128 resultados para RANDOM REGULAR GRAPHS


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In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

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Camera traps are used to estimate densities or abundances using capture-recapture and, more recently, random encounter models (REMs). We deploy REMs to describe an invasive-native species replacement process, and to demonstrate their wider application beyond abundance estimation. The Irish hare Lepus timidus hibernicus is a high priority endemic of conservation concern. It is threatened by an expanding population of non-native, European hares L. europaeus, an invasive species of global importance. Camera traps were deployed in thirteen 1 km squares, wherein the ratio of invader to native densities were corroborated by night-driven line transect distance sampling throughout the study area of 1652 km2. Spatial patterns of invasive and native densities between the invader’s core and peripheral ranges, and native allopatry, were comparable between methods. Native densities in the peripheral range were comparable to those in native allopatry using REM, or marginally depressed using Distance Sampling. Numbers of the invader were substantially higher than the native in the core range, irrespective of method, with a 5:1 invader-to-native ratio indicating species replacement. We also describe a post hoc optimization protocol for REM which will inform subsequent (re-)surveys, allowing survey effort (camera hours) to be reduced by up to 57% without compromising the width of confidence intervals associated with density estimates. This approach will form the basis of a more cost-effective means of surveillance and monitoring for both the endemic and invasive species. The European hare undoubtedly represents a significant threat to the endemic Irish hare.

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Learning or writing regular expressions to identify instances of a specific
concept within text documents with a high precision and recall is challenging.
It is relatively easy to improve the precision of an initial regular expression
by identifying false positives covered and tweaking the expression to avoid the
false positives. However, modifying the expression to improve recall is difficult
since false negatives can only be identified by manually analyzing all documents,
in the absence of any tools to identify the missing instances. We focus on partially
automating the discovery of missing instances by soliciting minimal user
feedback. We present a technique to identify good generalizations of a regular
expression that have improved recall while retaining high precision. We empirically
demonstrate the effectiveness of the proposed technique as compared to
existing methods and show results for a variety of tasks such as identification of
dates, phone numbers, product names, and course numbers on real world datasets

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What is meant by the term random? Do we understand how to identify which type of randomisation to use in our future research projects? We, as researchers, often explain randomisation to potential research participants as being a 50/50 chance of selection to either an intervention or control group, akin to drawing numbers out of a hat. Is this an accurate explanation? And are all methods of randomisation equal? This paper aims to guide the researcher through the different techniques used to randomise participants with examples of how they can be used in educational research.

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Objectives There is evidence from neuroscience, cognitive psychology and educational research that the delivery of a stimulus in a spaced format (over time) rather than a massed format (all at once) leads to more effective learning. This project aimed to pilot spaced learning materials using various spacing lengths for GCSE science by exploring the feasibility of introducing spaced leaning into regular classrooms and by evaluating teacher fidelity to the materials. The spaced learning methods will then be compared with traditional science revision techniques and a programme manual will be produced. Design A feasibility study. Methods A pilot study (4 schools) was carried out to examine the feasibility and teacher fidelity to the materials, using pupil workshops and teacher interviews. A subsequent random assignment experimental study (12 schools) will involve pre and post testing of students on a science attainment measure and a post-test implementation questionnaire. Results The literature review found that longer spacing intervals between repetitions of material (>24 hours) may be optimal for long term memory formation than shorter intervals. A logic model was developed to inform the design of various programme variants for the pilot and experimental study. This paper will report qualitative data from the initial pilot study. Conclusions The paper uses this research project as an example to explain the importance of conducting pilot work and small scale experimental studies to explore the feasibility and inform the design of educational interventions, rather than prematurely moving to RCT type studies.

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OBJECTIVES: Regular use of nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with a reduced risk of esophageal adenocarcinoma. Epidemiological studies examining the association between NSAID use and the risk of the precursor lesion, Barrett’s esophagus, have been inconclusive.

METHODS: We analyzed pooled individual-level participant data from six case-control studies of Barrett’s esophagus in the Barrett’s and Esophageal Adenocarcinoma Consortium (BEACON). We compared medication use from 1474 patients with Barrett’s esophagus separately with two control groups: 2256 population-based controls and 2018 gastroesophageal reflux disease (GERD) controls. Study-specific odds ratios (OR) and 95% confidence intervals (CI) were estimated using multivariable logistic regression models and were combined using a random effects meta-analytic model.

RESULTS: Regular (at least once weekly) use of any NSAIDs was not associated with the risk of Barrett’s esophagus (vs. population-based controls, adjusted OR = 1.00, 95% CI = 0.76–1.32; I2=61%; vs. GERD controls, adjusted OR = 0.99, 95% CI = 0.82–1.19; I2=19%). Similar null findings were observed among individuals who took aspirin or non-aspirin NSAIDs. We also found no association with highest levels of frequency (at least daily use) and duration (≥5 years) of NSAID use. There was evidence of moderate between-study heterogeneity; however, associations with NSAID use remained non-significant in “leave-one-out” sensitivity analyses.

CONCLUSIONS: Use of NSAIDs was not associated with the risk of Barrett’s esophagus. The previously reported inverse association between NSAID use and esophageal adenocarcinoma may be through reducing the risk of neoplastic progression in patients with Barrett’s esophagus.