Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation

PURPOSE: To evaluate the feasibility of using Mechanical Turk as a massively parallel platform to perform manual segmentations of macular spectral domain optical coherence tomography (SD-OCT) images using a MapReduce framework. METHODS: A macular SD-OCT volume of 61 slice images was map-distributed to Amazon Mechanical Turk. Each Human Intelligence Task was set to 0.01 and required the user to draw five lines to outline the sublayers of the retinal OCT image after being shown example images. Each image was submitted twice for segmentation, and interrater reliability was calculated. The interface was created using custom HTML5 and JavaScript code, and data analysis was performed using R. An automated pipeline was developed to handle the map and reduce steps of the framework. RESULTS: More than 93,500 data points were collected using this framework for the 61 images submitted. Pearson’s correlation of interrater reliability was 0.995 () and coefficient of determination was 0.991. The cost of segmenting the macular volume was 1.21. A total of 22 individual Mechanical Turk users provided segmentations, each completing an average of 5.5 HITs. Each HIT was completed in an average of 4.43 minutes. CONCLUSIONS: Amazon Mechanical Turk provides a cost-effective, scalable, high-availability infrastructure for manual segmentation of OCT images

Impact of dye interlayer on the performance of organic photovoltaic devices

The influences of buffer interlayer at the donor/acceptor interface on the open circuit voltage (VOC) of typical copper phthalocyanine (CuPc) / C60 organic photovoltaic devices are studied. Six fluorescent dyes with progressively increasing ionization potentials (I P) were used to investigate the factors influencing the VOC. The short-circuit current and fill factor of CuPc/ C60 device incorporating dye interlayer are lower than those of standard bilayer device. On the other hand, the VOC increases linearly with the I P of dye material and falls off when the I P is equal to or greater than 5.6 eV, in which the energy offset between the highest occupied molecular orbitals at the interlayer/ C60 heterojunction is smaller than the C60 exciton binding energy. The findings underscore the importance of energy offsets in photovoltaic responses. © 2009 American Institute of Physics.published_or_final_versio

Average-Case Optimal Approximate Circular String Matching

Approximate string matching is the problem of finding all factors of a text t of length n that are at a distance at most k from a pattern x of length m. Approximate circular string matching is the problem of finding all factors of t that are at a distance at most k from x or from any of its rotations. In this article, we present a new algorithm for approximate circular string matching under the edit distance model with optimal average-case search time O(n(k + log m)/m). Optimal average-case search time can also be achieved by the algorithms for multiple approximate string matching (Fredriksson and Navarro, 2004) using x and its rotations as the set of multiple patterns. Here we reduce the preprocessing time and space requirements compared to that approach

Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach

Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the differences in activity preference among the individual, adherence problems, and uncertainty of future health outcomes may reduce the effect of the mHealth information service. The current health service system usually provides recommendations based on fixed exercise plans that do not satisfy the user specific needs. This paper seeks an efficient way to make physical activity recommendation decisions on physical activity promotion in personalised mHealth information service by establishing data-driven model. In this study, we propose a real-time interaction model to select the optimal exercise plan for the individual considering the time-varying characteristics in maximising the long-term health utility of the user. We construct a framework for mHealth information service system comprising a personalised AI module, which is based on the scientific knowledge about physical activity to evaluate the individual exercise performance, which may increase the awareness of the mHealth artificial intelligence system. The proposed deep reinforcement learning (DRL) methodology combining two classes of approaches to improve the learning capability for the mHealth information service system. A deep learning method is introduced to construct the hybrid neural network combing long-short term memory (LSTM) network and deep neural network (DNN) techniques to infer the individual exercise behavior from the time series data. A reinforcement learning method is applied based on the asynchronous advantage actor-critic algorithm to find the optimal policy through exploration and exploitation

Quality of life (QoL) in southern Chinese with systemic lupus erythematosus (SLE)

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PD-0458: AFP response as a predictor of clinical outcome after stereotactic body radiotherapy (SBRT) for advanced HCC

POSTER DISCUSSION: YOUNG SCIENTISTS 2: LUNG AND GASTROINTESTINAL TUMOURSpublished_or_final_version2nd ESTRO Forum, Geneva, Switzerland, 19-23 April 2013, In Radiotherapy & Oncology, 2013, v. 106, p. S17

Waste reduction and recycling strategies for the in-flight services in the airline industry

Author name used in this publication: X. D. LiAuthor name used in this publication: S. C. Lee2002-2003 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe