Quality information retrieval for the World Wide Web

The World Wide Web is an unregulated communication medium which exhibits very limited means of quality control. Quality assurance has become a key issue for many information retrieval services on the Internet, e.g. web search engines. This paper introduces some quality evaluation and assessment methods to assess the quality of web pages. The proposed quality evaluation mechanisms are based on a set of quality criteria which were extracted from a targeted user survey. A weighted algorithmic interpretation of the most significant user quoted quality criteria is proposed. In addition, the paper utilizes machine learning methods to produce a prediction of quality for web pages before they are downloaded. The set of quality criteria allows us to implement a web search engine with quality ranking schemes, leading to web crawlers which can crawl directly quality web pages. The proposed approaches produce some very promising results on a sizable web repository

Energy cost of physical activities and sedentary behaviors in young children

Background: This study reports energy expenditure (EE) data for lifestyle and ambulatory activities in young children. Methods: Eleven children aged 3 to 6 years (mean age = 4.8 ± 0.9; 55% boys) completed 12 semistructured activities including sedentary behaviors (SB), light (LPA), and moderate-to-vigorous physical activities (MVPA) over 2 laboratory visits while wearing a portable metabolic system to measure EE. Results: Mean EE values for SB (TV, reading, tablet and toy play) were between 0.9 to 1.1 kcal/min. Standing art had an energy cost that was 1.5 times that of SB (mean = 1.4 kcal/min), whereas bike riding (mean = 2.5 kcal/min) was similar to LPA (cleaning-up, treasure hunt and walking) (mean = 2.3 to 2.5 kcal/min), which had EE that were 2.5 times SB. EE for MVPA (running, active games and obstacle course) was 4.2 times SB (mean = 3.8 to 3.9 kcal/ min). Conclusion: EE values reported in this study can contribute to the limited available data on the energy cost of lifestyle and ambulatory activities in young children

A step towards treatment planning for microbeam radiation therapy: fast peak and valley dose predictions with 3D U-Nets

Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose, a concept of macro peak doses and macro valley doses is introduced, describing the respective doses not on a microscopic level but as macroscopic quantities in larger voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Complex physical effects such as polarization are therefore automatically taking into account by the model. The macro dose distribution approach described in this study allows for superimposing single microbeam predictions to a beam array field making it an interesting candidate for treatment planning. It is shown that the proposed approach can overcome a main obstacle with microbeam dose predictions by predicting a full microbeam irradiation field in less than a minute while maintaining reasonable accuracy.Comment: accepted for publication in the IFMBE Proceedings on the World Congress on Medical Physics and Biomedical Engineering 202

Accurate and fast deep learning dose prediction for a preclinical microbeam radiation therapy study using low-statistics Monte Carlo simulations

Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumour diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Prescription dose estimations for treating preclinical gliosarcoma models in MRT studies at the Imaging and Medical Beamline at the Australian Synchrotron currently rely on Monte Carlo (MC) simulations. The steep dose gradients associated with the 50$\,\mu$m wide coplanar beamlets present a significant challenge for precise MC simulation of the MRT irradiation treatment field in a short time frame. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiation treatment options like MRT. In this work, the successful application of a fast and accurate machine learning dose prediction model in a retrospective preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the gliosarcoma in rodent patients. The predictions of the ML model show excellent agreement with low-noise MC simulations, especially within the investigated tumour volume. This agreement is despite the ML model being deliberately trained with MC-calculated samples exhibiting significantly higher statistical uncertainties. The successful use of high-noise training set data samples, which are much faster to generate, encourages and accelerates the transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies

Extensions and evaluations of adaptive processing of structured information using artifical neural networks

The application of Artificial Neural Networks has traditionally been restricted to fixed size data and data sequences. However, there are a large number of applications which are more appropriately represented in the form of graphs. Such applications include learning problems from the area of molecular chemistry, software engineering, artificial intelligence, image and document processing, and numerous others. The inability of conventional Artificial Neural Networks to encode this kind of data has motivated for research in this field

A supervised self-organizing map for structures

This work proposes an improvement of a supervised learning technique for self organizing maps. The ideas presented in This work differ from Kohonen\u27s approach to supervision in that a.) a rejection term is used, and b.) rejection affects the training only locally. This approach produces superior results because it does not affect network weights globally, and hence, prevents the addition of noise to the learning process of remote neurons. We implemented the ideas into self-organizing maps for structured data (SOM-SD) which is a more general form of self-organizing maps capable of processing graphs. The capabilities of the proposed ideas are demonstrated by utilizing a relatively large real world learning problem from the area of image recognition. It is shown that the proposed method produces better classification performances while being more robust and flexible than other supervised approaches to SOM

A self-organizing map for adaptive processing of structured data

Recent developments in the area of neural networks produced models capable of dealing with structured data. Here, we propose the first fully unsupervised model, namely an extension of traditional self-organizing maps (SOMs), for the processing of labeled directed acyclic graphs (DAGs). The extension is obtained by using the unfolding procedure adopted in recurrent and recursive neural networks, with the replicated neurons in the unfolded network comprising of a full SOM. This approach enables the discovery of similarities among objects including vectors consisting of numerical data. The capabilities of the model are analyzed in detail by utilizing a relatively large data set taken from an artificial benchmark problem involving visual patterns encoded as labeled DAGs. The experimental results demonstrate clearly that the proposed model is capable of exploiting both information conveyed in the labels attached to each node of the input DAGs and information encoded in the DAG topology