Instantaneous pressure measurements on a spherical grain under threshold flow conditions

River morphodynamics and sediment transportMechanics of sediment transpor

Incipient rolling of coarse particles in water flows: a dynamical perspective

River morphodynamics and sediment transportMechanics of sediment transpor

Summarizing and measuring development activity

Software developers pursue a wide range of activities as part of their work, and making sense of what they did in a given time frame is far from trivial as evidenced by the large number of awareness and coordination tools that have been developed in recent years. To inform tool design for making sense of the information available about a developer's activity, we conducted an empirical study with 156 GitHub users to investigate what information they would expect in a summary of development activity, how they would measure development activity, and what factors in uence how such activity can be condensed into textual summaries or numbers. We found that unexpected events are as important as expected events in summaries of what a developer did, and that many developers do not believe in measuring development activity. Among the factors that in uence summarization and measurement of development activity, we identified development experience and programming languages.Christoph Treude, Fernando Figueira Filho, Uirá Kulesz

Logistic model tree extraction from artificial neural networks

Artificial neural networks (ANNs) are a powerful and widely used pattern recognition technique. However, they remain “black boxes” giving no explanation for the decisions they make. This paper presents a new algorithm for extracting a logistic model tree (LMT) from a neural network, which gives a symbolic representation of the knowledge hidden within the ANN. Landwehr’s LMTs are based on standard decision trees, but the terminal nodes are replaced with logistic regression functions. This paper reports the results of an empirical evaluation that compares the new decision tree extraction algorithm with Quinlan’s C4.5 and ExTree. The evaluation used 12 standard benchmark datasets from the University of California, Irvine machine-learning repository. The results of this evaluation demonstrate that the new algorithm produces decision trees that have higher accuracy and higher fidelity than decision trees created by both C4.5 and ExTree

Decision tree extraction from trained neural networks

Artificial Neural Networks (ANNs) have proved both a popular and powerful technique for pattern recognition tasks in a number of problem domains. However, the adoption of ANNs in many areas has been impeded, due to their inability to explain how they came to their conclusion, or show in a readily comprehendible form the knowledge they have obtained. This paper presents an algorithm that addresses these problems. The algorithm achieves this by extracting a Decision Tree, a graphical and easily understood symbolic representation of a decision process, from a trained ANN. The algorithm does not make assumptions about the ANN’s architecture or training algorithm; therefore, it can be applied to any type of ANN. The algorithm is empirically compared with Quinlan’s C4.5 (a common Decision Tree induction algorithm) using standard benchmark datasets. For most of the datasets used in the evaluation, the new algorithm is shown to extract Decision Trees that have a higher predictive accuracy than those induced using C4.5 directly

Nucleotide supplementation: a randomised double-blind placebo controlled trial of IntestAidIB in people with Irritable Bowel Syndrome [ISRCTN67764449]

BACKGROUND: Dietary nucleotide supplementation has been shown to have important effects on the growth and development of cells which have a rapid turnover such as those in the immune system and the gastrointestinal tract. Work with infants has shown that the incidence and duration of diarrhoea is lower when nucleotide supplementation is given, and animal work shows that villi height and crypt depth in the intestine is increased as a result of dietary nucleotides. Dietary nucleotides may be semi-essential under conditions of ill-health, poor diet or stress. Since people with Irritable Bowel Syndrome tend to fulfil these conditions, we tested the hypothesis that symptoms would be improved with dietary nucleotide supplementation. METHODS: Thirty-seven people with a diagnosis of Irritable Bowel gave daily symptom severity ratings for abdominal pain, diarrhoea, urgency to have a bowel movement, incomplete feeling of evacuation after a bowel movement, bloating, flatulence and constipation for 28 days (baseline). They were then assigned to either placebo (56 days) followed by experimental (56 days) or the reverse. There was a four week washout period before crossover. During the placebo and experimental conditions participants took one 500 mg capsule three times a day; in the experimental condition the capsule contained the nutroceutical substances. Symptom severity ratings and psychological measures (anxiety, depression, illness intrusiveness and general health) were obtained and analysed by repeated measures ANOVAs. RESULTS: Symptom severity for all symptoms (except constipation) were in the expected direction of baseline>placebo>experimental condition. Symptom improvement was in the range 4 – 6%. A feeling of incomplete evacuation and abdominal pain showed the most improvement. The differences between conditions for diarrhoea, bloating and flatulence were not significant at the p < .05 level. There were no significant differences between the conditions for any of the psychological measures. CONCLUSION: Dietary nucleotide supplementation improves some of the symptoms of irritable bowel above baseline and placebo level. As expected, placebo effects were high. Apart from abdominal pain and urgency to have a bowel movement, the improvements, while consistent, are modest, and were not accompanied by improvements in any of the psychological measures. We suggest that the percentage improvement over and above the placebo effect is a physiological effect of the nucleotide supplement on the gut. The mechanisms by which these effects might improve symptoms are discussed

Skin Lesion Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods

© 2013 IEEE. Early detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth in the number of skin cancers, there is a growing need of computerised analysis for skin lesions. The state-of-the-art public available datasets for skin lesions are often accompanied with a very limited amount of segmentation ground truth labeling. Also, the available segmentation datasets consist of noisy expert annotations reflecting the fact that precise annotations to represent the boundary of skin lesions are laborious and expensive. The lesion boundary segmentation is vital to locate the lesion accurately in dermoscopic images and lesion diagnosis of different skin lesion types. In this work, we propose the fully automated deep learning ensemble methods to achieve high sensitivity and high specificity in lesion boundary segmentation. We trained the ensemble methods based on Mask R-CNN and DeeplabV3+ methods on ISIC-2017 segmentation training set and evaluate the performance of the ensemble networks on ISIC-2017 testing set and PH2 dataset. Our results showed that the proposed ensemble methods segmented the skin lesions with Sensitivity of 89.93% and Specificity of 97.94% for the ISIC-2017 testing set. The proposed ensemble method Ensemble-A outperformed FrCN, FCNs, U-Net, and SegNet in Sensitivity by 4.4%, 8.8%, 22.7%, and 9.8% respectively. Furthermore, the proposed ensemble method Ensemble-S achieved a specificity score of 97.98% for clinically benign cases, 97.30% for the melanoma cases, and 98.58% for the seborrhoeic keratosis cases on ISIC-2017 testing set, exhibiting better performance than FrCN, FCNs, U-Net, and SegNet

Radiation dose differences between thoracic radiotherapy planning CT and thoracic diagnostic CT scans

Purpose: To compare the absorbed dose from computed tomography (CT) in radiotherapy planning (RP CT) against those from diagnostic CT (DG CT) examinations and to explore the possible reasons for any dose differences. Method: Two groups of patients underwent CT-scans of the thorax with either DG-CT (n=55) or RP-CT (n=55). Patients from each group had similar weight and body mass index (BMI) and were divided into low (25). Parameters including CTDIvol, DLP and scan length were compared. Results: The mean CTDIvol and DLP values from RP-CT (38.1 mGy, 1472 mGy·cm) are approximately four times higher than for DG-CT (9.63 mGy, 376.5 mGy·cm). For low BMI group, the CTDIvol in the RP-CT scans (36.4 mGy) is 6.3 times higher than the one in the DG-CT scans (5.8 mGy). For high BMI group, the CTDIvol in the RP-CT (39.6 mGy) is 2.5 times higher than the one in the DG-CT scans (15.8 mGy). In the DG-CT scans a strong negative linear correlation between noise index (NI) and mean CTDIvol was observed (r =-0.954, p=0.004); the higher NI, the lower CTDIvol. This was not the case in the RP-DG scans. Conclusion: The absorbed radiation dose is significantly higher and less BMI dependent for RP-CT scans compared to DG-CT. Image quality requirements of the examinations should be researched to ensure that radiation doses are not unnecessarily high