Classification of human motion based on affective state descriptors Human body movements and postures carry emotion-specific information. On the basis of this motivation, the objective of this study is to analyze this information in the spatial and temporal structure of the motion capture data and extract features that are indicative of certain emotions in terms of affective state descriptors. Our contribution comprises identifying the directly or indirectly related descriptors to emotion classification in human motion and conducting a comprehensive analysis of these descriptors (features) that fall into three different categories: posture descriptors, dynamic descriptors, and frequency-based descriptors in order to measure their performance with respect to predicting the affective state of an input motion. The classification results demonstrate that no single category is sufficient by itself; the best prediction performance is achieved when all categories are combined. Much of the difficulty in creating character animation is to give the character representation of expression or personality. Even though, in traditional animation, animators can achieve this challenge in a time-consuming key-framing processes, the spreading availability of motion capture technology and large amount of human motion databases have increased the importance of reusability of motion capture data in human animation based on the analysis and understanding of human motion. It is well known that the personality of a character is conveyed through emotions, and human motion carries important visual cues associated with emotions. On the basis of this motivation, the objective of this study is to analyze these cues from spatial and temporal structure of the motion capture data and classify human motions according to one of their stylistic variations-emotion or the energy level of the actor (low energy interpreted as sad, relaxed; high energy as happy, angry).. The term emotion is sometimes used in reference to the emotional state and is also called the affective state, which † Supporting information may be found in the online version of this article.. has been studied in detail in emotion research and affective science. Affective science is mainly concerned with the questions of which features of human motion are important for emotional communication and how human beings use them in order to distinguish one emotion from another. Dimensional models of emotion or affective state can lie in two or three dimensions. Several two-dimensional models of emotion have been developed. In our study, among these models, we apply the circumplex model of emotion that was first developed by James Russell [1] . Figure 1 shows the circumplex model of affective states. In this context, we will use the terms emotion and affective state interchangeably.. Although it has been debated that the specific body movements may be only indicative of the intensity of the emotion, our study presents an affective state analysis and classification of human motion capture data, which attempts to demonstrate that emotion-specific cues in body movements and postures are indicative of certain emotions. Our main contributions comprise defining and categorizing the affective state descriptors that are mostly adapted from the past and current approaches in affective science and attempting to identify the directly or indirectly related descriptors to emotion classification in human motion. We have investigated three categories of descriptors-posturebased, dynamic, and frequency-based-and measured their performance with respect to predicting the affective state of the input motion. The results indicate that no single category is sufficient to predict the emotional state by itself, and the best prediction performance is achieved when all the categories are combined. On the basis of the motivation that the style of an actor is conveyed through emotion, the study presented here is an attempt to address the question whether there are emotionspecific indicators in human motion and whether they can be extracted and quantified into some measures in terms of affective state descriptors. We conducted a comprehensive analysis of these descriptors (features) that fall into three different categories-posture descriptors, dynamic descriptors, and frequency-based descriptors, and compared their prediction accuracy. According to the experimental results, the most accurate results are achieved when all the descriptors are combined. With a limited set of emotional states (sad, happy, angry, and relaxed) that represent each quadrant of Russell's circumplex model, the prediction accuracy was as high as 91%.. There are several limitations of the system, which we plan to address in the future work. First, we selected only four representative emotional states from the more comprehensive set of emotions, and the method would be beneficial with more states. Secondly, although our classifier was accurate, it would be necessary to compare its performance with the classification answers by real users. Third, we have investigated the walking motion, for a more accurate unification of the training set; however, more general types of motions need to be investigated. Furthermore, it would be beneficial to investigate other types of affective descriptors, particularly semantic ones. Finally, future work needs to address motion synthesis with the use of the learned affective state descriptors. In this section, the experimental results for several combinations of features that are explained in Section 3 are analyzed in terms of their classification accuracy. We designed seven experiments to evaluate the accuracy of features. In the first three experiments, the frequency-based feature set F , posture feature set P and dynamic feature set D were tested separately. For the following three experiments, these feature sets were paired together in a vector form of OEF ; P , OEF ; D and OEP ; D. In the seventh and last experiment, all features were combined together resulting in a vector OEF ; P ; D.. For the evaluation of the experiments, confusion matrices [20] are presented that contain information about actual and predicted classification carried out in the classification process. They are also used for evaluating the performance of the classification. Table 1 reports the confusion matrix for each experimental condition. In each section of the table, the values on the diagonal represent the correct recognition rates, whereas the remaining entries gives the rates of incorrect hits (classifications). Figure 4 shows the results of the style-based classification of human motion graphically. The overall success of the system is 91% where the feature vector is the combination of three feature sets OEF ; P ; D used in experiment 7. The experiment results show that the choice of features has a significant impact on the accuracy of prediction.. Because determining the best kernel to use in classification is important, we also compared the classification results of the linear kernel to the RBF kernel. Figure 5 shows that the RBF kernel yields better prediction results than the linear kernel for classification. However, a careful parameter search of the kernel parameters was necessary. Especially, for the RBF kernel parameters, we used C D 1000 and D 0:0001. The motion recognition and classification problem draws interest in a variety of major disciplines including robotics, computer animation, and psychology. It comprises identifying two different types of human actions-primary and secondary themes [2] . Whereas primary themes represent basic human actions such as walking, running, or jumping, secondary themes consider the stylistic variations in the motion caused by the actor's emotion, gender, or age. Our approach falls into the second type of classification with respect to variations caused by different actors.. A large body of work exists for the recognition and classification of human action based on the primary theme. These approaches aim the recognition of primary human actions such as walking, running, kicking, and punching and classifying them according to their spatial and temporal structure. Among them, a considerable amount of research focus on the recognition of human actions from video images [3, 4] . An alternative to detecting actions from video sequences is a data-driven approach, using available 3D motion capture technology [5, 6] .. In these works, the motion recognition and classification methods have been based on primary human actions regardless of the actor's style. Our goal is to analyze and classify human motions associated with specific emotions, which falls into the secondary theme. Related to our work, a number of studies target the classification of the emotional state in human motion. Troje has applied [7] Principal Component Analysis (PCA) in order to learn lower-dimensional representations of human walking in different emotional states. There have also been studies detecting emotion from static body posture descriptors. Coulson has conducted experiments [8] where observers classified the emotions from static images. Another important descriptor for the recognition of emotion in motion is kinematical information, such as velocity, acceleration, or jerk. Wallbot [9] found that spatio-temporal features of body movements, rather than static body postures are more indicative of differences between emotions and changes in energy states. Atkinson et al. [10] investigated the exaggeration of whole body movements and found that exaggeration of body movements would increase the emotional intensity and enhance emotion recognition. In another work, Kapur et al. [11] used dynamics of motion features and analyzed the performance of different classifiers including neural networks and support vector machines (SVMs). Although classifiers achieved a correct recognition rate of 84%, in the user study, humans were able to classify the motions with 93% of accuracy.. Although the main aim of this study is the classification of human action and variations, there are also publications that study how to synthesize human motion by understanding and computationally modeling them. Rose et al. present the Verbs-and-Adverbs approach for interpolating styles for motion synthesis [12] . Grochow et al. use the Scaled Gaussian Process Latent Variable Model [13] , which make use of a training set to capture the style of the actor for motion synthesis and inverse kinematics.

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


On the dual of (non)-weakly regular bent functions and self-dual bent functions Abstract. For weakly regular bent functions in odd characteristic the dual function is also bent. We analyse a recently introduced construction of nonweakly regular bent functions and show conditions under which their dual is bent as well. This leads to the definition of the class of dual-bent functions containing the class of weakly regular bent functions as a proper subclass. We analyse self-duality for bent functions in odd characteristic, and characterize quadratic self-dual bent functions. We construct non-weakly regular bent functions with and without a bent dual, and bent functions with a dual bent function of a different algebraic degree. For a prime p, let f be a function from F n p to F p . The Fourier transform of f is then defined to be the complex valued function f on F n p f (b) = x∈F n p f (x)−b·x p where p = e 2πi/p and b · x denotes the conventional dot product in F n p . The Fourier spectrum of f is the set of all values of f . We remark that one can equivalently consider functions from an arbitrary n-dimensional vector space over F p to F p , and substitute the dot product with any (non-degenerate) inner product. Frequently the finite field F p n with the inner product Tr n (bx) is used, where Tr n (z) denotes the absolute trace of z ∈ F p n .. The function f is called a bent function if | f (b)| 2 = p n for all b ∈ F n p . The normalized Fourier coefficient of a bent function f at b ∈ F n p is defined by p −n/2 f (b). For p = 2 bent functions can only exist when n is even, the normalized Fourier coefficients are obviously ±1. For p > 2 bent functions exist for both, n even and n odd. For the normalized Fourier coefficients we always have (cf. [11] When p = 2, a bent function is trivially regular, and as can be seen from (1), for p > 2 a regular bent function can only exist for even n and for odd n when p ≡ 1 mod 4.. A function f : F n p → F p is called weakly regular if, for all b ∈ F n p , we have. for some complex number ζ with |ζ| = 1, otherwise it is called non-weakly regular. By (1) , ζ can only be ±1 or ±i. Note that regular implies weakly regular.. A function f : F Only the condition n even (odd) has to be changed to n + 1 even (odd), and f * is now a function from supp( f ) to F p . The notion of weak regularity is then also meaningful for near-bent functions.. Weakly regular bent functions always appear in pairs, since the weak regularity of f guarantees that the function f * which is called the dual of f is also (weakly regular) bent (see also [11] ): For y ∈ F n p we get. and therefore f * is weakly regular bent. Furthermore we have f * * (x) = f (−x) -if p = 2 forming the dual is an involution -and f * * * * (x) = f (x). A weakly regular bent function f is called self-dual if f * = f . We observe that self-dual bent functions must satisfy f (x) = f (−x). A weakly regular bent function f is called anti-self-dual if f * = f + e for a constant e ∈ F bent as well. Until now the dual of a bent function has only been defined for weakly regular bent functions. This motivates the definition of a new class of bent functions, the class of bent functions for which the dual is also bent. In Section 3 we describe the duality properties of quadratic bent functions, and we completely characterize self-dual bent functions in odd characteristic. In Section 4 we give a general construction of self-dual non-quadratic bent functions in characteristic p ≡ 1 mod 4, and some results on self-dual bent functions for p ≡ 3 mod 4. In Section 5 we use our results and construct examples of bent functions and their duals with some interesting properties, amongst others non-weakly regular bent functions for which the dual is also bent, and self-dual bent functions.

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 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 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 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 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 O O O O O O O O O O O O O O O O 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


Manifold regularized kernel logistic regression for web image annotation With the rapid advance of Internet technology and smart devices, users often need to manage large amounts of multimedia information using smart devices, such as personal image and video accessing and browsing. These requirements heavily rely on the success of image (video) annotation, and thus large scale image annotation through innovative machine learning methods has attracted intensive attention in recent years. One representative work is support vector machine (SVM). Although it works well in binary classification, SVM has a non-smooth loss function and can not naturally cover multi-class case. In this paper, we propose manifold regularized kernel logistic regression (KLR) for web image annotation. Compared to SVM, KLR has the following advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; and (3) the KLR can naturally be generalized to the multi-class case. We carefully conduct experiments on MIR FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation. Today, smart devices e.g. smart phone, table PC which equipped with a digital camera have become more and more popular, and people can easily produce millions or even billions of multimedia information such as personal photos or videos. However, it is not convenient to effectively manage the photos or videos at the semantic level, and therefore large scale image/video annotation through innovative machine learning methods has attracted intensive attention in recent years and been successfully deployed for many practical applications in multimedia, computer vision and image processing [14] [16] [17] 0.. There are a number of machine leaning algorithms have been employed for image annotation. One of the representative methods is support vector machine (SVM) that tries to find a separating hyperplane to maximize the margin between two classes. [12] SVM usually minimizes a hinge loss to train the maximum-margin classifier.. Although hinge loss is a convex function, it is not differentiable and can not naturally be generalized to multi-class cases [15] .. On the other hand, it is a very expensive labor to label a large number of images to learn a robust model for image annotation. Then semi-supervised learning (SSL) has been employed for semi-automatic image annotation [7] [8] . SSL can improve the generalization ability with only a small number of labeled images by exploiting the intrinsic structure of all the training samples including labeled and unlabelled images [1] . The most traditional class of SSL methods is manifold regularization that tries to explore the geometry of intrinsic data probability distribution by penalizing the objective function along the potential manifold [1] [4] .. Considering the above analysis, in this paper, we replace hinge loss in SVM with logistic loss and propose manifold regularized kernel logistic regression (KLR) for web image annotation. Particularly, we employ the representative Laplacian graph to exploit the geometry of the underlying manifold. Compared to SVM, manifold regularized KLR has the following immediate advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; (3) the KLR can naturally be generalized to the multi-class case; and (4) Laplacian regularization can well utilize the intrinsic structure of the data distribution. This paper studies manifold regularized kernel logistic regression (KLR) for web image annotation. Technically, we develop Laplacian regularized kernel logistic regression and implement image annotation task on MIR Flickr dataset. Compared to the representative SVM classifier, the KLR has a smooth loss function and produces an explicit estimate of the probability instead of class label. The carefully conducted experiments demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation.. In the future, we will apply the proposed Laplacian regularized kernel logistic regression to other applications. We will also further extend the proposed method to other manifold regularizations and explore the relation of the different regularizations. In recent years, there are many algorithms been proposed for multimedia retrieval including image annotation/classification, video indexing, and 3D object retrieval etc.. Briefly, the related image/video annotation methods can be divided into three categories based on the employed machine leaning schemes which are unsupervised, supervised, and semi-supervised learning.. Unsupervised learning methods use unsupervised machine learning methods such as nonnegative matrix factorization [3] , clustering [10] to annotate images/videos.. Supervised leaning methods such as support vector machines [13] , decision trees [11] aim to find the relationship between labels and visual features. Considering the growing large amount of samples, some active learning methods [14] are introduced to interactively select only effective samples for labeling.. Considering the heavy user labeling effort, semi-supervised learning methods exploit both a small number of labeled samples and a large number of unlabeled samples to boost the generalization of learning model and receive more and more intensive attention recently [9] . In this section, we employ the conjugate gradient algorithm to optimize problem (4).. The gradient of the objective function in (4) can be written as:. Then we have the optimization procedure of conjugate gradient algorithm as below:. Step 1: Initialize .. Step 2: Do , .. Until .. Step 3: .. The optimization of problem (4) is efficient and effective due to the smoothness character of the objective function. From the illustration of different loss functions in Figure 1 , the logistic loss can achieve almost equivalent performance to hinge loss. In the following section we describe the comparison experiments. To evaluate the effectiveness of the proposed algorithm, we carefully conduct web image annotation experiments on the MIR Flickr dataset [6] that is offered by the LIACS Medialab at Leiden University, the Netherlands and introduced by the ACM MIR Committee in 2008 as an ACM sponsored image retrieval evaluation. The dataset contains 25,000 images of 38 categories including animals, baby, baby*, bird, bird*, car, car*, clouds, clouds*, dog, dog*, female, female*, flower, flower*, food, indoor, lake, male, male*, night, night*, people, people*, plant_life, portrait, portrait*, river, river*, sea, sea*, sky, structures, sunset, transport, tree, tree*, water. Figure 2 shows some example images in the dataset.

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


Job submission to grid computing environments The problem of enabling scientist users to submit jobs to grid computing environments will eventually limit the usability of grids. The eMinerals project has tackled this problem by developing the "my_condor_submit" (MCS) tool, which provides a simple scriptable interface to Globus, a flexible interaction with the Storage Resource Broker, metascheduling with load balancing within a grid environment, and automatic metadata harvesting. This paper provides an overview of MCS together with some use cases. We also describe the use of MCS within parameter-sweep studies. For grid computing infrastructures to be exploited, it is essential that the tools built to provide access have usability designed into them from the outset. In our experience, it is unrealistic to ask most scientists to work with raw Globus job-submission commands -in the end they are likely to end up compromising by merely using gsissh to log into grid resources and submit jobs using more familiar batch queue commands. However, we have found that asking them to work with Condor job submission scripts is quite feasible [1] . In this paper we describe work we have done to develop Condor-like grid job submission tools that encompass integration with the San Diego Storage Resource Broker (SRB) [2] and new metadata capture tools [3] .. The context for this work is the eMinerals minigrid structure [4, 5] . This is a heterogeneous compute grid integrated with a data grid based on the SRB, as illustrated in Figure 1 . Since the eMinerals project is an escience testbed project, we enforce a policy that access is controlled by the use of Globus job submission scripts (with authentication handled by Globus Grid Security Infrastructure (GSI) and X.509 digital certificates); we do not enable access via gsissh except for certain mission-critical exceptions (mostly for code developers and system administrators).. In previous papers we have described the eMinerals minigrid structure and access tools in some detail [4, 5] . Our main job submission tool is "my_condor_submit" (MCS). This uses Condor-G, a Condor wrapping of Globus jobsubmission tools. As noted above, we quickly learned within the eMinerals project that users can work quite easily with Condor job submission scripts, and working with MCS only requires users to prepare a short Condor-like job submission script [1] .. The first versions of MCS [4, 5] essentially carried out one small set of tasks, namely to retrieve data from the SRB, run a program on the eMinerals minigrid, and then upload generated files to the SRB. Subsequent to this, we have developed a new version of MCS with some innovations that will be described in this paper. These include metascheduling, generalisation to other grid infrastructures (including campus grids and the National Grid Service), and automatic metadata capture.. MCS is primarily designed for users to submit one job at a time to a grid infrastructure from a computer on which the Globus toolkit has been installed. An issue that requires us to go beyond this approach is the need to wrap MCS within an infrastructure than enables the scientist to submit many jobs at a single instance as part of a combinatorial or ensemble study. We have developed scripts enabling automatic generation of input files for parameter sweeps which use MCS for their submission to address this need.. This paper describes how the eMinerals project handles grid computing job submission in a way that hides the underlying middleware from the users, and makes grid computing genuinely usable for scientists.

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


Description and comparison of documentation of nursing assessment between paper-based and electronic systems in Australian aged care homes Description and comparison of documentation of nursing assessment between paper-based and electronic systems in Australian aged care homes Abstract Purpose: To describe nursing assessment documentation practices in aged care organizations and to evaluate the quality of electronic versus paper-based documentation of nursing assessment. Methods: This was a retrospective nursing documentation audit study. Study samples were 2299 paper-based and 6997 electronic resident assessment forms contained in 159 paper-based and 249 electronic resident nursing records, respectively, from three aged care organizations. The practice of nursing assessment documentation in participating aged care homes was described. Three attributes of quality of nursing assessment documentation were evaluated: format and structure, process, and content by seven measures: quantity, completeness, timeliness comprehensiveness, frequencies of documentation specific to care domains and data items, and whether assessment forms were signed and dated. Results: Varying practice in documentation of nursing assessment was found among different aged care organizations and homes. Electronic resident records contained higher numbers and more comprehensive resident assessment forms than paper-based records. The frequency of documentation was higher in electronic than in paper-based records in relation to most care domains. There was no difference between the two types of documentation systems on other aspects of nursing assessment documentation (overall completeness and timeliness, variation of frequencies among different care domains, and item completion in personal hygiene assessment forms). Conclusions: Electronic nursing documentation systems could improve the quality of documentation structure and format, process and content in the aspects of quantity, comprehensiveness and signing and dating of assessment forms. Further studies are needed to understand the factors leading to the variations of practice and the limitations of nursing assessment documentation and to evaluate documentation quality from a clinical perspective. © 2013 Elsevier Ireland Ltd. Methods: This was a retrospective nursing documentation audit study. Study samples were 2299 paper-based and 6997 electronic resident assessment forms contained in 159 paper-based and 249 electronic resident nursing records, respectively, from three aged care organizations. The practice of nursing assessment documentation in participating aged care homes was described. Three attributes of quality of nursing assessment documentation were evaluated: format and structure, process, and content by seven measures: quantity, completeness, timeliness comprehensiveness, frequencies of documentation specific to care domains and data items, and whether assessment forms were signed and dated.Results: Varying practice in documentation of nursing assessment was found among different aged care organisations and homes. Electronic resident records contained higher numbers and more comprehensive resident assessment forms than paper-based records. The frequency of documentation was higher in electronic than in paper-based records in relation to most care domains. There was no difference between the two types of documentation systems on other aspects of nursing assessment documentation (overall completeness and timeliness, variation of frequencies among different care domains, and item completion in personal hygiene assessment forms).Conclusions: Electronic nursing documentation systems could improve the quality of documentation structure and format, process and content in the aspects of quantity, comprehensiveness and signing and dating of assessment forms. Further studies are needed to understand the factors leading to the variations of practice and the limitations of nursing assessment documentation and to evaluate documentation quality from a clinical perspective.3 Electronic nursing documentation systems, which aggregate digital forms of data about clients and nursing care, have been increasingly introduced into care settings to assist nurses' daily work [1] . Such systems are able to improve documentation quality and allow reuse of data for various purposes, albeit with mixed overall effects on nursing care [2, 3, 4] .. Given the potential of information and communication technology (ICT) in supporting care provision, several aged care organisations have implemented electronic nursing documentation systems. These systems were expected to improve the quality of documentation and communication among care team members to facilitate care, and to provide better evidence that care services meet accreditation standards and government requirements.. Nursing documentation, a major clinical information source in Australian aged care, plays a vital role in the delivery of aged care services [5] . Resident assessment forms recording information about nursing assessment are a significant part of a resident's record. Nursing assessment is the first stage of the nursing process and an important task in daily care practice. During assessment, a nurse systematically collects, verifies, analyses and communicates a health care client's information to derive a nursing diagnosis and to plan individualized care for the client [6] . The accuracy and completeness of nursing assessment determines the accuracy of follow-on other steps of the nursing process [7] .. In aged care, nursing assessment documentation practice is shaped by the aged care accreditation standards in the sense that the scope of care to be assessed has been defined. This scope includes clients' care issues from health, personal care and safety to a range of lifestyle matters [8] . However, it is unclear how nursing assessment documentation is conducted at an operational level in individual aged care organisations or nursing homes.. Previous studies have addressed the content of nursing assessment documentation at different levels, considering whether and/or to what extent general or specific assessment data have been documented. For example, Björvell et al. [9] and Darmer et al. [10] measured the presence of the 19 pre-structured nursing status keywords in the VIPS documentation system such as communication, nutrition and pain on a patient's arrival and ongoing updates of care needs. At a specific care level, Barry et al. [11] identified poor documentation of vital signs and physical assessment for residents with acute change in condition in nursing home settings. Ehrenberg et al. [12] assessed the presence of relevant assessment data in relation to chronic heart failure.. Measurement approaches used in previous studies to only address the documentation content appear to be inadequate to comprehensively reflect the capacity of electronic documentation systems to assist nurses to undertake and document nursing assessment. In fact, the quality of documentation has three attributes: structure and format, process, and content, which refer to data presentation, collection and meaning [13] .. To obtain an understanding about nursing documentation practice relating assessment and to describe the effect of the electronic systems on the quality of nursing documentation, a nursing documentation audit on nursing assessment forms was carried out. The aims of the study were to investigate nursing assessment documentation practice in Australian aged care homes and to compare the quality of nursing assessment documentation in the paper-based and electronic nursing systems.. For this study, multiple measures were developed to address the three quality attributes of assessment documentation. Under the same legislative requirements, there can be variation in practice of nursing assessment documentation among different aged care organisations or homes. Such varying practices may not necessarily be standardized by the use of an electronic nursing documentation system. Electronic systems could improve the quality of documentation structure and format, process and content for some components of nursing assessment. Although the electronic systems contained more documented assessment forms, which covered a wider range of resident care needs, they did not perform better than paper-based systems in the completeness and timeliness of documentation. Therefore, further work on the usage of the electronic systems may focus on improving these aspects. There is also a need for improvement in compliance with standards in order to address the entire care needs of residents.. Several areas may need to be further studied. The relationship between the characteristics of an item in a form and its completion status may need to be further investigated for the improvement of design or upgrade of assessment forms in electronic systems. The quality of nursing assessment documentation for focused care issues such as pain, pressure ulcer and continence needs to be understood from the clinical point of view. Such a study should provide evidence reflecting the appropriateness of items designed in a form for a specific care issue and nurses' knowledge and skills in collecting relevant clinical data. It would be also valuable to investigate the factors causing flaws in the nursing assessment documentation such as incomplete and delayed documentation. In addition, variation in nursing assessment documentation practice among the organisations and its impact on quality of care and resident outcomes need to be investigated in the future studies.. the study design, analysis, interpretation of data and writing of the manuscript. The authors thank the residents in the participating aged care homes and their representatives for giving consent for collection and analysis of their records. The authors would also like to acknowledge nurses, nursing managers and management groups of the participant aged care organisations for giving the research team support and access to the aged care facilities to collect empirical data. To our knowledge, this is the first study to assess the quality of nursing assessment documentation in an Australian aged care setting. It used multiple measures to address the three quality attributes of nursing assessment documentation: structure and format, process and content. This provided an overall profile about the capacity of paperbased and electronic documentation systems in aggregating resident assessment data.

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


Breaking

the

computational

barriers

of

pairwise

genome

comparison

Background:

Conventional

pairwise

sequence

comparison

software

algorithms

are

being

used

to

process

much

larger

datasets

than

they

were

originally

designed

for.

This

can

result

in

processing

bottlenecks

that

limit

software

capabilities

or

prevent

full

use

of

the

available

hardware

resources.

Overcoming

the

barriers

that

limit

the

efficient

computational

analysis

of

large

biological

sequence

datasets

by

retrofitting

existing

algorithms

or

by

creating

new

applications

represents

a

major

challenge

for

the

bioinformatics

community.We

have

developed

C

libraries

for

pairwise

sequence

comparison

within

diverse

architectures,

ranging

from

commodity

systems

to

high

performance

and

cloud

computing

environments.

Exhaustive

tests

were

performed

using

different

datasets

of

closely-and

distantly-related

sequences

that

span

from

small

viral

genomes

to

large

mammalian

chromosomes.

The

tests

demonstrated

that

our

solution

is

capable

of

generating

high

quality

results

with

a

linear-time

response

and

controlled

memory

consumption,

being

comparable

or

faster

than

the

current

state-of-the-art

methods.We

have

addressed

the

problem

of

pairwise

and

all-versus-all

comparison

of

large

sequences

in

general,

greatly

increasing

the

limits

on

input

data

size.

The

approach

described

here

is

based

on

a

modular

out-ofcore

strategy

that

uses

secondary

storage

to

avoid

reaching

memory

limits

during

the

identification

of

High-scoring

Segment

Pairs

(HSPs)

between

the

sequences

under

comparison.

Software

engineering

concepts

were

applied

to

avoid

intermediate

result

re-calculation,

to

minimise

the

performance

impact

of

input/output

(I/O)

operations

and

to

modularise

the

process,

thus

enhancing

application

flexibility

and

extendibility.

Our

computationally-efficient

approach

allows

tasks

such

as

the

massive

comparison

of

complete

genomes,

evolutionary

event

detection,

the

identification

of

conserved

synteny

blocks

and

inter-genome

distance

calculations

to

be

performed

more

effectively.

Considering

that

GECKO's

implementation

was

designed

primarily

for

very

large

sequence

comparisons,

it

compares

surprisingly

well

with

the

reference

software

packages

when

analysing

short

sequences.

It

is

as

fast

as

Gepard

even

when

the

dictionaries

were

not

precalculated.

Gepard

reports

s

for

Mbp

genomes,

compared

with

s

for

our

implementation.

In

the

cases

of

MUMmer,

LAST

and

LASTZ,

our

execution

time

was

greater,

due

to

the

different

strategy

we

are

following

compared

to

the

suffix

array

indexing

they

are

using,

but

still

the

difference

is

acceptable

since

the

execution

time

is

not

that

high.

However,

for

longer

sequences,

our

method

strongly

outperforms

existing

methods.

GECKO

needed

less

than

h

in

average

to

compare

chromosome

from

different

species

(all

possessing

more

than

Mbp)

against

the

h

and

a

half

in

average

of

Gepard

and

MUMmer

and

the

h

of

LASTZ

(average

values

extracted

from

Table

Since

all

the

reference

software

packages

manage

data

structures

in

core

memory,

their

good

performance

with

short

sequences

was

predictable,

but

this

also

means

that

their

performance

degrades

as

sequence

size

grows,

entering

into

starvation

when

no

more

computational

resources

are

available.

This

is

due

in

part

to

the

use

of

the

Suffix

Array

data

structure

which

in

one

side

reduces

the

computational

complexity

but

in

the

other

increases

the

memory

consumption

up

to

times

the

length

of

the

input

sequence

in

the

most

efficient

implementations.

For

comparison

purposes

and

to

prove

the

mentioned

Suffix

Array

memory

consumption,

we

implemented

a

Suffix

Array

version

of

the

program

which

significantly

reduces

the

computation

time

compared

with

our

actual

dictionary

strategy,

but

as

mentioned

is

using

more

memory

(more

details

can

be

seen

in

the

Additional

file

Section

"Alternative

dictionary

calculation

using

Suffix

arrays").

Sus

scrofa.

Red

colour

indicates

forward

strand

fragments

and

black

the

reverse

strand

ones.

Plots

indicate

that

there

are

closelyrelated

(from

to

and

remotely-related

(from

to

sequences.

This

is

caused

by

the

fact

of

that

chromosome

numbering

was

based

on

their

length

and

not

their

content.

For

example,

human

chromosome

is

present

in

several

chromosomes

of

Bos

Taurus

(but

not

in

the

first

chromosome,

as

can

be

deduced

from

sub-

figure

An

image

with

the

first

five

sub-plots

projected

over

one

sequence

is

provided

in

the

Additional

file

The

results

of

these

comparisons

are

shown

in

Table

and

more

details

are

available

in

the

Additional

file

at

Section

GECKO's

implementation

showed

real-world

performance

gains

ranging

from

versus

Gepard

for

TYLCV

comparison,

to

versus

LAST

in

the

case

of

Drosophila

comparison

(see

Table

The

number

of

genome

sequencing

projects

has

grown

exponentially,

in

parallel

with

a

drastic

reduction

in

the

cost

of

sequencing.

For

example,

at

the

turn

of

the

millennium

the

cost

of

sequencing

Mbp

of

genomic

DNA

(million

DNA

base

pairs)

was

about

thousand

US

dollars,

compared

to

around

US

cents

at

the

time

of

writing

Scientists

are

continuing

to

develop

faster

and

cheaper

methods

that

will

allow

the

routine

sequencing

of

individual

patient

genomes,

thus

truly

ushering

in

the

era

of

genetics-based

personalised

medicine.

The

human

genome

is

not

the

only

one

of

interest

to

the

research

community,

and

the

progression

of

sequencing

technology

also

has

huge

consequences

for

studies

involving

the

genomes

of

other

organisms.

At

present,

hundreds

of

different

organisms,

from

all

living

kingdoms,

have

been

sequenced

and

thousands

more

projects

are

on-going.

These

developments

have

put

Comparative

Genomics

into

the

spotlight

in

order

to

provide

the

tools

for

studying

relationships

within

this

flood

of

data..

Pairwise

sequence

comparison

algorithms

have

been

implemented

since

the

early

days

of

bioinformatics.

Original

algorithms

for

global

and

local

alignments

were

designed

using

dynamic

programming

techniques

that

result

in

quadratic

calculation

time

and

memory

consumption

proportional

to

the

product

of

the

total

number

of

bases

analysed..

When

sequence

analysis

jumped

from

individual

genes

and

proteins

to

full

genomes,

new

software

appeared,

such

as

MegaBlast

MUMmer

and

Gepard

the

latter

of

which

has

been

reported

to

be

able

to

compare

more

than

Mbp

of

human

in

approximately

h

These

software

adopted

some

ideas

introduced

by

the

heuristic

sequence

database

searching

algorithms

FASTA

and

later

BLAST

These

algorithms

introduced

a

computational

space

reduction

strategy

based

on

the

fast

identification

of

matching

points

(hits)

that

are

in

turn

used

as

seed

points

for

the

extension

of

local

alignments.

In

FASTA,

these

matching

points

are

perfect

matches

between

K-mers

(words

of

length

k)

from

each

sequence,

while

BLAST

allows

certain

mismatches,

thus

enhancing

its

sensitivity.

Other

computational

space

reduction

strategies

confine

the

search

to

the

most

probable

matching

space

(FASTA),

or

limit

seed

extension

to

regions

with

a

minimal

concentration

of

hits

(BLAST)..

Additionally,

some

of

the

previous

software

adopted

other

ideas

coming

from

the

string

matching

field

such

as

the

Generalized

Suffix

Trees

and

Suffix

Array

data

structures

which

reduce

significantly

the

computational

complexity

but

still

involves

the

use

of

significant

memory

resources

(see

Section

of

the

Additional

file

In

order

to

overcome

the

mentioned

memory

issue,

a

number

of

disk-based

implementations

were

developed

Despite

using

customised

strategies

to

minimize

the

I/O

operation

overhead,

they

reported

higher

execution

times

for

indexing

the

Human

genome

h

in

and

h

in

compared

to

h

for

our

proposed

indexing

strategy..

In

general,

the

reference

software

was

designed

to

deal

with

genes,

proteins

and

small

genome

sequences

and

since

are

now

used

for

much

larger

datasets

than

they

were

originally

designed

for,

they

are

now

reaching

their

limit

in

terms

of

memory

capacity

and

efficient

computation

on

single-CPU

systems.

Consequently,

there

is

a

pressing

need

to

design

new

software

that

tackles

the

memory

consumption

problem

caused

by

the

analysis

of

very

large

genome

sequence

datasets.

A

good

strategy

to

deal

with

this

problem

is

to

move

data

that

does

not

fit

into

internal

memory

to

external

memory

(i.e.

hard

disks),

following

what

is

known

as

an

out-of-core

strategy

However,

since

there

is

a

difference

of

several

orders

of

magnitude

in

access

time

between

the

two

memory

layers,

special

care

must

be

taken

in

order

to

avoid

performance

degradation.

Some

of

these

approaches

have

previously

been

applied

to

bioinformatics

but

not

specifically

for

pairwise

genome

comparison..

In

this

document

we

report

on

GECKO

(GEnome

Comparison

with

K-mers

Out-of-core),

a

modular

application

designed

to

identify

collections

of

HSPs

by

pairwise

genome

comparison

procedures,

that

can

then

be

used

to

obtain

gapped

fragments.

Our

work

improves

on

previous

methods

by

introducing

controlled

memory

usage

and

a

modular

design

that

allows

further

comparisons

to

be

performed

without

the

need

to

recalculate

intermediate

results

and

thus

without

sacrificing

performance.

We

have

benchmarked

the

application

in

terms

of

both

performance

and

results

quality.

We

designed

experiments

with

datasets

ranging

from

short

sequences

in

the

kilobase

range

to

larger

sequences

up

to

Mbp

in

length

in

order

to

compare

GECKO

against

the

best

currently

available

software

under

both

unfavourable

and

favourable

conditions

respectively.

In

addition,

we

performed

a

massive

comparison

exercise

between

mammalian

chromosome

sequences

in

order

to

test

one

of

the

key

improvements

of

the

application:

the

avoidance

of

intermediate

result

re-calculation.

In

the

tests

with

short

sequences,

GECKO

was

slower

compared

to

existing

software,

but

with

long

sequences,

the

results

were

comparable

or

superior

in

terms

of

performance.

The

results

quality

in

both

cases

(short

and

long

sequences)

was

superior.

Binaries

are

available

from

Source

code

is

available

from:

To

overcome

the

limitations

of

existing

sequence

comparison

methods

we

focused

firstly

on

the

applicationspecific

reduction

of

main

memory

and

computational

space

usage,

and

secondly

on

modularising

the

process

using

classical

software

engineering

concepts.

In

the

next

sections,

we

explain

how

we

reduce

memory

usage

using

an

out-of-core

strategy

designed

to

manage

data

structures

that

are

too

large

to

fit

into

main

memory

at

one

time.

Naturally,

memory

management

could

be

delegated

to

the

Operating

System

using

virtual

memory

concepts;

however

poorer

data

locality

can

result

in

performance

degradation

in

memory

intensive

applications

such

as

large-scale

sequence

analyses.

In

addition,

we

explain

the

strategies

applied

to

the

design

of

GECKO

(see

Fig.

(a)

Dictionary

calculation,

(b)

Hits

determination,

(c)

HSP

detection,

and

(d)

HSP

post-processing.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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


Identification of subsurface structures using electromagnetic data and shape priors We consider the inverse problem of identifying large-scale subsurface structures using the controlled source electromagnetic method. To identify structures in the subsurface where the contrast in electric conductivity can be small, regularization is needed to bias the solution towards preserving structural information. We propose to combine two approaches for regularization of the inverse problem. In the first approach we utilize a model-based, reduced, composite representation of the electric conductivity that is highly flexible, even for a moderate number of degrees of freedom. With a low number of parameters, the inverse problem is efficiently solved using a standard, second-order gradient-based optimization algorithm. Further regularization is obtained using structural prior information, available, e.g., from interpreted seismic data. The reduced conductivity representation is suitable for incorporation of structural prior information. Such prior information cannot, however, be accurately modeled with a gaussian distribution. To alleviate this, we incorporate the structural information using shape priors. The shape prior technique requires the choice of kernel function, which is application dependent. We argue for using the conditionally positive definite kernel which is shown to have computational advantages over the commonly applied gaussian kernel for our problem. Numerical experiments on various test cases show that the methodology is able to identify fairly complex subsurface electric conductivity distributions while preserving structural prior information during the inversion. Marine geophysical exploration is important, for example, in the search for new offshore petroleum reservoirs. Several surface methods have been developed to attempt depiction of the subsurface geological structures, and thereby direct or indirect detection of a possible presence of hydrocarbons. The procedure for most surface methods is: transmission of energy into the subsurface; measurement on the surface, and; inversion to get a map of the subsurface physical properties. Over the last decades, seismic reflection methods have been widely used in hydrocarbon exploration, but more recently, electromagnetic methods have also gained interest, particularly controlled source electromagnetics (CSEM), see, e.g., [1] and references therein.. With CSEM, a towed electric dipole transmits low-frequency (0.1-10 Hz) electromagnetic (EM) signals into the subsurface. The transmitted signals are sensitive to the subsurface electric conductivity. The electric conductivities of brine and oil saturated porous media differs by 1-2 orders of magnitude, which makes CSEM attractive as a hydrocarbon indicator. The recorded response in the sea floor receivers is, however, the result of the interaction of the EM signals with a subsurface region much larger than that of a (potentially existing) petroleum reservoir. The usefulness of CSEM therefore relies on its ability to map the entire subsurface region that is being explored with reasonable accuracy.. Subsurface geology is typically dominated by large-scale structures (geological strata), although subsurface properties also vary within a structure. The main aim with the research presented in this paper is to develop a computationally efficient inversion methodology for CSEM data that is able to preserve prior information about geological strata, and we will therefore apply a model-based representation (see, e.g., [2] [3] [4] [5] [6] [7] [8] [9] ) of the unknown electric conductivity field. Alternatively, a pixel-based representation (see, e.g., [10] [11] [12] [13] ) could have been used, although the tendency for smoothing out the resulting conductivity field makes it less attractive. Additionally, large computational costs are associated with running second-order gradient-based optimization algorithms with a pixel-based representation, due to the large number of parameters.. The majority of subsurface geological strata are brine saturated. Electric conductivity contrasts between different brine saturated strata are typically much weaker than those occurring between a brine saturated stratum and a petroleum reservoir. Data from EM signals which have propagated through the brine saturated strata have low resolution power due to the diffusive nature of the low-frequency EM signals. This makes it very challenging to solve the inverse problem of obtaining correct placement of structure boundaries from CSEM data alone. In addition, there is a possibility of getting trapped in local minima when using gradient-based optimization algorithms, due to nonlinearity in the mapping from conductivity to EM data.. To enhance stability and to reduce model nonlinearity, we apply regularization by reduced parameterization and adjustable smoothing of the structure boundaries, extending the work in [8, 9] . The reduced representation allows for use of Gauss-Newton-type optimization algorithms as the low number of parameters permits the model to be updated using second-order derivative (Hessian) information. To further aid in obtaining accurate placement of structure boundaries, it would be an advantage if structural prior information could be applied. How to achieve this will be a major topic in the paper.. From the inversion methodology viewpoint, it is not crucial where the structural prior information comes from, but it seems natural to point to seismic data in this context. Seismic data are sensitive to elastic properties of the subsurface, and are applied to detect interfaces between geological strata. They are usually interpreted by geologists in combination with well logs and outcrop analogues. The outcome of this process is a set of possible structural subsurface models, from which one model may be selected as the most probable. There is often a fair amount of uncertainty and ambiguity associated with this selection process. Without going into any detail about geological interpretation, we will simply assume that uncertain structural subsurface information exist, and seek to utilize this information to build a prior model for the structural CSEM inversion to increase reliability of the inversion results. We will, however, sometimes refer to the structural prior information as seismic interpretations, etc., for convenience. (Note also that joint inversion of seismic data and CSEM data, see, e.g., [14] [15] [16] [17] , is an alternative way to utilize the information in seismic data in the context of CSEM inversion.) As a final remark concerning the origin of the structural information, we note that it is not guaranteed that all large-scale structures with respect to elastic properties always correspond exactly to large-scale structures with respect to electric properties. Further discussion of this complex issue is, however, outside the scope of the paper.. Use of prior information in an inversion requires that a prior probability density function (PDF) for the parameters involved in the estimation is specified. A very common assumption for many subsurfacerelated problems is that the prior PDF is multivariate gaussian. When the solution of the inverse problem is expected to contain several large-scale structures as a dominating feature, like in our case, any realistic prior PDF associated with a pixel-based parameterization would be multimodal, and therefore non-gaussian. We apply an entirely different parameterization, but even so, it is still not clear how to directly design a realistic prior PDF for the parameters involved. Our parameterization does, however, facilitate representation of structural prior information as training images, where one training image corresponds to a possible structural subsurface model. Subsequently, the training images are used to build a (not necessarily gaussian) PDF for the subsurface structures that is incorporated in the inversion methodology as a shape prior [18] . Shape priors allow for calculation of non-gaussian PDF's by transforming them to a space where they follow a gaussian distribution. The calculation requires a choice of kernel function, which to some degree is application dependent, with the gaussian kernel being the common choice. We will, however, argue for the use of a conditionally positive definite kernel [19] , as it is shown to aid the gradient-based algorithm in a favorable manner compared to the gaussian kernel.. The paper is organized as follows: In section 2, we describe the numerical solver for Maxwell's equations. The formulation of the inverse problem is outlined in section 3. This includes a description of the parameter representation in section 3.1, and a discussion of shape priors and kernel functions in section 3.2. A description of the procedure for solving the optimization problem is given in section 4. In section 5, we present numerical results involving various types of subsurface geological structures, where we demonstrate the performance of the novel inversion methodology. The main aim of the numerical investigation is to assess the importance of using structural prior information for achieving CSEM inversion results that preserves geological strata. We end with some concluding remarks in section 6. We have presented a methodology for inversion of controlled source electromagnetic (CSEM) data that allows for identification of large-scale geoelectric structures with weak conductivity contrasts (corresponding, e.g., to different brine-saturated geological strata). The inversion methodology applies two types of regularization; reduced parameterization, and structural prior information. We represent the structural subsurface electric conductivity distribution with a parameterization with similarities to a level set representation, which is very flexible with respect to the shapes of the structures it can represent. Compared to a pixel-based parameterization, a level set parameterization increases nonlinearities in the mapping from parameters to model output, which is challenging for any gradient-based optimization algorithm. This increase in model nonlinearities is, however, alleviated in our parameterization by using smoothing of exterior functions and reduced representation of interior functions, reducing the risk of getting trapped in local minima when applying gradient-based optimization. Moreover, the reduced representation uses a parameter grid which is detached from the forward model grid, allowing for implementations of large models in terms of forwardmodel grid cells using a low number of parameters. Consequently, Gauss-Newton-type optimization methods can be used with a reduced representation, which is usually not possible with a pixel-based representation due to high computational costs associated with the Hessian matrix. Contrary to a pixel-based representation, the selected representation facilitates use of shape priors to incorporate structural prior information, potentially originating from geological interpretations of seismic data, in the CSEM inversion. Shape priors handle non-gaussian prior distributions well, which is essential, since structural prior information typically comes in the form of non-gaussian probability distributions. The numerical experiments showed that in some cases, geoelectric structures with weak conductivity contrasts can be approximately identified from CSEM data when regularizing the problem only with reduced representation. Use of shape priors clearly improved the results, however, and in test case 2, shape priors had to be introduced in order to obtain even an approximate identification. Use of shape priors, in addition to reduced representation, is therefore recommended.. We have assumed that the c-coefficients (conductivity values) are known during the inversion and independent of the spatial coordinates. This seems a natural first step when the focus is on identification of subsurface structures. We have, however, made some preliminary numerical investigations where the c-coefficients (still independent of the spatial coordinates) have been included as parameters during the inversion with shape priors. For this investigation, we equipped the c-coefficients with prior models where the standard deviations reflected that the probability of changing the internal ordering of their numerical values during the inversion should be low. Simultaneous estimation of a and c then led to similar results as those presented in the paper for all cases except test case 2, where a poor estimation of the structures was obtained. Sequential estimation (first a, then c) led to a similar result as that presented in the paper, also for test case 2.

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


DESIGN AND IMPLEMENTATION OF ADAPTIVE WEB APPLICATION Web applications are now widely used for diffusing and processing information over the network. Methodological guidelines which assist web application developers in their task must take into account users' preferences to generate adaptable web interfaces. This paper presents MCSD-case, a generator of adaptive web application, which addresses the issue of designing and automatically deploying such application. MCSD-case's target is to facilitate the automatic execution of the design and the automatic generation of adaptable web interface. Using MCSD-case, designers can specify, at a conceptual level, the features of the web application to be generated. These features are made operational by instantiating different models dedicated to the description of the expected functionalities, the users and some features concerning adaptation. The implementation of MCSD-case is based on HTML and Java Servlets to instantiate the models which are translated in XML files then XSL files to generate the HTML page corresponding to the user aims. From its introduction in the early 90's the World Wide Web (WWW) is in a continuous development. Its rapid expansion results in an increasing number of Web applications being developed, especially with sources that contain frequently changing information such as databases (relational or objectoriented databases, XML repositories etc.) (Bieber et al., 1998) . This leads to the fact that there is a higher need to automate, at least partially, the design process of web application. For this reason, there are many methods like RMM (Isakowitz et al, 1995) , OOHDM (Schwabe and Rossi, 1998) , WSDM (De Troyer and Leune, 1998) , etc. These methods have been originally developed for a manual hypermedia design process; they are not particularly well-suited in the context of automated adaptive web application. In this context, efforts in the design of today's hypermedia system have to be pursued and, especially, to be concentrated on the adaptation issue.. Adaptation can be defined as the ability a system has to dynamically adapt itself to any user by giving her/him the feeling that the system has been designed especially for her/him. The need for adaptation arises from different aspects of the interaction between users and hypermedia systems. Users' categories which deal with these systems are increasingly heterogeneous due to their different interests and preferences. The web can be accessed through a number of different devices (PC, WebTV, WAP phone, etc.). Finally taking into account the user preferences (desired layout, navigation patterns, etc.) can lead to a more effective interaction. For this reason, in previous work (Djemaa et al., 2004) , we have presented a user driven method for modeling adaptive Web applications, called MCSD. The different models of this method are: the functionalities model, the audience model, the adaptation model, the analysis model, the conceptual model and the navigational model.. In this paper, we will present MCSD-case, a generator of adaptive web application, which addresses the issue of designing and automatically deploying such application. The design approach supported by MCSD-case is presented in section 2. The prototype architecture is detailed in section 3. Then, we compare our approach and prototype with some related works in section 4. Finally, section 5 concludes the paper and suggests future research directions. In this paper, we have presented MCSD-case, a generator for adaptive web application. We have demonstrated how this generator can facilitate the automatic execution of the design and the automatic generation of adaptive web interfaces.. During the different activities that compose the engineering process of MCSD-case there are a lot of ontologies involved: semantic level (Functionalities model and audience model), adaptation level (profile model) and presentation level (presentation details in terms of composition and graphic).. The implementation of MCSD-case is based on java Servlets interface to instantiate the models which are translated in XML files. MCSD-case uses then XSL files to generate the HTML page corresponding to the user. As future work, we mention:. − The extension of the MCSD-case implementation to take into account users' devices capabilities (hardware and software).. − The construction of a graphical tool that supports the presentation of the other models of MCSD and outputs XML files.. − The inclusion of the mecanism of the dynamic adaptation (adaptativity) in MCSD-case in order to dynamically elaborate and modify both the functional space and the navigation patterns, learning from the user's behavior. Most of the Web application design approaches proposed at the end of the 90's are inspired from earlier hypermedia design methodologies (RMM (Isakowitz et al, 1995) or OOHDM (Schwabe and Rossi, 1998) ) which address content, presentation and navigation issues. These three dimensions are also taken into account in our approach, with a special emphasis put on adaptability to user. As a consequence, we propose an explicit modeling of users as opposed to Araneus (Atezni et al., 1997) . WSDM (De Troyer and Leune, 1998 ) also represents users explicitly, but does not uses algorithm of generation of functional space for each user. The last concept allows us to adapt the content to user which constitutes a particularity of MCSDcase.. Adaptability also appears through the choice of a graphical charter, and through the composition of pages which can be define in MCSD-case for each audience class. The independence between content and presentation, notably allowed by the XML/XSL technology, promotes adaptability since it makes possible the association of different presentations for a same content. WebML (Ceri et al., 2000) and KIWIS (Villanova-Oliver et al., 2002 ) adopts a similar approach, but with lesser possibilities for user for instantiate his profile.. Similarly to MCSD-case, tools for Web application development support the steps of their associated methods, and generally perform the Web application generation. A particularity of MCSDcase is the ability of its use through the network.

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