Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is decomposed into intrinsic mode functions (IMFs) by using BEMD.At each decomposed level, the IMF is expanded and fused with the residue by grey-scale transformation and principal component analysis fusion technique, respectively. The enhanced image is then formed by the improved IMFs in reconstruction process. Subsequently, feature extraction is applied for the enhanced images to obtain histogram features which characterize the thermal image and contain useful information for diagnosis. The high dimensionality of the achieved feature set can be reduced by GDA implementation. Moreover, GDA also assists in the increase of the feature cluster separation. Finally, the diagnostic results are performed by RVM.The proposed system is applied and validated with the thermal images of a fault simulator. A comparative study of the classification results obtained from RVM, support vector machines, and adaptive neuro-fuzzy inference system is also performed to appraise the accuracy of these models. The results show that the proposed diagnosis system is capable of improving the classification accuracy and efficiently assisting in rotating machinery fault diagnosis. Rotating machinery covers a wide range of mechanical equipment and is of importance in industrial applications. Therefore, faults occurring in rotating machinery may severely affect operations in industry and even safety. In order to minimize the number of breakdowns as well as to increase the reliability, rotating machinery condition should be monitored for symptoms and incipient fault detection. By this, the life of machinery could be prolonged and the catastrophic consequences of unplanned failure could be avoided. Consequently, fault diagnosis and condition monitoring of rotating machinery have been the research subject in recent years.. Traditionally, to monitor the conditions and diagnose the faults of rotating machinery, acoustics and vibration are commonly used signals due to their easy-to-measure characteristics and analysis. Some outstanding works [1] [2] [3] [4] [5] [6] have successfully employed these signals for fault detection and diagnosis areas. However, it is necessary to increase the fault diagnosis capability and implement suitable signals which intensify the fault detection ability for highly automatic rotating machinery.. In recent times, infrared thermal image has been considered as a new signal which can be applied for fault diagnosis because of the possibility to indicate the object's operating condition through its temperature. In the study of Bagavathiappan et al. [7] , the operating conditions of blower bearings, shaft at the impeller end, and motors in ventilation systems of nuclear plants were monitored by the temperature obtained from thermal images. The result of this study. showed that thermography could assist in detecting the abnormal operation of various components at an early stage of impending failure. Younus et al. [8] used thermal imaging for diagnosing the faults of rotating machinery in which principal component analysis (PCA) and independent component analysis (ICA) were utilized as feature extraction tools whilst support vector machine (SVM) was employed as a classifier. Moreover, the classification accuracy of SVM was also compared with other classifiers such as parzen probabilistic neural networks, fuzzy k-nearest neighbor, and adaptive resonance theory-Kohonen neural network. In another work in [9] , thermal images of rotating machinery conditions were decomposed by twodimensional discrete wavelet transform. For each level obtained from the decomposition process, histogram features was extracted and selected by Mahalanobis distance and relief algorithm to choose salient features. Subsequently, SVM and linear discriminant analysis were applied as classifiers for each level.. Generally, the classification accuracy of those studies was not significant because the thermal images were not enhanced before implementing feature extraction or classification.. Image enhancement, which is a function of image processing, aims to augment some information in an image as a specific requirement, weaken or remove some unwanted information, so that it changes the original image into a more suitable form for human observations or computer analysis. Traditionally, image enhancement technologies can be divided into two categories [10] : image enhancement on spatial domain and image enhancement on frequency domain. Several methods consisting of histogram equalization, adaptive contrast enhancement, smoothing, sharpening, color processing, etc. have been utilized in these categories.. The first commonly used method for image enhancement is histogram equalization (HE). As a consequence, HE flattens the density distribution of the resultant image and enhances the contrast of the image, since it has an effect of stretching dynamic range [11] . However, HE changes the brightness of the image significantly and makes the image saturated with very bright or dark intensity values [12] . The second method is un-sharp masking (USM). The classic linear USM is implemented by passing a low-contrast image through a linear two-dimensional high-pass filter and then adding a fraction of its output to the origin. This method enlarges the image noise, particularly in uniform areas of even slightly noisy images and causes overshoot artifacts in high-contrast regions. HE and USM are only the compromise between de-noising and enhancing image details. Furthermore, they are also less sensitive to noise presented in the input. Another commonly used method is wavelet transformation. The main advantage of this method is that no artificial information is introduced into the enhanced image. This allows some flexibility in different applications. Nevertheless, wavelet transformation has a major drawback where the basis function has to be defined a priori and this choice may influence the final results.. To overcome the shortcomings of the above traditional methods, several image enhancement methods were proposed in which some of them were based on bi-dimensional empirical mode decomposition (BEMD). BEMD is a two-dimensional generalization of the classical empirical mode decomposition (EMD), which was proposed by Huang et al. [13] to extract the frequency components of a signal. Theoretically, any complicated signal can be decomposed into a set of intrinsic mode functions (IMFs) based upon the local characteristic time scale of the signal.. EMD is self-adaptive and highly efficient in non-stationary data analysis because IMFs, working as the basis functions, are determined by the signal itself. Accordingly, BEMD overcomes the wavelet transformation drawback and provides a promising image processing technique that can be applied in various areas of image analysis, e.g. image fusion [14] , image compression [15] [16] , texture analysis [17] [18] , feature extraction [19] , etc. Numerous expansions of BEMD are afterwards developed for image enhancement. Qin et al. [20] used BEMD to decompose the medical images and the high frequency information was then expanded to obtain the enhanced images. Çelebi et al. [21] utilized BEMD to separately decompose the color channels of underwater color images into IMFs; then, the weighted IMFs of each channel were combined to reconstruct the enhanced images [22] . Other image enhancement methods based on ensemble EMD [23] and fast and adaptive BEMD [24] were developed by Bakhtiari et al. in [22] and [25] , respectively. In this study, a novel method which is a combination of BEMD and PCA fusion technique [26] is proposed to enhance the thermal images of machine conditions. The thermal images are iteratively decomposed into the IMFs. At each decomposition step, the obtained IMF is expanded by grey-scale transformation and then fused with the residue by using PCA fusion. The enhanced image is obtained from the reconstruction once the decomposition process is terminated.. Next, the enhanced images are the input of the feature extraction process to draw out useful information. According to Umbaugh [27] , the image features consist of histogram, spectral, texture, and color. Among these, histogram features which are truly statistical features are a compact representation of image characteristics without requiring knowledge about them.. Moreover, histogram features also provide information about the characteristics of the intensity grey level distribution for the image. Hence, they are suitable for fully automatic characterization of images and are used in this study. Histogram features consist of mean, standard deviation, skewness, energy, entropy, and kurtosis. Normally, these features are rarely usable because of the huge dimensionality leading to the increase of computational complexity and the decrease of accuracy in fault diagnosis. Therefore, dimensionality reduction is the necessity for data preprocessing techniques. In fault diagnosis, there have been numerous approaches for feature reduction such as ICA, PCA [28] , and genetic algorithms [29] , etc. In this study, the generalized discriminant analysis (GDA) [30] based feature reduction is investigated with the aim of improving the classification performance.. Selecting the model is the last stage in this system in order to diagnose the machine conditions. The classification models have a wide range of approaches which are varied from model-based to pattern recognition-based. Amongst these, machine learning and artificial intelligence are regularly used for machine fault diagnosis system due to their accuracy and flexibility. In machine learning techniques, the relevance vector machine (RVM) which was originally introduced by Tipping [31] is one of the remarkable models. However, the original RVM could only be implemented for binary classification which merely encounters in real applications in general and fault diagnosis in particular. Therefore, RVM was developed from the original for multi-class classification by introducing multinomial logistic regression [32] and applied for fault diagnosis of low speed bearing [33] . In this study, multi-class RVM is used for classifying the different machine conditions such as normal, misalignment, mass unbalance, and bearing fault. A comparative study of the classification results obtained from RVM, SVM [34] , and adaptive neuro-fuzzy inference system (ANFIS) [35] is also carried out to appraise the accuracy of these models. In this study, the novel fault diagnosis system using thermal images consisting of BEMD based image enhancement, GDA for feature reduction, and RVM for classification have been presented. The thermal images captured from machine conditions are firstly preprocessed by the HE algorithm to enhance the image contrast, removing noise, and cropping to obtain the ROI.. These images are further improved by the enhancement method based on the combination of BEMD and PCA fusion. Then, the histogram feature extraction and the GDA feature reduction are respectively carried out to extract the features of enhanced images, reduce the high dimension and increase the cluster separability of the feature data. The classification process provides a comparative study of RVM, SVM including one-against-one and one-against-all algorithms, and ANFIS in fault diagnosis.. As a result, the classification accuracy of the enhanced images is much higher than that of the original images. This can conclude that the proposed image enhancement method has significantly assisted in improving the image quality. Furthermore, the performances of RVM and SVM-OAO are considerably superior to SVM-OAA in the case of the original images. It. indicates that RVM and SVM-OAO have great potential for fault diagnosis using thermal imaging. In training time comparison, RVM is faster than SVM-OAO, which is necessary and useful in real applications. Accordingly, the proposed system with RVM used as classifier is capable of efficiently assisting in machine fault diagnosis. • Storage temperature range: −40 °C to +70 °C. • Solid object materials and surface treatments exhibit emissivity. ranging from approximately 0.1 to 0.95.. • For short distance, humidity is default value of 50 %. • Thermal sensitivity: 0.08 °C at 30 °C. • Accuracy: ± 2°C. •

B I O O B I I I O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O B I I I O O O O O B I I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A Full-Brain, Bootstrapped Analysis of Diffusion Tensor Imaging Robustly Differentiates Parkinson Disease from Healthy Controls Abstract There is a compelling need for early, accurate diagnosis of Parkinson's disease (PD). Various magnetic resonance imaging modalities are being explored as an adjunct to diagnosis. A significant challenge in using MR imaging for diagnosis is developing appropriate algorithms for extracting diagnostically relevant information from brain images. In previous work, we have demonstrated that individual subject variability can have a substantial effect on identifying and determining the borders of regions of analysis, and that this variability may impact on prediction accuracy. In this paper we evaluate a new statistical algorithm to determine if we can improve accuracy of prediction using a subjects left-out validation of a DTI analysis. Twenty subjects with PD and 22 healthy controls were imaged to evaluate if a full brain diffusion tensor imaging-fractional anisotropy (DTI-FA) map might be capable of segregating PD from controls. In this paper, we present a new statistical algorithm based on bootstrapping. We compare the capacity of this algorithm to classify the identity of subjects left out of the analysis with the accuracy of other statistical techniques, including standard cluster-thresholding. The bootstrapped analysis approach was able to correctly discriminate the 20 subjects with PD from the 22 healthy controls (area under the receiver operator curve or AUROC 0.90); however the sensitivity and specificity of standard cluster-thresholding techniques at various voxel-specific thresholds were less effective (AUROC 0.72-0.75). Based on these results sufficient information to generate diagnostically relevant statistical maps may already be collected by current MRI scanners. We present one statistical technique that might be used to extract diagnostically relevant information from a full brain analysis. There is a compelling need for early and accurate diagnosis of Parkinson's disease (PD). Various MR imaging modalities are being explored to enhance diagnosis (Skidmore et al. 2011a, b, c; Gorell et al. 1995; Shalom Michaeli et al. 2007; Vaillancourt et al. 2009; Péran et al. 2010; Ma et al. 2010; Brodoehl et al. 2012; Focke et al. 2011; Haller et al. 2012) . Methodologies for developing predictions from imaging datasets vary. For example, some groups focus on individual regions, ignoring the vast majority of the image in order to focus on, for example, regions such as the substantia nigra (Gorell et al. 1995; Vaillancourt et al. 2009 ), or olfactory regions (Brodoehl et al. 2012) . In some cases, multiple modalities are used to look at a single regions or a small number of regions to boost diagnostic yield (Péran et al. 2010) . These region-specific approaches assume consistency of effects amongst all PD patients, which may not be a valid assumption. A few groups have attempted to perform full brain analyses to evaluate group differences using all the available imaging information (Skidmore et al. 2011a, b, c; Ma et al. 2010; Focke et al. 2011; Haller et al. 2012) . However, while multiple methods show robust group-level differences between individuals with PD and individuals without PD, prediction of diagnosis on an individual level is usually less robust, and only a few studies are attempting to examine sensitivity or specificity for diagnosis (Skidmore et al. 2011a, b, c; Haller et al. 2012) .. One possible limitation for generating predictive imaging maps may relate to the statistical techniques used for the analysis of imaging data. For example, cluster thresholding is a longstanding and accepted technique used to evaluate significance in imaging datasets (Forman et al 1995) . Methods used by Forman et al. have been generally adopted for statistical testing of imaging data for the purposes of hypothesistesting, and this method is still in widespread use (Leunissen et al. 2013; Monti et al. 2013; Junger et al. 2013; Gallivan et al. 2013) . However, the variability of the results derived by this technique to the voxel-wise thresholding criteria is well recognized (Nichols and Hayasaka 2003; Nichols and Hayasaka 2003; Bennet et al. 2009; Smith and Nichols 2009) . Our earlier work disclosed that individual subject variability within an imaging dataset can additionally have a significant effect. Specifically, leaving out individual subjects from modest sized analyses can result in substantial changes in the probability of individual voxels exceeding a statistical threshold. In addition, reliability on a regional basis of voxels to exceed a statistical threshold has an effect on the ability of a region to predict group membership of individuals within a given dataset (Skidmore et al. 2011a, b, c) .. Univariate techniques have long assumed that physical proximity (as judged by the size of a region of interest exceeding a certain statistical threshold) has an intrinsic value in distinguishing signal from noise in large imaging datasets (Forman et al. 1995) . Multivariate techniques such as principal component analysis (e.g. see Eidelberg 2009 ) examine the spatial covariance of the whole brain data structure for the purposes of identifying characteristic disease specific patterns rather than isolated anatomical deviations (e.g. see Eidelberg 2009 ). These methods have been primarily successful when applied to steady-state functional data such as in Positron Emission Tomography (PET) imaging, but have had limited success in DTI data thus-far (see e.g., Caprihan et al. 2008) , possibly because of difference in signal to noise ratio on a voxel-by-voxel basis between PET images and DTI imaging. Since regional averaging effects might improve signal-tonoise issues in DTI imaging, we wished to explore alternative techniques that explicitly allowed regional statistical relationships (e.g. Regions of Interest) to continue to play a role in selection of the voxels for the purposes of developing a statistical map, while controlling for individual subject effects.. Bootstrapping, using a univariate region-based approach, is a procedure that may be well suited for this purpose. Bootstrapping refers to a class of statistical methods that use resampling to generate an empirical estimate of population distribution. Classical bootstrapping, as defined by Efron et al. (Efron 1979 ) is similar but not identical to other permutation methods, which merely provide the probabilities of an occurrence. Bootstrapping may be distinguished from other permutation methods methodologically by two factors: 1) relative robustness as a measure, (bootstrapping can be applied with minimal assumptions to even complex datasets), and 2) generating as a principal output estimates of confidence intervals and standard error within the dataset (see DiCiccio and Romano 1989) .. In this paper, we estimate the capacity of a bootstrapped analysis approach to generate a predictive map using real imaging data by comparing a straightforward bootstrapped process (using confidence intervals to define our regions of interest for the purposes of prediction), compared to a standard statistical mapping technique (Forman et al 1995) . We also compare our previous predictive mapping strategy (Skidmore et al. 2011a, b, c) and discuss problems with this strategy that this bootstrapping procedure corrects. We generate this analysis in a dataset of clinical interest-evaluating how well we can distinguish PD from healthy controls in a DTI data set. The goal of our study was twofold. First, we had an overall goal of evaluating the feasibility of using diffusion tensor imaging for the purposes of identifying the presence of PD. In this specific work, we use a region of interest based approach to distinguish PD from a control sample, and we evaluate if a bootstrapped analysis allows us to better define the borders of our regions of interest from the perspective of identifying left-out subjects for validation. We find that a bootstrapped approach can generate more robust predictive maps (AUROC 0.901 at our 95 % confidence interval). We would interpret the improved prediction using our bootstrapped methodology as evidence that the bootstrapping constrained selection, shape, and precise localization of regions to maximize identification of those voxels within the selected regions that would be most likely to be predictive of the shape, size, and location of a similar identifying region in a new subject. The approach improves on standard clusterthresholding, and also provides an improved prediction compared to our previously published RMRD method (Skidmore et al 2011a, b, c) . In addition, unlike the RMRD method, we do not constrain our results by an initial analysis, removing some a feature in the RMRD method that could lead to circularity in the analysis and over-optimistic estimates of the effectiveness of the RMRD method to predict new data. We find again in this analysis that significance and reliability are dissociated properties in brain images, and that focusing on defining what regions of an image are reliably different leads to better prediction of new data. We accordingly propose consideration of a general standard in statistical analysis of brain imaging data, focusing on clinically relevant criteria of reliability of prediction of new data. Our findings of increased fractional anisotropy in several regions are ostensibly distinct from the findings of other laboratories that have evaluated white matter integrity in Parkinson disease. For example, both Zhang et al., and Zhan et al. studied white matter integrity in PD using voxel-based morphometry, and found decreased FA in a number of regions throughout the brain (Zhang et al 2011; Zhan et al. 2012) . A close examination of the papers in question, however, reveals that our analytical goals were quite different from those of Zhan and Zhang. In their studies, performed on highperformance 4 T MRIs, rigid thresholding was used to remove gray matter (for example, voxels with signal <0.20 were excluded from analysis). To maintain precise localization of often narrowly defined white matter tracts, smoothing of the Fig. 4 Effects of different analysis strategies on predictive maps data was not performed. The principal goal of the research of these two groups was the valuable, and valid goal of specifically evaluating white matter integrity in PD. However in removing large regions of the image to isolate only white matter tracts much information that might be useful for differentiating the samples may also have been removed.. In our case, we were not specifically focused on the goal of understanding white matter integrity in PD, but rather on the more prosaic goal of determining whether we could use an FA map to predict group identity. Our results may differ from those of Zhan and Zhang, due to the simple fact that we retained a significant amount of fractional anisotropy data within our images, information that was discarded by Zhan and Zhang. It can be noted, for example, that the mean FA value of several of our regions of largest FA difference (including the rectal gyrus and the supplementary motor cortex) were in healthy controls below the predetermined threshold of both labs (e.g. the regions would have been discarded and not included in their analyses). It would be reasonable to posit that in these regions FA values represented a mixture of gray and white matter signal. It is therefore entirely possible that the "increases" in FA in PD discerned by our analysis might represent a decrease in gray matter in the regions identified in at least some cases. Further study will be required to determine the nature of the underlying structural differences that might be responsible for the FA differences within our sample.. A further limitation of our study is our sample size, and the nature of our sample. Our PD sample pool included only 20 individuals with late mild to moderate PD, with disease duration between 5 and 12 years. All subjects clearly had defined PD under chronic treatment with dopaminergic agents, that would be identifiable by brief clinical examination by any qualified neurologist. An additional interest in collection of the dataset was the causes of apathy in PD (Skidmore et al 2011c) , and so the presence of apathy is relatively enriched in our data pool. Nevertheless, unless there is a method by PD interaction, our use of all three methods applied to the same individuals should still allow valuable conclusions. The validity of a map generated by such a population to identify PD in unmedicated individuals early in disease (an eventual goal of analysis) is open to question, but it is likely that sensitivity and specificity will suffer, perhaps dramatically.. We further are limited by the nature of our goals in this study-we show that we can segregate PD from healthy controls, but do not show that we can differentiate PD from other forms of parkinsonism. More specifically, we show we can segregate individuals with well-defined clinical disease from individuals who do not have PD; as we will discuss below, the clinical population we study is therefore quite distinct from a clinical population that would be of general clinical relevance-individuals in early stages of symptoms, where differential diagnosis and prognosis is a clinical question of significant relevance.. Finally, our study was a single-site study, performed using a specific 3 T Phillips MRI, with a defined obsolescence date. As is common in the field, we standardized our data according to a uniform atlas, and we face the usual issue of variability in size and shape of the brain of each subject. We used an established and valid method of mapping to the atlas that may however be distinct from methods used by other laboratories. Our laboratory is not alone in managing these types of difficulties, which are common features in a field of study in which individual variability in the analytical approach dramatically colors research output.. Given the above caveats, it would be inappropriate to conclude that we have discovered a "diagnostic map" for PD. Rather, we present an approach to analysis of imaging that may begin to lead towards better standardization and yields results that are more closely aligned with clinically relevant outcomes (prediction). Given that we show excellent sample segregation using only one aspect of the DTI image- a Raw results involved creating 42 separate renditions of the above tables. However, for the sake of clarity, we present location and size of regions derived from the full analysis (generated by taking the 90 % confidence interval from a map comprised of adding all 2,100 bootstrapped analyses). Similarly, mean and standard deviation of fractional anisotropy are shown based on the full bootstrapped analysis. Conversely, sensitivity and specificity (AUROC) is based on each of the 42 individual "left out" analysis b Mean value of each region, rather than mean of individual voxels (which would have weighted predictions to favor larger regions) was used to generate a "mean of regions" for the purposes of prediction fractional anisotropy (FA), we also show that full brain analysis of DTI images may be a fruitful area of study for developing biomarkers for PD. However, a larger sample group, collected across multiple sites will likely be required to generate predictive maps relevant across sites, and showing that PD can be differentiated from parkinsonism would be an important additional step. If identification of individuals with early-stage disease is a goal, then maps should be generated in a dataset of individuals with early disease. We present a proof of concept therefore; further optimization is likely to occur over time.. Our brute force approach (bootstrapping) applied here works as a univariate voxel based algorithm, and may suggest that bootstrapping can also be extended to multivariate covariance network based analysis of voxel interconnectivity such as SSM-PCA where bootstrapping has been previously used to validate predictive network pattern maps in PET images (Tang et al 2010a, b; Eidelberg 2009; Mure et al. 2011; Huang et al. 2007 Huang et al. , 2008 Habeck et al. 2008) . Combined approaches, using bootstrapping to constrain regions selected for analysis, might also bear fruit. Further, while in this initial analysis, we used a "one-voxel, one vote" analysis, creating averages for each region based on equal weighting of all voxels in the identified cluster, our current approach creates complex maps that are amenable to weighting. Finally, with respect to the choice of imaging technique, we evaluated the DTI FA signal, however FA is only a small fragment of information available in DTI images, and other imaging types, including cortical thickness maps, resting fMRI, p-CASL images, and a host of new and developing techniques for imaging brain iron and other characteristics will likely all contribute to improving the direct clinical relevancy of brain imaging.. In summary, we show that it is possible, using an iterative, full brain, bootstrapped analysis, to generate robust DTI brain maps that are predictive of new data that was generated and processed in a similar fashion. We propose that methods of statistical analysis focused on reliability of prediction of new data may over time be more useful than standard statistical methods in advancing brain imaging towards the goal of improved clinical relevancy.

O O O O O B I I O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Performing Flexible Control on Low-Cost Microcontrollers Using a Minimal Real-Time Kernel Abstract-In recent years, approaches to control performance and resource optimization for embedded control systems have been receiving increased attention. Most of them focus on theory, whereas practical aspects are omitted. Theoretical advances demand flexible real-time kernel support for multitasking and preemption, thus requiring more sophisticated and expensive software/hardware solutions. On the other hand, embedded control systems often have cost constraints related with mass production and strong industrial competition, thus demanding low-cost solutions.In this paper, it is shown that these conflicting demands can be softened and that a compromise solution can be reached. We advocate that recent research results on optimal resource management for control tasks can be implemented on simple multitasking preemptive real-time kernels targeting low-cost microprocessors, which can be easily built in-house and tailored to actual application needs. The experimental evaluation shows that significant control performance improvement can be achieved without increasing hardware costs. Recently, approaches to control performance and resource optimization for embedded control systems have been receiving substantial attention. Nevertheless, such approaches have essentially focused on theory, without sufficient concern with practical aspects. Several of the theoretical advances require the support of a software infrastructure such as real-time kernels thus requiring more sophisticated and expensive software/hardware solutions. This requirement conflicts with the frequently severe cost constraints that embedded control systems are subject to, for example related with mass production and strong industrial competition.. In this paper, however, we showed that using a minimal kernel just supporting task and time management with reflectivity allows achieving the desired goal of supporting modern and efficient control techniques, particularly with rate adaptation and multiple closed-loops integration, with low-cost microcontrollers, namely those of the PIC18Fxx8 family.. The paper presents the referred kernel, the RTKPIC18, which supports preemptive and nonpreemptive multitasking, automatic management of periodic task releases, EDF, RM and DM task scheduling, and access to reflective information to support application adaptation. Then, the paper discusses a case study that illustrates the integration of multiple control loops with online rate adaptation, how they can be implemented using the referred kernel, and the benefits of rate adaptation by means of practical experiments and quantitative results. Further work will focus on improving specific performance aspects of the kernel, namely reducing the worst-case execution time of the EDF scheduler, and on analyzing how to tune the period of the adaptation task for an optimal balance between the reactivity of the adaptation, the overhead imposed and the overall performance gains. To assess the benefits of the optimal policy, a baseline static policy has been also implemented. In the static policy both controllers run at a fixed rate, and no slack allocation is performed. One controller runs at fixed 38 ms and the other at fixed 22 ms. Therefore, they implement the traditional controller, that is, a constant controller gain, which coincides with the optimal gain designed in Section IV-C. Fig. 11 shows the results in accumulated cost, i.e., control error, of both static and dynamic (optimal) policies. The accumulated cost is the evaluation of the cost function (5) over the experiment time. The lower the curve, the better the performance. Both systems were run for two minutes.. As it can be seen in the Fig. 11 , the scenario with slack allocation (dynamic) outperforms the static by more than 10%. Note that the resource utilization in both runs is the same. In the dynamic case, the two control tasks cannot execute at the highest rate, so they alternate between 22 and 38 ms, giving an average for each task of 30 ms. In the static, both tasks always execute at a fixed rate, with average of 30 ms.

O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


NatalieQ:

A

web

server

for

protein-protein

interaction

network

querying

Background:

Molecular

interactions

need

to

be

taken

into

account

to

adequately

model

the

complex

behavior

of

biological

systems.

These

interactions

are

captured

by

various

types

of

biological

networks,

such

as

metabolic,

gene-regulatory,

signal

transduction

and

protein-protein

interaction

networks.

We

recently

developed

NATALIE,

which

computes

high-quality

network

alignments

via

advanced

methods

from

combinatorial

optimization.Here,

we

present

NATALIEQ,

a

web

server

for

topology-based

alignment

of

a

specified

query

protein-protein

interaction

network

to

a

selected

target

network

using

the

NATALIE

algorithm.

By

incorporating

similarity

at

both

the

sequence

and

the

network

level,

we

compute

alignments

that

allow

for

the

transfer

of

functional

annotation

as

well

as

for

the

prediction

of

missing

interactions.

We

illustrate

the

capabilities

of

NATALIEQ

with

a

biological

case

study

involving

the

Wnt

signaling

pathway.We

show

that

topology-based

network

alignment

can

produce

results

complementary

to

those

obtained

by

using

sequence

similarity

alone.

We

also

demonstrate

that

NATALIEQ

is

able

to

predict

putative

interactions.

The

server

is

available

at:

We

developed

NATALIEQ,

a

web

server

for

global

pairwise

network

alignment

of

a

pre-specified

query

PPI

network

to

a

selected

target

network.

The

underlying

alignment

method

computes

alignments

with

a

worst-case

bound

on

their

quality.

For

the

biological

query

networks

we

considered,

the

optimality

gap

was

closed

and

provably

optimal

alignments

with

respect

to

the

used

score

function

were

thus

found.

The

user

can

quickly

get

an

overview

of

the

alignment

through

the

interactive

visualization,

where

conserved

and

non-conserved

interactions

are

easily

visible..

Currently,

we

support

eight

different

target

species

from

both

STRING

and

IntAct.

NATALIEQ

is

extendible,

and

we

will

add

more

target

networks

in

the

future.

In

addition,

we

plan

to

exploit

the

general

applicability

of

the

underlying

NATALIE

method

by

facilitating

the

identification

of

network

motifs

through

more

sophisticated

query

networks

where

nodes

are

labeled

by

GO

terms

and

edges

are

labeled

by

different

interaction

types,

such

as

inhibition

and

activation.

To

adequately

model

complex

behavior

of

biological

systems

one

needs

to

take

molecular

interactions

into

account.

These

interactions

are

captured

by

various

types

of

biological

networks

such

as

metabolic,

gene-regulatory,

signal

transduction

and

protein-protein

interaction

(PPI)

networks.

Recent

advances

in

technological

developments

and

computational

methods

have

resulted

in

large

amounts

of

network

data.

For

instance,

STRING

a

database

of

experimentally

verified

and

computationally

predicted

protein

interactions,

grew

from

proteins

in

organisms

in

to

proteins

in

organisms

in

January

However,

the

development

of

solid

methods

for

analyzing

network

data

is

lagging

behind,

particularly

in

the

field

of

comparative

network

analysis.

Here,

one

wants

to

detect

commonalities

between

biological

networks

from

different

strains

or

species,

or

derived

from

different

conditions.

In

contrast

to

traditional

comparison

at

sequence

level,

topology-based

comparison

methods

explicitly

take

interactions

into

account

and

are

thus

more

suitable

to

compare

networks.

Subnetworks

with

shared

interactions

across

species

allow

for

improved

transfer

of

functional

annotations

from

one

species

to

the

other

by

using

more

information

than

sequence

alone

We

have

developed

NATALIEQ,

a

web

server

for

accurate

topology-based

protein-protein

interaction

network

queries.

It

provides

an

interface

to

the

general

network

alignment

method

NATALIE

which

is

fast

and

supports

various

scoring

schemes

taking

both

node-to-node

correspondences

and

network

topologies

into

account.

Briefly,

NATALIE

views

the

network

alignment

problem

as

a

generalization

of

the

well-studied

quadratic

assignment

problem

and

solves

it

using

techniques

from

integer

linear

programming..

Currently,

only

few

web

servers

for

comparative

network

analysis

exist.

The

PathBLAST

web

server

reports

exact

and

approximate

hits

in

a

target

PPI

network

for

a

user-defined

simple

query,

expressed

as

a

linear

path

of

up

to

five

proteins.

The

NetworkBLAST

web

server

finds

locally-conserved

protein

complexes

between

species-specific

PPI

networks.

NetAligner

a

recent

web

server,

allows

the

comparison

of

userdefined

networks

or

whole

interactomes

within

a

set

of

fixed

species

using

a

heuristic

network

alignment

with

no

guarantees

on

the

optimality

of

the

identified

solutions..

Our

contribution

is

twofold.

First,

NATALIEQ

employs

a

new

scoring

function

to

produce

high-quality

pairwise

alignments

between

a

user-specified

query

network

of

arbitrary

topology

and

interactomes

of

several

model

species

and

human.

The

score

of

an

alignment

is

primarily

based

on

the

number

of

conserved

interactions,

while

sequence

similarity

is

used

as

a

secondary,

subordinate

optimization

goal.

In

addition,

the

alignments

computed

by

the

underlying

NATALIE

algorithm

come

with

a

quality

guarantee

that

often

proves

their

optimality.

Second,

through

an

interactive

visualization

of

the

alignment,

the

user

can

quickly

get

an

overview

of

conserved

and

nonconserved

interactions

and

can

use

the

protein

descriptions

of

the

nodes

to

assess

the

alignment.

We

illustrate

a

usage

scenario

of

the

web

server

on

the

Wnt

signaling

pathway

and

demonstrate

that

NATALIEQ

is

able

to

predict

putative

interactions

that

are

not

detected

by

other

methods.

Network

alignment

algorithm

NATALIE,

the

alignment

method

of

NATALIEQ,

is

applicable

to

any

type

of

network

and

supports

any

additive

score

function

taking

both

node-to-node

correspondences

and

topology

into

account.

Here,

we

take

as

input

a

pair

of

PPI

networks

whose

nodes

and

edges

correspond

to

proteins

and

their

interactions.

Let

G

(V

E

and

G

(V

E

be

two

PPI

networks

whose

edges

have

a

confidence

value

above

a

user-defined

threshold

c

min

We

denote

by

E(v

v

the

E-value

of

proteins

v

V

and

v

V

obtained

by

an

all-against-all

sequence

alignment.

Typically,

G

is

a

smaller

query

network

such

as

a

specific

pathway

of

interest,

and

G

is

a

large

species-specific

PPI

network..

A

network

alignment

is

a

partial

injective

function

a

That

is,

every

node

v

V

is

related

to

at

most

one

node

v

V

with

E-value

E(v

v

below

a

pre-specified

cutoff

E

max

and

vice

versa.

We

score

the

topology

component

of

an

alignment

a

as

follows.

This

score

is

also

known

as

edge

correctness

and

denotes

the

fraction

of

edges

from

the

smaller

query

network

that

have

been

aligned.

The

problem

of

global

pairwise

network

alignment

is

to

find

the

highest-scoring

alignment..

Should

there

be

several

alignments

with

the

same

maximum

edge

correctness,

we

would

prefer

the

alignment

with

the

highest

overall

bit

score

as

obtained

by

an

allagainst-all

sequence

alignment.

We

achieve

this

in

the

following

way.

Let.

The

total

score

of

an

alignment

a

is

then.

That

is,

the

score

component

is

ensured

to

be

strictly

smaller

than

the

score

contribution

of

one

conserved

edge.

Therefore

ties

among

alignments

with

the

same

edge

correctness

are

broken

in

favor

of

those

with

the

highest

overall

bit

score..

We

use

NATALIE

to

compute

alignments

with

maximum

total

score.

A

specific

feature

of

NATALIE

is

that

any

identified

solution

comes

with

an

upper

bound

on

the

optimal

score

value.

In

the

NATALIEQ

setting

with

small

query

networks,

the

upper

bound

equals

the

score

of

the

alignment

found,

thereby

proving

its

optimality.

The

identified

alignment

is

not

necessarily

optimal

if

there

is

a

gap

between

the

score

and

the

upper

bound.

In

that

case

the

relative

size

of

the

gap

provides

a

bound

on

the

error

due

to

suboptimality.

In

a

recent

study

on

aligning

PPI

networks

of

six

different

species,

NATALIE

was

compared

to

state-of-the-art

network

alignment

methods,

evaluating

the

number

of

conserved

edges

as

well

as

functional

coherence

of

the

modules

in

terms

of

Gene

Ontology

annotation.

The

study

established

NATALIE

as

a

top

network

alignment

method

with

respect

to

both

alignment

quality

and

running

time.

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O


A Characterization of Concordance Relations The notion of concordance is central to many multiple criteria techniques relying on ordinal information, e.g. outranking methods. It leads to compare alternatives by pairs on the basis of a comparison of coalitions of attributes in terms of "importance". This paper proposes a characterization of the binary relations that can be obtained using such comparisons, within a general framework for conjoint measurement that allows for intransitive preferences. We show that such relations are mainly characterized by the very rough differentiation of preference differences that they induce on each attribute. A classical problem in the field of decision analysis with multiple attributes is to build a preference relation on a set of multi-attributed alternatives on the basis of preferences expressed on each attribute and "inter-attribute" information such as weights. The classical way to do so is to build a value function that aggregates into a real number the evaluations of each alternative on the set of attributes (see French, 1993; Keeney and Raiffa, 1976) . The construction of such a value function requires a detailed analysis of the tradeoffs between the various attributes. When such an analysis appears difficult, one may resort to techniques for comparing alternatives that have a more ordinal character. Several such techniques, the so-called outranking methods, were proposed by B. Roy (for presentations in English, see Bouyssou, 2001; Roy, 1991 Roy, , 1996 Vincke, 1992 Vincke, , 1999 . Most outranking methods use the notion of concordance. It leads to compare alternatives by pairs on the basis of a comparison of coalitions of attributes in terms of "importance". Such pairwise comparisons do not lead to preference relations having nice transitivity properties (Bouyssou, 1996) . These relations, henceforth called concordance relations, are therefore quite distinct from the transitive structures usually dealt with in conjoint measurement (Krantz, Luce, Suppes, and Tversky, 1971; Roberts, 1979; Wakker, 1989) .. The aim of this paper is to propose a characterization of concordance relations within a general framework for conjoint measurement allowing for incomplete and/or intransitive relations that was introduced in Bouyssou and Pirlot (2002b) . It will turn out that, within this framework, the main distinctive feature of concordance relations is the very rough differentiation of preference differences that they induce on each attribute. Our results extend to the case of-possibly incomplete-reflexive preference relations (interpreted as "at least as good as" relations), the results proposed in Bouyssou and Pirlot (2002a,c) for asymmetric relations (interpreted as "strict preference"). Pirlot (1997) proposes an alternative approach to the analysis of concordance relations that is not based on a conjoint measurement model. The paper is organized as follows. Section 2 introduces our main definitions and notation. Concordance relations are defined and illustrated in section 3. Section 4 characterizes concordance relations within our general framework for conjoint measurement. A final section compares our results with other approaches to concordance relations and presents directions for future research. All proofs are relegated in appendix.

O O O B O O O O B O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O


Cloud Data Management for Online Games: Potentials and Open Issues The number of players, for massively multiplayer online role-playing games (MMORPG), typically reaches millions of people, geographically distributed throughout the world. Worldwide revenues for these games increase by billions of dollars each year. Unfortunately, their complex architecture makes them hard to maintain, resulting in considerable costs and development risks. For normal operation, MMORPGs have to access huge amounts of diverse data. With increasing numbers of players, managing growing volumes of data in a relational database becomes a big challenge, which cannot be overcome by simply adding new servers. Cloud storage systems are emerging solutions focusing on providing scalability and high performance for Cloud applications, social media, etc. However, Cloud storage systems are in general not designed for processing transactions or providing high levels of consistency. In this paper, we present our current work-in-progress by analyzing the existing architecture of MMORPGs and classifying relevant data. Based on this, we highlight the design requirements, identify the major research challenges, and propose a CloudThis is an extended version of the paper "Cloud Data Management for A massively multiplayer online game (MMOG) can support hundreds or thousands of players from all over the world in parallel. In a virtual game world, players can choose a new identity, establish a new social network, compete or cooperate with other players, or even realize dreams that cannot be fulfilled in real life. It is estimated that, in 2011, the Americans spent 26 million hours and 2.6 billion dollars in total for playing MMOGs [15] . The game industry is developing rapidly, and the global MMOG market volume has grown to 13 billion dollars in 2012 [16] .. What differs between MMORPGs and other MMOGs, such as first-person shooters and real-time strategy games, is that the virtual world environment of MMORPGs runs even in the case of no players. In addition to the account information, the state data of objects and characters must be recorded on the server side in real-time, so that players can quit and continue the game at any time. Player behavior in the game should be monitored and backed up in order to maintain the order of the virtual world. Furthermore, MMORPGs usually have more concurrent players than any other MMOGs, (for example, World of Warcraft has millions of concurrent players), which also exacerbates the burden of managing data. A qualified database system for data persistence in MMORPGs must guarantee the data consistency and also be efficient and scalable [2] . However, existing RDBMS cannot fully satisfy all these requirements [7, 11] . Therefore, with an increasing data volume, the storage system becomes a bottleneck, and solving scalability and availability issues becomes a major cost factor and a development risk.. Cloud storage systems, which have the ability to support highly concurrent data accesses and huge storage, may become a solution. Unfortunately, contrary to conventional DBMSs, cloud systems are generally designed for Web applications that have different access characteristics and require lower or different consistency levels. Therefore, MMORPGs need to be analyzed in more detail to assess the usability of Cloud storage systems, open issues, and possible solutions for them.. The approach presented in this paper is work in progress. We are currently focusing on architectural issues, conceptual work on fulfilling specific data management requirements, and the creation of a testbed implementing these concepts for further evaluation. The core ideas were presented at the DMC workshop 2013. This paper extends the workshop contribution by -descriptions of more details on the current state of the testbed implementation, including lessons learned up to this stage of the project, -a more thorough discussion of problems and open issues of using Cloud storage systems for MMORPGs, and -a more fine-grained outline and discussion of our future research directions, which will most of all focus on evaluating critical aspects for this type of application, e.g., scalability and consistency.. The rest of the paper is structured as follows. In Sect. 2, we analyze the existing architecture and data management of MMORPGs. In Sect. 3, we classify data of MMORPGs into four groups and discuss their requirements. In Sects. 4 and 5, we propose a Cloud-based data management model for MMORPG, and, finally, conclude and summarize this paper in Sect. 6. In this paper, we proposed a Cloud-based data management for MMORPGs to improve system performance and scalability. We outlined the main criteria for this proposal, by classifying and describing the typical requirements of MMORPGs. The proposed model adopts a shared-nothing architecture to ensure scalability and customizes a set of data management solutions, so that the new system can operate efficiently. We classified data into four groups and found out that only state and log data should be recorded in different ways in the Cloud storage system and managed. For example, the model promises a strong consistency guarantee to state data, but only an eventual consistency guarantee to log data. To further improve the system's performance, we also proposed some solutions to reduce the number and size of messages. In our future work, we will design and carry out a series of experiments on our testbed in order to evaluate performance and scalability of it. Evaluation of performance while using different databases (e.g., MySQL, HBase, and Cassandra) in MMORPGs is also in our plan.

B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O


Flipping Cubical Meshes Abstract. We define and examine flip operations for quadrilateral and hexahedral meshes, similar to the flipping transformations previously used in triangular and tetrahedral mesh generation. In this paper we propose flipping transformations for cubical meshes, such as quad and hex meshes. These flipping transformations are precise analogues of the well-known flipping transformations for simplicial meshes. We did not invent these transformations. They have already appeared-although they were not explicitly enumerated-in the mathematical polytope literature [1, 25] . We have not found any previous references in the meshing literature. The closest work seems to be the quad refinement operations of Canann, Muthukrishnan, and Phillips [4] and the hex reconnection primitives of Knupp and Mitchell [12] .. As in the simplicial case, quad and hex flipping could be useful for mesh generation, improvement, and refinement/derefinement. We also use flipping to study existence questions for hexahedral meshes. Up until this present paper, the theoretical results on quad-to-hex extension have been positive, suggesting that (under simple necessary conditions) it should always be possible. Mitchell [17] and Thurston [22] independently showed that any quad surface mesh with an even number of quadrilaterals, and topologically equivalent to a sphere, can be extended to a topological hex volume mesh, which allows elements to have warped, nonplanar sides. (An even number of surface quadrilaterals is a necessary condition, because hex elements each have six sides, and each interior face will account for two hex sides.) Eppstein [6] gave another proof of this result and improved the complexity bound from O(n 2 ) to O(n) elements. Each of the two proofs generalize to certain more topologically complicated inputs as well. However, it has not been clear under what circumstances these topological meshes can be realized using flat-faced cuboids. In this context we discuss a simple 10-element boundary complex, the bicuboid, which we have been unable to mesh, and use flipping to show that many possible topological meshes for this complex are unrealizable. This is primarily a theoretical paper-realizability is not of great practical importance since the isoparametric trilinear elements typically used with unstructured hexahedral meshes allow the cells to have curved boundaries. However, the existence or nonexistence of flat-faced meshes may shed light on the question of how many curved elements are needed to achieve satisfactory mesh quality. In addition, our study of flip graph connectivity relates to mesh improvement algorithms: by characterizing the connected components of the quadrilateral flip graph for simply-connected regions (Theorem 5) we show that any local improvement operation that remeshes bounded regions of a quadrilateral mesh can be simulated by a sequence of simpler flip and parity-change operations. In this paper, we have introduced (or at least popularized) flipping moves for quad and hex meshes. We have also considered the conditions under which flips are realizable, tested the connectivity of the flip graph, and used flipping to show some evidence that not all quad surface meshes can be extended to hex volume meshes.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Mean field variational Bayesian inference for nonparametric regression with measurement error A fast mean field variational Bayes (MFVB) approach to nonparametric regression when the predictors are subject to classical measurement error is investigated. It is shown that the use of such technology to the measurement error setting achieves reasonable accuracy. In tandem with the methodological development, a customized Markov chain Monte Carlo method is developed to facilitate the evaluation of accuracy of the MFVB method. Flexible regression where the predictors are subject to measurement error continues to be an active area of research in the 2000s (Mallick et al., 2002; Liang et al., 2003; Carroll et al., 2004; Ganguli et al., 2005; Carroll et al., 2008) and is likely to be so in the 2010s. Carroll et al. (2006) offers a recent and comprehensive summary of the area.. Fitting and inference in such models is notoriously challenging. Berry et al. (2002) devised an elegant hierarchical Bayes approach to the simplest version of the problem and described Markov chain Monte Carlo (MCMC) based inference. Extensions have been considered by Carroll et al. (2004) and Ganguli et al. (2005) . However, inference based on MCMC can be very slow for such models and may take hours if using BUGS (Lunn et al., 2000) .. In this paper we investigate a faster mean field variational Bayes (MFVB) alternative to the problem. For an introduction to such techniques see Bishop (2006) , Ormerod and Wand (2010) or Wand et al. (2011) . We show that the transference of such technology to the measurement error setting achieves reasonable accuracy while being hundreds of times faster than MCMC. MFVB approximations to nonparametric regression problems with measurement error in the predictors is challenging due to spline basis functions entering the approximate posterior densities of the unobserved predictor. A streamlined discretization of these approximate posterior densities on a grid across the domain of the predictor is utilized to achieve computational efficiency.. In tandem with the methodological development a customized MCMC is developed to facilitate the evaluation of accuracy of the MFVB method. Both MCMC and MFVB are straightforward for all components of nonparametric regression measurement error model, with the exception of the unobserved predictors. Approximate sampling from the full conditionals for the unobserved predictors can be performed efficiently using griddy-Gibbs sampling steps (Ritter and Tanner, 1992) . Note that our MCMC and MFVB methods use an analogous approximation to the posterior distributions of the unobserved predictors.. After a brief introduction to MFVB methods (Section 2) we will develop these methods from the simplest case, simple linear regression (Section 3), and then extend these ideas to the more complex case of nonparametric regression with measurement error (Section 4) which could lay the foundation for more elaborate models such as additive models (see for example, Richardson and Green, 2002; Ganguli et al., 2005) . The methodology will be illustrated using a mix of simulated and real world examples (Section 5) and conclusions will be drawn (Section 6). In this paper we derived efficient MFVB and MCMC approaches to nonparametric regression problems with classical measurement error. The MFVB approach was shown to have reasonable accuracy with great improvements of speed over the MCMC approach. For the cases involving simple linear regression reasonable coverage probabilities were observed. For the nonparametric regression examples, using the MFVB approach, the estimated mean function approximated the true posterior mean with high accuracy and the 95% credible sets for the mean were only slightly underestimated.. Several simple extensions and a few less simple extensions can be envisaged. Simple extensions include binary response via the probit link, Berkson measurement error, semiparametric regression, non-Gaussian x i models and models where repeated w i observations are available to estimate σ 2 v . Extensions for various types of non-Gaussian response are also possible due to Wand et al. (2011) , however it is anticipated that these extensions of the techniques presented here would require a reasonable amount of modification.. Finally, the methods presented here are streamlined for one-dimensional spline models. We anticipate that our grid-based MFVB and MCMC approaches would be ex Figure 6: Diagnostic plots for MCMC analysis along with MFVB posterior density estimates for a random sample of x i s (corresponding to indices: 10, 27, 79, 85 and 96). The columns are: missing predictor, trace plot of MCMC sample, plot of sample against 1-lagged sample, sample autocorrelation function, kernel estimates posterior density and basic numerical summaries. For the density column MCMC estimates appear as solid black lines whereas MFVB estimates appear as dashed lines.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Marble track music sequencers for children We discuss the development of electronically enhanced construction kits for marble tracks that act as music sequencers for music education of young children. Marbles rolling along the tracks trigger sound events to produce the playback of musical notes and songs. Re-arrangeable tangible elements correspond to timing, durations and pitches of notes and thus allow electronic music sequencing by means of haptic programming. This approach to early music education separates the different tasks of melody making and instrument manipulation, aiming at faster success and fewer frustrating learning experiences. We present two marble track designs: an earlier one, which is based on a central synthesizer and a system-wide communication bus and a second one based on multiple decentralized synthesizer units. We discuss previous work in the field of machine-readable models and haptic programming and report on experiences with both sequencer designs including user tests of the second design. Marbles rolling through three-dimensional structures allow the translation of physical configurations into sequenced temporal events in a way that is comprehensible to children and beneficial to their music learning experiences. Systems of this kind seem appropriate in the context of primary school and possibly also pre-school children's music education. Both the concept of marble tracks and that of construction kits relate directly to children's active play experience and are understood intuitively. Offering children physical music sequencers appears helpful in separating learning acts related to instrument manipulation and to melody making. This can be helpful where the simultaneous learning of both acts is experienced as frustrating. Marble track sequencers seem appropriate to support active acquisition of theoretical concepts of music. We have presented two alternative technical systems for this purpose, which are based on opposite systemarchitectural concepts, each with its specific advantages and disadvantages. With respect to mechanical execution, fully open tracks that allow (multiple) marble placement and pick-up at arbitrary points, appear highly appropriate to allow much richer experiences including simultaneous interaction by multiple users and the partial playback of compositions and canons but pose challenges regarding the linearization of playback speed. Learning to play music involves multiple simultaneous learning processes. At the very least, it requires a learner to apprehend the process of memorizing or reading and reproducing a given piece of music as well as the motor and co-ordination skills required to manipulate an instrument at an appropriate speed. Assuming that doing things separately rather than simultaneously is generally less difficult, we propose to deal with these different learning activities separately to hopefully allow easier and less frustrating early music learning experiences. With the exception of the most intuitive instruments such as the singing voice, the skills required for instrument manipulation pose a major stumbling block to the musical novice.. Programmable music sequencers potentially avoid this problem -but only for those who fathom their interfaces and interaction logic. In some step sequencer designs, musical events are programmed using spatial arrangements in two dimensions (figure 1). Nevertheless, sequencer technology available today is typically not accessible to young learners, for whom the understanding of abstract qualities and formal notations of music might be particularly challenging.

B I O B O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Incompressible SPH method based on Rankine source solution for violent water wave simulation . Incompressible SPH method based on Rankine source solution for violent water wave simulation. Journal of Computational Physics, 276, pp. 291-314. doi: 10.1016Physics, 276, pp. 291-314. doi: 10. /j.jcp.2014 This is the accepted version of the paper.This version of the publication may differ from the final published version. PermanentWith wide applications, the smoothed particle hydrodynamics method (abbreviated as SPH) has become an important numerical tool for solving complex flows, in particular those with a rapidly moving free surface. For such problems, the incompressible Smoothed Particle Hydrodynamics (ISPH) has been shown to yield better and more stable pressure time histories than the traditional SPH by many papers in literature. However, the existing ISPH method directly approximates the second order derivatives of the functions to be solved by using the Poisson equation. The order of accuracy of the method becomes low, especially when particles are distributed in a disorderly manner, which generally happens for modelling violent water waves. This paper introduces a new formulation using the Rankine source solution. In the new approach to the ISPH, the Poisson equation is first transformed into another form that does not include any derivative of the functions to be solved, and as a result, does not need to numerically approximate derivatives. The advantage of the new approach without need of numerical approximation of derivatives is obvious, potentially leading to a more robust numerical method. The newly formulated method is tested by simulating various water waves, and its convergent behaviours are numerically studied in this paper. Its results are compared with experimental data in some cases and reasonably good agreement is achieved. More importantly, numerical results clearly show that the newly developed method does need less number of particles and so less computational costs to achieve the similar level of accuracy, or to produce more accurate results with the same number of particles compared with the traditional SPH and existing ISPH when it is applied to modelling water waves. Smoothed Particle Hydrodynamics (SPH) is a Lagrangian meshless particle method. It was originally developed to simulate astrodynamics [1, 2] but has been extended to model dynamics problems with violent motions in many areas . The work on the method has been continuously reviewed by many authors. We mainly take some of these related to water wave modelling here.. When the SPH is applied to modelling water waves, there are largely two different formulations in literature. The first one is weakly compressible SPH (WCSPH, also called traditional SPH in this paper), in which water is considered as slightly compressible and its pressure is related to its density through an equation of state with artificially specified sound speed [6] . The second formulation is incompressible SPH, also called ISPH, in which water is considered as incompressible and having constant density with pressure found by solving a boundary value problem. As indicated by many researchers, e.g., Rafiee et al. [13] and Lee et al. [22] , the WCSPH has several advantages, such as that it is easy to be programmed and does not need to solve pressure boundary value problem. However, it has at least two weaknesses [13, 22] : (a) requiring use of very small time steps and (b) resulting in significant spurious pressure fluctuations in space and time domain. The first one is inherent because the sound speed in the equation of state has to be large enough (even though much. where U is the fluid density, u is the fluid velocity, t is the time, p is the fluid pressure, g is the gravitational acceleration, and Q is the kinematic viscosity. In WCSPH, the pressure and density are usually related by the following equation of state for sound waves where h is equal to 1.8s (s is the initial distance between particles) in this paper and D d is taken as 15/(7*S*h 2 ) in 2D cases. Using Eq. (4), the velocity and density of each particle may be updated by the following equations: (8) where the artificial viscosity is given by 2 (8) is not the exact physical viscous term, instead, it is artificial in some sense but can lead to more stable results than the viscous force directly estimated by derivatives of velocity. Other artificial viscosity models are available [31] but will not be discussed here as they are not focus of this paper. Although the positions of particles may be found by directly integrating velocity, Monaghan [6] This paper introduced a new incompressible SPH based on Rankine source solution (abbreviated as ISPH_R) to simulate 2D violent waves. The method adopts the Rankine solution to decrease the order of the derivatives in the Poisson equation defining the boundary value problem for pressure. The transformed Poisson equation does not include any derivative of the functions to be solved. Using the new formulation, one just needs to approximate the functions themselves during discretisation, instead of approximating their second order derivatives as in the existing incompressible SPH. In the cases of dam-breaking and sloshing wave problems, the results of the ISPH_R method are in good agreement with available experimental data and analytical solution. By comparing its results with those from traditional SPH and different forms of existing ISPH, the ISPH_R method presented in this paper is demonstrated to need a smaller number of particles and less computational costs to achieve the similar level of accuracy, or to produce more accurate results with the same number of particles for the water wave problems. Although the conclusion is obtained by using the limited cases studied in this paper, it is expected to be hold for general water wave cases where the pressure is more dominant than viscous stresses. That is because the feature of no derivatives in governing equation for pressure adopted in this paper is applied for all the water wave cases. This method can be extended to three dimensional problems, which will be presented in future. In future, comparative studies may also be carried out in order to find out if combining the particle shifting technique with the new technique for solving the pressure Poisson introduced in the paper may give better results, and if the other free surface identification technique (e.g. the one based on the free surface divergence criteria, [21] ) other than the one employed in this paper would be more accurate. In addition, an adaptive time-step technique should be adopted and the viscosity caused by turbulence should be considered.

O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


An Evolutionary Approach to Realism-Based Adverse Event Representations The above mentioned manuscript which you submitted to the special topic "MIE 2009" of Methods of Information in Medicine has been reviewed. We would like to congratulate you that it has been selected for the special topic on MIE2009 after successful revision. The comments of the reviewers are included at the bottom of this letter. The reviewers have suggested "minor revision" of your paper. Backed by good evidence (broad range of contradicting and shallow authoritative definitions), the authors advocate the need of standardized adverse event definitions. To this end they propose an ontological framework and provide anecdotic evidence that this framework may be appropriate. Although there is no evaluation of the usability of the framework, this is reportable and important work, and the paper should be accepted with some modifications (see below).[R1] One concern of the reviewer refers to the distinction between universals and defined classes, which brings in additional complexity without any obvious benefit for the construction, maintenance, and use of the ontology. More convincing arguments why this distinction is necessary would be appreciated.We added a paragraph clarifying the distinction and the relevance 1 Editor's and reviewers' comments are printed in bold blue. Authors' responses in black.

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Higher-Order Genomic Organization of Cellular Functions in Yeast Previous studies have shown that the distribution of genes in prokaryotes and eukaryotic genomes is not random. Using the thousands of cellular functions that appear in the Gene Ontology (GO) project, we exhaustively studied the relation between functionality and genomic localization of genes across 16 organisms with rich GO ontologies (one prokaryote and 15 eukaryotes). Overall, we found that the genomic distribution of cellular functions tends to be more similar in organisms that have higher evolutionary proximity. At the primary level, which measures localization of functionally related genes, the prokaryote Escherichia coli exhibits the highest level of organization, as one would expect given its operon-based genomic organization. However, examining a higher level of genomic organization by analyzing the co-localization of pairs of different functional gene groups, we surprisingly find that the eukaryote yeast Saccharomyces cerevisiae is markedly more organized than E. coli. A network-based analysis further supports this notion and suggests that the eukaryotic genomic architecture is more organized than previously thought. See online Supplementary Material at www.liebertonline.com. One of the interesting results reported in this work is that, when considering co-localization of GO group pairs, E. coli seems less organized than S. cerevisiae. This result is surprising as in general prokaryotes are considered to be more organized than eukaryotes. Indeed, Figure 3A shows that most GO groups in E. coli exhibit a high level of order, reflecting the existence of operons in prokaryotic genomes. We term this level of gene organization the primary level. However, at a secondary level of organization (in which the relative organization between GO groups is measured), E. coli exhibits only low level of order (Fig. 4A) . In contrast, S. cerevisiae has no operons and a much lower primary level of functional organization; however, at the secondary level, the yeast genome demonstrates a high degree of co-localization between related GO groups. The differences in functional gene architecture can be clearly seen in the density and distribution of closely located, functionally related GO groups (Fig. 4B) . Our results thus uncover, at this secondary level, a high degree of organization of the eukaryotic genome that has not been explored to date.. Our observations imply an important distinction between prokaryotes and eukaryotic cells: in the first, gene regulation occurs mainly at the operon level, with each operon acting as an independent unit. Operons that affect related processes do not tend to map in the vicinity of each other. In contrast, yeast cells, which lack polycistronic units, show a low level of gene clustering, but gene distribution tends to be such that related functions co-localize. Potentially, such co-localized clusters may share particular chromosomal or nuclear domains, facilitating coordinated expression of many genes acting in related functions.. Interestingly, the human genome has a primary level of organization that is quite similar in its magnitude to that of the yeast (Fig. 3A) . Characterizing the secondary level of organization of human and other available organisms is an interesting challenge for future related research. This may be used to answer questions about the systemic links between different levels of organization, and for understanding the properties that correlate with co-localization. Further, a natural generalization of our approach is to recursively define and study even higher levels of organization (third level, fourth level, and so on).. In summary, eukaryote genomes are more organized than was previously believed, and more careful studies tracking down their higher-order organization are called for.

O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O