Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201

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

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

Using guided individualised feedback to review self-reported quality of life in health and its importance

© 2014 Taylor & Francis. This pilot study investigated the effects of providing guided, individualised feedback on subjective quality of life (QoL), using results from the multidimensional WHOQOL-BREF profile. Participants (n=129; 85 chronically ill) were recruited in the community, and primary care. They were randomised to receive written or verbal guidance on interpreting a new graphical summary profile, which simultaneously presented (a) their individual self-ratings of QoL and (b) the importance attributed to each QoL dimension. Before and after feedback, participants completed health status, subjective QoL, QoL Importance, goal-oriented QoL and mood measures. Receiving individualised feedback was associated with increased psychological QoL, with modest effect size. No effects were found for physical, social or environmental QoL or QoL importance, health status, mood or goal-oriented QoL. There were no differences between modes of delivering guidance, indicating equal effectiveness. Chronic illness participants reported poorer QoL, moved more slowly towards their QoL goals, and had larger differences between core QoL and QoL Importance than healthy participants. Guided individualised empirical feedback about QoL judgements could be used to promote psychological well-being. Although professional interpretation of feedback is unnecessary, if shared, patients’ profiled WHOQOL information could support self-monitoring, self-management and clinical decision-making

Porosity estimation of (Moso bamboo) by computed tomography and backscattered electron imaging

This study aims to investigate and quantify the porosity in the cross section of Phyllostachys edulis (Moso bamboo) culm wall. The porosity results are expected to be utilised in numerical study of heat and moisture transfer. Computed tomography (CT) and backscattered electron (BSE) imaging methods are utilised in this study because these two methods allow measurements of the anisotropic features of bamboo specimens. The results of these two methods can be represented as the function of the real dimension rather than the pore size distribution of the specimen. The specimens are obtained from eight different locations along the Moso bamboo culms. Both internodes and nodes specimens are measured in this study. The average porosity, standard deviation (SD) and coefficient of variation (COV) are calculated for BSE and CT results. Pearson product-moment correlation coefficient (PPMCC) is also calculated in this study to analyse the correlation between the BSE results and CT results. Typical porosity results from 400 sampling points and 10 portions average porosity are analysed in this study. The CT scanning results show similar trend with BSE results. The correlation relationship between BSE and CT results approaches moderate correlation level to strong correlation level. The average porosity of internode specimens is from 43.9 to 58.8 % by BSE measurement and from 44.9 to 63.4 % by CT measurement. The average porosity of node specimens is from 37.4 to 56.6 % by BSE measurement and from 32.1 to 62.2 % by CT measurement

Porosity estimation of Phyllostachys edulis (Moso bamboo) by computed tomography and backscattered electron imaging

This study aims to investigate and quantify the porosity in the cross section of Phyllostachys edulis (Moso bamboo) culm wall. The porosity results are expected to be utilised in numerical study of heat and moisture transfer. Computed tomography (CT) and backscattered electron (BSE) imaging methods are utilised in this study because these two methods allow measurements of the anisotropic features of bamboo specimens. The results of these two methods can be represented as the function of the real dimension rather than the pore size distribution of the specimen. The specimens are obtained from eight different locations along the Moso bamboo culms. Both internodes and nodes specimens are measured in this study. The average porosity, standard deviation (SD) and coefficient of variation (COV) are calculated for BSE and CT results. Pearson product-moment correlation coefficient (PPMCC) is also calculated in this study to analyse the correlation between the BSE results and CT results. Typical porosity results from 400 sampling points and 10 portions average porosity are analysed in this study. The CT scanning results show similar trend with BSE results. The correlation relationship between BSE and CT results approaches moderate correlation level to strong correlation level. The average porosity of internode specimens is from 43.9 to 58.8 % by BSE measurement and from 44.9 to 63.4 % by CT measurement. The average porosity of node specimens is from 37.4 to 56.6 % by BSE measurement and from 32.1 to 62.2 % by CT measurement

Arginine Deprivation With Pegylated Arginine Deiminase in Patients With Argininosuccinate Synthetase 1-Deficient Malignant Pleural Mesothelioma A Randomized Clinical Trial

IMPORTANCE: Preclinical studies show that arginine deprivation is synthetically lethal in argininosuccinate synthetase 1 (ASS1)-negative cancers, including mesothelioma. The role of the arginine-lowering agent pegylated arginine deiminase (ADI-PEG20) has not been evaluated in a randomized and biomarker-driven study among patients with cancer. OBJECTIVE: To assess the clinical impact of arginine depletion in patients with ASS1-deficient malignant pleural mesothelioma. DESIGN, SETTING, AND PARTICIPANTS: A multicenter phase 2 randomized clinical trial, the Arginine Deiminase and Mesothelioma (ADAM) study, was conducted between March 2, 2011, and May 21, 2013, at 8 academic cancer centers. Immunohistochemical screening of 201 patients (2011-2013) identified 68 with advanced ASS1-deficient malignant pleural mesothelioma. INTERVENTIONS: Randomization 2:1 to arginine deprivation (ADI-PEG20, 36.8 mg/m2, weekly intramuscular) plus best supportive care (BSC) or BSC alone. MAIN OUTCOMES AND MEASURES: The primary end point was progression-free survival (PFS) assessed by modified Response Evaluation Criteria in Solid Tumors (RECIST) (target hazard ratio, 0.60). Secondary end points were overall survival (OS), tumor response rate, safety, and quality of life, analyzed by intention to treat. We measured plasma arginine and citrulline levels, anti–ADI-PEG20 antibody titer, ASS1 methylation status, and metabolic response by 18F-fluorodeoxyglucose positron-emission tomography. RESULTS: Median (range) follow-up in 68 adults (median [range] age, 66 [48-83] years; 19% female) was 38 (2.5-39) months. The PFS hazard ratio was 0.56 (95% CI, 0.33-0.96), with a median of 3.2 months in the ADI-PEG20 group vs 2.0 months in the BSC group (P = .03) (absolute risk, 18% vs 0% at 6 months). Best response at 4 months (modified RECIST) was stable disease: 12 of 23 (52%) in the ADI-PEG20 group vs 2 of 9 (22%) in the BSC group (P = .23). The OS curves crossed, so life expectancy was used: 15.7 months in the ADI-PEG20 group vs 12.1 months in the BSC group (difference of 3.6 [95% CI, −1.0 to 8.1] months; P = .13). The incidence of symptomatic adverse events of grade at least 3 was 11 of 44 (25%) in the ADI-PEG20 group vs 4 of 24 (17%) in the BSC group (P = .43), the most common being immune related, nonfebrile neutropenia, gastrointestinal events, and fatigue. Differential ASS1 gene-body methylation correlated with ASS1 immunohistochemistry, and longer arginine deprivation correlated with improved PFS. CONCLUSIONS AND RELEVANCE: In this trial, arginine deprivation with ADI-PEG20 improved PFS in patients with ASS1-deficient mesothelioma. Targeting arginine is safe and warrants further clinical investigation in arginine-dependent cancers