmax < 2.2M⊙ (68%), 2.0M⊙ < mmax < 2.6M⊙ (90%), and evidence for a cut-off is robust against the choice of model for the mass distribution and to removing the most extreme (highest mass) neutron stars from the dataset. If this sharp cut-off is interpreted as the maximum stable neutron star mass allowed by the equation of state of dense matter, our measurement puts constraints on the equation of state. For a set of realistic equations of state that support >2M⊙ neutron stars, our inference of mmax is able to distinguish between models at odds ratios of up to 12: 1, whilst under a flexible piecewise polytropic equation of state model our maximum mass measurement improves constraints on the pressure at 3 - 7 × the nuclear saturation density by ˜30 - 50% compared to simply requiring mmax > 2M⊙. We obtain a lower bound on the maximum sound speed attained inside the neutron star of c_s^max > 0.63c (99.8%), ruling out c_s^max < c/√{3} at high significance. Our constraints on the maximum neutron star mass strengthen the case for neutron star-neutron star mergers as the primary source of short gamma-ray bursts.

Inference for Transition Network Grammars,

DTIC Science & Technology

1976-01-01

If the arc Is followed. language L(G) is said to be structurally complete if The power of an augmented transition network (Am) is each rewriting rule ...Clearly, a context-sensitive grammar can be represented as a context—free grarmar plus a set of transformationDbbbbb Eabbbbbb Dbb~~bb Ebbbbbb rules ...are the foun— as a CFG (base) and a set of transformationa l rules . datIons of grammars of different complexities. The The CSL Is obtained by appl

Determination of Orbital Parameters for Visual Binary Stars Using a Fourier-Series Approach

NASA Astrophysics Data System (ADS)

Brown, D. E.; Prager, J. R.; DeLeo, G. G.; McCluskey, G. E., Jr.

2001-12-01

We expand on the Fourier transform method of Monet (ApJ 234, 275, 1979) to infer the orbital parameters of visual binary stars, and we present results for several systems, both simulated and real. Although originally developed to address binary systems observed through at least one complete period, we have extended the method to deal explicitly with cases where the orbital data is less complete. This is especially useful in cases where the period is so long that only a fragment of the orbit has been recorded. We utilize Fourier-series fitting methods appropriate to data sets covering less than one period and containing random measurement errors. In so doing, we address issues of over-determination in fitting the data and the reduction of other deleterious Fourier-series artifacts. We developed our algorithm using the MAPLE mathematical software code, and tested it on numerous "synthetic" systems, and several real binaries, including Xi Boo, 24 Aqr, and Bu 738. This work was supported at Lehigh University by the Delaware Valley Space Grant Consortium and by NSF-REU grant PHY-9820301.

Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in campanulid angiosperms.

PubMed

Beaulieu, Jeremy M; O'Meara, Brian C; Donoghue, Michael J

2013-09-01

The growth of phylogenetic trees in scope and in size is promising from the standpoint of understanding a wide variety of evolutionary patterns and processes. With trees comprised of larger, older, and globally distributed clades, it is likely that the lability of a binary character will differ significantly among lineages, which could lead to errors in estimating transition rates and the associated inference of ancestral states. Here we develop and implement a new method for identifying different rates of evolution in a binary character along different branches of a phylogeny. We illustrate this approach by exploring the evolution of growth habit in Campanulidae, a flowering plant clade containing some 35,000 species. The distribution of woody versus herbaceous species calls into question the use of traditional models of binary character evolution. The recognition and accommodation of changes in the rate of growth form evolution in different lineages demonstrates, for the first time, a robust picture of growth form evolution across a very large, very old, and very widespread flowering plant clade.

First Photometric Investigation of the Neglected EW-type Binary System V502 Her

NASA Astrophysics Data System (ADS)

Zhao, Ergang; Qian, Shengbang; Liao, Wenping; He, Jiajia; Shi, Xiangdong; Zhang, Jia

2018-04-01

V502 Her is a neglected EW-type binary, which has been known for more than 60 years. The first multi-color CCD photometric light curve and spectroscopic observations of contact binary V502 Her was obtained. Based on the LAMOST data, its spectrum can be found to be F5. Together with solutions of light curves by using the Wilson-Devinney code, it infers that V502 Her is an A-type W UMa contact binary system with the mass ratio of q = 0.313 and the filling factor of f = 38.1%. According to all minimum times from the literature and our observations, the orbital period was analyzed and a long-term increase with a periodic change (P 3 = 26.8 years) was computed. The orbital period increase may be caused by the mass transfer from a less-massive component to the more massive one, which indicates that V502 Her is in the thermal relaxation oscillation (TRO) controller stage, while the light-travel time effect (LTTE) through the presence of a cool third body may lead to the periodic variation.

Searching Planets Around Some Selected Eclipsing Close Binary Stars Systems

NASA Astrophysics Data System (ADS)

Nasiroglu, Ilham; Slowikowska, Agnieszka; Krzeszowski, Krzysztof; Zejmo, M. Michal; Er, Hüseyin; Goździewski, Krzysztof; Zola, Stanislaw; Koziel-Wierzbowska, Dorota; Debski, Bartholomew; Ogloza, Waldemar; Drozdz, Marek

2016-07-01

We present updated O-C diagrams of selected short period eclipsing binaries observed since 2009 with the T100 Telescope at the TUBITAK National Observatory (Antalya, Turkey), the T60 Telescope at the Adiyaman University Observatory (Adiyaman, Turkey), the 60cm at the Mt. Suhora Observatory of the Pedagogical University (Poland) and the 50cm Cassegrain telescope at the Fort Skala Astronomical Observatory of the Jagiellonian University in Krakow, Poland. All four telescopes are equipped with sensitive, back-illuminated CCD cameras and sets of wide band filters. One of the targets in our sample is a post-common envelope eclipsing binary NSVS 14256825. We collected more than 50 new eclipses for this system that together with the literature data gives more than 120 eclipse timings over the time span of 8.5 years. The obtained O-C diagram shows quasi-periodic variations that can be well explained by the existence of the third body on Jupiter-like orbit. We also present new results indicating a possible light time travel effect inferred from the O-C diagrams of two other binary systems: HU Aqr and V470 Cam.

Inferring thermodynamic stability relationship of polymorphs from melting data.

PubMed

Yu, L

1995-08-01

This study investigates the possibility of inferring the thermodynamic stability relationship of polymorphs from their melting data. Thermodynamic formulas are derived for calculating the Gibbs free energy difference (delta G) between two polymorphs and its temperature slope from mainly the temperatures and heats of melting. This information is then used to estimate delta G, thus relative stability, at other temperatures by extrapolation. Both linear and nonlinear extrapolations are considered. Extrapolating delta G to zero gives an estimation of the transition (or virtual transition) temperature, from which the presence of monotropy or enantiotropy is inferred. This procedure is analogous to the use of solubility data measured near the ambient temperature to estimate a transition point at higher temperature. For several systems examined, the two methods are in good agreement. The qualitative rule introduced this way for inferring the presence of monotropy or enantiotropy is approximately the same as The Heat of Fusion Rule introduced previously on a statistical mechanical basis. This method is applied to 96 pairs of polymorphs from the literature. In most cases, the result agrees with the previous determination. The deviation of the calculated transition temperatures from their previous values (n = 18) is 2% on average and 7% at maximum.

TARGETED SEQUENTIAL DESIGN FOR TARGETED LEARNING INFERENCE OF THE OPTIMAL TREATMENT RULE AND ITS MEAN REWARD.

PubMed

Chambaz, Antoine; Zheng, Wenjing; van der Laan, Mark J

2017-01-01

This article studies the targeted sequential inference of an optimal treatment rule (TR) and its mean reward in the non-exceptional case, i.e. , assuming that there is no stratum of the baseline covariates where treatment is neither beneficial nor harmful, and under a companion margin assumption. Our pivotal estimator, whose definition hinges on the targeted minimum loss estimation (TMLE) principle, actually infers the mean reward under the current estimate of the optimal TR. This data-adaptive statistical parameter is worthy of interest on its own. Our main result is a central limit theorem which enables the construction of confidence intervals on both mean rewards under the current estimate of the optimal TR and under the optimal TR itself. The asymptotic variance of the estimator takes the form of the variance of an efficient influence curve at a limiting distribution, allowing to discuss the efficiency of inference. As a by product, we also derive confidence intervals on two cumulated pseudo-regrets, a key notion in the study of bandits problems. A simulation study illustrates the procedure. One of the corner-stones of the theoretical study is a new maximal inequality for martingales with respect to the uniform entropy integral.

Oxytocin conditions trait-based rule adherence.

PubMed

Gross, Jörg; De Dreu, Carsten K W

2017-03-01

Rules, whether in the form of norms, taboos or laws, regulate and coordinate human life. Some rules, however, are arbitrary and adhering to them can be personally costly. Rigidly sticking to such rules can be considered maladaptive. Here, we test whether, at the neurobiological level, (mal)adaptive rule adherence is reduced by oxytocin-a hypothalamic neuropeptide that biases the biobehavioural approach-avoidance system. Participants (N = 139) self-administered oxytocin or placebo intranasally, and reported their need for structure and approach-avoidance sensitivity. Next, participants made binary decisions and were given an arbitrary rule that demanded to forgo financial benefits. Under oxytocin, participants violated the rule more often, especially when they had high need for structure and high approach sensitivity. Possibly, oxytocin dampens the need for a highly structured environment and enables individuals to flexibly trade-off internal desires against external restrictions. Implications for the treatment of clinical disorders marked by maladaptive rule adherence are discussed. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Moving beyond Text Highlights: Inferring Users' Interests to Improve the Relevance of Retrieval

ERIC Educational Resources Information Center

Balakrishnan, Vimala; Mehmood, Yasir; Nagappan, Yoganathan

2016-01-01

Introduction: Studies have indicated that users' text highlighting behaviour can be further manipulated to improve the relevance of retrieved results. This article reports on a study that examined users' text highlight frequency, length and users' copy-paste actions. Method: A binary voting mechanism was employed to determine the weights for the…

Modelling dynamics with context-free grammars

NASA Astrophysics Data System (ADS)

García-Huerta, Juan-M.; Jiménez-Hernández, Hugo; Herrera-Navarro, Ana-M.; Hernández-Díaz, Teresa; Terol-Villalobos, Ivan

2014-03-01

This article presents a strategy to model the dynamics performed by vehicles in a freeway. The proposal consists on encode the movement as a set of finite states. A watershed-based segmentation is used to localize regions with high-probability of motion. Each state represents a proportion of a camera projection in a two-dimensional space, where each state is associated to a symbol, such that any combination of symbols is expressed as a language. Starting from a sequence of symbols through a linear algorithm a free-context grammar is inferred. This grammar represents a hierarchical view of common sequences observed into the scene. Most probable grammar rules express common rules associated to normal movement behavior. Less probable rules express themselves a way to quantify non-common behaviors and they might need more attention. Finally, all sequences of symbols that does not match with the grammar rules, may express itself uncommon behaviors (abnormal). The grammar inference is built with several sequences of images taken from a freeway. Testing process uses the sequence of symbols emitted by the scenario, matching the grammar rules with common freeway behaviors. The process of detect abnormal/normal behaviors is managed as the task of verify if any word generated by the scenario is recognized by the grammar.

SEARCHING FOR BINARY Y DWARFS WITH THE GEMINI MULTI-CONJUGATE ADAPTIVE OPTICS SYSTEM (GeMS)

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

Opitz, Daniela; Tinney, C. G.; Faherty, Jacqueline K.

The NASA Wide-field Infrared Survey Explorer (WISE) has discovered almost all the known members of the new class of Y-type brown dwarfs. Most of these Y dwarfs have been identified as isolated objects in the field. It is known that binaries with L- and T-type brown dwarf primaries are less prevalent than either M-dwarf or solar-type primaries, they tend to have smaller separations and are more frequently detected in near-equal mass configurations. The binary statistics for Y-type brown dwarfs, however, are sparse, and so it is unclear if the same trends that hold for L- and T-type brown dwarfs alsomore » hold for Y-type ones. In addition, the detection of binary companions to very cool Y dwarfs may well be the best means available for discovering even colder objects. We present results for binary properties of a sample of five WISE Y dwarfs with the Gemini Multi-Conjugate Adaptive Optics System. We find no evidence for binary companions in these data, which suggests these systems are not equal-luminosity (or equal-mass) binaries with separations larger than ∼0.5–1.9 AU. For equal-mass binaries at an age of 5 Gyr, we find that the binary binding energies ruled out by our observations (i.e., 10{sup 42} erg) are consistent with those observed in previous studies of hotter ultra-cool dwarfs.« less

Tropical geometry of statistical models.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.

Rule acquisition in formal decision contexts based on formal, object-oriented and property-oriented concept lattices.

PubMed

Ren, Yue; Li, Jinhai; Aswani Kumar, Cherukuri; Liu, Wenqi

2014-01-01

Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: "if conditions 1,2,…, and m hold, then decisions hold." In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency.

Rule Acquisition in Formal Decision Contexts Based on Formal, Object-Oriented and Property-Oriented Concept Lattices

PubMed Central

Ren, Yue; Aswani Kumar, Cherukuri; Liu, Wenqi

2014-01-01

Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: “if conditions 1,2,…, and m hold, then decisions hold.” In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency. PMID:25165744

Development of a novel forensic STR multiplex for ancestry analysis and extended identity testing.

PubMed

Phillips, Chris; Fernandez-Formoso, Luis; Gelabert-Besada, Miguel; Garcia-Magariños, Manuel; Santos, Carla; Fondevila, Manuel; Carracedo, Angel; Lareu, Maria Victoria

2013-04-01

There is growing interest in developing additional DNA typing techniques to provide better investigative leads in forensic analysis. These include inference of genetic ancestry and prediction of common physical characteristics of DNA donors. To date, forensic ancestry analysis has centered on population-divergent SNPs but these binary loci cannot reliably detect DNA mixtures, common in forensic samples. Furthermore, STR genotypes, forming the principal DNA profiling system, are not routinely combined with forensic SNPs to strengthen frequency data available for ancestry inference. We report development of a 12-STR multiplex composed of ancestry informative marker STRs (AIM-STRs) selected from 434 tetranucleotide repeat loci. We adapted our online Bayesian classifier for AIM-SNPs: Snipper, to handle multiallele STR data using frequency-based training sets. We assessed the ability of the 12-plex AIM-STRs to differentiate CEPH Human Genome Diversity Panel populations, plus their informativeness combined with established forensic STRs and AIM-SNPs. We found combining STRs and SNPs improves the success rate of ancestry assignments while providing a reliable mixture detection system lacking from SNP analysis alone. As the 12 STRs generally show a broad range of alleles in all populations, they provide highly informative supplementary STRs for extended relationship testing and identification of missing persons with incomplete reference pedigrees. Lastly, mixed marker approaches (combining STRs with binary loci) for simple ancestry inference tests beyond forensic analysis bring advantages and we discuss the genotyping options available. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Intersection point rule for the retention value with mobile phase composition and boiling point of the homologues and chlorobenzenes in soil leaching column chromatography].

PubMed

Xu, F; Liang, X; Lin, B; Su, F

1999-03-01

Based on the linear retention equation of the logarithm of the capacity factor (logk') vs. the methanol volume fraction (psi) of aqueous binary mobile phase in soil leaching column chromatography, the intersection point rule for the logk' of homologues and weak polar chlorobenzenes, with psi, as well as with boiling point, has been derived due to existence of the similar interactions among solutes of the same series, stationary phase (soil) and eluent (methanol-water). These rules were testified by experimental data of homologues (n-alkylbenzenes, methylbenzenes) and weak polar chlorobenzenes.

Discovery of a Highly Unequal-mass Binary T Dwarf with Keck Laser Guide Star Adaptive Optics: A Coevality Test of Substellar Theoretical Models and Effective Temperatures

NASA Astrophysics Data System (ADS)

Liu, Michael C.; Dupuy, Trent J.; Leggett, S. K.

2010-10-01

Highly unequal-mass ratio binaries are rare among field brown dwarfs, with the mass ratio distribution of the known census described by q (4.9±0.7). However, such systems enable a unique test of the joint accuracy of evolutionary and atmospheric models, under the constraint of coevality for the individual components (the "isochrone test"). We carry out this test using two of the most extreme field substellar binaries currently known, the T1 + T6 epsilon Ind Bab binary and a newly discovered 0farcs14 T2.0 + T7.5 binary, 2MASS J12095613-1004008AB, identified with Keck laser guide star adaptive optics. The latter is the most extreme tight binary resolved to date (q ≈ 0.5). Based on the locations of the binary components on the Hertzsprung-Russell (H-R) diagram, current models successfully indicate that these two systems are coeval, with internal age differences of log(age) = -0.8 ± 1.3(-1.0+1.2 -1.3) dex and 0.5+0.4 -0.3(0.3+0.3 -0.4) dex for 2MASS J1209-1004AB and epsilon Ind Bab, respectively, as inferred from the Lyon (Tucson) models. However, the total mass of epsilon Ind Bab derived from the H-R diagram (≈ 80 M Jup using the Lyon models) is strongly discrepant with the reported dynamical mass. This problem, which is independent of the assumed age of the epsilon Ind Bab system, can be explained by a ≈ 50-100 K systematic error in the model atmosphere fitting, indicating slightly warmer temperatures for both components; bringing the mass determinations from the H-R diagram and the visual orbit into consistency leads to an inferred age of ≈ 6 Gyr for epsilon Ind Bab, older than previously assumed. Overall, the two T dwarf binaries studied here, along with recent results from T dwarfs in age and mass benchmark systems, yield evidence for small (≈100 K) errors in the evolutionary models and/or model atmospheres, but not significantly larger. Future parallax, resolved spectroscopy, and dynamical mass measurements for 2MASS J1209-1004AB will enable a more stringent application of the isochrone test. Finally, the binary nature of this object reduces its utility as the primary T3 near-IR spectral typing standard; we suggest SDSS J1206+2813 as a replacement. Most of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation.

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

PubMed

Kim, J; Kasabov, N

1999-11-01

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

System and method for responding to ground and flight system malfunctions

NASA Technical Reports Server (NTRS)

Anderson, Julie J. (Inventor); Fussell, Ronald M. (Inventor)

2010-01-01

A system for on-board anomaly resolution for a vehicle has a data repository. The data repository stores data related to different systems, subsystems, and components of the vehicle. The data stored is encoded in a tree-based structure. A query engine is coupled to the data repository. The query engine provides a user and automated interface and provides contextual query to the data repository. An inference engine is coupled to the query engine. The inference engine compares current anomaly data to contextual data stored in the data repository using inference rules. The inference engine generates a potential solution to the current anomaly by referencing the data stored in the data repository.

Methodology for the inference of gene function from phenotype data.

PubMed

Ascensao, Joao A; Dolan, Mary E; Hill, David P; Blake, Judith A

2014-12-12

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures. We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes. We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

A self-adaptive algorithm for traffic sign detection in motion image based on color and shape features

NASA Astrophysics Data System (ADS)

Zhang, Ka; Sheng, Yehua; Gong, Zhijun; Ye, Chun; Li, Yongqiang; Liang, Cheng

2007-06-01

As an important sub-system in intelligent transportation system (ITS), the detection and recognition of traffic signs from mobile images is becoming one of the hot spots in the international research field of ITS. Considering the problem of traffic sign automatic detection in motion images, a new self-adaptive algorithm for traffic sign detection based on color and shape features is proposed in this paper. Firstly, global statistical color features of different images are computed based on statistics theory. Secondly, some self-adaptive thresholds and special segmentation rules for image segmentation are designed according to these global color features. Then, for red, yellow and blue traffic signs, the color image is segmented to three binary images by these thresholds and rules. Thirdly, if the number of white pixels in the segmented binary image exceeds the filtering threshold, the binary image should be further filtered. Fourthly, the method of gray-value projection is used to confirm top, bottom, left and right boundaries for candidate regions of traffic signs in the segmented binary image. Lastly, if the shape feature of candidate region satisfies the need of real traffic sign, this candidate region is confirmed as the detected traffic sign region. The new algorithm is applied to actual motion images of natural scenes taken by a CCD camera of the mobile photogrammetry system in Nanjing at different time. The experimental results show that the algorithm is not only simple, robust and more adaptive to natural scene images, but also reliable and high-speed on real traffic sign detection.

Research Note PSR B1929+10 and GSC 01060-01374 are not binary companions

NASA Astrophysics Data System (ADS)

Kouwenhoven, M. L. A.; van den Berg, M. C.

2001-03-01

We have observed the star GSC 01060-01374 to investigate whether it is in a binary with PSR B1929+10. Its spectral type is K4-6 and its luminosity class is III or II, therefore its distance is 2.4 kpc or higher. Since the dispersion measure distance of PSR B1929+10 is 0.17 kpc, we rule out the possibility that these two stars are associated in a binary. This poses further constraints on the lower limit of kick velocities in supernova explosions. Based on observations made with the William Herschel Telescope operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias.

Statistical inference of static analysis rules

NASA Technical Reports Server (NTRS)

Engler, Dawson Richards (Inventor)

2009-01-01

Various apparatus and methods are disclosed for identifying errors in program code. Respective numbers of observances of at least one correctness rule by different code instances that relate to the at least one correctness rule are counted in the program code. Each code instance has an associated counted number of observances of the correctness rule by the code instance. Also counted are respective numbers of violations of the correctness rule by different code instances that relate to the correctness rule. Each code instance has an associated counted number of violations of the correctness rule by the code instance. A respective likelihood of the validity is determined for each code instance as a function of the counted number of observances and counted number of violations. The likelihood of validity indicates a relative likelihood that a related code instance is required to observe the correctness rule. The violations may be output in order of the likelihood of validity of a violated correctness rule.

Learning the Rules of the Game

ERIC Educational Resources Information Center

Smith, Donald A.

2018-01-01

Games have often been used in the classroom to teach physics ideas and concepts, but there has been less published on games that can be used to teach scientific thinking. D. Maloney and M. Masters describe an activity in which students attempt to infer rules to a game from a history of moves, but the students do not actually play the game. Giving…

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

PubMed

Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab

2017-09-01

Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

Program for Experimentation With Expert Systems

NASA Technical Reports Server (NTRS)

Engle, S. W.

1986-01-01

CERBERUS is forward-chaining, knowledge-based system program useful for experimentation with expert systems. Inference-engine mechanism performs deductions according to user-supplied rule set. Information stored in intermediate area, and user interrogated only when no applicable data found in storage. Each assertion posed by CERBERUS answered with certainty ranging from 0 to 100 percent. Rule processor stops investigating applicable rules when goal reaches certainty of 95 percent or higher. Capable of operating for wide variety of domains. Sample rule files included for animal identification, pixel classification in image processing, and rudimentary car repair for novice mechanic. User supplies set of end goals or actions. System complexity decided by user's rule file. CERBERUS written in FORTRAN 77.

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

Del Pozzo, Walter; Nikhef National Institute for Subatomic Physics, Science Park 105, 1098 XG Amsterdam; Veitch, John

Second-generation interferometric gravitational-wave detectors, such as Advanced LIGO and Advanced Virgo, are expected to begin operation by 2015. Such instruments plan to reach sensitivities that will offer the unique possibility to test general relativity in the dynamical, strong-field regime and investigate departures from its predictions, in particular, using the signal from coalescing binary systems. We introduce a statistical framework based on Bayesian model selection in which the Bayes factor between two competing hypotheses measures which theory is favored by the data. Probability density functions of the model parameters are then used to quantify the inference on individual parameters. We alsomore » develop a method to combine the information coming from multiple independent observations of gravitational waves, and show how much stronger inference could be. As an introduction and illustration of this framework-and a practical numerical implementation through the Monte Carlo integration technique of nested sampling-we apply it to gravitational waves from the inspiral phase of coalescing binary systems as predicted by general relativity and a very simple alternative theory in which the graviton has a nonzero mass. This method can (and should) be extended to more realistic and physically motivated theories.« less

Spectroscopic observations of the detached binary PG 1413 + 015

NASA Technical Reports Server (NTRS)

Fulbright, Michael S.; Liebert, James; Bergeron, P.; Green, Richard

1993-01-01

We present improved estimates of the stellar parameters of the eclipsing, precataclysmic binary system PG 1413 + 015 (GH Vir), which has an orbital period of only 8h16m. Model atmosphere fits a Balmer line profiles yield T(eff) = 48,800 +/- 1200 K and log g = 7.70 +/- 0.11 for the DAO white dwarf primary star, from which a mass of 0.51 +/- 0.04 solar mass is inferred using evolutionary models. An ultraviolet spectrum obtained with the IUE Observatory has a slope consistent with this temperature and the assumption of no interstellar extinction. A red CCD spectrum of the secondary star during the 12-minute total eclipse indicates a spectral type of M3 V-M5 V. Reanalysis of the eclipse light curve leads to an inferred radius of 0.15 solar radius and a mass of 0.10 solar mass for the secondary, the latter being marginally consistent with the spectral type. Reprocessing on the facing side of the secondary produces phase-dependent Balmer line emission and detectable variations in the continuum from 6500-9000 A. The observed levels of reprocessing are consistent with expectations based on the above stellar parameters.

cit: hypothesis testing software for mediation analysis in genomic applications.

PubMed

Millstein, Joshua; Chen, Gary K; Breton, Carrie V

2016-08-01

The challenges of successfully applying causal inference methods include: (i) satisfying underlying assumptions, (ii) limitations in data/models accommodated by the software and (iii) low power of common multiple testing approaches. The causal inference test (CIT) is based on hypothesis testing rather than estimation, allowing the testable assumptions to be evaluated in the determination of statistical significance. A user-friendly software package provides P-values and optionally permutation-based FDR estimates (q-values) for potential mediators. It can handle single and multiple binary and continuous instrumental variables, binary or continuous outcome variables and adjustment covariates. Also, the permutation-based FDR option provides a non-parametric implementation. Simulation studies demonstrate the validity of the cit package and show a substantial advantage of permutation-based FDR over other common multiple testing strategies. The cit open-source R package is freely available from the CRAN website (https://cran.r-project.org/web/packages/cit/index.html) with embedded C ++ code that utilizes the GNU Scientific Library, also freely available (http://www.gnu.org/software/gsl/). joshua.millstein@usc.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Discovery and characterization of 3000+ main-sequence binaries from APOGEE spectra

NASA Astrophysics Data System (ADS)

El-Badry, Kareem; Ting, Yuan-Sen; Rix, Hans-Walter; Quataert, Eliot; Weisz, Daniel R.; Cargile, Phillip; Conroy, Charlie; Hogg, David W.; Bergemann, Maria; Liu, Chao

2018-05-01

We develop a data-driven spectral model for identifying and characterizing spatially unresolved multiple-star systems and apply it to APOGEE DR13 spectra of main-sequence stars. Binaries and triples are identified as targets whose spectra can be significantly better fit by a superposition of two or three model spectra, drawn from the same isochrone, than any single-star model. From an initial sample of ˜20 000 main-sequence targets, we identify ˜2500 binaries in which both the primary and secondary stars contribute detectably to the spectrum, simultaneously fitting for the velocities and stellar parameters of both components. We additionally identify and fit ˜200 triple systems, as well as ˜700 velocity-variable systems in which the secondary does not contribute detectably to the spectrum. Our model simplifies the process of simultaneously fitting single- or multi-epoch spectra with composite models and does not depend on a velocity offset between the two components of a binary, making it sensitive to traditionally undetectable systems with periods of hundreds or thousands of years. In agreement with conventional expectations, almost all the spectrally identified binaries with measured parallaxes fall above the main sequence in the colour-magnitude diagram. We find excellent agreement between spectrally and dynamically inferred mass ratios for the ˜600 binaries in which a dynamical mass ratio can be measured from multi-epoch radial velocities. We obtain full orbital solutions for 64 systems, including 14 close binaries within hierarchical triples. We make available catalogues of stellar parameters, abundances, mass ratios, and orbital parameters.

THE STELLAR INITIAL MASS FUNCTION OF ULTRA-FAINT DWARF GALAXIES: EVIDENCE FOR IMF VARIATIONS WITH GALACTIC ENVIRONMENT

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

Geha, Marla; Brown, Thomas M.; Tumlinson, Jason

2013-07-01

We present constraints on the stellar initial mass function (IMF) in two ultra-faint dwarf (UFD) galaxies, Hercules and Leo IV, based on deep Hubble Space Telescope Advanced Camera for Surveys imaging. The Hercules and Leo IV galaxies are extremely low luminosity (M{sub V} = -6.2, -5.5), metal-poor (([Fe/H]) = -2.4, -2.5) systems that have old stellar populations (>11 Gyr). Because they have long relaxation times, we can directly measure the low-mass stellar IMF by counting stars below the main-sequence turnoff without correcting for dynamical evolution. Over the stellar mass range probed by our data, 0.52-0.77 M{sub Sun }, the IMFmore » is best fit by a power-law slope of {alpha}= 1.2{sub -0.5}{sup +0.4} for Hercules and {alpha} = 1.3 {+-} 0.8 for Leo IV. For Hercules, the IMF slope is more shallow than a Salpeter ({alpha} = 2.35) IMF at the 5.8{sigma} level, and a Kroupa ({alpha} = 2.3 above 0.5 M{sub Sun }) IMF slope at 5.4{sigma} level. We simultaneously fit for the binary fraction, f{sub binary}, finding f{sub binary}= 0.47{sup +0.16}{sub -0.14} for Hercules, and 0.47{sup +0.37}{sub -0.17} for Leo IV. The UFD binary fractions are consistent with that inferred for Milky Way stars in the same mass range, despite very different metallicities. In contrast, the IMF slopes in the UFDs are shallower than other galactic environments. In the mass range 0.5-0.8 M{sub Sun }, we see a trend across the handful of galaxies with directly measured IMFs such that the power-law slopes become shallower (more bottom-light) with decreasing galactic velocity dispersion and metallicity. This trend is qualitatively consistent with results in elliptical galaxies inferred via indirect methods and is direct evidence for IMF variations with galactic environment.« less

Temperature Dependence of Densities and Excess Molar Volumes of the Ternary Mixture (1-Butanol + Chloroform + Benzene) and its Binary Constituents (1-Butanol + Chloroform and 1-Butanol + Benzene)

NASA Astrophysics Data System (ADS)

Smiljanić, Jelena D.; Kijevčanin, Mirjana Lj.; Djordjević, Bojan D.; Grozdanić, Dušan K.; Šerbanović, Slobodan P.

2008-04-01

Densities ρ of the 1-butanol + chloroform + benzene ternary mixture and the 1-butanol + chloroform and 1-butanol + benzene binaries have been measured at six temperatures (288.15, 293.15, 298.15, 303.15, 308.15, and 313.15) K and atmospheric pressure, using an oscillating U-tube densimeter. From these densities, excess molar volumes ( V E) were calculated and fitted to the Redlich Kister equation for all binary mixtures and to the Nagata and Tamura equation for the ternary system. The Radojković et al. equation has been used to predict excess molar volumes of the ternary mixtures. Also, V E data of the binary systems were correlated by the van der Waals (vdW1) and Twu Coon Bluck Tilton (TCBT) mixing rules coupled with the Peng Robinson Stryjek Vera (PRSV) equation of state. The prediction and correlation of V E data for the ternary system were performed by the same models.

Quick probabilistic binary image matching: changing the rules of the game

NASA Astrophysics Data System (ADS)

Mustafa, Adnan A. Y.

2016-09-01

A Probabilistic Matching Model for Binary Images (PMMBI) is presented that predicts the probability of matching binary images with any level of similarity. The model relates the number of mappings, the amount of similarity between the images and the detection confidence. We show the advantage of using a probabilistic approach to matching in similarity space as opposed to a linear search in size space. With PMMBI a complete model is available to predict the quick detection of dissimilar binary images. Furthermore, the similarity between the images can be measured to a good degree if the images are highly similar. PMMBI shows that only a few pixels need to be compared to detect dissimilarity between images, as low as two pixels in some cases. PMMBI is image size invariant; images of any size can be matched at the same quick speed. Near-duplicate images can also be detected without much difficulty. We present tests on real images that show the prediction accuracy of the model.

Bayesian Analysis of High Dimensional Classification

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Subhadeep; Liang, Faming

2009-12-01

Modern data mining and bioinformatics have presented an important playground for statistical learning techniques, where the number of input variables is possibly much larger than the sample size of the training data. In supervised learning, logistic regression or probit regression can be used to model a binary output and form perceptron classification rules based on Bayesian inference. In these cases , there is a lot of interest in searching for sparse model in High Dimensional regression(/classification) setup. we first discuss two common challenges for analyzing high dimensional data. The first one is the curse of dimensionality. The complexity of many existing algorithms scale exponentially with the dimensionality of the space and by virtue of that algorithms soon become computationally intractable and therefore inapplicable in many real applications. secondly, multicollinearities among the predictors which severely slowdown the algorithm. In order to make Bayesian analysis operational in high dimension we propose a novel 'Hierarchical stochastic approximation monte carlo algorithm' (HSAMC), which overcomes the curse of dimensionality, multicollinearity of predictors in high dimension and also it possesses the self-adjusting mechanism to avoid the local minima separated by high energy barriers. Models and methods are illustrated by simulation inspired from from the feild of genomics. Numerical results indicate that HSAMC can work as a general model selection sampler in high dimensional complex model space.

An Architecture for Performance Optimization in a Collaborative Knowledge-Based Approach for Wireless Sensor Networks

PubMed Central

Gadeo-Martos, Manuel Angel; Fernandez-Prieto, Jose Angel; Canada-Bago, Joaquin; Velasco, Juan Ramon

2011-01-01

Over the past few years, Intelligent Spaces (ISs) have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a) an optimized design for the inference engine; (b) a visual interface; (c) a module to reduce the redundancy and complexity of the knowledge bases; (d) a module to evaluate the accuracy of the new knowledge base; (e) a module to adapt the format of the rules to the structure used by the inference engine; and (f) a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern.) and repilo (caused by the fungus Spilocaea oleagina). The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery) without a substantial decrease in the accuracy of the inferred values. PMID:22163687

Empirical OPC rule inference for rapid RET application

NASA Astrophysics Data System (ADS)

Kulkarni, Anand P.

2006-10-01

A given technological node (45 nm, 65 nm) can be expected to process thousands of individual designs. Iterative methods applied at the node consume valuable days in determining proper placement of OPC features, and manufacturing and testing mask correspondence to wafer patterns in a trial-and-error fashion for each design. Repeating this fabrication process for each individual design is a time-consuming and expensive process. We present a novel technique which sidesteps the requirement to iterate through the model-based OPC analysis and pattern verification cycle on subsequent designs at the same node. Our approach relies on the inference of rules from a correct pattern at the wafer surface it relates to the OPC and pre-OPC pattern layout files. We begin with an offline phase where we obtain a "gold standard" design file that has been fab-tested at the node with a prepared, post-OPC layout file that corresponds to the intended on-wafer pattern. We then run an offline analysis to infer rules to be used in this method. During the analysis, our method implicitly identifies contextual OPC strategies for optimal placement of RET features on any design at that node. Using these strategies, we can apply OPC to subsequent designs at the same node with accuracy comparable to the original design file but significantly smaller expected runtimes. The technique promises to offer a rapid and accurate complement to existing RET application strategies.

How people explain their own and others’ behavior: a theory of lay causal explanations

PubMed Central

Böhm, Gisela; Pfister, Hans-Rüdiger

2015-01-01

A theoretical model is proposed that specifies lay causal theories of behavior; and supporting experimental evidence is presented. The model’s basic assumption is that different types of behavior trigger different hypotheses concerning the types of causes that may have brought about the behavior. Seven categories are distinguished that are assumed to serve as both behavior types and explanation types: goals, dispositions, temporary states such as emotions, intentional actions, outcomes, events, and stimulus attributes. The model specifies inference rules that lay people use when explaining behavior (actions are caused by goals; goals are caused by higher order goals or temporary states; temporary states are caused by dispositions, stimulus attributes, or events; outcomes are caused by actions, temporary states, dispositions, stimulus attributes, or events; events are caused by dispositions or preceding events). Two experiments are reported. Experiment 1 showed that free-response explanations followed the assumed inference rules. Experiment 2 demonstrated that explanations which match the inference rules are generated faster and more frequently than non-matching explanations. Together, the findings support models that incorporate knowledge-based aspects into the process of causal explanation. The results are discussed with respect to their implications for different stages of this process, such as the activation of causal hypotheses and their subsequent selection, as well as with respect to social influences on this process. PMID:25741306

An architecture for performance optimization in a collaborative knowledge-based approach for wireless sensor networks.

PubMed

Gadeo-Martos, Manuel Angel; Fernandez-Prieto, Jose Angel; Canada-Bago, Joaquin; Velasco, Juan Ramon

2011-01-01

Over the past few years, Intelligent Spaces (ISs) have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a) an optimized design for the inference engine; (b) a visual interface; (c) a module to reduce the redundancy and complexity of the knowledge bases; (d) a module to evaluate the accuracy of the new knowledge base; (e) a module to adapt the format of the rules to the structure used by the inference engine; and (f) a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern.) and repilo (caused by the fungus Spilocaea oleagina). The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery) without a substantial decrease in the accuracy of the inferred values.

Constraints on the inner accretion flow of 4U/MXB 1636-53 (V 801 Arae) from a comparison of X-ray burst and persistent emission

NASA Technical Reports Server (NTRS)

Damen, E.; Wijers, R. A. M. J.; Van Paradijs, J.; Penninx, W.; Oosterbroek, T.

1990-01-01

A detailed analysis is presented of the importance of Comptonization in burst and persistent spectra of the low-mass X-ray binary 4U/MXB 1636-53, and from this analysis it is inferred that the inner accretion flow is geometrically thin. It is found that burst spectra of 1636-53 are very nearly Planckian in shape; from an upper limit to a high-energy excess in these spectra it is inferred that the Thomson scattering optical depth of a possible intervening hot cloud must be less than 1 during bursts, and that the Compton y parameter of that cloud must be less than 0.5. During persistent emission, Thomson optical depth of 4-8, an electron temperature of 2-5 keV, and a value of 0.8-1.1 for y are inferred.

Evolution of bone microanatomy of the tetrapod tibia and its use in palaeobiological inference.

PubMed

Kriloff, A; Germain, D; Canoville, A; Vincent, P; Sache, M; Laurin, M

2008-05-01

Bone microanatomy appears to track changes in various physiological or ecological properties of the individual or the taxon. Analyses of sections of the tibia of 99 taxa show a highly significant (P

Functional networks inference from rule-based machine learning models.

PubMed

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 implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html.

Pre-Algebra Groups. Concepts & Applications.

ERIC Educational Resources Information Center

Montgomery County Public Schools, Rockville, MD.

Discussion material and exercises related to pre-algebra groups are provided in this five chapter manual. Chapter 1 (mappings) focuses on restricted domains, order of operations (parentheses and exponents), rules of assignment, and computer extensions. Chapter 2 considers finite number systems, including binary operations, clock arithmetic,…

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

NASA Astrophysics Data System (ADS)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

PubMed Central

Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

2012-01-01

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817

DOE PAGES

Abbott, B. P.; Abbott, R.; Abbott, T. D.; ...

2017-12-01

The source of the gravitational-wave (GW) signal GW170817, very likely a binary neutron star merger, was also observed electromagnetically, providing the first multi-messenger observations of this type. The two-week-long electromagnetic (EM) counterpart had a signature indicative of an r-process-induced optical transient known as a kilonova. This Letter examines how the mass of the dynamical ejecta can be estimated without a direct electromagnetic observation of the kilonova, using GW measurements and a phenomenological model calibrated to numerical simulations of mergers with dynamical ejecta. Specifically, we apply the model to the binary masses inferred from the GW measurements, and use the resulting mass of the dynamical ejecta to estimate its contribution (without the effects of wind ejecta) to the corresponding kilonova light curves from various models. The distributions of dynamical ejecta mass range betweenmore » $${M}_{\\mathrm{ej}}={10}^{-3}-{10}^{-2}\\,{M}_{\\odot }$$ for various equations of state, assuming that the neutron stars are rotating slowly. In addition, we use our estimates of the dynamical ejecta mass and the neutron star merger rates inferred from GW170817 to constrain the contribution of events like this to the r-process element abundance in the Galaxy when ejecta mass from post-merger winds is neglected. We find that if ≳10% of the matter dynamically ejected from binary neutron star (BNS) mergers is converted to r-process elements, GW170817-like BNS mergers could fully account for the amount of r-process material observed in the Milky Way.« less

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817

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

Abbott, B. P.; Abbott, R.; Abbott, T. D.

The source of the gravitational-wave (GW) signal GW170817, very likely a binary neutron star merger, was also observed electromagnetically, providing the first multi-messenger observations of this type. The two-week-long electromagnetic (EM) counterpart had a signature indicative of an r-process-induced optical transient known as a kilonova. This Letter examines how the mass of the dynamical ejecta can be estimated without a direct electromagnetic observation of the kilonova, using GW measurements and a phenomenological model calibrated to numerical simulations of mergers with dynamical ejecta. Specifically, we apply the model to the binary masses inferred from the GW measurements, and use the resulting mass of the dynamical ejecta to estimate its contribution (without the effects of wind ejecta) to the corresponding kilonova light curves from various models. The distributions of dynamical ejecta mass range betweenmore » $${M}_{\\mathrm{ej}}={10}^{-3}-{10}^{-2}\\,{M}_{\\odot }$$ for various equations of state, assuming that the neutron stars are rotating slowly. In addition, we use our estimates of the dynamical ejecta mass and the neutron star merger rates inferred from GW170817 to constrain the contribution of events like this to the r-process element abundance in the Galaxy when ejecta mass from post-merger winds is neglected. We find that if ≳10% of the matter dynamically ejected from binary neutron star (BNS) mergers is converted to r-process elements, GW170817-like BNS mergers could fully account for the amount of r-process material observed in the Milky Way.« less

Genie: An Inference Engine with Applications to Vulnerability Analysis.

DTIC Science & Technology

1986-06-01

Stanford Artifcial intelligence Laboratory, 1976. 15 D. A. Waterman and F. Hayes-Roth, eds. Pattern-Directed Inference Systems. Academic Press, Inc...Continue an reverse aide It nlecessary mid Identify by block rnmbor) ; f Expert Systems Artificial Intelligence % Vulnerability Analysis Knowledge...deduction it is used wherever possible in data -driven mode (forward chaining). Production rules - JIM 0 g79OOFMV55@S I INCLASSTpnF SECURITY CLASSIFICATION OF

Spitzer Observations of OGLE-2015-BLG-1212 Reveal a New Path toward Breaking Strong Microlens Degeneracies

NASA Technical Reports Server (NTRS)

Bozza, V.; Shvartzvald, Y.; Udalski, A.; Novati, S.Calchi; Bond, I. A.; Han, C.; Hundertmark, M.; Poleski, R.; Pawlak, M.; Szymanski, M. K.;

ON THE BINARY FREQUENCY OF THE LOWEST MASS MEMBERS OF THE PLEIADES WITH HUBBLE SPACE TELESCOPE WIDE FIELD CAMERA 3

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

Garcia, E. V.; Dupuy, Trent J.; Allers, Katelyn N.

2015-05-01

We present the results of a Hubble Space Telescope Wide Field Camera 3 (WFC3) imaging survey of 11 of the lowest mass brown dwarfs in the Pleiades known (25–40 M{sub Jup}). These objects represent the predecessors to T dwarfs in the field. Using a semi-empirical binary point-spread function (PSF)-fitting technique, we are able to probe to 0.″ 03 (0.75 pixel), better than 2x the WFC3/UVIS diffraction limit. We did not find any companions to our targets. From extensive testing of our PSF-fitting method on simulated binaries, we compute detection limits which rule out companions to our targets with mass ratiosmore » of ≳0.7 and separations ≳4 AU. Thus, our survey is the first to attain the high angular resolution needed to resolve brown dwarf binaries in the Pleiades at separations that are most common in the field population. We constrain the binary frequency over this range of separation and mass ratio of 25–40 M{sub Jup} Pleiades brown dwarfs to be <11% for 1σ (<26% at 2σ). This binary frequency is consistent with both younger and older brown dwarfs in this mass range.« less

SIRE: A Simple Interactive Rule Editor for NICBES

NASA Technical Reports Server (NTRS)

Bykat, Alex

1988-01-01

To support evolution of domain expertise, and its representation in an expert system knowledge base, a user-friendly rule base editor is mandatory. The Nickel Cadmium Battery Expert System (NICBES), a prototype of an expert system for the Hubble Space Telescope power storage management system, does not provide such an editor. In the following, a description of a Simple Interactive Rule Base Editor (SIRE) for NICBES is described. The SIRE provides a consistent internal representation of the NICBES knowledge base. It supports knowledge presentation and provides a user-friendly and code language independent medium for rule addition and modification. The SIRE is integrated with NICBES via an interface module. This module provides translation of the internal representation to Prolog-type rules (Horn clauses), latter rule assertion, and a simple mechanism for rule selection for its Prolog inference engine.

Combination Rules for Morse-Based van der Waals Force Fields.

PubMed

Yang, Li; Sun, Lei; Deng, Wei-Qiao

2018-02-15

In traditional force fields (FFs), van der Waals interactions have been usually described by the Lennard-Jones potentials. Conventional combination rules for the parameters of van der Waals (VDW) cross-termed interactions were developed for the Lennard-Jones based FFs. Here, we report that the Morse potentials were a better function to describe VDW interactions calculated by highly precise quantum mechanics methods. A new set of combination rules was developed for Morse-based FFs, in which VDW interactions were described by Morse potentials. The new set of combination rules has been verified by comparing the second virial coefficients of 11 noble gas mixtures. For all of the mixed binaries considered in this work, the combination rules work very well and are superior to all three other existing sets of combination rules reported in the literature. We further used the Morse-based FF by using the combination rules to simulate the adsorption isotherms of CH 4 at 298 K in four covalent-organic frameworks (COFs). The overall agreement is great, which supports the further applications of this new set of combination rules in more realistic simulation systems.

Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls

NASA Technical Reports Server (NTRS)

Anastasiadis, Stergios

1991-01-01

Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric.

Can binary stars test solar models?

NASA Technical Reports Server (NTRS)

Popper, D. M.; Ulrich, R. K.

1986-01-01

The position in the H-R diagram of the approximately solar-mass component of the Hyades eclipsing binary, HD 27130, is compared with the predictions of stellar structure theory. The stellar models are calibrated by matching a model with the solar heavy element composition and age to the solar radius and luminosity. The comparison to the Hyades binary then is a test of the prediction that the initial solar luminosity was only about 0.7 times the present solar luminosity. The agreement is satisfactory, lending a measure of confidence to the solar model employed, provided that the initial helium abundance of the Hyades stars is not greater than that of the sun and is not less by more than about 0.03 in Y. Unless the model is grossly incorrect, the inference of Stromgren, Olsen, and Gustafsson (1982) from the 'Hyades anomaly' in intermediate-band photometry that Y(Hyades) is less than Y(solar) by 0.1 or 0.15 is rejected by the observed properties of HD 27130.

Inferring the post-merger gravitational wave emission from binary neutron star coalescences

NASA Astrophysics Data System (ADS)

Chatziioannou, Katerina; Clark, James Alexander; Bauswein, Andreas; Millhouse, Margaret; Littenberg, Tyson B.; Cornish, Neil

2017-12-01

We present a robust method to characterize the gravitational wave emission from the remnant of a neutron star coalescence. Our approach makes only minimal assumptions about the morphology of the signal and provides a full posterior probability distribution of the underlying waveform. We apply our method on simulated data from a network of advanced ground-based detectors and demonstrate the gravitational wave signal reconstruction. We study the reconstruction quality for different binary configurations and equations of state for the colliding neutron stars. We show how our method can be used to constrain the yet-uncertain equation of state of neutron star matter. The constraints on the equation of state we derive are complementary to measurements of the tidal deformation of the colliding neutron stars during the late inspiral phase. In the case of nondetection of a post-merger signal following a binary neutron star inspiral, we show that we can place upper limits on the energy emitted.

No tension between assembly models of super massive black hole binaries and pulsar observations.

PubMed

Middleton, Hannah; Chen, Siyuan; Del Pozzo, Walter; Sesana, Alberto; Vecchio, Alberto

2018-02-08

Pulsar timing arrays are presently the only means to search for the gravitational wave stochastic background from super massive black hole binary populations, considered to be within the grasp of current or near-future observations. The stringent upper limit from the Parkes Pulsar Timing Array has been interpreted as excluding (>90% confidence) the current paradigm of binary assembly through galaxy mergers and hardening via stellar interaction, suggesting evolution is accelerated or stalled. Using Bayesian hierarchical modelling we consider implications of this upper limit for a range of astrophysical scenarios, without invoking stalling, nor more exotic physical processes. All scenarios are fully consistent with the upper limit, but (weak) bounds on population parameters can be inferred. Recent upward revisions of the black hole-galaxy bulge mass relation are disfavoured at 1.6σ against lighter models. Once sensitivity improves by an order of magnitude, a non-detection will disfavour the most optimistic scenarios at 3.9σ.

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks

PubMed Central

Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

2015-01-01

Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns. PMID:26291608

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

PubMed

Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

2015-08-01

Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns.

Supermassive Black Holes and Galaxy Evolution

NASA Technical Reports Server (NTRS)

Merritt, D.

2004-01-01

Supermassive black holes appear to be generic components of galactic nuclei. The formation and growth of black holes is intimately connected with the evolution of galaxies on a wide range of scales. For instance, mergers between galaxies containing nuclear black holes would produce supermassive binaries which eventually coalesce via the emission of gravitational radiation. The formation and decay of these binaries is expected to produce a number of observable signatures in the stellar distribution. Black holes can also affect the large-scale structure of galaxies by perturbing the orbits of stars that pass through the nucleus. Large-scale N-body simulations are beginning to generate testable predictions about these processes which will allow us to draw inferences about the formation history of supermassive black holes.

Developing a statistically powerful measure for quartet tree inference using phylogenetic identities and Markov invariants.

PubMed

Sumner, Jeremy G; Taylor, Amelia; Holland, Barbara R; Jarvis, Peter D

2017-12-01

Recently there has been renewed interest in phylogenetic inference methods based on phylogenetic invariants, alongside the related Markov invariants. Broadly speaking, both these approaches give rise to polynomial functions of sequence site patterns that, in expectation value, either vanish for particular evolutionary trees (in the case of phylogenetic invariants) or have well understood transformation properties (in the case of Markov invariants). While both approaches have been valued for their intrinsic mathematical interest, it is not clear how they relate to each other, and to what extent they can be used as practical tools for inference of phylogenetic trees. In this paper, by focusing on the special case of binary sequence data and quartets of taxa, we are able to view these two different polynomial-based approaches within a common framework. To motivate the discussion, we present three desirable statistical properties that we argue any invariant-based phylogenetic method should satisfy: (1) sensible behaviour under reordering of input sequences; (2) stability as the taxa evolve independently according to a Markov process; and (3) explicit dependence on the assumption of a continuous-time process. Motivated by these statistical properties, we develop and explore several new phylogenetic inference methods. In particular, we develop a statistically bias-corrected version of the Markov invariants approach which satisfies all three properties. We also extend previous work by showing that the phylogenetic invariants can be implemented in such a way as to satisfy property (3). A simulation study shows that, in comparison to other methods, our new proposed approach based on bias-corrected Markov invariants is extremely powerful for phylogenetic inference. The binary case is of particular theoretical interest as-in this case only-the Markov invariants can be expressed as linear combinations of the phylogenetic invariants. A wider implication of this is that, for models with more than two states-for example DNA sequence alignments with four-state models-we find that methods which rely on phylogenetic invariants are incapable of satisfying all three of the stated statistical properties. This is because in these cases the relevant Markov invariants belong to a class of polynomials independent from the phylogenetic invariants.

Equations for Scoring Rules When Data Are Missing

NASA Technical Reports Server (NTRS)

James, Mark

2006-01-01

A document presents equations for scoring rules in a diagnostic and/or prognostic artificial-intelligence software system of the rule-based inference-engine type. The equations define a set of metrics that characterize the evaluation of a rule when data required for the antecedence clause(s) of the rule are missing. The metrics include a primary measure denoted the rule completeness metric (RCM) plus a number of subsidiary measures that contribute to the RCM. The RCM is derived from an analysis of a rule with respect to its truth and a measure of the completeness of its input data. The derivation is such that the truth value of an antecedent is independent of the measure of its completeness. The RCM can be used to compare the degree of completeness of two or more rules with respect to a given set of data. Hence, the RCM can be used as a guide to choosing among rules during the rule-selection phase of operation of the artificial-intelligence system..

Neural network explanation using inversion.

PubMed

Saad, Emad W; Wunsch, Donald C

2007-01-01

An important drawback of many artificial neural networks (ANN) is their lack of explanation capability [Andrews, R., Diederich, J., & Tickle, A. B. (1996). A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8, 373-389]. This paper starts with a survey of algorithms which attempt to explain the ANN output. We then present HYPINV, a new explanation algorithm which relies on network inversion; i.e. calculating the ANN input which produces a desired output. HYPINV is a pedagogical algorithm, that extracts rules, in the form of hyperplanes. It is able to generate rules with arbitrarily desired fidelity, maintaining a fidelity-complexity tradeoff. To our knowledge, HYPINV is the only pedagogical rule extraction method, which extracts hyperplane rules from continuous or binary attribute neural networks. Different network inversion techniques, involving gradient descent as well as an evolutionary algorithm, are presented. An information theoretic treatment of rule extraction is presented. HYPINV is applied to example synthetic problems, to a real aerospace problem, and compared with similar algorithms using benchmark problems.

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

PubMed

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

2015-01-01

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

Modeling Protein Expression and Protein Signaling Pathways

PubMed Central

Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

2015-01-01

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

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

PubMed Central

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

2015-01-01

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

Reconstructing population histories from single nucleotide polymorphism data.

PubMed

Sirén, Jukka; Marttinen, Pekka; Corander, Jukka

2011-01-01

Population genetics encompasses a strong theoretical and applied research tradition on the multiple demographic processes that shape genetic variation present within a species. When several distinct populations exist in the current generation, it is often natural to consider the pattern of their divergence from a single ancestral population in terms of a binary tree structure. Inference about such population histories based on molecular data has been an intensive research topic in the recent years. The most common approach uses coalescent theory to model genealogies of individuals sampled from the current populations. Such methods are able to compare several different evolutionary scenarios and to estimate demographic parameters. However, their major limitation is the enormous computational complexity associated with the indirect modeling of the demographies, which limits the application to small data sets. Here, we propose a novel Bayesian method for inferring population histories from unlinked single nucleotide polymorphisms, which is applicable also to data sets harboring large numbers of individuals from distinct populations. We use an approximation to the neutral Wright-Fisher diffusion to model random fluctuations in allele frequencies. The population histories are modeled as binary rooted trees that represent the historical order of divergence of the different populations. A combination of analytical, numerical, and Monte Carlo integration techniques are utilized for the inferences. A particularly important feature of our approach is that it provides intuitive measures of statistical uncertainty related with the estimates computed, which may be entirely lacking for the alternative methods in this context. The potential of our approach is illustrated by analyses of both simulated and real data sets.

Forward-Chaining Versus A Graph Approach As The Inference Engine In Expert Systems

NASA Astrophysics Data System (ADS)

Neapolitan, Richard E.

1986-03-01

Rule-based expert systems are those in which a certain number of IF-THEN rules are assumed to be true. Based on the verity of some assertions, the rules deduce as many new conclusions as possible. A standard technique used to make these deductions is forward-chaining. In forward-chaining, the program or 'inference engine' cycles through the rules. At each rule, the premises for the rule are checked against the current true assertions. If all the premises are found, the conclusion is added to the list of true assertions. At that point it is necessary to start over at the first rule, since the new conclusion may be a premise in a rule already checked. Therefore, each time a new conclusion is deduced it is necessary to start the rule checking procedure over. This process continues until no new conclusions are added and the end of the list of rules is reached. The above process, although quite costly in terms of CPU cycles due to the necessity of repeatedly starting the process over, is necessary if the rules contain 'pattern variables'. An example of such a rule is, 'IF X IS A BACTERIA, THEN X CAN BE TREATED WITH ANTIBIOTICS'. Since the rule can lead to conclusions for many values of X, it is necessary to check each premise in the rule against every true assertion producing an association list to be used in the checking of the next premise. However, if the rule does not contain variable data, as is the case in many current expert systems, then a rule can lead to only one conclusion. In this case, the rules can be stored in a graph, and the true assertions in an assertion list. The assertion list is traversed only once; at each assertion a premise is triggered in all the rules which have that assertion as a premise. When all premises for a rule trigger, the rule's conclusion is added to the END of the list of assertions. It must be added at the end so that it will eventually be used to make further deductions. In the current paper, the two methods are described in detail, the relative advantages of each is discussed, and a benchmark comparing the CPU cycles consumed by each is included. It is also shown that, in the case of reasoning under uncertainty, it is possible to properly combine the certainties derived from rules arguing for the same conclusion when the graph approach is used.

KIC 7177553: A QUADRUPLE SYSTEM OF TWO CLOSE BINARIES

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

Lehmann, H.; Borkovits, T.; Rappaport, S. A.

2016-03-01

KIC 7177553 was observed by the Kepler satellite to be an eclipsing eccentric binary star system with an 18-day orbital period. Recently, an eclipse timing study of the Kepler binaries has revealed eclipse timing variations (ETVs) in this object with an amplitude of ∼100 s and an outer period of 529 days. The implied mass of the third body is that of a super-Jupiter, but below the mass of a brown dwarf. We therefore embarked on a radial velocity (RV) study of this binary to determine its system configuration and to check the hypothesis that it hosts a giant planet. Frommore » the RV measurements, it became immediately obvious that the same Kepler target contains another eccentric binary, this one with a 16.5-day orbital period. Direct imaging using adaptive optics reveals that the two binaries are separated by 0.″4 (∼167 AU) and have nearly the same magnitude (to within 2%). The close angular proximity of the two binaries and very similar γ velocities strongly suggest that KIC 7177553 is one of the rare SB4 systems consisting of two eccentric binaries where at least one system is eclipsing. Both systems consist of slowly rotating, nonevolved, solar-like stars of comparable masses. From the orbital separation and the small difference in γ velocity, we infer that the period of the outer orbit most likely lies in the range of 1000–3000 yr. New images taken over the next few years, as well as the high-precision astrometry of the Gaia satellite mission, will allow us to set much narrower constraints on the system geometry. Finally, we note that the observed ETVs in the Kepler data cannot be produced by the second binary. Further spectroscopic observations on a longer timescale will be required to prove the existence of the massive planet.« less

MULTIWAVELENGTH OBSERVATIONS OF THE RUNAWAY BINARY HD 15137

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

McSwain, M. Virginia; Aragona, Christina; Marsh, Amber N.

2010-03-15

HD 15137 is an intriguing runaway O-type binary system that offers a rare opportunity to explore the mechanism by which it was ejected from the open cluster of its birth. Here, we present recent blue optical spectra of HD 15137 and derive a new orbital solution for the spectroscopic binary and physical parameters of the O star primary. We also present the first XMM-Newton observations of the system. Fits of the EPIC spectra indicate soft, thermal X-ray emission consistent with an isolated O star. Upper limits on the undetected hard X-ray emission place limits on the emission from a proposedmore » compact companion in the system, and we rule out a quiescent neutron star (NS) in the propeller regime or a weakly accreting NS. An unevolved secondary companion is also not detected in our optical spectra of the binary, and it is difficult to conclude that a gravitational interaction could have ejected this runaway binary with a low mass optical star. HD 15137 may contain an elusive NS in the ejector regime or a quiescent black hole with conditions unfavorable for accretion at the time of our observations.« less

Toward Webscale, Rule-Based Inference on the Semantic Web Via Data Parallelism

DTIC Science & Technology

2013-02-01

Another work distinct from its peers is the work on approximate reasoning by Rudolph et al. [34] in which multiple inference sys- tems were combined not...Workshop Scalable Semantic Web Knowledge Base Systems, 2010, pp. 17–31. [34] S. Rudolph , T. Tserendorj, and P. Hitzler, “What is approximate reasoning...2013] [55] M. Duerst and M. Suignard. (2005, Jan .). RFC 3987 – internationalized resource identifiers (IRIs). IETF. [Online]. Available: http

A large systematic search for close supermassive binary and rapidly recoiling black holes - III. Radial velocity variations

NASA Astrophysics Data System (ADS)

Runnoe, Jessie C.; Eracleous, Michael; Pennell, Alison; Mathes, Gavin; Boroson, Todd; Sigurðsson, Steinn; Bogdanović, Tamara; Halpern, Jules P.; Liu, Jia; Brown, Stephanie

2017-06-01

We have been spectroscopically monitoring 88 quasars selected to have broad Hβ emission lines offset from their systemic redshift by thousands of km s-1. By analogy with single-lined spectroscopic binary stars, we consider these quasars to be candidates for hosting supermassive black hole binaries (SBHBs). In this work, we present new radial velocity measurements, typically three to four per object over a time period of up to 12 yr in the observer's frame. In 29/88 of the SBHB candidates, no variability of the shape of the broad Hβ profile is observed, which allows us to make reliable measurements of radial velocity changes. Among these, we identify three objects that have displayed systematic and monotonic velocity changes by several hundred km s-1 and are prime targets for further monitoring. Because the periods of the hypothetical binaries are expected to be long, we cannot hope to observe many orbital cycles during our lifetimes. Instead, we seek to evaluate the credentials of the SBHB candidates by attempting to rule out the SBHB hypothesis. In this spirit, we present a method for placing a lower limit on the period, and thus the mass, of the SBHBs under the assumption that the velocity changes we observe are due to orbital motion. Given the duration of our monitoring campaign and the uncertainties in the radial velocities, we were able to place a lower limit on the total mass in the range 4.7 × 104-3.8 × 108 M⊙, which does not yet allow us to rule out the SBHB hypothesis for any candidates.

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

PubMed

Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

2014-01-01

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.

Spotting Incorrect Rules in Signed-Number Arithmetic by the Individual Consistency Index.

DTIC Science & Technology

1981-08-01

meaning of dimensionality of achievement data. It also shows the importance of construct validity, even in criterion referenced testing of the cognitive ... aspect of performance, and that the traditional means of item analysis that are based on taking the variances of binary scores and content analysis

Decision Fusion with Channel Errors in Distributed Decode-Then-Fuse Sensor Networks

PubMed Central

Yan, Yongsheng; Wang, Haiyan; Shen, Xiaohong; Zhong, Xionghu

2015-01-01

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones. PMID:26251908

Domain repertoires as a tool to derive protein recognition rules.

PubMed

Zucconi, A; Panni, S; Paoluzi, S; Castagnoli, L; Dente, L; Cesareni, G

2000-08-25

Several approaches, some of which are described in this issue, have been proposed to assemble a complete protein interaction map. These are often based on high throughput methods that explore the ability of each gene product to bind any other element of the proteome of the organism. Here we propose that a large number of interactions can be inferred by revealing the rules underlying recognition specificity of a small number (a few hundreds) of families of protein recognition modules. This can be achieved through the construction and characterization of domain repertoires. A domain repertoire is assembled in a combinatorial fashion by allowing each amino acid position in the binding site of a given protein recognition domain to vary to include all the residues allowed at that position in the domain family. The repertoire is then searched by phage display techniques with any target of interest and from the primary structure of the binding site of the selected domains one derives rules that are used to infer the formation of complexes between natural proteins in the cell.

An inference engine for embedded diagnostic systems

NASA Technical Reports Server (NTRS)

Fox, Barry R.; Brewster, Larry T.

1987-01-01

The implementation of an inference engine for embedded diagnostic systems is described. The system consists of two distinct parts. The first is an off-line compiler which accepts a propositional logical statement of the relationship between facts and conclusions and produces data structures required by the on-line inference engine. The second part consists of the inference engine and interface routines which accept assertions of fact and return the conclusions which necessarily follow. Given a set of assertions, it will generate exactly the conclusions which logically follow. At the same time, it will detect any inconsistencies which may propagate from an inconsistent set of assertions or a poorly formulated set of rules. The memory requirements are fixed and the worst case execution times are bounded at compile time. The data structures and inference algorithms are very simple and well understood. The data structures and algorithms are described in detail. The system has been implemented on Lisp, Pascal, and Modula-2.

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

PubMed Central

2014-01-01

Introduction Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory or degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Methods Three multi-center genome-wide transcriptomic data sets (Affymetrix HG-U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule-based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. Results The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. Conclusion First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. PMID:24690414

The perception of rational, goal-directed action in nonhuman primates.

PubMed

Wood, Justin N; Glynn, David D; Phillips, Brenda C; Hauser, Marc D

2007-09-07

Humans are capable of making inferences about other individuals' intentions and goals by evaluating their actions in relation to the constraints imposed by the environment. This capacity enables humans to go beyond the surface appearance of behavior to draw inferences about an individual's mental states. Presently unclear is whether this capacity is uniquely human or is shared with other animals. We show that cotton-top tamarins, rhesus macaques, and chimpanzees all make spontaneous inferences about a human experimenter's goal by attending to the environmental constraints that guide rational action. These findings rule out simple associative accounts of action perception and show that our capacity to infer rational, goal-directed action likely arose at least as far back as the New World monkeys, some 40 million years ago.

Binary black hole mergers within the LIGO horizon: statistical properties and prospects for detecting electromagnetic counterparts

NASA Astrophysics Data System (ADS)

Perna, Rosalba; Chruslinska, Martyna; Corsi, Alessandra; Belczynski, Krzysztof

2018-07-01

Binary black holes (BBHs) are one of the endpoints of isolated binary evolution, and their mergers a leading channel for gravitational wave events. Here, using the evolutionary code STARTRACK, we study the statistical properties of the BBH population from isolated binary evolution for a range of progenitor star metallicities and BH natal kicks. We compute the mass function and the distribution of the primary BH spin a as a result of mass accretion during the binary evolution, and find that this is not an efficient process to spin-up BHs, producing an increase by at most a ˜ 0.2-0.3 for very low natal BH spins. We further compute the distribution of merger sites within the host galaxy, after tracking the motion of the binaries in the potentials of a massive spiral, a massive elliptical, and a dwarf galaxy. We find that a fraction of 70-90 per cent of mergers in massive galaxies and of 40-60 per cent in dwarfs (range mostly sensitive to the natal kicks) are expected to occur inside of their hosts. The number density distribution at the merger sites further allows us to estimate the broad-band luminosity distribution that BBH mergers would produce, if associated with a kinetic energy release in an outflow, which, as a reference, we assume at the level inferred for the Fermi GBM counterpart to GW150914, with the understanding that current limits from the O1 and O2 runs would require such emission to be produced within a jet of angular size within ≲50°.

Binary Black Hole Mergers within the LIGO Horizon: Statistical Properties and prospects for detecting Electromagnetic Counterparts

NASA Astrophysics Data System (ADS)

Perna, Rosalba; Chruslinska, Martyna; Corsi, Alessandra; Belczynski, Krzysztof

2018-03-01

Binary black holes (BBHs) are one of the endpoints of isolated binary evolution, and their mergers a leading channel for gravitational wave events. Here, using the evolutionary code STARTRACK, we study the statistical properties of the BBH population from isolated binary evolution for a range of progenitor star metallicities and BH natal kicks. We compute the mass function and the distribution of the primary BH spin a as a result of mass accretion during the binary evolution, and find that this is not an efficient process to spin up BHs, producing an increase by at most a ˜ 0.2-0.3 for very low natal BH spins. We further compute the distribution of merger sites within the host galaxy, after tracking the motion of the binaries in the potentials of a massive spiral, a massive elliptical, and a dwarf galaxy. We find that a fraction of 70-90% of mergers in massive galaxies and of 40-60% in dwarfs (range mostly sensitive to the natal kicks) is expected to occur inside of their hosts. The number density distribution at the merger sites further allows us to estimate the broadband luminosity distribution that BBH mergers would produce, if associated with a kinetic energy release in an outflow, which, as a reference, we assume at the level inferred for the Fermi GBM counterpart to GW150914, with the understanding that current limits from the O1 and O2 runs would require such emission to be produced within a jet of angular size within ≲ 50°.

ALMA and GMRT Constraints on the Off-axis Gamma-Ray Burst 170817A from the Binary Neutron Star Merger GW170817

NASA Astrophysics Data System (ADS)

Kim, S.; Schulze, S.; Resmi, L.; González-López, J.; Higgins, A. B.; Ishwara-Chandra, C. H.; Bauer, F. E.; de Gregorio-Monsalvo, I.; De Pasquale, M.; de Ugarte Postigo, A.; Kann, D. A.; Martín, S.; Oates, S. R.; Starling, R. L. C.; Tanvir, N. R.; Buchner, J.; Campana, S.; Cano, Z.; Covino, S.; Fruchter, A. S.; Fynbo, J. P. U.; Hartmann, D. H.; Hjorth, J.; Jakobsson, P.; Levan, A. J.; Malesani, D.; Michałowski, M. J.; Milvang-Jensen, B.; Misra, K.; O’Brien, P. T.; Sánchez-Ramírez, R.; Thöne, C. C.; Watson, D. J.; Wiersema, K.

2017-12-01

Binary neutron-star mergers (BNSMs) are among the most readily detectable gravitational-wave (GW) sources with the Laser Interferometer Gravitational-wave Observatory (LIGO). They are also thought to produce short γ-ray bursts (SGRBs) and kilonovae that are powered by r-process nuclei. Detecting these phenomena simultaneously would provide an unprecedented view of the physics during and after the merger of two compact objects. Such a Rosetta Stone event was detected by LIGO/Virgo on 2017 August 17 at a distance of ∼44 Mpc. We monitored the position of the BNSM with Atacama Large Millimeter/submillimeter Array (ALMA) at 338.5 GHz and the Giant Metrewave Radio Telescope (GMRT) at 1.4 GHz, from 1.4 to 44 days after the merger. Our observations rule out any afterglow more luminous than 3× {10}26 {erg} {{{s}}}-1 {{Hz}}-1 in these bands, probing >2–4 dex fainter than previous SGRB limits. We match these limits, in conjunction with public data announcing the appearance of X-ray and radio emission in the weeks after the GW event, to templates of off-axis afterglows. Our broadband modeling suggests that GW170817 was accompanied by an SGRB and that the γ-ray burst (GRB) jet, powered by {E}{AG,{iso}}∼ {10}50 erg, had a half-opening angle of ∼ 20^\\circ , and was misaligned by ∼ 41^\\circ from our line of sight. The data are also consistent with a more collimated jet: {E}{AG,{iso}}∼ {10}51 erg, {θ }1/2,{jet}∼ 5^\\circ ,{θ }{obs}∼ 17^\\circ . This is the most conclusive detection of an off-axis GRB afterglow and the first associated with a BNSM-GW event to date. We use the viewing angle estimates to infer the initial bulk Lorentz factor and true energy release of the burst.

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. VI. Radio Constraints on a Relativistic Jet and Predictions for Late-time Emission from the Kilonova Ejecta

NASA Astrophysics Data System (ADS)

Alexander, K. D.; Berger, E.; Fong, W.; Williams, P. K. G.; Guidorzi, C.; Margutti, R.; Metzger, B. D.; Annis, J.; Blanchard, P. K.; Brout, D.; Brown, D. A.; Chen, H.-Y.; Chornock, R.; Cowperthwaite, P. S.; Drout, M.; Eftekhari, T.; Frieman, J.; Holz, D. E.; Nicholl, M.; Rest, A.; Sako, M.; Soares-Santos, M.; Villar, V. A.

2017-10-01

We present Very Large Array (VLA) and Atacama Large Millimeter/submillimeter Array (ALMA) radio observations of GW170817, the first Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo gravitational wave (GW) event from a binary neutron star merger and the first GW event with an electromagnetic (EM) counterpart. Our data include the first observations following the discovery of the optical transient at both the centimeter (13.7 hr post-merger) and millimeter (2.41 days post-merger) bands. We detect faint emission at 6 GHz at 19.47 and 39.23 days after the merger, but not in an earlier observation at 2.46 days. We do not detect cm/mm emission at the position of the optical counterpart at frequencies of 10-97.5 GHz at times ranging from 0.6 to 30 days post-merger, ruling out an on-axis short gamma-ray burst (SGRB) for energies ≳ {10}48 erg. For fiducial SGRB parameters, our limits require an observer viewer angle of ≳20°. The radio and X-ray data can be jointly explained as the afterglow emission from an SGRB with a jet energy of ˜ {10}49{--}{10}50 erg that exploded in a uniform density environment with n˜ {10}-4{--}{10}-2 cm-3, viewed at an angle of ˜20°-40° from the jet axis. Using the results of our light curve and spectral modeling, in conjunction with the inference of the circumbinary density, we predict the emergence of late-time radio emission from the deceleration of the kilonova (KN) ejecta on a timescale of ˜5-10 years that will remain detectable for decades with next-generation radio facilities, making GW170817 a compelling target for long-term radio monitoring.

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. VI. Radio Constraints on a Relativistic Jet and Predictions for Late-time Emission from the Kilonova Ejecta

DOE PAGES

Alexander, K. D.; Berger, E.; Fong, W.; ...

2017-10-16

Here, we present Very Large Array (VLA) and Atacama Large Millimeter/sub-millimeter Array ALMA radio observations of GW\\,170817, the first Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo gravitational wave (GW) event from a binary neutron star merger and the first GW event with an electromagnetic (EM) counterpart. Our data include the first observations following the discovery of the optical transient at both the centimeter (more » $13.7$ hours post merger) and millimeter ($2.41$ days post merger) bands. We detect faint emission at 6 GHz at 19.47 and 39.23 days after the merger, but not in an earlier observation at 2.46 d. We do not detect cm/mm emission at the position of the optical counterpart at frequencies of 10-97.5 GHz at times ranging from 0.6 to 30 days post merger, ruling out an on-axis short gamma-ray burst (SGRB) for energies $$\\gtrsim 10^{48}$$ erg. For fiducial SGRB parameters, our limits require an observer viewer angle of $$\\gtrsim 20^{\\circ}$$. The radio and X-ray data can be jointly explained as the afterglow emission from an SGRB with a jet energy of $$\\sim 10^{49}-10^{50}$$ erg that exploded in a uniform density environment with $$n\\sim 10^{-4}-10^{-2}$$ cm$$^{-3}$$, viewed at an angle of $$\\sim 20^{\\circ}-40^{\\circ}$$ from the jet axis. Using the results of our light curve and spectral modeling, in conjunction with the inference of the circumbinary density, we predict the emergence of late-time radio emission from the deceleration of the kilonova (KN) ejecta on a timescale of $$\\sim 5-10$$ years that will remain detectable for decades with next-generation radio facilities, making GW\\,170817 a compelling target for long-term radio monitoring.« less

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. VI. Radio Constraints on a Relativistic Jet and Predictions for Late-time Emission from the Kilonova Ejecta

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

Alexander, K. D.; Berger, E.; Fong, W.

Here, we present Very Large Array (VLA) and Atacama Large Millimeter/sub-millimeter Array ALMA radio observations of GW\\,170817, the first Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo gravitational wave (GW) event from a binary neutron star merger and the first GW event with an electromagnetic (EM) counterpart. Our data include the first observations following the discovery of the optical transient at both the centimeter (more » $13.7$ hours post merger) and millimeter ($2.41$ days post merger) bands. We detect faint emission at 6 GHz at 19.47 and 39.23 days after the merger, but not in an earlier observation at 2.46 d. We do not detect cm/mm emission at the position of the optical counterpart at frequencies of 10-97.5 GHz at times ranging from 0.6 to 30 days post merger, ruling out an on-axis short gamma-ray burst (SGRB) for energies $$\\gtrsim 10^{48}$$ erg. For fiducial SGRB parameters, our limits require an observer viewer angle of $$\\gtrsim 20^{\\circ}$$. The radio and X-ray data can be jointly explained as the afterglow emission from an SGRB with a jet energy of $$\\sim 10^{49}-10^{50}$$ erg that exploded in a uniform density environment with $$n\\sim 10^{-4}-10^{-2}$$ cm$$^{-3}$$, viewed at an angle of $$\\sim 20^{\\circ}-40^{\\circ}$$ from the jet axis. Using the results of our light curve and spectral modeling, in conjunction with the inference of the circumbinary density, we predict the emergence of late-time radio emission from the deceleration of the kilonova (KN) ejecta on a timescale of $$\\sim 5-10$$ years that will remain detectable for decades with next-generation radio facilities, making GW\\,170817 a compelling target for long-term radio monitoring.« less

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. VI. Radio Constraints on a Relativistic Jet and Predictions for Late-time Emission from the Kilonova Ejecta

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

Alexander, K. D.; Berger, E.; Fong, W.

2017-10-16

We present Very Large Array (VLA) and Atacama Large Millimeter/sub-millimeter Array ALMA radio observations of GW\\,170817, the first Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo gravitational wave (GW) event from a binary neutron star merger and the first GW event with an electromagnetic (EM) counterpart. Our data include the first observations following the discovery of the optical transient at both the centimeter (more » $13.7$ hours post merger) and millimeter ($2.41$ days post merger) bands. We detect faint emission at 6 GHz at 19.47 and 39.23 days after the merger, but not in an earlier observation at 2.46 d. We do not detect cm/mm emission at the position of the optical counterpart at frequencies of 10-97.5 GHz at times ranging from 0.6 to 30 days post merger, ruling out an on-axis short gamma-ray burst (SGRB) for energies $$\\gtrsim 10^{48}$$ erg. For fiducial SGRB parameters, our limits require an observer viewer angle of $$\\gtrsim 20^{\\circ}$$. The radio and X-ray data can be jointly explained as the afterglow emission from an SGRB with a jet energy of $$\\sim 10^{49}-10^{50}$$ erg that exploded in a uniform density environment with $$n\\sim 10^{-4}-10^{-2}$$ cm$$^{-3}$$, viewed at an angle of $$\\sim 20^{\\circ}-40^{\\circ}$$ from the jet axis. Using the results of our light curve and spectral modeling, in conjunction with the inference of the circumbinary density, we predict the emergence of late-time radio emission from the deceleration of the kilonova (KN) ejecta on a timescale of $$\\sim 5-10$$ years that will remain detectable for decades with next-generation radio facilities, making GW\\,170817 a compelling target for long-term radio monitoring.« less

Generative models for discovering sparse distributed representations.

PubMed Central

Hinton, G E; Ghahramani, Z

1997-01-01

We describe a hierarchical, generative model that can be viewed as a nonlinear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Bayesian perceptual inference correctly. Once perceptual inference has been performed the connection strengths can be updated using a very simple learning rule that only requires locally available information. We demonstrate that the network learns to extract sparse, distributed, hierarchical representations. PMID:9304685

HIGH-RESOLUTION SPECTROSCOPY DURING ECLIPSE OF THE YOUNG SUBSTELLAR ECLIPSING BINARY 2MASS 0535-0546. II. SECONDARY SPECTRUM: NO EVIDENCE THAT SPOTS CAUSE THE TEMPERATURE REVERSAL

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

Mohanty, Subhanjoy; Stassun, Keivan G., E-mail: s.mohanty@imperial.ac.uk, E-mail: keivan.stassun@vanderbilt.edu

2012-10-10

We present high-resolution optical spectra of the young brown dwarf eclipsing binary 2M0535-05, obtained during eclipse of the higher-mass (primary) brown dwarf. Combined with our previous spectrum of the primary alone (Paper I), the new observations yield the spectrum of the secondary alone. We investigate, through a differential analysis of the two binary components, whether cool surface spots are responsible for suppressing the temperature of the primary. In Paper I, we found a significant discrepancy between the empirical surface gravity of the primary and that inferred via fine analysis of its spectrum. Here we find precisely the same discrepancy inmore » surface gravity, both qualitatively and quantitatively. While this may again be ascribed to either cool spots or model opacity errors, it implies that cool spots cannot be responsible for preferentially lowering the temperature of the primary: if they were, spot effects on the primary spectrum should be preferentially larger, and they are not. The T{sub eff}'s we infer for the primary and secondary, from the TiO-{epsilon} bands alone, show the same reversal, in the same ratio, as is empirically observed, bolstering the validity of our analysis. In turn, this implies that if suppression of convection by magnetic fields on the primary is the fundamental cause of the T{sub eff} reversal, then it cannot be a local suppression yielding spots mainly on the primary (though both components may be equally spotted), but a global suppression in the interior of the primary. We briefly discuss current theories of how this might work.« less

Efficient Variable Selection Method for Exposure Variables on Binary Data

NASA Astrophysics Data System (ADS)

Ohno, Manabu; Tarumi, Tomoyuki

In this paper, we propose a new variable selection method for "robust" exposure variables. We define "robust" as property that the same variable can select among original data and perturbed data. There are few studies of effective for the selection method. The problem that selects exposure variables is almost the same as a problem that extracts correlation rules without robustness. [Brin 97] is suggested that correlation rules are possible to extract efficiently using chi-squared statistic of contingency table having monotone property on binary data. But the chi-squared value does not have monotone property, so it's is easy to judge the method to be not independent with an increase in the dimension though the variable set is completely independent, and the method is not usable in variable selection for robust exposure variables. We assume anti-monotone property for independent variables to select robust independent variables and use the apriori algorithm for it. The apriori algorithm is one of the algorithms which find association rules from the market basket data. The algorithm use anti-monotone property on the support which is defined by association rules. But independent property does not completely have anti-monotone property on the AIC of independent probability model, but the tendency to have anti-monotone property is strong. Therefore, selected variables with anti-monotone property on the AIC have robustness. Our method judges whether a certain variable is exposure variable for the independent variable using previous comparison of the AIC. Our numerical experiments show that our method can select robust exposure variables efficiently and precisely.

Decision tree modeling using R.

PubMed

Zhang, Zhongheng

2016-08-01

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

Supplement: The Rate of Binary Black Hole Mergers Inferred from Advanced LIGO Observations Surrounding GW150914

NASA Technical Reports Server (NTRS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.;

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

The scent of mixtures: rules of odour processing in ants

PubMed Central

Perez, Margot; Giurfa, Martin; d'Ettorre, Patrizia

2015-01-01

Natural odours are complex blends of numerous components. Understanding how animals perceive odour mixtures is central to multiple disciplines. Here we focused on carpenter ants, which rely on odours in various behavioural contexts. We studied overshadowing, a phenomenon that occurs when animals having learnt a binary mixture respond less to one component than to the other, and less than when this component was learnt alone. Ants were trained individually with alcohols and aldehydes varying in carbon-chain length, either as single odours or binary mixtures. They were then tested with the mixture and the components. Overshadowing resulted from the interaction between chain length and functional group: alcohols overshadowed aldehydes, and longer chain lengths overshadowed shorter ones; yet, combinations of these factors could cancel each other and suppress overshadowing. Our results show how ants treat binary olfactory mixtures and set the basis for predictive analyses of odour perception in insects. PMID:25726692

Enthalpy and phase behavior of coal derived liquid mixtures. Technical progress report, April-June 1986

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

Yesavage, V.F.; Kidnay, A.J.

Enthalpy measurements for the m-cresol/tetralin binary system, and the quinoline/tertralin binary system have been completed and are included. A calibration check on the calorimeter was performed and is presented in Appendix C. Vapor liquid equilibria measurements for the quinoline/tetralin system have been completed for four isotherms; 250, 275, 300, and 325/sup 0/C. These results and a summary of progress to date for the VLE apparatus are in the appendix at the end of this report. Also, preliminary work has begun on the quinoline/m-cresol/tetralin ternary system. Correlational work has consisted of the development of mathematical expressions for fugacity and enthalpy usingmore » various combinations of mixing rules and equations of state discussed in earlier reports. Also maximum likelihood routines has been written to determine the necessary parameters for binary data obtained in this investigation.« less

Stationary Size Distributions of Growing Cells with Binary and Multiple Cell Division

NASA Astrophysics Data System (ADS)

Rading, M. M.; Engel, T. A.; Lipowsky, R.; Valleriani, A.

2011-10-01

Populations of unicellular organisms that grow under constant environmental conditions are considered theoretically. The size distribution of these cells is calculated analytically, both for the usual process of binary division, in which one mother cell produces always two daughter cells, and for the more complex process of multiple division, in which one mother cell can produce 2 n daughter cells with n=1,2,3,… . The latter mode of division is inspired by the unicellular algae Chlamydomonas reinhardtii. The uniform response of the whole population to different environmental conditions is encoded in the individual rates of growth and division of the cells. The analytical treatment of the problem is based on size-dependent rules for cell growth and stochastic transition processes for cell division. The comparison between binary and multiple division shows that these different division processes lead to qualitatively different results for the size distribution and the population growth rates.

The compatibility heuristic in non-categorical hypothetical reasoning: inferences between conditionals and disjunctions.

PubMed

Espino, Orlando; Byrne, Ruth M J

2013-11-01

A new theory explains how people make hypothetical inferences from a premise consistent with several alternatives to a conclusion consistent with several alternatives. The key proposal is that people rely on a heuristic that identifies compatible possibilities. It is tested in 7 experiments that examine inferences between conditionals and disjunctions. Participants accepted inferences between conditionals and inclusive disjunctions when a compatible possibility was immediately available, in their binary judgments that a conclusion followed or not (Experiment 1a) and ternary judgments that included it was not possible to know (Experiment 1b). The compatibility effect was amplified when compatible possibilities were more readily available, e.g., for 'A only if B' conditionals (Experiment 2). It was eliminated when compatible possibilities were not available, e.g., for 'if and only if A B' bi-conditionals and exclusive disjunctions (Experiment 3). The compatibility heuristic occurs even for inferences based on implicit negation e.g., 'A or B, therefore if C D' (Experiment 4), and between universals 'All A's are B's' and disjunctions (Experiment 5a) and universals and conditionals (Experiment 5b). The implications of the results for alternative theories of the cognitive processes underlying hypothetical deductions are discussed. Copyright © 2013. Published by Elsevier Inc.

Thermodynamic models for vapor-liquid equilibria of nitrogen + oxygen + carbon dioxide at low temperatures

NASA Astrophysics Data System (ADS)

Vrabec, Jadran; Kedia, Gaurav Kumar; Buchhauser, Ulrich; Meyer-Pittroff, Roland; Hasse, Hans

2009-02-01

For the design and optimization of CO 2 recovery from alcoholic fermentation processes by distillation, models for vapor-liquid equilibria (VLE) are needed. Two such thermodynamic models, the Peng-Robinson equation of state (EOS) and a model based on Henry's law constants, are proposed for the ternary mixture N 2 + O 2 + CO 2. Pure substance parameters of the Peng-Robinson EOS are taken from the literature, whereas the binary parameters of the Van der Waals one-fluid mixing rule are adjusted to experimental binary VLE data. The Peng-Robinson EOS describes both binary and ternary experimental data well, except at high pressures approaching the critical region. A molecular model is validated by simulation using binary and ternary experimental VLE data. On the basis of this model, the Henry's law constants of N 2 and O 2 in CO 2 are predicted by molecular simulation. An easy-to-use thermodynamic model, based on those Henry's law constants, is developed to reliably describe the VLE in the CO 2-rich region.

Architecture For The Optimization Of A Machining Process In Real Time Through Rule-Based Expert System

NASA Astrophysics Data System (ADS)

Serrano, Rafael; González, Luis Carlos; Martín, Francisco Jesús

2009-11-01

Under the project SENSOR-IA which has had financial funding from the Order of Incentives to the Regional Technology Centers of the Counsil of Innovation, Science and Enterprise of Andalusia, an architecture for the optimization of a machining process in real time through rule-based expert system has been developed. The architecture consists of an acquisition system and sensor data processing engine (SATD) from an expert system (SE) rule-based which communicates with the SATD. The SE has been designed as an inference engine with an algorithm for effective action, using a modus ponens rule model of goal-oriented rules.The pilot test demonstrated that it is possible to govern in real time the machining process based on rules contained in a SE. The tests have been done with approximated rules. Future work includes an exhaustive collection of data with different tool materials and geometries in a database to extract more precise rules.

Development of Silica Fibers and Microstructures with Large and Thermodynamically Stable Second Order Nonlinearity

DTIC Science & Technology

2011-06-22

high degree of symmetry directly leads to a symmetry-enforced selection rule that can produce quantum entanglement [21, 22]. This report is organized...page.) Then, using a Matlab program, we converted the microscope image to a binary bitmap, from which we extract fiber radius at any given location

Plagiarism as Literacy Practice: Recognizing and Rethinking Ethical Binaries

ERIC Educational Resources Information Center

Valentine, Kathryn

2006-01-01

In this article, I assert that plagiarism is a literacy practice that involves social relationships, attitudes, and values as much as it involves rules of citation and students' texts. In addition, I show how plagiarism is complicated by a discourse about academic dishonesty, and I consider the implications that recognizing such complexity has for…

CLIPS - C LANGUAGE INTEGRATED PRODUCTION SYSTEM (IBM PC VERSION)

NASA Technical Reports Server (NTRS)

Riley, G.

1994-01-01

The C Language Integrated Production System, CLIPS, is a shell for developing expert systems. It is designed to allow artificial intelligence research, development, and delivery on conventional computers. The primary design goals for CLIPS are portability, efficiency, and functionality. For these reasons, the program is written in C. CLIPS meets or outperforms most micro- and minicomputer based artificial intelligence tools. CLIPS is a forward chaining rule-based language. The program contains an inference engine and a language syntax that provide a framework for the construction of an expert system. It also includes tools for debugging an application. CLIPS is based on the Rete algorithm, which enables very efficient pattern matching. The collection of conditions and actions to be taken if the conditions are met is constructed into a rule network. As facts are asserted either prior to or during a session, CLIPS pattern-matches the number of fields. Wildcards and variables are supported for both single and multiple fields. CLIPS syntax allows the inclusion of externally defined functions (outside functions which are written in a language other than CLIPS). CLIPS itself can be embedded in a program such that the expert system is available as a simple subroutine call. Advanced features found in CLIPS version 4.3 include an integrated microEMACS editor, the ability to generate C source code from a CLIPS rule base to produce a dedicated executable, binary load and save capabilities for CLIPS rule bases, and the utility program CRSV (Cross-Reference, Style, and Verification) designed to facilitate the development and maintenance of large rule bases. Five machine versions are available. Each machine version includes the source and the executable for that machine. The UNIX version includes the source and binaries for IBM RS/6000, Sun3 series, and Sun4 series computers. The UNIX, DEC VAX, and DEC RISC Workstation versions are line oriented. The PC version and the Macintosh version each contain a windowing variant of CLIPS as well as the standard line oriented version. The mouse/window interface version for the PC works with a Microsoft compatible mouse or without a mouse. This window version uses the proprietary CURSES library for the PC, but a working executable of the window version is provided. The window oriented version for the Macintosh includes a version which uses a full Macintosh-style interface, including an integrated editor. This version allows the user to observe the changing fact base and rule activations in separate windows while a CLIPS program is executing. The IBM PC version is available bundled with CLIPSITS, The CLIPS Intelligent Tutoring System for a special combined price (COS-10025). The goal of CLIPSITS is to provide the student with a tool to practice the syntax and concepts covered in the CLIPS User's Guide. It attempts to provide expert diagnosis and advice during problem solving which is typically not available without an instructor. CLIPSITS is divided into 10 lessons which mirror the first 10 chapters of the CLIPS User's Guide. The program was developed for the IBM PC series with a hard disk. CLIPSITS is also available separately as MSC-21679. The CLIPS program is written in C for interactive execution and has been implemented on an IBM PC computer operating under DOS, a Macintosh and DEC VAX series computers operating under VMS or ULTRIX. The line oriented version should run on any computer system which supports a full (Kernighan and Ritchie) C compiler or the ANSI standard C language. CLIPS was developed in 1986 and Version 4.2 was released in July of 1988. Version 4.3 was released in June of 1989.

CLIPS - C LANGUAGE INTEGRATED PRODUCTION SYSTEM (MACINTOSH VERSION)

NASA Technical Reports Server (NTRS)

Culbert, C.

1994-01-01

The C Language Integrated Production System, CLIPS, is a shell for developing expert systems. It is designed to allow artificial intelligence research, development, and delivery on conventional computers. The primary design goals for CLIPS are portability, efficiency, and functionality. For these reasons, the program is written in C. CLIPS meets or outperforms most micro- and minicomputer based artificial intelligence tools. CLIPS is a forward chaining rule-based language. The program contains an inference engine and a language syntax that provide a framework for the construction of an expert system. It also includes tools for debugging an application. CLIPS is based on the Rete algorithm, which enables very efficient pattern matching. The collection of conditions and actions to be taken if the conditions are met is constructed into a rule network. As facts are asserted either prior to or during a session, CLIPS pattern-matches the number of fields. Wildcards and variables are supported for both single and multiple fields. CLIPS syntax allows the inclusion of externally defined functions (outside functions which are written in a language other than CLIPS). CLIPS itself can be embedded in a program such that the expert system is available as a simple subroutine call. Advanced features found in CLIPS version 4.3 include an integrated microEMACS editor, the ability to generate C source code from a CLIPS rule base to produce a dedicated executable, binary load and save capabilities for CLIPS rule bases, and the utility program CRSV (Cross-Reference, Style, and Verification) designed to facilitate the development and maintenance of large rule bases. Five machine versions are available. Each machine version includes the source and the executable for that machine. The UNIX version includes the source and binaries for IBM RS/6000, Sun3 series, and Sun4 series computers. The UNIX, DEC VAX, and DEC RISC Workstation versions are line oriented. The PC version and the Macintosh version each contain a windowing variant of CLIPS as well as the standard line oriented version. The mouse/window interface version for the PC works with a Microsoft compatible mouse or without a mouse. This window version uses the proprietary CURSES library for the PC, but a working executable of the window version is provided. The window oriented version for the Macintosh includes a version which uses a full Macintosh-style interface, including an integrated editor. This version allows the user to observe the changing fact base and rule activations in separate windows while a CLIPS program is executing. The IBM PC version is available bundled with CLIPSITS, The CLIPS Intelligent Tutoring System for a special combined price (COS-10025). The goal of CLIPSITS is to provide the student with a tool to practice the syntax and concepts covered in the CLIPS User's Guide. It attempts to provide expert diagnosis and advice during problem solving which is typically not available without an instructor. CLIPSITS is divided into 10 lessons which mirror the first 10 chapters of the CLIPS User's Guide. The program was developed for the IBM PC series with a hard disk. CLIPSITS is also available separately as MSC-21679. The CLIPS program is written in C for interactive execution and has been implemented on an IBM PC computer operating under DOS, a Macintosh and DEC VAX series computers operating under VMS or ULTRIX. The line oriented version should run on any computer system which supports a full (Kernighan and Ritchie) C compiler or the ANSI standard C language. CLIPS was developed in 1986 and Version 4.2 was released in July of 1988. Version 4.3 was released in June of 1989.

CLIPS - C LANGUAGE INTEGRATED PRODUCTION SYSTEM (IBM PC VERSION WITH CLIPSITS)

NASA Technical Reports Server (NTRS)

Riley, , .

1994-01-01

The C Language Integrated Production System, CLIPS, is a shell for developing expert systems. It is designed to allow artificial intelligence research, development, and delivery on conventional computers. The primary design goals for CLIPS are portability, efficiency, and functionality. For these reasons, the program is written in C. CLIPS meets or outperforms most micro- and minicomputer based artificial intelligence tools. CLIPS is a forward chaining rule-based language. The program contains an inference engine and a language syntax that provide a framework for the construction of an expert system. It also includes tools for debugging an application. CLIPS is based on the Rete algorithm, which enables very efficient pattern matching. The collection of conditions and actions to be taken if the conditions are met is constructed into a rule network. As facts are asserted either prior to or during a session, CLIPS pattern-matches the number of fields. Wildcards and variables are supported for both single and multiple fields. CLIPS syntax allows the inclusion of externally defined functions (outside functions which are written in a language other than CLIPS). CLIPS itself can be embedded in a program such that the expert system is available as a simple subroutine call. Advanced features found in CLIPS version 4.3 include an integrated microEMACS editor, the ability to generate C source code from a CLIPS rule base to produce a dedicated executable, binary load and save capabilities for CLIPS rule bases, and the utility program CRSV (Cross-Reference, Style, and Verification) designed to facilitate the development and maintenance of large rule bases. Five machine versions are available. Each machine version includes the source and the executable for that machine. The UNIX version includes the source and binaries for IBM RS/6000, Sun3 series, and Sun4 series computers. The UNIX, DEC VAX, and DEC RISC Workstation versions are line oriented. The PC version and the Macintosh version each contain a windowing variant of CLIPS as well as the standard line oriented version. The mouse/window interface version for the PC works with a Microsoft compatible mouse or without a mouse. This window version uses the proprietary CURSES library for the PC, but a working executable of the window version is provided. The window oriented version for the Macintosh includes a version which uses a full Macintosh-style interface, including an integrated editor. This version allows the user to observe the changing fact base and rule activations in separate windows while a CLIPS program is executing. The IBM PC version is available bundled with CLIPSITS, The CLIPS Intelligent Tutoring System for a special combined price (COS-10025). The goal of CLIPSITS is to provide the student with a tool to practice the syntax and concepts covered in the CLIPS User's Guide. It attempts to provide expert diagnosis and advice during problem solving which is typically not available without an instructor. CLIPSITS is divided into 10 lessons which mirror the first 10 chapters of the CLIPS User's Guide. The program was developed for the IBM PC series with a hard disk. CLIPSITS is also available separately as MSC-21679. The CLIPS program is written in C for interactive execution and has been implemented on an IBM PC computer operating under DOS, a Macintosh and DEC VAX series computers operating under VMS or ULTRIX. The line oriented version should run on any computer system which supports a full (Kernighan and Ritchie) C compiler or the ANSI standard C language. CLIPS was developed in 1986 and Version 4.2 was released in July of 1988. Version 4.3 was released in June of 1989.

Properties of the Binary Black Hole Merger GW150914

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allocca, A.; Altin, P. A.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Babak, S.; Bacon, P.; Bader, M. K. M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Baune, C.; Bavigadda, V.; Bazzan, M.; Behnke, B.; Bejger, M.; Bell, A. S.; Bell, C. J.; Berger, B. K.; Bergman, J.; Bergmann, G.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Birney, R.; Birnholtz, O.; Biscans, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Bodiya, T. P.; Boer, M.; Bogaert, G.; Bogan, C.; Bohe, A.; Bojtos, P.; Bond, C.; Bondu, F.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brown, N. M.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T.; Calloni, E.; Camp, J. B.; Cannon, K. C.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Carbon Baiardi, L.; Cerretani, G.; Cesarini, E.; Chakraborty, R.; Chalermsongsak, T.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Chen, H. Y.; Chen, Y.; Cheng, C.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, S.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C. G.; Cominsky, L.; Constancio, M.; Conte, A.; Conti, L.; Cook, D.; Corbitt, T. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Craig, K.; Creighton, J. D. E.; Cripe, J.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Darman, N. S.; Dattilo, V.; Dave, I.; Daveloza, H. P.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; DeBra, D.; Debreczeni, G.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dereli, H.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Devine, C.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Dojcinoski, G.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Douglas, R.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Engels, W.; Essick, R. C.; Etienne, Z.; Etzel, T.; Evans, M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Fang, Q.; Farinon, S.; Farr, B.; Farr, W. M.; Fauchon-Jones, E.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M.; Fournier, J.-D.; Franco, S.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fricke, T. T.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H. A. G.; Gaebel, S. M.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gatto, A.; Gaur, G.; Gehrels, N.; Gemme, G.; Gendre, B.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gordon, N. A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Hall, B. R.; Hall, E. D.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Hofman, D.; Hollitt, S. E.; Holt, K.; Holz, D. E.; Hopkins, P.; Hosken, D. J.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huang, S.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Idrisy, A.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J.-M.; Isi, M.; Islas, G.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jang, H.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Johnson-McDaniel, N. K.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; K, Haris; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karki, S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kawazoe, F.; Kéfélian, F.; Kehl, M. S.; Keitel, D.; Kelley, D. B.; Kells, W.; Kennedy, R.; Key, J. S.; Khalaidovski, A.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, C.; Kim, J.; Kim, K.; Kim, Nam-Gyu; Kim, Namjun; Kim, Y.-M.; King, E. J.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Koehlenbeck, S. M.; Kokeyama, K.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Kringel, V.; Krishnan, B.; Królak, A.; Krueger, C.; Kuehn, G.; Kumar, P.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lange, J.; Lantz, B.; Lasky, P. D.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, K.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Levine, B. M.; Li, T. G. F.; Libson, A.; Littenberg, T. B.; Lockerbie, N. A.; Logue, J.; Lombardi, A. L.; London, L. T.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lousto, C. O.; Lovelace, G.; Lück, H.; Lundgren, A. P.; Luo, J.; Lynch, R.; Ma, Y.; MacDonald, T.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Magee, R. M.; Mageswaran, M.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandel, I.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martynov, D. V.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mendoza-Gandara, D.; Mercer, R. A.; Merilh, E.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B. C.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Murphy, D. J.; Murray, P. G.; Mytidis, A.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Necula, V.; Nedkova, K.; Nelemans, G.; Neri, M.; Neunzert, A.; Newton, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; O'Shaughnessy, R.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pan, Y.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Patrick, Z.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perreca, A.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poggiani, R.; Popolizio, P.; Post, A.; Powell, J.; Prasad, J.; Predoi, V.; Premachandra, S. S.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Ricci, F.; Riles, K.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Röver, C.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sanchez, E. J.; Sandberg, V.; Sandeen, B.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Schilling, R.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Serna, G.; Setyawati, Y.; Sevigny, A.; Shaddock, D. A.; Shah, S.; Shahriar, M. S.; Shaltev, M.; Shao, Z.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sigg, D.; Silva, A. D.; Simakov, D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, J. R.; Smith, N. D.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stevenson, S. P.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strauss, N. A.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tápai, M.; Tarabrin, S. P.; Taracchini, A.; Taylor, R.; Theeg, T.; Thirugnanasambandam, M. P.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Tomlinson, C.; Tonelli, M.; Torres, C. V.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Tringali, M. C.; Trozzo, L.; Tse, M.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van der Sluys, M. V.; van Heijningen, J. V.; Vañó-Viñuales, A.; van Veggel, A. A.; Vardaro, M.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, X.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; White, D. J.; Whiting, B. F.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Worden, J.; Wright, J. L.; Wu, G.; Yablon, J.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yap, M. J.; Yu, H.; Yvert, M.; ZadroŻny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, F.; Zhang, L.; Zhang, M.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, X. J.; Zucker, M. E.; Zuraw, S. E.; Zweizig, J.; Boyle, M.; Brügamin, B.; Campanelli, M.; Clark, M.; Hamberger, D.; Kidder, L. E.; Kinsey, M.; Laguna, P.; Ossokine, S.; Scheel, M. A.; Szilagyi, B.; Teukolsky, S.; Zlochower, Y.; LIGO Scientific Collaboration; Virgo Collaboration

2016-06-01

On September 14, 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO) detected a gravitational-wave transient (GW150914); we characterize the properties of the source and its parameters. The data around the time of the event were analyzed coherently across the LIGO network using a suite of accurate waveform models that describe gravitational waves from a compact binary system in general relativity. GW150914 was produced by a nearly equal mass binary black hole of masses 3 6-4+5M⊙ and 2 9-4+4M⊙ ; for each parameter we report the median value and the range of the 90% credible interval. The dimensionless spin magnitude of the more massive black hole is bound to be <0.7 (at 90% probability). The luminosity distance to the source is 41 0-180+160 Mpc , corresponding to a redshift 0.0 9-0.04+0.03 assuming standard cosmology. The source location is constrained to an annulus section of 610 deg2 , primarily in the southern hemisphere. The binary merges into a black hole of mass 6 2-4+4M⊙ and spin 0.6 7-0.07+0.05. This black hole is significantly more massive than any other inferred from electromagnetic observations in the stellar-mass regime.

Black holes, disks, and jets following binary mergers and stellar collapse: The narrow range of electromagnetic luminosities and accretion rates.

PubMed

Shapiro, Stuart L

2017-05-15

We have performed magnetohydrodynamic simulations in general relativity of binary neutron star and binary black hole-neutron star mergers, as well as the magnetorotational collapse of supermassive stars. In many cases the outcome is a spinnng black hole (BH) immersed in a magnetized disk, with a jet emanating from the poles of the BH. While their formation scenarios differ and their BH masses, as well as their disk masses, densities, and magnetic field strengths, vary by orders of magnitude, these features conspire to generate jet Poynting luminosities that all lie in the same, narrow range of ~10 52±1 erg s -1 . A similar result applies to their BH accretion rates upon jet launch, which is ~0.1-10 M ⊙ s -1 . We provide a simple model that explains these unanticipated findings. Interestingly, these luminosities reside in the same narrow range characterizing the observed luminosity distributions of over 400 short and long GRBs with distances inferred from spectroscopic redshifts or host galaxies. This result, together with the GRB lifetimes predicted by the model, supports the belief that a compact binary merger is the progenitor of an SGRB, while a massive, stellar magnetorotational collapse is the progenitor of an LGRB.

Asteroseismology of KIC 7107778: a binary comprising almost identical subgiants

NASA Astrophysics Data System (ADS)

Li, Yaguang; Bedding, Timothy R.; Li, Tanda; Bi, Shaolan; Murphy, Simon J.; Corsaro, Enrico; Chen, Li; Tian, Zhijia

2018-05-01

We analyse an asteroseismic binary system: KIC 7107778, a non-eclipsing, unresolved target, with solar-like oscillations in both components. We used Kepler short cadence time series spanning nearly 2 yr to obtain the power spectrum. Oscillation mode parameters were determined using Bayesian inference and a nested sampling Monte Carlo algorithm with the DIAMONDS package. The power profiles of the two components fully overlap, indicating their close similarity. We modelled the two stars with MESA and calculated oscillation frequencies with GYRE. Stellar fundamental parameters (mass, radius, and age) were estimated by grid modelling with atmospheric parameters and the oscillation frequencies of l = 0, 2 modes as constraints. Most l = 1 mixed modes were identified with models searched using a bisection method. Stellar parameters for the two sub-giant stars are MA = 1.42 ± 0.06 M⊙, MB = 1.39 ± 0.03 M⊙, RA = 2.93 ± 0.05 R⊙, RB = 2.76 ± 0.04 R⊙, tA = 3.32 ± 0.54 Gyr and tB = 3.51 ± 0.33 Gyr. The mass difference of the system is ˜1 per cent. The results confirm their simultaneous birth and evolution, as is expected from binary formation. KIC 7107778 comprises almost identical twins, and is the first asteroseismic sub-giant binary to be detected.

Properties of the Binary Black Hole Merger GW150914

NASA Technical Reports Server (NTRS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Camp, J. B.

2016-01-01

On September 14, 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO) detected a gravitational-wave transient (GW150914); we characterize the properties of the source and its parameters. The data around the time of the event were analyzed coherently across the LIGO network using a suite of accurate waveform models that describe gravitational waves from a compact binary system in general relativity. GW150914 was produced by a nearly equal mass binary black hole of masses 36(+5/-4) solar mass and 29(+4/-4) solar mass; for each parameter we report the median value and the range of the 90% credible interval. The dimensionless spin magnitude of the more massive black hole is bound to be less than 0.7 (at 90% probability). The luminosity distance to the source is 410(+160/-180) Mpc, corresponding to a redshift 0.09(+0.03/-0.04) assuming standard cosmology. The source location is constrained to an annulus section of 610 sq deg, primarily in the southern hemisphere. The binary merges into a black hole of mass 62(+4/-4) solar mass and spin 0.67(+0.05/-0.07). This black hole is significantly more massive than any other inferred from electromagnetic observations in the stellar-mass regime.

Properties of the Binary Black Hole Merger GW150914.

PubMed

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Leonardi, M; Leong, J R; Leroy, N; Letendre, N; Levin, Y; Levine, B M; Li, T G F; Libson, A; Littenberg, T B; Lockerbie, N A; Logue, J; Lombardi, A L; London, L T; Lord, J E; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J D; Lousto, C O; Lovelace, G; Lück, H; Lundgren, A P; Luo, J; Lynch, R; Ma, Y; MacDonald, T; Machenschalk, B; MacInnis, M; Macleod, D M; Magaña-Sandoval, F; Magee, R M; Mageswaran, M; Majorana, E; Maksimovic, I; Malvezzi, V; Man, N; Mandel, I; Mandic, V; Mangano, V; Mansell, G L; Manske, M; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markosyan, A S; Maros, E; Martelli, F; Martellini, L; Martin, I W; Martin, R M; Martynov, D V; Marx, J N; Mason, K; Masserot, A; Massinger, T J; Masso-Reid, M; Matichard, F; Matone, L; Mavalvala, N; Mazumder, N; Mazzolo, G; McCarthy, R; McClelland, D E; McCormick, S; McGuire, S C; McIntyre, G; McIver, J; McManus, D J; McWilliams, S T; Meacher, D; Meadors, G D; Meidam, J; Melatos, A; Mendell, G; Mendoza-Gandara, D; Mercer, R A; Merilh, E; Merzougui, M; Meshkov, S; Messenger, C; Messick, C; Meyers, P M; Mezzani, F; Miao, H; Michel, C; Middleton, H; Mikhailov, E E; Milano, L; Miller, J; Millhouse, M; Minenkov, Y; Ming, J; Mirshekari, S; Mishra, C; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moggi, A; Mohan, M; Mohapatra, S R P; Montani, M; Moore, B C; Moore, C J; Moraru, D; Moreno, G; Morriss, S R; Mossavi, K; Mours, B; Mow-Lowry, C M; Mueller, C L; Mueller, G; Muir, A W; Mukherjee, Arunava; Mukherjee, D; Mukherjee, S; Mukund, N; Mullavey, A; Munch, J; Murphy, D J; Murray, P G; Mytidis, A; Nardecchia, I; Naticchioni, L; Nayak, R K; Necula, V; Nedkova, K; Nelemans, G; Neri, M; Neunzert, A; Newton, G; Nguyen, T T; Nielsen, A B; Nissanke, S; Nitz, A; Nocera, F; Nolting, D; Normandin, M E; Nuttall, L K; Oberling, J; Ochsner, E; O'Dell, J; Oelker, E; Ogin, G H; Oh, J J; Oh, S H; Ohme, F; Oliver, M; Oppermann, P; Oram, Richard J; O'Reilly, B; O'Shaughnessy, R; Ottaway, D J; Ottens, R S; Overmier, H; Owen, B J; Pai, A; Pai, S A; Palamos, J R; Palashov, O; Palomba, C; Pal-Singh, A; Pan, H; Pan, Y; Pankow, C; Pannarale, F; Pant, B C; Paoletti, F; Paoli, A; Papa, M A; Paris, H R; Parker, W; Pascucci, D; Pasqualetti, A; Passaquieti, R; Passuello, D; Patricelli, B; Patrick, Z; Pearlstone, B L; Pedraza, M; Pedurand, R; Pekowsky, L; Pele, A; Penn, S; Perreca, A; Pfeiffer, H P; Phelps, M; Piccinni, O; Pichot, M; Piergiovanni, F; Pierro, V; Pillant, G; Pinard, L; Pinto, I M; Pitkin, M; Poggiani, R; Popolizio, P; Post, A; Powell, J; Prasad, J; Predoi, V; Premachandra, S S; Prestegard, T; Price, L R; Prijatelj, M; Principe, M; Privitera, S; Prodi, G A; Prokhorov, L; Puncken, O; Punturo, M; Puppo, P; Pürrer, M; Qi, H; Qin, J; Quetschke, V; Quintero, E A; Quitzow-James, R; Raab, F J; Rabeling, D S; Radkins, H; Raffai, P; Raja, S; Rakhmanov, M; Rapagnani, P; Raymond, V; Razzano, M; Re, V; Read, J; Reed, C M; Regimbau, T; Rei, L; Reid, S; Reitze, D H; Rew, H; Reyes, S D; Ricci, F; Riles, K; Robertson, N A; Robie, R; Robinet, F; Rocchi, A; Rolland, L; Rollins, J G; Roma, V J; Romano, R; Romanov, G; Romie, J H; Rosińska, D; Röver, C; Rowan, S; Rüdiger, A; Ruggi, P; Ryan, K; Sachdev, S; Sadecki, T; Sadeghian, L; Salconi, L; Saleem, M; Salemi, F; Samajdar, A; Sammut, L; Sanchez, E J; Sandberg, V; Sandeen, B; Sanders, J R; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Sauter, O; Savage, R L; Sawadsky, A; Schale, P; Schilling, R; Schmidt, J; Schmidt, P; Schnabel, R; Schofield, R M S; Schönbeck, A; Schreiber, E; Schuette, D; Schutz, B F; Scott, J; Scott, S M; Sellers, D; Sengupta, A S; Sentenac, D; Sequino, V; Sergeev, A; Serna, G; Setyawati, Y; Sevigny, A; Shaddock, D A; Shah, S; Shahriar, M S; Shaltev, M; Shao, Z; Shapiro, B; Shawhan, P; Sheperd, A; Shoemaker, D H; Shoemaker, D M; Siellez, K; Siemens, X; Sigg, D; Silva, A D; Simakov, D; Singer, A; Singer, L P; Singh, A; Singh, R; Singhal, A; Sintes, A M; Slagmolen, B J J; Smith, J R; Smith, N D; Smith, R J E; Son, E J; Sorazu, B; Sorrentino, F; Souradeep, T; Srivastava, A K; Staley, A; Steinke, M; Steinlechner, J; Steinlechner, S; Steinmeyer, D; Stephens, B C; Stevenson, S P; Stone, R; Strain, K A; Straniero, N; Stratta, G; Strauss, N A; Strigin, S; Sturani, R; Stuver, A L; Summerscales, T Z; Sun, L; Sutton, P J; Swinkels, B L; Szczepańczyk, M J; Tacca, M; Talukder, D; Tanner, D B; Tápai, M; Tarabrin, S P; Taracchini, A; Taylor, R; Theeg, T; Thirugnanasambandam, M P; Thomas, E G; Thomas, M; Thomas, P; Thorne, K A; Thorne, K S; Thrane, E; Tiwari, S; Tiwari, V; Tokmakov, K V; Tomlinson, C; Tonelli, M; Torres, C V; Torrie, C I; Töyrä, D; Travasso, F; Traylor, G; Trifirò, D; Tringali, M C; Trozzo, L; Tse, M; Turconi, M; Tuyenbayev, D; Ugolini, D; Unnikrishnan, C S; Urban, A L; Usman, S A; Vahlbruch, H; Vajente, G; Valdes, G; van Bakel, N; van Beuzekom, M; van den Brand, J F J; Van Den Broeck, C; Vander-Hyde, D C; van der Schaaf, L; van der Sluys, M V; van Heijningen, J V; Vañó-Viñuales, A; van Veggel, A A; Vardaro, M; Vass, S; Vasúth, M; Vaulin, R; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Verkindt, D; Vetrano, F; Viceré, A; Vinciguerra, S; Vine, D J; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Voss, D; Vousden, W D; Vyatchanin, S P; Wade, A R; Wade, L E; Wade, M; Walker, M; Wallace, L; Walsh, S; Wang, G; Wang, H; Wang, M; Wang, X; Wang, Y; Ward, R L; Warner, J; Was, M; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Welborn, T; Wen, L; Weßels, P; Westphal, T; Wette, K; Whelan, J T; White, D J; Whiting, B F; Williams, R D; Williamson, A R; Willis, J L; Willke, B; Wimmer, M H; Winkler, W; Wipf, C C; Wittel, H; Woan, G; Worden, J; Wright, J L; Wu, G; Yablon, J; Yam, W; Yamamoto, H; Yancey, C C; Yap, M J; Yu, H; Yvert, M; Zadrożny, A; Zangrando, L; Zanolin, M; Zendri, J-P; Zevin, M; Zhang, F; Zhang, L; Zhang, M; Zhang, Y; Zhao, C; Zhou, M; Zhou, Z; Zhu, X J; Zucker, M E; Zuraw, S E; Zweizig, J; Boyle, M; Brügmann, B; Campanelli, M; Clark, M; Hamberger, D; Kidder, L E; Kinsey, M; Laguna, P; Ossokine, S; Scheel, M A; Szilagyi, B; Teukolsky, S; Zlochower, Y

2016-06-17

On September 14, 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO) detected a gravitational-wave transient (GW150914); we characterize the properties of the source and its parameters. The data around the time of the event were analyzed coherently across the LIGO network using a suite of accurate waveform models that describe gravitational waves from a compact binary system in general relativity. GW150914 was produced by a nearly equal mass binary black hole of masses 36_{-4}^{+5}M_{⊙} and 29_{-4}^{+4}M_{⊙}; for each parameter we report the median value and the range of the 90% credible interval. The dimensionless spin magnitude of the more massive black hole is bound to be <0.7 (at 90% probability). The luminosity distance to the source is 410_{-180}^{+160}  Mpc, corresponding to a redshift 0.09_{-0.04}^{+0.03} assuming standard cosmology. The source location is constrained to an annulus section of 610  deg^{2}, primarily in the southern hemisphere. The binary merges into a black hole of mass 62_{-4}^{+4}M_{⊙} and spin 0.67_{-0.07}^{+0.05}. This black hole is significantly more massive than any other inferred from electromagnetic observations in the stellar-mass regime.

Supernova remnant S 147 and its associated neutron star(s)

NASA Astrophysics Data System (ADS)

Gvaramadze, V. V.

2006-07-01

The supernova remnant S 147 harbors the pulsar PSR J 0538+2817 whose characteristic age is more than an order of magnitude greater than the kinematic age of the system (inferred from the angular offset of the pulsar from the geometric center of the supernova remnant and the pulsar proper motion). To reconcile this discrepancy we propose that PSR J 0538+2817 could be the stellar remnant of the first supernova explosion in a massive binary system and therefore could be as old as its characteristic age. Our proposal implies that S 147 is the diffuse remnant of the second supernova explosion (that disrupted the binary system) and that a much younger second neutron star (not necessarily manifesting itself as a radio pulsar) should be associated with S 147. We use the existing observational data on the system to suggest that the progenitor of the supernova that formed S 147 was a Wolf-Rayet star (so that the supernova explosion occurred within a wind bubble surrounded by a massive shell) and to constrain the parameters of the binary system. We also restrict the magnitude and direction of the kick velocity received by the young neutron star at birth and find that the kick vector should not strongly deviate from the orbital plane of the binary system.

Black holes, disks, and jets following binary mergers and stellar collapse: The narrow range of electromagnetic luminosities and accretion rates

PubMed Central

Shapiro, Stuart L.

2018-01-01

We have performed magnetohydrodynamic simulations in general relativity of binary neutron star and binary black hole-neutron star mergers, as well as the magnetorotational collapse of supermassive stars. In many cases the outcome is a spinnng black hole (BH) immersed in a magnetized disk, with a jet emanating from the poles of the BH. While their formation scenarios differ and their BH masses, as well as their disk masses, densities, and magnetic field strengths, vary by orders of magnitude, these features conspire to generate jet Poynting luminosities that all lie in the same, narrow range of ~1052±1 erg s−1. A similar result applies to their BH accretion rates upon jet launch, which is ~0.1–10 M⊙ s−1. We provide a simple model that explains these unanticipated findings. Interestingly, these luminosities reside in the same narrow range characterizing the observed luminosity distributions of over 400 short and long GRBs with distances inferred from spectroscopic redshifts or host galaxies. This result, together with the GRB lifetimes predicted by the model, supports the belief that a compact binary merger is the progenitor of an SGRB, while a massive, stellar magnetorotational collapse is the progenitor of an LGRB. PMID:29881790

High-Pressure Transport Properties Of Fluids: Theory And Data From Levitated Drops At Combustion-Relevant Temperatures

NASA Technical Reports Server (NTRS)

Bellan, Josette; Harstad, Kenneth; Ohsaka, Kenichi

2003-01-01

Although the high pressure multicomponent fluid conservation equations have already been derived and approximately validated for binary mixtures by this PI, the validation of the multicomponent theory is hampered by the lack of existing mixing rules for property calculations. Classical gas dynamics theory can provide property mixing-rules at low pressures exclusively. While thermal conductivity and viscosity high-pressure mixing rules have been documented in the literature, there is no such equivalent for the diffusion coefficients and the thermal diffusion factors. The primary goal of this investigation is to extend the low pressure mixing rule theory to high pressures and validate the new theory with experimental data from levitated single drops. The two properties that will be addressed are the diffusion coefficients and the thermal diffusion factors. To validate/determine the property calculations, ground-based experiments from levitated drops are being conducted.

Approximate inference on planar graphs using loop calculus and belief progagation

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

Chertkov, Michael; Gomez, Vicenc; Kappen, Hilbert

We introduce novel results for approximate inference on planar graphical models using the loop calculus framework. The loop calculus (Chertkov and Chernyak, 2006b) allows to express the exact partition function Z of a graphical model as a finite sum of terms that can be evaluated once the belief propagation (BP) solution is known. In general, full summation over all correction terms is intractable. We develop an algorithm for the approach presented in Chertkov et al. (2008) which represents an efficient truncation scheme on planar graphs and a new representation of the series in terms of Pfaffians of matrices. We analyzemore » in detail both the loop series and the Pfaffian series for models with binary variables and pairwise interactions, and show that the first term of the Pfaffian series can provide very accurate approximations. The algorithm outperforms previous truncation schemes of the loop series and is competitive with other state-of-the-art methods for approximate inference.« less

Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics.

PubMed

Ocone, Andrea; Millar, Andrew J; Sanguinetti, Guido

2013-04-01

Computational modelling of the dynamics of gene regulatory networks is a central task of systems biology. For networks of small/medium scale, the dominant paradigm is represented by systems of coupled non-linear ordinary differential equations (ODEs). ODEs afford great mechanistic detail and flexibility, but calibrating these models to data is often an extremely difficult statistical problem. Here, we develop a general statistical inference framework for stochastic transcription-translation networks. We use a coarse-grained approach, which represents the system as a network of stochastic (binary) promoter and (continuous) protein variables. We derive an exact inference algorithm and an efficient variational approximation that allows scalable inference and learning of the model parameters. We demonstrate the power of the approach on two biological case studies, showing that the method allows a high degree of flexibility and is capable of testable novel biological predictions. http://homepages.inf.ed.ac.uk/gsanguin/software.html. Supplementary data are available at Bioinformatics online.

Learning planar Ising models

DOE PAGES

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael; ...

2016-12-01

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Learning planar Ising models

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

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

U.S. stock market interaction network as learned by the Boltzmann machine

DOE PAGES

Borysov, Stanislav S.; Roudi, Yasser; Balatsky, Alexander V.

2015-12-07

Here, we study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented results show that binarization preserves the correlation structure of the market. Properties of distributions of external fields and couplings as well as themore » market interaction network and industry sector clustering structure are studied for different historical dates and moving window sizes. We demonstrate that the observed positive heavy tail in distribution of couplings is related to the sparse clustering structure of the market. We also show that discrepancies between the model’s parameters might be used as a precursor of financial instabilities.« less

Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance

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

Ahn, Tae-Hyuk; Chai, Juanjuan; Pan, Chongle

Motivation: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis. Results: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic readsmore » to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. In conclusion, the algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains. Availability and Implementation: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org.« less

Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance

DOE PAGES

Ahn, Tae-Hyuk; Chai, Juanjuan; Pan, Chongle

2014-09-29

Motivation: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis. Results: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic readsmore » to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. In conclusion, the algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains. Availability and Implementation: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org.« less

al3c: high-performance software for parameter inference using Approximate Bayesian Computation.

PubMed

Stram, Alexander H; Marjoram, Paul; Chen, Gary K

2015-11-01

The development of Approximate Bayesian Computation (ABC) algorithms for parameter inference which are both computationally efficient and scalable in parallel computing environments is an important area of research. Monte Carlo rejection sampling, a fundamental component of ABC algorithms, is trivial to distribute over multiple processors but is inherently inefficient. While development of algorithms such as ABC Sequential Monte Carlo (ABC-SMC) help address the inherent inefficiencies of rejection sampling, such approaches are not as easily scaled on multiple processors. As a result, current Bayesian inference software offerings that use ABC-SMC lack the ability to scale in parallel computing environments. We present al3c, a C++ framework for implementing ABC-SMC in parallel. By requiring only that users define essential functions such as the simulation model and prior distribution function, al3c abstracts the user from both the complexities of parallel programming and the details of the ABC-SMC algorithm. By using the al3c framework, the user is able to scale the ABC-SMC algorithm in parallel computing environments for his or her specific application, with minimal programming overhead. al3c is offered as a static binary for Linux and OS-X computing environments. The user completes an XML configuration file and C++ plug-in template for the specific application, which are used by al3c to obtain the desired results. Users can download the static binaries, source code, reference documentation and examples (including those in this article) by visiting https://github.com/ahstram/al3c. astram@usc.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Can We Distinguish Low-mass Black Holes in Neutron Star Binaries?

NASA Astrophysics Data System (ADS)

Yang, Huan; East, William E.; Lehner, Luis

2018-04-01

The detection of gravitational waves (GWs) from coalescing binary neutron stars (NS) represents another milestone in gravitational-wave astronomy. However, since LIGO is currently not as sensitive to the merger/ringdown part of the waveform, the possibility that such signals are produced by a black hole (BH)–NS binary can not be easily ruled out without appealing to assumptions about the underlying compact object populations. We review a few astrophysical channels that might produce BHs below 3 M ⊙ (roughly the upper bound on the maximum mass of an NS), as well as existing constraints for these channels. We show that, due to the uncertainty in the NS equation of state, it is difficult to distinguish GWs from a binary NS system from those of a BH–NS system with the same component masses, assuming Advanced LIGO sensitivity. This degeneracy can be broken by accumulating statistics from many events to better constrain the equation of state, or by third-generation detectors with higher sensitivity to the late-spiral to post-merger signal. We also discuss the possible differences in electromagnetic (EM) counterparts between binary NS and low-mass BH–NS mergers, arguing that it will be challenging to definitively distinguish the two without better understanding of the underlying astrophysical processes.

ROTATING STARS AND THE FORMATION OF BIPOLAR PLANETARY NEBULAE. II. TIDAL SPIN-UP

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

García-Segura, G.; Villaver, E.; Manchado, A.

We present new binary stellar evolution models that include the effects of tidal forces, rotation, and magnetic torques with the goal of testing planetary nebulae (PNs) shaping via binary interaction. We explore whether tidal interaction with a companion can spin-up the asymptotic giant brach (AGB) envelope. To do so, we have selected binary systems with main-sequence masses of 2.5 M {sub ⊙} and 0.8 M {sub ⊙} and evolve them allowing initial separations of 5, 6, 7, and 8 au. The binary stellar evolution models have been computed all the way to the PNs formation phase or until Roche lobemore » overflow (RLOF) is reached, whatever happens first. We show that with initial separations of 7 and 8 au, the binary avoids entering into RLOF, and the AGB star reaches moderate rotational velocities at the surface (∼3.5 and ∼2 km s{sup −1}, respectively) during the inter-pulse phases, but after the thermal pulses it drops to a final rotational velocity of only ∼0.03 km s{sup −1}. For the closest binary separations explored, 5 and 6 au, the AGB star reaches rotational velocities of ∼6 and ∼4 km s{sup −1}, respectively, when the RLOF is initiated. We conclude that the detached binary models that avoid entering the RLOF phase during the AGB will not shape bipolar PNs, since the acquired angular momentum is lost via the wind during the last two thermal pulses. This study rules out tidal spin-up in non-contact binaries as a sufficient condition to form bipolar PNs.« less

A density cusp of quiescent X-ray binaries in the central parsec of the Galaxy

NASA Astrophysics Data System (ADS)

Hailey, Charles J.; Mori, Kaya; Bauer, Franz E.; Berkowitz, Michael E.; Hong, Jaesub; Hord, Benjamin J.

2018-04-01

The existence of a ‘density cusp’—a localized increase in number—of stellar-mass black holes near a supermassive black hole is a fundamental prediction of galactic stellar dynamics. The best place to detect such a cusp is in the Galactic Centre, where the nearest supermassive black hole, Sagittarius A*, resides. As many as 20,000 black holes are predicted to settle into the central parsec of the Galaxy as a result of dynamical friction; however, so far no density cusp of black holes has been detected. Low-mass X-ray binary systems that contain a stellar-mass black hole are natural tracers of isolated black holes. Here we report observations of a dozen quiescent X-ray binaries in a density cusp within one parsec of Sagittarius A*. The lower-energy emission spectra that we observed in these binaries is distinct from the higher-energy spectra associated with the population of accreting white dwarfs that dominates the central eight parsecs of the Galaxy. The properties of these X-ray binaries, in particular their spatial distribution and luminosity function, suggest the existence of hundreds of binary systems in the central parsec of the Galaxy and many more isolated black holes. We cannot rule out a contribution to the observed emission from a population (of up to about one-half the number of X-ray binaries) of rotationally powered, millisecond pulsars. The spatial distribution of the binary systems is a relic of their formation history, either in the stellar disk around Sagittarius A* (ref. 7) or through in-fall from globular clusters, and constrains the number density of sources in the modelling of gravitational waves from massive stellar remnants, such as neutron stars and black holes.

A density cusp of quiescent X-ray binaries in the central parsec of the Galaxy.

PubMed

Hailey, Charles J; Mori, Kaya; Bauer, Franz E; Berkowitz, Michael E; Hong, Jaesub; Hord, Benjamin J

2018-04-04

The existence of a 'density cusp'-a localized increase in number-of stellar-mass black holes near a supermassive black hole is a fundamental prediction of galactic stellar dynamics. The best place to detect such a cusp is in the Galactic Centre, where the nearest supermassive black hole, Sagittarius A*, resides. As many as 20,000 black holes are predicted to settle into the central parsec of the Galaxy as a result of dynamical friction; however, so far no density cusp of black holes has been detected. Low-mass X-ray binary systems that contain a stellar-mass black hole are natural tracers of isolated black holes. Here we report observations of a dozen quiescent X-ray binaries in a density cusp within one parsec of Sagittarius A*. The lower-energy emission spectra that we observed in these binaries is distinct from the higher-energy spectra associated with the population of accreting white dwarfs that dominates the central eight parsecs of the Galaxy. The properties of these X-ray binaries, in particular their spatial distribution and luminosity function, suggest the existence of hundreds of binary systems in the central parsec of the Galaxy and many more isolated black holes. We cannot rule out a contribution to the observed emission from a population (of up to about one-half the number of X-ray binaries) of rotationally powered, millisecond pulsars. The spatial distribution of the binary systems is a relic of their formation history, either in the stellar disk around Sagittarius A* (ref. 7) or through in-fall from globular clusters, and constrains the number density of sources in the modelling of gravitational waves from massive stellar remnants, such as neutron stars and black holes.

Designing boosting ensemble of relational fuzzy systems.

PubMed

Scherer, Rafał

2010-10-01

A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights - elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.

Evaluation of the grand-canonical partition function using expanded Wang-Landau simulations. III. Impact of combining rules on mixtures properties

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

Desgranges, Caroline; Delhommelle, Jerome

2014-03-14

Combining rules, such as the Lorentz-Berthelot rules, are routinely used to calculate the thermodynamic properties of mixtures using molecular simulations. Here we extend the expanded Wang-Landau simulation approach to determine the impact of the combining rules on the value of the partition function of binary systems, and, in turn, on the phase coexistence and thermodynamics of these mixtures. We study various types of mixtures, ranging from systems of rare gases to biologically and technologically relevant mixtures, such as water-urea and water-carbon dioxide. Comparing the simulation results to the experimental data on mixtures of rare gases allows us to rank themore » performance of combining rules. We find that the widely used Lorentz-Berthelot rules exhibit the largest deviations from the experimental data, both for the bulk and at coexistence, while the Kong and Waldman-Hagler provide much better alternatives. In particular, in the case of aqueous solutions of urea, we show that the use of the Lorentz-Berthelot rules has a strong impact on the Gibbs free energy of the solute, overshooting the value predicted by the Waldman-Hagler rules by 7%. This result emphasizes the importance of the combining rule for the determination of hydration free energies using molecular simulations.« less

Adolescent Victimization and Early-Adult Psychopathology: Approaching Causal Inference Using a Longitudinal Twin Study to Rule Out Noncausal Explanations

PubMed Central

Schaefer, Jonathan D.; Moffitt, Terrie E.; Arseneault, Louise; Danese, Andrea; Fisher, Helen L.; Houts, Renate; Sheridan, Margaret A.; Wertz, Jasmin; Caspi, Avshalom

2017-01-01

Adolescence is the peak age for both victimization and mental disorder onset. Previous research has reported associations between victimization exposure and many psychiatric conditions. However, causality remains controversial. Within the Environmental Risk Longitudinal Twin Study, we tested whether seven types of adolescent victimization increased risk of multiple psychiatric conditions and approached causal inference by systematically ruling out noncausal explanations. Longitudinal within-individual analyses showed that victimization was followed by increased mental health problems over a childhood baseline of emotional/behavioral problems. Discordant-twin analyses showed that victimization increased risk of mental health problems independent of family background and genetic risk. Both childhood and adolescent victimization made unique contributions to risk. Victimization predicted heightened generalized liability (the “p factor”) to multiple psychiatric spectra, including internalizing, externalizing, and thought disorders. Results recommend violence reduction and identification and treatment of adolescent victims to reduce psychiatric burden. PMID:29805917

A Fuzzy Reasoning Design for Fault Detection and Diagnosis of a Computer-Controlled System

PubMed Central

Ting, Y.; Lu, W.B.; Chen, C.H.; Wang, G.K.

2008-01-01

A Fuzzy Reasoning and Verification Petri Nets (FRVPNs) model is established for an error detection and diagnosis mechanism (EDDM) applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree (FRDT) and a Petri nets (PNs) technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstratrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program. PMID:19255619

Your Understanding Is My Understanding: Evidence for a Community of Knowledge.

PubMed

Sloman, Steven A; Rabb, Nathaniel

2016-11-01

In four experiments, we tested the community-of-knowledge hypothesis, that people fail to distinguish their own knowledge from other people's knowledge. In all the experiments, despite the absence of any actual explanatory information, people rated their own understanding of novel natural phenomena as higher when they were told that scientists understood the phenomena than when they were told that scientists did not yet understand them. In Experiment 2, we found that this occurs only when people have ostensible access to the scientists' explanations; the effect does not occur when the explanations exist but are held in secret. In Experiment 3, we further ruled out two classes of alternative explanations (one appealing to task demands and the other proposing that judgments were mediated by inferences about a phenomenon's understandability). In Experiment 4, we ruled out the possibility that the effect could be attributed to a pragmatic inference. © The Author(s) 2016.

TIGER: A data analysis pipeline for testing the strong-field dynamics of general relativity with gravitational wave signals from coalescing compact binaries

NASA Astrophysics Data System (ADS)

Agathos, M.; Del Pozzo, W.; Li, T. G. F.; Van Den Broeck, C.; Veitch, J.; Vitale, S.

2014-04-01

The direct detection of gravitational waves with upcoming second-generation gravitational wave observatories such as Advanced LIGO and Advanced Virgo will allow us to probe the genuinely strong-field dynamics of general relativity (GR) for the first time. We have developed a data analysis pipeline called TIGER (test infrastructure for general relativity), which uses signals from compact binary coalescences to perform a model-independent test of GR. In this paper we focus on signals from coalescing binary neutron stars, for which sufficiently accurate waveform models are already available which can be generated fast enough on a computer that they can be used in Bayesian inference. By performing numerical experiments in stationary, Gaussian noise, we show that for such systems, TIGER is robust against a number of unmodeled fundamental, astrophysical, and instrumental effects, such as differences between waveform approximants, a limited number of post-Newtonian phase contributions being known, the effects of neutron star tidal deformability on the orbital motion, neutron star spins, and instrumental calibration errors.

Atlantis: An Open Architecture for Synergy of Process-Centered Environments and Computer-Supported Cooperative Work

DTIC Science & Technology

1998-04-01

revision phase would be followed, after which a second review would be scheduled , and so forth, until the review succeeds. 2.3 Realization of the...normal rules, when Summit rules are inferred they are enqueued in a separate Summit queue and are scheduled for execution only after local forward... scheduling and activating activities according to the defined process; reac- tively triggering activities based on state changes; monitoring the process

Self-Associations Influence Task-Performance through Bayesian Inference

PubMed Central

Bengtsson, Sara L.; Penny, Will D.

2013-01-01

The way we think about ourselves impacts greatly on our behavior. This paper describes a behavioral study and a computational model that shed new light on this important area. Participants were primed “clever” and “stupid” using a scrambled sentence task, and we measured the effect on response time and error-rate on a rule-association task. First, we observed a confirmation bias effect in that associations to being “stupid” led to a gradual decrease in performance, whereas associations to being “clever” did not. Second, we observed that the activated self-concepts selectively modified attention toward one’s performance. There was an early to late double dissociation in RTs in that primed “clever” resulted in RT increase following error responses, whereas primed “stupid” resulted in RT increase following correct responses. We propose a computational model of subjects’ behavior based on the logic of the experimental task that involves two processes; memory for rules and the integration of rules with subsequent visual cues. The model incorporates an adaptive decision threshold based on Bayes rule, whereby decision thresholds are increased if integration was inferred to be faulty. Fitting the computational model to experimental data confirmed our hypothesis that priming affects the memory process. This model explains both the confirmation bias and double dissociation effects and demonstrates that Bayesian inferential principles can be used to study the effect of self-concepts on behavior. PMID:23966937

Evidence for a maximum mass cut-off in the neutron star mass distribution and constraints on the equation of state

NASA Astrophysics Data System (ADS)

Alsing, Justin; Silva, Hector O.; Berti, Emanuele

2018-07-01

We infer the mass distribution of neutron stars in binary systems using a flexible Gaussian mixture model and use Bayesian model selection to explore evidence for multimodality and a sharp cut-off in the mass distribution. We find overwhelming evidence for a bimodal distribution, in agreement with previous literature, and report for the first time positive evidence for a sharp cut-off at a maximum neutron star mass. We measure the maximum mass to be 2.0 M⊙ < mmax < 2.2 M⊙ (68 per cent), 2.0 M⊙ < mmax < 2.6 M⊙ (90 per cent), and evidence for a cut-off is robust against the choice of model for the mass distribution and to removing the most extreme (highest mass) neutron stars from the data set. If this sharp cut-off is interpreted as the maximum stable neutron star mass allowed by the equation of state of dense matter, our measurement puts constraints on the equation of state. For a set of realistic equations of state that support >2 M⊙ neutron stars, our inference of mmax is able to distinguish between models at odds ratios of up to 12:1, whilst under a flexible piecewise polytropic equation-of-state model our maximum mass measurement improves constraints on the pressure at 3-7× the nuclear saturation density by ˜ 30-50 per cent compared to simply requiring mmax > 2 M⊙. We obtain a lower bound on the maximum sound speed attained inside the neutron star of c_ s^max > 0.63c (99.8 per cent), ruling out c_ s^max < c/√{3} at high significance. Our constraints on the maximum neutron star mass strengthen the case for neutron star-neutron star mergers as the primary source of short gamma-ray bursts.

Do people reason rationally about causally related events? Markov violations, weak inferences, and failures of explaining away.

PubMed

Rottman, Benjamin M; Hastie, Reid

2016-06-01

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X1→Y→X2, common cause structures X1←Y→X2, and common effect structures X1→Y←X2, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative "explaining away" pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations. Copyright © 2016 Elsevier Inc. All rights reserved.

Context recognition for a hyperintensional inference machine

NASA Astrophysics Data System (ADS)

Duží, Marie; Fait, Michal; Menšík, Marek

2017-07-01

The goal of this paper is to introduce the algorithm of context recognition in the functional programming language TIL-Script, which is a necessary condition for the implementation of the TIL-Script inference machine. The TIL-Script language is an operationally isomorphic syntactic variant of Tichý's Transparent Intensional Logic (TIL). From the formal point of view, TIL is a hyperintensional, partial, typed λ-calculus with procedural semantics. Hyperintensional, because TIL λ-terms denote procedures (defined as TIL constructions) producing set-theoretic functions rather than the functions themselves; partial, because TIL is a logic of partial functions; and typed, because all the entities of TIL ontology, including constructions, receive a type within a ramified hierarchy of types. These features make it possible to distinguish three levels of abstraction at which TIL constructions operate. At the highest hyperintensional level the object to operate on is a construction (though a higher-order construction is needed to present this lower-order construction as an object of predication). At the middle intensional level the object to operate on is the function presented, or constructed, by a construction, while at the lowest extensional level the object to operate on is the value (if any) of the presented function. Thus a necessary condition for the development of an inference machine for the TIL-Script language is recognizing a context in which a construction occurs, namely extensional, intensional and hyperintensional context, in order to determine the type of an argument at which a given inference rule can be properly applied. As a result, our logic does not flout logical rules of extensional logic, which makes it possible to develop a hyperintensional inference machine for the TIL-Script language.

When some is actually all: scalar inferences in face-threatening contexts.

PubMed

Bonnefon, Jean-François; Feeney, Aidan; Villejoubert, Gaëlle

2009-08-01

Accounts of the scalar inference from 'some X-ed' to 'not all X-ed' are central to the debate between contemporary theories of conversational pragmatics. An important contribution to this debate is to identify contexts that decrease the endorsement rate of the inference. We suggest that the inference is endorsed less often in face-threatening contexts, i.e., when X implies a loss of face for the listener. This claim is successfully tested in Experiment 1. Experiment 2 rules out a possible confound between face-threatening contexts and lower-bound contexts. Experiment 3 shows that whilst saying 'some X-ed' when one knew for a fact that all X-ed is always perceived as an underinformative utterance, it is also seen as a nice and polite thing to do when X threatens the face of the listener. These findings are considered from the perspective of Relevance Theory as well as that of the Generalized Conversational Inference approach.

Uncertain deduction and conditional reasoning.

PubMed

Evans, Jonathan St B T; Thompson, Valerie A; Over, David E

2015-01-01

There has been a paradigm shift in the psychology of deductive reasoning. Many researchers no longer think it is appropriate to ask people to assume premises and decide what necessarily follows, with the results evaluated by binary extensional logic. Most every day and scientific inference is made from more or less confidently held beliefs and not assumptions, and the relevant normative standard is Bayesian probability theory. We argue that the study of "uncertain deduction" should directly ask people to assign probabilities to both premises and conclusions, and report an experiment using this method. We assess this reasoning by two Bayesian metrics: probabilistic validity and coherence according to probability theory. On both measures, participants perform above chance in conditional reasoning, but they do much better when statements are grouped as inferences, rather than evaluated in separate tasks.

Simulation-based Bayesian inference for latent traits of item response models: Introduction to the ltbayes package for R.

PubMed

Johnson, Timothy R; Kuhn, Kristine M

2015-12-01

This paper introduces the ltbayes package for R. This package includes a suite of functions for investigating the posterior distribution of latent traits of item response models. These include functions for simulating realizations from the posterior distribution, profiling the posterior density or likelihood function, calculation of posterior modes or means, Fisher information functions and observed information, and profile likelihood confidence intervals. Inferences can be based on individual response patterns or sets of response patterns such as sum scores. Functions are included for several common binary and polytomous item response models, but the package can also be used with user-specified models. This paper introduces some background and motivation for the package, and includes several detailed examples of its use.

The anatomy of choice: dopamine and decision-making

PubMed Central

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

2014-01-01

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses—and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making. PMID:25267823

The anatomy of choice: dopamine and decision-making.

PubMed

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

2014-11-05

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding. In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses-and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

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.

Learning abstract visual concepts via probabilistic program induction in a Language of Thought.

PubMed

Overlan, Matthew C; Jacobs, Robert A; Piantadosi, Steven T

2017-11-01

The ability to learn abstract concepts is a powerful component of human cognition. It has been argued that variable binding is the key element enabling this ability, but the computational aspects of variable binding remain poorly understood. Here, we address this shortcoming by formalizing the Hierarchical Language of Thought (HLOT) model of rule learning. Given a set of data items, the model uses Bayesian inference to infer a probability distribution over stochastic programs that implement variable binding. Because the model makes use of symbolic variables as well as Bayesian inference and programs with stochastic primitives, it combines many of the advantages of both symbolic and statistical approaches to cognitive modeling. To evaluate the model, we conducted an experiment in which human subjects viewed training items and then judged which test items belong to the same concept as the training items. We found that the HLOT model provides a close match to human generalization patterns, significantly outperforming two variants of the Generalized Context Model, one variant based on string similarity and the other based on visual similarity using features from a deep convolutional neural network. Additional results suggest that variable binding happens automatically, implying that binding operations do not add complexity to peoples' hypothesized rules. Overall, this work demonstrates that a cognitive model combining symbolic variables with Bayesian inference and stochastic program primitives provides a new perspective for understanding people's patterns of generalization. Copyright © 2017 Elsevier B.V. All rights reserved.

A Comparison of Two Models for Cognitive Diagnosis. Research Report. ETS RR-04-02

ERIC Educational Resources Information Center

Yan, Duanli; Almond, Russell; Mislevy, Robert

2004-01-01

Diagnostic score reports linking assessment outcomes to instructional interventions are one of the most requested features of assessment products. There is a body of interesting work done in the last 20 years including Tatsuoka's rule space method (Tatsuoka, 1983), Haertal and Wiley's binary skills model (Haertal, 1984; Haertal & Wiley, 1993),…

When the Exception Is the Rule: School Shootings, Bare Life, and the Sovereign Self

ERIC Educational Resources Information Center

Shapiro, Harvey

2015-01-01

Much discourse on school shootings tends to imply a binary separation between what is considered normal and exceptional, between an expected course of human events and sociohistorical aberrations. In this article Harvey Shapiro suggests the need for new directions in our responses: First, he shows how responses to school shootings tend to…

Optimal two-phase sampling design for comparing accuracies of two binary classification rules.

PubMed

Xu, Huiping; Hui, Siu L; Grannis, Shaun

2014-02-10

In this paper, we consider the design for comparing the performance of two binary classification rules, for example, two record linkage algorithms or two screening tests. Statistical methods are well developed for comparing these accuracy measures when the gold standard is available for every unit in the sample, or in a two-phase study when the gold standard is ascertained only in the second phase in a subsample using a fixed sampling scheme. However, these methods do not attempt to optimize the sampling scheme to minimize the variance of the estimators of interest. In comparing the performance of two classification rules, the parameters of primary interest are the difference in sensitivities, specificities, and positive predictive values. We derived the analytic variance formulas for these parameter estimates and used them to obtain the optimal sampling design. The efficiency of the optimal sampling design is evaluated through an empirical investigation that compares the optimal sampling with simple random sampling and with proportional allocation. Results of the empirical study show that the optimal sampling design is similar for estimating the difference in sensitivities and in specificities, and both achieve a substantial amount of variance reduction with an over-sample of subjects with discordant results and under-sample of subjects with concordant results. A heuristic rule is recommended when there is no prior knowledge of individual sensitivities and specificities, or the prevalence of the true positive findings in the study population. The optimal sampling is applied to a real-world example in record linkage to evaluate the difference in classification accuracy of two matching algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

Identifying Emergency Department Patients at Low Risk for a Variceal Source of Upper Gastrointestinal Hemorrhage.

PubMed

Klein, Lauren R; Money, Joel; Maharaj, Kaveesh; Robinson, Aaron; Lai, Tarissa; Driver, Brian E

2017-11-01

Assessing the likelihood of a variceal versus nonvariceal source of upper gastrointestinal bleeding (UGIB) guides therapy, but can be difficult to determine on clinical grounds. The objective of this study was to determine if there are easily ascertainable clinical and laboratory findings that can identify a patient as low risk for a variceal source of hemorrhage. This was a retrospective cohort study of adult ED patients with UGIB between January 2008 and December 2014 who had upper endoscopy performed during hospitalization. Clinical and laboratory data were abstracted from the medical record. The source of the UGIB was defined as variceal or nonvariceal based on endoscopic reports. Binary recursive partitioning was utilized to create a clinical decision rule. The rule was internally validated and test characteristics were calculated with 1,000 bootstrap replications. A total of 719 patients were identified; mean age was 55 years and 61% were male. There were 71 (10%) patients with a variceal UGIB identified on endoscopy. Binary recursive partitioning yielded a two-step decision rule (platelet count > 200 × 10 9 /L and an international normalized ratio [INR] < 1.3), which identified patients who were low risk for a variceal source of hemorrhage. For the bootstrapped samples, the rule performed with 97% sensitivity (95% confidence interval [CI] = 91%-100%) and 49% specificity (95% CI = 44%-53%). Although this derivation study must be externally validated before widespread use, patients presenting to the ED with an acute UGIB with platelet count of >200 × 10 9 /L and an INR of <1.3 may be at very low risk for a variceal source of their upper gastrointestinal hemorrhage. © 2017 by the Society for Academic Emergency Medicine.

Inferring the Composition of Super-Jupiter Mass Companions of Pulsars with Radio Line Spectroscopy

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

Ray, Alak; Loeb, Abraham, E-mail: akr@tifr.res.in, E-mail: aloeb@cfa.harvard.edu

We propose using radio line spectroscopy to detect molecular absorption lines (such as OH at 1.6–1.7 GHz) before and after the total eclipse of black widow and other short orbital period binary pulsars with low-mass companions. The companion in such a binary may be ablated away by energetic particles and high-energy radiation produced by the pulsar wind. The observations will probe the eclipsing wind being ablated by the pulsar and constrain the nature of the companion and its surroundings. Maser emission from the interstellar medium stimulated by a pulsar beam might also be detected from the intrabinary medium. The shortmore » temporal resolution allowed by the millisecond pulsars can probe this medium with the high angular resolution of the pulsar beam.« less

Modeling Radial Velocities and Eclipse Photometry of the Kepler Target KIC 4054905: an Oscillating Red Giant in an Eclipsing Binary

NASA Astrophysics Data System (ADS)

Benbakoura, M.; Gaulme, P.; McKeever, J.; Beck, P. G.; Jackiewicz, J.; García, R. A.

2017-12-01

Asteroseismology is a powerful tool to measure the fundamental properties of stars and probe their interiors. This is particularly efficient for red giants because their modes are well detectable and give information on their deep layers. However, the seismic relations used to infer the mass and radius of a star have been calibrated on the Sun. Therefore, it is crucial to assess their accuracy for red giants which are not perfectly homologous to it. We study eclipsing binaries with a giant component to test their validity. We identified 16 systems for which we intend to compare the dynamical masses and radii obtained by combined photometry and spectroscopy to the values obtained from asteroseismology. In the present work, we illustrate our approach on a system from our sample.

Enhanced Hα activity at periastron in the young and massive spectroscopic binary HD 200775

NASA Astrophysics Data System (ADS)

Benisty, M.; Perraut, K.; Mourard, D.; Stee, P.; Lima, G. H. R. A.; Le Bouquin, J. B.; Borges Fernandes, M.; Chesneau, O.; Nardetto, N.; Tallon-Bosc, I.; McAlister, H.; Ten Brummelaar, T.; Ridgway, S.; Sturmann, J.; Sturmann, L.; Turner, N.; Farrington, C.; Goldfinger, P. J.

2013-07-01

Context. Young close binaries clear central cavities in their surrounding circumbinary disk from which the stellar objects can still accrete material. This process takes place within the first astronomical unit and is still not well constrained because the observational evidence has been gathered, until now, only by means of spectroscopy. Theoretical models for T Tauri stars in close binaries predict a variability of the hydrogen emission lines attributable to periodic changes in the accretion rates as the secondary approaches periastron. Whether a similar scenario applies to more massive objects is unclear, and still needs to be proven observationally. Aims: The young object HD 200775 (MWC 361) is a massive spectroscopic binary (separation of ~15.9 mas, ~5.0 AU), with uncertain classification (early/late Be), that shows a strong and variable Hα emission. We aim to study the mechanisms that produce the Hα line at the AU-scale, and their dependence on binarity. Methods: Combining the radial velocity measurements and astrometric data available in the literature, we determined new orbital parameters and revised the distance to 320 ± 51 pc. With the VEGA instrument on the CHARA array, we spatially and spectrally resolved the Hα emission of HD 200775 on a scale of a few milliarcseconds, at low and medium spectral resolutions (R ~ 1600 and 5000). Our observations cover a single orbital period (~3.6 years). Spectra, spectral visibilities, and differential phases have been derived. A simple analytical model of a face-on Gaussian located along the binary axis was used to analyze the interferometric observables over the spectral range. Results: We observe that the Hα equivalent width varies with the orbital phase, and increases close to periastron, as expected from theoretical models that predict an increase of the mass transfer from the circumbinary disk to the primary disk. In addition, using spectral visibilities and differential phases, we find marginal variations of the typical extent of the Hα emission (at 1 to 2σ level) and location (at 1 to 5σ level). The spatial extent of the Hα emission, as probed by the Gaussian FWHM, is minimum at the ascending node (0.67 ± 0.20 mas, i.e., 0.22 ± 0.06 AU), and more than doubles at the periastron. In addition, the Gaussian photocenter is slightly displaced in the direction opposite to the secondary, ruling out the scenario in which all or most of the Hα emission is due to accretion onto the secondary. This favors a scenario in which the primary is responsible for the enhanced Hα activity at periastron. These findings, together with the wide Hα line profile, may be due to a non-spherical wind enhanced at periastron. Conclusions: For the first time in a system of this kind, we spatially resolve the Hα line and estimate that it is emitted in a region larger than the one usually inferred in accretion processes. The Hα line could be emitted in a stellar or disk-wind, enhanced at periastron as a result of gravitational perturbation, after a period of increased mass accretion rate. Our results suggest a strong connection between accretion and ejection in these massive objects, consistent with the predictions for lower-mass close binaries. Based on observations made with the VEGA/CHARA instrument.

On the origin of Hill's causal criteria.

PubMed

Morabia, A

1991-09-01

The rules to assess causation formulated by the eighteenth century Scottish philosopher David Hume are compared to Sir Austin Bradford Hill's causal criteria. The strength of the analogy between Hume's rules and Hill's causal criteria suggests that, irrespective of whether Hume's work was known to Hill or Hill's predecessors, Hume's thinking expresses a point of view still widely shared by contemporary epidemiologists. The lack of systematic experimental proof to causal inferences in epidemiology may explain the analogy of Hume's and Hill's, as opposed to Popper's, logic.

Single board system for fuzzy inference

NASA Technical Reports Server (NTRS)

Symon, James R.; Watanabe, Hiroyuki

1991-01-01

The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.

United States Air Force Summer Faculty Research Program (1987). Program Technical Report. Volume 2.

DTIC Science & Technology

1987-12-01

the area of statistical inference, distribution theory and stochastic * •processes. I have taught courses in random processes and sample % j .functions...controlled phase separation of isotropic, binary mixtures, the theory of spinodal decomposition has been developed by Cahn and Hilliard.5 ,6 This theory is...peak and its initial rate of growth at a given temperature are predicted by the spinodal theory . The angle of maximum intensity is then determined by

Distances of Dwarf Carbon Stars

NASA Astrophysics Data System (ADS)

Harris, Hugh C.; Dahn, Conard C.; Subasavage, John P.; Munn, Jeffrey A.; Canzian, Blaise J.; Levine, Stephen E.; Monet, Alice B.; Pier, Jeffrey R.; Stone, Ronald C.; Tilleman, Trudy M.; Hartkopf, William I.

2018-06-01

Parallaxes are presented for a sample of 20 nearby dwarf carbon stars. The inferred luminosities cover almost two orders of magnitude. Their absolute magnitudes and tangential velocities confirm prior expectations that some originate in the Galactic disk, although more than half of this sample are halo stars. Three stars are found to be astrometric binaries, and orbital elements are determined; their semimajor axes are 1–3 au, consistent with the size of an AGB mass-transfer donor star.

Measuring the neutron star tidal deformability with equation-of-state-independent relations and gravitational waves

NASA Astrophysics Data System (ADS)

Chatziioannou, Katerina; Haster, Carl-Johan; Zimmerman, Aaron

2018-05-01

Gravitational wave measurements of binary neutron star coalescences offer information about the properties of the extreme matter that comprises the stars. Despite our expectation that all neutron stars in the Universe obey the same equation of state, i.e. the properties of the matter that forms them are universal, current tidal inference analyses treat the two bodies as independent. We present a method to measure the effect of tidal interactions in the gravitational wave signal—and hence constrain the equation of state—that assumes that the two binary components obey the same equation of state. Our method makes use of a relation between the tidal deformabilities of the two stars given the ratio of their masses, a relation that has been shown to only have a weak dependence on the equation of state. We use this to link the tidal deformabilities of the two stars in a realistic parameter inference study while simultaneously marginalizing over the error in the relation. This approach incorporates more physical information into our analysis, thus leading to a better measurement of tidal effects in gravitational wave signals. Through simulated signals we estimate that uncertainties in the measured tidal parameters are reduced by a factor of at least 2—and in some cases up to 10—depending on the equation of state and mass ratio of the system.

Resolved, expanding jets in the Galactic black hole candidate XTE J1908+094

NASA Astrophysics Data System (ADS)

Rushton, A. P.; Miller-Jones, J. C. A.; Curran, P. A.; Sivakoff, G. R.; Rupen, M. P.; Paragi, Z.; Spencer, R. E.; Yang, J.; Altamirano, D.; Belloni, T.; Fender, R. P.; Krimm, H. A.; Maitra, D.; Migliari, S.; Russell, D. M.; Russell, T. D.; Soria, R.; Tudose, V.

2017-07-01

Black hole X-ray binaries undergo occasional outbursts caused by changing inner accretion flows. Here we report high angular resolution radio observations of the 2013 outburst of the black hole candidate X-ray binary system XTE J1908+094, using data from the Very Long Baseline Array and European VLBI Network. We show that following a hard-to-soft state transition, we detect moving jet knots that appear asymmetric in morphology and brightness, and expand to become laterally resolved as they move away from the core, along an axis aligned approximately -11° east of north. We initially see only the southern component, whose evolution gives rise to a 15-mJy radio flare and generates the observed radio polarization. This fades and becomes resolved out after 4 days, after which a second component appears to the north, moving in the opposite direction. From the timing of the appearance of the knots relative to the X-ray state transition, a 90° swing of the inferred magnetic field orientation, the asymmetric appearance of the knots, their complex and evolving morphology, and their low speeds, we interpret the knots as working surfaces where the jets impact the surrounding medium. This would imply a substantially denser environment surrounding XTE J1908+094 than has been inferred to exist around the microquasar sources GRS 1915+105 and GRO J1655-40.

Context-Aware Generative Adversarial Privacy

NASA Astrophysics Data System (ADS)

Huang, Chong; Kairouz, Peter; Chen, Xiao; Sankar, Lalitha; Rajagopal, Ram

2017-12-01

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals' private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP's performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model, and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.

Inferring interplanetary magnetic field polarities from geomagnetic variations

NASA Astrophysics Data System (ADS)

Vokhmyanin, M. V.; Ponyavin, D. I.

2012-06-01

In this paper, we propose a modified procedure to infer the interplanetary magnetic field (IMF) polarities from geomagnetic observations. It allows to identify the polarity back to 1905. As previous techniques it is based on the well-known Svalgaard-Mansurov effect. We have improved the quality and accuracy of polarity inference compared with the previous results of Svalgaard (1975) and Vennerstroem et al. (2001) by adding new geomagnetic stations and extracting carefully diurnal curve. The data demonstrates an excess of one of the two IMF sectors within equinoxes (Rosenberg-Coleman rule) evidencing polar field reversals at least for the last eight solar cycles. We also found a predominance of the two-sector structure in late of descending phase of solar cycle 16.

Static analysis of class invariants in Java programs

NASA Astrophysics Data System (ADS)

Bonilla-Quintero, Lidia Dionisia

2011-12-01

This paper presents a technique for the automatic inference of class invariants from Java bytecode. Class invariants are very important for both compiler optimization and as an aid to programmers in their efforts to reduce the number of software defects. We present the original DC-invariant analysis from Adam Webber, talk about its shortcomings and suggest several different ways to improve it. To apply the DC-invariant analysis to identify DC-invariant assertions, all that one needs is a monotonic method analysis function and a suitable assertion domain. The DC-invariant algorithm is very general; however, the method analysis can be highly tuned to the problem in hand. For example, one could choose shape analysis as the method analysis function and use the DC-invariant analysis to simply extend it to an analysis that would yield class-wide invariants describing the shapes of linked data structures. We have a prototype implementation: a system we refer to as "the analyzer" that infers DC-invariant unary and binary relations and provides them to the user in a human readable format. The analyzer uses those relations to identify unnecessary array bounds checks in Java programs and perform null-reference analysis. It uses Adam Webber's relational constraint technique for the class-invariant binary relations. Early results with the analyzer were very imprecise in the presence of "dirty-called" methods. A dirty-called method is one that is called, either directly or transitively, from any constructor of the class, or from any method of the class at a point at which a disciplined field has been altered. This result was unexpected and forced an extensive search for improved techniques. An important contribution of this paper is the suggestion of several ways to improve the results by changing the way dirty-called methods are handled. The new techniques expand the set of class invariants that can be inferred over Webber's original results. The technique that produces better results uses in-line analysis. Final results are promising: we can infer sound class invariants for full-scale, not just toy applications.

Probing Cold Dark Matter Substructure with Wide Binaries in Dwarf Spheroidal Galaxies

NASA Astrophysics Data System (ADS)

Chaname, Julio

2013-10-01

The mass function of dark matter {DM} halos is a central piece in the current framework of hierarchical structure formation. Although a wealth of information is available on the properties of DM halos with M>1e8 solar masses {Msun}, lower-mass halos remain virtually inaccessible. In particular, we do not know whether there is substructure on scales below dwarf spheroidal {dSph} galaxies, nor whether the DM power spectrum cuts off at some low-mass value. Here we propose an experiment that, using resolved binary systems as gravitational test particles, will probe these unexplored regimes for the first time. We will measure the stellar 2-point correlation function in 370 square arcmin of the Ursa Minor dSph down to separations of 40 mas, corresponding to 3000 AU. If there is no DM substructure on small scales, we will detect a 6-sigma excess due to "wide" binaries at the smallest separations. On the other hand, if DM substructure exists on scales of 1e4 Msun at even 10% of the level predicted by standard theory, then these binaries will have been destroyed and there will be no excess at small separations. Because the wide-binary separation function is identical in the Milky Way disk and halo {despite being radically different dynamical environments}, it is almost certain that dSphs were originally endowed with the same wide-binary distribution. Moreover, the interpretation of the resulting data is free from ambiguities, as there are no known mechanisms for destroying these binaries within dSph environments, other than DM subhalos. Thus this is, to the best of our knowledge, the only current experiment that could detect or rule out DM clustering on M=1e4 Msun scales.

Object/rule integration in CLIPS. [C Language Integrated Production System

NASA Technical Reports Server (NTRS)

Donnell, Brian L.

1993-01-01

This paper gives a brief overview of the C Language Integrated Production System (CLIPS) with a focus on the object-oriented features. The advantages of an object data representation over the traditional working memory element (WME), i.e., facts, are discussed, and the implementation of the Rete inference algorithm in CLIPS is presented in detail. A few methods for achieving pattern-matching on objects with the current inference engine are given, and finally, the paper examines the modifications necessary to the Rete algorithm to allow direct object pattern-matching.

Signal Processing Expert Code (SPEC)

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

Ames, H.S.

1985-12-01

The purpose of this paper is to describe a prototype expert system called SPEC which was developed to demonstrate the utility of providing an intelligent interface for users of SIG, a general purpose signal processing code. The expert system is written in NIL, runs on a VAX 11/750 and consists of a backward chaining inference engine and an English-like parser. The inference engine uses knowledge encoded as rules about the formats of SIG commands and about how to perform frequency analyses using SIG. The system demonstrated that expert system can be used to control existing codes.

Low-Luminosity AGN As Analogues of Galactic Black Holes in the Low/Hard State: Evidence from X-Ray Timing of NGC 4258

NASA Technical Reports Server (NTRS)

Markowitz, A.; Uttley, P.

2005-01-01

We present a broadband power spectral density function (PSD) measured from extensive RXTE monitoring data of the low-luminosity AGN NGC 4258, which has an accurate, maser-determined black hole mass of (3.9 plus or minus 0.1) x 10(exp 7) solar mass. We constrain the PSD break time scale to be greater than 4.5 d at greater than 90% confidence, which appears to rule out the possibility that NGC 4258 is an analogue of black hole X-ray binaries (BHXRBs) in the high/soft state. In this sense, the PSD of NGC 4258 is different to that of some more-luminous Seyferts, which appear similar to the PSDs of high/soft state X-ray binaries. This result supports previous analogies between LLAGN and X-ray binaries in the low/hard state based on spectral energy distributions, indicating that the AGN/BHXRB analogy is valid across a broad range of accretion rates.

How important is thermodynamics for identifying elementary flux modes?

PubMed Central

Peres, Sabine; Jolicœur, Mario; Moulin, Cécile

2017-01-01

We present a method for computing thermodynamically feasible elementary flux modes (tEFMs) using equilibrium constants without need of internal metabolite concentrations. The method is compared with the method based on a binary distinction between reversible and irreversible reactions. When all reactions are reversible, adding the constraints based on equilibrium constants reduces the number of elementary flux modes (EFMs) by a factor of two. Declaring in advance some reactions as irreversible, based on reliable biochemical expertise, can in general reduce the number of EFMs by a greater factor. But, even in this case, computing tEFMs can rule out some EFMs which are biochemically irrelevant. We applied our method to two published models described with binary distinction: the monosaccharide metabolism and the central carbon metabolism of Chinese hamster ovary cells. The results show that the binary distinction is in good agreement with biochemical observations. Moreover, the suppression of the EFMs that are not consistent with the equilibrium constants appears to be biologically relevant. PMID:28222104

Phenomenological study of exclusive binary light particle production from antiproton-proton annihilation at FAIR/PANDA

NASA Astrophysics Data System (ADS)

Ying, Wang

2016-08-01

Exclusive binary annihilation reactions induced by antiprotons of momentum from 1.5 to 15 GeV/c can be extensively investigated at FAIR/PANDA [1]. We are especially interested in the channel of charged pion pairs. Whereas this very probable channel constitutes the major background for other processes of interest in the PANDA experiment, it carries unique physical information on the quark content of proton, allowing to test different models (quark counting rules, statistical models,..). To study the binary reactions of light meson formation, we are developing an effective Lagrangian model based on Feynman diagrams which takes into account the virtuality of the exchanged particles. Regge factors [2] and form factors are introduced with parameters which may be adjusted on the existing data. We present preliminary results of our formalism for different reactions of light meson production leading to reliable predictions of cross sections, energy and angular dependencies in the PANDA kinematical range.

Tests of General Relativity with GW150914

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allocca, A.; Altin, P. A.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Babak, S.; Bacon, P.; Bader, M. K. M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Baune, C.; Bavigadda, V.; Bazzan, M.; Behnke, B.; Bejger, M.; Bell, A. S.; Bell, C. J.; Berger, B. K.; Bergman, J.; Bergmann, G.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Birney, R.; Birnholtz, O.; Biscans, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Bodiya, T. P.; Boer, M.; Bogaert, G.; Bogan, C.; Bohe, A.; Bojtos, P.; Bond, C.; Bondu, F.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brown, N. M.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T.; Calloni, E.; Camp, J. B.; Cannon, K. C.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Cerboni Baiardi, L.; Cerretani, G.; Cesarini, E.; Chakraborty, R.; Chalermsongsak, T.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Chen, H. Y.; Chen, Y.; Cheng, C.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, S.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C. G.; Cominsky, L.; Constancio, M.; Conte, A.; Conti, L.; Cook, D.; Corbitt, T. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Craig, K.; Creighton, J. D. E.; Cripe, J.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Darman, N. S.; Dattilo, V.; Dave, I.; Daveloza, H. P.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; DeBra, D.; Debreczeni, G.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dereli, H.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Dojcinoski, G.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Douglas, R.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Engels, W.; Essick, R. C.; Etzel, T.; Evans, M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Fang, Q.; Farinon, S.; Farr, B.; Farr, W. M.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M.; Fournier, J.-D.; Franco, S.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fricke, T. T.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H. A. G.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gatto, A.; Gaur, G.; Gehrels, N.; Gemme, G.; Gendre, B.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gordon, N. A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Hall, B. R.; Hall, E. D.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Hofman, D.; Hollitt, S. E.; Holt, K.; Holz, D. E.; Hopkins, P.; Hosken, D. J.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huang, S.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Idrisy, A.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J.-M.; Isi, M.; Islas, G.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jang, H.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Johnson-McDaniel, N. K.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Haris, M. K.; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karki, S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kawazoe, F.; Kéfélian, F.; Kehl, M. S.; Keitel, D.; Kelley, D. B.; Kells, W.; Kennedy, R.; Key, J. S.; Khalaidovski, A.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, C.; Kim, J.; Kim, K.; Kim, Nam-Gyu; Kim, Namjun; Kim, Y.-M.; King, E. J.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Koehlenbeck, S. M.; Kokeyama, K.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Kringel, V.; Krishnan, B.; Królak, A.; Krueger, C.; Kuehn, G.; Kumar, P.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lange, J.; Lantz, B.; Lasky, P. D.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, K.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Levine, B. M.; Li, T. G. F.; Libson, A.; Littenberg, T. B.; Lockerbie, N. A.; Logue, J.; Lombardi, A. L.; London, L. T.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lousto, C. O.; Lovelace, G.; Lück, H.; Lundgren, A. P.; Luo, J.; Lynch, R.; Ma, Y.; MacDonald, T.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Magee, R. M.; Mageswaran, M.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandel, I.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martynov, D. V.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mendoza-Gandara, D.; Mercer, R. A.; Merilh, E.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B. C.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Murphy, D. J.; Murray, P. G.; Mytidis, A.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Necula, V.; Nedkova, K.; Nelemans, G.; Neri, M.; Neunzert, A.; Newton, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; O'Shaughnessy, R.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pan, Y.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Patrick, Z.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perreca, A.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poggiani, R.; Popolizio, P.; Post, A.; Powell, J.; Prasad, J.; Predoi, V.; Premachandra, S. S.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prix, R.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Ricci, F.; Riles, K.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sanchez, E. J.; Sandberg, V.; Sandeen, B.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Schilling, R.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Serna, G.; Setyawati, Y.; Sevigny, A.; Shaddock, D. A.; Shah, S.; Shahriar, M. S.; Shaltev, M.; Shao, Z.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sigg, D.; Silva, A. D.; Simakov, D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, J. R.; Smith, N. D.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strauss, N. A.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tápai, M.; Tarabrin, S. P.; Taracchini, A.; Taylor, R.; Theeg, T.; Thirugnanasambandam, M. P.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Tomlinson, C.; Tonelli, M.; Torres, C. V.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Tringali, M. C.; Trozzo, L.; Tse, M.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; Vallisneri, M.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, X.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; White, D. J.; Whiting, B. F.; Williams, D.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Worden, J.; Wright, J. L.; Wu, G.; Yablon, J.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yap, M. J.; Yu, H.; Yvert, M.; ZadroŻny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, F.; Zhang, L.; Zhang, M.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, X. J.; Zucker, M. E.; Zuraw, S. E.; Zweizig, J.; Boyle, M.; Campanelli, M.; Hemberger, D. A.; Kidder, L. E.; Ossokine, S.; Scheel, M. A.; Szilagyi, B.; Teukolsky, S.; Zlochower, Y.; LIGO Scientific; Virgo Collaborations

2016-06-01

The LIGO detection of GW150914 provides an unprecedented opportunity to study the two-body motion of a compact-object binary in the large-velocity, highly nonlinear regime, and to witness the final merger of the binary and the excitation of uniquely relativistic modes of the gravitational field. We carry out several investigations to determine whether GW150914 is consistent with a binary black-hole merger in general relativity. We find that the final remnant's mass and spin, as determined from the low-frequency (inspiral) and high-frequency (postinspiral) phases of the signal, are mutually consistent with the binary black-hole solution in general relativity. Furthermore, the data following the peak of GW150914 are consistent with the least-damped quasinormal mode inferred from the mass and spin of the remnant black hole. By using waveform models that allow for parametrized general-relativity violations during the inspiral and merger phases, we perform quantitative tests on the gravitational-wave phase in the dynamical regime and we determine the first empirical bounds on several high-order post-Newtonian coefficients. We constrain the graviton Compton wavelength, assuming that gravitons are dispersed in vacuum in the same way as particles with mass, obtaining a 90%-confidence lower bound of 1013 km . In conclusion, within our statistical uncertainties, we find no evidence for violations of general relativity in the genuinely strong-field regime of gravity.

The magnetic fields, ages, and original spin periods of millisecond pulsars

NASA Technical Reports Server (NTRS)

Camilo, F.; Thorsett, S. E.; Kulkarni, S. R.

1994-01-01

Accurate determination of the spin-down rates of millisecond pulsars requires consideration of the apparent acceleration of the pulsars due to their high transverse velocities. We show that for several nearby pulsars the neglect of this effect leads to substantial errors in inferred pulsar ages and magnetic fields. Two important ramifications follow. (1) The intrinsic magnetic field strengths of all millisecond pulsars lie below 5 x 10(exp 8) G, strengthening an earlier suggestion of a 'gap' between the magnetic field strengths of millisecond pulsars and of high-mass binary pulsars such as PSR B1913+16, which are thought to have been formed by mass transfer in low-mass and high-mass X-ray binaries, respectively. This result suggests that the magnetic field strengths of recycled pulsars are related to their formation and evolution in binary systems. (2) The corrected characteristic ages of several millisecond pulsars appear to be greater than the age of the Galactic disk. We reconcile this apparent paradox by suggesting that some millisecond pulsars were born with periods close to their current periods. This conclusion has important implications for the interpretation of the cooling ages of white dwarf companions, the birthrate discrepancy between millisecond pulsars and their X-ray binary progenitors, and the possible existence of a class of weakly magnetized (B much less than 10(exp 8)G), rapidly rotating neutron stars.

Tests of General Relativity with GW150914.

PubMed

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Rolland, L; Rollins, J G; Roma, V J; Romano, R; Romanov, G; Romie, J H; Rosińska, D; Rowan, S; Rüdiger, A; Ruggi, P; Ryan, K; Sachdev, S; Sadecki, T; Sadeghian, L; Salconi, L; Saleem, M; Salemi, F; Samajdar, A; Sammut, L; Sanchez, E J; Sandberg, V; Sandeen, B; Sanders, J R; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Sauter, O; Savage, R L; Sawadsky, A; Schale, P; Schilling, R; Schmidt, J; Schmidt, P; Schnabel, R; Schofield, R M S; Schönbeck, A; Schreiber, E; Schuette, D; Schutz, B F; Scott, J; Scott, S M; Sellers, D; Sengupta, A S; Sentenac, D; Sequino, V; Sergeev, A; Serna, G; Setyawati, Y; Sevigny, A; Shaddock, D A; Shah, S; Shahriar, M S; Shaltev, M; Shao, Z; Shapiro, B; Shawhan, P; Sheperd, A; Shoemaker, D H; Shoemaker, D M; Siellez, K; Siemens, X; Sigg, D; Silva, A D; Simakov, D; Singer, A; Singer, L P; Singh, A; Singh, R; Singhal, A; Sintes, A M; Slagmolen, B J J; Smith, J R; Smith, N D; Smith, R J E; Son, E J; Sorazu, B; Sorrentino, F; Souradeep, T; Srivastava, A K; Staley, A; Steinke, M; Steinlechner, J; Steinlechner, S; Steinmeyer, D; Stephens, B C; Stone, R; Strain, K A; Straniero, N; Stratta, G; Strauss, N A; Strigin, S; Sturani, R; Stuver, A L; Summerscales, T Z; Sun, L; Sutton, P J; Swinkels, B L; Szczepańczyk, M J; Tacca, M; Talukder, D; Tanner, D B; Tápai, M; Tarabrin, S P; Taracchini, A; Taylor, R; Theeg, T; Thirugnanasambandam, M P; Thomas, E G; Thomas, M; Thomas, P; Thorne, K A; Thorne, K S; Thrane, E; Tiwari, S; Tiwari, V; Tokmakov, K V; Tomlinson, C; Tonelli, M; Torres, C V; Torrie, C I; Töyrä, D; Travasso, F; Traylor, G; Trifirò, D; Tringali, M C; Trozzo, L; Tse, M; Turconi, M; Tuyenbayev, D; Ugolini, D; Unnikrishnan, C S; Urban, A L; Usman, S A; Vahlbruch, H; Vajente, G; Valdes, G; Vallisneri, M; van Bakel, N; van Beuzekom, M; van den Brand, J F J; Van Den Broeck, C; Vander-Hyde, D C; van der Schaaf, L; van Heijningen, J V; van Veggel, A A; Vardaro, M; Vass, S; Vasúth, M; Vaulin, R; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Verkindt, D; Vetrano, F; Viceré, A; Vinciguerra, S; Vine, D J; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Voss, D; Vousden, W D; Vyatchanin, S P; Wade, A R; Wade, L E; Wade, M; Walker, M; Wallace, L; Walsh, S; Wang, G; Wang, H; Wang, M; Wang, X; Wang, Y; Ward, R L; Warner, J; Was, M; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Welborn, T; Wen, L; Weßels, P; Westphal, T; Wette, K; Whelan, J T; White, D J; Whiting, B F; Williams, D; Williams, R D; Williamson, A R; Willis, J L; Willke, B; Wimmer, M H; Winkler, W; Wipf, C C; Wittel, H; Woan, G; Worden, J; Wright, J L; Wu, G; Yablon, J; Yam, W; Yamamoto, H; Yancey, C C; Yap, M J; Yu, H; Yvert, M; Zadrożny, A; Zangrando, L; Zanolin, M; Zendri, J-P; Zevin, M; Zhang, F; Zhang, L; Zhang, M; Zhang, Y; Zhao, C; Zhou, M; Zhou, Z; Zhu, X J; Zucker, M E; Zuraw, S E; Zweizig, J; Boyle, M; Campanelli, M; Hemberger, D A; Kidder, L E; Ossokine, S; Scheel, M A; Szilagyi, B; Teukolsky, S; Zlochower, Y

2016-06-03

The LIGO detection of GW150914 provides an unprecedented opportunity to study the two-body motion of a compact-object binary in the large-velocity, highly nonlinear regime, and to witness the final merger of the binary and the excitation of uniquely relativistic modes of the gravitational field. We carry out several investigations to determine whether GW150914 is consistent with a binary black-hole merger in general relativity. We find that the final remnant's mass and spin, as determined from the low-frequency (inspiral) and high-frequency (postinspiral) phases of the signal, are mutually consistent with the binary black-hole solution in general relativity. Furthermore, the data following the peak of GW150914 are consistent with the least-damped quasinormal mode inferred from the mass and spin of the remnant black hole. By using waveform models that allow for parametrized general-relativity violations during the inspiral and merger phases, we perform quantitative tests on the gravitational-wave phase in the dynamical regime and we determine the first empirical bounds on several high-order post-Newtonian coefficients. We constrain the graviton Compton wavelength, assuming that gravitons are dispersed in vacuum in the same way as particles with mass, obtaining a 90%-confidence lower bound of 10^{13}  km. In conclusion, within our statistical uncertainties, we find no evidence for violations of general relativity in the genuinely strong-field regime of gravity.

Parameter estimation method that directly compares gravitational wave observations to numerical relativity

NASA Astrophysics Data System (ADS)

Lange, J.; O'Shaughnessy, R.; Boyle, M.; Calderón Bustillo, J.; Campanelli, M.; Chu, T.; Clark, J. A.; Demos, N.; Fong, H.; Healy, J.; Hemberger, D. A.; Hinder, I.; Jani, K.; Khamesra, B.; Kidder, L. E.; Kumar, P.; Laguna, P.; Lousto, C. O.; Lovelace, G.; Ossokine, S.; Pfeiffer, H.; Scheel, M. A.; Shoemaker, D. M.; Szilagyi, B.; Teukolsky, S.; Zlochower, Y.

2017-11-01

We present and assess a Bayesian method to interpret gravitational wave signals from binary black holes. Our method directly compares gravitational wave data to numerical relativity (NR) simulations. In this study, we present a detailed investigation of the systematic and statistical parameter estimation errors of this method. This procedure bypasses approximations used in semianalytical models for compact binary coalescence. In this work, we use the full posterior parameter distribution for only generic nonprecessing binaries, drawing inferences away from the set of NR simulations used, via interpolation of a single scalar quantity (the marginalized log likelihood, ln L ) evaluated by comparing data to nonprecessing binary black hole simulations. We also compare the data to generic simulations, and discuss the effectiveness of this procedure for generic sources. We specifically assess the impact of higher order modes, repeating our interpretation with both l ≤2 as well as l ≤3 harmonic modes. Using the l ≤3 higher modes, we gain more information from the signal and can better constrain the parameters of the gravitational wave signal. We assess and quantify several sources of systematic error that our procedure could introduce, including simulation resolution and duration; most are negligible. We show through examples that our method can recover the parameters for equal mass, zero spin, GW150914-like, and unequal mass, precessing spin sources. Our study of this new parameter estimation method demonstrates that we can quantify and understand the systematic and statistical error. This method allows us to use higher order modes from numerical relativity simulations to better constrain the black hole binary parameters.

Photometric Properties for Selected Algol-type Binaries. VIII. The Triple Systems DI Peg and AF Gem Revisited

NASA Astrophysics Data System (ADS)

Yang, Yuan-Gui; Yang, Ying; Li, Shu-Zheng

2014-06-01

New extensive photometry for two triple binary stars, DI Peg and AF Gem, was performed from 2012 October to 2013 January, with two small telescopes at Xinglong station (XLs) of NAOC. From new multi-color observations and previously published ones in literature, the photometric models were (re)deduced using the updated Wilson-Devinney code. The results indicated that the low third lights exist in two classic Algol-type binaries, whose fill-out factors for the more massive components are fp = 78.2(± 0.4)% for DI Peg, and fp = 69.0(± 0.3)% for AF Gem, respectively. Through analyzing the O-C curves, the orbital periods for two binaries change in the complicated mode. The period of DI Peg possibly appears to show two light-time orbits, whose modulated periods are P 3 = 54.6(± 0.5) yr and P 4 = 23.0(± 0.6) yr, respectively. The inferred minimum masses for the inner and outer sub-stellar companions are M in = 0.095 M ⊙ and M out = 0.170 M ⊙, respectively. Therefore, DI Peg may be a quadruple star. The orbital period of AF Gem appears to show a continuous period decrease or a cyclic variation; the latter may be preferable. The cyclic oscillation, with a period of 120.3(± 2.5) yr, may be attributed to the light-time effect due to the third body. This kind of additional companion may extract angular momentum from the central system, which may play a key role in the evolution of the binary.

Einstein@Home Discovery of a PALFA Millisecond Pulsar in an Eccentric Binary Orbit

NASA Astrophysics Data System (ADS)

Knispel, B.; Lyne, A. G.; Stappers, B. W.; Freire, P. C. C.; Lazarus, P.; Allen, B.; Aulbert, C.; Bock, O.; Bogdanov, S.; Brazier, A.; Camilo, F.; Cardoso, F.; Chatterjee, S.; Cordes, J. M.; Crawford, F.; Deneva, J. S.; Eggenstein, H.-B.; Fehrmann, H.; Ferdman, R.; Hessels, J. W. T.; Jenet, F. A.; Karako-Argaman, C.; Kaspi, V. M.; van Leeuwen, J.; Lorimer, D. R.; Lynch, R.; Machenschalk, B.; Madsen, E.; McLaughlin, M. A.; Patel, C.; Ransom, S. M.; Scholz, P.; Siemens, X.; Spitler, L. G.; Stairs, I. H.; Stovall, K.; Swiggum, J. K.; Venkataraman, A.; Wharton, R. S.; Zhu, W. W.

2015-06-01

We report the discovery of the millisecond pulsar (MSP) PSR J1950+2414 (P = 4.3 ms) in a binary system with an eccentric (e = 0.08) 22 day orbit in Pulsar Arecibo L-band Feed Array survey observations with the Arecibo telescope. Its companion star has a median mass of 0.3 M⊙ and is most likely a white dwarf (WD). Fully recycled MSPs like this one are thought to be old neutron stars spun-up by mass transfer from a companion star. This process should circularize the orbit, as is observed for the vast majority of binary MSPs, which predominantly have orbital eccentricities e < 0.001. However, four recently discovered binary MSPs have orbits with 0. 027 < e < 0.44; PSR J1950+2414 is the fifth such system to be discovered. The upper limits for its intrinsic spin period derivative and inferred surface magnetic field strength are comparable to those of the general MSP population. The large eccentricities are incompatible with the predictions of the standard recycling scenario: something unusual happened during their evolution. Proposed scenarios are (a) initial evolution of the pulsar in a triple system which became dynamically unstable, (b) origin in an exchange encounter in an environment with high stellar density, (c) rotationally delayed accretion-induced collapse of a super-Chandrasekhar WD, and (d) dynamical interaction of the binary with a circumbinary disk. We compare the properties of all five known eccentric MSPs with the predictions of these formation channels. Future measurements of the masses and proper motion might allow us to firmly exclude some of the proposed formation scenarios.

EMISSION SIGNATURES FROM SUB-PARSEC BINARY SUPERMASSIVE BLACK HOLES. I. DIAGNOSTIC POWER OF BROAD EMISSION LINES

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

Nguyen, Khai; Bogdanović, Tamara

Motivated by advances in observational searches for sub-parsec supermassive black hole binaries (SBHBs) made in the past few years, we develop a semi-analytic model to describe spectral emission-line signatures of these systems. The goal of this study is to aid the interpretation of spectroscopic searches for binaries and to help test one of the leading models of binary accretion flows in the literature: SBHB in a circumbinary disk. In this work, we present the methodology and a comparison of the preliminary model with the data. We model SBHB accretion flows as a set of three accretion disks: two mini-disks thatmore » are gravitationally bound to the individual black holes and a circumbinary disk. Given a physically motivated parameter space occupied by sub-parsec SBHBs, we calculate a synthetic database of nearly 15 million broad optical emission-line profiles and explore the dependence of the profile shapes on characteristic properties of SBHBs. We find that the modeled profiles show distinct statistical properties as a function of the semimajor axis, mass ratio, eccentricity of the binary, and the degree of alignment of the triple disk system. This suggests that the broad emission-line profiles from SBHB systems can in principle be used to infer the distribution of these parameters and as such merit further investigation. Calculated profiles are more morphologically heterogeneous than the broad emission lines in observed SBHB candidates and we discuss improved treatment of radiative transfer effects, which will allow a direct statistical comparison of the two groups.« less

A GDP-driven model for the binary and weighted structure of the International Trade Network

NASA Astrophysics Data System (ADS)

Almog, Assaf; Squartini, Tiziano; Garlaschelli, Diego

2015-01-01

Recent events such as the global financial crisis have renewed the interest in the topic of economic networks. One of the main channels of shock propagation among countries is the International Trade Network (ITN). Two important models for the ITN structure, the classical gravity model of trade (more popular among economists) and the fitness model (more popular among networks scientists), are both limited to the characterization of only one representation of the ITN. The gravity model satisfactorily predicts the volume of trade between connected countries, but cannot reproduce the missing links (i.e. the topology). On the other hand, the fitness model can successfully replicate the topology of the ITN, but cannot predict the volumes. This paper tries to make an important step forward in the unification of those two frameworks, by proposing a new gross domestic product (GDP) driven model which can simultaneously reproduce the binary and the weighted properties of the ITN. Specifically, we adopt a maximum-entropy approach where both the degree and the strength of each node are preserved. We then identify strong nonlinear relationships between the GDP and the parameters of the model. This ultimately results in a weighted generalization of the fitness model of trade, where the GDP plays the role of a ‘macroeconomic fitness’ shaping the binary and the weighted structure of the ITN simultaneously. Our model mathematically explains an important asymmetry in the role of binary and weighted network properties, namely the fact that binary properties can be inferred without the knowledge of weighted ones, while the opposite is not true.

Mechanisms of rule acquisition and rule following in inductive reasoning.

PubMed

Crescentini, Cristiano; Seyed-Allaei, Shima; De Pisapia, Nicola; Jovicich, Jorge; Amati, Daniele; Shallice, Tim

2011-05-25

Despite the recent interest in the neuroanatomy of inductive reasoning processes, the regional specificity within prefrontal cortex (PFC) for the different mechanisms involved in induction tasks remains to be determined. In this study, we used fMRI to investigate the contribution of PFC regions to rule acquisition (rule search and rule discovery) and rule following. Twenty-six healthy young adult participants were presented with a series of images of cards, each consisting of a set of circles numbered in sequence with one colored blue. Participants had to predict the position of the blue circle on the next card. The rules that had to be acquired pertained to the relationship among succeeding stimuli. Responses given by subjects were categorized in a series of phases either tapping rule acquisition (responses given up to and including rule discovery) or rule following (correct responses after rule acquisition). Mid-dorsolateral PFC (mid-DLPFC) was active during rule search and remained active until successful rule acquisition. By contrast, rule following was associated with activation in temporal, motor, and medial/anterior prefrontal cortex. Moreover, frontopolar cortex (FPC) was active throughout the rule acquisition and rule following phases before a rule became familiar. We attributed activation in mid-DLPFC to hypothesis generation and in FPC to integration of multiple separate inferences. The present study provides evidence that brain activation during inductive reasoning involves a complex network of frontal processes and that different subregions respond during rule acquisition and rule following phases.

How do pre-adolescent children interpret conditionals?

PubMed

Markovits, Henry; Brisson, Janie; de Chantal, Pier-Luc

2016-12-01

Studies examining children's basic understanding of conditionals have led to very different conclusions. On the one hand, conditional inference tasks suggest that young children are able to interpret familiar conditionals in a complex manner. In contrast, truth-table tasks suggest that before adolescence, children have limited (conjunctive) representations of conditionals. We hypothesized that the latter results are due to use of what are essentially arbitrary conditionals. To examine this, we gave a truth-table task using two kinds of conditional rules, Arbitrary and Imaginary categorical rules (If an animal is a bori, then it has red wings) to 9- and 12-year-olds. Results with the Arbitrary rules were consistent with those found in previous studies, with the most frequent interpretation being the Conjunctive one. However, among even the youngest children, the most frequent interpretation of the Imaginary categorical rules was the defective conditional, which is only found with much older adolescents with Arbitrary rules. These results suggest that working memory limitations are not an important developmental factor in how young children interpret conditional rules.

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

PubMed Central

Lo, Benjamin W. Y.; Macdonald, R. Loch; Baker, Andrew; Levine, Mitchell A. H.

2013-01-01

Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication. PMID:23690884

Bayesian networks improve causal environmental assessments for evidence-based policy

EPA Science Inventory

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

COMPUTERIZED RISK AND BIOACCUMULATION SYSTEM (VERSION 1.0)

EPA Science Inventory

CRABS is a combination of a rule-based expert system and more traditional procedural programming techniques. ule-based expert systems attempt to emulate the decision making process of human experts within a clearly defined subject area. xpert systems consist of an "inference engi...

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

PubMed

Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao

2014-10-07

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.

Extracting the information of coastline shape and its multiple representations

NASA Astrophysics Data System (ADS)

Liu, Ying; Li, Shujun; Tian, Zhen; Chen, Huirong

2007-06-01

According to studying the coastline, a new way of multiple representations is put forward in the paper. That is stimulating human thinking way when they generalized, building the appropriate math model and describing the coastline with graphics, extracting all kinds of the coastline shape information. The coastline automatic generalization will be finished based on the knowledge rules and arithmetic operators. Showing the information of coastline shape by building the curve Douglas binary tree, it can reveal the shape character of coastline not only microcosmically but also macroscopically. Extracting the information of coastline concludes the local characteristic point and its orientation, the curve structure and the topology trait. The curve structure can be divided the single curve and the curve cluster. By confirming the knowledge rules of the coastline generalization, the generalized scale and its shape parameter, the coastline automatic generalization model is established finally. The method of the multiple scale representation of coastline in this paper has some strong points. It is human's thinking mode and can keep the nature character of the curve prototype. The binary tree structure can control the coastline comparability, avoid the self-intersect phenomenon and hold the unanimous topology relationship.

Searching for Dust around Hyper Metal Poor Stars

NASA Astrophysics Data System (ADS)

Venn, Kim A.; Puzia, Thomas H.; Divell, Mike; Côté, Stephanie; Lambert, David L.; Starkenburg, Else

2014-08-01

We examine the mid-infrared fluxes and spectral energy distributions for stars with iron abundances [Fe/H] <-5, and other metal-poor stars, to eliminate the possibility that their low metallicities are related to the depletion of elements onto dust grains in the formation of a debris disk. Six out of seven stars examined here show no mid-IR excesses. These non-detections rule out many types of circumstellar disks, e.g., a warm debris disk (T <= 290 K), or debris disks with inner radii <=1 AU, such as those associated with the chemically peculiar post-asymptotic giant branch spectroscopic binaries and RV Tau variables. However, we cannot rule out cooler debris disks, nor those with lower flux ratios to their host stars due to, e.g., a smaller disk mass, a larger inner disk radius, an absence of small grains, or even a multicomponent structure, as often found with the chemically peculiar Lambda Bootis stars. The only exception is HE0107-5240, for which a small mid-IR excess near 10 μm is detected at the 2σ level; if the excess is real and associated with this star, it may indicate the presence of (recent) dust-gas winnowing or a binary system.

Minimal state-dependent proof of measurement contextuality for a qubit

NASA Astrophysics Data System (ADS)

Kunjwal, Ravi; Ghosh, Sibasish

2014-04-01

We show that three unsharp binary qubit measurements are enough to violate a generalized noncontextuality inequality, the Liang-Spekkens-Wiseman inequality, in a state-dependent manner. For the case of trine spin axes we calculate the optimal quantum violation of this inequality. In addition, we show that unsharp qubit measurements do not allow a state-independent violation of this inequality. We thus provide a minimal state-dependent proof of measurement contextuality requiring one qubit and three unsharp measurements. Our result rules out generalized noncontextual models of these measurements which were previously conjectured to exist. More importantly, this class of generalized noncontextual models includes the traditional Kochen-Specker (KS) noncontextual models as a proper subset, so our result rules out a larger class of models than those ruled out by a violation of the corresponding KS inequality in this scenario.

Modularising ontology and designing inference patterns to personalise health condition assessment: the case of obesity.

PubMed

Sojic, Aleksandra; Terkaj, Walter; Contini, Giorgia; Sacco, Marco

2016-05-04

The public health initiatives for obesity prevention are increasingly exploiting the advantages of smart technologies that can register various kinds of data related to physical, physiological, and behavioural conditions. Since individual features and habits vary among people, the design of appropriate intervention strategies for motivating changes in behavioural patterns towards a healthy lifestyle requires the interpretation and integration of collected information, while considering individual profiles in a personalised manner. The ontology-based modelling is recognised as a promising approach in facing the interoperability and integration of heterogeneous information related to characterisation of personal profiles. The presented ontology captures individual profiles across several obesity-related knowledge-domains structured into dedicated modules in order to support inference about health condition, physical features, behavioural habits associated with a person, and relevant changes over time. The modularisation strategy is designed to facilitate ontology development, maintenance, and reuse. The domain-specific modules formalised in the Web Ontology Language (OWL) integrate the domain-specific sets of rules formalised in the Semantic Web Rule Language (SWRL). The inference rules follow a modelling pattern designed to support personalised assessment of health condition as age- and gender-specific. The test cases exemplify a personalised assessment of the obesity-related health conditions for the population of teenagers. The paper addresses several issues concerning the modelling of normative concepts related to obesity and depicts how the public health concern impacts classification of teenagers according to their phenotypes. The modelling choices regarding the ontology-structure are explained in the context of the modelling goal to integrate multiple knowledge-domains and support reasoning about the individual changes over time. The presented modularisation pattern enhances reusability of the domain-specific modules across various health care domains.

ARNetMiT R Package: association rules based gene co-expression networks of miRNA targets.

PubMed

Özgür Cingiz, M; Biricik, G; Diri, B

2017-03-31

miRNAs are key regulators that bind to target genes to suppress their gene expression level. The relations between miRNA-target genes enable users to derive co-expressed genes that may be involved in similar biological processes and functions in cells. We hypothesize that target genes of miRNAs are co-expressed, when they are regulated by multiple miRNAs. With the usage of these co-expressed genes, we can theoretically construct co-expression networks (GCNs) related to 152 diseases. In this study, we introduce ARNetMiT that utilize a hash based association rule algorithm in a novel way to infer the GCNs on miRNA-target genes data. We also present R package of ARNetMiT, which infers and visualizes GCNs of diseases that are selected by users. Our approach assumes miRNAs as transactions and target genes as their items. Support and confidence values are used to prune association rules on miRNA-target genes data to construct support based GCNs (sGCNs) along with support and confidence based GCNs (scGCNs). We use overlap analysis and the topological features for the performance analysis of GCNs. We also infer GCNs with popular GNI algorithms for comparison with the GCNs of ARNetMiT. Overlap analysis results show that ARNetMiT outperforms the compared GNI algorithms. We see that using high confidence values in scGCNs increase the ratio of the overlapped gene-gene interactions between the compared methods. According to the evaluation of the topological features of ARNetMiT based GCNs, the degrees of nodes have power-law distribution. The hub genes discovered by ARNetMiT based GCNs are consistent with the literature.

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

Shen, Yue; Liu, Xin; Loeb, Abraham

We perform a systematic search for sub-parsec binary supermassive black holes (BHs) in normal broad-line quasars at z < 0.8, using multi-epoch Sloan Digital Sky Survey (SDSS) spectroscopy of the broad Hβ line. Our working model is that (1) one and only one of the two BHs in the binary is active; (2) the active BH dynamically dominates its own broad-line region (BLR) in the binary system, so that the mean velocity of the BLR reflects the mean velocity of its host BH; (3) the inactive companion BH is orbiting at a distance of a few R{sub BLR}, where R{submore » BLR} ∼ 0.01-0.1 pc is the BLR size. We search for the expected line-of-sight acceleration of the broad-line velocity from binary orbital motion by cross-correlating SDSS spectra from two epochs separated by up to several years in the quasar rest frame. Out of ∼700 pairs of spectra for which we have good measurements of the velocity shift between two epochs (1σ error ∼40 km s{sup –1}), we detect 28 systems with significant velocity shifts in broad Hβ, among which 7 are the best candidates for the hypothesized binaries, 4 are most likely due to broad-line variability in single BHs, and the rest are ambiguous. Continued spectroscopic observations of these candidates will easily strengthen or disprove these claims. We use the distribution of the observed accelerations (mostly non-detections) to place constraints on the abundance of such binary systems among the general quasar population. Excess variance in the velocity shift is inferred for observations separated by longer than 0.4 yr (quasar rest frame). Attributing all the excess to binary motion would imply that most of the quasars in this sample must be in binaries, that the inactive BH must be on average more massive than the active one, and that the binary separation is at most a few times the size of the BLR. However, if this excess variance is partly or largely due to long-term broad-line variability, the requirement of a large population of close binaries is much weakened or even disfavored for massive companions. Future time-domain spectroscopic surveys of normal quasars can provide vital prior information on the structure function of stochastic velocity shifts induced by broad-line variability in single BHs. Such surveys with improved spectral quality, increased time baseline, and more epochs can greatly improve the statistical constraints of this method on the general binary population in broad-line quasars, further shrink the allowed binary parameter space, and detect true sub-parsec binaries.« less

Uncertain deduction and conditional reasoning

PubMed Central

Evans, Jonathan St. B. T.; Thompson, Valerie A.; Over, David E.

2015-01-01

There has been a paradigm shift in the psychology of deductive reasoning. Many researchers no longer think it is appropriate to ask people to assume premises and decide what necessarily follows, with the results evaluated by binary extensional logic. Most every day and scientific inference is made from more or less confidently held beliefs and not assumptions, and the relevant normative standard is Bayesian probability theory. We argue that the study of “uncertain deduction” should directly ask people to assign probabilities to both premises and conclusions, and report an experiment using this method. We assess this reasoning by two Bayesian metrics: probabilistic validity and coherence according to probability theory. On both measures, participants perform above chance in conditional reasoning, but they do much better when statements are grouped as inferences, rather than evaluated in separate tasks. PMID:25904888

Supplement: “The Rate of Binary Black Hole Mergers Inferred from Advanced LIGO Observations Surrounding GW150914” (2016, ApJL, 833, L1)

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allocca, A.; Altin, P. A.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Babak, S.; Bacon, P.; Bader, M. K. M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Baune, C.; Bavigadda, V.; Bazzan, M.; Behnke, B.; Bejger, M.; Bell, A. S.; Bell, C. J.; Berger, B. K.; Bergman, J.; Bergmann, G.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Birney, R.; Biscans, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Bodiya, T. P.; Boer, M.; Bogaert, G.; Bogan, C.; Bohe, A.; Bojtos, P.; Bond, C.; Bondu, F.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brown, N. M.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T.; Calloni, E.; Camp, J. B.; Cannon, K. C.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Cerboni Baiardi, L.; Cerretani, G.; Cesarini, E.; Chakraborty, R.; Chalermsongsak, T.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Chen, H. Y.; Chen, Y.; Cheng, C.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, S.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C. G.; Cominsky, L.; Constancio, M., Jr.; Conte, A.; Conti, L.; Cook, D.; Corbitt, T. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Craig, K.; Creighton, J. D. E.; Cripe, J.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Danilishin, S. L.; D’Antonio, S.; Danzmann, K.; Darman, N. S.; Dattilo, V.; Dave, I.; Daveloza, H. P.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; De, S.; DeBra, D.; Debreczeni, G.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dereli, H.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Dojcinoski, G.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Douglas, R.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Engels, W.; Essick, R. C.; Etzel, T.; Evans, M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Fang, Q.; Farinon, S.; Farr, B.; Farr, W. M.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M.; Fong, H.; Fournier, J.-D.; Franco, S.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fricke, T. T.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H. A. G.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gatto, A.; Gaur, G.; Gehrels, N.; Gemme, G.; Gendre, B.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gordon, N. A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Hall, B. R.; Hall, E. D.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Hofman, D.; Hollitt, S. E.; Holt, K.; Holz, D. E.; Hopkins, P.; Hosken, D. J.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huang, S.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Idrisy, A.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J.-M.; Isi, M.; Islas, G.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jang, H.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; K, Haris; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karki, S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kawazoe, F.; Kéfélian, F.; Kehl, M. S.; Keitel, D.; Kelley, D. B.; Kells, W.; Kennedy, R.; Key, J. S.; Khalaidovski, A.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, C.; Kim, J.; Kim, K.; Kim, Nam-Gyu; Kim, Namjun; Kim, Y.-M.; King, E. J.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Koehlenbeck, S. M.; Kokeyama, K.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Kringel, V.; Krishnan, B.; Królak, A.; Krueger, C.; Kuehn, G.; Kumar, P.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lange, J.; Lantz, B.; Lasky, P. D.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, K.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Levine, B. M.; Li, T. G. F.; Libson, A.; Littenberg, T. B.; Lockerbie, N. A.; Logue, J.; Lombardi, A. L.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lück, H.; Lundgren, A. P.; Luo, J.; Lynch, R.; Ma, Y.; MacDonald, T.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Magee, R. M.; Mageswaran, M.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandel, I.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martynov, D. V.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mendoza-Gandara, D.; Mercer, R. A.; Merilh, E.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B. C.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Murphy, D. J.; Murray, P. G.; Mytidis, A.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Necula, V.; Nedkova, K.; Nelemans, G.; Neri, M.; Neunzert, A.; Newton, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; O’Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O’Reilly, B.; O’Shaughnessy, R.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Patrick, Z.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perreca, A.; Phelps, M.; Piccinni, O.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poggiani, R.; Popolizio, P.; Porter, E. K.; Post, A.; Powell, J.; Prasad, J.; Predoi, V.; Premachandra, S. S.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Ricci, F.; Riles, K.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sampson, L.; Sanchez, E. J.; Sandberg, V.; Sandeen, B.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Schilling, R.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Serna, G.; Setyawati, Y.; Sevigny, A.; Shaddock, D. A.; Shah, S.; Shahriar, M. S.; Shaltev, M.; Shao, Z.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sigg, D.; Silva, A. D.; Simakov, D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, J. R.; Smith, N. D.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stevenson, S.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strauss, N. A.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tápai, M.; Tarabrin, S. P.; Taracchini, A.; Taylor, R.; Theeg, T.; Thirugnanasambandam, M. P.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Tomlinson, C.; Tonelli, M.; Torres, C. V.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Tringali, M. C.; Trozzo, L.; Tse, M.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; Vallisneri, M.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, X.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Wesels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; White, D. J.; Whiting, B. F.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Worden, J.; Wright, J. L.; Wu, G.; Yablon, J.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yap, M. J.; Yu, H.; Yvert, M.; Zadrożny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, F.; Zhang, L.; Zhang, M.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, X. J.; Zucker, M. E.; Zuraw, S. E.; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration

2016-12-01

This article provides supplemental information for a Letter reporting the rate of (BBH) coalescences inferred from 16 days of coincident Advanced LIGO observations surrounding the transient (GW) signal GW150914. In that work we reported various rate estimates whose 90% confidence intervals fell in the range 2–600 Gpc‑3 yr‑1. Here we give details on our method and computations, including information about our search pipelines, a derivation of our likelihood function for the analysis, a description of the astrophysical search trigger distribution expected from merging BBHs, details on our computational methods, a description of the effects and our model for calibration uncertainty, and an analytic method for estimating our detector sensitivity, which is calibrated to our measurements.

X-ray mapping of the stellar wind in the binary PSR J2032+4127/MT91 213

NASA Astrophysics Data System (ADS)

Petropoulou, M.; Vasilopoulos, G.; Christie, I. M.; Giannios, D.; Coe, M. J.

2018-02-01

PSR J2032+4127 is a young and rapidly rotating pulsar on a highly eccentric orbit around the high-mass Be star MT91 213. X-ray monitoring of the binary system over an ˜4000 d period with Swift has revealed an increase of the X-ray luminosity which we attribute to the synchrotron emission of the shocked pulsar wind. We use Swift X-ray observations to infer a clumpy stellar wind with r-2 density profile and constrain the Lorentz factor of the pulsar wind to 105 < γw < 106. We investigate the effects of an axisymmetric stellar wind with polar gradient on the X-ray emission. Comparison of the X-ray light curve hundreds of days before and after the periastron can be used to explore the polar structure of the wind.

A feature dictionary supporting a multi-domain medical knowledge base.

PubMed

Naeymi-Rad, F

1989-01-01

Because different terminology is used by physicians of different specialties in different locations to refer to the same feature (signs, symptoms, test results), it is essential that our knowledge development tools provide a means to access a common pool of terms. This paper discusses the design of an online medical dictionary that provides a solution to this problem for developers of multi-domain knowledge bases for MEDAS (Medical Emergency Decision Assistance System). Our Feature Dictionary supports phrase equivalents for features, feature interactions, feature classifications, and translations to the binary features generated by the expert during knowledge creation. It is also used in the conversion of a domain knowledge to the database used by the MEDAS inference diagnostic sessions. The Feature Dictionary also provides capabilities for complex queries across multiple domains using the supported relations. The Feature Dictionary supports three methods for feature representation: (1) for binary features, (2) for continuous valued features, and (3) for derived features.

Classical Cepheid luminosities from binary companions

NASA Technical Reports Server (NTRS)

Evans, Nancy Remage

1991-01-01

Luminosities for the classical Cepheids Eta Aql, W Sgr, and SU Cas are determined from IUE spectra of their binary companions. Spectral types of the companions are determined from the spectra by comparison with the spectra of standard stars. The absolute magnitude inferred from these spectral types is used to determine the absolute magnitude of the Cepheid, either directly or from the magnitude difference between the two stars. For the temperature range of the companions (A0 V), distinctions of a quarter of a spectral subclass can be made in the comparison between the companions and standard stars. The absolute magnitudes for Eta Aql and W Sgr agree well with the period-luminosity-color relation of Feast and Walker (1987). Random errors are estimated to be 0.3 mag. SU Cas, however, is overluminous for pulsation in the fundamental mode, implying that it is pulsating in an overtone.

THE PUZZLING MUTUAL ORBIT OF THE BINARY TROJAN ASTEROID (624) HEKTOR

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

Marchis, F.; Cuk, M.; Durech, J.

Asteroids with satellites are natural laboratories to constrain the formation and evolution of our solar system. The binary Trojan asteroid (624) Hektor is the only known Trojan asteroid to possess a small satellite. Based on W. M. Keck adaptive optics observations, we found a unique and stable orbital solution, which is uncommon in comparison to the orbits of other large multiple asteroid systems studied so far. From lightcurve observations recorded since 1957, we showed that because the large Req = 125 km primary may be made of two joint lobes, the moon could be ejecta of the low-velocity encounter, which formedmore » the system. The inferred density of Hektor's system is comparable to the L5 Trojan doublet (617) Patroclus but due to their difference in physical properties and in reflectance spectra, both captured Trojan asteroids could have a different composition and origin.« less

VizieR Online Data Catalog: Massive stars in 30 Dor (Schneider+, 2018)

NASA Astrophysics Data System (ADS)

Schneider, F. R. N.; Sana, H.; Evans, C. J.; Bestenlehner, J. M.; Castro, N.; Fossati, L.; Grafener, G.; Langer, N.; Ramirez-Agudelo, O. H.; Sabin-Sanjulian, C.; Simon-Diaz, S.; Tramper, F.; Crowther, P. A.; de Koter, A.; de Mink, S. E.; Dufton, P. L.; Garcia, M.; Gieles, M.; Henault-Brunet, V.; Herrero, A.; Izzard, R. G.; Kalari, V.; Lennon, D. J.; Apellaniz, J. M.; Markova, N.; Najarro, F.; Podsiadlowski, P.; Puls, J.; Taylor, W. D.; van Loon, J. T.; Vink, J. S.; Norman, C.

2018-02-01

Through the use of the Fibre Large Array Multi Element Spectrograph (FLAMES) on the Very Large Telescope (VLT), the VLT-FLAMES Tarantula Survey (VFTS) has obtained optical spectra of ~800 massive stars in 30 Dor, avoiding the core region of the dense star cluster R136 because of difficulties with crowding. Repeated observations at multiple epochs allow determination of the orbital motion of potentially binary objects. For a sample of 452 apparently single stars, robust stellar parameters-such as effective temperatures, luminosities, surface gravities, and projected rotational velocities-are determined by modeling the observed spectra. Composite spectra of visual multiple systems and spectroscopic binaries are not considered here because their parameters cannot be reliably inferred from the VFTS data. To match the derived atmospheric parameters of the apparently single VFTS stars to stellar evolutionary models, we use the Bayesian code Bonnsai. (2 data files).

Live phylogeny with polytomies: Finding the most compact parsimonious trees.

PubMed

Papamichail, D; Huang, A; Kennedy, E; Ott, J-L; Miller, A; Papamichail, G

2017-08-01

Construction of phylogenetic trees has traditionally focused on binary trees where all species appear on leaves, a problem for which numerous efficient solutions have been developed. Certain application domains though, such as viral evolution and transmission, paleontology, linguistics, and phylogenetic stemmatics, often require phylogeny inference that involves placing input species on ancestral tree nodes (live phylogeny), and polytomies. These requirements, despite their prevalence, lead to computationally harder algorithmic solutions and have been sparsely examined in the literature to date. In this article we prove some unique properties of most parsimonious live phylogenetic trees with polytomies, and their mapping to traditional binary phylogenetic trees. We show that our problem reduces to finding the most compact parsimonious tree for n species, and describe a novel efficient algorithm to find such trees without resorting to exhaustive enumeration of all possible tree topologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mediation Analysis: A Practitioner's Guide.

PubMed

VanderWeele, Tyler J

2016-01-01

This article provides an overview of recent developments in mediation analysis, that is, analyses used to assess the relative magnitude of different pathways and mechanisms by which an exposure may affect an outcome. Traditional approaches to mediation in the biomedical and social sciences are described. Attention is given to the confounding assumptions required for a causal interpretation of direct and indirect effect estimates. Methods from the causal inference literature to conduct mediation in the presence of exposure-mediator interactions, binary outcomes, binary mediators, and case-control study designs are presented. Sensitivity analysis techniques for unmeasured confounding and measurement error are introduced. Discussion is given to extensions to time-to-event outcomes and multiple mediators. Further flexible modeling strategies arising from the precise counterfactual definitions of direct and indirect effects are also described. The focus throughout is on methodology that is easily implementable in practice across a broad range of potential applications.

A learning framework for age rank estimation based on face images with scattering transform.

PubMed

Chang, Kuang-Yu; Chen, Chu-Song

2015-03-01

This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age estimation based on face images. The proposed approach exploits relative-order information among the age labels for rank prediction. In our approach, the age rank is obtained by aggregating a series of binary classification results, where cost sensitivities among the labels are introduced to improve the aggregating performance. In addition, we give a theoretical analysis on designing the cost of individual binary classifier so that the misranking cost can be bounded by the total misclassification costs. An efficient descriptor, scattering transform, which scatters the Gabor coefficients and pooled with Gaussian smoothing in multiple layers, is evaluated for facial feature extraction. We show that this descriptor is a generalization of conventional bioinspired features and is more effective for face-based age inference. Experimental results demonstrate that our method outperforms the state-of-the-art age estimation approaches.

REMOTE LAND MINE(FIELD) DETECTION. An Overview of Techniques (DETECTIE VAN LANDMIJNEN EN MIJNENVELDEN OP AFSTAND. Een Overzicht van de technieken),

DTIC Science & Technology

1994-09-01

titel DETECTIE VAN LANDMIJNEN EN MIJNENVELDEN OP AFSTAND, een overzicht van de technieken auteur (s) Drs. J.S. Groot, Ir. Y.H.L. Janssen datum september...functions based on set theory . The fundamental theory is developed in the sixties. This theory was applicable to binary images (black-and-white images...held at TNO-FEL. Various subjects related to fusion techniques: Dempster Shafer theory , Bayesian inference, Kalman filtering, fuzzy logic. [A15], [B4

Constraints on Short, Hard Gamma-Ray Burst Beaming Angles from Gravitational Wave Observations

NASA Astrophysics Data System (ADS)

Williams, D.; Clark, J. A.; Williamson, A. R.; Heng, I. S.

2018-05-01

The first detection of a binary neutron star merger, GW170817, and an associated short gamma-ray burst confirmed that neutron star mergers are responsible for at least some of these bursts. The prompt gamma-ray emission from these events is thought to be highly relativistically beamed. We present a method for inferring limits on the extent of this beaming by comparing the number of short gamma-ray bursts (SGRBs) observed electromagnetically with the number of neutron star binary mergers detected in gravitational waves. We demonstrate that an observing run comparable to the expected Advanced LIGO (aLIGO) 2016–2017 run would be capable of placing limits on the beaming angle of approximately θ \\in (2\\buildrel{\\circ}\\over{.} 88,14\\buildrel{\\circ}\\over{.} 15), given one binary neutron star detection, under the assumption that all mergers produce a gamma-ray burst, and that SGRBs occur at an illustrative rate of {{ \\mathcal R }}grb}=10 {Gpc}}-3 {yr}}-1. We anticipate that after a year of observations with aLIGO at design sensitivity in 2020, these constraints will improve to θ \\in (8\\buildrel{\\circ}\\over{.} 10,14\\buildrel{\\circ}\\over{.} 95), under the same efficiency and SGRB rate assumptions.

A Periodically Varying Luminous Quasar at z = 2 from the Pan-STARRS1 Medium Deep Survey: A Candidate Supermassive Black Hole Binary in the Gravitational Wave-Driven Regime

NASA Astrophysics Data System (ADS)

Liu, Tingting; Gezari, Suvi

Supermassive black hole binaries (SMBHBs) should be an inevitable consequence of the hierarchical growth of massive galaxies through mergers and the strongest sirens of gravitational waves (GWs) in the cosmos. Yet, their direct detection has remained elusive due to the compact (sub-parsec) orbital separations of gravitationally bound SMBHBs. Here we exploit a theoretically predicted signature of SMBHBs in the time domain. We have begun a systematic search for SMBHB candidates in the Pan-STARRS1 Medium Deep Survey (MDS) and reported our first significant detection of such a candidate from our pilot study of MD09 in Liu et al. (2015). Our candidate PSO J334.2028+01.4075 has a detected period of 542 days, varying persistently over the available baseline. From its archival spectrum, we estimated the black hole mass of the z = 2.06 quasar to be ~1010 M⊙. The inferred ~7 R s binary separation therefore puts this candidate in the regime of GW-dominated orbital decay, opening up the exciting possibility of finding GW sources detectable by pulsar timing arrays (PTAs) in a wide-field optical synoptic survey.

Knowledge Acquisition from Structural Descriptions.

ERIC Educational Resources Information Center

Hayes-Roth, Frederick; McDermott, John

The learning machine described in this paper acquires concepts representable as conjunctive forms of the predicate calculus and behaviors representable as productions (antecedent-consequent pairs of such conjunctive forms): these concepts and behavior rules are inferred from sequentially presented pairs of examples by an algorithm that is probably…

Premature ventricular contraction detection combining deep neural networks and rules inference.

PubMed

Zhou, Fei-Yan; Jin, Lin-Peng; Dong, Jun

2017-06-01

Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH arrhythmia database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD). The PVC detection accuracy on the MIT-BIH-AR database was 99.41%, with a sensitivity and specificity of 97.59% and 99.54%, respectively, which were better than the results from other existing methods. To test the generalization capability, the detection performance was also evaluated on the CCDD. The effectiveness of the proposed method was confirmed by the accuracy (98.03%), sensitivity (96.42%) and specificity (98.06%) with the dataset over 140,000 ECG recordings of the CCDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Structure identification in fuzzy inference using reinforcement learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1993-01-01

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

Controlling false-negative errors in microarray differential expression analysis: a PRIM approach.

PubMed

Cole, Steve W; Galic, Zoran; Zack, Jerome A

2003-09-22

Theoretical considerations suggest that current microarray screening algorithms may fail to detect many true differences in gene expression (Type II analytic errors). We assessed 'false negative' error rates in differential expression analyses by conventional linear statistical models (e.g. t-test), microarray-adapted variants (e.g. SAM, Cyber-T), and a novel strategy based on hold-out cross-validation. The latter approach employs the machine-learning algorithm Patient Rule Induction Method (PRIM) to infer minimum thresholds for reliable change in gene expression from Boolean conjunctions of fold-induction and raw fluorescence measurements. Monte Carlo analyses based on four empirical data sets show that conventional statistical models and their microarray-adapted variants overlook more than 50% of genes showing significant up-regulation. Conjoint PRIM prediction rules recover approximately twice as many differentially expressed transcripts while maintaining strong control over false-positive (Type I) errors. As a result, experimental replication rates increase and total analytic error rates decline. RT-PCR studies confirm that gene inductions detected by PRIM but overlooked by other methods represent true changes in mRNA levels. PRIM-based conjoint inference rules thus represent an improved strategy for high-sensitivity screening of DNA microarrays. Freestanding JAVA application at http://microarray.crump.ucla.edu/focus

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Rule-based expert system for maritime anomaly detection

NASA Astrophysics Data System (ADS)

Roy, Jean

2010-04-01

Maritime domain operators/analysts have a mandate to be aware of all that is happening within their areas of responsibility. This mandate derives from the needs to defend sovereignty, protect infrastructures, counter terrorism, detect illegal activities, etc., and it has become more challenging in the past decade, as commercial shipping turned into a potential threat. In particular, a huge portion of the data and information made available to the operators/analysts is mundane, from maritime platforms going about normal, legitimate activities, and it is very challenging for them to detect and identify the non-mundane. To achieve such anomaly detection, they must establish numerous relevant situational facts from a variety of sensor data streams. Unfortunately, many of the facts of interest just cannot be observed; the operators/analysts thus use their knowledge of the maritime domain and their reasoning faculties to infer these facts. As they are often overwhelmed by the large amount of data and information, automated reasoning tools could be used to support them by inferring the necessary facts, ultimately providing indications and warning on a small number of anomalous events worthy of their attention. Along this line of thought, this paper describes a proof-of-concept prototype of a rule-based expert system implementing automated rule-based reasoning in support of maritime anomaly detection.

Implications of Binary Black Hole Detections on the Merger Rates of Double Neutron Stars and Neutron Star–Black Holes

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

Gupta, Anuradha; Arun, K. G.; Sathyaprakash, B. S., E-mail: axg645@psu.edu, E-mail: kgarun@cmi.ac.in, E-mail: bss25@psu.edu

We show that the inferred merger rate and chirp masses of binary black holes (BBHs) detected by advanced LIGO (aLIGO) can be used to constrain the rate of double neutron star (DNS) and neutron star–black hole (NSBH) mergers in the universe. We explicitly demonstrate this by considering a set of publicly available population synthesis models of Dominik et al. and show that if all the BBH mergers, GW150914, LVT151012, GW151226, and GW170104, observed by aLIGO arise from isolated binary evolution, the predicted DNS merger rate may be constrained to be 2.3–471.0 Gpc{sup −3} yr{sup −1} and that of NSBH mergersmore » will be constrained to 0.2–48.5 Gpc{sup −3} yr{sup −1}. The DNS merger rates are not constrained much, but the NSBH rates are tightened by a factor of ∼4 as compared to their previous rates. Note that these constrained DNS and NSBH rates are extremely model-dependent and are compared to the unconstrained values 2.3–472.5 Gpc{sup −3} yr{sup −1} and 0.2–218 Gpc{sup −3} yr{sup −1}, respectively, using the same models of Dominik et al. (2012a). These rate estimates may have implications for short Gamma Ray Burst progenitor models assuming they are powered (solely) by DNS or NSBH mergers. While these results are based on a set of open access population synthesis models, which may not necessarily be the representative ones, the proposed method is very general and can be applied to any number of models, thereby yielding more realistic constraints on the DNS and NSBH merger rates from the inferred BBH merger rate and chirp mass.« less

'Do not resuscitate order' in neonatology: authority rules.

PubMed

Niebrój, L T; Jadamus-Niebrój, D

2007-11-01

Ethical dilemmas in medicine should be resolved in light of four essential principles. To specify and guide concrete actions, it is necessary to 'supplement' these principles by certain other (substantive, authority and procedural) rules. The purpose of this paper is to establish and justify the authority rules regarding the order not to resuscitate newborns. The authority rules are intended to indicate who should decide, but they do not determine what should be chosen. Decision regarding newborn's treatment/letting die depends on medical and quality-of-life judgments. Parents, doctors, and society are considered to possess decisional authority in the matter. However, who in a given case should decide ought to be inferred from the reasoning which assumes, as its premises, the medical and quality-of-life judgments. The 'logical' syntax of this reasoning is presented in this paper.

The mechanisms of temporal inference

NASA Technical Reports Server (NTRS)

Fox, B. R.; Green, S. R.

1987-01-01

The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.

The design and application of a Transportable Inference Engine (TIE1)

NASA Technical Reports Server (NTRS)

Mclean, David R.

1986-01-01

A Transportable Inference Engine (TIE1) system has been developed by the author as part of the Interactive Experimenter Planning System (IEPS) task which is involved with developing expert systems in support of the Spacecraft Control Programs Branch at Goddard Space Flight Center in Greenbelt, Maryland. Unlike traditional inference engines, TIE1 is written in the C programming language. In the TIE1 system, knowledge is represented by a hierarchical network of objects which have rule frames. The TIE1 search algorithm uses a set of strategies, including backward chaining, to obtain the values of goals. The application of TIE1 to a spacecraft scheduling problem is described. This application involves the development of a strategies interpreter which uses TIE1 to do constraint checking.

Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

NASA Astrophysics Data System (ADS)

Khoury, Mehdi; Liu, Honghai

This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.

RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination

PubMed Central

Mirzaei, Sajad; Wu, Yufeng

2017-01-01

Abstract Motivation: Haplotypes from one or multiple related populations share a common genealogical history. If this shared genealogy can be inferred from haplotypes, it can be very useful for many population genetics problems. However, with the presence of recombination, the genealogical history of haplotypes is complex and cannot be represented by a single genealogical tree. Therefore, inference of genealogical history with recombination is much more challenging than the case of no recombination. Results: In this paper, we present a new approach called RENT+ for the inference of local genealogical trees from haplotypes with the presence of recombination. RENT+ builds on a previous genealogy inference approach called RENT, which infers a set of related genealogical trees at different genomic positions. RENT+ represents a significant improvement over RENT in the sense that it is more effective in extracting information contained in the haplotype data about the underlying genealogy than RENT. The key components of RENT+ are several greatly enhanced genealogy inference rules. Through simulation, we show that RENT+ is more efficient and accurate than several existing genealogy inference methods. As an application, we apply RENT+ in the inference of population demographic history from haplotypes, which outperforms several existing methods. Availability and Implementation: RENT+ is implemented in Java, and is freely available for download from: https://github.com/SajadMirzaei/RentPlus. Contacts: sajad@engr.uconn.edu or ywu@engr.uconn.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28065901

Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance.

PubMed

MacGillivray, Brian H

2017-08-01

In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting "recovery schemes" to correct for known biases; and basing decision rules on clearly articulated values and evidence, rather than convention. Copyright © 2017. Published by Elsevier Ltd.

Revising the "Rule of Three" for inferring seizure freedom.

PubMed

Westover, M Brandon; Cormier, Justine; Bianchi, Matt T; Shafi, Mouhsin; Kilbride, Ronan; Cole, Andrew J; Cash, Sydney S

2012-02-01

How long after starting a new medication must a patient go without seizures before they can be regarded as seizure-free? A recent International League Against Epilepsy (ILAE) task force proposed using a "Rule of Three" as an operational definition of seizure freedom, according to which a patient should be considered seizure-free following an intervention after a period without seizures has elapsed equal to three times the longest preintervention interseizure interval over the previous year. This rule was motivated in large part by statistical considerations advanced in a classic 1983 paper by Hanley and Lippman-Hand. However, strict adherence to the statistical logic of this rule generally requires waiting much longer than recommended by the ILAE task force. Therefore, we set out to determine whether an alternative approach to the Rule of Three might be possible, and under what conditions the rule may be expected to hold or would need to be extended. Probabilistic modeling and application of Bayes' rule. We find that an alternative approach to the problem of inferring seizure freedom supports using the Rule of Three in the way proposed by the ILAE in many cases, particularly in evaluating responses to a first trial of antiseizure medication, and to favorably-selected epilepsy surgical candidates. In cases where the a priori odds of success are less favorable, our analysis requires longer seizure-free observation periods before declaring seizure freedom, up to six times the average preintervention interseizure interval. The key to our approach is to take into account not only the time elapsed without seizures but also empirical data regarding the a priori probability of achieving seizure freedom conferred by a particular intervention. In many cases it may be reasonable to consider a patient seizure-free after they have gone without seizures for a period equal to three times the preintervention interseizure interval, as proposed on pragmatic grounds in a recent ILAE position paper, although in other commonly encountered cases a waiting time up to six times this interval is required. In this work we have provided a coherent theoretical basis for modified criterion for seizure freedom, which we call the "Rule of Three-To-Six." Wiley Periodicals, Inc. © 2011 International League Against Epilepsy.

Revising the Rule Of Three For Inferring Seizure Freedom

PubMed Central

Westover, M. Brandon; Cormier, Justine; Bianchi, Matt T.; Shafi, Mouhsin; Kilbride, Ronan; Cole, Andrew J.; Cash, Sydney S.

2011-01-01

Summary Purpose How long after starting a new medication must a patient go without seizures before they can be regarded as seizure free? A recent ILAE task force proposed using a “Rule of Three” as an operational definition of seizure freedom, according to which a patient should be considered seizure-free following an intervention after a period without seizures has elapsed equal to three times the longest pre-intervention inter-seizure interval over the previous year. This rule was motivated in large part by statistical considerations advanced in a classic 1983 paper by Hanley and Lippman-Hand. However, strict adherence to the statistical logic of this rule generally requires waiting much longer than recommended by the ILAE task force. Therefore, we set out to determine whether an alternative approach to the Rule of Three might be possible, and under what conditions the rule may be expected to hold or would need to be extended. Methods Probabilistic modeling and application of Bayes’ rule. Key Findings We find that an alternative approach to the problem of inferring seizure freedom supports using the Rule of Three in the way proposed by the ILAE in many cases, particularly in evaluating responses to a first trial of anti-seizure medication, and to favorably-selected epilepsy surgical candidates. In cases where the a priori odds of success are less favorable, our analysis requires longer seizure-free observation periods before declaring seizure freedom, up to six times the average pre-intervention insterseizure interval. The key to our approach is to take into account not only the time elapsed without seizures but also empirical data regarding the a priori probability of achieving seizure freedom conferred by a particular intervention. Significance In many cases it may be reasonable to consider a patient seizure free after they have gone without seizures for a period equal to three times the pre-intervention inter-seizure interval, as proposed on pragmatic grounds in a recent ILAE position paper, though in other commonly encountered cases a waiting time up to six times this interval is required. In this work we have provided a coherent theoretical basis for modified criterion for seizure freedom, which we call the “Rule of Three-To-Six”. PMID:22191711

DELTA: An Expert System for Diesel Electric Locomotive Repair

DTIC Science & Technology

1984-06-01

Rules and Inference Mechanisms. AD-P003 943 The ACE (Automated Cable Expert) Exlpelient: Initial Evaluation of an Expert System for Preventive...tions. The first field prototype expert system, designated CATS -i (Computer-Aided Troubleshooting System - Version 1), was delivered in July 1983 and is

26 CFR 1.279-1 - General rule; purpose.

Code of Federal Regulations, 2011 CFR

2011-04-01

... described in sections 279(d) (3), (4), and (5), 279(f), or 279(i) are present. However, no inference should... interest exceeds $5 million. However, the $5 million limitation is reduced by the amount of interest paid... obligation. [T.D. 7262, 38 FR 5844, Mar. 5, 1973] ...

37 CFR 353.5 - Participation not required.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... 353.5 Section 353.5 Patents, Trademarks, and Copyrights COPYRIGHT ROYALTY BOARD, LIBRARY OF CONGRESS COPYRIGHT ROYALTY JUDGES RULES AND PROCEDURES REHEARING § 353.5 Participation not required. In any case in... inference from a lack of participation in a rehearing. Nonparticipation in rehearing proceedings may limit...

37 CFR 353.5 - Participation not required.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... 353.5 Section 353.5 Patents, Trademarks, and Copyrights COPYRIGHT ROYALTY BOARD, LIBRARY OF CONGRESS COPYRIGHT ROYALTY JUDGES RULES AND PROCEDURES REHEARING § 353.5 Participation not required. In any case in... inference from a lack of participation in a rehearing. Nonparticipation in rehearing proceedings may limit...

On Modeling of If-Then Rules for Probabilistic Inference

DTIC Science & Technology

1993-02-01

conditionals b -- a. This space contains A strictly. Contrary to a statement in Gilio and Spezzaferri (1992), these conditionals are equivalent to...Wiley, N.Y. [4] Gilio , A. and Spezzaferri, F. (1992). Knowledge integration for condi- tional probability assessmn-ts. Proceedings 8th Conf. Uncertainty

Living or Nonliving?

ERIC Educational Resources Information Center

Legaspi, Britt; Straits, William

2011-01-01

Categorizing organisms as living or nonliving things may seem to be intuitive by nature. Yet, it is regulated by scientific criteria. Students come to school with rules already in place. Their categorizing criteria have already been influenced by their personal experiences, also known as observations and inferences. They believe that all things…

Using Blue Stragglers to Predict Retained Black Hole Population in Globular Clusters

NASA Astrophysics Data System (ADS)

Hermanek, Keith; Chatterjee, Sourav; Rasio, Frederic

2018-01-01

Large numbers of black holes (BHs) are expected to form in massive star clusters typical of the globular clusters (GCs). Sophisticated theoretical models suggest that many of these BHs can be retained in present-day GCs. Observations have also identified several BH candidates in Galactic and extragalactic GCs (e.g., Macarone et al. 2007; Irwin et al. 2010; Strader et al. 2012; Chomiuk et al. 2013; Miller-Jones et al. 2014). It has also been shown that high-mass and high-density clusters such as GCs are efficient factories of merging binary BHs similar to those observed by the LIGO observatories (Abbott et al. 2016a,b,c,d,e; Rodriguez et al. 2016). Understanding the formation rate and properties of binary BHs are dependent on a detailed understanding of how the BHs dynamically evolve within GCs. Nevertheless, directly detecting BHs in GCs is extremely challenging; BHs only in binaries with limited configurations can be directly detected by the detection of gravitational wave, X-ray, or radio emissions. We propose an indirect of inferring the number of undetected retained BHs in a GC by investigating the dynamical effects of a large number of BHs on the production of other tracer populations such as Blue Straggler Stars (BSS). Using a large grid of detailed GC models we show that there is a clear anti-correlation between the number of BSS in a cluster and the number of retained BHs. Being the most massive species, large numbers of retained BHs will dominate the core of the cluster as a result of mass-segregation driving away other low-mass species such as main-sequence stars from central high-density regions. BSS are expected to form from physical collisions between main-sequence (MS) stars mediated by binary encounters (e.g., Chatterjee et al. 2013) in cores of GCs. Production of BSS by collisions or mass transfer channels are suppressed if a large number of retained BHs in a cluster restrict the number of MS stars in the core. Extensive observational data exist on the number and radial distribution of BSS in GCs. Thus, this anti-correlation between the number of retained BHs and the number of BSS, once carefully calibrated by theoretical models, can be used to infer the population of undetected BHs in GCs.

A mass transfer origin for blue stragglers in NGC 188 as revealed by half-solar-mass companions.

PubMed

Geller, Aaron M; Mathieu, Robert D

2011-10-19

In open star clusters, where all members formed at about the same time, blue straggler stars are typically observed to be brighter and bluer than hydrogen-burning main-sequence stars, and therefore should already have evolved into giant stars and stellar remnants. Correlations between blue straggler frequency and cluster binary star fraction, core mass and radial position suggest that mass transfer or mergers in binary stars dominates the production of blue stragglers in open clusters. Analytic models, detailed observations and sophisticated N-body simulations, however, argue in favour of stellar collisions. Here we report that the blue stragglers in long-period binaries in the old (7 × 10(9)-year) open cluster NGC 188 have companions with masses of about half a solar mass, with a surprisingly narrow mass distribution. This conclusively rules out a collisional origin, as the collision hypothesis predicts a companion mass distribution with significantly higher masses. Mergers in hierarchical triple stars are marginally permitted by the data, but the observations do not favour this hypothesis. The data are highly consistent with a mass transfer origin for the long-period blue straggler binaries in NGC 188, in which the companions would be white dwarfs of about half a solar mass.

Thermodynamics and structural transition of binary atomic Bose-Fermi mixtures in box or harmonic potentials: A path-integral study

NASA Astrophysics Data System (ADS)

Kim, Tom; Chien, Chih-Chun

2018-03-01

Experimental realizations of a variety of atomic binary Bose-Fermi mixtures have brought opportunities for studying composite quantum systems with different spin statistics. The binary atomic mixtures can exhibit a structural transition from a mixture into phase separation as the boson-fermion interaction increases. By using a path-integral formalism to evaluate the grand partition function and the thermodynamic grand potential, we obtain the effective potential of binary Bose-Fermi mixtures. Thermodynamic quantities in a broad range of temperatures and interactions are also derived. The structural transition can be identified as a loop of the effective potential curve, and the volume fraction of phase separation can be determined by the lever rule. For 6Li-7Li and 6Li-41K mixtures, we present the phase diagrams of the mixtures in a box potential at zero and finite temperatures. Due to the flexible densities of atomic gases, the construction of phase separation is more complicated when compared to conventional liquid or solid mixtures where the individual densities are fixed. For harmonically trapped mixtures, we use the local density approximation to map out the finite-temperature density profiles and present typical trap structures, including the mixture, partially separated phases, and fully separated phases.

On the luminosity function, lifetimes, and origin of blue stragglers in globular clusters

NASA Technical Reports Server (NTRS)

Bailyn, Charles D.; Pinsonneault, Marc H.

1995-01-01

We compute theoretical evolutionary tracks of blue stragglers created by mergers. Two formation scenarios are considered: mergers of primordial binaries, and stellar collisions. These two scenarios predict strikingly different luminosity functions, which are potentially distinguishable observationally. Tabulated theoretical luminosity functions and lifetimes are presented for blue stragglers formed under a variety of input conditions. We compare our results with observations of the blue straggler sequences in 47 Tucanae and M3. In the case of 47 Tuc, the luminosity function and the formation rate are compatible with the hypothesis that the blue stragglers formed through the collision of single stars. Mergers of primordial binaries are only marginally cosistent with the data, and a significant enhancement of the collision cross section by binary-single-star encounters appears to be ruled out. In the case of M3, we find that the innermost blue stragglers have a luminosity function significantly different from that of the outer stragglers, thus confirming earlier suggestions that there are two distinct populations of blue stragglers in this cluster. The inner stragglers are preferentially brighter and bluer, as would be expected if they were made by collisions, but there are so many of them that the collision rate would need to be enhanced by interactions involving wide binaries. The luminosity function of the outer stragglers is almost identical to the predictions of mergers from primordial binaries and is inconsistent with the collision hypothesis.

Spectroscopic classification of X-ray sources in the Galactic Bulge Survey

NASA Astrophysics Data System (ADS)

Wevers, T.; Torres, M. A. P.; Jonker, P. G.; Nelemans, G.; Heinke, C.; Mata Sánchez, D.; Johnson, C. B.; Gazer, R.; Steeghs, D. T. H.; Maccarone, T. J.; Hynes, R. I.; Casares, J.; Udalski, A.; Wetuski, J.; Britt, C. T.; Kostrzewa-Rutkowska, Z.; Wyrzykowski, Ł.

2017-10-01

We present the classification of 26 optical counterparts to X-ray sources discovered in the Galactic Bulge Survey. We use (time-resolved) photometric and spectroscopic observations to classify the X-ray sources based on their multiwavelength properties. We find a variety of source classes, spanning different phases of stellar/binary evolution. We classify CX21 as a quiescent cataclysmic variable (CV) below the period gap, and CX118 as a high accretion rate (nova-like) CV. CXB12 displays excess UV emission, and could contain a compact object with a giant star companion, making it a candidate symbiotic binary or quiescent low-mass X-ray binary (although other scenarios cannot be ruled out). CXB34 is a magnetic CV (polar) that shows photometric evidence for a change in accretion state. The magnetic classification is based on the detection of X-ray pulsations with a period of 81 ± 2 min. CXB42 is identified as a young stellar object, namely a weak-lined T Tauri star exhibiting (to date unexplained) UX Ori-like photometric variability. The optical spectrum of CXB43 contains two (resolved) unidentified double-peaked emission lines. No known scenario, such as an active galactic nucleus or symbiotic binary, can easily explain its characteristics. We additionally classify 20 objects as likely active stars based on optical spectroscopy, their X-ray to optical flux ratios and photometric variability. In four cases we identify the sources as binary stars.

Photometric properties for selected algol-type binaries. VIII. The triple systems DI Peg and AF Gem revisited

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

Yang, Yuan-Gui; Li, Shu-Zheng; Yang, Ying, E-mail: yygcn@163.com, E-mail: yangyg@chnu.edu.cn, E-mail: yangy818@yeah.net

2014-06-01

New extensive photometry for two triple binary stars, DI Peg and AF Gem, was performed from 2012 October to 2013 January, with two small telescopes at Xinglong station (XLs) of NAOC. From new multi-color observations and previously published ones in literature, the photometric models were (re)deduced using the updated Wilson-Devinney code. The results indicated that the low third lights exist in two classic Algol-type binaries, whose fill-out factors for the more massive components are f{sub p} = 78.2(± 0.4)% for DI Peg, and f{sub p} = 69.0(± 0.3)% for AF Gem, respectively. Through analyzing the O–C curves, the orbital periodsmore » for two binaries change in the complicated mode. The period of DI Peg possibly appears to show two light-time orbits, whose modulated periods are P {sub 3} = 54.6(± 0.5) yr and P {sub 4} = 23.0(± 0.6) yr, respectively. The inferred minimum masses for the inner and outer sub-stellar companions are M {sub in} = 0.095 M {sub ☉} and M {sub out} = 0.170 M {sub ☉}, respectively. Therefore, DI Peg may be a quadruple star. The orbital period of AF Gem appears to show a continuous period decrease or a cyclic variation; the latter may be preferable. The cyclic oscillation, with a period of 120.3(± 2.5) yr, may be attributed to the light-time effect due to the third body. This kind of additional companion may extract angular momentum from the central system, which may play a key role in the evolution of the binary.« less

Einstein@Home DISCOVERY OF A PALFA MILLISECOND PULSAR IN AN ECCENTRIC BINARY ORBIT

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

Knispel, B.; Allen, B.; Lyne, A. G.

2015-06-10

We report the discovery of the millisecond pulsar (MSP) PSR J1950+2414 (P = 4.3 ms) in a binary system with an eccentric (e = 0.08) 22 day orbit in Pulsar Arecibo L-band Feed Array survey observations with the Arecibo telescope. Its companion star has a median mass of 0.3 M{sub ⊙} and is most likely a white dwarf (WD). Fully recycled MSPs like this one are thought to be old neutron stars spun-up by mass transfer from a companion star. This process should circularize the orbit, as is observed for the vast majority of binary MSPs, which predominantly have orbitalmore » eccentricities e < 0.001. However, four recently discovered binary MSPs have orbits with 0. 027 < e < 0.44; PSR J1950+2414 is the fifth such system to be discovered. The upper limits for its intrinsic spin period derivative and inferred surface magnetic field strength are comparable to those of the general MSP population. The large eccentricities are incompatible with the predictions of the standard recycling scenario: something unusual happened during their evolution. Proposed scenarios are (a) initial evolution of the pulsar in a triple system which became dynamically unstable, (b) origin in an exchange encounter in an environment with high stellar density, (c) rotationally delayed accretion-induced collapse of a super-Chandrasekhar WD, and (d) dynamical interaction of the binary with a circumbinary disk. We compare the properties of all five known eccentric MSPs with the predictions of these formation channels. Future measurements of the masses and proper motion might allow us to firmly exclude some of the proposed formation scenarios.« less

TESTING THE ASTEROSEISMIC SCALING RELATIONS FOR RED GIANTS WITH ECLIPSING BINARIES OBSERVED BY KEPLER

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

Gaulme, P.; McKeever, J.; Jackiewicz, J.

2016-12-01

Given the potential of ensemble asteroseismology for understanding fundamental properties of large numbers of stars, it is critical to determine the accuracy of the scaling relations on which these measurements are based. From several powerful validation techniques, all indications so far show that stellar radius estimates from the asteroseismic scaling relations are accurate to within a few percent. Eclipsing binary systems hosting at least one star with detectable solar-like oscillations constitute the ideal test objects for validating asteroseismic radius and mass inferences. By combining radial velocity (RV) measurements and photometric time series of eclipses, it is possible to determine themore » masses and radii of each component of a double-lined spectroscopic binary. We report the results of a four-year RV survey performed with the échelle spectrometer of the Astrophysical Research Consortium’s 3.5 m telescope and the APOGEE spectrometer at Apache Point Observatory. We compare the masses and radii of 10 red giants (RGs) obtained by combining radial velocities and eclipse photometry with the estimates from the asteroseismic scaling relations. We find that the asteroseismic scaling relations overestimate RG radii by about 5% on average and masses by about 15% for stars at various stages of RG evolution. Systematic overestimation of mass leads to underestimation of stellar age, which can have important implications for ensemble asteroseismology used for Galactic studies. As part of a second objective, where asteroseismology is used for understanding binary systems, we confirm that oscillations of RGs in close binaries can be suppressed enough to be undetectable, a hypothesis that was proposed in a previous work.« less

The Temperature and Cooling Age of the White Dwarf Companion to the Millisecond Pulsar PSR B1855+09.

PubMed

van Kerkwijk MH; Bell; Kaspi; Kulkarni

2000-02-10

We report on Keck and Hubble Space Telescope observations of the binary millisecond pulsar PSR B1855+09. We detect its white dwarf companion and measure mF555W=25.90+/-0.12 and mF814W=24.19+/-0.11 (Vega system). From the reddening-corrected color, (mF555W-mF814W&parr0;0=1.06+/-0.21, we infer a temperature Teff=4800+/-800 K. The white dwarf mass is known accurately from measurements of the Shapiro delay of the pulsar signal, MC=0.258+0.028-0.016 M middle dot in circle. Hence, given a cooling model, one can use the measured temperature to determine the cooling age. The main uncertainty in the cooling models for such low-mass white dwarfs is the amount of residual nuclear burning, which is set by the thickness of the hydrogen layer surrounding the helium core. From the properties of similar systems, it has been inferred that helium white dwarfs form with thick hydrogen layers, with mass greater, similar3x10-3 M middle dot in circle, which leads to significant additional heating. This is consistent with expectations from simple evolutionary models of the preceding binary evolution. For PSR B1855+09, though, such models lead to a cooling age of approximately 10 Gyr, which is twice the spin-down age of the pulsar. It could be that the spin-down age were incorrect, which would call the standard vacuum dipole braking model into question. For two other pulsar companions, however, ages well over 10 Gyr are inferred, indicating that the problem may lie with the cooling models. There is no age discrepancy for models in which the white dwarfs are formed with thinner hydrogen layers ( less, similar3x10-4 M middle dot in circle).

The new physician as unwitting quantum mechanic: is adapting Dirac's inference system best practice for personalized medicine, genomics, and proteomics?

PubMed

Robson, Barry

2007-08-01

What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets and bras where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus. An alternative interpretation with the same consequences is if every i = radical-1 in Dirac's QM is replaced by h, an entity distinct from 1 and i and arguably a hidden root of 1 such that h2 = 1. With that exception, this paper is thus primarily a review of the application of Dirac's system, by application of linear algebra in the complex domain to help manipulate information about associations and ontology in complicated data. Any combined bra-ket can be shown to be composed only of the sum of QM-like bra and ket weights c(), times an exponential function of Fano's mutual information measure I(A; B) about the association between A and B, that is, an association rule from data mining. With the weights and Fano measure re-expressed as expectations on finite data using Riemann's Incomplete (i.e., Generalized) Zeta Functions, actual counts of observations for real world sparse data can be readily utilized. Finally, the paper compares identical character, distinguishability of states events or measurements, correlation, mutual information, and orthogonal character, important issues in data mining and biomedical analytics, as in QM.

Evidence Accumulation and Change Rate Inference in Dynamic Environments.

PubMed

Radillo, Adrian E; Veliz-Cuba, Alan; Josić, Krešimir; Kilpatrick, Zachary P

2017-06-01

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.

The Effect of Household Smoking Bans on Household Smoking

PubMed Central

Bleakley, Amy; Mallya, Giridhar; Romer, Daniel

2014-01-01

Objectives. Because household smoking levels and adoption of domestic smoking rules may be endogenously related, we estimated a nonrecursive regression model to determine the simultaneous relationship between home smoking restrictions and household smoking. Methods. We used data from a May–June 2012 survey of Philadelphia, Pennsylvania, households with smokers (n = 456) to determine the simultaneous association between smoking levels in the home and the presence of home restrictions on smoking. Results. We found that home smoking rules predicted smoking in the home but smoking in the home had no effect on home smoking restrictions. Conclusions. Absent in-home randomized experiments, a quasi-experimental causal inference suggesting that home smoking rules result in lower home smoking levels may be plausible. PMID:24524533

Tidal Deformability from GW170817 as a Direct Probe of the Neutron Star Radius

NASA Astrophysics Data System (ADS)

Raithel, Carolyn A.; Özel, Feryal; Psaltis, Dimitrios

2018-04-01

Gravitational waves from the coalescence of two neutron stars were recently detected for the first time by the LIGO–Virgo Collaboration, in event GW170817. This detection placed an upper limit on the effective tidal deformability of the two neutron stars and tightly constrained the chirp mass of the system. We report here on a new simplification that arises in the effective tidal deformability of the binary, when the chirp mass is specified. We find that, in this case, the effective tidal deformability of the binary is surprisingly independent of the component masses of the individual neutron stars, and instead depends primarily on the ratio of the chirp mass to the neutron star radius. Thus, a measurement of the effective tidal deformability can be used to directly measure the neutron star radius. We find that the upper limit on the effective tidal deformability from GW170817 implies that the radius cannot be larger than ∼13 km, at the 90% level, independent of the assumed masses for the component stars. The result can be applied generally, to probe the stellar radii in any neutron star–neutron star merger with a measured chirp mass. The approximate mass independence disappears for neutron star–black hole mergers. Finally, we discuss a Bayesian inference of the equation of state that uses the measured chirp mass and tidal deformability from GW170817 combined with nuclear and astrophysical priors and discuss possible statistical biases in this inference.

The UV reflectance of Patroclus: Exploring the surface composition and origins of Jupiter Trojans

NASA Astrophysics Data System (ADS)

Molyneux, Pippa

2017-08-01

(617) Patroclus is a binary system comprising two almost equally sized Trojan asteroids, Patroclus and Menoetius. (617) Patroclus has never been observed in the UV spectral region, which contains important diagnostic features of major Trojan surface constituents inferred from fits to visible-near IR spectra. Previous spectral observations have not been spatially resolved, precluding a direct spectral comparison of the two bodies. We propose to obtain full surface UV reflectance maps of both Patroclus and Menoetius using the STIS G230L mode, to search for characteristic absorption features of silicates, carbons/graphites and NH3, which together make up the major inferred Jupiter Trojan surface constituents, and for signs of ''spectral bluing'' that occurs for space-weathered objects. The Jupiter Trojans are believed to represent the most readily accessible Kuiper Belt material in the solar system, having been scattered from that region to their current orbits following a dynamical instability. A direct spectral comparison of Patroclus and Menoetius, indicating whether the objects share a common origin and evolution, will explore the hypothesis that the system is a rare binary survivor of this scattering. (617) Patroclus is also a target of the upcoming Lucy mission, and constraints on surface composition would represent a valuable input to instrument configuration and observation planning work for the mission. As Lucy will not carry a UV instrument, the proposed observations would remain unique and complementary to the results of the mission.

IN-SYNC. VII. Evidence for a Decreasing Spectroscopic Binary Fraction (from 1 to 100 Myr) within the IN-SYNC Sample

NASA Astrophysics Data System (ADS)

Jaehnig, Karl; Bird, Jonathan C.; Stassun, Keivan G.; Da Rio, Nicola; Tan, Jonathan C.; Cotaar, Michiel; Somers, Garrett

2017-12-01

We study the occurrence of spectroscopic binaries in young star-forming regions using the INfrared Spectroscopy of Young Nebulous Clusters (IN-SYNC) survey, carried out in SDSS-III with the APOGEE spectrograph. Multi-epoch observations of thousands of low-mass stars in Orion A, NGC 2264, NGC 1333, IC 348, and the Pleiades have been carried out, yielding H-band spectra with a nominal resolution of R = 22,500 for sources with H < 12 mag. Radial velocity precisions of ˜0.3 {km} {{{s}}}-1 were achieved, which we use to identify radial velocity variations indicative of undetected companions. We use Monte Carlo simulations to assess the types of spectroscopic binaries to which we are sensitive, finding sensitivity to binaries with orbital periods ≲ {10}3.5 days, for stars with 2500 {{K}}≤slant {T}{eff}≤slant 6000 {{K}} and v \\sin i < 100 {km} {{{s}}}-1. Using Bayesian inference, we find evidence for a decline in the spectroscopic binary fraction, by a factor of 3-4, from the age of our pre-main-sequence (PMS) sample to the Pleiades age . The significance of this decline is weakened if spot-induced radial-velocity jitter is strong in the sample, and is only marginally significant when comparing any one of the PMS clusters against the Pleiades. However, the same decline in both sense and magnitude is found for each of the five PMS clusters, and the decline reaches a statistical significance of greater than 95% confidence when considering the PMS clusters jointly. Our results suggest that dynamical processes disrupt the widest spectroscopic binaries ({P}{orb}≈ {10}3{--}{10}4 days) as clusters age, indicating that this occurs early in the stars’ evolution, while they still reside within their nascent clusters.

Deduction of reservoir operating rules for application in global hydrological models

NASA Astrophysics Data System (ADS)

Coerver, Hubertus M.; Rutten, Martine M.; van de Giesen, Nick C.

2018-01-01

A big challenge in constructing global hydrological models is the inclusion of anthropogenic impacts on the water cycle, such as caused by dams. Dam operators make decisions based on experience and often uncertain information. In this study information generally available to dam operators, like inflow into the reservoir and storage levels, was used to derive fuzzy rules describing the way a reservoir is operated. Using an artificial neural network capable of mimicking fuzzy logic, called the ANFIS adaptive-network-based fuzzy inference system, fuzzy rules linking inflow and storage with reservoir release were determined for 11 reservoirs in central Asia, the US and Vietnam. By varying the input variables of the neural network, different configurations of fuzzy rules were created and tested. It was found that the release from relatively large reservoirs was significantly dependent on information concerning recent storage levels, while release from smaller reservoirs was more dependent on reservoir inflows. Subsequently, the derived rules were used to simulate reservoir release with an average Nash-Sutcliffe coefficient of 0.81.

System and method for creating expert systems

NASA Technical Reports Server (NTRS)

Hughes, Peter M. (Inventor); Luczak, Edward C. (Inventor)

1998-01-01

A system and method provides for the creation of a highly graphical expert system without the need for programming in code. An expert system is created by initially building a data interface, defining appropriate Mission, User-Defined, Inferred, and externally-generated GenSAA (EGG) data variables whose data values will be updated and input into the expert system. Next, rules of the expert system are created by building appropriate conditions of the rules which must be satisfied and then by building appropriate actions of rules which are to be executed upon corresponding conditions being satisfied. Finally, an appropriate user interface is built which can be highly graphical in nature and which can include appropriate message display and/or modification of display characteristics of a graphical display object, to visually alert a user of the expert system of varying data values, upon conditions of a created rule being satisfied. The data interface building, rule building, and user interface building are done in an efficient manner and can be created without the need for programming in code.

Hydrodynamical Evolution of Merging Carbon-Oxygen White Dwarfs: Their Pre-supernova Structure and Observational Counterparts

NASA Astrophysics Data System (ADS)

Tanikawa, Ataru; Nakasato, Naohito; Sato, Yushi; Nomoto, Ken'ichi; Maeda, Keiichi; Hachisu, Izumi

2015-07-01

We perform smoothed particle hydrodynamics simulations for merging binary carbon-oxygen (CO) WDs with masses of 1.1 and 1.0 {M}⊙ , until the merger remnant reaches a dynamically steady state. Using these results, we assess whether the binary could induce a thermonuclear explosion, and whether the explosion could be observed as a type Ia supernova (SN Ia). We investigate three explosion mechanisms: a helium-ignition following the dynamical merger (“helium-ignited violent merger model”), a carbon-ignition (“carbon-ignited violent merger model”), and an explosion following the formation of the Chandrasekhar mass WD (“Chandrasekhar mass model”). An explosion of the helium-ignited violent merger model is possible, while we predict that the resulting SN ejecta are highly asymmetric since its companion star is fully intact at the time of the explosion. The carbon-ignited violent merger model can also lead to an explosion. However, the envelope of the exploding WD spreads out to ˜ 0.1 {R}⊙ ; it is much larger than that inferred for SN 2011fe (\\lt 0.1 {R}⊙ ) while much smaller than that for SN 2014J (˜ 1 {R}⊙ ). For the particular combination of the WD masses studied in this work, the Chandrasekhar mass model does not successfully lead to an SN Ia explosion. Besides these assessments, we investigate the evolution of unbound materials ejected through the merging process (“merger ejecta”), assuming a case where the SN Ia explosion is not triggered by the helium- or carbon-ignition during the merger. The merger ejecta interact with the surrounding interstellar medium and form a shell. The shell has a bolometric luminosity of more than 2× {10}35 {erg} {{{s}}}-1, lasting for ˜ 2× {10}4 years. If this is the case, the Milky Way should harbor about 10 such shells at any given time. The detection of the shell(s) can therefore rule out the helium-ignited and carbon-ignited violent merger models as major paths to SN Ia explosions.

Minimal perceptrons for memorizing complex patterns

NASA Astrophysics Data System (ADS)

Pastor, Marissa; Song, Juyong; Hoang, Danh-Tai; Jo, Junghyo

2016-11-01

Feedforward neural networks have been investigated to understand learning and memory, as well as applied to numerous practical problems in pattern classification. It is a rule of thumb that more complex tasks require larger networks. However, the design of optimal network architectures for specific tasks is still an unsolved fundamental problem. In this study, we consider three-layered neural networks for memorizing binary patterns. We developed a new complexity measure of binary patterns, and estimated the minimal network size for memorizing them as a function of their complexity. We formulated the minimal network size for regular, random, and complex patterns. In particular, the minimal size for complex patterns, which are neither ordered nor disordered, was predicted by measuring their Hamming distances from known ordered patterns. Our predictions agree with simulations based on the back-propagation algorithm.

From the Beauty of Genomic Landscapes to the Strength of Transcriptional Mechanisms.

PubMed

Natoli, Gioacchino

2016-03-24

Genomic analyses are commonly used to infer trends and broad rules underlying transcriptional control. The innovative approach by Tong et al. to interrogate genomic datasets allows extracting mechanistic information on the specific regulation of individual genes. Copyright © 2016 Elsevier Inc. All rights reserved.

A Priori Knowledge and Heuristic Reasoning in Architectural Design.

ERIC Educational Resources Information Center

Rowe, Peter G.

1982-01-01

It is proposed that the various classes of a priori knowledge incorporated in heuristic reasoning processes exert a strong influence over architectural design activity. Some design problems require exercise of some provisional set of rules, inference, or plausible strategy which requires heuristic reasoning. A case study illustrates this concept.…

CORMIX1: AN EXPERT SYSTEM FOR MIXING ZONE ANALYSIS OF TOXIC AND CONVENTIONAL, SINGLE PORT AQUATIC DISCHARGES

EPA Science Inventory

An expert system, CORMIX1, was developed to predict the dilution and trajectory of a single buoyant discharge into an unstratified aquatic environment with and without crossflow. The system uses knowledge and inference rules obtained from hydrodynamic experts to classify and pred...

Reliability of Multi-Category Rating Scales

ERIC Educational Resources Information Center

Parker, Richard I.; Vannest, Kimberly J.; Davis, John L.

2013-01-01

The use of multi-category scales is increasing for the monitoring of IEP goals, classroom and school rules, and Behavior Improvement Plans (BIPs). Although they require greater inference than traditional data counting, little is known about the inter-rater reliability of these scales. This simulation study examined the performance of nine…

Using Patterned Discourse in the Language of Law and Medicine.

ERIC Educational Resources Information Center

Hoppe, Ronald A.; Kess, Joseph F.

A discussion of discourse patterns in the professional interactions of lawyers and physicians suggests that these encounters are programmatic exchanges to which the psycholinguistic rules of processing and inference can be applied. In these professions, the constraints of normal discourse patterns interact with the conventions of giving and…

SUPPLEMENT: “THE RATE OF BINARY BLACK HOLE MERGERS INFERRED FROM ADVANCED LIGO OBSERVATIONS SURROUNDING GW150914” (2016, ApJL, 833, L1)

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

Abbott, B. P.; Abbott, R.; Abernathy, M. R.

This article provides supplemental information for a Letter reporting the rate of (BBH) coalescences inferred from 16 days of coincident Advanced LIGO observations surrounding the transient (GW) signal GW150914. In that work we reported various rate estimates whose 90% confidence intervals fell in the range 2–600 Gpc{sup −3} yr{sup −1}. Here we give details on our method and computations, including information about our search pipelines, a derivation of our likelihood function for the analysis, a description of the astrophysical search trigger distribution expected from merging BBHs, details on our computational methods, a description of the effects and our model for calibration uncertainty,more » and an analytic method for estimating our detector sensitivity, which is calibrated to our measurements.« less

Design of synthetic bacterial communities for predictable plant phenotypes

PubMed Central

Herrera Paredes, Sur; Gao, Tianxiang; Law, Theresa F.; Finkel, Omri M.; Mucyn, Tatiana; Teixeira, Paulo José Pereira Lima; Salas González, Isaí; Feltcher, Meghan E.; Powers, Matthew J.; Shank, Elizabeth A.; Jones, Corbin D.; Jojic, Vladimir; Dangl, Jeffery L.; Castrillo, Gabriel

2018-01-01

Specific members of complex microbiota can influence host phenotypes, depending on both the abiotic environment and the presence of other microorganisms. Therefore, it is challenging to define bacterial combinations that have predictable host phenotypic outputs. We demonstrate that plant–bacterium binary-association assays inform the design of small synthetic communities with predictable phenotypes in the host. Specifically, we constructed synthetic communities that modified phosphate accumulation in the shoot and induced phosphate starvation–responsive genes in a predictable fashion. We found that bacterial colonization of the plant is not a predictor of the plant phenotypes we analyzed. Finally, we demonstrated that characterizing a subset of all possible bacterial synthetic communities is sufficient to predict the outcome of untested bacterial consortia. Our results demonstrate that it is possible to infer causal relationships between microbiota membership and host phenotypes and to use these inferences to rationally design novel communities. PMID:29462153

Reference-dependent risk sensitivity as rational inference.

PubMed

Denrell, Jerker C

2015-07-01

Existing explanations of reference-dependent risk sensitivity attribute it to cognitive imperfections and heuristic choice processes. This article shows that behavior consistent with an S-shaped value function could be an implication of rational inferences about the expected values of alternatives. Theoretically, I demonstrate that even a risk-neutral Bayesian decision maker, who is uncertain about the reliability of observations, should use variability in observed outcomes as a predictor of low expected value for outcomes above a reference level, and as a predictor of high expected value for outcomes below a reference level. Empirically, I show that combining past outcomes using an S-shaped value function leads to accurate predictions about future values. The theory also offers a rationale for why risk sensitivity consistent with an inverse S-shaped value function should occur in experiments on decisions from experience with binary payoff distributions. (c) 2015 APA, all rights reserved).

TMS for Instantiating a Knowledge Base With Incomplete Data

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

A computer program that belongs to the class known among software experts as output truth-maintenance-systems (output TMSs) has been devised as one of a number of software tools for reducing the size of the knowledge base that must be searched during execution of artificial- intelligence software of the rule-based inference-engine type in a case in which data are missing. This program determines whether the consequences of activation of two or more rules can be combined without causing a logical inconsistency. For example, in a case involving hypothetical scenarios that could lead to turning a given device on or off, the program determines whether a scenario involving a given combination of rules could lead to turning the device both on and off at the same time, in which case that combination of rules would not be included in the scenario.

Optical Generation of Fuzzy-Based Rules

NASA Astrophysics Data System (ADS)

Gur, Eran; Mendlovic, David; Zalevsky, Zeev

2002-08-01

In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.

K2 eclipsing binaries in the benchmark open cluster Ruprecht 147

NASA Astrophysics Data System (ADS)

Torres, Guillermo

Open clusters are ideal laboratories to study stellar astrophysics. They represent homogeneous collections of hundreds or thousands of stars that were formed together and should therefore have the same age, chemical composition, space motion, and distance. Easily measured properties for member stars such as the brightness and color can be used to infer some of the characteristics of the ensemble including the age and distance, by comparing with model isochrones in the color-magnitude diagram. In recent years space missions such as CoRoT and Kepler have enabled the detection of solar-like oscillations in some of the brighter open cluster members, which can yield asteroseismic estimates of the stellar masses and radii through simple scaling relations anchored on the Sun, and also ages under certain assumptions. Furthermore, when photometric rotation periods of stars can be measured in them, clusters will well-known ages then become essential calibrators for gyrochronology relations, which describe how stars spin down as they get older due to magnetic braking from stellar winds. These relations are important because they provide one of the few empirical ways to age-date field stars. For clusters endowed with detached, double-lined eclipsing binaries amenable to study, even stronger constraints on their properties become available that are of an entirely different nature. The absolute masses and radii of the binary components can be measured very accurately and in a model-independent way, providing an opportunity for stringent tests of stellar evolution theory. The ages that can also be obtained by comparison with models can serve to validate other age estimates mentioned above. Ruprecht 147 is remarkable in that it permits all of these types of studies at the same time. It is the oldest nearby open cluster, with an age of about 3 Gyr and a distance of only 300 pc. This makes it a favorable target for follow-up studies. The metallicity is well determined from previous spectroscopic investigations. It was observed photometrically by the K2 mission for 80 days in late 2015, enabling both asteroseismic and rotation period studies of dozens of members. What makes it truly unique, however, is that it has no less than five eclipsing binaries brighter than 13th magnitude that lend themselves to high-precision mass and radius determinations. No other open cluster has as many, let alone an old one. The brightest binary happens to be at the tip of the turnoff and provides an unusually strong constraint on age. A very special opportunity for study has thus presented itself. This is a proposal to analyze publicly available K2 photometry for the five bright eclipsing binaries discovered in Ruprecht 147, with the goal of fashioning the cluster into an important new benchmark for high-precision testing of stellar astrophysics. We will supplement the K2 light curves, processed with special detrending techniques, with ground-based spectroscopic observations yielding radial velocities for the stars. With these we will derive accurate masses, radii, and temperatures for the components of each binary using well-proven classical methodologies. The impact of the project is that the large number of binaries will allow for an unprecedented and extraordinarily strong test of stellar evolution theory over a range of masses, not available for any other open cluster. The ages we will infer are completely independent of, and of a different nature than other estimates in Ruprecht 147, coming from isochrone fitting in the colormagnitude diagram, asteroseismology of the brighter cluster members, or the use of gyrochronology relations. We will thus have a unique opportunity to cross-validate four different age-dating techniques in the same cluster. Additionally, our accurate eclipsing binary masses and radii will enable crucial tests of the asteroseismic scaling relations, which will improve their use for single stars.

Lyotropic liquid crystal engineering moving beyond binary compositional space - ordered nanostructured amphiphile self-assembly materials by design.

PubMed

van 't Hag, Leonie; Gras, Sally L; Conn, Charlotte E; Drummond, Calum J

2017-05-22

Ordered amphiphile self-assembly materials with a tunable three-dimensional (3D) nanostructure are of fundamental interest, and crucial for progressing several biological and biomedical applications, including in meso membrane protein crystallization, as drug and medical contrast agent delivery vehicles, and as biosensors and biofuel cells. In binary systems consisting of an amphiphile and a solvent, the ability to tune the 3D cubic phase nanostructure, lipid bilayer properties and the lipid mesophase is limited. A move beyond the binary compositional space is therefore required for efficient engineering of the required material properties. In this critical review, the phase transitions upon encapsulation of more than 130 amphiphilic and soluble additives into the bicontinuous lipidic cubic phase under excess hydration are summarized. The data are interpreted using geometric considerations, interfacial curvature, electrostatic interactions, partition coefficients and miscibility of the alkyl chains. The obtained lyotropic liquid crystal engineering design rules can be used to enhance the formulation of self-assembly materials and provides a large library of these materials for use in biomedical applications (242 references).

Web-based Weather Expert System (WES) for Space Shuttle Launch

NASA Technical Reports Server (NTRS)

Bardina, Jorge E.; Rajkumar, T.

2003-01-01

The Web-based Weather Expert System (WES) is a critical module of the Virtual Test Bed development to support 'go/no go' decisions for Space Shuttle operations in the Intelligent Launch and Range Operations program of NASA. The weather rules characterize certain aspects of the environment related to the launching or landing site, the time of the day or night, the pad or runway conditions, the mission durations, the runway equipment and landing type. Expert system rules are derived from weather contingency rules, which were developed over years by NASA. Backward chaining, a goal-directed inference method is adopted, because a particular consequence or goal clause is evaluated first, and then chained backward through the rules. Once a rule is satisfied or true, then that particular rule is fired and the decision is expressed. The expert system is continuously verifying the rules against the past one-hour weather conditions and the decisions are made. The normal procedure of operations requires a formal pre-launch weather briefing held on Launch minus 1 day, which is a specific weather briefing for all areas of Space Shuttle launch operations. In this paper, the Web-based Weather Expert System of the Intelligent Launch and range Operations program is presented.

Data-driven backward chaining

NASA Technical Reports Server (NTRS)

Haley, Paul

1991-01-01

The C Language Integrated Production System (CLIPS) cannot effectively perform sound and complete logical inference in most real-world contexts. The problem facing CLIPS is its lack of goal generation. Without automatic goal generation and maintenance, forward chaining can only deduce all instances of a relationship. Backward chaining, which requires goal generation, allows deduction of only that subset of what is logically true which is also relevant to ongoing problem solving. Goal generation can be mimicked in simple cases using forward chaining. However, such mimicry requires manual coding of additional rules which can assert an inadequate goal representation for every condition in every rule that can have corresponding facts derived by backward chaining. In general, for N rules with an average of M conditions per rule the number of goal generation rules required is on the order of N*M. This is clearly intractable from a program maintenance perspective. We describe the support in Eclipse for backward chaining which it automatically asserts as it checks rule conditions. Important characteristics of this extension are that it does not assert goals which cannot match any rule conditions, that 2 equivalent goals are never asserted, and that goals persist as long as, but no longer than, they remain relevant.

RENT+: an improved method for inferring local genealogical trees from haplotypes with recombination.

PubMed

Mirzaei, Sajad; Wu, Yufeng

2017-04-01

: Haplotypes from one or multiple related populations share a common genealogical history. If this shared genealogy can be inferred from haplotypes, it can be very useful for many population genetics problems. However, with the presence of recombination, the genealogical history of haplotypes is complex and cannot be represented by a single genealogical tree. Therefore, inference of genealogical history with recombination is much more challenging than the case of no recombination. : In this paper, we present a new approach called RENT+  for the inference of local genealogical trees from haplotypes with the presence of recombination. RENT+  builds on a previous genealogy inference approach called RENT , which infers a set of related genealogical trees at different genomic positions. RENT+  represents a significant improvement over RENT in the sense that it is more effective in extracting information contained in the haplotype data about the underlying genealogy than RENT . The key components of RENT+  are several greatly enhanced genealogy inference rules. Through simulation, we show that RENT+  is more efficient and accurate than several existing genealogy inference methods. As an application, we apply RENT+  in the inference of population demographic history from haplotypes, which outperforms several existing methods. : RENT+  is implemented in Java, and is freely available for download from: https://github.com/SajadMirzaei/RentPlus . : sajad@engr.uconn.edu or ywu@engr.uconn.edu. : Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A model for amalgamation in group decision making

NASA Technical Reports Server (NTRS)

Cutello, Vincenzo; Montero, Javier

1992-01-01

In this paper we present a generalization of the model proposed by Montero, by allowing non-complete fuzzy binary relations for individuals. A degree of unsatisfaction can be defined in this case, suggesting that any democratic aggregation rule should take into account not only ethical conditions or some degree of rationality in the amalgamating procedure, but also a minimum support for the set of alternatives subject to the group analysis.

Fuzzy multiobjective models for optimal operation of a hydropower system

NASA Astrophysics Data System (ADS)

Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

2013-06-01

Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

Incremental Learning of Context Free Grammars by Parsing-Based Rule Generation and Rule Set Search

NASA Astrophysics Data System (ADS)

Nakamura, Katsuhiko; Hoshina, Akemi

This paper discusses recent improvements and extensions in Synapse system for inductive inference of context free grammars (CFGs) from sample strings. Synapse uses incremental learning, rule generation based on bottom-up parsing, and the search for rule sets. The form of production rules in the previous system is extended from Revised Chomsky Normal Form A→βγ to Extended Chomsky Normal Form, which also includes A→B, where each of β and γ is either a terminal or nonterminal symbol. From the result of bottom-up parsing, a rule generation mechanism synthesizes minimum production rules required for parsing positive samples. Instead of inductive CYK algorithm in the previous version of Synapse, the improved version uses a novel rule generation method, called ``bridging,'' which bridges the lacked part of the derivation tree for the positive string. The improved version also employs a novel search strategy, called serial search in addition to minimum rule set search. The synthesis of grammars by the serial search is faster than the minimum set search in most cases. On the other hand, the size of the generated CFGs is generally larger than that by the minimum set search, and the system can find no appropriate grammar for some CFL by the serial search. The paper shows experimental results of incremental learning of several fundamental CFGs and compares the methods of rule generation and search strategies.

Foreign-grammar acquisition while watching subtitled television programmes.

PubMed

Van Lommel, Sven; Laenen, Annouschka; d'Ydewalle, Géry

2006-06-01

Past research has shown that watching a subtitled foreign movie (i.e. foreign language in the soundtrack and native language in the subtitles) leads to considerable foreign-language vocabulary acquisition; however, acquisition of the grammatical rules has failed to emerge. The aim of this study was to obtain evidence for the acquisition of grammatical rules in watching subtitled foreign movies. Given an informal context, younger children were predicted to outperform older children in acquiring a foreign language; however, older children will take more advantage of explicit instruction compared with younger children. In Experiment 1, 62 sixth-graders from a primary school and 47 sixth-graders from a secondary school volunteered to participate. The participants in Experiment 2 were 94 sixth-graders from primary schools and 84 sixth-graders from secondary schools. The two experiments manipulated the instructions (incidental- vs. intentional-language learning). Moreover, before the experiments began, some participants explicitly received some of the foreign grammatical rules (presented rules), while the movie contained cases of presented rules as well as cases of rules which had to be inferred (not-presented rules). Rule acquisition through the movie only was not obtained; there was a strong effect of advance rule presentation but only on the items of presented rules, particularly among the older participants. Contrary to vocabulary, grammar may be too complicated to acquire from a rather short movie presentation.

Ages of young star clusters, massive blue stragglers, and the upper mass limit of stars: Analyzing age-dependent stellar mass functions

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

Schneider, F. R. N.; Izzard, R. G.; Langer, N.

2014-01-10

Massive stars rapidly change their masses through strong stellar winds and mass transfer in binary systems. The latter aspect is important for populations of massive stars as more than 70% of all O stars are expected to interact with a binary companion during their lifetime. We show that such mass changes leave characteristic signatures in stellar mass functions of young star clusters that can be used to infer their ages and to identify products of binary evolution. We model the observed present-day mass functions of the young Galactic Arches and Quintuplet star clusters using our rapid binary evolution code. Wemore » find that the shaping of the mass function by stellar wind mass loss allows us to determine the cluster ages as 3.5 ± 0.7 Myr and 4.8 ± 1.1 Myr, respectively. Exploiting the effects of binary mass exchange on the cluster mass function, we find that the most massive stars in both clusters are rejuvenated products of binary mass transfer, i.e., the massive counterpart of classical blue straggler stars. This resolves the problem of an apparent age spread among the most luminous stars exceeding the expected duration of star formation in these clusters. We perform Monte Carlo simulations to probe stochastic sampling, which support the idea of the most massive stars being rejuvenated binary products. We find that the most massive star is expected to be a binary product after 1.0 ± 0.7 Myr in Arches and after 1.7 ± 1.0 Myr in Quintuplet. Today, the most massive 9 ± 3 stars in Arches and 8 ± 3 in Quintuplet are expected to be such objects. Our findings have strong implications for the stellar upper mass limit and solve the discrepancy between the claimed 150 M {sub ☉} limit and observations of four stars with initial masses of 165-320 M {sub ☉} in R136 and of supernova 2007bi, which is thought to be a pair-instability supernova from an initial 250 M {sub ☉} star. Using the stellar population of R136, we revise the upper mass limit to values in the range 200-500 M {sub ☉}.« less

Ages of Young Star Clusters, Massive Blue Stragglers, and the Upper Mass Limit of Stars: Analyzing Age-dependent Stellar Mass Functions

NASA Astrophysics Data System (ADS)

Schneider, F. R. N.; Izzard, R. G.; de Mink, S. E.; Langer, N.; Stolte, A.; de Koter, A.; Gvaramadze, V. V.; Hußmann, B.; Liermann, A.; Sana, H.

2014-01-01

Massive stars rapidly change their masses through strong stellar winds and mass transfer in binary systems. The latter aspect is important for populations of massive stars as more than 70% of all O stars are expected to interact with a binary companion during their lifetime. We show that such mass changes leave characteristic signatures in stellar mass functions of young star clusters that can be used to infer their ages and to identify products of binary evolution. We model the observed present-day mass functions of the young Galactic Arches and Quintuplet star clusters using our rapid binary evolution code. We find that the shaping of the mass function by stellar wind mass loss allows us to determine the cluster ages as 3.5 ± 0.7 Myr and 4.8 ± 1.1 Myr, respectively. Exploiting the effects of binary mass exchange on the cluster mass function, we find that the most massive stars in both clusters are rejuvenated products of binary mass transfer, i.e., the massive counterpart of classical blue straggler stars. This resolves the problem of an apparent age spread among the most luminous stars exceeding the expected duration of star formation in these clusters. We perform Monte Carlo simulations to probe stochastic sampling, which support the idea of the most massive stars being rejuvenated binary products. We find that the most massive star is expected to be a binary product after 1.0 ± 0.7 Myr in Arches and after 1.7 ± 1.0 Myr in Quintuplet. Today, the most massive 9 ± 3 stars in Arches and 8 ± 3 in Quintuplet are expected to be such objects. Our findings have strong implications for the stellar upper mass limit and solve the discrepancy between the claimed 150 M ⊙ limit and observations of four stars with initial masses of 165-320 M ⊙ in R136 and of supernova 2007bi, which is thought to be a pair-instability supernova from an initial 250 M ⊙ star. Using the stellar population of R136, we revise the upper mass limit to values in the range 200-500 M ⊙.

How the brain predicts people's behavior in relation to rules and desires. Evidence of a medio-prefrontal dissociation.

PubMed

Corradi-Dell'Acqua, Corrado; Turri, Francesco; Kaufmann, Laurence; Clément, Fabrice; Schwartz, Sophie

2015-09-01

Forming and updating impressions about others is critical in everyday life and engages portions of the dorsomedial prefrontal cortex (dMPFC), the posterior cingulate cortex (PCC) and the amygdala. Some of these activations are attributed to "mentalizing" functions necessary to represent people's mental states, such as beliefs or desires. Evolutionary psychology and developmental studies, however, suggest that interpersonal inferences can also be obtained through the aid of deontic heuristics, which dictate what must (or must not) be done in given circumstances. We used fMRI and asked 18 participants to predict whether unknown characters would follow their desires or obey external rules. Participants had no means, at the beginning, to make accurate predictions, but slowly learned (throughout the experiment) each character's behavioral profile. We isolated brain regions whose activity changed during the experiment, as a neural signature of impression updating: whereas dMPFC was progressively more involved in predicting characters' behavior in relation to their desires, the medial orbitofrontal cortex and the amygdala were progressively more recruited in predicting rule-based behavior. Our data provide evidence of a neural dissociation between deontic inference and theory-of-mind (ToM), and support a differentiation of orbital and dorsal prefrontal cortex in terms of low- and high-level social cognition. Copyright © 2015 Elsevier Ltd. All rights reserved.

An expert system for diagnosing environmentally induced spacecraft anomalies

NASA Technical Reports Server (NTRS)

Rolincik, Mark; Lauriente, Michael; Koons, Harry C.; Gorney, David

1992-01-01

A new rule-based, machine independent analytical tool was designed for diagnosing spacecraft anomalies using an expert system. Expert systems provide an effective method for saving knowledge, allow computers to sift through large amounts of data pinpointing significant parts, and most importantly, use heuristics in addition to algorithms, which allow approximate reasoning and inference and the ability to attack problems not rigidly defined. The knowledge base consists of over two-hundred (200) rules and provides links to historical and environmental databases. The environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose. The system's driver translates forward chaining rules into a backward chaining sequence, prompting the user for information pertinent to the causes considered. The use of heuristics frees the user from searching through large amounts of irrelevant information and allows the user to input partial information (varying degrees of confidence in an answer) or 'unknown' to any question. The modularity of the expert system allows for easy updates and modifications. It not only provides scientists with needed risk analysis and confidence not found in algorithmic programs, but is also an effective learning tool, and the window implementation makes it very easy to use. The system currently runs on a Micro VAX II at Goddard Space Flight Center (GSFC). The inference engine used is NASA's C Language Integrated Production System (CLIPS).

Wolf-Rayet stars

NASA Technical Reports Server (NTRS)

Abbott, David C.; Conti, Peter S.

1987-01-01

The properties and evolutionary status of WR stars are examined, reviewing the results of recent observational and theoretical investigations. Topics discussed include spectral types and line strengths, magnitudes and colors, intrinsic variability, IR and radio observations, X-ray observations, the Galactic distribution of WR stars, WR stars in other galaxies, and WR binaries. Consideration is given to the inferred masses, composition, and stellar winds of WR stars; model atmospheres; WR stars and the Galactic environment; and WR stars as a phase of stellar evolution. Diagrams, graphs, and tables of numerical data are provided.

Some properties of low-vapor-pressure braze alloys for thermionic converters

NASA Technical Reports Server (NTRS)

Bair, V. L.

1978-01-01

Property measurements were made for arc-melted, rod-shaped specimens. Density and dc electrical resistivity at 296 K were measured for various binary eutectic alloys. Thermal conductivity was inferred from the electrical conductivity using the Wiedemann, Franz, Lorenz relation. Linear thermal expansion from 293 K to two-thirds melting point, under a helium atmosphere, was measured for Zr, 21.7-wt percent Ru; Zr, 13-wt percent W; Zr, 22.3-wt percent Nb; Nb, 66.9-wt percent Ru; and Zr, 25.7-wt percent Ta.

Response Surface Modeling Tolerance and Inference Error Risk Specifications: Proposed Industry Standards

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2012-01-01

This paper reviews the derivation of an equation for scaling response surface modeling experiments. The equation represents the smallest number of data points required to fit a linear regression polynomial so as to achieve certain specified model adequacy criteria. Specific criteria are proposed which simplify an otherwise rather complex equation, generating a practical rule of thumb for the minimum volume of data required to adequately fit a polynomial with a specified number of terms in the model. This equation and the simplified rule of thumb it produces can be applied to minimize the cost of wind tunnel testing.

POPPER, a simple programming language for probabilistic semantic inference in medicine.

PubMed

Robson, Barry

2015-01-01

Our previous reports described the use of the Hyperbolic Dirac Net (HDN) as a method for probabilistic inference from medical data, and a proposed probabilistic medical Semantic Web (SW) language Q-UEL to provide that data. Rather like a traditional Bayes Net, that HDN provided estimates of joint and conditional probabilities, and was static, with no need for evolution due to "reasoning". Use of the SW will require, however, (a) at least the semantic triple with more elaborate relations than conditional ones, as seen in use of most verbs and prepositions, and (b) rules for logical, grammatical, and definitional manipulation that can generate changes in the inference net. Here is described the simple POPPER language for medical inference. It can be automatically written by Q-UEL, or by hand. Based on studies with our medical students, it is believed that a tool like this may help in medical education and that a physician unfamiliar with SW science can understand it. It is here used to explore the considerable challenges of assigning probabilities, and not least what the meaning and utility of inference net evolution would be for a physician. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impact of flow routing on catchment area calculations, slope estimates, and numerical simulations of landscape development

NASA Astrophysics Data System (ADS)

Shelef, Eitan; Hilley, George E.

2013-12-01

Flow routing across real or modeled topography determines the modeled discharge and wetness index and thus plays a central role in predicting surface lowering rate, runoff generation, likelihood of slope failure, and transition from hillslope to channel forming processes. In this contribution, we compare commonly used flow-routing rules as well as a new routing rule, to commonly used benchmarks. We also compare results for different routing rules using Airborne Laser Swath Mapping (ALSM) topography to explore the impact of different flow-routing schemes on inferring the generation of saturation overland flow and the transition between hillslope to channel forming processes, as well as on location of saturation overland flow. Finally, we examined the impact of flow-routing and slope-calculation rules on modeled topography produced by Geomorphic Transport Law (GTL)-based simulations. We found that different rules produce substantive differences in the structure of the modeled topography and flow patterns over ALSM data. Our results highlight the impact of flow-routing and slope-calculation rules on modeled topography, as well as on calculated geomorphic metrics across real landscapes. As such, studies that use a variety of routing rules to analyze and simulate topography are necessary to determine those aspects that most strongly depend on a chosen routing rule.

iNJclust: Iterative Neighbor-Joining Tree Clustering Framework for Inferring Population Structure.

PubMed

Limpiti, Tulaya; Amornbunchornvej, Chainarong; Intarapanich, Apichart; Assawamakin, Anunchai; Tongsima, Sissades

2014-01-01

Understanding genetic differences among populations is one of the most important issues in population genetics. Genetic variations, e.g., single nucleotide polymorphisms, are used to characterize commonality and difference of individuals from various populations. This paper presents an efficient graph-based clustering framework which operates iteratively on the Neighbor-Joining (NJ) tree called the iNJclust algorithm. The framework uses well-known genetic measurements, namely the allele-sharing distance, the neighbor-joining tree, and the fixation index. The behavior of the fixation index is utilized in the algorithm's stopping criterion. The algorithm provides an estimated number of populations, individual assignments, and relationships between populations as outputs. The clustering result is reported in the form of a binary tree, whose terminal nodes represent the final inferred populations and the tree structure preserves the genetic relationships among them. The clustering performance and the robustness of the proposed algorithm are tested extensively using simulated and real data sets from bovine, sheep, and human populations. The result indicates that the number of populations within each data set is reasonably estimated, the individual assignment is robust, and the structure of the inferred population tree corresponds to the intrinsic relationships among populations within the data.

Astrophysical Model Selection in Gravitational Wave Astronomy

NASA Technical Reports Server (NTRS)

Adams, Matthew R.; Cornish, Neil J.; Littenberg, Tyson B.

2012-01-01

Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.

Lidov-Kozai Cycles with Gravitational Radiation: Merging Black Holes in Isolated Triple Systems

NASA Astrophysics Data System (ADS)

Silsbee, Kedron; Tremaine, Scott

2017-02-01

We show that a black-hole binary with an external companion can undergo Lidov-Kozai cycles that cause a close pericenter passage, leading to a rapid merger due to gravitational-wave emission. This scenario occurs most often for systems in which the companion has a mass comparable to the reduced mass of the binary and the companion orbit has a semimajor axis within a factor of ˜10 of the binary semimajor axis. Using a simple population-synthesis model and three-body simulations, we estimate the rate of mergers in triple black-hole systems in the field to be about six per Gpc3 per year in the absence of natal kicks during black-hole formation. This value is within the low end of the 90% credible interval for the total black hole-black hole merger rate inferred from the current LIGO results. There are many uncertainties in these calculations, the largest of which is the unknown distribution of natal kicks. Even modest natal kicks of 40 km s-1 will reduce the merger rate by a factor of 40. A few percent of these systems will have eccentricity greater than 0.999 when they first enter the frequency band detectable by aLIGO (above 10 Hz).

Marginal and Random Intercepts Models for Longitudinal Binary Data With Examples From Criminology.

PubMed

Long, Jeffrey D; Loeber, Rolf; Farrington, David P

2009-01-01

Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides individual-level information including information about heterogeneity of growth. It is shown how a type of numerical averaging can be used with the random intercepts model to obtain group-level information, thus approximating individual and marginal aspects of the LMM. The types of inferences associated with each model are illustrated with longitudinal criminal offending data based on N = 506 males followed over a 22-year period. Violent offending indexed by official records and self-report were analyzed, with the marginal model estimated using generalized estimating equations and the random intercepts model estimated using maximum likelihood. The results show that the numerical averaging based on the random intercepts can produce prediction curves almost identical to those obtained directly from the marginal model parameter estimates. The results provide a basis for contrasting the models and the estimation procedures and key features are discussed to aid in selecting a method for empirical analysis.

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

Lau, R. M.; Hankins, M. J.; Herter, T. L.

Massive, evolved stars play a crucial role in the metal enrichment, dust budget, and energetics of the interstellar medium; however, the details of their evolution are uncertain because of their rarity and short lifetimes before exploding as supernovae. Discrepancies between theoretical predictions from single-star evolutionary models and observations of massive stars have evoked a shifting paradigm that implicates the importance of binary interaction. We present mid- to far-infrared observations from the Stratospheric Observatory for Infrared Astronomy of a conical “helix” of warm dust (∼180 K) that appears to extend from the Wolf–Rayet star WR102c. Our interpretation of the helix ismore » a precessing, collimated outflow that emerged from WR102c during a previous evolutionary phase as a rapidly rotating luminous blue variable. We attribute the precession of WR102c to gravitational interactions with an unseen compact binary companion whose orbital period can be constrained to 800 days < P < 1400 days from the inferred precession period, τ{sub p} ∼ 1.4 × 10{sup 4} yr, and limits imposed on the stellar and orbital parameters of the system. Our results concur with the range of orbital periods (P ≲ 1500 days) where spin-up via mass exchange is expected to occur for massive binary systems.« less