exploit prior knowledge: Topics by Science.gov

Sample records for exploit prior knowledge

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

PubMed Central

Shah, Abhik; Woolf, Peter

2009-01-01

Summary In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing. PMID:20161541
Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

PubMed

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.
Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

PubMed Central

Mikut, Ralf

2017-01-01

Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. PMID:29095927
Superposing pure quantum states with partial prior information

NASA Astrophysics Data System (ADS)

Dogra, Shruti; Thomas, George; Ghosh, Sibasish; Suter, Dieter

2018-05-01

The principle of superposition is an intriguing feature of quantum mechanics, which is regularly exploited in many different circumstances. A recent work [M. Oszmaniec et al., Phys. Rev. Lett. 116, 110403 (2016), 10.1103/PhysRevLett.116.110403] shows that the fundamentals of quantum mechanics restrict the process of superimposing two unknown pure states, even though it is possible to superimpose two quantum states with partial prior knowledge. The prior knowledge imposes geometrical constraints on the choice of input states. We discuss an experimentally feasible protocol to superimpose multiple pure states of a d -dimensional quantum system and carry out an explicit experimental realization for two single-qubit pure states with partial prior information on a two-qubit NMR quantum information processor.
Exploiting Genome Structure in Association Analysis

PubMed Central

Kim, Seyoung

2014-01-01

Abstract A genome-wide association study involves examining a large number of single-nucleotide polymorphisms (SNPs) to identify SNPs that are significantly associated with the given phenotype, while trying to reduce the false positive rate. Although haplotype-based association methods have been proposed to accommodate correlation information across nearby SNPs that are in linkage disequilibrium, none of these methods directly incorporated the structural information such as recombination events along chromosome. In this paper, we propose a new approach called stochastic block lasso for association mapping that exploits prior knowledge on linkage disequilibrium structure in the genome such as recombination rates and distances between adjacent SNPs in order to increase the power of detecting true associations while reducing false positives. Following a typical linear regression framework with the genotypes as inputs and the phenotype as output, our proposed method employs a sparsity-enforcing Laplacian prior for the regression coefficients, augmented by a first-order Markov process along the sequence of SNPs that incorporates the prior information on the linkage disequilibrium structure. The Markov-chain prior models the structural dependencies between a pair of adjacent SNPs, and allows us to look for association SNPs in a coupled manner, combining strength from multiple nearby SNPs. Our results on HapMap-simulated datasets and mouse datasets show that there is a significant advantage in incorporating the prior knowledge on linkage disequilibrium structure for marker identification under whole-genome association. PMID:21548809
Towards a machine learning framework for acquiring and exploiting monitoring and diagnostic knowledge

NASA Technical Reports Server (NTRS)

Manganaris, Stefanos; Fisher, Doug; Kulkarni, Deepak

1993-01-01

In this paper we address the problem of detecting and diagnosing faults in physical systems, for which neither prior expertise for the task nor suitable system models are available. We propose an architecture that integrates the on-line acquisition and exploitation of monitoring and diagnostic knowledge. The focus of the paper is on the component of the architecture that discovers classes of behaviors with similar characteristics by observing a system in operation. We investigate a characterization of behaviors based on best fitting approximation models. An experimental prototype has been implemented to test it. We present preliminary results in diagnosing faults of the Reaction Control System of the Space Shuttle. The merits and limitations of the approach are identified and directions for future work are set.
Multilingual Learners and Foreign Language Acquisition: Insights into the Effects of Prior Linguistic Knowledge

ERIC Educational Resources Information Center

de la Fuente, Anahí Alba; Lacroix, Hugues

2015-01-01

In foreign language classrooms we often find that, in addition to their mother tongue (L1), learners already speak--or are learning--at least one other language. As a result, they already have an array of linguistic and cognitive skills that may prove very useful if they are adequately exploited during the language learning process. However, in…
Schema-driven facilitation of new hierarchy learning in the transitive inference paradigm

PubMed Central

Kumaran, Dharshan

2013-01-01

Prior knowledge, in the form of a mental schema or framework, is viewed to facilitate the learning of new information in a range of experimental and everyday scenarios. Despite rising interest in the cognitive and neural mechanisms underlying schema-driven facilitation of new learning, few paradigms have been developed to examine this issue in humans. Here we develop a multiphase experimental scenario aimed at characterizing schema-based effects in the context of a paradigm that has been very widely used across species, the transitive inference task. We show that an associative schema, comprised of prior knowledge of the rank positions of familiar items in the hierarchy, has a marked effect on transitivity performance and the development of relational knowledge of the hierarchy that cannot be accounted for by more general changes in task strategy. Further, we show that participants are capable of deploying prior knowledge to successful effect under surprising conditions (i.e., when corrective feedback is totally absent), but only when the associative schema is robust. Finally, our results provide insights into the cognitive mechanisms underlying such schema-driven effects, and suggest that new hierarchy learning in the transitive inference task can occur through a contextual transfer mechanism that exploits the structure of associative experiences. PMID:23782509
Schema-driven facilitation of new hierarchy learning in the transitive inference paradigm.

PubMed

Kumaran, Dharshan

2013-06-19

Prior knowledge, in the form of a mental schema or framework, is viewed to facilitate the learning of new information in a range of experimental and everyday scenarios. Despite rising interest in the cognitive and neural mechanisms underlying schema-driven facilitation of new learning, few paradigms have been developed to examine this issue in humans. Here we develop a multiphase experimental scenario aimed at characterizing schema-based effects in the context of a paradigm that has been very widely used across species, the transitive inference task. We show that an associative schema, comprised of prior knowledge of the rank positions of familiar items in the hierarchy, has a marked effect on transitivity performance and the development of relational knowledge of the hierarchy that cannot be accounted for by more general changes in task strategy. Further, we show that participants are capable of deploying prior knowledge to successful effect under surprising conditions (i.e., when corrective feedback is totally absent), but only when the associative schema is robust. Finally, our results provide insights into the cognitive mechanisms underlying such schema-driven effects, and suggest that new hierarchy learning in the transitive inference task can occur through a contextual transfer mechanism that exploits the structure of associative experiences.
Enriching 3D optical surface scans with prior knowledge: tissue thickness computation by exploiting local neighborhoods.

PubMed

Wissel, Tobias; Stüber, Patrick; Wagner, Benjamin; Bruder, Ralf; Schweikard, Achim; Ernst, Floris

2016-04-01

Patient immobilization and X-ray-based imaging provide neither a convenient nor a very accurate way to ensure low repositioning errors or to compensate for motion in cranial radiotherapy. We therefore propose an optical tracking device that exploits subcutaneous structures as landmarks in addition to merely spatial registration. To develop such head tracking algorithms, precise and robust computation of these structures is necessary. Here, we show that the tissue thickness can be predicted with high accuracy and moreover exploit local neighborhood information within the laser spot grid on the forehead to further increase this estimation accuracy. We use statistical learning with Support Vector Regression and Gaussian Processes to learn a relationship between optical backscatter features and an MR tissue thickness ground truth. We compare different kernel functions for the data of five different subjects. The incident angle of the laser on the forehead as well as local neighborhoods is incorporated into the feature space. The latter represent the backscatter features from four neighboring laser spots. We confirm that the incident angle has a positive effect on the estimation error of the tissue thickness. The root-mean-square error falls even below 0.15 mm when adding the complete neighborhood information. This prior knowledge also leads to a smoothing effect on the reconstructed skin patch. Learning between different head poses yields similar results. The partial overlap of the point clouds makes the trade-off between novel information and increased feature space dimension obvious and hence feature selection by e.g., sequential forward selection necessary.
The conscious, the unconscious, and familiarity.

PubMed

Scott, Ryan B; Dienes, Zoltán

2008-09-01

This article examines the role of subjective familiarity in the implicit and explicit learning of artificial grammars. Experiment 1 found that objective measures of similarity (including fragment frequency and repetition structure) predicted ratings of familiarity, that familiarity ratings predicted grammaticality judgments, and that the extremity of familiarity ratings predicted confidence. Familiarity was further shown to predict judgments in the absence of confidence, hence contributing to above-chance guessing. Experiment 2 found that confidence developed as participants refined their knowledge of the distribution of familiarity and that differences in familiarity could be exploited prior to confidence developing. Experiment 3 found that familiarity was consciously exploited to make grammaticality judgments including those made without confidence and that familiarity could in some instances influence participants' grammaticality judgments apparently without their awareness. All 3 experiments found that knowledge distinct from familiarity was derived only under deliberate learning conditions. The results provide decisive evidence that familiarity is the essential source of knowledge in artificial grammar learning while also supporting a dual-process model of implicit and explicit learning. (c) 2008 APA, all rights reserved.
Elapsed decision time affects the weighting of prior probability in a perceptual decision task

PubMed Central

Hanks, Timothy D.; Mazurek, Mark E.; Kiani, Roozbeh; Hopp, Elizabeth; Shadlen, Michael N.

2012-01-01

Decisions are often based on a combination of new evidence with prior knowledge of the probable best choice. Optimal combination requires knowledge about the reliability of evidence, but in many realistic situations, this is unknown. Here we propose and test a novel theory: the brain exploits elapsed time during decision formation to combine sensory evidence with prior probability. Elapsed time is useful because (i) decisions that linger tend to arise from less reliable evidence, and (ii) the expected accuracy at a given decision time depends on the reliability of the evidence gathered up to that point. These regularities allow the brain to combine prior information with sensory evidence by weighting the latter in accordance with reliability. To test this theory, we manipulated the prior probability of the rewarded choice while subjects performed a reaction-time discrimination of motion direction using a range of stimulus reliabilities that varied from trial to trial. The theory explains the effect of prior probability on choice and reaction time over a wide range of stimulus strengths. We found that prior probability was incorporated into the decision process as a dynamic bias signal that increases as a function of decision time. This bias signal depends on the speed-accuracy setting of human subjects, and it is reflected in the firing rates of neurons in the lateral intraparietal cortex (LIP) of rhesus monkeys performing this task. PMID:21525274
Elapsed decision time affects the weighting of prior probability in a perceptual decision task.

PubMed

Hanks, Timothy D; Mazurek, Mark E; Kiani, Roozbeh; Hopp, Elisabeth; Shadlen, Michael N

2011-04-27

Decisions are often based on a combination of new evidence with prior knowledge of the probable best choice. Optimal combination requires knowledge about the reliability of evidence, but in many realistic situations, this is unknown. Here we propose and test a novel theory: the brain exploits elapsed time during decision formation to combine sensory evidence with prior probability. Elapsed time is useful because (1) decisions that linger tend to arise from less reliable evidence, and (2) the expected accuracy at a given decision time depends on the reliability of the evidence gathered up to that point. These regularities allow the brain to combine prior information with sensory evidence by weighting the latter in accordance with reliability. To test this theory, we manipulated the prior probability of the rewarded choice while subjects performed a reaction-time discrimination of motion direction using a range of stimulus reliabilities that varied from trial to trial. The theory explains the effect of prior probability on choice and reaction time over a wide range of stimulus strengths. We found that prior probability was incorporated into the decision process as a dynamic bias signal that increases as a function of decision time. This bias signal depends on the speed-accuracy setting of human subjects, and it is reflected in the firing rates of neurons in the lateral intraparietal area (LIP) of rhesus monkeys performing this task.
Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

PubMed Central

2012-01-01

Background An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge. PMID:22578440
Knowledge-based nonuniform sampling in multidimensional NMR.

PubMed

Schuyler, Adam D; Maciejewski, Mark W; Arthanari, Haribabu; Hoch, Jeffrey C

2011-07-01

The full resolution afforded by high-field magnets is rarely realized in the indirect dimensions of multidimensional NMR experiments because of the time cost of uniformly sampling to long evolution times. Emerging methods utilizing nonuniform sampling (NUS) enable high resolution along indirect dimensions by sampling long evolution times without sampling at every multiple of the Nyquist sampling interval. While the earliest NUS approaches matched the decay of sampling density to the decay of the signal envelope, recent approaches based on coupled evolution times attempt to optimize sampling by choosing projection angles that increase the likelihood of resolving closely-spaced resonances. These approaches employ knowledge about chemical shifts to predict optimal projection angles, whereas prior applications of tailored sampling employed only knowledge of the decay rate. In this work we adapt the matched filter approach as a general strategy for knowledge-based nonuniform sampling that can exploit prior knowledge about chemical shifts and is not restricted to sampling projections. Based on several measures of performance, we find that exponentially weighted random sampling (envelope matched sampling) performs better than shift-based sampling (beat matched sampling). While shift-based sampling can yield small advantages in sensitivity, the gains are generally outweighed by diminished robustness. Our observation that more robust sampling schemes are only slightly less sensitive than schemes highly optimized using prior knowledge about chemical shifts has broad implications for any multidimensional NMR study employing NUS. The results derived from simulated data are demonstrated with a sample application to PfPMT, the phosphoethanolamine methyltransferase of the human malaria parasite Plasmodium falciparum.
A novel tracing method for the segmentation of cell wall networks.

PubMed

De Vylder, Jonas; Rooms, Filip; Dhondt, Stijn; Inze, Dirk; Philips, Wilfried

2013-01-01

Cell wall networks are a common subject of research in biology, which are important for plant growth analysis, organ studies, etc. In order to automate the detection of individual cells in such cell wall networks, we propose a new segmentation algorithm. The proposed method is a network tracing algorithm, exploiting the prior knowledge of the network structure. The method is applicable on multiple microscopy modalities such as fluorescence, but also for images captured using non invasive microscopes such as differential interference contrast (DIC) microscopes.
Filtering genetic variants and placing informative priors based on putative biological function.

PubMed

Friedrichs, Stefanie; Malzahn, Dörthe; Pugh, Elizabeth W; Almeida, Marcio; Liu, Xiao Qing; Bailey, Julia N

2016-02-03

High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure.
Prior-knowledge-based feedforward network simulation of true boiling point curve of crude oil.

PubMed

Chen, C W; Chen, D Z

2001-11-01

Theoretical results and practical experience indicate that feedforward networks can approximate a wide class of functional relationships very well. This property is exploited in modeling chemical processes. Given finite and noisy training data, it is important to encode the prior knowledge in neural networks to improve the fit precision and the prediction ability of the model. In this paper, as to the three-layer feedforward networks and the monotonic constraint, the unconstrained method, Joerding's penalty function method, the interpolation method, and the constrained optimization method are analyzed first. Then two novel methods, the exponential weight method and the adaptive method, are proposed. These methods are applied in simulating the true boiling point curve of a crude oil with the condition of increasing monotonicity. The simulation experimental results show that the network models trained by the novel methods are good at approximating the actual process. Finally, all these methods are discussed and compared with each other.
Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

PubMed

Li, Jiansen; Song, Ying; Zhu, Zhen; Zhao, Jun

2017-05-01

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.
On a full Bayesian inference for force reconstruction problems

NASA Astrophysics Data System (ADS)

Aucejo, M.; De Smet, O.

2018-05-01

In a previous paper, the authors introduced a flexible methodology for reconstructing mechanical sources in the frequency domain from prior local information on both their nature and location over a linear and time invariant structure. The proposed approach was derived from Bayesian statistics, because of its ability in mathematically accounting for experimenter's prior knowledge. However, since only the Maximum a Posteriori estimate was computed, the posterior uncertainty about the regularized solution given the measured vibration field, the mechanical model and the regularization parameter was not assessed. To answer this legitimate question, this paper fully exploits the Bayesian framework to provide, from a Markov Chain Monte Carlo algorithm, credible intervals and other statistical measures (mean, median, mode) for all the parameters of the force reconstruction problem.

Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

PubMed Central

Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

2016-01-01

Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993
Opportunism or aquatic specialization? Evidence of freshwater fish exploitation at Ohalo II- A waterlogged Upper Paleolithic site

PubMed Central

Dayan, Tamar; Goren, Menachem; Nadel, Dani; Hershkovitz, Israel

2018-01-01

Analysis of ca. 17,000 fish remains recovered from the late Upper Paleolithic/early Epi-Paleolithic (LGM; 23,000 BP) waterlogged site of Ohalo II (Rift Valley, Israel) provides new insights into the role of wetland habitats and the fish inhabiting them during the evolution of economic strategies prior to the agricultural evolution. Of the current 19 native fish species in Lake Kinneret (Sea of Galilee), eight species were identified at Ohalo II, belonging to two freshwater families: Cyprinidae (carps) and Cichlidae (St. Peter fish). Employing a large set of quantitative and qualitative criteria (NISP, species richness, diversity, skeletal element representation, fragmentation, color, spatial distribution, etc.), we demonstrate that the inhabitants of Ohalo II used their knowledge of the breeding behavior of different species of fish, for year-round intensive exploitation. PMID:29912923
Opportunism or aquatic specialization? Evidence of freshwater fish exploitation at Ohalo II- A waterlogged Upper Paleolithic site.

PubMed

Zohar, Irit; Dayan, Tamar; Goren, Menachem; Nadel, Dani; Hershkovitz, Israel

2018-01-01

Analysis of ca. 17,000 fish remains recovered from the late Upper Paleolithic/early Epi-Paleolithic (LGM; 23,000 BP) waterlogged site of Ohalo II (Rift Valley, Israel) provides new insights into the role of wetland habitats and the fish inhabiting them during the evolution of economic strategies prior to the agricultural evolution. Of the current 19 native fish species in Lake Kinneret (Sea of Galilee), eight species were identified at Ohalo II, belonging to two freshwater families: Cyprinidae (carps) and Cichlidae (St. Peter fish). Employing a large set of quantitative and qualitative criteria (NISP, species richness, diversity, skeletal element representation, fragmentation, color, spatial distribution, etc.), we demonstrate that the inhabitants of Ohalo II used their knowledge of the breeding behavior of different species of fish, for year-round intensive exploitation.
“Super-deblended” Dust Emission in Galaxies. I. The GOODS-North Catalog and the Cosmic Star Formation Rate Density out to Redshift 6

NASA Astrophysics Data System (ADS)

Liu, Daizhong; Daddi, Emanuele; Dickinson, Mark; Owen, Frazer; Pannella, Maurilio; Sargent, Mark; Béthermin, Matthieu; Magdis, Georgios; Gao, Yu; Shu, Xinwen; Wang, Tao; Jin, Shuowen; Inami, Hanae

2018-02-01

We present a new technique to measure multi-wavelength “super-deblended” photometry from highly confused images, which we apply to Herschel and ground-based far-infrared (FIR) and (sub-)millimeter (mm) data in the northern field of the Great Observatories Origins Deep Survey. There are two key novelties. First, starting with a large database of deep Spitzer 24 μm and VLA 20 cm detections that are used to define prior positions for fitting the FIR/submm data, we perform an active selection of useful priors independently at each frequency band, moving from less to more confused bands. Exploiting knowledge of redshift and all available photometry, we identify hopelessly faint priors that we remove from the fitting pool. This approach significantly reduces blending degeneracies and allows reliable photometry to be obtained for galaxies in FIR+mm bands. Second, we obtain well-behaved, nearly Gaussian flux density uncertainties, individually tailored to all fitted priors for each band. This is done by exploiting extensive simulations that allow us to calibrate the conversion of formal fitting uncertainties to realistic uncertainties, depending on directly measurable quantities. We achieve deeper detection limits with high fidelity measurements and uncertainties at FIR+mm bands. As an illustration of the utility of these measurements, we identify 70 galaxies with z≥slant 3 and reliable FIR+mm detections. We present new constraints on the cosmic star formation rate density at 3< z< 6, finding a significant contribution from z≥slant 3 dusty galaxies that are missed by optical-to-near-infrared color selection. Photometric measurements for 3306 priors, including more than 1000 FIR+mm detections, are released publicly with our catalog.
The early Earth Observing System reference handbook: Earth Science and Applications Division missions, 1990-1997

NASA Technical Reports Server (NTRS)

1990-01-01

Prior to the launch of the Earth Observing System (EOS) series, NASA will launch and operate a wide variety of new earth science satellites and instruments, as well as undertake several efforts collecting and using the data from existing and planned satellites from other agencies and nations. These initiatives will augment the knowledge base gained from ongoing Earth Science and Applications Division (ESAD) programs. This volume describes three sets of ESAD activities -- ongoing exploitation of operational satellite data, research missions with upcoming launches between now and the first launch of EOS, and candidate earth probes.
Target-Oriented High-Resolution SAR Image Formation via Semantic Information Guided Regularizations

NASA Astrophysics Data System (ADS)

Hou, Biao; Wen, Zaidao; Jiao, Licheng; Wu, Qian

2018-04-01

Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature enhanced high resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image. However, since the simple prior of low level features are insufficient to describe different semantic contents in the image, this type of regularizer will be incapable of distinguishing between the target of interest and unconcerned background clutters. As a consequence, the features belonging to the target and clutters are simultaneously affected in the generated image without concerning their underlying semantic labels. To address this problem, we propose a novel semantic information guided framework for target oriented SAR image formation, which aims at enhancing the interested target scatters while suppressing the background clutters. Firstly, we develop a new semantics-specific regularizer for image formation by exploiting the statistical properties of different semantic categories in a target scene SAR image. In order to infer the semantic label for each pixel in an unsupervised way, we moreover induce a novel high-level prior-driven regularizer and some semantic causal rules from the prior knowledge. Finally, our regularized framework for image formation is further derived as a simple iteratively reweighted $\\ell_1$ minimization problem which can be conveniently solved by many off-the-shelf solvers. Experimental results demonstrate the effectiveness and superiority of our framework for SAR image formation in terms of target enhancement and clutters suppression, compared with the state of the arts. Additionally, the proposed framework opens a new direction of devoting some machine learning strategies to image formation, which can benefit the subsequent decision making tasks.
The influence of prior knowledge on the retrieval-directed function of note taking in prior knowledge activation.

PubMed

Wetzels, Sandra A J; Kester, Liesbeth; van Merriënboer, Jeroen J G; Broers, Nick J

2011-06-01

Prior knowledge activation facilitates learning. Note taking during prior knowledge activation (i.e., note taking directed at retrieving information from memory) might facilitate the activation process by enabling learners to build an external representation of their prior knowledge. However, taking notes might be less effective in supporting prior knowledge activation if available prior knowledge is limited. This study investigates the effects of the retrieval-directed function of note taking depending on learners' level of prior knowledge. It is hypothesized that the effectiveness of note taking is influenced by the amount of prior knowledge learners already possess. Sixty-one high school students participated in this study. A prior knowledge test was used to ascertain differences in level of prior knowledge and assign participants to a low or a high prior knowledge group. A 2×2 factorial design was used to investigate the effects of note taking during prior knowledge activation (yes, no) depending on learners' level of prior knowledge (low, high) on mental effort, performance, and mental efficiency. Note taking during prior knowledge activation lowered mental effort and increased mental efficiency for high prior knowledge learners. For low prior knowledge learners, note taking had the opposite effect on mental effort and mental efficiency. The effects of the retrieval-directed function of note taking are influenced by learners' level of prior knowledge. Learners with high prior knowledge benefit from taking notes while activating prior knowledge, whereas note taking has no beneficial effects for learners with limited prior knowledge. ©2010 The British Psychological Society.
A space-frequency multiplicative regularization for force reconstruction problems

NASA Astrophysics Data System (ADS)

Aucejo, M.; De Smet, O.

2018-05-01

Dynamic forces reconstruction from vibration data is an ill-posed inverse problem. A standard approach to stabilize the reconstruction consists in using some prior information on the quantities to identify. This is generally done by including in the formulation of the inverse problem a regularization term as an additive or a multiplicative constraint. In the present article, a space-frequency multiplicative regularization is developed to identify mechanical forces acting on a structure. The proposed regularization strategy takes advantage of one's prior knowledge of the nature and the location of excitation sources, as well as that of their spectral contents. Furthermore, it has the merit to be free from the preliminary definition of any regularization parameter. The validity of the proposed regularization procedure is assessed numerically and experimentally. It is more particularly pointed out that properly exploiting the space-frequency characteristics of the excitation field to identify can improve the quality of the force reconstruction.
Heat-Passing Framework for Robust Interpretation of Data in Networks

PubMed Central

Fang, Yi; Sun, Mengtian; Ramani, Karthik

2015-01-01

Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents. PMID:25668316
Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication

PubMed Central

Thepsoonthorn, C.; Yokozuka, T.; Miura, S.; Ogawa, K.; Miyake, Y.

2016-01-01

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony. PMID:27910902
Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication.

PubMed

Thepsoonthorn, C; Yokozuka, T; Miura, S; Ogawa, K; Miyake, Y

2016-12-02

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony.
The effects of activating prior topic and metacognitive knowledge on text comprehension scores.

PubMed

Kostons, Danny; van der Werf, Greetje

2015-09-01

Research on prior knowledge activation has consistently shown that activating learners' prior knowledge has beneficial effects on learning. If learners activate their prior knowledge, this activated knowledge serves as a framework for establishing relationships between the knowledge they already possess and new information provided to them. Thus far, prior knowledge activation has dealt primarily with topic knowledge in specific domains. Students, however, likely also possess at least some metacognitive knowledge useful in those domains, which, when activated, should aid in the deployment of helpful strategies during reading. In this study, we investigated the effects of both prior topic knowledge activation (PTKA) and prior metacognitive knowledge activation (PMKA) on text comprehension scores. Eighty-eight students in primary education were randomly distributed amongst the conditions of the 2 × 2 (PTKA yes/no × PMKA yes/no) designed experiment. Results show that activating prior metacognitive knowledge had a beneficial effect on text comprehension, whereas activating prior topic knowledge, after correcting for the amount of prior knowledge, did not. Most studies deal with explicit instruction of metacognitive knowledge, but our results show that this may not be necessary, specifically in the case of students who already have some metacognitive knowledge. However, existing metacognitive knowledge needs to be activated in order for students to make better use of this knowledge. © 2015 The British Psychological Society.
Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature.

PubMed

Radhakrishnan, Srinivasan; Erbis, Serkan; Isaacs, Jacqueline A; Kamarthi, Sagar

2017-01-01

Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword co-occurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map.
Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature

PubMed Central

Isaacs, Jacqueline A.

2017-01-01

Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword co-occurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map. PMID:28328983
Real time MRI prostate segmentation based on wavelet multiscale products flow tracking.

PubMed

Flores-Tapia, Daniel; Venugopal, Niranjan; Thomas, Gabriel; McCurdy, Boyd; Ryner, Lawrence; Pistorius, Stephen

2010-01-01

Currently, prostate cancer is the third leading cause of cancer-related deaths among men in North America. As with many others types of cancer, early detection and treatment greatly increases the patient's chance of survival. Combined Magnetic Resonance Imaging and Spectroscopic Imaging (MRI/MRSI) techniques have became a reliable tool for early stage prostate cancer detection. Nevertheless, their performance is strongly affected by the determination of the region of interest (ROI) prior to data acquisition process. The process of executing prostate MRI/MRSI techniques can be significantly enhanced by segmenting the whole prostate. A novel method for segmentation of the prostate in MRI datasets is presented. This method exploits the different behavior presented by signal singularities and noise in the wavelet domain in order to accurately detect the borders around the prostate. The prostate contour is then traced by using a set of spatially variant rules that are based on prior knowledge about the general shape of the prostate. The proposed method yielded promising results when applied to clinical datasets.
The Influence of Prior Knowledge on the Retrieval-Directed Function of Note Taking in Prior Knowledge Activation

ERIC Educational Resources Information Center

Wetzels, Sandra A. J.; Kester, Liesbeth; van Merrienboer, Jeroen J. G.; Broers, Nick J.

2011-01-01

Background: Prior knowledge activation facilitates learning. Note taking during prior knowledge activation (i.e., note taking directed at retrieving information from memory) might facilitate the activation process by enabling learners to build an external representation of their prior knowledge. However, taking notes might be less effective in…
Graphical derivations of radar, sonar, and communication signals

NASA Technical Reports Server (NTRS)

Altes, R. A.; Titlebaum, E. L.

1975-01-01

The designer of a communication system often has knowledge concerning the changes in distance between transmitter and receiver as a function of time. This information can be exploited to reduce multipath interference via proper signal design. A radar or sonar may also have good a priori information about possible target trajectories. Such knowledge can again be used to reduce the receiver's response to clutter (MTI), to enhance signal-to-noise ratio, or to simplify receiver design. There are also situations in which prior knowledge about trajectories is lacking. The system should then utilize a single-filter pair which is insensitive to the effects induced by relative motion between transmitter, receiver, and reflectors. For waveforms with large time-bandwidth products, such as long pulse trains, it is possible to graphically derive signal formats for both situations (trajectory known and unknown). Although the exact form of the signal is sometimes not specified by the graphical procedure, the problem in such cases is reduced to one which has already been solved, i.e., the generation of an impulse equivalent code.
Balancing exploration and exploitation in transferring research into practice: a comparison of five knowledge translation entity archetypes

PubMed Central

2013-01-01

Background Translating knowledge from research into clinical practice has emerged as a practice of increasing importance. This has led to the creation of new organizational entities designed to bridge knowledge between research and practice. Within the UK, the Collaborations for Leadership in Applied Health Research and Care (CLAHRC) have been introduced to ensure that emphasis is placed in ensuring research is more effectively translated and implemented in clinical practice. Knowledge translation (KT) can be accomplished in various ways and is affected by the structures, activities, and coordination practices of organizations. We draw on concepts in the innovation literature—namely exploration, exploitation, and ambidexterity—to examine these structures and activities as well as the ensuing tensions between research and implementation. Methods Using a qualitative research approach, the study was based on 106 semi-structured, in-depth interviews with the directors, theme leads and managers, key professionals involved in research and implementation in nine CLAHRCs. Data was also collected from intensive focus group workshops. Results In this article we develop five archetypes for organizing KT. The results show how the various CLAHRC entities work through partnerships to create explorative research and deliver exploitative implementation. The different archetypes highlight a range of structures that can achieve ambidextrous balance as they organize activity and coordinate practice on a continuum of exploration and exploitation. Conclusion This work suggests that KT entities aim to reach their goals through a balance between exploration and exploitation in the support of generating new research and ensuring knowledge implementation. We highlight different organizational archetypes that support various ways to maintain ambidexterity, where both exploration and exploitation are supported in an attempt to narrow the knowledge gaps. The KT entity archetypes offer insights on strategies in structuring collaboration to facilitate an effective balance of exploration and exploitation learning in the KT process. PMID:24007259
Balancing exploration and exploitation in transferring research into practice: a comparison of five knowledge translation entity archetypes.

PubMed

Oborn, Eivor; Barrett, Michael; Prince, Karl; Racko, Girts

2013-09-05

Translating knowledge from research into clinical practice has emerged as a practice of increasing importance. This has led to the creation of new organizational entities designed to bridge knowledge between research and practice. Within the UK, the Collaborations for Leadership in Applied Health Research and Care (CLAHRC) have been introduced to ensure that emphasis is placed in ensuring research is more effectively translated and implemented in clinical practice. Knowledge translation (KT) can be accomplished in various ways and is affected by the structures, activities, and coordination practices of organizations. We draw on concepts in the innovation literature--namely exploration, exploitation, and ambidexterity--to examine these structures and activities as well as the ensuing tensions between research and implementation. Using a qualitative research approach, the study was based on 106 semi-structured, in-depth interviews with the directors, theme leads and managers, key professionals involved in research and implementation in nine CLAHRCs. Data was also collected from intensive focus group workshops. In this article we develop five archetypes for organizing KT. The results show how the various CLAHRC entities work through partnerships to create explorative research and deliver exploitative implementation. The different archetypes highlight a range of structures that can achieve ambidextrous balance as they organize activity and coordinate practice on a continuum of exploration and exploitation. This work suggests that KT entities aim to reach their goals through a balance between exploration and exploitation in the support of generating new research and ensuring knowledge implementation. We highlight different organizational archetypes that support various ways to maintain ambidexterity, where both exploration and exploitation are supported in an attempt to narrow the knowledge gaps. The KT entity archetypes offer insights on strategies in structuring collaboration to facilitate an effective balance of exploration and exploitation learning in the KT process.
The Exploration-Exploitation Dilemma: A Multidisciplinary Framework

PubMed Central

Berger-Tal, Oded; Meron, Ehud; Saltz, David

2014-01-01

The trade-off between the need to obtain new knowledge and the need to use that knowledge to improve performance is one of the most basic trade-offs in nature, and optimal performance usually requires some balance between exploratory and exploitative behaviors. Researchers in many disciplines have been searching for the optimal solution to this dilemma. Here we present a novel model in which the exploration strategy itself is dynamic and varies with time in order to optimize a definite goal, such as the acquisition of energy, money, or prestige. Our model produced four very distinct phases: Knowledge establishment, Knowledge accumulation, Knowledge maintenance, and Knowledge exploitation, giving rise to a multidisciplinary framework that applies equally to humans, animals, and organizations. The framework can be used to explain a multitude of phenomena in various disciplines, such as the movement of animals in novel landscapes, the most efficient resource allocation for a start-up company, or the effects of old age on knowledge acquisition in humans. PMID:24756026

The critical success factors and impact of prior knowledge to nursing students when transferring nursing knowledge during nursing clinical practise.

PubMed

Tsai, Ming-Tien; Tsai, Ling-Long

2005-11-01

Nursing practise plays an important role in transferring nursing knowledge to nursing students. From the related literature review, prior knowledge will affect how learners gain new knowledge. There has been no direct examination of the prior knowledge interaction effect on students' performance and its influence on nursing students when evaluating the knowledge transfer success factors. This study explores (1) the critical success factors in transferring nursing knowledge, (2) the impact of prior knowledge when evaluating the success factors for transferring nursing knowledge. This research utilizes in-depth interviews to probe the initial success factor phase. A total of 422 valid questionnaires were conducted by the authors. The data were analysed by comparing the mean score and t-test between two groups. Seventeen critical success factors were identified by the two groups of students. Twelve items were selected to examine the diversity in the two groups. Students with prior knowledge were more independent than the other group. They also preferred self-directed learning over students without prior knowledge. Students who did not have prior knowledge were eager to take every opportunity to gain experience and more readily adopted new knowledge.
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

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

Xiao, H., E-mail: hengxiao@vt.edu; Wu, J.-L.; Wang, J.-X.

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations.more » Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes. - Highlights: • Proposed a physics–informed framework to quantify uncertainty in RANS simulations. • Framework incorporates physical prior knowledge and observation data. • Based on a rigorous Bayesian framework yet fully utilizes physical model. • Applicable for many complex physical systems beyond turbulent flows.« less
Microarray missing data imputation based on a set theoretic framework and biological knowledge.

PubMed

Gan, Xiangchao; Liew, Alan Wee-Chung; Yan, Hong

2006-01-01

Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set theoretic framework based on projection onto convex sets (POCS) for missing data imputation. POCS allows us to incorporate different types of a priori knowledge about missing values into the estimation process. The main idea of POCS is to formulate every piece of prior knowledge into a corresponding convex set and then use a convergence-guaranteed iterative procedure to obtain a solution in the intersection of all these sets. In this work, we design several convex sets, taking into consideration the biological characteristic of the data: the first set mainly exploit the local correlation structure among genes in microarray data, while the second set captures the global correlation structure among arrays. The third set (actually a series of sets) exploits the biological phenomenon of synchronization loss in microarray experiments. In cyclic systems, synchronization loss is a common phenomenon and we construct a series of sets based on this phenomenon for our POCS imputation algorithm. Experiments show that our algorithm can achieve a significant reduction of error compared to the KNNimpute, SVDimpute and LSimpute methods.
Effects of Prior Knowledge on Memory: Implications for Education

ERIC Educational Resources Information Center

Shing, Yee Lee; Brod, Garvin

2016-01-01

The encoding, consolidation, and retrieval of events and facts form the basis for acquiring new skills and knowledge. Prior knowledge can enhance those memory processes considerably and thus foster knowledge acquisition. But prior knowledge can also hinder knowledge acquisition, in particular when the to-be-learned information is inconsistent with…
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach

NASA Astrophysics Data System (ADS)

Xiao, H.; Wu, J.-L.; Wang, J.-X.; Sun, R.; Roy, C. J.

2016-11-01

Despite their well-known limitations, Reynolds-Averaged Navier-Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes.
Low Resolution Refinement of Atomic Models Against Crystallographic Data.

PubMed

Nicholls, Robert A; Kovalevskiy, Oleg; Murshudov, Garib N

2017-01-01

This review describes some of the problems encountered during low-resolution refinement and map calculation. Refinement is considered as an application of Bayes' theorem, allowing combination of information from various sources including crystallographic experimental data and prior chemical and structural knowledge. The sources of prior knowledge relevant to macromolecules include basic chemical information such as bonds and angles, structural information from reference models of known homologs, knowledge about secondary structures, hydrogen bonding patterns, and similarity of non-crystallographically related copies of a molecule. Additionally, prior information encapsulating local conformational conservation is exploited, keeping local interatomic distances similar to those in the starting atomic model. The importance of designing an accurate likelihood function-the only link between model parameters and observed data-is emphasized. The review also reemphasizes the importance of phases, and describes how the use of raw observed amplitudes could give a better correlation between the calculated and "true" maps. It is shown that very noisy or absent observations can be replaced by calculated structure factors, weighted according to the accuracy of the atomic model. This approach helps to smoothen the map. However, such replacement should be used sparingly, as the bias toward errors in the model could be too much to avoid. It is in general recommended that, whenever a new map is calculated, map quality should be judged by inspection of the parts of the map where there is no atomic model. It is also noted that it is advisable to work with multiple blurred and sharpened maps, as different parts of a crystal may exhibit different degrees of mobility. Doing so can allow accurate building of atomic models, accounting for overall shape as well as finer structural details. Some of the results described in this review have been implemented in the programs REFMAC5, ProSMART and LORESTR, which are available as part of the CCP4 software suite.
“Once upon a Time in the Mediterranean” Long Term Trends of Mediterranean Fisheries Resources Based on Fishers’ Traditional Ecological Knowledge

PubMed Central

Damalas, Dimitrios; Maravelias, Christos D.; Osio, Giacomo C.; Maynou, Francesc; Sbrana, Mario; Sartor, Paolo

2015-01-01

We investigate long-term changes in the Mediterranean marine resources driving the trawl fisheries by analysing fishers’ perceptions (Traditional Ecological Knowledge, TEK) throughout the Mediterranean Sea during the last 80 years. To this end, we conducted an extended set of interviews with experienced fishers that enabled us to classify species (or taxa) as ‘decreasing’ or ‘increasing’ both in terms of abundance, as well as average size in the catch. The aspect that most clearly emerged in all the investigated areas over time was the notable increase of fishing capacity indicators, such as engine power and fishing depth range. Atlantic mackerel, poor cod, scorpionfishes, striped seabream, and John Dory demonstrated a decreasing trend in the fishers’ perceived abundance, while Mediterranean parrotfish, common pandora, cuttlefish, blue and red shrimp, and mullets gave indications of an increasing temporal trend. Although, as a rule, trawler captains did not report any cataclysmic changes (e.g. extinctions), when they were invited to estimate total catches, a clear decreasing pattern emerged; this being a notable finding taking into account the steep escalation of fishing efficiency during the past century. The overall deteriorating status of stocks in most Mediterranean regions calls for responsible management and design of rebuilding plans. This should include historical information accounting for past exploitation patterns that could help defining a baseline of fish abundance prior to heavy industrial fisheries exploitation. PMID:25781459
The Effects of Prior Knowledge Activation on Free Recall and Study Time Allocation.

ERIC Educational Resources Information Center

Machiels-Bongaerts, Maureen; And Others

The effects of mobilizing prior knowledge on information processing were studied. Two hypotheses, the cognitive set-point hypothesis and the selective attention hypothesis, try to account for the facilitation effects of prior knowledge activation. These hypotheses predict different recall patterns as a result of mobilizing prior knowledge. In…
Deepening Understanding of Prior Knowledge: What Diverse First-Generation College Students in the U.S. Can Teach Us

ERIC Educational Resources Information Center

Castillo-Montoya, Milagros

2017-01-01

Educational research indicates that teachers revealing and utilizing students' prior knowledge supports students' academic learning. Yet, the variation in students' prior knowledge is not fully known. To better understand students' prior knowledge, I drew on sociocultural learning theories to examine racially and ethnically diverse college…
Ascertainment-adjusted parameter estimation approach to improve robustness against misspecification of health monitoring methods

NASA Astrophysics Data System (ADS)

Juesas, P.; Ramasso, E.

2016-12-01

Condition monitoring aims at ensuring system safety which is a fundamental requirement for industrial applications and that has become an inescapable social demand. This objective is attained by instrumenting the system and developing data analytics methods such as statistical models able to turn data into relevant knowledge. One difficulty is to be able to correctly estimate the parameters of those methods based on time-series data. This paper suggests the use of the Weighted Distribution Theory together with the Expectation-Maximization algorithm to improve parameter estimation in statistical models with latent variables with an application to health monotonic under uncertainty. The improvement of estimates is made possible by incorporating uncertain and possibly noisy prior knowledge on latent variables in a sound manner. The latent variables are exploited to build a degradation model of dynamical system represented as a sequence of discrete states. Examples on Gaussian Mixture Models, Hidden Markov Models (HMM) with discrete and continuous outputs are presented on both simulated data and benchmarks using the turbofan engine datasets. A focus on the application of a discrete HMM to health monitoring under uncertainty allows to emphasize the interest of the proposed approach in presence of different operating conditions and fault modes. It is shown that the proposed model depicts high robustness in presence of noisy and uncertain prior.
Novice and expert teachers' conceptions of learners' prior knowledge

NASA Astrophysics Data System (ADS)

Meyer, Helen

2004-11-01

This study presents comparative case studies of preservice and first-year teachers' and expert teachers' conceptions of the concept of prior knowledge. Kelly's (The Psychology of Personal Construct, New York: W.W. Norton, 1955) theory of personal constructs as discussed by Akerson, Flick, and Lederman (Journal of Research in Science Teaching, 2000, 37, 363-385) in relationship to prior knowledge underpins the study. Six teachers were selected to participate in the case studies based upon their level experience teaching science and their willingness to take part. The comparative case studies of the novice and expert teachers provide insights into (a) how novice and expert teachers understand the concept of prior knowledge and (b) how they use this knowledge to make instructional decisions. Data collection consisted of interviews, classroom observations, and document analysis. Findings suggest that novice teachers hold insufficient conceptions of prior knowledge and its role in instruction to effectively implement constructivist teaching practices. While expert teachers hold a complex conception of prior knowledge and make use of their students' prior knowledge in significant ways during instruction. A second finding was an apparent mismatch between the novice teachers' beliefs about their urban students' life experiences and prior knowledge and the wealth of knowledge the expert teachers found to draw upon.
Use of Elaborative Interrogation to Help Students Acquire Information Consistent with Prior Knowledge and Information Inconsistent with Prior Knowledge.

ERIC Educational Resources Information Center

Woloshyn, Vera E.; And Others

1994-01-01

Thirty-two factual statements, half consistent and half not consistent with subjects' prior knowledge, were processed by 140 sixth and seventh graders. Half were directed to use elaborative interrogation (using prior knowledge) to answer why each statement was true. Across all memory measures, elaborative interrogation subjects performed better…
Brief Report: Teachers' Awareness of the Relationship between Prior Knowledge and New Learning

ERIC Educational Resources Information Center

Journal for Research in Mathematics Education, 2016

2016-01-01

The author examined the degree to which experienced teachers are aware of the relationship between prior knowledge and new learning. Interviews with teachers revealed that they were explicitly aware of when students made connections between prior knowledge and new learning, when they applied their prior knowledge to new contexts, and when they…
"Dare I Ask?": Eliciting Prior Knowledge and Its Implications for Teaching and Learning

ERIC Educational Resources Information Center

Dávila, Liv Thorstensson

2015-01-01

This article examines high school teachers' engagement of newcomer English learner students' prior knowledge. Three central research questions guided this study: 1) To what extent do teachers function as mediators of their students' prior knowledge? 2) What goes into teachers' thinking about how and when to elicit prior knowledge? and 3) How do…
Asymmetric threat data mining and knowledge discovery

NASA Astrophysics Data System (ADS)

Gilmore, John F.; Pagels, Michael A.; Palk, Justin

2001-03-01

Asymmetric threats differ from the conventional force-on- force military encounters that the Defense Department has historically been trained to engage. Terrorism by its nature is now an operational activity that is neither easily detected or countered as its very existence depends on small covert attacks exploiting the element of surprise. But terrorism does have defined forms, motivations, tactics and organizational structure. Exploiting a terrorism taxonomy provides the opportunity to discover and assess knowledge of terrorist operations. This paper describes the Asymmetric Threat Terrorist Assessment, Countering, and Knowledge (ATTACK) system. ATTACK has been developed to (a) data mine open source intelligence (OSINT) information from web-based newspaper sources, video news web casts, and actual terrorist web sites, (b) evaluate this information against a terrorism taxonomy, (c) exploit country/region specific social, economic, political, and religious knowledge, and (d) discover and predict potential terrorist activities and association links. Details of the asymmetric threat structure and the ATTACK system architecture are presented with results of an actual terrorist data mining and knowledge discovery test case shown.
Ethics, Collaboration, and Presentation Methods for Local and Traditional Knowledge for Understanding Arctic Change

NASA Astrophysics Data System (ADS)

Parsons, M. A.; Gearheard, S.; McNeave, C.

2009-12-01

Local and traditional knowledge (LTK) provides rich information about the Arctic environment at spatial and temporal scales that scientific knowledge often does not have access to (e.g. localized observations of fine-scale ecological change potentially from many different communities, or local sea ice and conditions prior to 1950s ice charts and 1970s satellite records). Community-based observations and monitoring are an opportunity for Arctic residents to provide ‘frontline’ observations and measurements that are an early warning system for Arctic change. The Exchange for Local Observations and Knowledge of the Arctic (ELOKA) was established in response to the growing number of community-based and community-oriented research and observation projects in the Arctic. ELOKA provides data management and user support to facilitate the collection, preservation, exchange, and use of local observations and knowledge. Managing these data presents unique ethical challenges in terms of appropriate use of rare human knowledge and ensuring that knowledge is not lost from the local communities and not exploited in ways antithetical to community culture and desires. Local Arctic residents must be engaged as true collaborative partners while respecting their perspectives, which may vary substantially from a western science perspective. At the same time, we seek to derive scientific meaning from the local knowledge that can be used in conjunction with quantitative science data. This creates new challenges in terms of data presentation, knowledge representations, and basic issues of metadata. This presentation reviews these challenges, some initial approaches to addressing them, and overall lessons learned and future directions.
Using a numerical model to understand the connection between the ocean and acoustic travel-time measurements.

PubMed

Powell, Brian S; Kerry, Colette G; Cornuelle, Bruce D

2013-10-01

Measurements of acoustic ray travel-times in the ocean provide synoptic integrals of the ocean state between source and receiver. It is known that the ray travel-time is sensitive to variations in the ocean at the transmission time, but the sensitivity of the travel-time to spatial variations in the ocean prior to the acoustic transmission have not been quantified. This study examines the sensitivity of ray travel-time to the temporally and spatially evolving ocean state in the Philippine Sea using the adjoint of a numerical model. A one year series of five day backward integrations of the adjoint model quantify the sensitivity of travel-times to varying dynamics that can alter the travel-time of a 611 km ray by 200 ms. The early evolution of the sensitivities reveals high-mode internal waves that dissipate quickly, leaving the lowest three modes, providing a connection to variations in the internal tide generation prior to the sample time. They are also strongly sensitive to advective effects that alter density along the ray path. These sensitivities reveal how travel-time measurements are affected by both nearby and distant waters. Temporal nonlinearity of the sensitivities suggests that prior knowledge of the ocean state is necessary to exploit the travel-time observations.
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
Predictive top-down integration of prior knowledge during speech perception.

PubMed

Sohoglu, Ediz; Peelle, Jonathan E; Carlyon, Robert P; Davis, Matthew H

2012-06-20

A striking feature of human perception is that our subjective experience depends not only on sensory information from the environment but also on our prior knowledge or expectations. The precise mechanisms by which sensory information and prior knowledge are integrated remain unclear, with longstanding disagreement concerning whether integration is strictly feedforward or whether higher-level knowledge influences sensory processing through feedback connections. Here we used concurrent EEG and MEG recordings to determine how sensory information and prior knowledge are integrated in the brain during speech perception. We manipulated listeners' prior knowledge of speech content by presenting matching, mismatching, or neutral written text before a degraded (noise-vocoded) spoken word. When speech conformed to prior knowledge, subjective perceptual clarity was enhanced. This enhancement in clarity was associated with a spatiotemporal profile of brain activity uniquely consistent with a feedback process: activity in the inferior frontal gyrus was modulated by prior knowledge before activity in lower-level sensory regions of the superior temporal gyrus. In parallel, we parametrically varied the level of speech degradation, and therefore the amount of sensory detail, so that changes in neural responses attributable to sensory information and prior knowledge could be directly compared. Although sensory detail and prior knowledge both enhanced speech clarity, they had an opposite influence on the evoked response in the superior temporal gyrus. We argue that these data are best explained within the framework of predictive coding in which sensory activity is compared with top-down predictions and only unexplained activity propagated through the cortical hierarchy.
Improving transmission efficiency of large sequence alignment/map (SAM) files.

PubMed

Sakib, Muhammad Nazmus; Tang, Jijun; Zheng, W Jim; Huang, Chin-Tser

2011-01-01

Research in bioinformatics primarily involves collection and analysis of a large volume of genomic data. Naturally, it demands efficient storage and transfer of this huge amount of data. In recent years, some research has been done to find efficient compression algorithms to reduce the size of various sequencing data. One way to improve the transmission time of large files is to apply a maximum lossless compression on them. In this paper, we present SAMZIP, a specialized encoding scheme, for sequence alignment data in SAM (Sequence Alignment/Map) format, which improves the compression ratio of existing compression tools available. In order to achieve this, we exploit the prior knowledge of the file format and specifications. Our experimental results show that our encoding scheme improves compression ratio, thereby reducing overall transmission time significantly.

Support Vector Machine-Based Endmember Extraction

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

Filippi, Anthony M; Archibald, Richard K

Introduced in this paper is the utilization of Support Vector Machines (SVMs) to automatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality, and hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this Support Vector Machine-Based Endmembermore » Extraction (SVM-BEE) algorithm has the capability of autonomously determining endmembers from multiple clusters with computational speed and accuracy, while maintaining a robust tolerance to noise.« less
An Adaptive Immune Genetic Algorithm for Edge Detection

NASA Astrophysics Data System (ADS)

Li, Ying; Bai, Bendu; Zhang, Yanning

An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.
Learning builds on learning: Infants' use of native language sound patterns to learn words

PubMed Central

Graf Estes, Katharine

2014-01-01

The present research investigated how infants apply prior knowledge of environmental regularities to support new learning. The experiments tested whether infants could exploit experience with native language (English) phonotactic patterns to facilitate associating sounds with meanings during word learning. Fourteen-month-olds heard fluent speech that contained cues for detecting target words; they were embedded in sequences that occur across word boundaries. A separate group heard the target words embedded without word boundary cues. Infants then participated in an object label-learning task. With the opportunity to use native language patterns to segment the target words, infants subsequently learned the labels. Without this experience, infants failed. Novice word learners can take advantage of early learning about sounds scaffold lexical development. PMID:24980741
Identifying potential disaster zones around the Verkhnekamskoye potash deposit (Russia) using advanced information technology (IT)

NASA Astrophysics Data System (ADS)

Royer, J. J.; Filippov, L. O.

2017-07-01

This work aims at improving the exploitation of the K, Mg, salts ore of the Verkhnekamskoye deposit using advanced information technology (IT) such as 3D geostatistical modeling techniques together with high performance flotation. It is expected to provide a more profitable exploitation of the actual deposit avoiding the formation of dramatic sinkholes by a better knowledge of the deposit. The GeoChron modelling method for sedimentary formations (Mallet, 2014) was used to improve the knowledge of the Verkhnekamskoye potash deposit, Perm region, Russia. After a short introduction on the modern theory of mathematical modelling applied to mineral resources exploitation and geology, new results are presented on the sedimentary architecture of the ore deposit. They enlighten the structural geology and the fault orientations, a key point for avoiding catastrophic water inflows recharging zone during exploitation. These results are important for avoiding catastrophic sinkholes during exploitation.
Historical ecology with real numbers: past and present extent and biomass of an imperilled estuarine habitat

PubMed Central

Zu Ermgassen, Philine S. E.; Spalding, Mark D.; Blake, Brady; Coen, Loren D.; Dumbauld, Brett; Geiger, Steve; Grabowski, Jonathan H.; Grizzle, Raymond; Luckenbach, Mark; McGraw, Kay; Rodney, William; Ruesink, Jennifer L.; Powers, Sean P.; Brumbaugh, Robert

2012-01-01

Historic baselines are important in developing our understanding of ecosystems in the face of rapid global change. While a number of studies have sought to determine changes in extent of exploited habitats over historic timescales, few have quantified such changes prior to late twentieth century baselines. Here, we present, to our knowledge, the first ever large-scale quantitative assessment of the extent and biomass of marine habitat-forming species over a 100-year time frame. We examined records of wild native oyster abundance in the United States from a historic, yet already exploited, baseline between 1878 and 1935 (predominantly 1885–1915), and a current baseline between 1968 and 2010 (predominantly 2000–2010). We quantified the extent of oyster grounds in 39 estuaries historically and 51 estuaries from recent times. Data from 24 estuaries allowed comparison of historic to present extent and biomass. We found evidence for a 64 per cent decline in the spatial extent of oyster habitat and an 88 per cent decline in oyster biomass over time. The difference between these two numbers illustrates that current areal extent measures may be masking significant loss of habitat through degradation. PMID:22696522
The physician as perpetrator of abuse.

PubMed

Kluft, R P

1993-06-01

Although the exploitation and abuse of patients is forbidden by every code of medical ethics, physicians are in a power position vis-a-vis their patients, and this power may be misused. The spectrum of abusive physician behaviors includes doctors functioning as agents of control, exploiting physicianly perogatives, acting out personal problems in the medical setting, allowing subversion of their judgment, deliberately delivering suboptimal care, dehumanizing care, and sexually exploiting patients. Guidelines for the treatment of patients with such prior experiences are offered.
Relations among conceptual knowledge, procedural knowledge, and procedural flexibility in two samples differing in prior knowledge.

PubMed

Schneider, Michael; Rittle-Johnson, Bethany; Star, Jon R

2011-11-01

Competence in many domains rests on children developing conceptual and procedural knowledge, as well as procedural flexibility. However, research on the developmental relations between these different types of knowledge has yielded unclear results, in part because little attention has been paid to the validity of the measures or to the effects of prior knowledge on the relations. To overcome these problems, we modeled the three constructs in the domain of equation solving as latent factors and tested (a) whether the predictive relations between conceptual and procedural knowledge were bidirectional, (b) whether these interrelations were moderated by prior knowledge, and (c) how both constructs contributed to procedural flexibility. We analyzed data from 2 measurement points each from two samples (Ns = 228 and 304) of middle school students who differed in prior knowledge. Conceptual and procedural knowledge had stable bidirectional relations that were not moderated by prior knowledge. Both kinds of knowledge contributed independently to procedural flexibility. The results demonstrate how changes in complex knowledge structures contribute to competence development.
The Influence of Prior Knowledge on Memory: A Developmental Cognitive Neuroscience Perspective

PubMed Central

Brod, Garvin; Werkle-Bergner, Markus; Shing, Yee Lee

2013-01-01

Across ontogenetic development, individuals gather manifold experiences during which they detect regularities in their environment and thereby accumulate knowledge. This knowledge is used to guide behavior, make predictions, and acquire further new knowledge. In this review, we discuss the influence of prior knowledge on memory from both the psychology and the emerging cognitive neuroscience literature and provide a developmental perspective on this topic. Recent neuroscience findings point to a prominent role of the medial prefrontal cortex (mPFC) and of the hippocampus (HC) in the emergence of prior knowledge and in its application during the processes of successful memory encoding, consolidation, and retrieval. We take the lateral PFC into consideration as well and discuss changes in both medial and lateral PFC and HC across development and postulate how these may be related to the development of the use of prior knowledge for remembering. For future direction, we argue that, to measure age differential effects of prior knowledge on memory, it is necessary to distinguish the availability of prior knowledge from its accessibility and use. PMID:24115923
When generating answers benefits arithmetic skill: the importance of prior knowledge.

PubMed

Rittle-Johnson, Bethany; Kmicikewycz, Alexander Oleksij

2008-09-01

People remember information better if they generate the information while studying rather than read the information. However, prior research has not investigated whether this generation effect extends to related but unstudied items and has not been conducted in classroom settings. We compared third graders' success on studied and unstudied multiplication problems after they spent a class period generating answers to problems or reading the answers from a calculator. The effect of condition interacted with prior knowledge. Students with low prior knowledge had higher accuracy in the generate condition, but as prior knowledge increased, the advantage of generating answers decreased. The benefits of generating answers may extend to unstudied items and to classroom settings, but only for learners with low prior knowledge.
Two Distinct Exploratory Behaviors in Decisions from Experience: Comment on Gonzalez and Dutt (2011)

ERIC Educational Resources Information Center

Hills, Thomas T.; Hertwig, Ralph

2012-01-01

Gonzalez and Dutt (2011) recently reported that trends during sampling, prior to a consequential risky decision, reveal a gradual movement from exploration to exploitation. That is, even when search imposes no immediate costs, people adopt the same pattern manifest in costly search: early exploration followed by later exploitation. From this…
The Effects of the Timing of Isolated FFI on the Explicit Knowledge and Written Accuracy of Learners with Different Prior Knowledge of the Linguistic Target

ERIC Educational Resources Information Center

Shintani, Natsuko

2017-01-01

This study examines the effects of the timing of explicit instruction (EI) on grammatical accuracy. A total of 123 learners were divided into two groups: those with some productive knowledge of past-counterfactual conditionals (+Prior Knowledge) and those without such knowledge (-Prior Knowledge). Each group was divided into four conditions. Two…
Bayesian Retrieval of Complete Posterior PDFs of Oceanic Rain Rate From Microwave Observations

NASA Technical Reports Server (NTRS)

Chiu, J. Christine; Petty, Grant W.

2005-01-01

This paper presents a new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measurements Mission (TRMM) Microwave Imager (TMI) over the ocean, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes Theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance our understanding of theoretical benefits of the Bayesian approach, we have conducted sensitivity analyses based on two synthetic datasets for which the true conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak, due to saturation effects. It is also suggested that the choice of the estimators and the prior information are both crucial to the retrieval. In addition, the performance of our Bayesian algorithm is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.
Active Prior Tactile Knowledge Transfer for Learning Tactual Properties of New Objects

PubMed Central

Feng, Di

2018-01-01

Reusing the tactile knowledge of some previously-explored objects (prior objects) helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to actively transfer the prior tactile exploratory action experiences when it learns the detailed physical properties of new objects. These experiences, or prior tactile knowledge, are built by the feature observations that the robot perceives from multiple sensory modalities, when it applies the pressing, sliding, and static contact movements on objects with different action parameters. We call our method Active Prior Tactile Knowledge Transfer (APTKT), and systematically evaluated its performance by several experiments. Results show that the robot improved the discrimination accuracy by around 10% when it used only one training sample with the feature observations of prior objects. By further incorporating the predictions from the observation models of prior objects as auxiliary features, our method improved the discrimination accuracy by over 20%. The results also show that the proposed method is robust against transferring irrelevant prior tactile knowledge (negative knowledge transfer). PMID:29466300
Knowledge Management to Exploit Agrarian Resources as Part of Late-Eighteenth-Century Cultures of Innovation: Friedrich Casimir Medicus and Franz Von Paula Schrank

ERIC Educational Resources Information Center

Popplow, Marcus

2012-01-01

This essay contributes to a recent strain of research that questions clear-cut dichotomies between "scientists" and "artisans" in the early modern period. With a focus on the exploitation of agrarian resources, it argues for the appreciation of a more complex panorama of intersecting knowledge systems spanning from botany as…
Knowledge Structures of Entering Computer Networking Students and Their Instructors

ERIC Educational Resources Information Center

DiCerbo, Kristen E.

2007-01-01

Students bring prior knowledge to their learning experiences. This prior knowledge is known to affect how students encode and later retrieve new information learned. Teachers and content developers can use information about students' prior knowledge to create more effective lessons and materials. In many content areas, particularly the sciences,…
Nudging toward Inquiry: Awakening and Building upon Prior Knowledge

ERIC Educational Resources Information Center

Fontichiaro, Kristin, Comp.

2010-01-01

"Prior knowledge" (sometimes called schema or background knowledge) is information one already knows that helps him/her make sense of new information. New learning builds on existing prior knowledge. In traditional reporting-style research projects, students bypass this crucial step and plow right into answer-finding. It's no wonder that many…
Mind wandering during film comprehension: The role of prior knowledge and situational interest.

PubMed

Kopp, Kristopher; Mills, Caitlin; D'Mello, Sidney

2016-06-01

This study assessed the occurrence and factors that influence mind wandering (MW) in the domain of film comprehension. The cascading model of inattention assumes that a stronger mental representation (i.e., a situation model) during comprehension results in less MW. Accordingly, a suppression hypothesis suggests that MW would decrease as a function of having the knowledge of the plot of a film prior to viewing, because the prior-knowledge would help to strengthen the situation model during comprehension. Furthermore, an interest-moderation hypothesis would predict that the suppression effect of prior-knowledge would only emerge when there was interest in viewing the film. In the current experiment, 108 participants either read a short story that depicted the plot (i.e., prior-knowledge condition) or read an unrelated story of equal length (control condition) prior to viewing the short film (32.5 minutes) entitled The Red Balloon. Participants self-reported their interest in viewing the film immediately before the film was presented. MW was tracked using a self-report method targeting instances of MW with metacognitive awareness. Participants in the prior-knowledge condition reported less MW compared with the control condition, thereby supporting the suppression hypothesis. MW also decreased over the duration of the film, but only for those with prior-knowledge of the film. Finally, prior-knowledge effects on MW were only observed when interest was average or high, but not when interest was low.
GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

PubMed Central

Wagner, Florian

2015-01-01

Method Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. Results I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets. PMID:26575370
GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

PubMed

Wagner, Florian

2015-01-01

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.
Assessing the Previous Economic Knowledge of Beginning Students in Germany: Implications for Teaching Economics in Basic Courses

ERIC Educational Resources Information Center

Happ, Roland; Förster, Manuel; Zlatkin-Troitschanskaia, Olga; Carstensen, Vivian

2016-01-01

Study-related prior knowledge plays a decisive role in business and economics degree courses. Prior knowledge has a significant influence on knowledge acquisition in higher education, and teachers need information on it to plan their introductory courses accordingly. Very few studies have been conducted of first-year students' prior economic…

Enzymatic and chemical treatment limits on the controlled solubilization of brewers' spent grain.

PubMed

Robertson, James A; Castro-Mariñas, Luis; Collins, Samuel R A; Faulds, Craig B; Waldron, Keith W

2011-10-26

The enzymatic hydrolysis of brewers' spent grain (BSG) has been investigated through treatment with commercial carbohydrases and proteases. Resultant residues were then chemically fractionated and delignified. Enzymatic treatments released 25-30% of the BSG mass and yielded precursors suitable for subsequent conversion to potentially value-added products. Controlled chemical fractionation selectively solubilized arabinoxylan but with no differences apparent due to prior enzyme treatment. The loss of non-polysaccharide components during alkali treatment suggests the presence of a high proportion of alkali-soluble lignin. Further delignification of the alkali-insoluble residues and further chemical fractionation released the remaining hemicellulose, to yield a residue which was >90% cellulose. Further knowledge of the properties and interaction between BSG polymers will facilitate an improved enzyme-assisted total deconstruction of BSG and hence the exploitation of its biomass.
Towards Robust Self-Calibration for Handheld 3d Line Laser Scanning

NASA Astrophysics Data System (ADS)

Bleier, M.; Nüchter, A.

2017-11-01

This paper studies self-calibration of a structured light system, which reconstructs 3D information using video from a static consumer camera and a handheld cross line laser projector. Intersections between the individual laser curves and geometric constraints on the relative position of the laser planes are exploited to achieve dense 3D reconstruction. This is possible without any prior knowledge of the movement of the projector. However, inaccurrately extracted laser lines introduce noise in the detected intersection positions and therefore distort the reconstruction result. Furthermore, when scanning objects with specular reflections, such as glossy painted or metalic surfaces, the reflections are often extracted from the camera image as erroneous laser curves. In this paper we investiagte how robust estimates of the parameters of the laser planes can be obtained despite of noisy detections.
A spatially adaptive spectral re-ordering technique for lossless coding of hyper-spectral images

NASA Technical Reports Server (NTRS)

Memon, Nasir D.; Galatsanos, Nikolas

1995-01-01

In this paper, we propose a new approach, applicable to lossless compression of hyper-spectral images, that alleviates some limitations of linear prediction as applied to this problem. According to this approach, an adaptive re-ordering of the spectral components of each pixel is performed prior to prediction and encoding. This re-ordering adaptively exploits, on a pixel-by pixel basis, the presence of inter-band correlations for prediction. Furthermore, the proposed approach takes advantage of spatial correlations, and does not introduce any coding overhead to transmit the order of the spectral bands. This is accomplished by using the assumption that two spatially adjacent pixels are expected to have similar spectral relationships. We thus have a simple technique to exploit spectral and spatial correlations in hyper-spectral data sets, leading to compression performance improvements as compared to our previously reported techniques for lossless compression. We also look at some simple error modeling techniques for further exploiting any structure that remains in the prediction residuals prior to entropy coding.
Absorptive capacity as a guiding concept for effective public sector management and conservation of freshwater ecosystems.

PubMed

Murray, K; Roux, D J; Nel, J L; Driver, A; Freimund, W

2011-05-01

The ability of an organisation to recognise the value of new external information, acquire it, assimilate it, transform, and exploit it, namely its absorptive capacity (AC), has been much researched in the context of commercial organisations and even applied to national innovation. This paper considers four key AC-related concepts and their relevance to public sector organisations with mandates to manage and conserve freshwater ecosystems for the common good. The concepts are the importance of in-house prior related knowledge, the importance of informal knowledge transfer, the need for motivation and intensity of effort, and the importance of gatekeepers. These concepts are used to synthesise guidance for a way forward in respect of such freshwater management and conservation, using the imminent release of a specific scientific conservation planning and management tool in South Africa as a case study. The tool comprises a comprehensive series of maps that depict national freshwater ecosystem priority areas for South Africa. Insights for implementing agencies relate to maintaining an internal science, rather than research capacity; making unpublished and especially tacit knowledge available through informal knowledge transfer; not underestimating the importance of intensity of effort required to create AC, driven by focussed motivation; and the potential use of a gatekeeper at national level (external to the implementing organisations), possibly playing a more general 'bridging' role, and multiple internal (organisational) gatekeepers playing the more limited role of 'knowledge translators'. The role of AC as a unifying framework is also proposed.
Quarries as educational resources - a research with students of a secondary school of Portugal

NASA Astrophysics Data System (ADS)

Filipe, Fernanda; Henriques, Maria Helena

2015-04-01

This work describes the results obtained in a research on science education involving 18 students of Biology and Geology of the 10th grade (15 years old) of the Secondary School of Figueiró dos Vinhos (Central Portugal). Framed on the curricular topic "Earth, a very special planet", the research included the conception, implementation and evaluation of an educational intervention aiming to answer the question: "How to stimulate meaningful and relevant learning about sustainable exploitation of geological resources, namely limestone?" The intervention occurred along 8 classes of 90 minutes each, which included practical work developed in small groups (3 students/each), and several activities both in the field and in the classroom (prior and after the fieldtrip). From the methodological point of view, this research is qualitative in nature, a study-case type, with data resulting from direct observation and content analysis of the answers presented by students to questionnaires (diagnostic and intervention assessment) and to worksheets, expressly created for the research. The main goal of the intervention was that the students, by developing practical activities centered upon a field trip to an abandoned limestone quarry located close to their homes, could learn to recognize the geological impacts arising from the exploitation of geological resources and acquire skills for collecting and processing relevant information about existing rules that control the operations in quarries, in order to develop critical thinking about the nature of exploitation of these types of resources, which may hinder the promotion of sustainable development. Concerning the intervention assessment, results reinforced the idea that quarries can provide an educational resource of great value for promoting substantive knowledge on geosciences, urgently needed and consistent with the development of critical and intervenient citizens, able to decide, at the right moment, how to behave responsibly and actively in the society. Moreover, the results show that the strategies adopted appear to have contributed to encourage the development of students' skills, particularly in terms of knowledge, reasoning, communication and adoption of individual and collective attitudes and behaviors consistent with the promotion of sustainable development. Both educational strategies and resources implemented for this specific project can inspire other initiatives for other classes and schools located near to quarries, thus increasing among citizens meaningful and relevant knowledge on geosciences.
The Effects of Prior-knowledge and Online Learning Approaches on Students' Inquiry and Argumentation Abilities

NASA Astrophysics Data System (ADS)

Yang, Wen-Tsung; Lin, Yu-Ren; She, Hsiao-Ching; Huang, Kai-Yi

2015-07-01

This study investigated the effects of students' prior science knowledge and online learning approaches (social and individual) on their learning with regard to three topics: science concepts, inquiry, and argumentation. Two science teachers and 118 students from 4 eighth-grade science classes were invited to participate in this research. Students in each class were divided into three groups according to their level of prior science knowledge; they then took either our social- or individual-based online science learning program. The results show that students in the social online argumentation group performed better in argumentation and online argumentation learning. Qualitative analysis indicated that the students' social interactions benefited the co-construction of sound arguments and the accurate understanding of science concepts. In constructing arguments, students in the individual online argumentation group were limited to knowledge recall and self-reflection. High prior-knowledge students significantly outperformed low prior-knowledge students in all three aspects of science learning. However, the difference in inquiry and argumentation performance between low and high prior-knowledge students decreased with the progression of online learning topics.
Explanation and Prior Knowledge Interact to Guide Learning

ERIC Educational Resources Information Center

Williams, Joseph J.; Lombrozo, Tania

2013-01-01

How do explaining and prior knowledge contribute to learning? Four experiments explored the relationship between explanation and prior knowledge in category learning. The experiments independently manipulated whether participants were prompted to explain the category membership of study observations and whether category labels were informative in…
Finding gene regulatory network candidates using the gene expression knowledge base.

PubMed

Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.
Poverty-Exploitation-Alienation.

ERIC Educational Resources Information Center

Bronfenbrenner, Martin

1980-01-01

Illustrates how knowledge derived from the discipline of economics can be used to help shed light on social problems such as poverty, exploitation, and alienation, and can help decision makers form policy to minimize these and similar problems. (DB)
The Importance of Prior Knowledge.

ERIC Educational Resources Information Center

Cleary, Linda Miller

1989-01-01

Recounts a college English teacher's experience of reading and rereading Noam Chomsky, building up a greater store of prior knowledge. Argues that Frank Smith provides a theory for the importance of prior knowledge and Chomsky's work provided a personal example with which to interpret and integrate that theory. (RS)
Exploring expectation effects in EMDR: does prior treatment knowledge affect the degrading effects of eye movements on memories?

PubMed Central

Littel, Marianne; van Schie, Kevin; van den Hout, Marcel A.

2017-01-01

ABSTRACT Background: Eye movement desensitization and reprocessing (EMDR) is an effective psychological treatment for posttraumatic stress disorder. Recalling a memory while simultaneously making eye movements (EM) decreases a memory’s vividness and/or emotionality. It has been argued that non-specific factors, such as treatment expectancy and experimental demand, may contribute to the EMDR’s effectiveness. Objective: The present study was designed to test whether expectations about the working mechanism of EMDR would alter the memory attenuating effects of EM. Two experiments were conducted. In Experiment 1, we examined the effects of pre-existing (non-manipulated) knowledge of EMDR in participants with and without prior knowledge. In Experiment 2, we experimentally manipulated prior knowledge by providing participants without prior knowledge with correct or incorrect information about EMDR’s working mechanism. Method: Participants in both experiments recalled two aversive, autobiographical memories during brief sets of EM (Recall+EM) or keeping eyes stationary (Recall Only). Before and after the intervention, participants scored their memories on vividness and emotionality. A Bayesian approach was used to compare two competing hypotheses on the effects of (existing/given) prior knowledge: (1) Prior (correct) knowledge increases the effects of Recall+EM vs. Recall Only, vs. (2) prior knowledge does not affect the effects of Recall+EM. Results: Recall+EM caused greater reductions in memory vividness and emotionality than Recall Only in all groups, including the incorrect information group. In Experiment 1, both hypotheses were supported by the data: prior knowledge boosted the effects of EM, but only modestly. In Experiment 2, the second hypothesis was clearly supported over the first: providing knowledge of the underlying mechanism of EMDR did not alter the effects of EM. Conclusions: Recall+EM appears to be quite robust against the effects of prior expectations. As Recall+EM is the core component of EMDR, expectancy effects probably contribute little to the effectiveness of EMDR treatment. PMID:29038685
Calculus Instructors' Responses to Prior Knowledge Errors

ERIC Educational Resources Information Center

Talley, Jana Renee

2009-01-01

This study investigates the responses to prior knowledge errors that Calculus I instructors make when assessing students. Prior knowledge is operationalized as any skill or understanding that a student needs to successfully navigate through a Calculus I course. A two part qualitative study consisting of student exams and instructor interviews was…
Signaling Text-Picture Relations in Multimedia Learning: The Influence of Prior Knowledge

ERIC Educational Resources Information Center

Richter, Juliane; Scheiter, Katharina; Eitel, Alexander

2018-01-01

Multimedia integration signals highlight correspondences between text and pictures with the aim of supporting learning from multimedia. A recent meta-analysis revealed that only learners with low domain-specific prior knowledge benefit from multimedia integration signals. To more thoroughly investigate the influence of prior knowledge on the…
Menarche: Prior Knowledge and Experience.

ERIC Educational Resources Information Center

Skandhan, K. P.; And Others

1988-01-01

Recorded menstruation information among 305 young women in India, assessing the differences between those who did and did not have knowledge of menstruation prior to menarche. Those with prior knowledge considered menarche to be a normal physiological function and had a higher rate of regularity, lower rate of dysmenorrhea, and earlier onset of…
Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules.

PubMed

Ursino, Mauro; Magosso, Elisa; La Cara, Giuseppe-Emiliano; Cuppini, Cristiano

2006-09-01

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and gamma-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.
Preparing learners with partly incorrect intuitive prior knowledge for learning

PubMed Central

Ohst, Andrea; Fondu, Béatrice M. E.; Glogger, Inga; Nückles, Matthias; Renkl, Alexander

2014-01-01

Learners sometimes have incoherent and fragmented intuitive prior knowledge that is (partly) “incompatible” with the to-be-learned contents. Such knowledge in pieces can cause conceptual disorientation and cognitive overload while learning. We hypothesized that a pre-training intervention providing a generalized schema as a structuring framework for such knowledge in pieces would support (re)organizing-processes of prior knowledge and thus reduce unnecessary cognitive load during subsequent learning. Fifty-six student teachers participated in the experiment. A framework group underwent a pre-training intervention providing a generalized, categorical schema for categorizing primary learning strategies and related but different strategies as a cognitive framework for (re-)organizing their prior knowledge. Our control group received comparable factual information but no framework. Afterwards, all participants learned about primary learning strategies. The framework group claimed to possess higher levels of interest and self-efficacy, achieved higher learning outcomes, and learned more efficiently. Hence, providing a categorical framework can help overcome the barrier of incorrect prior knowledge in pieces. PMID:25071638
Supported Workplace Learning: A Knowledge Transfer Paradigm

ERIC Educational Resources Information Center

Burns, George R.; Paton, Robert R.

2005-01-01

The importance of knowledge to the effective development of economic growth in the twenty-first century has led to a number of initiatives such as lifelong learning, skills development and knowledge transfer. Of these, knowledge transfer has predominantly been concerned with the commercial exploitation of research knowledge. This article suggests…
A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

NASA Astrophysics Data System (ADS)

Qi, Pei-Han; Li, Zan; Si, Jiang-Bo; Gao, Rui

2014-12-01

Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density (PSD), a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method (PSC), is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform, the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman—Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty, and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds.
Using Hypermedia: Effects of Prior Knowledge and Goal Strength.

ERIC Educational Resources Information Center

Last, David A.; O'Donnell, Angela M.; Kelly, Anthony E.

The influences of a student's prior knowledge and desired goal on the difficulties and benefits associated with using hypertext were examined in this study. Participants, 12 students from an undergraduate course in educational psychology, were assigned to either the low or high prior knowledge category. Within these two groups, subjects were…
The Role of Prior Knowledge in Learning from Analogies in Science Texts

ERIC Educational Resources Information Center

Braasch, Jason L. G.; Goldman, Susan R.

2010-01-01

Two experiments examined whether inconsistent effects of analogies in promoting new content learning from text are related to prior knowledge of the analogy "per se." In Experiment 1, college students who demonstrated little understanding of weather systems and different levels of prior knowledge (more vs. less) of an analogous everyday…

Power relations and reciprocity: dialectics of knowledge construction.

PubMed

Ben-Ari, Adital; Enosh, Guy

2013-03-01

In this article we suggest a theoretical framework of knowledge construction by employing the concept of dialectics to power relationships between researcher and participants. Power distribution in research is perceived as dichotomous and asymmetrical in favor of the researcher, creating unequal power relations that make exploitation possible. Acknowledging such exploitation has led to a critical stance and attempts to bridge gaps through egalitarianism and empowerment of participants. Some scholars have focused on shifting expert knowledge differentials between researcher and participants throughout the research project. Others have evaluated such gaps as a source of knowledge construction. In the present work we applied a dialectical approach to understanding research relationships, suggesting reciprocity as their defining attribute, regardless of symmetry or asymmetry and as a source of knowledge construction. In this article we recommend avoiding a taken-for-granted attitude, because we see it as a direct obstacle to the construction of knowledge.
Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference

PubMed Central

Ting, Chih-Chung; Yu, Chia-Chen; Maloney, Laurence T.

2015-01-01

In Bayesian decision theory, knowledge about the probabilities of possible outcomes is captured by a prior distribution and a likelihood function. The prior reflects past knowledge and the likelihood summarizes current sensory information. The two combined (integrated) form a posterior distribution that allows estimation of the probability of different possible outcomes. In this study, we investigated the neural mechanisms underlying Bayesian integration using a novel lottery decision task in which both prior knowledge and likelihood information about reward probability were systematically manipulated on a trial-by-trial basis. Consistent with Bayesian integration, as sample size increased, subjects tended to weigh likelihood information more compared with prior information. Using fMRI in humans, we found that the medial prefrontal cortex (mPFC) correlated with the mean of the posterior distribution, a statistic that reflects the integration of prior knowledge and likelihood of reward probability. Subsequent analysis revealed that both prior and likelihood information were represented in mPFC and that the neural representations of prior and likelihood in mPFC reflected changes in the behaviorally estimated weights assigned to these different sources of information in response to changes in the environment. Together, these results establish the role of mPFC in prior-likelihood integration and highlight its involvement in representing and integrating these distinct sources of information. PMID:25632152
Exploitation of pocket gophers and their food caches by grizzly bears

USGS Publications Warehouse

Mattson, D.J.

2004-01-01

I investigated the exploitation of pocket gophers (Thomomys talpoides) by grizzly bears (Ursus arctos horribilis) in the Yellowstone region of the United States with the use of data collected during a study of radiomarked bears in 1977-1992. My analysis focused on the importance of pocket gophers as a source of energy and nutrients, effects of weather and site features, and importance of pocket gophers to grizzly bears in the western contiguous United States prior to historical extirpations. Pocket gophers and their food caches were infrequent in grizzly bear feces, although foraging for pocket gophers accounted for about 20-25% of all grizzly bear feeding activity during April and May. Compared with roots individually excavated by bears, pocket gopher food caches were less digestible but more easily dug out. Exploitation of gopher food caches by grizzly bears was highly sensitive to site and weather conditions and peaked during and shortly after snowmelt. This peak coincided with maximum success by bears in finding pocket gopher food caches. Exploitation was most frequent and extensive on gently sloping nonforested sites with abundant spring beauty (Claytonia lanceolata) and yampah (Perdieridia gairdneri). Pocket gophers are rare in forests, and spring beauty and yampah roots are known to be important foods of both grizzly bears and burrowing rodents. Although grizzly bears commonly exploit pocket gophers only in the Yellowstone region, this behavior was probably widespread in mountainous areas of the western contiguous United States prior to extirpations of grizzly bears within the last 150 years.
Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2016-12-01

In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior construction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In this paper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.
Engineering Knowledge for Assistive Living

NASA Astrophysics Data System (ADS)

Chen, Liming; Nugent, Chris

This paper introduces a knowledge based approach to assistive living in smart homes. It proposes a system architecture that makes use of knowledge in the lifecycle of assistive living. The paper describes ontology based knowledge engineering practices and discusses mechanisms for exploiting knowledge for activity recognition and assistance. It presents system implementation and experiments, and discusses initial results.
Using cross correlations to investigate how chimpanzees (Pan troglodytes) use conspecific gaze cues to extract and exploit information in a foraging competition.

PubMed

Hall, Katie; Oram, Mike W; Campbell, Matthew W; Eppley, Timothy M; Byrne, Richard W; De Waal, Frans B M

2014-10-01

In a dyadic informed forager task, chimpanzees (Pan troglodytes) are known to exploit the knowledge of informed subordinates; however, the behavioral mechanisms they employ are unknown. It is tempting to interpret outcome measures, such as which individual obtained the food, in a cognitively richer way than the outcomes may justify. We employed a different approach from prior research, asking how chimpanzees compete by maneuvering around each other, whether they use gaze cues to acquire information from others, and what information they use in moment-to-moment decision-making. We used cross correlations, which plot the correlation between two variables as a function of time, systematically to examine chimpanzee interactions in a series of dyadic informed forager contests. We used cross correlations as a "proof of concept" so as to determine whether the target actions were contingent on, or occurred in a time-locked pattern relative to, the referent actions. A subordinate individual was given privileged knowledge of food location. As expected, an ignorant dominant followed the informed subordinate's movement in the enclosure. The dominant also followed the subordinate's gaze direction: after she looked at the subordinate, she was more likely to gaze toward this same direction within one second. In contrast, the subordinate only occasionally followed the dominant's movement and gaze. The dominant also changed her own direction of movement to converge on the location to which the subordinate directed her gaze and movement. Cross correlation proves an effective technique for charting contingencies in social interactions, an important step in understanding the use of cognition in natural situations. © 2014 Wiley Periodicals, Inc.
Using cross correlations to investigate how chimpanzees (Pan troglodytes) use conspecific gaze cues to extract and exploit information in a foraging competition

PubMed Central

Hall, Katie; Oram, Mike W.; Campbell, Matthew W.; Eppley, Timothy M.; Byrne, Richard W.; de Waal, Frans B.M.

2014-01-01

In a dyadic informed forager task, chimpanzees (Pan troglodytes) are known to exploit the knowledge of informed subordinates; however, the behavioral mechanisms they employ are unknown. It is tempting to interpret outcome measures, such as which individual obtained the food, in a cognitively richer way than the outcomes may justify. We employed a different approach from prior research, asking how chimpanzees compete by maneuvering around each other, whether they use gaze cues to acquire information from others, and what information they use in moment-to-moment decision-making. We used cross correlations, which plot the correlation between two variables as a function of time, systematically to examine chimpanzee interactions in a series of dyadic informed forager contests. We used cross correlations as a “proof of concept” so as to determine whether the target actions were contingent on, or occurred in a time-locked pattern relative to, the referent actions. A subordinate individual was given privileged knowledge of food location. As expected, an ignorant dominant followed the informed subordinate’s movement in the enclosure. The dominant also followed the subordinate’s gaze direction: after she looked at the subordinate, she was more likely to gaze towards this same direction within one second. In contrast, the subordinate only occasionally followed the dominant’s movement and gaze. The dominant also changed her own direction of movement to converge on the location to which the subordinate directed her gaze and movement. Cross correlation proves an effective technique for charting contingencies in social interactions, an important step in understanding the use of cognition in natural situations. PMID:24710756
The Effect of Prior Knowledge Activation on Text Recall: An Investigation of Two Conflicting Hypotheses.

ERIC Educational Resources Information Center

Machiels-Bongaerts, Maureen; And Others

Two hypotheses, the cognitive capacity hypothesis and the selective attention hypothesis, try to account for the facilitation effects of prior knowledge activation. They appear to be mutually exclusive since they predict different recall patterns as a result of prior knowledge activation. This study was designed to determine whether the two…
Understanding the Role of Prior Knowledge in a Multimedia Learning Application

ERIC Educational Resources Information Center

Rias, Riaza Mohd; Zaman, Halimah Badioze

2013-01-01

This study looked at the effects that individual differences in prior knowledge have on student understanding in learning with multimedia in a computer science subject. Students were identified as having either low or high prior knowledge from a series of questions asked in a survey conducted at the Faculty of Computer and Mathematical Sciences at…
A Fuzzy-Based Prior Knowledge Diagnostic Model with Multiple Attribute Evaluation

ERIC Educational Resources Information Center

Lin, Yi-Chun; Huang, Yueh-Min

2013-01-01

Prior knowledge is a very important part of teaching and learning, as it affects how instructors and students interact with the learning materials. In general, tests are used to assess students' prior knowledge. Nevertheless, conventional testing approaches usually assign only an overall score to each student, and this may mean that students are…
Quantum theory for 1D X-ray free electron laser

DOE PAGES

Anisimov, Petr Mikhaylovich

2017-09-19

Classical 1D X-ray Free Electron Laser (X-ray FEL) theory has stood the test of time by guiding FEL design and development prior to any full-scale analysis. Future X-ray FELs and inverse-Compton sources, where photon recoil approaches an electron energy spread value, push the classical theory to its limits of applicability. After substantial efforts by the community to find what those limits are, there is no universally agreed upon quantum approach to design and development of future X-ray sources. We offer a new approach to formulate the quantum theory for 1D X-ray FELs that has an obvious connection to the classicalmore » theory, which allows for immediate transfer of knowledge between the two regimes. In conclusion, we exploit this connection in order to draw quantum mechanical conclusions about the quantum nature of electrons and generated radiation in terms of FEL variables.« less
Quantum theory for 1D X-ray free electron laser

NASA Astrophysics Data System (ADS)

Anisimov, Petr M.

2018-06-01

Classical 1D X-ray Free Electron Laser (X-ray FEL) theory has stood the test of time by guiding FEL design and development prior to any full-scale analysis. Future X-ray FELs and inverse-Compton sources, where photon recoil approaches an electron energy spread value, push the classical theory to its limits of applicability. After substantial efforts by the community to find what those limits are, there is no universally agreed upon quantum approach to design and development of future X-ray sources. We offer a new approach to formulate the quantum theory for 1D X-ray FELs that has an obvious connection to the classical theory, which allows for immediate transfer of knowledge between the two regimes. We exploit this connection in order to draw quantum mechanical conclusions about the quantum nature of electrons and generated radiation in terms of FEL variables.
Human body segmentation via data-driven graph cut.

PubMed

Li, Shifeng; Lu, Huchuan; Shao, Xingqing

2014-11-01

Human body segmentation is a challenging and important problem in computer vision. Existing methods usually entail a time-consuming training phase for prior knowledge learning with complex shape matching for body segmentation. In this paper, we propose a data-driven method that integrates top-down body pose information and bottom-up low-level visual cues for segmenting humans in static images within the graph cut framework. The key idea of our approach is first to exploit human kinematics to search for body part candidates via dynamic programming for high-level evidence. Then, by using the body parts classifiers, obtaining bottom-up cues of human body distribution for low-level evidence. All the evidence collected from top-down and bottom-up procedures are integrated in a graph cut framework for human body segmentation. Qualitative and quantitative experiment results demonstrate the merits of the proposed method in segmenting human bodies with arbitrary poses from cluttered backgrounds.
DART: a practical reconstruction algorithm for discrete tomography.

PubMed

Batenburg, Kees Joost; Sijbers, Jan

2011-09-01

In this paper, we present an iterative reconstruction algorithm for discrete tomography, called discrete algebraic reconstruction technique (DART). DART can be applied if the scanned object is known to consist of only a few different compositions, each corresponding to a constant gray value in the reconstruction. Prior knowledge of the gray values for each of the compositions is exploited to steer the current reconstruction towards a reconstruction that contains only these gray values. Based on experiments with both simulated CT data and experimental μCT data, it is shown that DART is capable of computing more accurate reconstructions from a small number of projection images, or from a small angular range, than alternative methods. It is also shown that DART can deal effectively with noisy projection data and that the algorithm is robust with respect to errors in the estimation of the gray values.
A compressed sensing based approach on Discrete Algebraic Reconstruction Technique.

PubMed

Demircan-Tureyen, Ezgi; Kamasak, Mustafa E

2015-01-01

Discrete tomography (DT) techniques are capable of computing better results, even using less number of projections than the continuous tomography techniques. Discrete Algebraic Reconstruction Technique (DART) is an iterative reconstruction method proposed to achieve this goal by exploiting a prior knowledge on the gray levels and assuming that the scanned object is composed from a few different densities. In this paper, DART method is combined with an initial total variation minimization (TvMin) phase to ensure a better initial guess and extended with a segmentation procedure in which the threshold values are estimated from a finite set of candidates to minimize both the projection error and the total variation (TV) simultaneously. The accuracy and the robustness of the algorithm is compared with the original DART by the simulation experiments which are done under (1) limited number of projections, (2) limited view problem and (3) noisy projections conditions.
A robust fuzzy local Information c-means clustering algorithm with noise detection

NASA Astrophysics Data System (ADS)

Shang, Jiayu; Li, Shiren; Huang, Junwei

2018-04-01

Fuzzy c-means clustering (FCM), especially with spatial constraints (FCM_S), is an effective algorithm suitable for image segmentation. Its reliability contributes not only to the presentation of fuzziness for belongingness of every pixel but also to exploitation of spatial contextual information. But these algorithms still remain some problems when processing the image with noise, they are sensitive to the parameters which have to be tuned according to prior knowledge of the noise. In this paper, we propose a new FCM algorithm, combining the gray constraints and spatial constraints, called spatial and gray-level denoised fuzzy c-means (SGDFCM) algorithm. This new algorithm conquers the parameter disadvantages mentioned above by considering the possibility of noise of each pixel, which aims to improve the robustness and obtain more detail information. Furthermore, the possibility of noise can be calculated in advance, which means the algorithm is effective and efficient.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Anisimov, Petr Mikhaylovich

Classical 1D X-ray Free Electron Laser (X-ray FEL) theory has stood the test of time by guiding FEL design and development prior to any full-scale analysis. Future X-ray FELs and inverse-Compton sources, where photon recoil approaches an electron energy spread value, push the classical theory to its limits of applicability. After substantial efforts by the community to find what those limits are, there is no universally agreed upon quantum approach to design and development of future X-ray sources. We offer a new approach to formulate the quantum theory for 1D X-ray FELs that has an obvious connection to the classicalmore » theory, which allows for immediate transfer of knowledge between the two regimes. In conclusion, we exploit this connection in order to draw quantum mechanical conclusions about the quantum nature of electrons and generated radiation in terms of FEL variables.« less
Temporal cross-correlation asymmetry and departure from equilibrium in a bistable chemical system.

PubMed

Bianca, C; Lemarchand, A

2014-06-14

This paper aims at determining sustained reaction fluxes in a nonlinear chemical system driven in a nonequilibrium steady state. The method relies on the computation of cross-correlation functions for the internal fluctuations of chemical species concentrations. By employing Langevin-type equations, we derive approximate analytical formulas for the cross-correlation functions associated with nonlinear dynamics. Kinetic Monte Carlo simulations of the chemical master equation are performed in order to check the validity of the Langevin equations for a bistable chemical system. The two approaches are found in excellent agreement, except for critical parameter values where the bifurcation between monostability and bistability occurs. From the theoretical point of view, the results imply that the behavior of cross-correlation functions cannot be exploited to measure sustained reaction fluxes in a specific nonlinear system without the prior knowledge of the associated chemical mechanism and the rate constants.
Spatio-temporal alignment of multiple sensors

NASA Astrophysics Data System (ADS)

Zhang, Tinghua; Ni, Guoqiang; Fan, Guihua; Sun, Huayan; Yang, Biao

2018-01-01

Aiming to achieve the spatio-temporal alignment of multi sensor on the same platform for space target observation, a joint spatio-temporal alignment method is proposed. To calibrate the parameters and measure the attitude of cameras, an astronomical calibration method is proposed based on star chart simulation and collinear invariant features of quadrilateral diagonal between the observed star chart. In order to satisfy a temporal correspondence and spatial alignment similarity simultaneously, the method based on the astronomical calibration and attitude measurement in this paper formulates the video alignment to fold the spatial and temporal alignment into a joint alignment framework. The advantage of this method is reinforced by exploiting the similarities and prior knowledge of velocity vector field between adjacent frames, which is calculated by the SIFT Flow algorithm. The proposed method provides the highest spatio-temporal alignment accuracy compared to the state-of-the-art methods on sequences recorded from multi sensor at different times.
Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints.

PubMed

Xiao Yang; Jianjiang Feng; Jie Zhou

2014-05-01

Dictionary based orientation field estimation approach has shown promising performance for latent fingerprints. In this paper, we seek to exploit stronger prior knowledge of fingerprints in order to further improve the performance. Realizing that ridge orientations at different locations of fingerprints have different characteristics, we propose a localized dictionaries-based orientation field estimation algorithm, in which noisy orientation patch at a location output by a local estimation approach is replaced by real orientation patch in the local dictionary at the same location. The precondition of applying localized dictionaries is that the pose of the latent fingerprint needs to be estimated. We propose a Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all orientation patches in the latent fingerprint are accumulated. Experimental results on challenging latent fingerprint datasets show the proposed method outperforms previous ones markedly.

Making Knowledge Services Work in Higher Education

ERIC Educational Resources Information Center

Norris, Donald M.; Lefrere, Paul; Mason, Jon

2006-01-01

Over the past three years, knowledge-based practices in higher education have advanced, driving the development of low/no-cost, mass-market tools for knowledge sharing and reducing some barriers to change. New investors in higher education are developing strategies to exploit the knowledge-driven value propositions. Existing institutions, anxious…
Informed walks: whispering hints to gene hunters inside networks' jungle.

PubMed

Bourdakou, Marilena M; Spyrou, George M

2017-10-11

Systemic approaches offer a different point of view on the analysis of several types of molecular associations as well as on the identification of specific gene communities in several cancer types. However, due to lack of sufficient data needed to construct networks based on experimental evidence, statistical gene co-expression networks are widely used instead. Many efforts have been made to exploit the information hidden in these networks. However, these approaches still need to capitalize comprehensively the prior knowledge encrypted into molecular pathway associations and improve their efficiency regarding the discovery of both exclusive subnetworks as candidate biomarkers and conserved subnetworks that may uncover common origins of several cancer types. In this study we present the development of the Informed Walks model based on random walks that incorporate information from molecular pathways to mine candidate genes and gene-gene links. The proposed model has been applied to TCGA (The Cancer Genome Atlas) datasets from seven different cancer types, exploring the reconstructed co-expression networks of the whole set of genes and driving to highlighted sub-networks for each cancer type. In the sequel, we elucidated the impact of each subnetwork on the indication of underlying exclusive and common molecular mechanisms as well as on the short-listing of drugs that have the potential to suppress the corresponding cancer type through a drug-repurposing pipeline. We have developed a method of gene subnetwork highlighting based on prior knowledge, capable to give fruitful insights regarding the underlying molecular mechanisms and valuable input to drug-repurposing pipelines for a variety of cancer types.
When does prior knowledge disproportionately benefit older adults’ memory?

PubMed Central

Badham, Stephen P.; Hay, Mhairi; Foxon, Natasha; Kaur, Kiran; Maylor, Elizabeth A.

2016-01-01

ABSTRACT Material consistent with knowledge/experience is generally more memorable than material inconsistent with knowledge/experience – an effect that can be more extreme in older adults. Four experiments investigated knowledge effects on memory with young and older adults. Memory for familiar and unfamiliar proverbs (Experiment 1) and for common and uncommon scenes (Experiment 2) showed similar knowledge effects across age groups. Memory for person-consistent and person-neutral actions (Experiment 3) showed a greater benefit of prior knowledge in older adults. For cued recall of related and unrelated word pairs (Experiment 4), older adults benefited more from prior knowledge only when it provided uniquely useful additional information beyond the episodic association itself. The current data and literature suggest that prior knowledge has the age-dissociable mnemonic properties of (1) improving memory for the episodes themselves (age invariant), and (2) providing conceptual information about the tasks/stimuli extrinsically to the actual episodic memory (particularly aiding older adults). PMID:26473767
Is an Illustration Always Worth Ten Thousand Words? Effects of Prior Knowledge, Learning Style and Multimedia Illustrations on Text Comprehension.

ERIC Educational Resources Information Center

Ollerenshaw, Alison; Aidman, Eugene; Kidd, Garry

1997-01-01

This study examined comprehension in four groups of undergraduates under text only, multimedia, and two diagram conditions of text supplementation. Results indicated that effects of text supplementation are mediated by prior knowledge and learning style: multimedia appears more beneficial to surface learners with little prior knowledge and makes…
Effects of Prior Knowledge in Mathematics on Learner-Interface Interactions in a Learning-by-Teaching Intelligent Tutoring System

ERIC Educational Resources Information Center

Bringula, Rex P.; Basa, Roselle S.; Dela Cruz, Cecilio; Rodrigo, Ma. Mercedes T.

2016-01-01

This study attempted to determine the influence of prior knowledge in mathematics of students on learner-interface interactions in a learning-by-teaching intelligent tutoring system. One hundred thirty-nine high school students answered a pretest (i.e., the prior knowledge in mathematics) and a posttest. In between the pretest and posttest, they…
The Effect of the States of Prior Knowledge on Question Answering.

ERIC Educational Resources Information Center

Holmes, Betty C.

A study was conducted to gain insight into the question answering abilities of good and poor readers by comparing how well they answered questions when their prior knowledge was at two different levels (high, low) and in four different states. These states of prior knowledge consisted of the ways in which answers to the questions were stored in…
Learning from Instructional Animations: How Does Prior Knowledge Mediate the Effect of Visual Cues?

ERIC Educational Resources Information Center

Arslan-Ari, I.

2018-01-01

The purpose of this study was to investigate the effects of cueing and prior knowledge on learning and mental effort of students studying an animation with narration. This study employed a 2 (no cueing vs. visual cueing) × 2 (low vs. high prior knowledge) between-subjects factorial design. The results revealed a significant interaction effect…
MQW Optical Feedback Modulators And Phase Shifters

NASA Technical Reports Server (NTRS)

Jackson, Deborah J.

1995-01-01

Laser diodes equipped with proposed multiple-quantum-well (MQW) optical feedback modulators prove useful in variety of analog and digital optical-communication applications, including fiber-optic signal-distribution networks and high-speed, low-crosstalk interconnections among super computers or very-high-speed integrated circuits. Development exploits accompanying electro-optical aspect of QCSE - variation in index of refraction with applied electric field. Also exploits sensitivity of laser diodes to optical feedback. Approach is reverse of prior approach.
New Proposals for Generating and Exploiting Solution-Oriented Knowledge

ERIC Educational Resources Information Center

Gredig, Daniel; Sommerfeld, Peter

2008-01-01

The claim that professional social work should be based on scientific knowledge is many decades old with knowledge transfer usually moving in the direction from science to practice. The authors critique this model of knowledge transfer and support a hybrid one that places more of an emphasis on professional knowledge and action occurring in the…
Knowledge discovery from data as a framework to decision support in medical domains

PubMed Central

Gibert, Karina

2009-01-01

Introduction Knowledge discovery from data (KDD) is a multidisciplinary discipline which appeared in 1996 for “non trivial identifying of valid, novel, potentially useful, ultimately understandable patterns in data”. Pre-treatment of data and post-processing is as important as the data exploitation (Data Mining) itself. Different analysis techniques can be properly combined to produce explicit knowledge from data. Methods Hybrid KDD methodologies combining Artificial Intelligence with Statistics and visualization have been used to identify patterns in complex medical phenomena: experts provide prior knowledge (pK); it biases the search of distinguishable groups of homogeneous objects; support-interpretation tools (CPG) assisted experts in conceptualization and labelling of discovered patterns, consistently with pK. Results Patterns of dependency in mental disabilities supported decision-making on legislation of the Spanish Dependency Law in Catalonia. Relationships between type of neurorehabilitation treatment and patterns of response for brain damage are assessed. Patterns of the perceived QOL along time are used in spinal cord lesion to improve social inclusion. Conclusion Reality is more and more complex and classical data analyses are not powerful enough to model it. New methodologies are required including multidisciplinarity and stressing on production of understandable models. Interaction with the experts is critical to generate meaningful results which can really support decision-making, particularly convenient transferring the pK to the system, as well as interpreting results in close interaction with experts. KDD is a valuable paradigm, particularly when facing very complex domains, not well understood yet, like many medical phenomena.
Predicting fifth-grade students' understanding of ecological science concepts with motivational and cognitive variables

NASA Astrophysics Data System (ADS)

Alao, Solomon

The need to identify factors that contribute to students' understanding of ecological concepts has been widely expressed in recent literature. The purpose of this study was to investigate the relationship between fifth grade students' prior knowledge, learning strategies, interest, and learning goals and their conceptual understanding of ecological science concepts. Subject were 72 students from three fifth grade classrooms located in a metropolitan area of the eastern United States. Students completed the goal commitment, interest, and strategy use questionnaire (GISQ), and a knowledge test designed to assess their prior knowledge and conceptual understanding of ecological science concepts. The learning goals scale assessed intentions to try to learn and understand ecological concepts. The interest scale assessed the feeling and value-related valences that students ascribed to science and ecological science concepts. The strategy use scale assessed the use of two cognitive strategies (monitoring and elaboration). The knowledge test assessed students' understanding of ecological concepts (the relationship between living organisms and their environment). Scores on all measures were examined for gender differences; no significant gender differences were observed. The motivational and cognitive variables contributed to students' understanding of ecological concepts. After accounting for interest, learning goals, and strategy use, prior knowledge accounted for 28% of the total variance in conceptual understanding. After accounting for prior knowledge, interest, learning goals, and strategy use explained 7%, 6%, and 4% of the total variance in conceptual understanding, respectively. More importantly, these variables were interrelated to each other and to conceptual understanding. After controlling for prior knowledge, learning goals, and strategy use, interest did not predict the variance in conceptual understanding. After controlling for prior knowledge, interest, and strategy use, learning goals did not predict the variance in conceptual understanding. And, after controlling for prior knowledge, interest, and learning goals, strategy use did not predict the variance in conceptual understanding. Results of this study indicated that prior knowledge, interest, learning goals, and strategy use should be included in theoretical models design to explain and to predict fifth grade students' understanding of ecological concepts. Results of this study further suggested that curriculum developers and science teachers need to take fifth grade students' prior knowledge of ecological concepts, interest in science and ecological concepts; intentions to learn and understand ecological concepts, and use of cognitive strategies into account when designing instructional contexts to support these students' understanding of ecological concepts.
Knowledge.

PubMed

Jost, Jürgen

2017-06-01

We investigate the basic principles of structural knowledge. Structural knowledge underlies cognition, and it organizes, selects and assigns meaning to information. It is the result of evolutionary, cultural and developmental processes. Because of its own constraints, it needs to discover and exploit regularities and thereby achieve a complexity reduction.
The relation between prior knowledge and students' collaborative discovery learning processes

NASA Astrophysics Data System (ADS)

Gijlers, Hannie; de Jong, Ton

2005-03-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction with the environment was logged. Based on students' individual judgments of the truth-value and testability of a series of domain-specific propositions, a detailed description of the knowledge configuration for each dyad was created before they entered the learning environment. Qualitative analyses of two dialogues illustrated that prior knowledge influences the discovery learning processes, and knowledge development in a pair of students. Assessments of student and dyad definitional (domain-specific) knowledge, generic (mathematical and graph) knowledge, and generic (discovery) skills were related to the students' dialogue in different discovery learning processes. Results show that a high level of definitional prior knowledge is positively related to the proportion of communication regarding the interpretation of results. Heterogeneity with respect to generic prior knowledge was positively related to the number of utterances made in the discovery process categories hypotheses generation and experimentation. Results of the qualitative analyses indicated that collaboration between extremely heterogeneous dyads is difficult when the high achiever is not willing to scaffold information and work in the low achiever's zone of proximal development.
Parameter Estimation and Model Selection for Indoor Environments Based on Sparse Observations

NASA Astrophysics Data System (ADS)

Dehbi, Y.; Loch-Dehbi, S.; Plümer, L.

2017-09-01

This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.
The Influence of Self-Regulated Learning and Prior Knowledge on Knowledge Acquisition in Computer-Based Learning Environments

ERIC Educational Resources Information Center

Bernacki, Matthew

2010-01-01

This study examined how learners construct textbase and situation model knowledge in hypertext computer-based learning environments (CBLEs) and documented the influence of specific self-regulated learning (SRL) tactics, prior knowledge, and characteristics of the learner on posttest knowledge scores from exposure to a hypertext. A sample of 160…
Historical spatial reconstruction of a spawning-aggregation fishery.

PubMed

Buckley, Sarah M; Thurstan, Ruth H; Tobin, Andrew; Pandolfi, John M

2017-12-01

Aggregations of individual animals that form for breeding purposes are a critical ecological process for many species, yet these aggregations are inherently vulnerable to exploitation. Studies of the decline of exploited populations that form breeding aggregations tend to focus on catch rate and thus often overlook reductions in geographic range. We tested the hypothesis that catch rate and site occupancy of exploited fish-spawning aggregations (FSAs) decline in synchrony over time. We used the Spanish mackerel (Scomberomorus commerson) spawning-aggregation fishery in the Great Barrier Reef as a case study. Data were compiled from historical newspaper archives, fisher knowledge, and contemporary fishery logbooks to reconstruct catch rates and exploitation trends from the inception of the fishery. Our fine-scale analysis of catch and effort data spanned 103 years (1911-2013) and revealed a spatial expansion of fishing effort. Effort shifted offshore at a rate of 9.4 nm/decade, and 2.9 newly targeted FSAs were reported/decade. Spatial expansion of effort masked the sequential exploitation, commercial extinction, and loss of 70% of exploited FSAs. After standardizing for improvements in technological innovations, average catch rates declined by 90.5% from 1934 to 2011 (from 119.4 to 11.41 fish/vessel/trip). Mean catch rate of Spanish mackerel and occupancy of exploited mackerel FSAs were not significantly related. Our study revealed a special kind of shifting spatial baseline in which a contraction in exploited FSAs occurred undetected. Knowledge of temporally and spatially explicit information on FSAs can be relevant for the conservation and management of FSA species. © 2017 Society for Conservation Biology.
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

PubMed Central

Sheth, Amit; Perera, Sujan; Wijeratne, Sanjaya; Thirunarayan, Krishnaprasad

2018-01-01

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.
Does prior domain-specific content knowledge influence students' recall of arguments surrounding interdisciplinary topics?

PubMed

Schmidt, Hiemke K; Rothgangel, Martin; Grube, Dietmar

2017-12-01

Awareness of various arguments can help interactants present opinions, stress points, and build counterarguments during discussions. At school, some topics are taught in a way that students learn to accumulate knowledge and gather arguments, and later employ them during debates. Prior knowledge may facilitate recalling information on well structured, fact-based topics, but does it facilitate recalling arguments during discussions on complex, interdisciplinary topics? We assessed the prior knowledge in domains related to a bioethical topic of 277 students from Germany (approximately 15 years old), their interest in the topic, and their general knowledge. The students read a text with arguments for and against prenatal diagnostics and tried to recall the arguments one week later and again six weeks later. Prior knowledge in various domains related to the topic individually and separately helped students recall the arguments. These relationships were independent of students' interest in the topic and their general knowledge. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
North-South benefit sharing arrangements in bioprospecting and genetic research: a critical ethical and legal analysis.

PubMed

Schüklenk, Udo; Kleinsmidt, Anita

2006-12-01

Most pharmaceutical research carried out today is focused on the treatment and management of the lifestyle diseases of the developed world. Diseases that affect mainly poor people are neglected in research advancements in treatment because they cannot generate large financial returns on research and development costs. Benefit sharing arrangements for the use of indigenous resources and genetic research could only marginally address this gap in research and development in diseases that affect the poor. Benefit sharing as a strategy is conceptually problematic, even if one, as we do, agrees that impoverished indigenous communities should not be exploited and that they should be assisted in improving their living conditions. The accepted concept of intellectual property protection envisages clearly defined originators and owners of knowledge, whereas the concept of community membership is fluid and indigenous knowledge is, by its very nature, open, with the originator(s) lost in the mists of time. The delineation of 'community' presents serious conceptual and practical difficulties as few communities form discrete, easily discernable groups, and most have problematic leadership structures. Benefit sharing is no substitute for governments' responsibility to uplift impoverished communities. Benefit sharing arrangements may be fraught with difficulties but considerations of respect and equity demand that prior informed consent and consultation around commercialisation of knowledge take place with the source community and their government.
Prior Knowledge Guides Speech Segregation in Human Auditory Cortex.

PubMed

Wang, Yuanye; Zhang, Jianfeng; Zou, Jiajie; Luo, Huan; Ding, Nai

2018-05-18

Segregating concurrent sound streams is a computationally challenging task that requires integrating bottom-up acoustic cues (e.g. pitch) and top-down prior knowledge about sound streams. In a multi-talker environment, the brain can segregate different speakers in about 100 ms in auditory cortex. Here, we used magnetoencephalographic (MEG) recordings to investigate the temporal and spatial signature of how the brain utilizes prior knowledge to segregate 2 speech streams from the same speaker, which can hardly be separated based on bottom-up acoustic cues. In a primed condition, the participants know the target speech stream in advance while in an unprimed condition no such prior knowledge is available. Neural encoding of each speech stream is characterized by the MEG responses tracking the speech envelope. We demonstrate that an effect in bilateral superior temporal gyrus and superior temporal sulcus is much stronger in the primed condition than in the unprimed condition. Priming effects are observed at about 100 ms latency and last more than 600 ms. Interestingly, prior knowledge about the target stream facilitates speech segregation by mainly suppressing the neural tracking of the non-target speech stream. In sum, prior knowledge leads to reliable speech segregation in auditory cortex, even in the absence of reliable bottom-up speech segregation cue.

Stewards of children education: Increasing undergraduate nursing student knowledge of child sexual abuse.

PubMed

Taylor, L Elaine; Harris, Heather S

2018-01-01

Child sexual abuse and exploitation are an increasing public health problem. In spite of the fact that nurses are in a unique position to identify and intervene in the lives of children suffering from abuse due to their role in providing health care in a variety of settings, nursing curricula does not routinely include this focus. The goal was to document the effectiveness of the Stewards of Children child sexual abuse training as an effective educational intervention to increase the knowledge level of undergraduate nursing students on how to prevent, recognize, and react responsibly to child sexual abuse and trafficking. Undergraduate nursing students were required to take the Stewards of Children training in their last semester prior to graduation. Students in the study were given a pre-test prior to the class and a post-test following the class. Pre- and post-tests were graded and the results were compared along with an item indicating the participants' perception of the educational intervention in improving their confidence and competence in this area. Data analysis revealed that post-test scores following training were significantly improved: pre-test mean=45.5%; post-test mean score=91.9%. The statistical significance of the improvement was marked, p<0.01, N=119. The mean response for the perceived values scale was 1.65 from a potential score of 2. This study found a statistically significant increase in the knowledge level of undergraduate nursing students on how to prevent, recognize, and react responsibly to child sexual abuse and trafficking following the Stewards of Children training. Students also reported a high level of confidence in how to prevent abuse and react skillfully when child sexual abuse had occurred. The authors concluded that Stewards of Children is an effective option to educate nursing students on this topic. Copyright © 2017. Published by Elsevier Ltd.
Contribution of prior semantic knowledge to new episodic learning in amnesia.

PubMed

Kan, Irene P; Alexander, Michael P; Verfaellie, Mieke

2009-05-01

We evaluated whether prior semantic knowledge would enhance episodic learning in amnesia. Subjects studied prices that are either congruent or incongruent with prior price knowledge for grocery and household items and then performed a forced-choice recognition test for the studied prices. Consistent with a previous report, healthy controls' performance was enhanced by price knowledge congruency; however, only a subset of amnesic patients experienced the same benefit. Whereas patients with relatively intact semantic systems, as measured by an anatomical measure (i.e., lesion involvement of anterior and lateral temporal lobes), experienced a significant congruency benefit, patients with compromised semantic systems did not experience a congruency benefit. Our findings suggest that when prior knowledge structures are intact, they can support acquisition of new episodic information by providing frameworks into which such information can be incorporated.
The Sexual Exploitation of Missing Children: A Research Review.

ERIC Educational Resources Information Center

Hotaling, Gerald T.; Finkelhor, David

This paper evaluates current knowledge about the prevalence, dynamics, and short- and long-term effects of sexual exploitation among missing children. It is based upon empirical research findings from books, papers presented at professional meetings, doctoral dissertations, works in progress, and more than 75 articles in professional journals.…
The Role of Knowledge Acquisition in Facilitating Customer Involvement in Product Development: Examining the Mediation Effect of Absorptive Capacity

ERIC Educational Resources Information Center

Dahiyat, Samer E.; Al-Zu'bi, Zu'bi M. F.

2012-01-01

Knowledge management has often been linked to product development, innovation, and customisation. In particular, effective exploitation of customer knowledge, through engaging customers in a process of co-creation of products, exemplifies such a link. Accordingly, this research aims to identify those dimensions of knowledge management activities…
Fast dictionary-based reconstruction for diffusion spectrum imaging.

PubMed

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar

2013-11-01

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.
Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging

PubMed Central

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F.; Yendiki, Anastasia; Wald, Lawrence L.; Adalsteinsson, Elfar

2015-01-01

Diffusion Spectrum Imaging (DSI) reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation (TV) transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using Matlab running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using Principal Component Analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. PMID:23846466
Assessment of knowledge transfer in the context of biomechanics

NASA Astrophysics Data System (ADS)

Hutchison, Randolph E.

The dynamic act of knowledge transfer, or the connection of a student's prior knowledge to features of a new problem, could be considered one of the primary goals of education. Yet studies highlight more instances of failure than success. This dissertation focuses on how knowledge transfer takes place during individual problem solving, in classroom settings and during group work. Through the lens of dynamic transfer, or how students connect prior knowledge to problem features, this qualitative study focuses on a methodology to assess transfer in the context of biomechanics. The first phase of this work investigates how a pedagogical technique based on situated cognition theory affects students' ability to transfer knowledge gained in a biomechanics class to later experiences both in and out of the classroom. A post-class focus group examined events the students remembered from the class, what they learned from them, and how they connected them to later relevant experiences inside and outside the classroom. These results were triangulated with conceptual gains evaluated through concept inventories and pre- and post- content tests. Based on these results, the next two phases of the project take a more in-depth look at dynamic knowledge transfer during independent problem-solving and group project interactions, respectively. By categorizing prior knowledge (Source Tools), problem features (Target Tools) and the connections between them, results from the second phase of this study showed that within individual problem solving, source tools were almost exclusively derived from "propagated sources," i.e. those based on an authoritative source. This differs from findings in the third phase of the project, in which a mixture of "propagated" sources and "fabricated" sources, i.e. those based on student experiences, were identified within the group project work. This methodology is effective at assessing knowledge transfer in the context of biomechanics through evidence of the ability to identify differing patterns of how different students apply prior knowledge and make new connections between prior knowledge and current problem features in different learning situations. Implications for the use of this methodology include providing insight into not only students' prior knowledge, but also how they connect this prior knowledge to problem features (i.e. dynamic knowledge transfer). It also allows the identification of instances in which external input from other students or the instructor prompted knowledge transfer to take place. The use of this dynamic knowledge transfer lens allows the addressing of gaps in student understanding, and permits further investigations of techniques that increase instances of successful knowledge transfer.
GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

PubMed

Sadowski, Krzysztof L; Thierens, Dirk; Bosman, Peter A N

2018-01-01

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.
Scalable Learning for Geostatistics and Speaker Recognition

DTIC Science & Technology

2011-01-01

of prior knowledge of the model or due to improved robustness requirements). Both these methods have their own advantages and disadvantages. The use...application. If the data is well-correlated and low-dimensional, any prior knowledge available on the data can be used to build a parametric model. In the...absence of prior knowledge , non-parametric methods can be used. If the data is high-dimensional, PCA based dimensionality reduction is often the first
SU-E-J-71: Spatially Preserving Prior Knowledge-Based Treatment Planning

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

Wang, H; Xing, L

2015-06-15

Purpose: Prior knowledge-based treatment planning is impeded by the use of a single dose volume histogram (DVH) curve. Critical spatial information is lost from collapsing the dose distribution into a histogram. Even similar patients possess geometric variations that becomes inaccessible in the form of a single DVH. We propose a simple prior knowledge-based planning scheme that extracts features from prior dose distribution while still preserving the spatial information. Methods: A prior patient plan is not used as a mere starting point for a new patient but rather stopping criteria are constructed. Each structure from the prior patient is partitioned intomore » multiple shells. For instance, the PTV is partitioned into an inner, middle, and outer shell. Prior dose statistics are then extracted for each shell and translated into the appropriate Dmin and Dmax parameters for the new patient. Results: The partitioned dose information from a prior case has been applied onto 14 2-D prostate cases. Using prior case yielded final DVHs that was comparable to manual planning, even though the DVH for the prior case was different from the DVH for the 14 cases. Solely using a single DVH for the entire organ was also performed for comparison but showed a much poorer performance. Different ways of translating the prior dose statistics into parameters for the new patient was also tested. Conclusion: Prior knowledge-based treatment planning need to salvage the spatial information without transforming the patients on a voxel to voxel basis. An efficient balance between the anatomy and dose domain is gained through partitioning the organs into multiple shells. The use of prior knowledge not only serves as a starting point for a new case but the information extracted from the partitioned shells are also translated into stopping criteria for the optimization problem at hand.« less
Diffusion-Cooled Tantalum Hot-Electron Bolometer Mixers

NASA Technical Reports Server (NTRS)

Skalare, Anders; McGrath, William; Bumble, Bruce; LeDuc, Henry

2004-01-01

A batch of experimental diffusion-cooled hot-electron bolometers (HEBs), suitable for use as mixers having input frequencies in the terahertz range and output frequencies up to about a gigahertz, exploit the superconducting/normal-conducting transition in a thin strip of tantalum. The design and operation of these HEB mixers are based on mostly the same principles as those of a prior HEB mixer that exploited the superconducting/normal- conducting transition in a thin strip of niobium and that was described elsewhere.
Baseline Characteristics of Dependent Youth Who Have Been Commercially Sexually Exploited: Findings From a Specialized Treatment Program.

PubMed

Landers, Monica; McGrath, Kimberly; Johnson, Melissa H; Armstrong, Mary I; Dollard, Norin

2017-01-01

Commercial sexual exploitation of children has emerged as a critical issue within child welfare, but little is currently known about this population or effective treatment approaches to address their unique needs. Children in foster care and runaways are reported to be vulnerable to exploitation because they frequently have unmet needs for family relationships, and they have had inadequate supervision and histories of trauma of which traffickers take advantage. The current article presents data on the demographic characteristics, trauma history, mental and behavioral health needs, physical health needs, and strengths collected on a sample of 87 commercially sexually exploited youth. These youth were served in a specialized treatment program in Miami-Dade County, Florida, for exploited youth involved with the child welfare system. Findings revealed that the youth in this study have high rates of previous sexual abuse (86% of the youth) and other traumatic experiences prior to their exploitation. Youth also exhibited considerable mental and behavioral health needs. Given that few programs emphasize the unique needs of children who have been sexually exploited, recommendations are offered for providing a continuum of specialized housing and treatment services to meet the needs of sexually exploited youth, based on the authors' experiences working with this population.
Optimization-based image reconstruction in x-ray computed tomography by sparsity exploitation of local continuity and nonlocal spatial self-similarity

NASA Astrophysics Data System (ADS)

Han-Ming, Zhang; Lin-Yuan, Wang; Lei, Li; Bin, Yan; Ai-Long, Cai; Guo-En, Hu

2016-07-01

The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography (CT) reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts. To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated. The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation. Project supported by the National Natural Science Foundation of China (Grant No. 61372172).
Comfort and experience with online learning: trends over nine years and associations with knowledge.

PubMed

Cook, David A; Thompson, Warren G

2014-07-01

Some evidence suggests that attitude toward computer-based instruction is an important determinant of success in online learning. We sought to determine how comfort using computers and perceptions of prior online learning experiences have changed over the past decade, and how these associate with learning outcomes. Each year from 2003-2011 we conducted a prospective trial of online learning. As part of each year's study, we asked medicine residents about their comfort using computers and if their previous experiences with online learning were favorable. We assessed knowledge using a multiple-choice test. We used regression to analyze associations and changes over time. 371 internal medicine and family medicine residents participated. Neither comfort with computers nor perceptions of prior online learning experiences showed a significant change across years (p > 0.61), with mean comfort rating 3.96 (maximum 5 = very comfortable) and mean experience rating 4.42 (maximum 6 = strongly agree [favorable]). Comfort showed no significant association with knowledge scores (p = 0.39) but perceptions of prior experiences did, with a 1.56% rise in knowledge score for a 1-point rise in experience score (p = 0.02). Correlations among comfort, perceptions of prior experiences, and number of prior experiences were all small and not statistically significant. Comfort with computers and perceptions of prior experience with online learning remained stable over nine years. Prior good experiences (but not comfort with computers) demonstrated a modest association with knowledge outcomes, suggesting that prior course satisfaction may influence subsequent learning.
The Collaborative Seismic Earth Model Project

NASA Astrophysics Data System (ADS)

Fichtner, A.; van Herwaarden, D. P.; Afanasiev, M.

2017-12-01

We present the first generation of the Collaborative Seismic Earth Model (CSEM). This effort is intended to address grand challenges in tomography that currently inhibit imaging the Earth's interior across the seismically accessible scales: [1] For decades to come, computational resources will remain insufficient for the exploitation of the full observable seismic bandwidth. [2] With the man power of individual research groups, only small fractions of available waveform data can be incorporated into seismic tomographies. [3] The limited incorporation of prior knowledge on 3D structure leads to slow progress and inefficient use of resources. The CSEM is a multi-scale model of global 3D Earth structure that evolves continuously through successive regional refinements. Taking the current state of the CSEM as initial model, these refinements are contributed by external collaborators, and used to advance the CSEM to the next state. This mode of operation allows the CSEM to [1] harness the distributed man and computing power of the community, [2] to make consistent use of prior knowledge, and [3] to combine different tomographic techniques, needed to cover the seismic data bandwidth. Furthermore, the CSEM has the potential to serve as a unified and accessible representation of tomographic Earth models. Generation 1 comprises around 15 regional tomographic refinements, computed with full-waveform inversion. These include continental-scale mantle models of North America, Australasia, Europe and the South Atlantic, as well as detailed regional models of the crust beneath the Iberian Peninsula and western Turkey. A global-scale full-waveform inversion ensures that regional refinements are consistent with whole-Earth structure. This first generation will serve as the basis for further automation and methodological improvements concerning validation and uncertainty quantification.
7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2014 CFR

2014-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...
7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2011 CFR

2011-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...
7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2013 CFR

2013-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...
7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2012 CFR

2012-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...
7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2010 CFR

2010-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...

A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition.

PubMed

Kurashige, Hiroki; Yamashita, Yuichi; Hanakawa, Takashi; Honda, Manabu

2018-01-01

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called "preplay" in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain's knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself.
A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition

PubMed Central

Kurashige, Hiroki; Yamashita, Yuichi; Hanakawa, Takashi; Honda, Manabu

2018-01-01

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called “preplay” in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain’s knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself. PMID:29662446
An exploratory study of adolescent pimping relationships.

PubMed

Anderson, Pamela M; Coyle, Karin K; Johnson, Anisha; Denner, Jill

2014-04-01

In the last decade, public attention to the problem of commercially sexually exploited children (CSEC) has grown. This exploratory qualitative study examines adolescent pimping relationships, including how urban youth perceive these types of relationships. Study data stem from interviews with three young adult informants with first-hand knowledge of adolescent pimping, as well as three gender-specific focus group discussions with a convenience sample of 26 urban high school students who have first- or second-hand knowledge of adolescent pimping. Findings indicate that respondents believe teen pimping exists in their schools and communities, and that those exploited typically do not self-identify as victims. Respondents also believed that younger pimps are more likely to use violence to induce compliance among the girls they exploit, whereas older pimps are more likely to emotionally manipulate young women into exploitation. Further, respondents indicated that some young people agreed to exchange or sell sex for money as a favor to their boyfriends or girlfriends, and some young people believed that selling sex is acceptable under certain circumstances. The growing attention to CSEC provides an important opportunity to expand prevention efforts to reach those most affected and at risk for exploitation. The findings highlight critical areas for augmenting traditional content in school-based HIV/STI and sexuality education classes.
Self-Explanation in the Domain of Statistics: An Expertise Reversal Effect

ERIC Educational Resources Information Center

Leppink, Jimmie; Broers, Nick J.; Imbos, Tjaart; van der Vleuten, Cees P. M.; Berger, Martijn P. F.

2012-01-01

This study investigated the effects of four instructional methods on cognitive load, propositional knowledge, and conceptual understanding of statistics, for low prior knowledge students and for high prior knowledge students. The instructional methods were (1) a reading-only control condition, (2) answering open-ended questions, (3) answering…
Web-Based Knowledge Exchange through Social Links in the Workplace

ERIC Educational Resources Information Center

Filipowski, Tomasz; Kazienko, Przemyslaw; Brodka, Piotr; Kajdanowicz, Tomasz

2012-01-01

Knowledge exchange between employees is an essential feature of recent commercial organisations on the competitive market. Based on the data gathered by various information technology (IT) systems, social links can be extracted and exploited in knowledge exchange systems of a new kind. Users of such a system ask their queries and the system…
A Finnish Concept for Academic Entrepreneurship: The Case of Satakunta University of Applied Sciences

ERIC Educational Resources Information Center

Lain, Kari

2008-01-01

In a knowledge-driven economy there is a growing need for deeper and more productive interaction between higher education and industry. The full exploitation of knowledge requires strategies, incentives, appropriate systems and strong interaction between the transfer processes and the main processes in higher education. In a knowledge-based…
Comfort and experience with online learning: trends over nine years and associations with knowledge

PubMed Central

2014-01-01

Background Some evidence suggests that attitude toward computer-based instruction is an important determinant of success in online learning. We sought to determine how comfort using computers and perceptions of prior online learning experiences have changed over the past decade, and how these associate with learning outcomes. Methods Each year from 2003–2011 we conducted a prospective trial of online learning. As part of each year’s study, we asked medicine residents about their comfort using computers and if their previous experiences with online learning were favorable. We assessed knowledge using a multiple-choice test. We used regression to analyze associations and changes over time. Results 371 internal medicine and family medicine residents participated. Neither comfort with computers nor perceptions of prior online learning experiences showed a significant change across years (p > 0.61), with mean comfort rating 3.96 (maximum 5 = very comfortable) and mean experience rating 4.42 (maximum 6 = strongly agree [favorable]). Comfort showed no significant association with knowledge scores (p = 0.39) but perceptions of prior experiences did, with a 1.56% rise in knowledge score for a 1-point rise in experience score (p = 0.02). Correlations among comfort, perceptions of prior experiences, and number of prior experiences were all small and not statistically significant. Conclusions Comfort with computers and perceptions of prior experience with online learning remained stable over nine years. Prior good experiences (but not comfort with computers) demonstrated a modest association with knowledge outcomes, suggesting that prior course satisfaction may influence subsequent learning. PMID:24985690
Middle school students' knowledge of autism.

PubMed

Campbell, Jonathan M; Barger, Brian D

2011-06-01

Authors examined 1,015 middle school students' knowledge of autism using a single item of prior awareness and a 10-item Knowledge of Autism (KOA) scale. The KOA scale was designed to assess students' knowledge of the course, etiology, and symptoms associated with autism. Less than half of students (46.1%) reported having heard of autism; however, most students correctly responded that autism was a chronic condition that was not communicable. Students reporting prior awareness of autism scored higher on 9 of 10 KOA scale items when compared to their naïve counterparts. Prior awareness of autism and KOA scores also differed across schools. A more detailed understanding of developmental changes in students' knowledge of autism should improve peer educational interventions.
Using ontologies for structuring organizational knowledge in Home Care assistance.

PubMed

Valls, Aida; Gibert, Karina; Sánchez, David; Batet, Montserrat

2010-05-01

Information Technologies and Knowledge-based Systems can significantly improve the management of complex distributed health systems, where supporting multidisciplinarity is crucial and communication and synchronization between the different professionals and tasks becomes essential. This work proposes the use of the ontological paradigm to describe the organizational knowledge of such complex healthcare institutions as a basis to support their management. The ontology engineering process is detailed, as well as the way to maintain the ontology updated in front of changes. The paper also analyzes how such an ontology can be exploited in a real healthcare application and the role of the ontology in the customization of the system. The particular case of senior Home Care assistance is addressed, as this is a highly distributed field as well as a strategic goal in an ageing Europe. The proposed ontology design is based on a Home Care medical model defined by an European consortium of Home Care professionals, framed in the scope of the K4Care European project (FP6). Due to the complexity of the model and the knowledge gap existing between the - textual - medical model and the strict formalization of an ontology, an ontology engineering methodology (On-To-Knowledge) has been followed. After applying the On-To-Knowledge steps, the following results were obtained: the feasibility study concluded that the ontological paradigm and the expressiveness of modern ontology languages were enough to describe the required medical knowledge; after the kick-off and refinement stages, a complete and non-ambiguous definition of the Home Care model, including its main components and interrelations, was obtained; the formalization stage expressed HC medical entities in the form of ontological classes, which are interrelated by means of hierarchies, properties and semantically rich class restrictions; the evaluation, carried out by exploiting the ontology into a knowledge-driven e-health application running on a real scenario, showed that the ontology design and its exploitation brought several benefits with regards to flexibility, adaptability and work efficiency from the end-user point of view; for the maintenance stage, two software tools are presented, aimed to address the incorporation and modification of healthcare units and the personalization of ontological profiles. The paper shows that the ontological paradigm and the expressiveness of modern ontology languages can be exploited not only to represent terminology in a non-ambiguous way, but also to formalize the interrelations and organizational structures involved in a real and distributed healthcare environment. This kind of ontologies facilitates the adaptation in front of changes in the healthcare organization or Care Units, supports the creation of profile-based interaction models in a transparent and seamless way, and increases the reusability and generality of the developed software components. As a conclusion of the exploitation of the developed ontology in a real medical scenario, we can say that an ontology formalizing organizational interrelations is a key component for building effective distributed knowledge-driven e-health systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
Relationship of prior knowledge and working engineers' learning preferences: implications for designing effective instruction

NASA Astrophysics Data System (ADS)

Baukal, Charles E.; Ausburn, Lynna J.

2017-05-01

Continuing engineering education (CEE) is important to ensure engineers maintain proficiency over the life of their careers. However, relatively few studies have examined designing effective training for working engineers. Research has indicated that both learner instructional preferences and prior knowledge can impact the learning process, but it has not established if these factors are interrelated. The study reported here considered relationships of prior knowledge and three aspects of learning preferences of working engineers at a manufacturing company: learning strategy choices, verbal-visual cognitive styles, and multimedia preferences. Prior knowledge was not found to be significantly related to engineers' learning preferences, indicating independence of effects of these variables on learning. The study also examined relationships of this finding to the Multimedia Cone of Abstraction and implications for its use as an instructional design tool for CEE.
Exploring the Impact of Prior Knowledge and Appropriate Feedback on Students' Perceived Cognitive Load and Learning Outcomes: Animation-based earthquakes instruction

NASA Astrophysics Data System (ADS)

Yeh, Ting-Kuang; Tseng, Kuan-Yun; Cho, Chung-Wen; Barufaldi, James P.; Lin, Mei-Shin; Chang, Chun-Yen

2012-07-01

The aim of this study was to develop an animation-based curriculum and to evaluate the effectiveness of animation-based instruction; the report involved the assessment of prior knowledge and the appropriate feedback approach, for the purpose of reducing perceived cognitive load and improving learning. The curriculum was comprised of five subunits designed to teach the 'Principles of Earthquakes.' Each subunit consisted of three modules: evaluation of prior knowledge with/without in-time feedback; animation-based instruction; and evaluation of learning outcomes with feedback. The 153 participants consisted of 10th grade high-school students. Seventy-eight students participated in the animation-based instruction, involving assessment of prior knowledge and appropriate feedback mechanism (APA group). A total of 75 students participated in animation-based learning that did not take into account their prior knowledge (ANPA group). The effectiveness of the instruction was then evaluated by using a Science Conception Test (SCT), a self-rating cognitive load questionnaire (CLQ), as well as a structured interview. The results indicated that: (1) Students' perceived cognitive load was reduced effectively through improving their prior knowledge by providing appropriate feedback. (2) When students perceived lower levels of cognitive load, they showed better learning outcome. The result of this study revealed that students of the APA group showed better performance than those of the ANPA group in an open-ended question. Furthermore, students' perceived cognitive load was negatively associated with their learning outcomes.
Investigating the Effectiveness of Inquiry-Based Instruction on Students with Different Prior Knowledge and Reading Abilities

ERIC Educational Resources Information Center

Wang, Jing-Ru; Wang, Yuh-Chao; Tai, Hsin-Jung; Chen, Wen-Ju

2010-01-01

This study examined the differential impacts of an inquiry-based instruction on conceptual changes across levels of prior knowledge and reading ability. The instrument emphasized four simultaneously important components: conceptual knowledge, reading ability, attitude toward science, and learning environment. Although the learning patterns and…
The Relation between Prior Knowledge and Students' Collaborative Discovery Learning Processes

ERIC Educational Resources Information Center

Gijlers, Hannie; de Jong, Ton

2005-01-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction…
Prior Knowledge and Story Processing: Integration, Selection, and Variation. Technical Report No. 138.

ERIC Educational Resources Information Center

Spiro, Rand J.

Psychological research concerning several aspects of the relationship between existing knowledge schemata and the processing of text is summarized in this report. The first section is concerned with dynamic processes of story understanding, with emphasis on the integration of information. The role of prior knowledge in accommodating parts of…
Preparation for College General Chemistry: More than Just a Matter of Content Knowledge Acquisition

ERIC Educational Resources Information Center

Cracolice, Mark S.; Busby, Brittany D.

2015-01-01

This study investigates the potential of five factors that may be predictive of success in college general chemistry courses: prior knowledge of common alternate conceptions, intelligence, scientific reasoning ability, proportional reasoning ability, and attitude toward chemistry. We found that both prior knowledge and scientific reasoning ability…
Third-Grade Students' Mental Models of Energy Expenditure during Exercise

ERIC Educational Resources Information Center

Pasco, Denis; Ennis, Catherine D.

2015-01-01

Background: Students' prior knowledge plays an important role in learning new knowledge. In physical education (PE) and physical activity settings, studies have confirmed the role of students' prior knowledge. According to Placek and Griffin, these studies demonstrate that: "our students are not empty balls waiting to be filled with knowledge…
Machine Learning Methods for Attack Detection in the Smart Grid.

PubMed

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.
Traffic Light Detection Using Conic Section Geometry

NASA Astrophysics Data System (ADS)

Hosseinyalmdary, S.; Yilmaz, A.

2016-06-01

Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.
Mathematics understanding and anxiety in collaborative teaching

NASA Astrophysics Data System (ADS)

Ansari, B. I.; Wahyu, N.

2017-12-01

This study aims to examine students’ mathematical understanding and anxiety using collaborative teaching. The sample consists of 51 students in the 7th-grade of MTs N Jeureula, one of the Islamic public junior high schools in Jeureula, Aceh, Indonesia. A test of mathematics understanding was administered to the students twice during the period of two months. The result suggests that there is a significant increase in mathematical understanding in the pre-test and post-test. We categorized the students into the high, intermediate, and low level of prior mathematics knowledge. In the high-level prior knowledge, there is no difference of mathematical understanding between the experiment and control group. Meanwhile, in the intermediate and low level of prior knowledge, there is a significant difference of mathematical understanding between the experiment and control group. The mathematics anxiety is at an intermediate level in the experiment class and at a high level in the control group. There is no interaction between the learning model and the students’ prior knowledge towards the mathematical understanding, but there are interactions towards the mathematics anxiety. It indicates that the collaborative teaching model and the students’ prior knowledge do not simultaneously impacts on the mathematics understanding but the mathematics anxiety.
Developmental Change in the Influence of Domain-General Abilities and Domain-Specific Knowledge on Mathematics Achievement: An Eight-Year Longitudinal Study

PubMed Central

Geary, David C.; Nicholas, Alan; Li, Yaoran; Sun, Jianguo

2016-01-01

The contributions of domain-general abilities and domain-specific knowledge to subsequent mathematics achievement were longitudinally assessed (n = 167) through 8th grade. First grade intelligence and working memory and prior grade reading achievement indexed domain-general effects and domain-specific effects were indexed by prior grade mathematics achievement and mathematical cognition measures of prior grade number knowledge, addition skills, and fraction knowledge. Use of functional data analysis enabled grade-by-grade estimation of overall domain-general and domain-specific effects on subsequent mathematics achievement, the relative importance of individual domain-general and domain-specific variables on this achievement, and linear and non-linear across-grade estimates of these effects. The overall importance of domain-general abilities for subsequent achievement was stable across grades, with working memory emerging as the most important domain-general ability in later grades. The importance of prior mathematical competencies on subsequent mathematics achievement increased across grades, with number knowledge and arithmetic skills critical in all grades and fraction knowledge in later grades. Overall, domain-general abilities were more important than domain-specific knowledge for mathematics learning in early grades but general abilities and domain-specific knowledge were equally important in later grades. PMID:28781382

Mondegreens and Soramimi as a Method to Induce Misperceptions of Speech Content – Influence of Familiarity, Wittiness, and Language Competence

PubMed Central

Beck, Claudia; Kardatzki, Bernd; Ethofer, Thomas

2014-01-01

Expectations and prior knowledge can strongly influence our perception. In vision research, such top-down modulation of perceptual processing has been extensively studied using ambiguous stimuli, such as reversible figures. Here, we propose a novel method to address this issue in the auditory modality during speech perception by means of Mondgreens and Soramimi which represent song lyrics with the potential for misperception within one or across two languages, respectively. We demonstrate that such phenomena can be induced by visual presentation of the alternative percept and occur with a sufficient probability to exploit them in neuroscientific experiments. Song familiarity did not influence the occurrence of such altered perception indicating that this tool can be employed irrespective of the participants’ knowledge of music. On the other hand, previous knowledge of the alternative percept had a strong impact on the strength of altered perception which is in line with frequent reports that these phenomena can have long-lasting effects. Finally, we demonstrate that the strength of changes in perception correlated with the extent to which they were experienced as amusing as well as the vocabulary of the participants as source of potential interpretations. These findings suggest that such perceptional phenomena might be linked to the pleasant experience of resolving ambiguity which is in line with the long-existing theory of Hermann von Helmholtz that perception and problem-solving recruit similar processes. PMID:24416261
Prior schemata transfer as an account for assessing the intuitive use of new technology.

PubMed

Fischer, Sandrine; Itoh, Makoto; Inagaki, Toshiyuki

2015-01-01

New devices are considered intuitive when they allow users to transfer prior knowledge. Drawing upon fundamental psychology experiments that distinguish prior knowledge transfer from new schema induction, a procedure was specified for assessing intuitive use. This procedure was tested with 31 participants who, prior to using an on-board computer prototype, studied its screenshots in reading vs. schema induction conditions. Distinct patterns of transfer or induction resulted for features of the prototype whose functions were familiar or unfamiliar, respectively. Though moderated by participants' cognitive style, these findings demonstrated a means for quantitatively assessing transfer of prior knowledge as the operation that underlies intuitive use. Implications for interface evaluation and design, as well as potential improvements to the procedure, are discussed. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Deception in plants: mimicry or perceptual exploitation?

PubMed

Schaefer, H Martin; Ruxton, Graeme D

2009-12-01

Mimicry involves adaptive resemblance between a mimic and a model. However, despite much recent research, it remains contentious in plants. Here, we review recent progress on studying deception by flowers, distinguishing between plants relying on mimicry to achieve pollination and those relying on the exploitation of the perceptual biases of animals. We disclose fundamental differences between both mechanisms and explain why the evolution of exploitation is less constrained than that of mimicry. Exploitation of perceptual biases might thus be a precursor for the gradual evolution of mimicry. Increasing knowledge on the sensory and cognitive filters in animals, and on the selective pressures that maintain them, should aid researchers in tracing the evolutionary dynamics of deception in plants.
How Prior Knowledge and Colour Contrast Interfere Visual Search Processes in Novice Learners: An Eye Tracking Study

ERIC Educational Resources Information Center

Sonmez, Duygu; Altun, Arif; Mazman, Sacide Guzin

2012-01-01

This study investigates how prior content knowledge and prior exposure to microscope slides on the phases of mitosis effect students' visual search strategies and their ability to differentiate cells that are going through any phases of mitosis. Two different sets of microscope slide views were used for this purpose; with high and low colour…
Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.

PubMed

Leifeld, Thomas; Zhang, Zhihua; Zhang, Ping

2018-01-01

Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.
Executive Functions as Moderators of the Worked Example Effect: When Shifting Is More Important than Working Memory Capacity

ERIC Educational Resources Information Center

Schwaighofer, Matthias; Bühner, Markus; Fischer, Frank

2016-01-01

Worked examples have proven to be effective for knowledge acquisition compared with problem solving, particularly when prior knowledge is low (e.g., Kalyuga, 2007). However, in addition to prior knowledge, executive functions and fluid intelligence might be potential moderators of the effectiveness of worked examples. The present study examines…
Can Prior Knowledge Hurt Text Comprehension? An Answer Borrowed from Plato, Aristotle, and Descartes.

ERIC Educational Resources Information Center

Friedman, Lawrence B.

Taking a philosophical approach based on what Plato, Aristotle, and Descartes said about knowledge, this paper addresses some of the murkiness in the conceptual space surrounding the issue of whether prior knowledge does or does not facilitate text comprehension. Specifically, the paper first develops a non-exhaustive typology of cases in which…
Effects of Activation of Prior Knowledge on the Recall of a Clinical Case.

ERIC Educational Resources Information Center

Schmidt, Henk G.; Boshuizen, Henny P. A.

A study investigated the known phenomenon of "intermediate effect" in which medical students with an intermediate amount of knowledge and experience demonstrate higher amounts of recall of the text of a medical case than either experienced clinicians or novices. In this study the amount of activation of prior knowledge was controlled by…
Effect of Altered Prior Knowledge on Passage Recall.

ERIC Educational Resources Information Center

Langer, Judith A.; Nicolich, Mark

A study was conducted to determine: (1) the relationships between prior knowledge and passage recall; (2) the effect of a prereading activity (PReP) on available knowledge; and (3) the effect of the PReP activity on total comprehension scores. The subjects were 161 sixth grade students from a middle class suburban Long Island, New York, public…
Does Teaching Experience Matter? Examining Biology Teachers' Prior Knowledge for Teaching in an Alternative Certification Program

ERIC Educational Resources Information Center

Friedrichsen, Patricia J.; Abell, Sandra K.; Pareja, Enrique M.; Brown, Patrick L.; Lankford, Deanna M.; Volkmann, Mark J.

2009-01-01

Alternative certification programs (ACPs) have been proposed as a viable way to address teacher shortages, yet we know little about how teacher knowledge develops within such programs. The purpose of this study was to investigate prior knowledge for teaching among students entering an ACP, comparing individuals with teaching experience to those…
The Importance of Prior Knowledge when Comparing Examples: Influences on Conceptual and Procedural Knowledge of Equation Solving

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Star, Jon R.; Durkin, Kelley

2009-01-01

Comparing multiple examples typically supports learning and transfer in laboratory studies and is considered a key feature of high-quality mathematics instruction. This experimental study investigated the importance of prior knowledge in learning from comparison. Seventh- and 8th-grade students (N = 236) learned to solve equations by comparing…
Collapse and recovery of forage fish populations prior to commercial exploitation

NASA Astrophysics Data System (ADS)

McClatchie, S.; Hendy, I. L.; Thompson, A. R.; Watson, W.

2017-02-01

We use a new, well-calibrated 500 year paleorecord off southern California to determine collapse frequency, cross correlation, persistence, and return times of exploited forage fish populations. The paleorecord shows that "collapse" (defined as <10% of the mean peak biomass) is a normal state repeatedly experienced by northern anchovy, Pacific hake, and Pacific sardine which were collapsed 29-40% of the time, prior to commercial fishing exploitation. Mean (± SD) persistence of "fishable biomass" (defined as one third mean peak biomass from the paleorecord) was 19 ± 18, 15 ± 17, and 12 ± 7 years for anchovy, hake, and sardine. Mean return times to the same biomass was 8 years for anchovy but 22 years for sardine and hake. Further, we find that sardine and anchovy are positively correlated over 400 years, consistent with coherent declines of both species off California. Persistence and return times combined with positive sardine-anchovy correlation indicate that on average 1-2 decades of fishable biomass will be followed by 1-2 decades of low forage. Forage populations are resilient on the 500 year time scale, but their collapse and recovery cycle (based on the paleorecord) are suited to alternating periods of high fishing mortality and periods of little or no fishing.
Partially supervised speaker clustering.

PubMed

Tang, Hao; Chu, Stephen Mingyu; Hasegawa-Johnson, Mark; Huang, Thomas S

2012-05-01

Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical model-based distance metrics, 2) our advocated use of the cosine distance metric yields consistent increases in the speaker clustering performance as compared to the commonly used euclidean distance metric, 3) our partially supervised speaker clustering concept and strategies significantly improve the speaker clustering performance over the baselines, and 4) our proposed LSDA algorithm further leads to state-of-the-art speaker clustering performance.
What You Know Can Hurt You: Effects of Age and Prior Knowledge on the Accuracy of Judgments of Learning

PubMed Central

Toth, Jeffrey P.; Daniels, Karen A.; Solinger, Lisa A.

2011-01-01

How do aging and prior knowledge affect memory and metamemory? We explored this question in the context of a dual-process approach to Judgments of Learning (JOLs) which require people to predict their ability to remember information at a later time. Young and older adults (n's = 36, mean ages = 20.2 & 73.1) studied the names of actors that were famous in the 1950s or 1990s, providing a JOL for each. Recognition memory for studied and unstudied actors was then assessed using a Recollect/Know/No-Memory (R/K/N) judgment task. Results showed that prior knowledge increased recollection in both age groups such that older adults recollected significantly more 1950s actors than younger adults. Also, for both age groups and both decades, actors judged R at test garnered significantly higher JOLs at study than actors judged K or N. However, while the young showed benefits of prior knowledge on relative JOL accuracy, older adults did not, showing lower levels of JOL accuracy for 1950s actors despite having higher recollection for, and knowledge about, those actors. Overall, the data suggest that prior knowledge can be a double-edged sword, increasing the availability of details that can support later recollection, but also increasing non-diagnostic feelings of familiarity that can reduce the accuracy of memory predictions. PMID:21480715
Myxozoan infections of caecilians demonstrate broad host specificity and indicate a link with human activity.

PubMed

Hartigan, Ashlie; Wilkinson, Mark; Gower, David J; Streicher, Jeffrey W; Holzer, Astrid S; Okamura, Beth

2016-05-01

Myxozoans are parasitic cnidarians that infect a wide variety of hosts. Vertebrates typically serve as intermediate hosts whereas definitive hosts are invertebrates, including annelids and bryozoans. Myxozoans are known to exploit species in two of the three extant amphibian orders (Anura: frogs and toads; Caudata: newts and salamanders). Here we use museum collections to determine, to our knowledge for the first time, whether myxozoans also exploit the third amphibian order (Gymnophiona: caecilians). Caecilians are a poorly known group of limbless amphibians, the ecologies of which range from aquatic to fully terrestrial. We examined 12 caecilian species in seven families (148 individuals total) characterised by a diversity of ecologies and life histories. Using morphological and molecular surveys, we discovered the presence of the myxozoan Cystodiscus axonis in two South American species (one of seven examined families) of aquatic caecilians - Typhlonectes natans and Typhlonectes compressicauda. All infected caecilians had been maintained in captivity in the United Kingdom prior to their preservation. Cystodiscus axonis is known from several Australian frog species and its presence in caecilians indicates a capacity for infecting highly divergent amphibian hosts. This first known report of myxozoan infections in caecilians provides evidence of a broad geographic and host range. However, the source of these infections remains unknown and could be related to exposure in South America, the U.K. or to conditions in captivity. Copyright © 2016 Australian Society for Parasitology Inc. All rights reserved.
On the Evolution of Colleges and Universities

ERIC Educational Resources Information Center

Fugazzotto, Sam J.

2010-01-01

Generating knowledge for various constituents and for society has always defined colleges and universities. Recent decades, though, have witnessed shifts in knowledge production and consumption. Research-oriented higher education institutions have developed closer linkages to for-profit firms, which have sought to exploit and commercialize…
Collaborative real-time motion video analysis by human observer and image exploitation algorithms

NASA Astrophysics Data System (ADS)

Hild, Jutta; Krüger, Wolfgang; Brüstle, Stefan; Trantelle, Patrick; Unmüßig, Gabriel; Heinze, Norbert; Peinsipp-Byma, Elisabeth; Beyerer, Jürgen

2015-05-01

Motion video analysis is a challenging task, especially in real-time applications. In most safety and security critical applications, a human observer is an obligatory part of the overall analysis system. Over the last years, substantial progress has been made in the development of automated image exploitation algorithms. Hence, we investigate how the benefits of automated video analysis can be integrated suitably into the current video exploitation systems. In this paper, a system design is introduced which strives to combine both the qualities of the human observer's perception and the automated algorithms, thus aiming to improve the overall performance of a real-time video analysis system. The system design builds on prior work where we showed the benefits for the human observer by means of a user interface which utilizes the human visual focus of attention revealed by the eye gaze direction for interaction with the image exploitation system; eye tracker-based interaction allows much faster, more convenient, and equally precise moving target acquisition in video images than traditional computer mouse selection. The system design also builds on prior work we did on automated target detection, segmentation, and tracking algorithms. Beside the system design, a first pilot study is presented, where we investigated how the participants (all non-experts in video analysis) performed in initializing an object tracking subsystem by selecting a target for tracking. Preliminary results show that the gaze + key press technique is an effective, efficient, and easy to use interaction technique when performing selection operations on moving targets in videos in order to initialize an object tracking function.
Effects of Teacher Use of Analogies on Achievement of High School Biology Students with Varying Levels of Cognitive Ability and Prior Knowledge.

ERIC Educational Resources Information Center

Burns, Joseph C.; Okey, James R.

This study investigated the effects of analogy-based and conventional lecture-based instructional strategies on the achievement of four classes of high school biology students (N=123). Prior to treatment, students were assessed for cognitive ability and prior knowledge of the analogy vehicle. The analogy-based treatment consisted of teacher…
The impact of curiosity on learning during a school field trip to the zoo

NASA Astrophysics Data System (ADS)

Carlin, Kerry Ann

1999-11-01

This study was designed to examine (a) differences in cognitive learning as a result of a zoo field trip, (b) if the trip to the zoo had an impact on epistemic curiosity, (c) the role epistemic curiosity plays in learning, (d) the effect of gender, race, prior knowledge and prior visitation to the zoo on learning and epistemic curiosity, (e) participants' affect for the zoo animals, and (f) if prior visitation to the zoo contributes to prior knowledge. Ninety-six fourth and fifth grade children completed curiosity, cognitive, and affective written tests before and after a field trip to the Lowery Park Zoo in Tampa, Florida. The data showed that students were very curious about zoo animals. Dependent T-tests indicated no significant difference between pretest and posttest curiosity levels. The trip did not influence participants' curiosity levels. Multiple regression analysis was used to determine the relationship between the dependent variable, curiosity, and the independent variables, gender, race, prior knowledge, and prior visitation. No significant differences were found. Dependent T-tests indicated no significant difference between pretest and posttest cognitive scores. The field trip to the zoo did not cause an increase in participants' knowledge. However, participants did learn on the trip. After the field trip, participants identified more animals displayed by the zoo than they did before. Also, more animals were identified by species and genus names after the trip than before. These differences were significant (alpha = .05). Multiple regression analysis was used to determine the relationship between the dependent variable, posttest cognitive performance, and the independent variables, curiosity, gender, race, prior knowledge, and prior visitation. A significant difference was found for prior knowledge (alpha = .05). No significant differences were found for the other independent variables. Chi-square tests of significance indicated significant differences (alpha = .05) in preferences for types of animals and preference for animals by gender. Significant differences (alpha = .05) were also found between the reasons why animals were preferred. Differences occurred between animals that were liked and disliked, between genders, and between the pretest and the posttest.
Dissemination and Exploitation: Project Goals beyond Science

NASA Astrophysics Data System (ADS)

Hamann, Kristin; Reitz, Anja

2017-04-01

Dissemination and Exploitation are essential parts of public funded projects. In Horizon 2020 a plan for the exploitation and dissemination of results (PEDR) is a requirement. The plan should contain a clear vision on the objectives of the project in relation to actions for dissemination and potential exploitation of the project results. The actions follow the basic idea to spread the knowledge and results gathered within the project and face the challenge of how to bring the results into potentially relevant policy circle and how they impact the market. The plan follows the purpose to assess the impact of the project and to address various target groups who are interested in the project results. Simply put, dissemination concentrates on the transfer of knowledge and exploitation on the commercialization of the project. Beyond the question of the measurability of project`s impact, strategies within science marketing can serve purposes beyond internal and external communication. Accordingly, project managers are facing the challenge to implement a dissemination and exploitation strategy that ideally supports the identification of all partners with the project and matches the current discourse of the project`s content within the society, politics and economy. A consolidated plan might unite all projects partners under a central idea and supports the identification with the project beyond the individual research questions. Which applications, strategies and methods can be used to bring forward a PEDR that accompanies a project successfully and allows a comprehensive assessment of the project afterwards? Which hurdles might project managers experience in the dissemination process and which tasks should be fulfilled by the project manager?

A Markov model for blind image separation by a mean-field EM algorithm.

PubMed

Tonazzini, Anna; Bedini, Luigi; Salerno, Emanuele

2006-02-01

This paper deals with blind separation of images from noisy linear mixtures with unknown coefficients, formulated as a Bayesian estimation problem. This is a flexible framework, where any kind of prior knowledge about the source images and the mixing matrix can be accounted for. In particular, we describe local correlation within the individual images through the use of Markov random field (MRF) image models. These are naturally suited to express the joint pdf of the sources in a factorized form, so that the statistical independence requirements of most independent component analysis approaches to blind source separation are retained. Our model also includes edge variables to preserve intensity discontinuities. MRF models have been proved to be very efficient in many visual reconstruction problems, such as blind image restoration, and allow separation and edge detection to be performed simultaneously. We propose an expectation-maximization algorithm with the mean field approximation to derive a procedure for estimating the mixing matrix, the sources, and their edge maps. We tested this procedure on both synthetic and real images, in the fully blind case (i.e., no prior information on mixing is exploited) and found that a source model accounting for local autocorrelation is able to increase robustness against noise, even space variant. Furthermore, when the model closely fits the source characteristics, independence is no longer a strict requirement, and cross-correlated sources can be separated, as well.
An investigation of multitasking information behavior and the influence of working memory and flow

NASA Astrophysics Data System (ADS)

Alexopoulou, Peggy; Hepworth, Mark; Morris, Anne

2015-02-01

This study explored the multitasking information behaviour of Web users and how this is influenced by working memory, flow and Personal, Artefact and Task characteristics, as described in the PAT model. The research was exploratory using a pragmatic, mixed method approach. Thirty University students participated; 10 psychologists, 10 accountants and 10 mechanical engineers. The data collection tools used were: pre and post questionnaires, a working memory test, a flow state scale test, audio-visual data, web search logs, think aloud data, observation, and the critical decision method. All participants searched information on the Web for four topics: two for which they had prior knowledge and two more without prior knowledge. Perception of task complexity was found to be related to working memory. People with low working memory reported a significant increase in task complexity after they had completed information searching tasks for which they had no prior knowledge, this was not the case for tasks with prior knowledge. Regarding flow and task complexity, the results confirmed the suggestion of the PAT model (Finneran and Zhang, 2003), which proposed that a complex task can lead to anxiety and low flow levels as well as to perceived challenge and high flow levels. However, the results did not confirm the suggestion of the PAT model regarding the characteristics of web search systems and especially perceived vividness. All participants experienced high vividness. According to the PAT model, however, only people with high flow should experience high levels of vividness. Flow affected the degree of change of knowledge of the participants. People with high flow gained more knowledge for tasks without prior knowledge rather than people with low flow. Furthermore, accountants felt that tasks without prior knowledge were less complex at the end of the web seeking procedure than psychologists and mechanical engineers. Finally, the three disciplines appeared to differ regarding the multitasking information behaviour characteristics such as queries, web search sessions and opened tabs/windows.
Powerful Learning Tools for ELLs: Using Native Language, Familiar Examples, and Concept Mapping to Teach English Language Learners

ERIC Educational Resources Information Center

Dong, Yu Ren

2013-01-01

This article highlights how English language learners' (ELLs) prior knowledge can be used to help learn science vocabulary. The article explains that the concept of prior knowledge needs to encompass the ELL student's native language, previous science learning, native literacy skills, and native cultural knowledge and life experiences.…
Teachers' Beliefs about the Role of Prior Language Knowledge in Learning and How These Influence Teaching Practices

ERIC Educational Resources Information Center

De Angelis, Gessica

2011-01-01

The present study was developed to assess teachers' beliefs on (1) the role of prior language knowledge in language learning; (2) the perceived usefulness of language knowledge in modern society; and (3) the teaching practices to be used with multilingual students. Subjects were 176 secondary schoolteachers working in Italy (N = 103), Austria (N =…
Modifying Cookbook Labs.

ERIC Educational Resources Information Center

Clark, Robert, L.; Clough, Michael P.; Berg, Craig A.

2000-01-01

Modifies an extended lab activity from a cookbook approach for determining the percent mass of water in copper sulfate pentahydrate crystals to one which incorporates students' prior knowledge, engenders active mental struggling with prior knowledge and new experiences, and encourages metacognition. (Contains 12 references.) (ASK)
Knowledge Modeling in Prior Art Search

NASA Astrophysics Data System (ADS)

Graf, Erik; Frommholz, Ingo; Lalmas, Mounia; van Rijsbergen, Keith

This study explores the benefits of integrating knowledge representations in prior art patent retrieval. Key to the introduced approach is the utilization of human judgment available in the form of classifications assigned to patent documents. The paper first outlines in detail how a methodology for the extraction of knowledge from such an hierarchical classification system can be established. Further potential ways of integrating this knowledge with existing Information Retrieval paradigms in a scalable and flexible manner are investigated. Finally based on these integration strategies the effectiveness in terms of recall and precision is evaluated in the context of a prior art search task for European patents. As a result of this evaluation it can be established that in general the proposed knowledge expansion techniques are particularly beneficial to recall and, with respect to optimizing field retrieval settings, further result in significant precision gains.
Effects of Prior Economic Education, Native Language, and Gender on Economic Knowledge of First-Year Students in Higher Education. A Comparative Study between Germany and the USA

ERIC Educational Resources Information Center

Brückner, Sebastian; Förster, Manuel; Zlatkin-Troitschanskaia, Olga; Walstad, William B.

2015-01-01

The assessment of university students' economic knowledge has become an increasingly important research area within and across countries. Particularly, the different influences of prior education, native language, and gender as some of the main prerequisites on students' economic knowledge have been highlighted since long. However, the findings…
Language knowledge and event knowledge in language use.

PubMed

Willits, Jon A; Amato, Michael S; MacDonald, Maryellen C

2015-05-01

This paper examines how semantic knowledge is used in language comprehension and in making judgments about events in the world. We contrast knowledge gleaned from prior language experience ("language knowledge") and knowledge coming from prior experience with the world ("world knowledge"). In two corpus analyses, we show that previous research linking verb aspect and event representations have confounded language and world knowledge. Then, using carefully chosen stimuli that remove this confound, we performed four experiments that manipulated the degree to which language knowledge or world knowledge should be salient and relevant to performing a task, finding in each case that participants use the type of knowledge most appropriate to the task. These results provide evidence for a highly context-sensitive and interactionist perspective on how semantic knowledge is represented and used during language processing. Copyright © 2015. Published by Elsevier Inc.
Expert Views on TPACK for Early Literacy: Priorities for Teacher Education

ERIC Educational Resources Information Center

McKenney, Susan; Voogt, Joke

2017-01-01

Technology applications can make important contributions to improving learning outcomes in the domain of early literacy. However, to fully exploit the potential of educational technologies, teachers must have specific knowledge and skills. This study aimed to articulate the technological pedagogical content knowledge teachers need to make…
The Ignorance of the Knowledge-Based Economy. The Iconoclast.

ERIC Educational Resources Information Center

McMurtry, John

1996-01-01

Castigates the supposed "knowledge-based economy" as simply a public relations smokescreen covering up the free market exploitation of people and resources serving corporate interests. Discusses the many ways that private industry, often with government collusion, has controlled or denied dissemination of information to serve its own interests.…
The Challenge of a Knowledge Society. A Philippine Plan of Action.

ERIC Educational Resources Information Center

Diaz, Rony V.; And Others

This volume explores the potentials of advanced technologies as they apply to education and training and looks at efforts to exploit these potentials in three areas: vocational-technical training, formal education, and corporate programs. An introduction, "The Age of Knowledge Work" (Emmanuel T. Velasco, Gayla C. Carreon), defines…
Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.
Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581
Immaterial Boys? A Large-Scale Exploration of Gender-Based Differences in Child Sexual Exploitation Service Users.

PubMed

Cockbain, Ella; Ashby, Matthew; Brayley, Helen

2017-10-01

Child sexual exploitation is increasingly recognized nationally and internationally as a pressing child protection, crime prevention, and public health issue. In the United Kingdom, for example, a recent series of high-profile cases has fueled pressure on policy makers and practitioners to improve responses. Yet, prevailing discourse, research, and interventions around child sexual exploitation have focused overwhelmingly on female victims. This study was designed to help redress fundamental knowledge gaps around boys affected by sexual exploitation. This was achieved through rigorous quantitative analysis of individual-level data for 9,042 users of child sexual exploitation services in the United Kingdom. One third of the sample were boys, and gender was associated with statistically significant differences on many variables. The results of this exploratory study highlight the need for further targeted research and more nuanced and inclusive counter-strategies.
Estimating the effect of lay knowledge and prior contact with pulmonary TB patients, on health-belief model in a high-risk pulmonary TB transmission population.

PubMed

Zein, Rizqy Amelia; Suhariadi, Fendy; Hendriani, Wiwin

2017-01-01

The research aimed to investigate the effect of lay knowledge of pulmonary tuberculosis (TB) and prior contact with pulmonary TB patients on a health-belief model (HBM) as well as to identify the social determinants that affect lay knowledge. Survey research design was conducted, where participants were required to fill in a questionnaire, which measured HBM and lay knowledge of pulmonary TB. Research participants were 500 residents of Semampir, Asemrowo, Bubutan, Pabean Cantian, and Simokerto districts, where the risk of pulmonary TB transmission is higher than other districts in Surabaya. Being a female, older in age, and having prior contact with pulmonary TB patients significantly increase the likelihood of having a higher level of lay knowledge. Lay knowledge is a substantial determinant to estimate belief in the effectiveness of health behavior and personal health threat. Prior contact with pulmonary TB patients is able to explain the belief in the effectiveness of a health behavior, yet fails to estimate participants' belief in the personal health threat. Health authorities should prioritize males and young people as their main target groups in a pulmonary TB awareness campaign. The campaign should be able to reconstruct people's misconception about pulmonary TB, thereby bringing around the health-risk perception so that it is not solely focused on improving lay knowledge.
The positive and negative consequences of multiple-choice testing.

PubMed

Roediger, Henry L; Marsh, Elizabeth J

2005-09-01

Multiple-choice tests are commonly used in educational settings but with unknown effects on students' knowledge. The authors examined the consequences of taking a multiple-choice test on a later general knowledge test in which students were warned not to guess. A large positive testing effect was obtained: Prior testing of facts aided final cued-recall performance. However, prior testing also had negative consequences. Prior reading of a greater number of multiple-choice lures decreased the positive testing effect and increased production of multiple-choice lures as incorrect answers on the final test. Multiple-choice testing may inadvertently lead to the creation of false knowledge.
Collaborative Interactive Visualization Exploratory Concept

DTIC Science & Technology

2015-06-01

the FIAC concepts. It consists of various DRDC-RDDC-2015-N004 intelligence analysis web services build of top of big data technologies exploited...sits on the UDS where validated common knowledge is stored. Based on the Lumify software2, this important component exploits big data technologies such...interfaces. Above this database resides the Big Data Manager responsible for transparent data transmission between the UDS and the rest of the S3
Automated Discovery of Machine-Specific Code Improvements

DTIC Science & Technology

1984-12-01

operation of the source language. Additional analysis may reveal special features of the target architecture that may be exploited to generate efficient...Additional analysis may reveal special features of the target architecture that may be exploited to generate efficient code. Such analysis is optional...incorporate knowledge of the source language, but do not refer to features of the target machine. These early phases are sometimes referred to as the
Processing and memory of information presented in narrative or expository texts.

PubMed

Wolfe, Michael B W; Woodwyk, Joshua M

2010-09-01

Previous research suggests that narrative and expository texts differ in the extent to which they prompt students to integrate to-be-learned content with relevant prior knowledge during comprehension. We expand on previous research by examining on-line processing and representation in memory of to-be-learned content that is embedded in narrative or expository texts. We are particularly interested in how differences in the use of relevant prior knowledge leads to differences in terms of levels of discourse representation (textbase vs. situation model). A total of 61 university undergraduates in Expt 1, and 160 in Expt 2. In Expt 1, subjects thought out loud while comprehending circulatory system content embedded in a narrative or expository text, followed by free recall of text content. In Expt 2, subjects read silently and completed a sentence recognition task to assess memory. In Expt 1, subjects made more associations to prior knowledge while reading the expository text, and recalled more content. Content recall was also correlated with amount of relevant prior knowledge for subjects who read the expository text but not the narrative text. In Expt 2, subjects reading the expository text (compared to the narrative text) had a weaker textbase representation of the to-be-learned content, but a marginally stronger situation model. Results suggest that in terms of to-be-learned content, expository texts trigger students to utilize relevant prior knowledge more than narrative texts.
Understanding the Financial Knowledge Gap: A New Dimension of Inequality in Later Life.

PubMed

Khan, Mohammad Nuruzzaman; Rothwell, David W; Cherney, Katrina; Sussman, Tamara

2017-01-01

To understand individuals' financial behaviors, it is important to understand the financial knowledge gap - the distance between one's objective and subjective financial knowledge. Overestimating one's financial knowledge can lead to risky financial behaviors. To date, limited empirical work has examined how financial knowledge gap varies across age groups. We analyze the size and nature of the financial knowledge gap and its variation across age groups. Using nationally representative data, we find robust evidence that older adults overestimate their financial knowledge. Social workers can assess the financial knowledge gap and educate their clients to protect from financial fraud, exploitation, and abuse.

External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising

NASA Astrophysics Data System (ADS)

Xu, Jun; Zhang, Lei; Zhang, David

2018-06-01

Most of existing image denoising methods learn image priors from either external data or the noisy image itself to remove noise. However, priors learned from external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real noisy images.
Language knowledge and event knowledge in language use

PubMed Central

Willits, Jon A.; Amato, Michael S.; MacDonald, Maryellen C.

2018-01-01

This paper examines how semantic knowledge is used in language comprehension and in making judgments about events in the world. We contrast knowledge gleaned from prior language experience (“language knowledge”) and knowledge coming from prior experience with the world (“world knowledge”). In two corpus analyses, we show that previous research linking verb aspect and event representations have confounded language and world knowledge. Then, using carefully chosen stimuli that remove this confound, we performed four experiments that manipulated the degree to which language knowledge or world knowledge should be salient and relevant to performing a task, finding in each case that participants use the type of knowledge most appropriate to the task. These results provide evidence for a highly context-sensitive and interactionist perspective on how semantic knowledge is represented and used during language processing. PMID:25791750
Explosion yield estimation from pressure wave template matching

PubMed Central

Arrowsmith, Stephen; Bowman, Daniel

2017-01-01

A method for estimating the yield of explosions from shock-wave and acoustic-wave measurements is presented. The method exploits full waveforms by comparing pressure measurements against an empirical stack of prior observations using scaling laws. The approach can be applied to measurements across a wide-range of source-to-receiver distances. The method is applied to data from two explosion experiments in different regions, leading to mean relative errors in yield estimates of 0.13 using prior data from the same region, and 0.2 when applied to a new region. PMID:28618805
An empirical Bayes approach to network recovery using external knowledge.

PubMed

Kpogbezan, Gino B; van der Vaart, Aad W; van Wieringen, Wessel N; Leday, Gwenaël G R; van de Wiel, Mark A

2017-09-01

Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure that automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for posterior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study, we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In particular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
78 FR 29071 - Assessment of Mediation and Arbitration Procedures

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-17

... proceeding. Program participants in the new arbitration program will have prior knowledge of the issues to be... final rules, all parties opting into the arbitration program will have full prior knowledge that these... including discovery, the submission of evidence, and the treatment of confidential information, and the...
Teaching Practice: A Perspective on Inter-Text and Prior Knowledge

ERIC Educational Resources Information Center

Costley, Kevin C.; West, Howard G.

2012-01-01

The use of teaching practices that involve intertextual relationship discovery in today's elementary classrooms is increasingly essential to the success of young learners of reading. Teachers must constantly strive to expand their perspective of how to incorporate the dialogue included in prior knowledge assessment. Teachers must also consider how…
Elaborative-Interrogation and Prior-Knowledge Effects on Learning of Facts.

ERIC Educational Resources Information Center

Woloshyn, Vera E.; And Others

1992-01-01

The differences among elaborative-interrogation, reading-to-understand, and no-exposure control conditions with familiar domain material in contrast to unfamiliar domain material were studied for 50 Canadian and 50 west German undergraduates. Results provide evidence of effects of both elaborative interrogation and prior knowledge on learning.…
Effects of Example Variability and Prior Knowledge in How Students Learn to Solve Equations

ERIC Educational Resources Information Center

Guo, Jian-Peng; Yang, Ling-Yan; Ding, Yi

2014-01-01

Researchers have consistently demonstrated that multiple examples are better than one example in facilitating learning because the comparison evoked by multiple examples supports learning and transfer. However, research outcomes are unclear regarding the effects of example variability and prior knowledge on learning from comparing multiple…
Relationship of Students' Prior Knowledge and Order of Questions on Tests to Students' Test Scores.

ERIC Educational Resources Information Center

Papp, Klara K.; And Others

1987-01-01

A study examined whether students beginning a cell biology course with prior knowledge of its three areas (genetics, histology, and biochemistry) would retain that advantage throughout the course and whether achievement was influenced by the order of questions in a test. (MSE)
The Impact of Prior Programming Knowledge on Lecture Attendance and Final Exam

ERIC Educational Resources Information Center

Veerasamy, Ashok Kumar; D'Souza, Daryl; Lindén, Rolf; Laakso, Mikko-Jussi

2018-01-01

In this article, we report the results of the impact of prior programming knowledge (PPK) on lecture attendance (LA) and on subsequent final programming exam performance in a university level introductory programming course. This study used Spearman's rank correlation coefficient, multiple regression, Kruskal-Wallis, and Bonferroni correction…
Composing Knowledge: Writing, Rhetoric, and Reflection in Prior Learning Assessment

ERIC Educational Resources Information Center

Leaker, Cathy; Ostman, Heather

2010-01-01

In this article, we argue that prior learning assessment (PLA) essays manifest a series of issues central to composition research and practice: they foreground the "contact zone" between the unauthorized writer, institutional power, and the articulation of knowledge claims; they reinforce the central role of a multifaceted approach to…
Using Analogies to Facilitate Conceptual Change in Mathematics Learning

ERIC Educational Resources Information Center

Vamvakoussi, Xenia

2017-01-01

The problem of adverse effects of prior knowledge in mathematics learning has been amply documented and theorized by mathematics educators as well as cognitive/developmental psychologists. This problem emerges when students' prior knowledge about a mathematical notion comes in contrast with new information coming from instruction, giving rise to…
Specific Previous Experience Affects Perception of Harmony and Meter

ERIC Educational Resources Information Center

Creel, Sarah C.

2011-01-01

Prior knowledge shapes our experiences, but which prior knowledge shapes which experiences? This question is addressed in the domain of music perception. Three experiments were used to determine whether listeners activate specific musical memories during music listening. Each experiment provided listeners with one of two musical contexts that was…
Pre-Test Assessment

ERIC Educational Resources Information Center

Berry, Thomas

2008-01-01

Pre-tests are a non-graded assessment tool used to determine pre-existing subject knowledge. Typically pre-tests are administered prior to a course to determine knowledge baseline, but here they are used to test students prior to topical material coverage throughout the course. While counterintuitive, the pre-tests cover material the student is…
Healthcare knowledge management through building and operationalising healthcare enterprise memory.

PubMed

Cheah, Y N; Abidi, S S

1999-01-01

In this paper we suggest that the healthcare enterprise needs to be more conscious of its vast knowledge resources vis-à-vis the exploitation of knowledge management techniques to efficiently manage its knowledge. The development of healthcare enterprise memory is suggested as a solution, together with a novel approach advocating the operationalisation of healthcare enterprise memories leading to the modelling of healthcare processes for strategic planning. As an example, we present a simulation of Service Delivery Time in a hospital's OPD.
Class-Size Effects in Secondary School

ERIC Educational Resources Information Center

Krassel, Karl Fritjof; Heinesen, Eskil

2014-01-01

We analyze class-size effects on academic achievement in secondary school in Denmark exploiting an institutional setting where pupils cannot predict class size prior to enrollment, and where post-enrollment responses aimed at affecting realized class size are unlikely. We identify class-size effects combining a regression discontinuity design with…
Effect of abamectin on feeding response, mortality, and reproduction of adult bollworm (Lepidoptera: Noctuidae)

USDA-ARS?s Scientific Manuscript database

Newly eclosed adult bollworm, Helicoverpa zea (Boddie) feeds on carbohydrate sources from plants and other exudates prior to dispersal and reproduction. The objective of this study was to determine whether or not this nocturnal behavior could be exploited for pest management by presenting the insect...
Japan's Learning Communities in Hewlett-Packard Consulting and Integration: Challenging One-Size Fits All Solutions

ERIC Educational Resources Information Center

Kohlbacher, Florian; Mukai, Kazuo

2007-01-01

Purpose: This paper aims to explain and analyze community-based corporate knowledge sharing and organizational learning, the actual use of communities in Hewlett Packard (HP) Consulting and Integration (CI) and their role in leveraging and exploiting existing and creating new knowledge. Design/methodology/approach: The paper presents an…
Supporting Collocation Learning with a Digital Library

ERIC Educational Resources Information Center

Wu, Shaoqun; Franken, Margaret; Witten, Ian H.

2010-01-01

Extensive knowledge of collocations is a key factor that distinguishes learners from fluent native speakers. Such knowledge is difficult to acquire simply because there is so much of it. This paper describes a system that exploits the facilities offered by digital libraries to provide a rich collocation-learning environment. The design is based on…
Practical Experiences for the Development of Educational Systems in the Semantic Web

ERIC Educational Resources Information Center

Sánchez Vera, Ma. del Mar; Tomás Fernández Breis, Jesualdo; Serrano Sánchez, José Luis; Prendes Espinosa, Ma. Paz

2013-01-01

Semantic Web technologies have been applied in educational settings for different purposes in recent years, with the type of application being mainly defined by the way in which knowledge is represented and exploited. The basic technology for knowledge representation in Semantic Web settings is the ontology, which represents a common, shareable…

Common IED exploitation target set ontology

NASA Astrophysics Data System (ADS)

Russomanno, David J.; Qualls, Joseph; Wowczuk, Zenovy; Franken, Paul; Robinson, William

2010-04-01

The Common IED Exploitation Target Set (CIEDETS) ontology provides a comprehensive semantic data model for capturing knowledge about sensors, platforms, missions, environments, and other aspects of systems under test. The ontology also includes representative IEDs; modeled as explosives, camouflage, concealment objects, and other background objects, which comprise an overall threat scene. The ontology is represented using the Web Ontology Language and the SPARQL Protocol and RDF Query Language, which ensures portability of the acquired knowledge base across applications. The resulting knowledge base is a component of the CIEDETS application, which is intended to support the end user sensor test and evaluation community. CIEDETS associates a system under test to a subset of cataloged threats based on the probability that the system will detect the threat. The associations between systems under test, threats, and the detection probabilities are established based on a hybrid reasoning strategy, which applies a combination of heuristics and simplified modeling techniques. Besides supporting the CIEDETS application, which is focused on efficient and consistent system testing, the ontology can be leveraged in a myriad of other applications, including serving as a knowledge source for mission planning tools.
Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks.

PubMed

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

2017-02-08

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.
Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks.

PubMed

Rumschinski, Philipp; Borchers, Steffen; Bosio, Sandro; Weismantel, Robert; Findeisen, Rolf

2010-05-25

Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.
Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks

PubMed Central

2010-01-01

Background Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. Results In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. Conclusions The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates. PMID:20500862
The Interpretation of a Knowledge Claim in the Recognition of Prior Learning (RPL) and the Impact of This on RPL Practice

ERIC Educational Resources Information Center

de Graaff, Frederika

2014-01-01

The question addressed in this paper is: what does a knowledge claim consist of in the context of the Recognition of Prior Learning (RPL)? The research comprises a case study of RPL applicants' entry into a postgraduate diploma (a fourth-year programme) in project management. The focus is on the knowledge claims made as part of the RPL application…
Fast Low-Rank Bayesian Matrix Completion With Hierarchical Gaussian Prior Models

NASA Astrophysics Data System (ADS)

Yang, Linxiao; Fang, Jun; Duan, Huiping; Li, Hongbin; Zeng, Bing

2018-06-01

The problem of low rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a Wishart distribution is specified as a hyperprior over the precision matrix. We show that such a hierarchical Gaussian prior has the potential to encourage a low-rank solution. Based on the proposed hierarchical prior model, a variational Bayesian method is developed for matrix completion, where the generalized approximate massage passing (GAMP) technique is embedded into the variational Bayesian inference in order to circumvent cumbersome matrix inverse operations. Simulation results show that our proposed method demonstrates superiority over existing state-of-the-art matrix completion methods.
Creating illusions of knowledge: learning errors that contradict prior knowledge.

PubMed

Fazio, Lisa K; Barber, Sarah J; Rajaram, Suparna; Ornstein, Peter A; Marsh, Elizabeth J

2013-02-01

Most people know that the Pacific is the largest ocean on Earth and that Edison invented the light bulb. Our question is whether this knowledge is stable, or if people will incorporate errors into their knowledge bases, even if they have the correct knowledge stored in memory. To test this, we asked participants general-knowledge questions 2 weeks before they read stories that contained errors (e.g., "Franklin invented the light bulb"). On a later general-knowledge test, participants reproduced story errors despite previously answering the questions correctly. This misinformation effect was found even for questions that were answered correctly on the initial test with the highest level of confidence. Furthermore, prior knowledge offered no protection against errors entering the knowledge base; the misinformation effect was equivalent for previously known and unknown facts. Errors can enter the knowledge base even when learners have the knowledge necessary to catch the errors. 2013 APA, all rights reserved
Age differences in suggestibility to contradictions of demonstrated knowledge: the influence of prior knowledge.

PubMed

Umanath, Sharda

2016-11-01

People maintain intact general knowledge into very old age and use it to support remembering. Interestingly, when older and younger adults encounter errors that contradict general knowledge, older adults suffer fewer memorial consequences: Older adults use fewer recently-encountered errors as answers for later knowledge questions. Why do older adults show this reduced suggestibility, and what role does their intact knowledge play? In three experiments, I examined suggestibility following exposure to errors in fictional stories that contradict general knowledge. Older adults consistently demonstrated more prior knowledge than younger adults but also gained access to even more across time. Additionally, they did not show a reduction in new learning from the stories, indicating lesser involvement of episodic memory failures. Critically, when knowledge was stably accessible, older adults relied more heavily on that knowledge compared to younger adults, resulting in reduced suggestibility. Implications for the broader role of knowledge in aging are discussed.
MRAC Control with Prior Model Knowledge for Asymmetric Damaged Aircraft

PubMed Central

Zhang, Jing

2015-01-01

This paper develops a novel state-tracking multivariable model reference adaptive control (MRAC) technique utilizing prior knowledge of plant models to recover control performance of an asymmetric structural damaged aircraft. A modification of linear model representation is given. With prior knowledge on structural damage, a polytope linear parameter varying (LPV) model is derived to cover all concerned damage conditions. An MRAC method is developed for the polytope model, of which the stability and asymptotic error convergence are theoretically proved. The proposed technique reduces the number of parameters to be adapted and thus decreases computational cost and requires less input information. The method is validated by simulations on NASA generic transport model (GTM) with damage. PMID:26180839
An integrated approach to historical population assessment of the great whales: case of the New Zealand southern right whale.

PubMed

Jackson, Jennifer A; Carroll, Emma L; Smith, Tim D; Zerbini, Alexandre N; Patenaude, Nathalie J; Baker, C Scott

2016-03-01

Accurate estimation of historical abundance provides an essential baseline for judging the recovery of the great whales. This is particularly challenging for whales hunted prior to twentieth century modern whaling, as population-level catch records are often incomplete. Assessments of whale recovery using pre-modern exploitation indices are therefore rare, despite the intensive, global nature of nineteenth century whaling. Right whales (Eubalaena spp.) were particularly exploited: slow swimmers with strong fidelity to sheltered calving bays, the species made predictable and easy targets. Here, we present the first integrated population-level assessment of the whaling impact and pre-exploitation abundance of a right whale, the New Zealand southern right whale (E. australis). In this assessment, we use a Bayesian population dynamics model integrating multiple data sources: nineteenth century catches, genetic constraints on bottleneck size and individual sightings histories informing abundance and trend. Different catch allocation scenarios are explored to account for uncertainty in the population's offshore distribution. From a pre-exploitation abundance of 28 800-47 100 whales, nineteenth century hunting reduced the population to approximately 30-40 mature females between 1914 and 1926. Today, it stands at less than 12% of pre-exploitation abundance. Despite the challenges of reconstructing historical catches and population boundaries, conservation efforts of historically exploited species benefit from targets for ecological restoration.
An integrated approach to historical population assessment of the great whales: case of the New Zealand southern right whale

PubMed Central

Jackson, Jennifer A.; Carroll, Emma L.; Smith, Tim D.; Zerbini, Alexandre N.; Patenaude, Nathalie J.; Baker, C. Scott

2016-01-01

Accurate estimation of historical abundance provides an essential baseline for judging the recovery of the great whales. This is particularly challenging for whales hunted prior to twentieth century modern whaling, as population-level catch records are often incomplete. Assessments of whale recovery using pre-modern exploitation indices are therefore rare, despite the intensive, global nature of nineteenth century whaling. Right whales (Eubalaena spp.) were particularly exploited: slow swimmers with strong fidelity to sheltered calving bays, the species made predictable and easy targets. Here, we present the first integrated population-level assessment of the whaling impact and pre-exploitation abundance of a right whale, the New Zealand southern right whale (E. australis). In this assessment, we use a Bayesian population dynamics model integrating multiple data sources: nineteenth century catches, genetic constraints on bottleneck size and individual sightings histories informing abundance and trend. Different catch allocation scenarios are explored to account for uncertainty in the population's offshore distribution. From a pre-exploitation abundance of 28 800–47 100 whales, nineteenth century hunting reduced the population to approximately 30–40 mature females between 1914 and 1926. Today, it stands at less than 12% of pre-exploitation abundance. Despite the challenges of reconstructing historical catches and population boundaries, conservation efforts of historically exploited species benefit from targets for ecological restoration. PMID:27069657
Potentiation in young infants: The origin of the prior knowledge effect?

PubMed Central

Barr, Rachel; Rovee-Collier, Carolyn; Learmonth, Amy

2011-01-01

In two experiments with 6-month-old infants, we found that prior learning of an operant task (remembered for 2 weeks) mediated new learning of a modeling event (remembered for only 1 day) and increased its recall. Infants first learned to associate lever pressing with moving a toy train housed in a large box. One or 2 weeks later, three target actions were modeled on a hand puppet while the train box (a retrieval cue) was in view. Merely retrieving the train memory strengthened it, and simultaneously pairing its retrieved memory with the modeled actions potentiated their learning and recall. When paired 1 week later, deferred imitation increased from 1 day to 4 weeks; when paired 2 weeks later, it increased from 1 day to 6 weeks. The striking parallels between potentiated learning in infants and the prior knowledge effect in adults suggests that the prior knowledge effect originates in early infancy. PMID:21264602
CRAFT (complete reduction to amplitude frequency table)--robust and time-efficient Bayesian approach for quantitative mixture analysis by NMR.

PubMed

Krishnamurthy, Krish

2013-12-01

The intrinsic quantitative nature of NMR is increasingly exploited in areas ranging from complex mixture analysis (as in metabolomics and reaction monitoring) to quality assurance/control. Complex NMR spectra are more common than not, and therefore, extraction of quantitative information generally involves significant prior knowledge and/or operator interaction to characterize resonances of interest. Moreover, in most NMR-based metabolomic experiments, the signals from metabolites are normally present as a mixture of overlapping resonances, making quantification difficult. Time-domain Bayesian approaches have been reported to be better than conventional frequency-domain analysis at identifying subtle changes in signal amplitude. We discuss an approach that exploits Bayesian analysis to achieve a complete reduction to amplitude frequency table (CRAFT) in an automated and time-efficient fashion - thus converting the time-domain FID to a frequency-amplitude table. CRAFT uses a two-step approach to FID analysis. First, the FID is digitally filtered and downsampled to several sub FIDs, and secondly, these sub FIDs are then modeled as sums of decaying sinusoids using the Bayesian approach. CRAFT tables can be used for further data mining of quantitative information using fingerprint chemical shifts of compounds of interest and/or statistical analysis of modulation of chemical quantity in a biological study (metabolomics) or process study (reaction monitoring) or quality assurance/control. The basic principles behind this approach as well as results to evaluate the effectiveness of this approach in mixture analysis are presented. Copyright © 2013 John Wiley & Sons, Ltd.
A deep learning framework for the automated inspection of complex dual-energy x-ray cargo imagery

NASA Astrophysics Data System (ADS)

Rogers, Thomas W.; Jaccard, Nicolas; Griffin, Lewis D.

2017-05-01

Previously, we investigated the use of Convolutional Neural Networks (CNNs) to detect so-called Small Metallic Threats (SMTs) hidden amongst legitimate goods inside a cargo container. We trained a CNN from scratch on data produced by a Threat Image Projection (TIP) framework that generates images with realistic variation to robustify performance. The system achieved 90% detection of containers that contained a single SMT, while raising 6% false positives on benign containers. The best CNN architecture used the raw high energy image (single-energy) and its logarithm as input channels. Use of the logarithm improved performance, thus echoing studies on human operator performance. However, it is an unexpected result with CNNs. In this work, we (i) investigate methods to exploit material information captured in dual-energy images, and (ii) introduce a new CNN training scheme that generates `spot-the-difference' benign and threat pairs on-the-fly. To the best of our knowledge, this is the first time that CNNs have been applied directly to raw dual-energy X-ray imagery, in any field. To exploit dual-energy, we experiment with adapting several physics-derived approaches to material discrimination from the cargo literature, and introduce three novel variants. We hypothesise that CNNs can implicitly learn about the material characteristics of objects from the raw dual-energy images, and use this to suppress false positives. The best performing method is able to detect 95% of containers containing a single SMT, while raising 0.4% false positives on benign containers. This is a step change improvement in performance over our prior work
Template-based protein-protein docking exploiting pairwise interfacial residue restraints.

PubMed

Xue, Li C; Rodrigues, João P G L M; Dobbs, Drena; Honavar, Vasant; Bonvin, Alexandre M J J

2017-05-01

Although many advanced and sophisticated ab initio approaches for modeling protein-protein complexes have been proposed in past decades, template-based modeling (TBM) remains the most accurate and widely used approach, given a reliable template is available. However, there are many different ways to exploit template information in the modeling process. Here, we systematically evaluate and benchmark a TBM method that uses conserved interfacial residue pairs as docking distance restraints [referred to as alpha carbon-alpha carbon (CA-CA)-guided docking]. We compare it with two other template-based protein-protein modeling approaches, including a conserved non-pairwise interfacial residue restrained docking approach [referred to as the ambiguous interaction restraint (AIR)-guided docking] and a simple superposition-based modeling approach. Our results show that, for most cases, the CA-CA-guided docking method outperforms both superposition with refinement and the AIR-guided docking method. We emphasize the superiority of the CA-CA-guided docking on cases with medium to large conformational changes, and interactions mediated through loops, tails or disordered regions. Our results also underscore the importance of a proper refinement of superimposition models to reduce steric clashes. In summary, we provide a benchmarked TBM protocol that uses conserved pairwise interface distance as restraints in generating realistic 3D protein-protein interaction models, when reliable templates are available. The described CA-CA-guided docking protocol is based on the HADDOCK platform, which allows users to incorporate additional prior knowledge of the target system to further improve the quality of the resulting models. © The Author 2016. Published by Oxford University Press.
PAD-MAC: Primary User Activity-Aware Distributed MAC for Multi-Channel Cognitive Radio Networks

PubMed Central

Ali, Amjad; Piran, Md. Jalil; Kim, Hansoo; Yun, Jihyeok; Suh, Doug Young

2015-01-01

Cognitive radio (CR) has emerged as a promising technology to solve problems related to spectrum scarcity and provides a ubiquitous wireless access environment. CR-enabled secondary users (SUs) exploit spectrum white spaces opportunistically and immediately vacate the acquired licensed channels as primary users (PUs) arrive. Accessing the licensed channels without the prior knowledge of PU traffic patterns causes severe throughput degradation due to excessive channel switching and PU-to-SU collisions. Therefore, it is significantly important to design a PU activity-aware medium access control (MAC) protocol for cognitive radio networks (CRNs). In this paper, we first propose a licensed channel usage pattern identification scheme, based on a two-state Markov model, and then estimate the future idle slots using previous observations of the channels. Furthermore, based on these past observations, we compute the rank of each available licensed channel that gives SU transmission success assessment during the estimated idle slot. Secondly, we propose a PU activity-aware distributed MAC (PAD-MAC) protocol for heterogeneous multi-channel CRNs that selects the best channel for each SU to enhance its throughput. PAD-MAC controls SU activities by allowing them to exploit the licensed channels only for the duration of estimated idle slots and enables predictive and fast channel switching. To evaluate the performance of the proposed PAD-MAC, we compare it with the distributed QoS-aware MAC (QC-MAC) and listen-before-talk MAC schemes. Extensive numerical results show the significant improvements of the PAD-MAC in terms of the SU throughput, SU channel switching rate and PU-to-SU collision rate. PMID:25831084
When Relationships Depicted Diagrammatically Conflict with Prior Knowledge: An Investigation of Students' Interpretations of Evolutionary Trees

ERIC Educational Resources Information Center

Novick, Laura R.; Catley, Kefyn M.

2014-01-01

Science is an important domain for investigating students' responses to information that contradicts their prior knowledge. In previous studies of this topic, this information was communicated verbally. The present research used diagrams, specifically trees (cladograms) depicting evolutionary relationships among taxa. Effects of college…
Building Knowledge through Portfolio Learning in Prior Learning Assessment and Recognition

ERIC Educational Resources Information Center

Conrad, Dianne

2008-01-01

It is important for academic credibility that the process of prior learning assessment and recognition (PLAR) keeps learning and knowledge as its foundational tenets. Doing so ensures PLAR's recognition as a fertile ground for learners' cognitive and personal growth. In many postsecondary venues, PLAR is often misunderstood and confused with…
Temporal Learning in 4 1/2- and 6-Year-Old Children: Role of Instructions and Prior Knowledge.

ERIC Educational Resources Information Center

Droit, Sylvie; And Others

1990-01-01

Examined the role of prior temporal knowledge of 4 1/2- and 6-year-olds through the use of high-rate, interval, and minimal instructions in a fixed-interval training schedule. Determined that the subjects' learning depended on their verbal self-control skills. (BC)
Understanding the Complexities of Prior Knowledge

ERIC Educational Resources Information Center

Soiferman, L. Karen

2014-01-01

The purpose of the study was to gain an understanding of the kinds of prior knowledge students bring with them from high school as it relates to the conventions of writing that they are expected to follow in ARTS 1110 Introduction to University. The research questions were "Can first-year students taking the Arts 1110 Introduction to…

An Effectiveness Index and Profile for Instructional Media.

ERIC Educational Resources Information Center

Bond, Jack H.

A scale was developed for judging the relative value of various media in teaching children. Posttest scores were partitioned into several components: error, prior knowledge, guessing, and gain from the learning exercise. By estimating the amounts of prior knowledge, guessing, and error, and then subtracting these from the total score, an index of…
Making Connections in Math: Activating a Prior Knowledge Analogue Matters for Learning

ERIC Educational Resources Information Center

Sidney, Pooja G.; Alibali, Martha W.

2015-01-01

This study investigated analogical transfer of conceptual structure from a prior-knowledge domain to support learning in a new domain of mathematics: division by fractions. Before a procedural lesson on division by fractions, fifth and sixth graders practiced with a surface analogue (other operations on fractions) or a structural analogue (whole…
An Investigation of the Relationships between Prior Knowledge and Vocabulary Development with Culturally Diverse Students.

ERIC Educational Resources Information Center

Karbon, Jacqueline C.

Using a semantic mapping technique for vocabulary instruction, a study explored how children of diverse groups bring different cultural backgrounds and prior knowledge to tasks involved in learning new words. The study was conducted in three sixth-grade classrooms--one containing rural Native American (especially Menominee) children, another…
The Influence of Prior Knowledge on Perception and Action: Relationships to Autistic Traits

ERIC Educational Resources Information Center

Buckingham, Gavin; Michelakakis, Elizabeth Evgenia; Rajendran, Gnanathusharan

2016-01-01

Autism is characterised by a range of perceptual and sensorimotor deficits, which might be related to abnormalities in how autistic individuals use prior knowledge. We investigated this proposition in a large non-clinical population in the context of the size-weight illusion, where individual's expectations about object weight influence their…
Interplay of Prior Knowledge, Self-Regulation and Motivation in Complex Multimedia Learning Environments

ERIC Educational Resources Information Center

Song, H. S.; Kalet, A. L.; Plass, J. L.

2016-01-01

This study examined the direct and indirect effects of medical clerkship students' prior knowledge, self-regulation and motivation on learning performance in complex multimedia learning environments. The data from 386 medical clerkship students from six medical schools were analysed using structural equation modeling. The structural model revealed…
Effects of Students' Prior Knowledge on Scientific Reasoning in Density.

ERIC Educational Resources Information Center

Yang, Il-Ho; Kwon, Yong-Ju; Kim, Young-Shin; Jang, Myoung-Duk; Jeong, Jin-Woo; Park, Kuk-Tae

2002-01-01

Investigates the effects of students' prior knowledge on the scientific reasoning processes of performing the task of controlling variables with computer simulation and identifies a number of problems that students encounter in scientific discovery. Involves (n=27) 5th grade students and (n=33) 7th grade students. Indicates that students' prior…
Relationship of Prior Knowledge and Working Engineers' Learning Preferences: Implications for Designing Effective Instruction

ERIC Educational Resources Information Center

Baukal, Charles E.; Ausburn, Lynna J.

2017-01-01

Continuing engineering education (CEE) is important to ensure engineers maintain proficiency over the life of their careers. However, relatively few studies have examined designing effective training for working engineers. Research has indicated that both learner instructional preferences and prior knowledge can impact the learning process, but it…
The Influence of Prior Knowledge and Viewing Repertoire on Learning from Video

ERIC Educational Resources Information Center

de Boer, Jelle; Kommers, Piet A. M.; de Brock, Bert; Tolboom, Jos

2016-01-01

Video is increasingly used as an instructional tool. It is therefore becoming more important to improve learning of students from video. We investigated whether student learning effects are influenced through an instruction about other viewing behaviours, and whether these learning effects depend on their prior knowledge. In a controlled…
Prior Knowledge and Online Inquiry-Based Science Reading: Evidence from Eye Tracking

ERIC Educational Resources Information Center

Ho, Hsin Ning Jessie; Tsai, Meng-Jung; Wang, Ching-Yeh; Tsai, Chin-Chung

2014-01-01

This study employed eye-tracking technology to examine how students with different levels of prior knowledge process text and data diagrams when reading a web-based scientific report. Students' visual behaviors were tracked and recorded when they read a report demonstrating the relationship between the greenhouse effect and global climate…
Students' Achievement in Relation to Reasoning Ability, Prior Knowledge and Gender

ERIC Educational Resources Information Center

Yenilmez, Ayse; Sungur, Semra; Tekkaya, Ceren

2006-01-01

This study investigated students' achievement regarding photosynthesis and respiration in plants in relation to reasoning ability, prior knowledge and gender. A total of 117 eighth-grade students participated in the study. Test of logical thinking and the two-tier multiple choice tests were administered to determine students' reasoning ability and…
The Effectiveness of Using Incorrect Examples to Support Learning about Decimal Magnitude

ERIC Educational Resources Information Center

Durkin, Kelley; Rittle-Johnson, Bethany

2012-01-01

Comparing common mathematical errors to correct examples may facilitate learning, even for students with limited prior domain knowledge. We examined whether studying incorrect and correct examples was more effective than studying two correct examples across prior knowledge levels. Fourth- and fifth-grade students (N = 74) learned about decimal…
Determining similarity of scientific entities in annotation datasets

PubMed Central

Palma, Guillermo; Vidal, Maria-Esther; Haag, Eric; Raschid, Louiqa; Thor, Andreas

2015-01-01

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug–drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called ‘AnnSim’ that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1–1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/ PMID:25725057
Determining similarity of scientific entities in annotation datasets.

PubMed

Palma, Guillermo; Vidal, Maria-Esther; Haag, Eric; Raschid, Louiqa; Thor, Andreas

2015-01-01

Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in the datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge, which is captured in annotation datasets. Determining relatedness between annotated entities becomes a building block for pattern mining, e.g. identifying drug-drug relationships may depend on the similarity of the targets that interact with each drug. A diversity of similarity measures has been proposed in the literature to compute relatedness between a pair of entities. Each measure exploits some knowledge including the name, function, relationships with other entities, taxonomic neighborhood and semantic knowledge. We propose a novel general-purpose annotation similarity measure called 'AnnSim' that measures the relatedness between two entities based on the similarity of their annotations. We model AnnSim as a 1-1 maximum weight bipartite match and exploit properties of existing solvers to provide an efficient solution. We empirically study the performance of AnnSim on real-world datasets of drugs and disease associations from clinical trials and relationships between drugs and (genomic) targets. Using baselines that include a variety of measures, we identify where AnnSim can provide a deeper understanding of the semantics underlying the relatedness of a pair of entities or where it could lead to predicting new links or identifying potential novel patterns. Although AnnSim does not exploit knowledge or properties of a particular domain, its performance compares well with a variety of state-of-the-art domain-specific measures. Database URL: http://www.yeastgenome.org/ © The Author(s) 2015. Published by Oxford University Press.
Students' inductive reasoning skills and the relevance of prior knowledge: an exploratory study with a computer-based training course on the topic of acne vulgaris.

PubMed

Horn-Ritzinger, Sabine; Bernhardt, Johannes; Horn, Michael; Smolle, Josef

2011-04-01

The importance of inductive instruction in medical education is increasingly growing. Little is known about the relevance of prior knowledge regarding students' inductive reasoning abilities. The purpose is to evaluate this inductive teaching method as a means of fostering higher levels of learning and to explore how individual differences in prior knowledge (high [HPK] vs. low [LPK]) contribute to students' inductive reasoning skills. Twenty-six LPK and 18 HPK students could train twice with an interactive computer-based training object to discover the underlying concept before doing the final comprehension check. Students had a median of 76.9% of correct answers in the first, 90.9% in the second training, and answered 92% of the final assessment questions correctly. More important, 86% of all students succeeded with inductive learning, among them 83% of the HPK students and 89% of the LPK students. Prior knowledge did not predict performance on overall comprehension. This inductive instructional strategy fostered students' deep approaches to learning in a time-effective way.
The Fast Follower: Coming Up Behind Development Leaders

DTIC Science & Technology

2015-06-01

DoD faces a shrinking defense industrial base and a more global tech marketplace and competes with the rise of consumer electronics that have short...to others and positions itself to rapidly exploit the newly discovered technical knowledge by quickly applying that knowledge to the unique needs of...technical aware- ness, organized for speed in innovation and has an intimate knowledge of its customer. From its vantage point on the first mover’s
Gaining the Long View: Reforming Organization and Empowering Knowledge Workers to Improve Strategy and Intelligence

DTIC Science & Technology

2017-03-31

processes. Hierarchal bureaucracies also provide the workforce with a predictable, structured work environment , a sense of status, and other...processes in response to changes in the environment . As they age and acquire a corporate culture, members become more entrenched in their work ...inability of managers and leaders of knowledge workers to foster a work environment that effectively exploits the knowledge worker’s drive to apply his or
Exploitation of deep-sea resources: the urgent need to understand the role of high pressure in the toxicity of chemical pollutants to deep-sea organisms.

PubMed

Mestre, Nélia C; Calado, Ricardo; Soares, Amadeu M V M

2014-02-01

The advent of industrial activities in the deep sea will inevitably expose deep-sea organisms to potentially toxic compounds. Although international regulations require environmental risk assessment prior to exploitation activities, toxicity tests remain focused on shallow-water model species. Moreover, current tests overlook potential synergies that may arise from the interaction of chemicals with natural stressors, such as the high pressures prevailing in the deep sea. As pressure affects chemical reactions and the physiology of marine organisms, it will certainly affect the toxicity of pollutants arising from the exploitation of deep-sea resources. We emphasize the need for environmental risk assessments based on information generated from ecotoxicological trials that mimic, as close as possible, the deep-sea environment, with emphasis to a key environmental factor - high hydrostatic pressure. Copyright © 2013 Elsevier Ltd. All rights reserved.
Exploiting Quantum Resonance to Solve Combinatorial Problems

NASA Technical Reports Server (NTRS)

Zak, Michail; Fijany, Amir

2006-01-01

Quantum resonance would be exploited in a proposed quantum-computing approach to the solution of combinatorial optimization problems. In quantum computing in general, one takes advantage of the fact that an algorithm cannot be decoupled from the physical effects available to implement it. Prior approaches to quantum computing have involved exploitation of only a subset of known quantum physical effects, notably including parallelism and entanglement, but not including resonance. In the proposed approach, one would utilize the combinatorial properties of tensor-product decomposability of unitary evolution of many-particle quantum systems for physically simulating solutions to NP-complete problems (a class of problems that are intractable with respect to classical methods of computation). In this approach, reinforcement and selection of a desired solution would be executed by means of quantum resonance. Classes of NP-complete problems that are important in practice and could be solved by the proposed approach include planning, scheduling, search, and optimal design.
32 CFR 34.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... increasing knowledge or understanding in science and engineering. Applied research is defined as efforts that attempt to determine and exploit the potential of scientific discoveries or improvements in technology...
Intelligent indexing: a semi-automated, trainable system for field labeling

NASA Astrophysics Data System (ADS)

Clawson, Robert; Barrett, William

2015-01-01

We present Intelligent Indexing: a general, scalable, collaborative approach to indexing and transcription of non-machinereadable documents that exploits visual consensus and group labeling while harnessing human recognition and domain expertise. In our system, indexers work directly on the page, and with minimal context switching can navigate the page, enter labels, and interact with the recognition engine. Interaction with the recognition engine occurs through preview windows that allow the indexer to quickly verify and correct recommendations. This interaction is far superior to conventional, tedious, inefficient post-correction and editing. Intelligent Indexing is a trainable system that improves over time and can provide benefit even without prior knowledge. A user study was performed to compare Intelligent Indexing to a basic, manual indexing system. Volunteers report that using Intelligent Indexing is less mentally fatiguing and more enjoyable than the manual indexing system. Their results also show that it reduces significantly (30.2%) the time required to index census records, while maintaining comparable accuracy. (a video demonstration is available at http://youtube.com/gqdVzEPnBEw)

The kinetics and location of intra-host HIV evolution to evade cellular immunity are predictable

NASA Astrophysics Data System (ADS)

Barton, John; Goonetilleke, Nilu; Butler, Thomas; Walker, Bruce; McMichael, Andrew; Chakraborty, Arup

Human immunodeficiency virus (HIV) evolves within infected persons to escape targeting and clearance by the host immune system, thereby preventing effective immune control of infection. Knowledge of the timing and pathways of escape that result in loss of control of the virus could aid in the design of effective strategies to overcome the challenge of viral diversification and immune escape. We combined methods from statistical physics and evolutionary dynamics to predict the course of in vivo viral sequence evolution in response to T cell-mediated immune pressure in a cohort of 17 persons with acute HIV infection. Our predictions agree well with both the location of documented escape mutations and the clinically observed time to escape. We also find that that the mutational pathways to escape depend on the viral sequence background due to epistatic interactions. The ability to predict escape pathways, and the duration over which control is maintained by specific immune responses prior to escape, could be exploited for the rational design of immunotherapeutic strategies that may enable long-term control of HIV infection.
Through-barrier electromagnetic imaging with an atomic magnetometer.

PubMed

Deans, Cameron; Marmugi, Luca; Renzoni, Ferruccio

2017-07-24

We demonstrate the penetration of thick metallic and ferromagnetic barriers for imaging of conductive targets underneath. Our system is based on an 85 Rb radio-frequency atomic magnetometer operating in electromagnetic induction imaging modality in an unshielded environment. Detrimental effects, including unpredictable magnetic signatures from ferromagnetic screens and variations in the magnetic background, are automatically compensated by active compensation coils controlled by servo loops. We exploit the tunability and low-frequency sensitivity of the atomic magnetometer to directly image multiple conductive targets concealed by a 2.5 mm ferromagnetic steel shield and/or a 2.0 mm aluminium shield, in a single scan. The performance of the atomic magnetometer allows imaging without any prior knowledge of the barriers or the targets, and without the need of background subtraction. A dedicated edge detection algorithm allows automatic estimation of the targets' size within 3.3 mm and of their position within 2.4 mm. Our results prove the feasibility of a compact, sensitive and automated sensing platform for imaging of concealed objects in a range of applications, from security screening to search and rescue.
Should Science be Taught in Early Childhood?

NASA Astrophysics Data System (ADS)

Eshach, Haim; Fried, Michael N.

2005-09-01

This essay considers the question of why we should teach science to K-2. After initial consideration of two traditional reasons for studying science, six assertions supporting the idea that even small children should be exposed to science are given. These are, in order: (1) Children naturally enjoy observing and thinking about nature. (2) Exposing students to science develops positive attitudes towards science. (3) Early exposure to scientific phenomena leads to better understanding of the scientific concepts studied later in a formal way. (4) The use of scientifically informed language at an early age influences the eventual development of scientific concepts. (5) Children can understand scientific concepts and reason scientifically. (6) Science is an efficient means for developing scientific thinking. Concrete illustrations of some of the ideas discussed in this essay, particularly, how language and prior knowledge may influence the development of scientific concepts, are then provided. The essay concludes by emphasizing that there is a window of opportunity that educators should exploit by presenting science as part of the curriculum in both kindergarten and the first years of primary school.
An information-based approach to change-point analysis with applications to biophysics and cell biology.

PubMed

Wiggins, Paul A

2015-07-21

This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics.

PubMed

Benedek, C; Descombes, X; Zerubia, J

2012-01-01

In this paper, we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: 1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low-level change information between the time layers and object-level building description to recognize and separate changed and unaltered buildings. 2) To answer the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature-based modules. 3) To simultaneously ensure the convergence, optimality, and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel nonuniform stochastic object birth process which generates relevant objects with higher probability based on low-level image features.
Relationship of resident characteristics, attitudes, prior training and clinical knowledge to communication skills performance.

PubMed

Laidlaw, Toni Suzuki; Kaufman, David M; MacLeod, Heather; van Zanten, Sander; Simpson, David; Wrixon, William

2006-01-01

A substantial body of literature demonstrates that communication skills in medicine can be taught and retained through teaching and practice. Considerable evidence also reveals that characteristics such as gender, age, language and attitudes affect communication skills performance. Our study examined the characteristics, attitudes and prior communication skills training of residents to determine the relationship of each to patient-doctor communication. The relationship between communication skills proficiency and clinical knowledge application (biomedical and ethical) was also examined through the use of doctor-developed clinical content checklists, as very little research has been conducted in this area. A total of 78 first- and second-year residents across all departments at Dalhousie Medical School participated in a videotaped 4-station objective structured clinical examination presenting a range of communication and clinical knowledge challenges. A variety of instruments were used to gather information and assess performance. Two expert raters evaluated the videotapes. Significant relationships were observed between resident characteristics, prior communication skills training, clinical knowledge and communication skills performance. Females, younger residents and residents with English as first language scored significantly higher, as did residents with prior communication skills training. A significant positive relationship was found between the clinical content checklist and communication performance. Gender was the only characteristic related significantly to attitudes. Gender, age, language and prior communication skills training are related to communication skills performance and have implications for resident education. The positive relationship between communication skills proficiency and clinical knowledge application is important and should be explored further.
Semantic Analysis of Military Relevant Texts for Intelligence Purposes

DTIC Science & Technology

2011-06-01

Topic 8: Architectures, Technologies, and Tools Topic 4: Information and Knowledge Exploitation Topic 3: Information and Knowledge... Information Processing and Ergonomics FKIE Fraunhofer Institute for Communication, Dr. Matthias Hecking Sandra Noubours (point of contact...Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour
U.S. Army Symposium on Artificial Intelligence Research for Exploitation of the Battlefield Environment Held in El Paso, Texas on 15-16 November 1988

DTIC Science & Technology

1988-11-16

Pheasant Run Lodge , !t. Chdrles, IL. Antony, R. and Emmerman, P., 1986: Spatial Reasoning and Knowledge Representation, Geographic Information Systems...Reasoning about Action and Plans Workshop, Timberline Ore., M. Georgeff and A. Lansky, ed., Morgan Kaufman. Kuan, D.1984: Terraiij Map Knowledge
Constructing an Entrepreneurial Architecture: An Emergent Framework for Studying the Contemporary University beyond the Entrepreneurial Turn

ERIC Educational Resources Information Center

Nelles, Jen; Vorley, Tim

2010-01-01

Universities are engines of the knowledge-based economy, both as sites of knowledge production and exploitation. Over the past two decades a "Third Mission" for universities has been articulated, alongside teaching and research; and this third mission is understood as commercial engagement. While growing literatures on the entrepreneurial…
Improving Memory after Interruption: Exploiting Soft Constraints and Manipulating Information Access Cost

ERIC Educational Resources Information Center

Morgan, Phillip L.; Patrick, John; Waldron, Samuel M.; King, Sophia L.; Patrick, Tanya

2009-01-01

Forgetting what one was doing prior to interruption is an everyday problem. The recent soft constraints hypothesis (Gray, Sims, Fu, & Schoelles, 2006) emphasizes the strategic adaptation of information processing strategy to the task environment. It predicts that increasing information access cost (IAC: the time, and physical and mental effort…
Knowledge Discovery from Growing Social Networks

DTIC Science & Technology

2009-12-24

a trackback. We exploited the blog “Theme salon of blogs” in the site “goo” 2, where a blogger can recruit trackbacks of other bloggers by registering...using trackbacks. Thus, a piece of information can propagate from one blogger to another blogger through a trackback. We exploited the blog “Theme salon ...interesting propagation properties. The circle is a URL that corresponds to the musical baton which is a kind of telephone game on the Internet. It has the
Prior Knowledge Activation: How Different Concept Mapping Tasks Lead to Substantial Differences in Cognitive Processes, Learning Outcomes, and Perceived Self-Efficacy

ERIC Educational Resources Information Center

Gurlitt, Johannes; Renkl, Alexander

2010-01-01

Two experiments investigated the effects of characteristic features of concept mapping used for prior knowledge activation. Characteristic demands of concept mapping include connecting lines representing the relationships between concepts and labeling these lines, specifying the type of the semantic relationships. In the first experiment,…
Concept Development and Meaningful Learning among Electrical Engineering Students Engaged in a Problem-Based Laboratory Experience

ERIC Educational Resources Information Center

Bledsoe, Karen E.; Flick, Lawrence

2012-01-01

This phenomenographic study documented changes in student-held electrical concepts the development of meaningful learning among students with both low and high prior knowledge within a problem-based learning (PBL) undergraduate electrical engineering course. This paper reports on four subjects: two with high prior knowledge and two with low prior…
Formative Assessment Pre-Test to Identify College Students' Prior Knowledge, Misconceptions and Learning Difficulties in Biology

ERIC Educational Resources Information Center

Lazarowitz, Reuven; Lieb, Carl

2006-01-01

A formative assessment pretest was administered to undergraduate students at the beginning of a science course in order to find out their prior knowledge, misconceptions and learning difficulties on the topic of the human respiratory system and energy issues. Those findings could provide their instructors with the valuable information required in…
The Influence of Prior Knowledge, Peer Review, Age, and Gender in Online Philosophy Discussions

ERIC Educational Resources Information Center

Cuddy, Lucas Stebbins

2016-01-01

Using a primarily experimental design, this study investigated whether discussion boards in online community college philosophy classes can be designed in the Blackboard course management system to lead to higher order thinking. Discussions were designed using one of two teaching techniques: the activation of prior knowledge or the use of peer…
Thai University Students' Prior Knowledge about P-Waves Generated during Particle Motion

ERIC Educational Resources Information Center

Rakkapao, Suttida; Arayathanikul, Kwan; Pananont, Passakorn

2009-01-01

The goal of this study is to identify Thai students' prior knowledge about particle motion when P-waves arrive. This existing idea significantly influences what and how students learn in the classroom. The data were collected via conceptual open-ended questions designed by the researchers and through explanatory follow-up interviews. Participants…
The Interpretation of Cellular Transport Graphics by Students with Low and High Prior Knowledge

ERIC Educational Resources Information Center

Cook, Michelle; Carter, Glenda; Wiebe, Eric N.

2008-01-01

The purpose of this study was to examine how prior knowledge of cellular transport influenced how high school students in the USA viewed and interpreted graphic representations of this topic. The participants were Advanced Placement Biology students (n = 65); each participant had previously taken a biology course in high school. After assessing…
The Effects of Prior-Knowledge and Online Learning Approaches on Students' Inquiry and Argumentation Abilities

ERIC Educational Resources Information Center

Yang, Wen-Tsung; Lin, Yu-Ren; She, Hsiao-Ching; Huang, Kai-Yi

2015-01-01

This study investigated the effects of students' prior science knowledge and online learning approaches (social and individual) on their learning with regard to three topics: science concepts, inquiry, and argumentation. Two science teachers and 118 students from 4 eighth-grade science classes were invited to participate in this research. Students…
The Instructional Effectiveness of Animated Signaling among Learners with High and Low Prior Knowledge

ERIC Educational Resources Information Center

Li, Shanshan

2012-01-01

The purpose of this study was to investigate the instructional effectiveness of animated signals among learners with high and low prior knowledge. Each of the two treatments was presented with animated instruction either with signals or without signals on the content of how an airplane achieves lift. Subjects were eighty-seven undergraduate…
A Fair and Balanced Look at the News: What Affects Memory for Controversial Arguments?

ERIC Educational Resources Information Center

Wiley, J.

2005-01-01

This research demonstrates how prior knowledge may allow for qualitative differences in representation of texts about controversial issues. People often experience a memory bias in favor of information with which they agree. In several experiments it was found that individuals with high prior knowledge about the topic were better able to recall…

Prior Knowledge Influence on Self-Explanation Effectiveness when Solving Problems: An Exploratory Study in Science Learning

ERIC Educational Resources Information Center

Ionas, Ioan Gelu; Cernusca, Dan; Collier, Harvest L.

2012-01-01

This exploratory study presents the outcomes of using self-explanation to improve learners' performance in solving basic chemistry problems. The results of the randomized experiment show the existence of a moderation effect between prior knowledge and the level of support self-explanation provides to learners, suggestive of a synergistic effect…
The Impact of Learner's Prior Knowledge on Their Use of Chemistry Computer Simulations: A Case Study

ERIC Educational Resources Information Center

Liu, Han-Chin; Andre, Thomas; Greenbowe, Thomas

2008-01-01

It is complicated to design a computer simulation that adapts to students with different characteristics. This study documented cases that show how college students' prior chemistry knowledge level affected their interaction with peers and their approach to solving problems with the use of computer simulations that were designed to learn…
Feedback Both Helps and Hinders Learning: The Causal Role of Prior Knowledge

ERIC Educational Resources Information Center

Fyfe, Emily R.; Rittle-Johnson, Bethany

2016-01-01

Feedback can be a powerful learning tool, but its effects vary widely. Research has suggested that learners' prior knowledge may moderate the effects of feedback; however, no causal link has been established. In Experiment 1, we randomly assigned elementary school children (N = 108) to a condition based on a crossing of 2 factors: induced strategy…
The Effect of Prior Knowledge and Feedback Type Design on Student Achievement and Satisfaction in Introductory Accounting

ERIC Educational Resources Information Center

Campbell, Donald P.

2013-01-01

This study investigated the effect of student prior knowledge and feedback type on student achievement and satisfaction in an introductory managerial accounting course using computer-based formative assessment tools. The study involved a redesign of the existing Job Order Costing unit using the ADDIE model of instructional design. The…
Activating Junior Secondary School Students' Prior Knowledge for the Development of Vocabulary, Concepts and Mathematics through Instructional Strategies

ERIC Educational Resources Information Center

Oyinloye, Olu; Popoola, Abiodun A.

2013-01-01

This paper investigates the activation of students' prior knowledge for the development of vocabulary, concepts and mathematics. It has been observed that many secondary school students are not performing well in the examination conducted by the West African Examinations Council and National Examinations Council of Nigeria. The situation became…
Mechanisms underlying comprehension of health information in adulthood: the roles of prior knowledge and working memory capacity.

PubMed

Soederberg Miller, Lisa M; Gibson, Tanja N; Applegate, Elizabeth A; de Dios, Jeannette

2011-07-01

Prior knowledge, working memory capacity (WMC), and conceptual integration (attention allocated to integrating concepts in text) are critical within many contexts; however, their impact on the acquisition of health information (i.e. learning) is relatively unexplored.We examined how these factors impact learning about nutrition within a cross-sectional study of adults ages 18 to 81. Results showed that conceptual integration mediated the effects of knowledge and WMC on learning, confirming that attention to concepts while reading is important for learning about health. We also found that when knowledge was controlled, age declines in learning increased, suggesting that knowledge mitigates the effects of age on learning about nutrition.
A schema theory analysis of students' think aloud protocols in an STS biology context

NASA Astrophysics Data System (ADS)

Quinlan, Catherine Louise

This dissertation study is a conglomerate of the fields of Science Education and Applied Cognitive Psychology. The goal of this study is to determine what organizational features and knowledge representation patterns high school students exhibit over time for issues pertinent to science and society. Participants are thirteen tenth grade students in a diverse suburban-urban classroom in a northeastern state. Students' think alouds are recorded, pre-, post-, and late-post treatment. Treatment consists of instruction in three Science, Technology, and Society (STS) biology issues, namely the human genome project, nutrition and health, and stem cell research. Coding and analyses are performed using Marshall's knowledge representations---identification knowledge, elaboration knowledge, planning knowledge, and execution knowledge, as well as qualitative research analysis methods. Schema theory, information processing theory, and other applied cognitive theory provide a framework in which to understand and explain students' schema descriptions and progressions over time. The results show that students display five organizational features in their identification and elaboration knowledge. Students also fall into one of four categories according to if they display prior schema or no prior schema, and their orientation "for" or "against," some of the issues. Students with prior schema and orientation "against" display the most robust schema descriptions and schema progressions. Those with no prior schemas and orientation "against" show very modest schema progressions best characterized by their keyword searches. This study shows the importance in considering not only students' integrated schemas but also their individual schemes. A role for the use of a more schema-based instruction that scaffolds student learning is implicated.
Perceptual learning of degraded speech by minimizing prediction error.

PubMed

Sohoglu, Ediz; Davis, Matthew H

2016-03-22

Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech.
Perceptual learning of degraded speech by minimizing prediction error

PubMed Central

Sohoglu, Ediz

2016-01-01

Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech. PMID:26957596
The ups and downs of trophic control in continental shelf ecosystems.

PubMed

Frank, Kenneth T; Petrie, Brian; Shackell, Nancy L

2007-05-01

Traditionally, marine ecosystem structure was thought to be determined by phytoplankton dynamics. However, an integrated view on the relative roles of top-down (consumer-driven) and bottom-up (resource-driven) forcing in large-scale, exploited marine ecosystems is emerging. Long time series of scientific survey data, underpinning the management of commercially exploited species such as cod, are being used to diagnose mechanisms that could affect the composition and relative abundance of species in marine food webs. By assembling published data from studies in exploited North Atlantic ecosystems, we found pronounced geographical variation in top-down and bottom-up trophic forcing. The data suggest that ecosystem susceptibility to top-down control and their resiliency to exploitation are related to species richness and oceanic temperature conditions. Such knowledge could be used to produce ecosystem guidelines to regulate and manage fisheries in a sustainable fashion.
Trauma and its aftermath for commercially sexually exploited women as told by front-line service providers.

PubMed

Hom, Kristin A; Woods, Stephanie J

2013-02-01

Commercial sexual exploitation of women and girls through forced prostitution and sex-trafficking is a human rights and public health issue, with survivors facing complex mental health problems from trauma and violence. An international and domestic problem, the average age of recruitment into sex-trafficking is between 11 and 14 years old. Given its secrecy and brutality, such exploitation remains difficult to study, which results in a lack of knowledge related to trauma and how best to develop specific services that effectively engage and meet the unique needs of survivors. This qualitative research, using thematic analysis, explored the stories of trauma and its aftermath for commercially sexually exploited women as told by front-line service providers. Three themes emerged regarding the experience of sex-trafficking and its outcomes-Pimp Enculturation, Aftermath, and Healing the Wound-along with seven subthemes. These have important implications for all service and healthcare providers.
The Role of Specificity, Targeted Learning Activities, and Prior Knowledge for the Effects of Relevance Instructions

ERIC Educational Resources Information Center

Roelle, Julian; Lehmkuhl, Nina; Beyer, Martin-Uwe; Berthold, Kirsten

2015-01-01

In 2 experiments we examined the role of (a) specificity, (b) the type of targeted learning activities, and (c) learners' prior knowledge for the effects of relevance instructions on learning from instructional explanations. In Experiment 1, we recruited novices regarding the topic of atomic structure (N = 80) and found that "specific"…
Effects of Prior Knowledge of Topics and the Instructional Objectives on Students' Achievement in Literature-in-English

ERIC Educational Resources Information Center

Mbah, Blessing Akaraka

2015-01-01

This study investigated the effects of prior knowledge of topics with their instructional objectives on senior secondary school class two (SS II) students. The study was carried out in Abakaliki Education Zone of Ebonyi State, Nigeria. The design of the study is quasi experimental of pretest-posttest of non-equivalent control group. Two research…
Polite Web-Based Intelligent Tutors: Can They Improve Learning in Classrooms?

ERIC Educational Resources Information Center

McLaren, Bruce M.; DeLeeuw, Krista E.; Mayer, Richard E.

2011-01-01

Should an intelligent software tutor be polite, in an effort to motivate and cajole students to learn, or should it use more direct language? If it should be polite, under what conditions? In a series of studies in different contexts (e.g., lab versus classroom) with a variety of students (e.g., low prior knowledge versus high prior knowledge),…
"She Has to Drink Blood of the Snake": Culture and Prior Knowledge in Science|Health Education

ERIC Educational Resources Information Center

Bricker, Leah A.; Reeve, Suzanne; Bell, Philip

2014-01-01

In this analysis, we argue that science education should attend more deeply to youths' cultural resources and practices (e.g. material, social, and intellectual). Inherent in our argument is a call for revisiting conceptions of "prior knowledge" to theorize how people make sense of the complex ecologies of experience, ideas, and cultural…
Effects of Different Types of True-False Questions on Memory Awareness and Long-Term Retention

ERIC Educational Resources Information Center

Schaap, Lydia; Verkoeijen, Peter; Schmidt, Henk

2014-01-01

This study investigated the effects of two different true-false questions on memory awareness and long-term retention of knowledge. Participants took four subsequent knowledge tests on curriculum learning material that they studied at different retention intervals prior to the start of this study (i.e. prior to the first test). At the first and…
Effects of Prior Knowledge and Concept-Map Structure on Disorientation, Cognitive Load, and Learning

ERIC Educational Resources Information Center

Amadieu, Franck; van Gog, Tamara; Paas, Fred; Tricot, Andre; Marine, Claudette

2009-01-01

This study explored the effects of prior knowledge (high vs. low; HPK and LPK) and concept-map structure (hierarchical vs. network; HS and NS) on disorientation, cognitive load, and learning from non-linear documents on "the infection process of a retrograde virus (HIV)". Participants in the study were 24 adults. Overall subjective ratings of…
Relevant Prior Knowledge Moderates the Effect of Elaboration during Small Group Discussion on Academic Achievement

ERIC Educational Resources Information Center

Van Blankenstein, Floris M.; Dolmans, Diana H. J. M.; Van der Vleuten, Cees P. M.; Schmidt, Henk G.

2013-01-01

This study set out to test whether relevant prior knowledge would moderate a positive effect on academic achievement of elaboration during small-group discussion. In a 2 × 2 experimental design, 66 undergraduate students observed a video showing a small-group problem-based discussion about thunder and lightning. In the video, a teacher asked…
The Mediation Effect of In-Game Performance between Prior Knowledge and Posttest Score. CRESST Report 819

ERIC Educational Resources Information Center

Kerr, Deirdre; Chung, Gregory K. W. K.

2012-01-01

Though video games are commonly considered to hold great potential as learning environments, their effectiveness as a teaching tool has yet to be determined. One reason for this is that researchers often run into the problem of multicollinearity between prior knowledge, in-game performance, and posttest scores, thereby making the determination of…
Developmental Change in the Influence of Domain-General Abilities and Domain-Specific Knowledge on Mathematics Achievement: An Eight-Year Longitudinal Study

ERIC Educational Resources Information Center

Geary, David C.; Nicholas, Alan; Li, Yaoran; Sun, Jianguo

2017-01-01

The contributions of domain-general abilities and domain-specific knowledge to subsequent mathematics achievement were longitudinally assessed (n = 167) through 8th grade. First grade intelligence and working memory and prior grade reading achievement indexed domain-general effects, and domain-specific effects were indexed by prior grade…

Blended Learning Based on Schoology: Effort of Improvement Learning Outcome and Practicum Chance in Vocational High School

ERIC Educational Resources Information Center

Irawan, Vincentius Tjandra; Sutadji, Eddy; Widiyanti

2017-01-01

The aims of this study were to determine: (1) the differences in learning outcome between Blended Learning based on Schoology and Problem-Based Learning, (2) the differences in learning outcome between students with prior knowledge of high, medium, and low, and (3) the interaction between Blended Learning based on Schoology and prior knowledge to…
Why is a Pomegranate an Apple? The Role of Shape, Taxonomic Relatedness, and Prior Lexical Knowledge in Children's Overextensions of "Apple" and "Dog."

ERIC Educational Resources Information Center

Gelman, Susan A.; Croft, William; Fu, Panfang; Clausner, Timothy; Gottfried, Gail

1998-01-01

Examined how object shape, taxonomic relatedness, and prior lexical knowledge influenced children's overextensions (e.g., referring to pomegranates as apples). Researchers presented items that disentangled the three factors and used a novel comprehension task where children could indicate negative exemplars. Error patterns differed by task and by…
Building dynamical models from data and prior knowledge: the case of the first period-doubling bifurcation.

PubMed

Aguirre, Luis Antonio; Furtado, Edgar Campos

2007-10-01

This paper reviews some aspects of nonlinear model building from data with (gray box) and without (black box) prior knowledge. The model class is very important because it determines two aspects of the final model, namely (i) the type of nonlinearity that can be accurately approximated and (ii) the type of prior knowledge that can be taken into account. Such features are usually in conflict when it comes to choosing the model class. The problem of model structure selection is also reviewed. It is argued that such a problem is philosophically different depending on the model class and it is suggested that the choice of model class should be performed based on the type of a priori available. A procedure is proposed to build polynomial models from data on a Poincaré section and prior knowledge about the first period-doubling bifurcation, for which the normal form is also polynomial. The final models approximate dynamical data in a least-squares sense and, by design, present the first period-doubling bifurcation at a specified value of parameters. The procedure is illustrated by means of simulated examples.
Mixture class recovery in GMM under varying degrees of class separation: frequentist versus Bayesian estimation.

PubMed

Depaoli, Sarah

2013-06-01

Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth. The aim of the current article was to explore the impact of latent class separation (i.e., how similar growth trajectories are across latent classes) on GMM performance. Several estimation conditions were compared: maximum likelihood via the expectation maximization (EM) algorithm and the Bayesian framework implementing diffuse priors, "accurate" informative priors, weakly informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and "inaccurate" (but informative) priors. The main goal was to provide insight about the optimal estimation condition under different degrees of latent class separation for GMM. Results indicated that optimal parameter recovery was obtained though the Bayesian approach using "accurate" informative priors, and partial-knowledge priors showed promise for the recovery of the growth trajectory parameters. Maximum likelihood and the remaining Bayesian estimation conditions yielded poor parameter recovery for the latent class proportions and the growth trajectories. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
Three-dimensional propagation in near-field tomographic X-ray phase retrieval

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

Ruhlandt, Aike, E-mail: aruhlan@gwdg.de; Salditt, Tim

An extension of phase retrieval algorithms for near-field X-ray (propagation) imaging to three dimensions is presented, enhancing the quality of the reconstruction by exploiting previously unused three-dimensional consistency constraints. This paper presents an extension of phase retrieval algorithms for near-field X-ray (propagation) imaging to three dimensions, enhancing the quality of the reconstruction by exploiting previously unused three-dimensional consistency constraints. The approach is based on a novel three-dimensional propagator and is derived for the case of optically weak objects. It can be easily implemented in current phase retrieval architectures, is computationally efficient and reduces the need for restrictive prior assumptions, resultingmore » in superior reconstruction quality.« less
Automated Software Vulnerability Analysis

NASA Astrophysics Data System (ADS)

Sezer, Emre C.; Kil, Chongkyung; Ning, Peng

Despite decades of research, software continues to have vulnerabilities. Successful exploitations of these vulnerabilities by attackers cost millions of dollars to businesses and individuals. Unfortunately, most effective defensive measures, such as patching and intrusion prevention systems, require an intimate knowledge of the vulnerabilities. Many systems for detecting attacks have been proposed. However, the analysis of the exploited vulnerabilities is left to security experts and programmers. Both the human effortinvolved and the slow analysis process are unfavorable for timely defensive measure to be deployed. The problem is exacerbated by zero-day attacks.
Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem.

PubMed

Lu, Qiang; Ren, Jun; Wang, Zhiguang

2016-01-01

A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR.
PRIOR-WK&E: Social Software for Policy Making in the Knowledge Society

NASA Astrophysics Data System (ADS)

Turón, Alberto; Aguarón, Juan; Escobar, María Teresa; Gallardo, Carolina; Moreno-Jiménez, José María; Salazar, José Luis

This paper presents a social software application denominated as PRIOR-WK&E. It has been developed by the Zaragoza Multicriteria Decision Making Group (GDMZ) with the aim of responding to the challenges of policy making in the Knowledge Society. Three specific modules have been added to PRIOR, the collaborative tool used by the research group (GDMZ) for considering the multicriteria selection of a discrete set of alternatives. The first module (W), that deals with multiactor decision making through the Web, and the second (K), that concerns the extraction and diffusion of knowledge related to the scientific resolution of the problem, were explained in [1]. The new application strengthens securitization and includes a third module (E) that evaluates the effectiveness of public administrations policy making.
Measuring Knowledge Elaboration Based on a Computer-Assisted Knowledge Map Analytical Approach to Collaborative Learning

ERIC Educational Resources Information Center

Zheng, Lanqin; Huang, Ronghuai; Hwang, Gwo-Jen; Yang, Kaicheng

2015-01-01

The purpose of this study is to quantitatively measure the level of knowledge elaboration and explore the relationships between prior knowledge of a group, group performance, and knowledge elaboration in collaborative learning. Two experiments were conducted to investigate the level of knowledge elaboration. The collaborative learning objective in…
An Expert System toward Buiding An Earth Science Knowledge Graph

NASA Astrophysics Data System (ADS)

Zhang, J.; Duan, X.; Ramachandran, R.; Lee, T. J.; Bao, Q.; Gatlin, P. N.; Maskey, M.

2017-12-01

In this ongoing work, we aim to build foundations of Cognitive Computing for Earth Science research. The goal of our project is to develop an end-to-end automated methodology for incrementally constructing Knowledge Graphs for Earth Science (KG4ES). These knowledge graphs can then serve as the foundational components for building cognitive systems in Earth science, enabling researchers to uncover new patterns and hypotheses that are virtually impossible to identify today. In addition, this research focuses on developing mining algorithms needed to exploit these constructed knowledge graphs. As such, these graphs will free knowledge from publications that are generated in a very linear, deterministic manner, and structure knowledge in a way that users can both interact and connect with relevant pieces of information. Our major contributions are two-fold. First, we have developed an end-to-end methodology for constructing Knowledge Graphs for Earth Science (KG4ES) using existing corpus of journal papers and reports. One of the key challenges in any machine learning, especially deep learning applications, is the need for robust and large training datasets. We have developed techniques capable of automatically retraining models and incrementally building and updating KG4ES, based on ever evolving training data. We also adopt the evaluation instrument based on common research methodologies used in Earth science research, especially in Atmospheric Science. Second, we have developed an algorithm to infer new knowledge that can exploit the constructed KG4ES. In more detail, we have developed a network prediction algorithm aiming to explore and predict possible new connections in the KG4ES and aid in new knowledge discovery.
The Impacts of Virtual Manipulatives and Prior Knowledge on Geometry Learning Performance in Junior High School

ERIC Educational Resources Information Center

Lee, Chun-Yi; Chen, Ming-Jang

2014-01-01

Previous studies on the effects of virtual and physical manipulatives have failed to consider the impact of prior knowledge on the efficacy of manipulatives. This study focuses on the learning of plane geometry in junior high schools, including the sum of interior angles in polygons, the sum of exterior angles in polygons, and the properties of…
Exploring the Impact of Prior Knowledge and Appropriate Feedback on Students' Perceived Cognitive Load and Learning Outcomes: Animation-Based Earthquakes Instruction

ERIC Educational Resources Information Center

Yeh, Ting-Kuang; Tseng, Kuan-Yun; Cho, Chung-Wen; Barufaldi, James P.; Lin, Mei-Shin; Chang, Chun-Yen

2012-01-01

The aim of this study was to develop an animation-based curriculum and to evaluate the effectiveness of animation-based instruction; the report involved the assessment of prior knowledge and the appropriate feedback approach, for the purpose of reducing perceived cognitive load and improving learning. The curriculum was comprised of five subunits…
How Word Decoding, Vocabulary and Prior Topic Knowledge Predict Reading Comprehension. A Study of Language-Minority Students in Norwegian Fifth Grade Classrooms

ERIC Educational Resources Information Center

Rydland, Veslemoy; Aukrust, Vibeke Grover; Fulland, Helene

2012-01-01

This study examined the contribution of word decoding, first-language (L1) and second-language (L2) vocabulary and prior topic knowledge to L2 reading comprehension. For measuring reading comprehension we employed two different reading tasks: Woodcock Passage Comprehension and a researcher-developed content-area reading assignment (the Global…
The Effectiveness of Worked Examples Associated with Presentation Format and Prior Knowledge: A Web-Based Experiment

ERIC Educational Resources Information Center

Hsiao, E-Ling

2010-01-01

The aim of this study is to explore whether presentation format and prior knowledge affect the effectiveness of worked examples. The experiment was conducted through a specially designed online instrument. A 2X2X3 factorial before-and-after design was conducted. Three-way ANOVA was employed for data analysis. The result showed first, that prior…
Case-based reasoning for space applications: Utilization of prior experience in knowledge-based systems

NASA Technical Reports Server (NTRS)

King, James A.

1987-01-01

The goal is to explain Case-Based Reasoning as a vehicle to establish knowledge-based systems based on experimental reasoning for possible space applications. This goal will be accomplished through an examination of reasoning based on prior experience in a sample domain, and also through a presentation of proposed space applications which could utilize Case-Based Reasoning techniques.
Effects of Reading Ability, Prior Knowledge, Topic Interest, and Locus of Control on At-Risk College Students' Use of Graphic Organizers and Summarizing.

ERIC Educational Resources Information Center

Balajthy, Ernest; Weisberg, Renee

A study investigated the influence of key factors (general comprehension ability, prior knowledge of passage topic, interest in passage topic, and locus of control) on training at-risk college students in the use of graphic organizers as a cognitive learning strategy. Subjects, 60 college freshmen required to take a developmental reading/study…
Frontopolar cortex and decision-making efficiency: comparing brain activity of experts with different professional background during an exploration-exploitation task.

PubMed

Laureiro-Martínez, Daniella; Canessa, Nicola; Brusoni, Stefano; Zollo, Maurizio; Hare, Todd; Alemanno, Federica; Cappa, Stefano F

2013-01-01

An optimal balance between efficient exploitation of available resources and creative exploration of alternatives is critical for adaptation and survival. Previous studies associated these behavioral drives with, respectively, the dopaminergic mesocorticolimbic system and frontopolar-intraparietal networks. We study the activation of these systems in two age and gender-matched groups of experienced decision-makers differing in prior professional background, with the aim to understand the neural bases of individual differences in decision-making efficiency (performance divided by response time). We compare brain activity of entrepreneurs (who currently manage the organization they founded based on their venture idea) and managers (who are constantly involved in making strategic decisions but have no venture experience) engaged in a gambling-task assessing exploitative vs. explorative decision-making. Compared with managers, entrepreneurs showed higher decision-making efficiency, and a stronger activation in regions of frontopolar cortex (FPC) previously associated with explorative choice. Moreover, activity across a network of regions previously linked to explore/exploit tradeoffs explained individual differences in choice efficiency. These results suggest new avenues for the study of individual differences in the neural antecedents of efficient decision-making.
Frontopolar cortex and decision-making efficiency: comparing brain activity of experts with different professional background during an exploration-exploitation task

PubMed Central

Laureiro-Martínez, Daniella; Canessa, Nicola; Brusoni, Stefano; Zollo, Maurizio; Hare, Todd; Alemanno, Federica; Cappa, Stefano F.

2014-01-01

An optimal balance between efficient exploitation of available resources and creative exploration of alternatives is critical for adaptation and survival. Previous studies associated these behavioral drives with, respectively, the dopaminergic mesocorticolimbic system and frontopolar-intraparietal networks. We study the activation of these systems in two age and gender-matched groups of experienced decision-makers differing in prior professional background, with the aim to understand the neural bases of individual differences in decision-making efficiency (performance divided by response time). We compare brain activity of entrepreneurs (who currently manage the organization they founded based on their venture idea) and managers (who are constantly involved in making strategic decisions but have no venture experience) engaged in a gambling-task assessing exploitative vs. explorative decision-making. Compared with managers, entrepreneurs showed higher decision-making efficiency, and a stronger activation in regions of frontopolar cortex (FPC) previously associated with explorative choice. Moreover, activity across a network of regions previously linked to explore/exploit tradeoffs explained individual differences in choice efficiency. These results suggest new avenues for the study of individual differences in the neural antecedents of efficient decision-making. PMID:24478664
Self-Monitoring and Knowledge-Building in Learning by Teaching

ERIC Educational Resources Information Center

Roscoe, Rod D.

2014-01-01

Prior research has established that learning by teaching depends upon peer tutors' engagement in knowledge-building, in which tutors integrate their knowledge and generate new knowledge through reasoning. However, many tutors adopt a "knowledge-telling bias" defined by shallow summarizing of source materials and didactic lectures.…
Lending a Helping Hand: Voluntary Engagement in Knowledge Sharing

ERIC Educational Resources Information Center

Mergel, Ines; Lazer, David; Binz-Scharf, Maria Christina

2008-01-01

Knowledge is essential for the functioning of every social system, especially for professionals in knowledge-intensive organisations. Since individuals do not possess all the work-related knowledge that they require, they turn to others in search for that knowledge. While prior research has mainly focused on antecedents and consequences of…

Science Literacy and Prior Knowledge of Astronomy MOOC Students

NASA Astrophysics Data System (ADS)

Impey, Chris David; Buxner, Sanlyn; Wenger, Matthew; Formanek, Martin

2018-01-01

Many of science classes offered on Coursera fall into fall into the category of general education or general interest classes for lifelong learners, including our own, Astronomy: Exploring Time and Space. Very little is known about the backgrounds and prior knowledge of these students. In this talk we present the results of a survey of our Astronomy MOOC students. We also compare these results to our previous work on undergraduate students in introductory astronomy courses. Survey questions examined student demographics and motivations as well as their science and information literacy (including basic science knowledge, interest, attitudes and beliefs, and where they get their information about science). We found that our MOOC students are different than the undergraduate students in more ways than demographics. Many MOOC students demonstrated high levels of science and information literacy. With a more comprehensive understanding of our students’ motivations and prior knowledge about science and how they get their information about science, we will be able to develop more tailored learning experiences for these lifelong learners.
Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks

PubMed Central

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

2017-01-01

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency. PMID:28208735
On the Frontier of Knowledge: A Discussion of Alien Civilizations

NASA Astrophysics Data System (ADS)

Thomson, Shelley

Possible States Theory discusses change in the abstract; it has a single description of change, equally applicable to acts of mind and physical phenomena. Change is defined as an interaction between collections of possible states, which include past, future and possible outcomes. All possible outcomes coincide in the complex present. This allows a competent observer to participate in possible states interactions that are unconstrained by time, distance or conservation laws. The technique of coordinate remote viewing was used in a study of technologically advanced alien life forms. The primary focus of the study was on two specific species but general knowledge of multiple others was also obtained. One of the two major species may be characterized as well disposed while the other may be classed as exploitative. Both species maintain facilities on Earth. The differences between the human species and these species are profound and go well beyond levels of technological development. Both alien species are forms of collective intelligence, which was true of all intelligent species studied. The human decision to militarize interaction with alien species may be characterized as inappropriate and potentially counterproductive. Unsuccessful efforts to communicate were made by all sides prior to this study. Substantial obstacles to communication exist based upon attitudes and beliefs of both humans and aliens. Among the most important findings are the unusual, perhaps unique biodiversity found on the Earth and the value aliens place on sentience. To the extent that the conclusions are correct, these findings hold significant implications for the future of humanity.
Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

PubMed Central

O'Hagan, Steve; Knowles, Joshua; Kell, Douglas B.

2012-01-01

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information). PMID:23185279
Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Haynor, David R.; Thompson, Carol L.; Lein, Ed; Hawrylycz, Michael

2009-02-01

Understanding the geography of genetic expression in the mouse brain has opened previously unexplored avenues in neuroinformatics. The Allen Brain Atlas (www.brain-map.org) (ABA) provides genome-wide colorimetric in situ hybridization (ISH) gene expression images at high spatial resolution, all mapped to a common three-dimensional 200μm3 spatial framework defined by the Allen Reference Atlas (ARA) and is a unique data set for studying expression based structural and functional organization of the brain. The goal of this study was to facilitate an unbiased data-driven structural partitioning of the major structures in the mouse brain. We have developed an algorithm that uses nonnegative matrix factorization (NMF) to perform parts based analysis of ISH gene expression images. The standard NMF approach and its variants are limited in their ability to flexibly integrate prior knowledge, in the context of spatial data. In this paper, we introduce spatial connectivity as an additional regularization in NMF decomposition via the use of Markov Random Fields (mNMF). The mNMF algorithm alternates neighborhood updates with iterations of the standard NMF algorithm to exploit spatial correlations in the data. We present the algorithm and show the sub-divisions of hippocampus and somatosensory-cortex obtained via this approach. The results are compared with established neuroanatomic knowledge. We also highlight novel gene expression based sub divisions of the hippocampus identified by using the mNMF algorithm.
Blocky inversion of multichannel elastic impedance for elastic parameters

NASA Astrophysics Data System (ADS)

Mozayan, Davoud Karami; Gholami, Ali; Siahkoohi, Hamid Reza

2018-04-01

Petrophysical description of reservoirs requires proper knowledge of elastic parameters like P- and S-wave velocities (Vp and Vs) and density (ρ), which can be retrieved from pre-stack seismic data using the concept of elastic impedance (EI). We propose an inversion algorithm which recovers elastic parameters from pre-stack seismic data in two sequential steps. In the first step, using the multichannel blind seismic inversion method (exploited recently for recovering acoustic impedance from post-stack seismic data), high-resolution blocky EI models are obtained directly from partial angle-stacks. Using an efficient total-variation (TV) regularization, each angle-stack is inverted independently in a multichannel form without prior knowledge of the corresponding wavelet. The second step involves inversion of the resulting EI models for elastic parameters. Mathematically, under some assumptions, the EI's are linearly described by the elastic parameters in the logarithm domain. Thus a linear weighted least squares inversion is employed to perform this step. Accuracy of the concept of elastic impedance in predicting reflection coefficients at low and high angles of incidence is compared with that of exact Zoeppritz elastic impedance and the role of low frequency content in the problem is discussed. The performance of the proposed inversion method is tested using synthetic 2D data sets obtained from the Marmousi model and also 2D field data sets. The results confirm the efficiency and accuracy of the proposed method for inversion of pre-stack seismic data.
Automatic Segmentation of the Cortical Grey and White Matter in MRI Using a Region-Growing Approach Based on Anatomical Knowledge

NASA Astrophysics Data System (ADS)

Wasserthal, Christian; Engel, Karin; Rink, Karsten; Brechmann, Andr'e.

We propose an automatic procedure for the correct segmentation of grey and white matter in MR data sets of the human brain. Our method exploits general anatomical knowledge for the initial segmentation and for the subsequent refinement of the estimation of the cortical grey matter. Our results are comparable to manual segmentations.
Predicting the outcome of roulette

NASA Astrophysics Data System (ADS)

Small, Michael; Tse, Chi Kong

2012-09-01

There have been several popular reports of various groups exploiting the deterministic nature of the game of roulette for profit. Moreover, through its history, the inherent determinism in the game of roulette has attracted the attention of many luminaries of chaos theory. In this paper, we provide a short review of that history and then set out to determine to what extent that determinism can really be exploited for profit. To do this, we provide a very simple model for the motion of a roulette wheel and ball and demonstrate that knowledge of initial position, velocity, and acceleration is sufficient to predict the outcome with adequate certainty to achieve a positive expected return. We describe two physically realizable systems to obtain this knowledge both incognito and in situ. The first system relies only on a mechanical count of rotation of the ball and the wheel to measure the relevant parameters. By applying these techniques to a standard casino-grade European roulette wheel, we demonstrate an expected return of at least 18%, well above the -2.7% expected of a random bet. With a more sophisticated, albeit more intrusive, system (mounting a digital camera above the wheel), we demonstrate a range of systematic and statistically significant biases which can be exploited to provide an improved guess of the outcome. Finally, our analysis demonstrates that even a very slight slant in the roulette table leads to a very pronounced bias which could be further exploited to substantially enhance returns.
Atypical combinations and scientific impact.

PubMed

Uzzi, Brian; Mukherjee, Satyam; Stringer, Michael; Jones, Ben

2013-10-25

Novelty is an essential feature of creative ideas, yet the building blocks of new ideas are often embodied in existing knowledge. From this perspective, balancing atypical knowledge with conventional knowledge may be critical to the link between innovativeness and impact. Our analysis of 17.9 million papers spanning all scientific fields suggests that science follows a nearly universal pattern: The highest-impact science is primarily grounded in exceptionally conventional combinations of prior work yet simultaneously features an intrusion of unusual combinations. Papers of this type were twice as likely to be highly cited works. Novel combinations of prior work are rare, yet teams are 37.7% more likely than solo authors to insert novel combinations into familiar knowledge domains.
48 CFR 31.205-18 - Independent research and development and bid and proposal costs.

Code of Federal Regulations, 2012 CFR

2012-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... systematic use, under whatever name, of scientific and technical knowledge in the design, development, test...
48 CFR 31.205-18 - Independent research and development and bid and proposal costs.

Code of Federal Regulations, 2013 CFR

2013-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... systematic use, under whatever name, of scientific and technical knowledge in the design, development, test...
48 CFR 31.205-18 - Independent research and development and bid and proposal costs.

Code of Federal Regulations, 2011 CFR

2011-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... systematic use, under whatever name, of scientific and technical knowledge in the design, development, test...
48 CFR 31.205-18 - Independent research and development and bid and proposal costs.

Code of Federal Regulations, 2014 CFR

2014-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... systematic use, under whatever name, of scientific and technical knowledge in the design, development, test...
Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors

NASA Astrophysics Data System (ADS)

Gloger, Oliver; Tönnies, Klaus; Bülow, Robin; Völzke, Henry

2017-07-01

To develop the first fully automated 3D spleen segmentation framework derived from T1-weighted magnetic resonance (MR) imaging data and to verify its performance for spleen delineation and volumetry. This approach considers the issue of low contrast between spleen and adjacent tissue in non-contrast-enhanced MR images. Native T1-weighted MR volume data was performed on a 1.5 T MR system in an epidemiological study. We analyzed random subsamples of MR examinations without pathologies to develop and verify the spleen segmentation framework. The framework is modularized to include different kinds of prior knowledge into the segmentation pipeline. Classification by support vector machines differentiates between five different shape types in computed foreground probability maps and recognizes characteristic spleen regions in axial slices of MR volume data. A spleen-shape space generated by training produces subject-specific prior shape knowledge that is then incorporated into a final 3D level set segmentation method. Individually adapted shape-driven forces as well as image-driven forces resulting from refined foreground probability maps steer the level set successfully to the segment the spleen. The framework achieves promising segmentation results with mean Dice coefficients of nearly 0.91 and low volumetric mean errors of 6.3%. The presented spleen segmentation approach can delineate spleen tissue in native MR volume data. Several kinds of prior shape knowledge including subject-specific 3D prior shape knowledge can be used to guide segmentation processes achieving promising results.
Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions.

PubMed

Yan, Yuguang; Wu, Qingyao; Tan, Mingkui; Ng, Michael K; Min, Huaqing; Tsang, Ivor W

2017-10-10

In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data. After that, an online decision function is learned from the target data. Last, we employ a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces. We also provide a theoretical analysis regarding the mistake bounds of the proposed approach. Comprehensive experiments on three real-world data sets demonstrate the effectiveness of the proposed technique.
Prior health expenditures and risk sharing with insurers competing on quality.

PubMed

Marchand, Maurice; Sato, Motohiro; Schokkaert, Erik

2003-01-01

Insurers can exploit the heterogeneity within risk-adjustment classes to select the good risks because they have more information than the regulator on the expected expenditures of individual insurees. To counteract this cream skimming, mixed systems combining capitation and cost-based payments have been adopted that do not, however, generally use the past expenditures of insurees as a risk adjuster. In this article, two symmetric insurers compete for clients by differentiating the quality of service offered to them according to some private information about their risk. In our setting it is always welfare improving to use prior expenditures as a risk adjuster.
The Role of "Creative Transfer" in Professional Transitions

ERIC Educational Resources Information Center

Triantafyllaki, Angeliki

2016-01-01

This paper discusses the concept of "knowledge transfer" in terms of expansion of prior knowledge, creativity and approaches to generating new knowledge. It explores professional transitions in which knowledge restructuring and identity reformation are pathways into greater work flexibility and adjustment. Two studies, exploring…
Effects of Process-Oriented and Product-Oriented Worked Examples and Prior Knowledge on Learner Problem Solving and Attitude: A Study in the Domain of Microeconomics

ERIC Educational Resources Information Center

Brooks, Christopher Darren

2009-01-01

The purpose of this study was to investigate the effectiveness of process-oriented and product-oriented worked example strategies and the mediating effect of prior knowledge (high versus low) on problem solving and learner attitude in the domain of microeconomics. In addition, the effect of these variables on learning efficiency as well as the…
Influence of Prior Knowledge and Interest on Fourth- and Fifth-Grade Passage Comprehension on the Qualitative Reading Inventory-4

ERIC Educational Resources Information Center

Clark, Mary Kristen; Kamhi, Alan G.

2014-01-01

Purpose: In 2 experiments, we examined the influence of prior knowledge and interest on 4th- and 5th-grade students' passage comprehension scores on the Qualitative Reading Inventory-4 (QRI-4) and 2 experimenter constructed passages. Method: In Experiment 1, 4th- and 5th-grade students were administered 4 Level 4 passages or 4 Level 5…
Effects of Type of Exploratory Strategy and Prior Knowledge on Middle School Students' Learning of Chemical Formulas from a 3D Role-Playing Game

ERIC Educational Resources Information Center

Chen, Ming-Puu; Wong, Yu-Ting; Wang, Li-Chun

2014-01-01

The purpose of this study was to examine the effects of the type of exploratory strategy and level of prior knowledge on middle school students' performance and motivation in learning chemical formulas via a 3D role-playing game (RPG). Two types of exploratory strategies-RPG exploratory with worked-example and RPG exploratory without…

Counting-On, Trading and Partitioning: Effects of Training and Prior Knowledge on Performance on Base-10 Tasks

ERIC Educational Resources Information Center

Saxton, Matthew; Cakir, Kadir

2006-01-01

Factors affecting performance on base-10 tasks were investigated in a series of four studies with a total of 453 children aged 5-7 years. Training in counting-on was found to enhance child performance on base-10 tasks (Studies 2, 3, and 4), while prior knowledge of counting-on (Study 1), trading (Studies 1 and 3), and partitioning (Studies 1 and…
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition.

PubMed

Wong, Sebastien C; Stamatescu, Victor; Gatt, Adam; Kearney, David; Lee, Ivan; McDonnell, Mark D

2017-10-01

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.
Red spruce stand dynamics, simulations, and restoration opportunities in the central Appalachians

Treesearch

James S. Rentch; Thomas M. Schuler; W. Mark Ford; Gergory J. Nowacki

2007-01-01

Red spruce (Picea rubens)-dominated forests occupied as much as 600,000 ha in West Virginia prior to exploitive logging era of the late nineteenth and early twentieth centuries. Subsequently, much of this forest type was converted to northern hardwoods. As an important habitat type for a number of rare or sensitive species, only about 12,000 ha of...
Collaborative learning in networks.

PubMed

Mason, Winter; Watts, Duncan J

2012-01-17

Complex problems in science, business, and engineering typically require some tradeoff between exploitation of known solutions and exploration for novel ones, where, in many cases, information about known solutions can also disseminate among individual problem solvers through formal or informal networks. Prior research on complex problem solving by collectives has found the counterintuitive result that inefficient networks, meaning networks that disseminate information relatively slowly, can perform better than efficient networks for problems that require extended exploration. In this paper, we report on a series of 256 Web-based experiments in which groups of 16 individuals collectively solved a complex problem and shared information through different communication networks. As expected, we found that collective exploration improved average success over independent exploration because good solutions could diffuse through the network. In contrast to prior work, however, we found that efficient networks outperformed inefficient networks, even in a problem space with qualitative properties thought to favor inefficient networks. We explain this result in terms of individual-level explore-exploit decisions, which we find were influenced by the network structure as well as by strategic considerations and the relative payoff between maxima. We conclude by discussing implications for real-world problem solving and possible extensions.
Collaborative learning in networks

PubMed Central

Mason, Winter; Watts, Duncan J.

2012-01-01

Complex problems in science, business, and engineering typically require some tradeoff between exploitation of known solutions and exploration for novel ones, where, in many cases, information about known solutions can also disseminate among individual problem solvers through formal or informal networks. Prior research on complex problem solving by collectives has found the counterintuitive result that inefficient networks, meaning networks that disseminate information relatively slowly, can perform better than efficient networks for problems that require extended exploration. In this paper, we report on a series of 256 Web-based experiments in which groups of 16 individuals collectively solved a complex problem and shared information through different communication networks. As expected, we found that collective exploration improved average success over independent exploration because good solutions could diffuse through the network. In contrast to prior work, however, we found that efficient networks outperformed inefficient networks, even in a problem space with qualitative properties thought to favor inefficient networks. We explain this result in terms of individual-level explore-exploit decisions, which we find were influenced by the network structure as well as by strategic considerations and the relative payoff between maxima. We conclude by discussing implications for real-world problem solving and possible extensions. PMID:22184216
The Extension-Reduction Strategy: Activating Prior Knowledge

ERIC Educational Resources Information Center

Sloyer, Cliff W.

2004-01-01

A mathematical problem is solved using the extension-reduction or build it up-tear it down tactic. This technique is implemented in reviving students' earlier knowledge to enable them to apply this knowledge to solving new problems.
Interactive web-based learning modules prior to general medicine advanced pharmacy practice experiences.

PubMed

Isaacs, Alex N; Walton, Alison M; Nisly, Sarah A

2015-04-25

To implement and evaluate interactive web-based learning modules prior to advanced pharmacy practice experiences (APPEs) on inpatient general medicine. Three clinical web-based learning modules were developed for use prior to APPEs in 4 health care systems. The aim of the interactive modules was to strengthen baseline clinical knowledge before the APPE to enable the application of learned material through the delivery of patient care. For the primary endpoint, postassessment scores increased overall and for each individual module compared to preassessment scores. Postassessment scores were similar among the health care systems. The survey demonstrated positive student perceptions of this learning experience. Prior to inpatient general medicine APPEs, web-based learning enabled the standardization and assessment of baseline student knowledge across 4 health care systems.
A Novel Multiple Choice Question Generation Strategy: Alternative Uses for Controlled Vocabulary Thesauri in Biomedical-Sciences Education.

PubMed

Lopetegui, Marcelo A; Lara, Barbara A; Yen, Po-Yin; Çatalyürek, Ümit V; Payne, Philip R O

2015-01-01

Multiple choice questions play an important role in training and evaluating biomedical science students. However, the resource intensive nature of question generation limits their open availability, reducing their contribution to evaluation purposes mainly. Although applied-knowledge questions require a complex formulation process, the creation of concrete-knowledge questions (i.e., definitions, associations) could be assisted by the use of informatics methods. We envisioned a novel and simple algorithm that exploits validated knowledge repositories and generates concrete-knowledge questions by leveraging concepts' relationships. In this manuscript we present the development and validation of a prototype which successfully produced meaningful concrete-knowledge questions, opening new applications for existing knowledge repositories, potentially benefiting students of all biomedical sciences disciplines.
Interplay between Content Knowledge and Scientific Argumentation

ERIC Educational Resources Information Center

Hakyolu, Hanife; Ogan-Bekiroglu, Feral

2016-01-01

This research study aimed to analyze the relationship between content knowledge and argumentation by examining students' prior subject matter knowledge and their production of arguments as well as by comparing students' arguments with their knowledge-in-use during scientific argumentation sessions. A correlational research design was carried out…
Creating Illusions of Knowledge: Learning Errors that Contradict Prior Knowledge

ERIC Educational Resources Information Center

Fazio, Lisa K.; Barber, Sarah J.; Rajaram, Suparna; Ornstein, Peter A.; Marsh, Elizabeth J.

2013-01-01

Most people know that the Pacific is the largest ocean on Earth and that Edison invented the light bulb. Our question is whether this knowledge is stable, or if people will incorporate errors into their knowledge bases, even if they have the correct knowledge stored in memory. To test this, we asked participants general-knowledge questions 2 weeks…
The Neuropsychological Function of Older First-Time Child Exploitation Material Offenders: A Pilot Study.

PubMed

Rodriguez, Marcelo; Ellis, Andrew

2018-06-01

Despite the growing incidence of child exploitation offences, there is little knowledge of the neuropsychological function of older child exploitation material offenders (CEMOs). Given that studies have reported that sex offenders demonstrate deficits attributed to frontal and temporal lobe function, the aim of this pilot study was to investigate the frontotemporal function of older first-time child exploitation material offenders (FTCEMOs). The neuropsychological performance of 11 older FTCEMOs was compared with 34 older historical sex offenders (HSOs) and 32 older nonsex offender (NSO) controls. Forty-five percent of FTCEMOs admitted to a pedophilic interest, which was significantly lower than those reported by HSOs. FTCEMOs provided significantly higher intellectual function scores than HSOs. Results revealed no evidence of mild or major neurocognitive disorder in FTCEMOs. Although the groups were not significantly different, compared with normative data, FTCEMOs reported a high incidence of impairment on a measure of decision making and on a measure of facial emotional recognition.
Profiles of Inconsistent Knowledge in Children's Pathways of Conceptual Change

ERIC Educational Resources Information Center

Schneider, Michael; Hardy, Ilonca

2013-01-01

Conceptual change requires learners to restructure parts of their conceptual knowledge base. Prior research has identified the fragmentation and the integration of knowledge as 2 important component processes of knowledge restructuring but remains unclear as to their relative importance and the time of their occurrence during development. Previous…
49 CFR 240.209 - Procedures for making the determination on knowledge.

Code of Federal Regulations, 2010 CFR

2010-10-01

... knowledge. 240.209 Section 240.209 Transportation Other Regulations Relating to Transportation (Continued... determination on knowledge. (a) Each railroad, prior to initially certifying or recertifying any person as an... with the requirements of § 240.125 of this part, demonstrated sufficient knowledge of the railroad's...
Activation of Background Knowledge for Inference Making: Effects on Reading Comprehension

ERIC Educational Resources Information Center

Elbro, Carsten; Buch-Iversen, Ida

2013-01-01

Failure to "activate" relevant, existing background knowledge may be a cause of poor reading comprehension. This failure may cause particular problems with inferences that depend heavily on prior knowledge. Conversely, teaching how to use background knowledge in the context of gap-filling inferences could improve reading comprehension in…
Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy

PubMed Central

Shepherd, T; Teras, M; Beichel, RR; Boellaard, R; Bruynooghe, M; Dicken, V; Gooding, MJ; Julyan, PJ; Lee, JA; Lefèvre, S; Mix, M; Naranjo, V; Wu, X; Zaidi, H; Zeng, Z; Minn, H

2017-01-01

The impact of positron emission tomography (PET) on radiation therapy is held back by poor methods of defining functional volumes of interest. Many new software tools are being proposed for contouring target volumes but the different approaches are not adequately compared and their accuracy is poorly evaluated due to the ill-definition of ground truth. This paper compares the largest cohort to date of established, emerging and proposed PET contouring methods, in terms of accuracy and variability. We emphasize spatial accuracy and present a new metric that addresses the lack of unique ground truth. Thirty methods are used at 13 different institutions to contour functional volumes of interest in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring methods are grouped according to algorithmic type, level of interactivity and how they exploit structural information in hybrid images. Experiments reveal benefits of high levels of user interaction, as well as simultaneous visualization of CT images and PET gradients to guide interactive procedures. Method-wise evaluation identifies the danger of over-automation and the value of prior knowledge built into an algorithm. PMID:22692898
Simultaneous Local Binary Feature Learning and Encoding for Homogeneous and Heterogeneous Face Recognition.

PubMed

Lu, Jiwen; Erin Liong, Venice; Zhou, Jie

2017-08-09

In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike existing hand-crafted face descriptors such as local binary pattern (LBP) and Gabor features which usually require strong prior knowledge, our SLBFLE is an unsupervised feature learning approach which automatically learns face representation from raw pixels. Unlike existing binary face descriptors such as the LBP, discriminant face descriptor (DFD), and compact binary face descriptor (CBFD) which use a two-stage feature extraction procedure, our SLBFLE jointly learns binary codes and the codebook for local face patches so that discriminative information from raw pixels from face images of different identities can be obtained by using a one-stage feature learning and encoding procedure. Moreover, we propose a coupled simultaneous local binary feature learning and encoding (C-SLBFLE) method to make the proposed approach suitable for heterogeneous face matching. Unlike most existing coupled feature learning methods which learn a pair of transformation matrices for each modality, we exploit both the common and specific information from heterogeneous face samples to characterize their underlying correlations. Experimental results on six widely used face datasets are presented to demonstrate the effectiveness of the proposed method.
COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

PubMed

Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

2016-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.
COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS

PubMed Central

REGENBOGEN, SAM; WILKINS, ANGELA D.; LICHTARGE, OLIVIER

2015-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses. PMID:26776170
Accelerating 4D flow MRI by exploiting vector field divergence regularization.

PubMed

Santelli, Claudio; Loecher, Michael; Busch, Julia; Wieben, Oliver; Schaeffter, Tobias; Kozerke, Sebastian

2016-01-01

To improve velocity vector field reconstruction from undersampled four-dimensional (4D) flow MRI by penalizing divergence of the measured flow field. Iterative image reconstruction in which magnitude and phase are regularized separately in alternating iterations was implemented. The approach allows incorporating prior knowledge of the flow field being imaged. In the present work, velocity data were regularized to reduce divergence, using either divergence-free wavelets (DFW) or a finite difference (FD) method using the ℓ1-norm of divergence and curl. The reconstruction methods were tested on a numerical phantom and in vivo data. Results of the DFW and FD approaches were compared with data obtained with standard compressed sensing (CS) reconstruction. Relative to standard CS, directional errors of vector fields and divergence were reduced by 55-60% and 38-48% for three- and six-fold undersampled data with the DFW and FD methods. Velocity vector displays of the numerical phantom and in vivo data were found to be improved upon DFW or FD reconstruction. Regularization of vector field divergence in image reconstruction from undersampled 4D flow data is a valuable approach to improve reconstruction accuracy of velocity vector fields. © 2014 Wiley Periodicals, Inc.
Joint image reconstruction method with correlative multi-channel prior for x-ray spectral computed tomography

NASA Astrophysics Data System (ADS)

Kazantsev, Daniil; Jørgensen, Jakob S.; Andersen, Martin S.; Lionheart, William R. B.; Lee, Peter D.; Withers, Philip J.

2018-06-01

Rapid developments in photon-counting and energy-discriminating detectors have the potential to provide an additional spectral dimension to conventional x-ray grayscale imaging. Reconstructed spectroscopic tomographic data can be used to distinguish individual materials by characteristic absorption peaks. The acquired energy-binned data, however, suffer from low signal-to-noise ratio, acquisition artifacts, and frequently angular undersampled conditions. New regularized iterative reconstruction methods have the potential to produce higher quality images and since energy channels are mutually correlated it can be advantageous to exploit this additional knowledge. In this paper, we propose a novel method which jointly reconstructs all energy channels while imposing a strong structural correlation. The core of the proposed algorithm is to employ a variational framework of parallel level sets to encourage joint smoothing directions. In particular, the method selects reference channels from which to propagate structure in an adaptive and stochastic way while preferring channels with a high data signal-to-noise ratio. The method is compared with current state-of-the-art multi-channel reconstruction techniques including channel-wise total variation and correlative total nuclear variation regularization. Realistic simulation experiments demonstrate the performance improvements achievable by using correlative regularization methods.

Parentage assignment with genomic markers: a major advance for understanding and exploiting genetic variation of quantitative traits in farmed aquatic animals

PubMed Central

Vandeputte, Marc; Haffray, Pierrick

2014-01-01

Since the middle of the 1990s, parentage assignment using microsatellite markers has been introduced as a tool in aquaculture breeding. It now allows close to 100% assignment success, and offered new ways to develop aquaculture breeding using mixed family designs in commercial conditions. Its main achievements are the knowledge and control of family representation and inbreeding, especially in mass spawning species, above all the capacity to estimate reliable genetic parameters in any species and rearing system with no prior investment in structures, and the development of new breeding programs in many species. Parentage assignment should not be seen as a way to replace physical tagging, but as a new way to conceive breeding programs, which have to be optimized with its specific constraints, one of the most important being to well define the number of individuals to genotype to limit costs, maximize genetic gain while minimizing inbreeding. The recent possible shift to (for the moment) more costly single nucleotide polymorphism markers should benefit from future developments in genomics and marker-assisted selection to combine parentage assignment and indirect prediction of breeding values. PMID:25566319
Direct elicitation of template concentration from quantification cycle (Cq) distributions in digital PCR.

PubMed

Mojtahedi, Mitra; Fouquier d'Hérouël, Aymeric; Huang, Sui

2014-01-01

Digital PCR (dPCR) exploits limiting dilution of a template into an array of PCR reactions. From this array the number of reactions that contain at least one (as opposed to zero) initial template is determined, allowing inferring the original template concentration. Here we present a novel protocol to efficiently infer the concentration of a sample and its optimal dilution for dPCR from few targeted qPCR assays. By taking advantage of the real-time amplification feature of qPCR as opposed to relying on endpoint PCR assessment as in standard dPCR prior knowledge of template concentration is not necessary. This eliminates the need for serial dilutions in a separate titration and reduces the number of necessary reactions. We describe the theory underlying our approach and discuss experimental moments that contribute to uncertainty. We present data from a controlled experiment where the initial template concentration is known as proof of principle and apply our method on directly monitoring transcript level change during cell differentiation as well as gauging amplicon numbers in cDNA samples after pre-amplification. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
Building on prior knowledge without building it in.

PubMed

Hansen, Steven S; Lampinen, Andrew K; Suri, Gaurav; McClelland, James L

2017-01-01

Lake et al. propose that people rely on "start-up software," "causal models," and "intuitive theories" built using compositional representations to learn new tasks more efficiently than some deep neural network models. We highlight the many drawbacks of a commitment to compositional representations and describe our continuing effort to explore how the ability to build on prior knowledge and to learn new tasks efficiently could arise through learning in deep neural networks.
Smoothing Spline ANOVA Decomposition of Arbitrary Splines: An Application to Eye Movements in Reading

PubMed Central

Matuschek, Hannes; Kliegl, Reinhold; Holschneider, Matthias

2015-01-01

The Smoothing Spline ANOVA (SS-ANOVA) requires a specialized construction of basis and penalty terms in order to incorporate prior knowledge about the data to be fitted. Typically, one resorts to the most general approach using tensor product splines. This implies severe constraints on the correlation structure, i.e. the assumption of isotropy of smoothness can not be incorporated in general. This may increase the variance of the spline fit, especially if only a relatively small set of observations are given. In this article, we propose an alternative method that allows to incorporate prior knowledge without the need to construct specialized bases and penalties, allowing the researcher to choose the spline basis and penalty according to the prior knowledge of the observations rather than choosing them according to the analysis to be done. The two approaches are compared with an artificial example and with analyses of fixation durations during reading. PMID:25816246
Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data

PubMed Central

2014-01-01

Background High-throughput omics technologies have enabled the measurement of many genes or metabolites simultaneously. The resulting high dimensional experimental data poses significant challenges to transcriptomics and metabolomics data analysis methods, which may lead to spurious instead of biologically relevant results. One strategy to improve the results is the incorporation of prior biological knowledge in the analysis. This strategy is used to reduce the solution space and/or to focus the analysis on biological meaningful regions. In this article, we review a selection of these methods used in transcriptomics and metabolomics. We combine the reviewed methods in three groups based on the underlying mathematical model: exploratory methods, supervised methods and estimation of the covariance matrix. We discuss which prior knowledge has been used, how it is incorporated and how it modifies the mathematical properties of the underlying methods. PMID:25033193
Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles

NASA Astrophysics Data System (ADS)

Cook, Michelle Patrick

2006-11-01

Visual representations are essential for communicating ideas in the science classroom; however, the design of such representations is not always beneficial for learners. This paper presents instructional design considerations providing empirical evidence and integrating theoretical concepts related to cognitive load. Learners have a limited working memory, and instructional representations should be designed with the goal of reducing unnecessary cognitive load. However, cognitive architecture alone is not the only factor to be considered; individual differences, especially prior knowledge, are critical in determining what impact a visual representation will have on learners' cognitive structures and processes. Prior knowledge can determine the ease with which learners can perceive and interpret visual representations in working memory. Although a long tradition of research has compared experts and novices, more research is necessary to fully explore the expert-novice continuum and maximize the potential of visual representations.
Sleep Spindle Density Predicts the Effect of Prior Knowledge on Memory Consolidation

PubMed Central

Lambon Ralph, Matthew A.; Kempkes, Marleen; Cousins, James N.; Lewis, Penelope A.

2016-01-01

Information that relates to a prior knowledge schema is remembered better and consolidates more rapidly than information that does not. Another factor that influences memory consolidation is sleep and growing evidence suggests that sleep-related processing is important for integration with existing knowledge. Here, we perform an examination of how sleep-related mechanisms interact with schema-dependent memory advantage. Participants first established a schema over 2 weeks. Next, they encoded new facts, which were either related to the schema or completely unrelated. After a 24 h retention interval, including a night of sleep, which we monitored with polysomnography, participants encoded a second set of facts. Finally, memory for all facts was tested in a functional magnetic resonance imaging scanner. Behaviorally, sleep spindle density predicted an increase of the schema benefit to memory across the retention interval. Higher spindle densities were associated with reduced decay of schema-related memories. Functionally, spindle density predicted increased disengagement of the hippocampus across 24 h for schema-related memories only. Together, these results suggest that sleep spindle activity is associated with the effect of prior knowledge on memory consolidation. SIGNIFICANCE STATEMENT Episodic memories are gradually assimilated into long-term memory and this process is strongly influenced by sleep. The consolidation of new information is also influenced by its relationship to existing knowledge structures, or schemas, but the role of sleep in such schema-related consolidation is unknown. We show that sleep spindle density predicts the extent to which schemas influence the consolidation of related facts. This is the first evidence that sleep is associated with the interaction between prior knowledge and long-term memory formation. PMID:27030764
Selected aspects of prior and likelihood information for a Bayesian classifier in a road safety analysis.

PubMed

Nowakowska, Marzena

2017-04-01

The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models. Copyright © 2017 Elsevier Ltd. All rights reserved.
45 CFR 1616.3 - Qualifications.

Code of Federal Regulations, 2010 CFR

2010-10-01

...) Academic training and performance; (b) The nature and extent of prior legal experience; (c) Knowledge and understanding of the legal problems and needs of the poor; (d) Prior working experience in the client community...
Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence?

PubMed

Griffen, Edward J; Dossetter, Alexander G; Leach, Andrew G; Montague, Shane

2018-03-22

AI comes to lead optimization: medicinal chemistry in all disease areas can be accelerated by exploiting our pre-competitive knowledge in an unbiased way. Copyright © 2018 Elsevier Ltd. All rights reserved.
A Study about Placement Support Using Semantic Similarity

ERIC Educational Resources Information Center

Katz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

2014-01-01

This paper discusses Latent Semantic Analysis (LSA) as a method for the assessment of prior learning. The Accreditation of Prior Learning (APL) is a procedure to offer learners an individualized curriculum based on their prior experiences and knowledge. The placement decisions in this process are based on the analysis of student material by domain…
The Effects of Feedback during Exploratory Mathematics Problem Solving: Prior Knowledge Matters

ERIC Educational Resources Information Center

Fyfe, Emily R.; Rittle-Johnson, Bethany; DeCaro, Marci S.

2012-01-01

Providing exploratory activities prior to explicit instruction can facilitate learning. However, the level of guidance provided during the exploration has largely gone unstudied. In this study, we examined the effects of 1 form of guidance, feedback, during exploratory mathematics problem solving for children with varying levels of prior domain…
Apogee, Perigee, and Recovery: Chronology of Army Exploitation of Space

DTIC Science & Technology

1989-01-01

46 17. A BMD Advanced Technology Center infrared optical sensor is shown prior to mounting into a specially designed payload...wave infrared sensors to detect and track enemy ballistic missile warheads ..................... 50 21. In June 1984, the U.S. Army launched the...LWIR Long Wavelength Infrared MAAG Military Assistance Advisory Group MET SAT Meteorology Satellite MHV Miniature Homing Device MICOM Missile Command
Learners' strategies for reconstructing cognitive frameworks and navigating conceptual change from prior conception to consensual genetics knowledge

NASA Astrophysics Data System (ADS)

Parrott, Annette M.

Problem. Science teachers are charged with preparing students to become scientifically literate individuals. Teachers are given curriculum that specifies the knowledge that students should come away with; however, they are not necessarily aware of the knowledge with which the student arrives or how best to help them navigate between the two knowledge states. Educators must be aware, not only of where their students are conceptually, but how their students move from their prior knowledge and naive theories, to scientifically acceptable theories. The understanding of how students navigate this course has the potential to revolutionize educational practices. Methods. This study explored how five 9th grade biology students reconstructed their cognitive frameworks and navigated conceptual change from prior conception to consensual genetics knowledge. The research questions investigated were: (1) how do students in the process of changing their naive science theories to accepted science theories describe their journey from prior knowledge to current conception, and (2) what are the methods that students utilize to bridge the gap between alternate and consensual science conceptions to effect conceptual change. Qualitative and quantitative methods were employed to gather and analyze the data. In depth, semi-structured interviews formed the primary data for probing the context and details of students' conceptual change experience. Primary interview data was coded by thematic analysis. Results and discussion. This study revealed information about students' perceived roles in learning, the role of articulation in the conceptual change process, and ways in which a community of learners aids conceptual change. It was ascertained that students see their role in learning primarily as repeating information until they could add that information to their knowledge. Students are more likely to consider challenges to their conceptual frameworks and be more motivated to become active participants in constructing their knowledge when they are working collaboratively with peers instead of receiving instruction from their teacher. Articulation was found to be instrumental in aiding learners in identifying their alternate conceptions as well as in revisiting, investigating and reconstructing their conceptual frameworks. Based on the assumptions generated, suggestions were offered to inform pedagogical practice in support of the conceptual change process.
Generic domain models in software engineering

NASA Technical Reports Server (NTRS)

Maiden, Neil

1992-01-01

This paper outlines three research directions related to domain-specific software development: (1) reuse of generic models for domain-specific software development; (2) empirical evidence to determine these generic models, namely elicitation of mental knowledge schema possessed by expert software developers; and (3) exploitation of generic domain models to assist modelling of specific applications. It focuses on knowledge acquisition for domain-specific software development, with emphasis on tool support for the most important phases of software development.
Exploiting Early Intent Recognition for Competitive Advantage

DTIC Science & Technology

2009-01-01

basketball [Bhan- dari et al., 1997; Jug et al., 2003], and Robocup soccer sim- ulations [Riley and Veloso, 2000; 2002; Kuhlmann et al., 2006] and non...actions (e.g. before, after, around). Jug et al. [2003] used a similar framework for offline basketball game analysis. More recently, Hess et al...and K. Ramanujam. Advanced Scout: Data mining and knowledge discovery in NBA data. Data Mining and Knowledge Discovery, 1(1):121–125, 1997. [Chang
Temporal dynamics of the knowledge-mediated visual disambiguation process in humans: a magnetoencephalography study.

PubMed

Urakawa, Tomokazu; Ogata, Katsuya; Kimura, Takahiro; Kume, Yuko; Tobimatsu, Shozo

2015-01-01

Disambiguation of a noisy visual scene with prior knowledge is an indispensable task of the visual system. To adequately adapt to a dynamically changing visual environment full of noisy visual scenes, the implementation of knowledge-mediated disambiguation in the brain is imperative and essential for proceeding as fast as possible under the limited capacity of visual image processing. However, the temporal profile of the disambiguation process has not yet been fully elucidated in the brain. The present study attempted to determine how quickly knowledge-mediated disambiguation began to proceed along visual areas after the onset of a two-tone ambiguous image using magnetoencephalography with high temporal resolution. Using the predictive coding framework, we focused on activity reduction for the two-tone ambiguous image as an index of the implementation of disambiguation. Source analysis revealed that a significant activity reduction was observed in the lateral occipital area at approximately 120 ms after the onset of the ambiguous image, but not in preceding activity (about 115 ms) in the cuneus when participants perceptually disambiguated the ambiguous image with prior knowledge. These results suggested that knowledge-mediated disambiguation may be implemented as early as approximately 120 ms following an ambiguous visual scene, at least in the lateral occipital area, and provided an insight into the temporal profile of the disambiguation process of a noisy visual scene with prior knowledge. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Physical health symptoms reported by trafficked women receiving post-trafficking support in Moldova: prevalence, severity and associated factors

PubMed Central

2012-01-01

Background Many trafficked people suffer high levels of physical, sexual and psychological abuse. Yet, there has been limited research on the physical health problems associated with human trafficking or how the health needs of women in post-trafficking support settings vary according to socio-demographic or trafficking characteristics. Methods We analysed the prevalence and severity of 15 health symptoms reported by 120 trafficked women who had returned to Moldova between December 2007 and December 2008 and were registered with the International Organisation for Migration Assistance and Protection Programme. Women had returned to Moldova an average of 5.9 months prior to interview (range 2-12 months). Results Headaches (61.7%), stomach pain (60.9%), memory problems (44.2%), back pain (42.5%), loss of appetite (35%), and tooth pain (35%) were amongst the most commonly reported symptoms amongst both women trafficked for sexual exploitation and women trafficked for labour exploitation. The prevalence of headache and memory problems was strongly associated with duration of exploitation. Conclusions Trafficked women who register for post-trafficking support services after returning to their country of origin are likely to have long-term physical and dental health needs and should be provided with access to comprehensive medical services. Health problems among women who register for post-trafficking support services after returning to their country of origin are not limited to women trafficked for sexual exploitation but are also experienced by victims of labour exploitation. PMID:22834807
Physical health symptoms reported by trafficked women receiving post-trafficking support in Moldova: prevalence, severity and associated factors.

PubMed

Oram, Siân; Ostrovschi, Nicolae V; Gorceag, Viorel I; Hotineanu, Mihai A; Gorceag, Lilia; Trigub, Carolina; Abas, Melanie

2012-07-26

Many trafficked people suffer high levels of physical, sexual and psychological abuse. Yet, there has been limited research on the physical health problems associated with human trafficking or how the health needs of women in post-trafficking support settings vary according to socio-demographic or trafficking characteristics. We analysed the prevalence and severity of 15 health symptoms reported by 120 trafficked women who had returned to Moldova between December 2007 and December 2008 and were registered with the International Organisation for Migration Assistance and Protection Programme. Women had returned to Moldova an average of 5.9 months prior to interview (range 2-12 months). Headaches (61.7%), stomach pain (60.9%), memory problems (44.2%), back pain (42.5%), loss of appetite (35%), and tooth pain (35%) were amongst the most commonly reported symptoms amongst both women trafficked for sexual exploitation and women trafficked for labour exploitation. The prevalence of headache and memory problems was strongly associated with duration of exploitation. Trafficked women who register for post-trafficking support services after returning to their country of origin are likely to have long-term physical and dental health needs and should be provided with access to comprehensive medical services. Health problems among women who register for post-trafficking support services after returning to their country of origin are not limited to women trafficked for sexual exploitation but are also experienced by victims of labour exploitation.
Mitigating Information Overload: The Impact of Context-Based Approach to the Design of Tools for Intelligence Analysts

DTIC Science & Technology

2008-03-01

amount of arriving data, extract actionable information, and integrate it with prior knowledge. Add to that the pressures of today’s fusion center...information, and integrate it with prior knowledge. Add to that the pressures of today’s fusion center climate and it becomes clear that analysts, police... fusion centers, including specifics about how these problems manifest at the Illinois State Police (ISP) Statewide Terrorism and Intelligence Center

The dynamics of fidelity over the time course of long-term memory.

PubMed

Persaud, Kimele; Hemmer, Pernille

2016-08-01

Bayesian models of cognition assume that prior knowledge about the world influences judgments. Recent approaches have suggested that the loss of fidelity from working to long-term (LT) memory is simply due to an increased rate of guessing (e.g. Brady, Konkle, Gill, Oliva, & Alvarez, 2013). That is, recall is the result of either remembering (with some noise) or guessing. This stands in contrast to Bayesian models of cognition while assume that prior knowledge about the world influences judgments, and that recall is a combination of expectations learned from the environment and noisy memory representations. Here, we evaluate the time course of fidelity in LT episodic memory, and the relative contribution of prior category knowledge and guessing, using a continuous recall paradigm. At an aggregate level, performance reflects a high rate of guessing. However, when aggregate data is partitioned by lag (i.e., the number of presentations from study to test), or is un-aggregated, performance appears to be more complex than just remembering with some noise and guessing. We implemented three models: the standard remember-guess model, a three-component remember-guess model, and a Bayesian mixture model and evaluated these models against the data. The results emphasize the importance of taking into account the influence of prior category knowledge on memory. Copyright © 2016 Elsevier Inc. All rights reserved.
MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma

PubMed Central

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S.; Theis, Fabian J.

2015-01-01

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic. PMID:26694379
MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma.

PubMed

Sass, Steffen; Pitea, Adriana; Unger, Kristian; Hess, Julia; Mueller, Nikola S; Theis, Fabian J

2015-12-18

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method "miRlastic", which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic.
Learning from returnee Ethiopian migrant domestic workers: a qualitative assessment to reduce the risk of human trafficking.

PubMed

Busza, Joanna; Teferra, Sehin; Omer, Serawit; Zimmerman, Cathy

2017-09-11

International migration has become a global political priority, with growing concern about the scale of human trafficking, hazardous work conditions, and resulting psychological and physical morbidity among migrants. Ethiopia remains a significant "source" country for female domestic workers to the Middle East and Gulf States, despite widespread reports of exploitation and abuse. Prior to introduction of a "safe migration" intervention, we conducted formative research to elicit lessons learned by women who had worked as domestic workers abroad. The aim of the study was to identify realistic measures future migrants could take to protect themselves, based on the collective insights and experience of returnees. We conducted a qualitative assessment among returnee domestic labour migrants in Amhara Region, Ethiopia, an area considered a "hotspot" for outmigration. We conducted in-depth interviews and focus group discussions with a total of 35 female returnees, exploring risk and protective factors experienced by Ethiopian women during domestic work abroad. We used thematic content analysis to identify practical messages that could improve prospective migrants' preparedness. Returnees described the knowledge and skills they acquired prior to departure and during migration, and shared advice they would give to prospective migrants in their community. Facilitators of positive migration included conforming to cultural and behavioural expectations, learning basic Arabic, using household appliances, and ensuring safety in employers' homes. Respondents also associated confidence and assertiveness with better treatment and respect, and emphasized the importance of access to external communication (e.g. a mobile phone, local sim card, and contact details) for help in an emergency. Following their own challenging or even traumatic experiences, returnees were keen to support resilience among the next wave of migrants. There is little evidence on practices that foster safer migration, yet attention to human trafficking has led to an increase in pre-migration interventions. These require robust evidence about local risk and protective factors. Our findings identify knowledge, skills, attributes and resources found useful by returnee domestic workers in Amhara region, and have been used to inform a community-based programme aiming to foster better decision-making and preparation, with potential to offer insights for safer migration elsewhere.
Assessment of Knowledge Transfer in the Context of Biomechanics

ERIC Educational Resources Information Center

Hutchison, Randolph E.

2011-01-01

The dynamic act of knowledge transfer, or the connection of a student's prior knowledge to features of a new problem, could be considered one of the primary goals of education. Yet studies highlight more instances of failure than success. This dissertation focuses on how knowledge transfer takes place during individual problem solving, in…
New Knowledge Derived from Learned Knowledge: Functional-Anatomic Correlates of Stimulus Equivalence

ERIC Educational Resources Information Center

Schlund, Michael W.; Hoehn-Saric, Rudolf; Cataldo, Michael F.

2007-01-01

Forming new knowledge based on knowledge established through prior learning is a central feature of higher cognition that is captured in research on stimulus equivalence (SE). Numerous SE investigations show that reinforcing behavior under control of distinct sets of arbitrary conditional relations gives rise to stimulus control by new, "derived"…
They're Lovin' It: How Preschool Children Mediated Their Funds of Knowledge into Dramatic Play

ERIC Educational Resources Information Center

Karabon, Anne

2017-01-01

The funds of knowledge framework promotes connecting community contexts with curriculum aimed to activate children's prior knowledge. Typically, teachers determine what knowledge sources harmonise best with their existing programming, potentially omitting particular resources that may not align. Young children, on the other hand, can act as agents…
Is knowledge important? Empirical research on nuclear risk communication in two countries.

PubMed

Perko, Tanja; Zeleznik, Nadja; Turcanu, Catrinel; Thijssen, Peter

2012-06-01

Increasing audience knowledge is often set as a primary objective of risk communication efforts. But is it worthwhile focusing risk communication strategies solely on enhancing specific knowledge? The main research questions tackled in this paper were: (1) if prior audience knowledge related to specific radiation risks is influential for the perception of these risks and the acceptance of communicated messages and (2) if gender, attitudes, risk perception of other radiation risks, confidence in authorities, and living in the vicinity of nuclear/radiological installations may also play an important role in this matter. The goal of this study was to test empirically the mentioned predictors in two independent case studies in different countries. The first case study was an information campaign for iodine pre-distribution in Belgium (N = 1035). The second was the information campaign on long-term radioactive waste disposal in Slovenia (N = 1,200). In both cases, recurrent and intensive communication campaigns were carried out by the authorities aiming, among other things, at increasing specific audience knowledge. Results show that higher prior audience knowledge leads to more willingness to accept communicated messages, but it does not affect people’s perception of the specific risk communicated. In addition, the influence of prior audience knowledge on the acceptance of communicated messages is shown to be no stronger than that of general radiation risk perception. The results in both case studies suggest that effective risk communication has to focus not only on knowledge but also on other more heuristic predictors, such as risk perception or attitudes toward communicated risks.
Relations among Conceptual Knowledge, Procedural Knowledge, and Procedural Flexibility in Two Samples Differing in Prior Knowledge

ERIC Educational Resources Information Center

Schneider, Michael; Rittle-Johnson, Bethany; Star, Jon R.

2011-01-01

Competence in many domains rests on children developing conceptual and procedural knowledge, as well as procedural flexibility. However, research on the developmental relations between these different types of knowledge has yielded unclear results, in part because little attention has been paid to the validity of the measures or to the effects of…
Managing the data deluge: data-driven GO category assignment improves while complexity of functional annotation increases.

PubMed

Gobeill, Julien; Pasche, Emilie; Vishnyakova, Dina; Ruch, Patrick

2013-01-01

The available curated data lag behind current biological knowledge contained in the literature. Text mining can assist biologists and curators to locate and access this knowledge, for instance by characterizing the functional profile of publications. Gene Ontology (GO) category assignment in free text already supports various applications, such as powering ontology-based search engines, finding curation-relevant articles (triage) or helping the curator to identify and encode functions. Popular text mining tools for GO classification are based on so called thesaurus-based--or dictionary-based--approaches, which exploit similarities between the input text and GO terms themselves. But their effectiveness remains limited owing to the complex nature of GO terms, which rarely occur in text. In contrast, machine learning approaches exploit similarities between the input text and already curated instances contained in a knowledge base to infer a functional profile. GO Annotations (GOA) and MEDLINE make possible to exploit a growing amount of curated abstracts (97 000 in November 2012) for populating this knowledge base. Our study compares a state-of-the-art thesaurus-based system with a machine learning system (based on a k-Nearest Neighbours algorithm) for the task of proposing a functional profile for unseen MEDLINE abstracts, and shows how resources and performances have evolved. Systems are evaluated on their ability to propose for a given abstract the GO terms (2.8 on average) used for curation in GOA. We show that since 2006, although a massive effort was put into adding synonyms in GO (+300%), our thesaurus-based system effectiveness is rather constant, reaching from 0.28 to 0.31 for Recall at 20 (R20). In contrast, thanks to its knowledge base growth, our machine learning system has steadily improved, reaching from 0.38 in 2006 to 0.56 for R20 in 2012. Integrated in semi-automatic workflows or in fully automatic pipelines, such systems are more and more efficient to provide assistance to biologists. DATABASE URL: http://eagl.unige.ch/GOCat/
Bayesian inference with historical data-based informative priors improves detection of differentially expressed genes

PubMed Central

Li, Ben; Sun, Zhaonan; He, Qing; Zhu, Yu; Qin, Zhaohui S.

2016-01-01

Motivation: Modern high-throughput biotechnologies such as microarray are capable of producing a massive amount of information for each sample. However, in a typical high-throughput experiment, only limited number of samples were assayed, thus the classical ‘large p, small n’ problem. On the other hand, rapid propagation of these high-throughput technologies has resulted in a substantial collection of data, often carried out on the same platform and using the same protocol. It is highly desirable to utilize the existing data when performing analysis and inference on a new dataset. Results: Utilizing existing data can be carried out in a straightforward fashion under the Bayesian framework in which the repository of historical data can be exploited to build informative priors and used in new data analysis. In this work, using microarray data, we investigate the feasibility and effectiveness of deriving informative priors from historical data and using them in the problem of detecting differentially expressed genes. Through simulation and real data analysis, we show that the proposed strategy significantly outperforms existing methods including the popular and state-of-the-art Bayesian hierarchical model-based approaches. Our work illustrates the feasibility and benefits of exploiting the increasingly available genomics big data in statistical inference and presents a promising practical strategy for dealing with the ‘large p, small n’ problem. Availability and implementation: Our method is implemented in R package IPBT, which is freely available from https://github.com/benliemory/IPBT. Contact: yuzhu@purdue.edu; zhaohui.qin@emory.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26519502
Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

NASA Astrophysics Data System (ADS)

Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

2018-02-01

Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model
Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less
Order priors for Bayesian network discovery with an application to malware phylogeny

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

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less
An investigation of prior knowledge in Automatic Music Transcription systems.

PubMed

Cazau, Dorian; Revillon, Guillaume; Krywyk, Julien; Adam, Olivier

2015-10-01

Automatic transcription of music is a long-studied research field with many operational systems available commercially. In this paper, a generic transcription system able to host various prior knowledge parameters has been developed, followed by an in-depth investigation of their impact on music transcription. Explicit links between musical knowledge and algorithmic formalism have been made. Musical knowledge covers classes of timbre, musicology, and playing style of an instrument repertoire. An evaluation sound corpus gathering musical pieces played by human performers from three different instrument repertoires, namely, classical piano, steel-string acoustic guitar, and the marovany zither from Madagascar, has been developed. The different components of musical knowledge have been successively incorporated in a complete transcription system, consisting mainly of a Probabilistic Latent Component Analysis algorithm post-processed with a Hidden Markov Model, and their impact on transcription results have been comparatively evaluated.
Anhydrous Proton-Conducting Membranes for Fuel Cells

NASA Technical Reports Server (NTRS)

Narayanan, Sekharipuram; Yen, Shiao-Pin S.

2005-01-01

Polymeric electrolyte membranes that do not depend on water for conduction of protons are undergoing development for use in fuel cells. Prior polymeric electrolyte fuel-cell membranes (e.g., those that contain perfluorosulfonic acid) depend on water and must be limited to operation below a temperature of 125 C because they retain water poorly at higher temperatures. In contrast, the present developmental anhydrous membranes are expected to function well at temperatures up to 200 C. The developmental membranes exploit a hopping-and-reorganization proton- conduction process that can occur in the solid state in organic amine salts and is similar to a proton-conduction process in a liquid. This process was studied during the 1970s, but until now, there has been no report of exploiting organic amine salts for proton conduction in fuel cells.
Life-Long Radar Tracking of Bumblebees

PubMed Central

Lim, Ka S.; Reynolds, Andrew M.; Chittka, Lars

2016-01-01

Insect pollinators such as bumblebees play a vital role in many ecosystems, so it is important to understand their foraging movements on a landscape scale. We used harmonic radar to record the natural foraging behaviour of Bombus terrestris audax workers over their entire foraging career. Every flight ever made outside the nest by four foragers was recorded. Our data reveal where the bees flew and how their behaviour changed with experience, at an unprecedented level of detail. We identified how each bee’s flights fit into two categories—which we named exploration and exploitation flights—examining the differences between the two types of flight and how their occurrence changed over the course of the bees’ foraging careers. Exploitation of learned resources takes place during efficient, straight trips, usually to a single foraging location, and is seldom combined with exploration of other areas. Exploration of the landscape typically occurs in the first few flights made by each bee, but our data show that further exploration flights can be made throughout the bee’s foraging career. Bees showed striking levels of variation in how they explored their environment, their fidelity to particular patches, ratio of exploration to exploitation, duration and frequency of their foraging bouts. One bee developed a straight route to a forage patch within four flights and followed this route exclusively for six days before abandoning it entirely for a closer location; this second location had not been visited since her first exploratory flight nine days prior. Another bee made only rare exploitation flights and continued to explore widely throughout its life; two other bees showed more frequent switches between exploration and exploitation. Our data shed light on the way bumblebees balance exploration of the environment with exploitation of resources and reveal extreme levels of variation between individuals. PMID:27490662
Life-Long Radar Tracking of Bumblebees.

PubMed

Woodgate, Joseph L; Makinson, James C; Lim, Ka S; Reynolds, Andrew M; Chittka, Lars

2016-01-01

Insect pollinators such as bumblebees play a vital role in many ecosystems, so it is important to understand their foraging movements on a landscape scale. We used harmonic radar to record the natural foraging behaviour of Bombus terrestris audax workers over their entire foraging career. Every flight ever made outside the nest by four foragers was recorded. Our data reveal where the bees flew and how their behaviour changed with experience, at an unprecedented level of detail. We identified how each bee's flights fit into two categories-which we named exploration and exploitation flights-examining the differences between the two types of flight and how their occurrence changed over the course of the bees' foraging careers. Exploitation of learned resources takes place during efficient, straight trips, usually to a single foraging location, and is seldom combined with exploration of other areas. Exploration of the landscape typically occurs in the first few flights made by each bee, but our data show that further exploration flights can be made throughout the bee's foraging career. Bees showed striking levels of variation in how they explored their environment, their fidelity to particular patches, ratio of exploration to exploitation, duration and frequency of their foraging bouts. One bee developed a straight route to a forage patch within four flights and followed this route exclusively for six days before abandoning it entirely for a closer location; this second location had not been visited since her first exploratory flight nine days prior. Another bee made only rare exploitation flights and continued to explore widely throughout its life; two other bees showed more frequent switches between exploration and exploitation. Our data shed light on the way bumblebees balance exploration of the environment with exploitation of resources and reveal extreme levels of variation between individuals.
Prior knowledge-based approach for associating ...

EPA Pesticide Factsheets

Evaluating the potential human health and/or ecological risks associated with exposures to complex chemical mixtures in the ambient environment is one of the central challenges of chemical safety assessment and environmental protection. There is a need for approaches that can help to integrate chemical monitoring and bio-effects data to evaluate risks associated with chemicals present in the environment. We used prior knowledge about chemical-gene interactions to develop a knowledge assembly model for detected chemicals at five locations near two wastewater treatment plants. The assembly model was used to generate hypotheses about the biological impacts of the chemicals at each location. The hypotheses were tested using empirical hepatic gene expression data from fathead minnows exposed for 12 d at each location. Empirical gene expression data was also mapped to the assembly models to statistically evaluate the likelihood of a chemical contributing to the observed biological responses. The prior knowledge approach was able reasonably hypothesize the biological impacts at one site but not the other. Chemicals most likely contributing to the observed biological responses were identified at each location. Despite limitations to the approach, knowledge assembly models have strong potential for associating chemical occurrence with potential biological effects and providing a foundation for hypothesis generation to guide research and/or monitoring efforts relat
Altered behavioral and neural responsiveness to counterfactual gains in the elderly.

PubMed

Tobia, Michael J; Guo, Rong; Gläscher, Jan; Schwarze, Ulrike; Brassen, Stefanie; Büchel, Christian; Obermayer, Klaus; Sommer, Tobias

2016-06-01

Counterfactual information processing refers to the consideration of events that did not occur in comparison to those actually experienced, in order to determine optimal actions, and can be formulated as computational learning signals, referred to as fictive prediction errors. Decision making and the neural circuitry for counterfactual processing are altered in healthy elderly adults. This experiment investigated age differences in neural systems for decision making with knowledge of counterfactual outcomes. Two groups of healthy adult participants, young (N = 30; ages 19-30 years) and elderly (N = 19; ages 65-80 years), were scanned with fMRI during 240 trials of a strategic sequential investment task in which a particular strategy of differentially weighting counterfactual gains and losses during valuation is associated with more optimal performance. Elderly participants earned significantly less than young adults, differently weighted counterfactual consequences and exploited task knowledge, and exhibited altered activity in a fronto-striatal circuit while making choices, compared to young adults. The degree to which task knowledge was exploited was positively correlated with modulation of neural activity by expected value in the vmPFC for young adults, but not in the elderly. These findings demonstrate that elderly participants' poor task performance may be related to different counterfactual processing.

What are they up to? The role of sensory evidence and prior knowledge in action understanding.

PubMed

Chambon, Valerian; Domenech, Philippe; Pacherie, Elisabeth; Koechlin, Etienne; Baraduc, Pierre; Farrer, Chlöé

2011-02-18

Explaining or predicting the behaviour of our conspecifics requires the ability to infer the intentions that motivate it. Such inferences are assumed to rely on two types of information: (1) the sensory information conveyed by movement kinematics and (2) the observer's prior expectations--acquired from past experience or derived from prior knowledge. However, the respective contribution of these two sources of information is still controversial. This controversy stems in part from the fact that "intention" is an umbrella term that may embrace various sub-types each being assigned different scopes and targets. We hypothesized that variations in the scope and target of intentions may account for variations in the contribution of visual kinematics and prior knowledge to the intention inference process. To test this hypothesis, we conducted four behavioural experiments in which participants were instructed to identify different types of intention: basic intentions (i.e. simple goal of a motor act), superordinate intentions (i.e. general goal of a sequence of motor acts), or social intentions (i.e. intentions accomplished in a context of reciprocal interaction). For each of the above-mentioned intentions, we varied (1) the amount of visual information available from the action scene and (2) participant's prior expectations concerning the intention that was more likely to be accomplished. First, we showed that intentional judgments depend on a consistent interaction between visual information and participant's prior expectations. Moreover, we demonstrated that this interaction varied according to the type of intention to be inferred, with participant's priors rather than perceptual evidence exerting a greater effect on the inference of social and superordinate intentions. The results are discussed by appealing to the specific properties of each type of intention considered and further interpreted in the light of a hierarchical model of action representation.
RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse

PubMed Central

Liu, Zhi-Ping; Wu, Canglin; Miao, Hongyu; Wu, Hulin

2015-01-01

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org PMID:26424082
Mining knowledge from corpora: an application to retrieval and indexing.

PubMed

Soualmia, Lina F; Dahamna, Badisse; Darmoni, Stéfan

2008-01-01

The present work aims at discovering new associations between medical concepts to be exploited as input in retrieval and indexing. Association rules method is applied to documents. The process is carried out on three major document categories referring to e-health information consumers: health professionals, students and lay people. Association rules evaluation is founded on statistical measures combined with domain knowledge. Association rules represent existing relations between medical concepts (60.62%) and new knowledge (54.21%). Based on observations, 463 expert rules are defined by medical librarians for retrieval and indexing. Association rules bear out existing relations, produce new knowledge and support users and indexers in document retrieval and indexing.
Collaborative Knowledge Building with Wikis: The Impact of Redundancy and Polarity

ERIC Educational Resources Information Center

Moskaliuk, Johannes; Kimmerle, Joachim; Cress, Ulrike

2012-01-01

Wikis as shared digital artifacts may enable users to participate in processes of knowledge building. To what extent and with which quality knowledge building can take place is assumed to depend on the interrelation between people's prior knowledge and the information available in a wiki. In two experimental studies we examined the impact on…
The Effects of Prior Knowledge on Children's Memory and Suggestibility

ERIC Educational Resources Information Center

Elischberger, Holger B.

2005-01-01

In this study, 5- and 6-year-olds were read a story and asked to recall its details. Two independent factors-prestory knowledge and poststory suggestions-were crossed to examine the effects on children's story recall. The results indicated that prestory social knowledge about the story protagonist as well as academic knowledge relating to the…
Approaching Multidimensional Forms of Knowledge through Personal Meaning Mapping in Science Integrating Teaching outside the Classroom

ERIC Educational Resources Information Center

Hartmeyer, Rikke; Bølling, Mads; Bentsen, Peter

2017-01-01

Current research points to Personal Meaning Mapping (PMM) as a method useful in investigating students' prior and current science knowledge. However, studies investigating PMM as a method for exploring specific knowledge dimensions are lacking. Ensuring that students are able to access specific knowledge dimensions is important, especially in…
Topic-Specific Pedagogical Content Knowledge (TSPCK) in Redox and Electrochemistry of Experienced Teachers

ERIC Educational Resources Information Center

O'Brien, Stephanie

2017-01-01

Topic specific pedagogical content knowledge (TSPCK) is the basis by which knowledge of subject matter of a particular topic is conveyed to students. This includes students' prior knowledge, curricular saliency, what makes a topic easy or difficult to teach, representations, and teaching strategies. The goal of this study is to assess the…
Creating Connections: Using the Internet to Support Struggling Readers' Background Knowledge. Issues in Technology

ERIC Educational Resources Information Center

Karchmer, Rachel A.

2004-01-01

Background knowledge plays an important role in one?s ability to learn. We learn new knowledge by relating it to our prior knowledge, which in turn provides concrete understanding (Piaget, 1969). Rosenblatt (1996) explained, "The reader brings to the work personality traits, memories of past events, present needs and preoccupations, a…
Interplay of Secondary Pre-Service Teacher Content Knowledge (CK), Pedagogical Content Knowledge (PCK) and Attitudes Regarding Scientific Inquiry Teaching within Teacher Training

ERIC Educational Resources Information Center

Smit, Robbert; Weitzel, Holger; Blank, Robert; Rietz, Florian; Tardent, Josiane; Robin, Nicolas

2017-01-01

Background: Beginning teachers encounter several constraints with respect to scientific inquiry. Depending on their prior beliefs, knowledge and understanding, these constraints affect their teaching of inquiry. Purpose: To investigate quantitatively the longitudinal relationship between pre-service teachers' knowledge and attitudes on scientific…
Substrate-borne vibrational signals in intraspecific communication of GWSS

USDA-ARS?s Scientific Manuscript database

Exploitation of vibrational signals for suppressing glassy-winged sharpshooter (GWSS) populations in citrus orchards and vineyards could prove to be a useful tool. However, existing knowledge of GWSS vibrational communication is insufficient to implement a management program for this pest in Califor...
48 CFR 35.001 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... aim is the design, development, or testing of specific items or services to be considered for sale..., means the systematic use of scientific and technical knowledge in the design, development, testing, or...
48 CFR 35.001 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... aim is the design, development, or testing of specific items or services to be considered for sale..., means the systematic use of scientific and technical knowledge in the design, development, testing, or...
48 CFR 35.001 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... aim is the design, development, or testing of specific items or services to be considered for sale..., means the systematic use of scientific and technical knowledge in the design, development, testing, or...
48 CFR 35.001 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... aim is the design, development, or testing of specific items or services to be considered for sale..., means the systematic use of scientific and technical knowledge in the design, development, testing, or...
Time, Technology, and Exploitation - Can Future Military Command and Control (C2) Domination be Linked to Effective Knowledge Management?

DTIC Science & Technology

2008-04-24

Domination be Linked to Effective Knowledge 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...necessarily endorsed by the NWC or the Department of the Navy. 14. ABSTRACT Highly effective employment of military force at the operational level...The most effective strategy to optimize this operational function is by increasing the speed of the operational commander’s decision- making process by
Prior knowledge guided active modules identification: an integrated multi-objective approach.

PubMed

Chen, Weiqi; Liu, Jing; He, Shan

2017-03-14

Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.
Graded-Index "Whispering-Gallery" Optical Microresonators

NASA Technical Reports Server (NTRS)

Savchenkov, Anatoliy; Maleki, Lute; Iltchenko, Vladimir; Matsko, Andrey

2006-01-01

Graded-index-of-refraction dielectric optical microresonators have been proposed as a superior alternative to prior dielectric optical microresonators, which include microspheres and microtori wherein electromagnetic waves propagate along circumferential paths in "whispering-gallery" modes. The design and method of fabrication of the proposed microresonators would afford improved performance by exploiting a combination of the propagation characteristics of the whisperinggallery modes and the effect of a graded index of refraction on the modes.
Quantum dialogue by nonselective measurements

NASA Astrophysics Data System (ADS)

Nguyen, Ba An

2018-06-01

Unlike classical measurements, quantum measurements may be useful even without reading the outcome. Such so called nonselective measurements are exploited in this paper to design a quantum dialogue protocol that allows exchanging secret data without prior key distributions. The relevant data to be exchanged are in terms of the high-dimensional mutually unbiased bases of quantum measurements. Appropriate modes of bidirectional controlling are devised to ensure the protocol security which is asymptotic.
Measuring the Effectiveness of Border Security Between Ports of Entry

DTIC Science & Technology

2010-01-01

missions. Th is report describes the results of a short study on such measures. It should be of interest to analysts and leaders responsible for...32 6.2 Identifying and Exploiting Opportunities to Estimate Attempted Illegal Crossings . . . . . . . 33 6.3 Translating Studies of...ected discussions with DHS component agencies engaged in border-security eff orts, review of prior studies of border security, and fi eld visits to
Scattering-Type Surface-Plasmon-Resonance Biosensors

NASA Technical Reports Server (NTRS)

Wang, Yu; Pain, Bedabrata; Cunningham, Thomas; Seshadri, Suresh

2005-01-01

Biosensors of a proposed type would exploit scattering of light by surface plasmon resonance (SPR). Related prior biosensors exploit absorption of light by SPR. Relative to the prior SPR biosensors, the proposed SPR biosensors would offer greater sensitivity in some cases, enough sensitivity to detect bioparticles having dimensions as small as nanometers. A surface plasmon wave can be described as a light-induced collective oscillation in electron density at the interface between a metal and a dielectric. At SPR, most incident photons are either absorbed or scattered at the metal/dielectric interface and, consequently, reflected light is greatly attenuated. The resonance wavelength and angle of incidence depend upon the permittivities of the metal and dielectric. An SPR sensor of the type most widely used heretofore includes a gold film coated with a ligand a substance that binds analyte molecules. The gold film is thin enough to support evanescent-wave coupling through its thickness. The change in the effective index of refraction at the surface, and thus the change in the SPR response, increases with the number of bound analyte molecules. The device is illuminated at a fixed wavelength, and the intensity of light reflected from the gold surface opposite the ligand-coated surface is measured as a function of the angle of incidence. From these measurements, the angle of minimum reflection intensity is determined

Joint reconstruction of PET-MRI by exploiting structural similarity

NASA Astrophysics Data System (ADS)

Ehrhardt, Matthias J.; Thielemans, Kris; Pizarro, Luis; Atkinson, David; Ourselin, Sébastien; Hutton, Brian F.; Arridge, Simon R.

2015-01-01

Recent advances in technology have enabled the combination of positron emission tomography (PET) with magnetic resonance imaging (MRI). These PET-MRI scanners simultaneously acquire functional PET and anatomical or functional MRI data. As function and anatomy are not independent of one another the images to be reconstructed are likely to have shared structures. We aim to exploit this inherent structural similarity by reconstructing from both modalities in a joint reconstruction framework. The structural similarity between two modalities can be modelled in two different ways: edges are more likely to be at similar positions and/or to have similar orientations. We analyse the diffusion process generated by minimizing priors that encapsulate these different models. It turns out that the class of parallel level set priors always corresponds to anisotropic diffusion which is sometimes forward and sometimes backward diffusion. We perform numerical experiments where we jointly reconstruct from blurred Radon data with Poisson noise (PET) and under-sampled Fourier data with Gaussian noise (MRI). Our results show that both modalities benefit from each other in areas of shared edge information. The joint reconstructions have less artefacts and sharper edges compared to separate reconstructions and the ℓ2-error can be reduced in all of the considered cases of under-sampling.
The influence of simulated exploitation on Patella vulgata populations: protandric sex change is size-dependent.

PubMed

Borges, Carla D G; Hawkins, Stephen J; Crowe, Tasman P; Doncaster, C Patrick

2016-01-01

Grazing mollusks are used as a food resource worldwide, and limpets are harvested commercially for both local consumption and export in several countries. This study describes a field experiment to assess the effects of simulated human exploitation of limpets Patella vulgata on their population ecology in terms of protandry (age-related sex change from male to female), growth, recruitment, migration, and density regulation. Limpet populations at two locations in southwest England were artificially exploited by systematic removal of the largest individuals for 18 months in plots assigned to three treatments at each site: no (control), low, and high exploitation. The shell size at sex change (L 50: the size at which there is a 50:50 sex ratio) decreased in response to the exploitation treatments, as did the mean shell size of sexual stages. Size-dependent sex change was indicated by L 50 occurring at smaller sizes in treatments than controls, suggesting an earlier switch to females. Mean shell size of P. vulgata neuters changed little under different levels of exploitation, while males and females both decreased markedly in size with exploitation. No differences were detected in the relative abundances of sexual stages, indicating some compensation for the removal of the bigger individuals via recruitment and sex change as no migratory patterns were detected between treatments. At the end of the experiment, 0-15 mm recruits were more abundant at one of the locations but no differences were detected between treatments. We conclude that sex change in P. vulgata can be induced at smaller sizes by reductions in density of the largest individuals reducing interage class competition. Knowledge of sex-change adaptation in exploited limpet populations should underpin strategies to counteract population decline and improve rocky shore conservation and resource management.
Prior Conceptual Knowledge and Textbook Search.

ERIC Educational Resources Information Center

Byrnes, James P.; Guthrie, John T.

1992-01-01

The role of a subject's conceptual knowledge in the procedural task of searching a text for information was studied for 51 college undergraduates in 2 experiments involving knowledge of anatomy. Students with more anatomical information were able to search a text more quickly. Educational implications are discussed. (SLD)
How Knowledge Powers Reading

ERIC Educational Resources Information Center

Lemov, Doug

2017-01-01

Recent research shows that reading comprehension relies heavily on prior knowledge. Far more than generic "reading skills" like drawing inferences, making predictions, and knowing the function of subheads, how well students learn from a nonfiction text depends on their background knowledge of the text's subject matter. And in a cyclical…
Employees and Creativity: Social Ties and Access to Heterogeneous Knowledge

ERIC Educational Resources Information Center

Huang, Chiung-En; Liu, Chih-Hsing Sam

2015-01-01

This study dealt with employee social ties, knowledge heterogeneity contacts, and the generation of creativity. Although prior studies demonstrated a relationship between network position and creativity, inadequate attention has been paid to network ties and heterogeneity knowledge contacts. This study considered the social interaction processes…
Computer Experiences, Self-Efficacy and Knowledge of Students Enrolled in Introductory University Agriculture Courses.

ERIC Educational Resources Information Center

Johnson, Donald M.; Ferguson, James A.; Lester, Melissa L.

1999-01-01

Of 175 freshmen agriculture students, 74% had prior computer courses, 62% owned computers. The number of computer topics studied predicted both computer self-efficacy and computer knowledge. A substantial positive correlation was found between self-efficacy and computer knowledge. (SK)
Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding.

PubMed

Olsher, Daniel

2014-10-01

Noise-resistant and nuanced, COGBASE makes 10 million pieces of commonsense data and a host of novel reasoning algorithms available via a family of semantically-driven prior probability distributions. Machine learning, Big Data, natural language understanding/processing, and social AI can draw on COGBASE to determine lexical semantics, infer goals and interests, simulate emotion and affect, calculate document gists and topic models, and link commonsense knowledge to domain models and social, spatial, cultural, and psychological data. COGBASE is especially ideal for social Big Data, which tends to involve highly implicit contexts, cognitive artifacts, difficult-to-parse texts, and deep domain knowledge dependencies. Copyright © 2014 Elsevier Ltd. All rights reserved.
Advanced prior modeling for 3D bright field electron tomography

NASA Astrophysics Data System (ADS)

Sreehari, Suhas; Venkatakrishnan, S. V.; Drummy, Lawrence F.; Simmons, Jeffrey P.; Bouman, Charles A.

2015-03-01

Many important imaging problems in material science involve reconstruction of images containing repetitive non-local structures. Model-based iterative reconstruction (MBIR) could in principle exploit such redundancies through the selection of a log prior probability term. However, in practice, determining such a log prior term that accounts for the similarity between distant structures in the image is quite challenging. Much progress has been made in the development of denoising algorithms like non-local means and BM3D, and these are known to successfully capture non-local redundancies in images. But the fact that these denoising operations are not explicitly formulated as cost functions makes it unclear as to how to incorporate them in the MBIR framework. In this paper, we formulate a solution to bright field electron tomography by augmenting the existing bright field MBIR method to incorporate any non-local denoising operator as a prior model. We accomplish this using a framework we call plug-and-play priors that decouples the log likelihood and the log prior probability terms in the MBIR cost function. We specifically use 3D non-local means (NLM) as the prior model in the plug-and-play framework, and showcase high quality tomographic reconstructions of a simulated aluminum spheres dataset, and two real datasets of aluminum spheres and ferritin structures. We observe that streak and smear artifacts are visibly suppressed, and that edges are preserved. Also, we report lower RMSE values compared to the conventional MBIR reconstruction using qGGMRF as the prior model.
Multi-object segmentation using coupled nonparametric shape and relative pose priors

NASA Astrophysics Data System (ADS)

Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep

2009-02-01

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.
Do Runner Beans Really Make You Run Fast? Young Children Learning About Science-Related Food Concepts in Informal Settings

NASA Astrophysics Data System (ADS)

Cumming, Jenny

2003-08-01

Early years practitioners acknowledge that much learning takes place in a family context. Science educators, in particular, recognise the importance of children's prior knowledge, both as a foundation on which to build and as a possible source of misconceptions. However, little work has been done to discover what young children learn outside school. This study utilised parent diaries and questionnaires to elucidate the experiences of children aged four to seven which might contribute to their knowledge about the origin of food and its destiny after being eaten. The findings indicate that children learn more scientifically correct information with friends and family than teachers might realise. Awareness of children's informal knowledge can assist teachers when planning activities. As well as this, children's prior knowledge can be utilised in classroom discourse to promote understanding.
Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks.

PubMed

Khan, Taimoor; De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.
Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

PubMed Central

De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results. PMID:27382616
Tacit knowledge.

PubMed

Walker, Alexander Muir

2017-04-01

Information that is not made explicit is nonetheless embedded in most of our standard procedures. In its simplest form, embedded information may take the form of prior knowledge held by the researcher and presumed to be agreed to by consumers of the research product. More interesting are the settings in which the prior information is held unconsciously by both researcher and reader, or when the very form of an "effective procedure" incorporates its creator's (unspoken) understanding of a problem. While it may not be productive to exhaustively detail the embedded or tacit knowledge that manifests itself in creative scientific work, at least at the beginning, we may want to routinize methods for extracting and documenting the ways of thinking that make "experts" expert. We should not back away from both expecting and respecting the tacit knowledge the pervades our work and the work of others.
Preservice Agricultural Education Teachers' Experiences in and Anticipation of Content Knowledge Preparation

ERIC Educational Resources Information Center

Rice, Amber H.; Kitchel, Tracy

2015-01-01

This study explored the experiences of preservice agriculture teachers in content knowledge preparation for pedagogical content knowledge (PCK) development. The researchers employed a phenomenological approach in which six preservice teachers were interviewed the semester prior to student teaching. The researchers found there was general…
Conditional Reasoning in Autism: Activation and Integration of Knowledge and Belief

ERIC Educational Resources Information Center

McKenzie, Rebecca; Evans, Jonathan St. B. T.; Handley, Simon J.

2010-01-01

Everyday conditional reasoning is typically influenced by prior knowledge and belief in the form of specific exceptions known as counterexamples. This study explored whether adolescents with autism spectrum disorder (ASD; N = 26) were less influenced by background knowledge than typically developing adolescents (N = 38) when engaged in conditional…
The Effects of Prior Knowledge and Instruction on Understanding Image Formation.

ERIC Educational Resources Information Center

Galili, Igal; And Others

1993-01-01

Reports a study (n=27) concerning the knowledge about image formation exhibited by students following instruction in geometrical optics in an activity-based college physics course for prospective elementary teachers. Student diagrams and verbal comments indicate their knowledge can be described as an intermediate state: a hybridization of…
Designing Knowledge Scaffolds to Support Mathematical Problem Solving

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Koedinger, Kenneth R.

2005-01-01

We present a methodology for designing better learning environments. In Phase 1, 6th-grade students' (n = 223) prior knowledge was assessed using a difficulty factors assessment (DFA). The assessment revealed that scaffolds designed to elicit contextual, conceptual, or procedural knowledge each improved students' ability to add and subtract…
Uncovering and Informing Preservice Teachers' Prior Knowledge about Poverty

ERIC Educational Resources Information Center

Mundy, Charlotte Anne; Leko, Melinda Marie

2015-01-01

This study explored 30 preservice teachers' knowledge on issues related to poverty. In an open-ended questionnaire, preservice teachers' perceptions of poverty and how teachers should respond to students from poverty were explored. Results indicated that preservice teachers' knowledge was nonspecific and lacked focus on the relationship among…
Determinants of Academic Entrepreneurship Behavior: A Multilevel Model

ERIC Educational Resources Information Center

Llano, Joseph Anthony

2010-01-01

It is well established that universities encourage the acquisition and dissemination of new knowledge among university community members and beyond. However, what is less well understood is how universities encourage entrepreneurial (opportunity discovery, evaluation, and exploiting) behavior. This research investigated a multilevel model of the…
Substrate-borne vibrational signals in intraspecific communication of glassy-winged sharpshooters (GWSS)

USDA-ARS?s Scientific Manuscript database

Exploitation of vibrational signals for suppressing glassy-winged sharpshooter (GWSS) populations could prove to be a useful tool. However, existing knowledge on GWSS vibrational communication is insufficient to implement a management program for this pest in California. Therefore, the objective of ...

Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning.

PubMed

Jackowski, Konrad; Krawczyk, Bartosz; Woźniak, Michał

2014-05-01

Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues and propose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide range of computer experiments that show that AdaSS+ can outperform the original method and several reference classifiers.
Intelligent Systems: Terrestrial Observation and Prediction Using Remote Sensing Data

NASA Technical Reports Server (NTRS)

Coughlan, Joseph C.

2005-01-01

NASA has made science and technology investments to better utilize its large space-borne remote sensing data holdings of the Earth. With the launch of Terra, NASA created a data-rich environment where the challenge is to fully utilize the data collected from EOS however, despite unprecedented amounts of observed data, there is a need for increasing the frequency, resolution, and diversity of observations. Current terrestrial models that use remote sensing data were constructed in a relatively data and compute limited era and do not take full advantage of on-line learning methods and assimilation techniques that can exploit these data. NASA has invested in visualization, data mining and knowledge discovery methods which have facilitated data exploitation, but these methods are insufficient for improving Earth science models that have extensive background knowledge nor do these methods refine understanding of complex processes. Investing in interdisciplinary teams that include computational scientists can lead to new models and systems for online operation and analysis of data that can autonomously improve in prediction skill over time.
Eggs, ethics and exploitation? Investigating women's experiences of an egg sharing scheme

PubMed Central

Haimes, Erica; Taylor, Ken; Turkmendag, Ilke

2012-01-01

Abstract There is a growing global demand for human eggs for the treatment of sub-fertile women and for stem cell-related research. This demand provokes concerns for the women providing the eggs, including their possible exploitation, whether they should be paid, whether they can give properly informed consent and whether their eggs and bodies are becoming commodified. However, few of the debates have benefitted from insights from the women themselves. We address this gap in knowledge by reporting on a study investigating women’s views and experiences of a scheme in which they can volunteer, in their capacity as fertility patients, to ‘share’ their eggs with researchers and receive a reduction in in vitro fertilisation fees. We focus our discussion on the question of exploitation, a concept central to many sociological and ethical interests. In brief, our analysis suggests that while interviewees acknowledge the potential of this scheme to be exploitative, they argue that this is not the case, emphasising their ability to act autonomously in deciding to volunteer. Nonetheless, these freely made decisions do not necessarily take place under circumstances of their choosing. We discuss the implications of this for egg provision in general and for understandings of exploitation. PMID:22443419
Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

PubMed Central

2010-01-01

Background Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. Method This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. Results EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. Discussion This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research. PMID:20920289
Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support.

PubMed

Gibert, Karina; García-Alonso, Carlos; Salvador-Carulla, Luis

2010-09-30

Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.
Using expert knowledge for test linking.

PubMed

Bolsinova, Maria; Hoijtink, Herbert; Vermeulen, Jorine Adinda; Béguin, Anton

2017-12-01

Linking and equating procedures are used to make the results of different test forms comparable. In the cases where no assumption of random equivalent groups can be made some form of linking design is used. In practice the amount of data available to link the two tests is often very limited due to logistic and security reasons, which affects the precision of linking procedures. This study proposes to enhance the quality of linking procedures based on sparse data by using Bayesian methods which combine the information in the linking data with background information captured in informative prior distributions. We propose two methods for the elicitation of prior knowledge about the difference in difficulty of two tests from subject-matter experts and explain how these results can be used in the specification of priors. To illustrate the proposed methods and evaluate the quality of linking with and without informative priors, an empirical example of linking primary school mathematics tests is presented. The results suggest that informative priors can increase the precision of linking without decreasing the accuracy. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Knowledge representation of motor activity of patients with Parkinson's disease.

PubMed

Kostek, Bożena; Kupryjanow, Adam; Czyżewski, Andrzej

An approach to the knowledge representation extraction from biomedical signals analysis concerning motor activity of Parkinson disease patients is proposed in this paper. This is done utilizing accelerometers attached to their body as well as exploiting video image of their hand movements. Experiments are carried out employing artificial neural networks and support vector machine to the recognition of characteristic motor activity disorders in patients. Obtained results indicate that it is possible to interpret some selected patient's body movements with a sufficiently high effectiveness.
Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

PubMed

Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

2016-01-01

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.
[Current status of the knowledge on Moroccan anophelines (Diptera: Culicidae): systematic, geographical distribution and vectorial competence].

PubMed

Faraj, C; Ouahabi, S; Adlaoui, E; Elaouad, R

2010-10-01

This bibliographical study, based on published works, ministry of Health Reports, exploitation of the database relative to the entomological surveillance conducted in the framework of the National Malaria Control Program, as well as unpublished results obtained within the framework of the European project "Emerging disease in a changing European environment", summarizes and completes with new data current knowledge on the systematics, the distribution and the vectorial competence of moroccan anophelines. Copyright © 2010 Elsevier Masson SAS. All rights reserved.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Yitan; Xu, Yanxun; Helseth, Donald L.

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood modelmore » derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.« less
Fostering Upper Secondary Students' Ability to Engage in Practices of Scientific Investigation: a Comparative Analysis of an Explicit and an Implicit Instructional Approach

NASA Astrophysics Data System (ADS)

Vorholzer, Andreas; von Aufschnaiter, Claudia; Boone, William J.

2018-02-01

Inquiry-based teaching is considered as contributing to content-related, procedural, and epistemic learning goals of science education. In this study, a quasi-experimental research design was utilized to investigate to what extent embedding inquiry activities in an explicit and an implicit instructional approach fosters students' ability to engage in three practices of scientific investigation (POSI): (1) formulating questions and hypotheses, (2) planning investigations, (3) analyzing and interpreting data. Both approaches were implemented in a classroom-based intervention conducted in a German upper secondary school (N = 222). Students' procedural knowledge of the three POSI was assessed with a paper-pencil test prior and post to the intervention, their content knowledge and dispositional factors (e.g., cognitive abilities) were gathered once. Results show that not only explicit but also implicit instruction fosters students' knowledge of POSI. While overall explicit instruction was found to be more effective, the findings indicate that the effectiveness depends considerably on the practice addressed. Moreover, findings suggest that both approaches were equally beneficial for all students regardless of their prior content knowledge and their prior procedural knowledge of POSI. Potential conditions for the success of explicit and implicit approaches as well as implications for instruction on POSI in science classrooms and for future research are discussed.
Adaptive zooming in X-ray computed tomography.

PubMed

Dabravolski, Andrei; Batenburg, Kees Joost; Sijbers, Jan

2014-01-01

In computed tomography (CT), the source-detector system commonly rotates around the object in a circular trajectory. Such a trajectory does not allow to exploit a detector fully when scanning elongated objects. Increase the spatial resolution of the reconstructed image by optimal zooming during scanning. A new approach is proposed, in which the full width of the detector is exploited for every projection angle. This approach is based on the use of prior information about the object's convex hull to move the source as close as possible to the object, while avoiding truncation of the projections. Experiments show that the proposed approach can significantly improve reconstruction quality, producing reconstructions with smaller errors and revealing more details in the object. The proposed approach can lead to more accurate reconstructions and increased spatial resolution in the object compared to the conventional circular trajectory.
PTAL Database and Website: Developing a Novel Information System for the Scientific Exploitation of the Planetary Terrestrial Analogues Library

NASA Astrophysics Data System (ADS)

Veneranda, M.; Negro, J. I.; Medina, J.; Rull, F.; Lantz, C.; Poulet, F.; Cousin, A.; Dypvik, H.; Hellevang, H.; Werner, S. C.

2018-04-01

The PTAL website will store multispectral analysis of samples collected from several terrestrial analogue sites and pretend to become a cornerstone tool for the scientific community interested in deepening the knowledge on Mars geological processes.
Phase Zero: How China Exploits It, Why the United States Does Not

DTIC Science & Technology

2012-06-01

in the United States and Chile found it impossible to contact certain Web sites that the Chinese gov- ernment has deemed to be politically ...They enlisted the aid of administrators and strategists who would eventually catalog the principles that came to epitomize Chinese political and...concentrated on destruction of enemy forces, not on the larger political context. Prior to the Second World War, he states, “the United States usually
Activists within the Academy: The Role of Prior Experience in Adult Learners' Acquisition of Postgraduate Literacies in a Postapartheid South African University

ERIC Educational Resources Information Center

Cooper, Linda

2011-01-01

The article takes as a case study a group of disability rights activists who were given access to a master's program via Recognition of Prior Learning. The question explored is "Can adult learners' prior experiential knowledge act as a resource for the successful acquisition of postgraduate academic literacy practices?" The analysis is…
Bayesian inference with historical data-based informative priors improves detection of differentially expressed genes.

PubMed

Li, Ben; Sun, Zhaonan; He, Qing; Zhu, Yu; Qin, Zhaohui S

2016-03-01

Modern high-throughput biotechnologies such as microarray are capable of producing a massive amount of information for each sample. However, in a typical high-throughput experiment, only limited number of samples were assayed, thus the classical 'large p, small n' problem. On the other hand, rapid propagation of these high-throughput technologies has resulted in a substantial collection of data, often carried out on the same platform and using the same protocol. It is highly desirable to utilize the existing data when performing analysis and inference on a new dataset. Utilizing existing data can be carried out in a straightforward fashion under the Bayesian framework in which the repository of historical data can be exploited to build informative priors and used in new data analysis. In this work, using microarray data, we investigate the feasibility and effectiveness of deriving informative priors from historical data and using them in the problem of detecting differentially expressed genes. Through simulation and real data analysis, we show that the proposed strategy significantly outperforms existing methods including the popular and state-of-the-art Bayesian hierarchical model-based approaches. Our work illustrates the feasibility and benefits of exploiting the increasingly available genomics big data in statistical inference and presents a promising practical strategy for dealing with the 'large p, small n' problem. Our method is implemented in R package IPBT, which is freely available from https://github.com/benliemory/IPBT CONTACT: yuzhu@purdue.edu; zhaohui.qin@emory.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.
Memory instability as a gateway to generalization

PubMed Central

2018-01-01

Our present frequently resembles our past. Patterns of actions and events repeat throughout our lives like a motif. Identifying and exploiting these patterns are fundamental to many behaviours, from creating grammar to the application of skill across diverse situations. Such generalization may be dependent upon memory instability. Following their formation, memories are unstable and able to interact with one another, allowing, at least in principle, common features to be extracted. Exploiting these common features creates generalized knowledge that can be applied across varied circumstances. Memory instability explains many of the biological and behavioural conditions necessary for generalization and offers predictions for how generalization is produced. PMID:29554094
The largest renewable, easily exploitable, and economically sustainable energy resource

NASA Astrophysics Data System (ADS)

Abbate, Giancarlo; Saraceno, Eugenio

2018-02-01

Sun, the ultimate energy resource of our planet, transfers energy to the Earth at an average power of 23,000 TW. Earth surface can be regarded as a huge panel transforming solar energy into a more convenient mechanical form, the wind. Since millennia wind is recognized as an exploitable form of energy and it is common knowledge that the higher you go, the stronger the winds flow. To go high is difficult; however Bill Gates cites high wind among possible energy miracles in the near future. Public awareness of this possible miracle is still missing, but today's technology is ready for it.
Necroptosis in tumorigenesis, activation of anti-tumor immunity, and cancer therapy

PubMed Central

Wu, Zhi-Qiang; Shi, Yang-Yang; Zaorsky, Nicholas G.; Deng, Lei; Yuan, Zhi-Yong; Lu, You; Wang, Ping

2016-01-01

While the mechanisms underlying apoptosis and autophagy have been well characterized over recent decades, another regulated cell death event, necroptosis, remains poorly understood. Elucidating the signaling networks involved in the regulation of necroptosis may allow this form of regulated cell death to be exploited for diagnosis and treatment of cancer, and will contribute to the understanding of the complex tumor microenvironment. In this review, we have summarized the mechanisms and regulation of necroptosis, the converging and diverging features of necroptosis in tumorigenesis, activation of anti-tumor immunity, and cancer therapy, as well as attempts to exploit this newly gained knowledge to provide therapeutics for cancer. PMID:27429198
Gradient-based reliability maps for ACM-based segmentation of hippocampus.

PubMed

Zarpalas, Dimitrios; Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

2014-04-01

Automatic segmentation of deep brain structures, such as the hippocampus (HC), in MR images has attracted considerable scientific attention due to the widespread use of MRI and to the principal role of some structures in various mental disorders. In this literature, there exists a substantial amount of work relying on deformable models incorporating prior knowledge about structures' anatomy and shape information. However, shape priors capture global shape characteristics and thus fail to model boundaries of varying properties; HC boundaries present rich, poor, and missing gradient regions. On top of that, shape prior knowledge is blended with image information in the evolution process, through global weighting of the two terms, again neglecting the spatially varying boundary properties, causing segmentation faults. An innovative method is hereby presented that aims to achieve highly accurate HC segmentation in MR images, based on the modeling of boundary properties at each anatomical location and the inclusion of appropriate image information for each of those, within an active contour model framework. Hence, blending of image information and prior knowledge is based on a local weighting map, which mixes gradient information, regional and whole brain statistical information with a multi-atlas-based spatial distribution map of the structure's labels. Experimental results on three different datasets demonstrate the efficacy and accuracy of the proposed method.

An Optimization-Based Framework for the Transformation of Incomplete Biological Knowledge into a Probabilistic Structure and Its Application to the Utilization of Gene/Protein Signaling Pathways in Discrete Phenotype Classification.

PubMed

Esfahani, Mohammad Shahrokh; Dougherty, Edward R

2015-01-01

Phenotype classification via genomic data is hampered by small sample sizes that negatively impact classifier design. Utilization of prior biological knowledge in conjunction with training data can improve both classifier design and error estimation via the construction of the optimal Bayesian classifier. In the genomic setting, gene/protein signaling pathways provide a key source of biological knowledge. Although these pathways are neither complete, nor regulatory, with no timing associated with them, they are capable of constraining the set of possible models representing the underlying interaction between molecules. The aim of this paper is to provide a framework and the mathematical tools to transform signaling pathways to prior probabilities governing uncertainty classes of feature-label distributions used in classifier design. Structural motifs extracted from the signaling pathways are mapped to a set of constraints on a prior probability on a Multinomial distribution. Being the conjugate prior for the Multinomial distribution, we propose optimization paradigms to estimate the parameters of a Dirichlet distribution in the Bayesian setting. The performance of the proposed methods is tested on two widely studied pathways: mammalian cell cycle and a p53 pathway model.
Improving Learning Outcome Using Six Sigma Methodology

ERIC Educational Resources Information Center

Tetteh, Godson A.

2015-01-01

Purpose: The purpose of this research paper is to apply the Six Sigma methodology to identify the attributes of a lecturer that will help improve a student's prior knowledge of a discipline from an initial "x" per cent knowledge to a higher "y" per cent of knowledge. Design/methodology/approach: The data collection method…
Reflective Assessment in Knowledge Building by Students with Low Academic Achievement

ERIC Educational Resources Information Center

Yang, Yuqin; van Aalst, Jan; Chan, Carol K. K.; Tian, Wen

2016-01-01

This study investigated whether and how students with low prior achievement can carry out and benefit from reflective assessment supported by the Knowledge Connections Analyzer (KCA) to collaboratively improve their knowledge-building discourse. Participants were a class of 20 Grade 11 students with low achievement taking visual art from an…
Knowledge of Algebra for Teaching: A Framework of Knowledge and Practices

ERIC Educational Resources Information Center

McCrory, Raven; Floden, Robert; Ferrini-Mundy, Joan; Reckase, Mark D.; Senk, Sharon L.

2012-01-01

Defining what teachers need to know to teach algebra successfully is important for informing teacher preparation and professional development efforts. Based on prior research, analysis of video, interviews with teachers, and analysis of textbooks, we define categories of knowledge and practices of teaching for understanding and assessing teachers'…
An Investigation of Preservice Teachers' Beliefs about the Certainty of Teaching Knowledge

ERIC Educational Resources Information Center

Ferguson, Leila E.; Brownlee, Jo Lunn

2018-01-01

Beliefs about the certainty of teaching knowledge may influence how preservice teachers engage with and learn from knowledge sources in teacher education, and their subsequent practice. In light of inconsistencies in prior findings that mainly employ epistemic questionnaires, we extended research focusing on a contextual analysis. Sixty-six…
Exploiting the Temperature/Concentration Dependence of Magnetic Susceptibility to Control Convection in Fundamental Studies of Solidification Phenomena

NASA Technical Reports Server (NTRS)

Evans, J. W.; Xu, Dong; Jones, W. Kinzy, Jr.; Szofran, Frank R.

1999-01-01

The objective of this new research project is to demonstrate by experiment, supplemented by mathematical modeling and physical property measurement, that the effects of buoyancy driven convection can be largely eliminated in ground-based experiments, and further reduced in flight, by applying a new technique. That technique exploits the dependence of magnetic susceptibility on composition or temperature. It is emphasized at the outset that the phenomenon to be exploited is fundamentally and practically different from the magnetic damping of convection in conducting liquids that has been the subject of much prior research. The concept suggesting this research is that all materials, even non-conductors, when placed in a magnetic field gradient, experience a force. Of particular interest here are paramagnetic and diamagnetic materials, classes which embrace the "model alloys", such as succinonitrile-acetone, that have been used by others investigating the fundamentals of solidification. Such alloys will exhibit a dependence of susceptibility on composition. The consequence is that, with a properly oriented field (gradient) a force will arise that can be made to be equal to, but opposite, the buoyancy force arising from concentration (or temperature) gradients. In this way convection can be stilled. The role of convection in determining the microstructure, and thereby properties, of materials is well known. Elimination of that convection has both scientific and technological consequences. Our knowledge of diffusive phenomena in solidification, phenomena normally hidden by the dominance of convection, is enhanced if we can study solidification of quiescent liquids. Furthermore, the microstructure, microchemistry and properties of materials (thereby practical value) are affected by the convection occurring during their solidification. Hitherto the method of choice for elimination of convection has been experimentation in microgravity. However, even in low Earth orbit, residual convection has effects. That residual convection arises from acceleration (drag on the spacecraft), displacement from the center of mass or transients in the gravitational field (g-jitter). There is therefore a need for both further reducing buoyancy driven flow in flight and allowing the simulation of microgravity during ground based experiments. Previous investigations, the research project description, theory behind the study and experimental methods as well as plots of magnetic fields and forces are presented.
Prior Knowledge Assessment Guide

DTIC Science & Technology

2014-12-01

marksmanship, advanced rifle marksmanship, and even specialized shooting courses. A comparison of the means on the test for the two groups showed that the...hands- on evaluations of student knowledge and/or skills. Pretests however, determine how much knowledge a student currently possesses of the course...content; thus, questions on pretests assess knowledge about what is to be taught in the course. Also, most pretests will include test items
The Interaction of Knowledge and Text Structure on the Ability to Identify Main Ideas in Texts. Content Knowledge and Reading Comprehension.

ERIC Educational Resources Information Center

Day, Jeanne D.; Engelhardt, Jean

Two studies examined how the factors of content-relevant knowledge and text organization influence students' abilities to study and to remember text information. The first experiment examined the effect of prior content knowledge on students' ability to identify important information in the text. Forty 7th- and forty 11th-grade students, experts…
Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation. PMID:19796406
Nutrition Knowledge of Teen-Agers.

ERIC Educational Resources Information Center

Skinner, Jean D.; Woodburn, Margy J.

1984-01-01

Nutrition knowledge tests were administered to 1,193 adolescents in Oregon prior to instructional units on nutrition in health and home economics classes. Mean scores on the tests were low. Guidelines for nutrition educators of adolescents are presented. (Author/CJB)
Gene regulation knowledge commons: community action takes care of DNA binding transcription factors

PubMed Central

Tripathi, Sushil; Vercruysse, Steven; Chawla, Konika; Christie, Karen R.; Blake, Judith A.; Huntley, Rachael P.; Orchard, Sandra; Hermjakob, Henning; Thommesen, Liv; Lægreid, Astrid; Kuiper, Martin

2016-01-01

A large gap remains between the amount of knowledge in scientific literature and the fraction that gets curated into standardized databases, despite many curation initiatives. Yet the availability of comprehensive knowledge in databases is crucial for exploiting existing background knowledge, both for designing follow-up experiments and for interpreting new experimental data. Structured resources also underpin the computational integration and modeling of regulatory pathways, which further aids our understanding of regulatory dynamics. We argue how cooperation between the scientific community and professional curators can increase the capacity of capturing precise knowledge from literature. We demonstrate this with a project in which we mobilize biological domain experts who curate large amounts of DNA binding transcription factors, and show that they, although new to the field of curation, can make valuable contributions by harvesting reported knowledge from scientific papers. Such community curation can enhance the scientific epistemic process. Database URL: http://www.tfcheckpoint.org PMID:27270715
Semantic Maps Capturing Organization Knowledge in e-Learning

NASA Astrophysics Data System (ADS)

Mavridis, Androklis; Koumpis, Adamantios; Demetriadis, Stavros N.

e-learning, shows much promise in accessibility and opportunity to learn, due to its asynchronous nature and its ability to transmit knowledge fast and effectively. However without a universal standard for online learning and teaching, many systems are proclaimed as “e-learning-compliant”, offering nothing more than automated services for delivering courses online, providing no additional enhancement to reusability and learner personalization. Hence, the focus is not on providing reusable and learner-centered content, but on developing the technology aspects of e-learning. This current trend has made it crucial to find a more refined definition of what constitutes knowledge in the e-learning context. We propose an e-learning system architecture that makes use of a knowledge model to facilitate continuous dialogue and inquiry-based knowledge learning, by exploiting the full benefits of the semantic web as a medium capable for supplying the web with formalized knowledge.
Experience as Knowledge in a New Product Development Team: Implications for Knowledge Management

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.

2009-01-01

This study was conducted to better understand how New Product Development (NPD) team members apply their experiences to meet the task needs of their project. Although "experience" is highly valued in team members, little research has looked specifically at experiences as a type of knowledge, and how this knowledge is used in work settings. This research evaluated nearly 200 instances where team members referenced past experiences during team meetings. During these experience exchanges, team members structured the sharing of their experiences to include three common elements: the source of the experience, the nature of the experience, and the degree of relevance to the current work of the team. The experiences fell into four categories: people (relationships), process, product, and politics. This paper describes how team members structured, applied, and integrated their individual experiences and presents the resulting implications for knowledge management systems that wish to exploit experience knowledge.
Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.

PubMed

Xi, Jianing; Wang, Minghui; Li, Ao

2018-06-05

Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.
N-mixture models for estimating population size from spatially replicated counts

USGS Publications Warehouse

Royle, J. Andrew

2004-01-01

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, i describe a class of models (n-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, n, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for n. Carroll and lombard (1985, journal of american statistical association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on n that is exploited by the n-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the n-mixture estimator compared to the caroll and lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.
Conceptual change strategies in teaching genetics

NASA Astrophysics Data System (ADS)

Batzli, Laura Elizabeth

The purpose of this study was to evaluate the effectiveness of utilizing conceptual change strategies when teaching high school genetics. The study examined the effects of structuring instruction to provide students with cognitive situations which promote conceptual change, specifically instruction was structured to elicit students' prior knowledge. The goal of the study was that the students would not only be able to solve genetics problems and define basic terminology but they would also have constructed more scientific schemas of the actual processes involved in inheritance. This study is based on the constructivist theory of learning and conceptual change research which suggest that students are actively involved in the process of relating new information to prior knowledge as they construct new knowledge. Two sections of biology II classes received inquiry based instruction and participated in structured cooperative learning groups. However, the unique difference in the treatment group's instruction was the use of structured thought time and the resulting social interaction between the students. The treatment group students' instructional design allowed students to socially construct their cognitive knowledge after elicitation of their prior knowledge. In contrast, the instructional design for the control group students allowed them to socially construct their cognitive knowledge of genetics without the individually structured thought time. The results indicated that the conceptual change strategies with individually structured thought time improved the students' scientific mastery of genetics concepts and they maintained fewer post instructional alternative conceptions. Although all students gained the ability to correctly solve genetics problems, the treatment group students were able to explain the processes involved in terms of meiosis. The treatment group students were also able to better apply their knowledge to novel genetic situations. The implications for genetics instruction from these results were discussed.
An ethnomethodological approach to examine exploitation in the context of capacity, trust and experience of commercial surrogacy in India.

PubMed

Saravanan, Sheela

2013-08-20

The socio-ethical concerns regarding exploitation in commercial surrogacy are premised on asymmetric vulnerability and the commercialization of women's reproductive capacity to suit individualistic motives. In examining the exploitation argument, this article reviews the social contract theory that describes an individual as an 'economic man' with moral and/or political motivations to satisfy individual desires. This study considers the critique by feminists, who argue that patriarchal and medical control prevails in the surrogacy contracts. It also explores the exploitative dynamics amongst actors in the light of Baier's conceptualization of trust and human relationship, within which both justice and exploitation thrive, and Foucault's concept of bio-power. Drawing on these concepts, this paper aims to investigate the manifestations of exploitation in commercial surrogacy in the context of trust, power and experiences of actors, using a case study of one clinic in India. The actors' experiences are evaluated at different stages of the surrogacy process: recruitment, medical procedures, living in the surrogate home, bonding with the child and amongst actors, financial dealings, relinquishment and post-relinquishment.This study applies ethnomethodology to identify phenomena as perceived by the actors in a situation, giving importance to their interpretations of the rules that make collective activity possible. The methods include semi-structured interviews, discussions, participant observation and explanation of the phenomena from the actors' perspectives. Between August 2009 and April 2010, 13 surrogate mothers (SMs), 4 intended parents (IPs) and 2 medical practitioners (MPs) from one clinic in Western India were interviewed.This study reveals that asymmetries of capacity amongst the MPs, SMs, IPs and surrogate agents (SAs) lead to a network of trust and designation of powers through rules, bringing out the relevance of Baier's conceptualization of asymmetric vulnerability, trust and potential exploitation in human relationships. The IPs are exploited, especially in monetary terms. The SMs are relatively the most exploited, given their vulnerability. Their remuneration through surrogacy is significant for them, and their acquired knowledge as ex-surrogates is used for their own benefit and for exploiting others. Foucault's conceptualization of power is hence relevant, since the ex-SMs re-invest the power of their exploitative experience in exploiting others.
An ethnomethodological approach to examine exploitation in the context of capacity, trust and experience of commercial surrogacy in India

PubMed Central

2013-01-01

The socio-ethical concerns regarding exploitation in commercial surrogacy are premised on asymmetric vulnerability and the commercialization of women’s reproductive capacity to suit individualistic motives. In examining the exploitation argument, this article reviews the social contract theory that describes an individual as an ‘economic man’ with moral and/or political motivations to satisfy individual desires. This study considers the critique by feminists, who argue that patriarchal and medical control prevails in the surrogacy contracts. It also explores the exploitative dynamics amongst actors in the light of Baier’s conceptualization of trust and human relationship, within which both justice and exploitation thrive, and Foucault’s concept of bio-power. Drawing on these concepts, this paper aims to investigate the manifestations of exploitation in commercial surrogacy in the context of trust, power and experiences of actors, using a case study of one clinic in India. The actors’ experiences are evaluated at different stages of the surrogacy process: recruitment, medical procedures, living in the surrogate home, bonding with the child and amongst actors, financial dealings, relinquishment and post-relinquishment. This study applies ethnomethodology to identify phenomena as perceived by the actors in a situation, giving importance to their interpretations of the rules that make collective activity possible. The methods include semi-structured interviews, discussions, participant observation and explanation of the phenomena from the actors’ perspectives. Between August 2009 and April 2010, 13 surrogate mothers (SMs), 4 intended parents (IPs) and 2 medical practitioners (MPs) from one clinic in Western India were interviewed. This study reveals that asymmetries of capacity amongst the MPs, SMs, IPs and surrogate agents (SAs) lead to a network of trust and designation of powers through rules, bringing out the relevance of Baier’s conceptualization of asymmetric vulnerability, trust and potential exploitation in human relationships. The IPs are exploited, especially in monetary terms. The SMs are relatively the most exploited, given their vulnerability. Their remuneration through surrogacy is significant for them, and their acquired knowledge as ex-surrogates is used for their own benefit and for exploiting others. Foucault’s conceptualization of power is hence relevant, since the ex-SMs re-invest the power of their exploitative experience in exploiting others. PMID:23962325
Knowledge representation and management: benefits and challenges of the semantic web for the fields of KRM and NLP.

PubMed

Rassinoux, A-M

2011-01-01

To summarize excellent current research in the field of knowledge representation and management (KRM). A synopsis of the articles selected for the IMIA Yearbook 2011 is provided and an attempt to highlight the current trends in the field is sketched. This last decade, with the extension of the text-based web towards a semantic-structured web, NLP techniques have experienced a renewed interest in knowledge extraction. This trend is corroborated through the five papers selected for the KRM section of the Yearbook 2011. They all depict outstanding studies that exploit NLP technologies whenever possible in order to accurately extract meaningful information from various biomedical textual sources. Bringing semantic structure to the meaningful content of textual web pages affords the user with cooperative sharing and intelligent finding of electronic data. As exemplified by the best paper selection, more and more advanced biomedical applications aim at exploiting the meaningful richness of free-text documents in order to generate semantic metadata and recently to learn and populate domain ontologies. These later are becoming a key piece as they allow portraying the semantics of the Semantic Web content. Maintaining their consistency with documents and semantic annotations that refer to them is a crucial challenge of the Semantic Web for the coming years.
Advances in Pediatric Cardiology Boot Camp: Boot Camp Training Promotes Fellowship Readiness and Enables Retention of Knowledge.

PubMed

Ceresnak, Scott R; Axelrod, David M; Sacks, Loren D; Motonaga, Kara S; Johnson, Emily R; Krawczeski, Catherine D

2017-03-01

We previously demonstrated that a pediatric cardiology boot camp can improve knowledge acquisition and decrease anxiety for trainees. We sought to determine if boot camp participants entered fellowship with a knowledge advantage over fellows who did not attend and if there was moderate-term retention of that knowledge. A 2-day training program was provided for incoming pediatric cardiology fellows from eight fellowship programs in April 2016. Hands-on, immersive experiences and simulations were provided in all major areas of pediatric cardiology. Knowledge-based examinations were completed by each participant prior to boot camp (PRE), immediately post-training (POST), and prior to the start of fellowship in June 2016 (F/U). A control group of fellows who did not attend boot camp also completed an examination prior to fellowship (CTRL). Comparisons of scores were made for individual participants and between participants and controls. A total of 16 participants and 16 control subjects were included. Baseline exam scores were similar between participants and controls (PRE 47 ± 11% vs. CTRL 52 ± 10%; p = 0.22). Participants' knowledge improved with boot camp training (PRE 47 ± 11% vs. POST 70 ± 8%; p < 0.001) and there was excellent moderate-term retention of the information taught at boot camp (PRE 47 ± 11% vs. F/U 71 ± 8%; p < 0.001). Testing done at the beginning of fellowship demonstrated significantly better scores in participants versus controls (F/U 71 ± 8% vs. CTRL 52 ± 10%; p < 0.001). Boot camp participants demonstrated a significant improvement in basic cardiology knowledge after the training program and had excellent moderate-term retention of that knowledge. Participants began fellowship with a larger fund of knowledge than those fellows who did not attend.

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