Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes Background: Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.Results: A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student's t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. Conclusions: The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease. A better understanding of complex disease relies on the integration of all available domains of data. There are gene-gene, gene-covariate, covariate-covariate interactions that are lost when one variable is considered at a time (t-test) or one domain at a time (single-domain clustering). The decision tree method applies these interactions between the domains as a tool for dimension reduction resulting in a more accurate model that is easily interpretable. The application and usefulness of models created to increase understanding of complex disease are dependent on the available data and the manner in which these data are incorporated into the model (Figure 2 ). In particular, much care was taken to incorporate both the covariate information and asthma status in a way that would guide discoveries toward those most informative for distinguishing asthma endotypes while at the same time minimizing the bias against discovery of novel mechanisms associated with a heavy reliance on known asthma etiology.. Several considerations drove the development of the multi-step decision tree method. The first consideration was the segregation of known asthmatics and nonasthmatics. The earlier in the analysis the covariate on which you want to segregate is introduced, the more influence it will have and the more likely that optimal segregation will be achieved. However, this must be balanced with avoiding undue influence on the natural segregation of individuals based on the mechanistic drivers of interest. We adopted a multi-step analysis pipeline largely to address this particular issue. The second consideration was ease of interpretation. Decision trees are particularly powerful with respect to interpretation since the process is completely transparent to the user. The final consideration was the amount of mechanistic information revealed by the different methods. By pre-assembling our gene expression and covariate information, we carried all gene annotation and knowledge regarding the biological pathways associated with the covariates into the interpretation phase. This greatly increased our ability to translate the branches from our decision tree into biological insights. As a result, we believe our multi-step decision tree method represents a novel approach to the discovery of new mechanisms underlying complex disease and complements existing methods that convert these mechanistic insights into informative biomarkers for the diagnosis of disease. Complex diseases, caused by multiple genetic and environmental factors acting together, are often difficult to diagnose, treat, and study due to the limited understanding of their mechanistic underpinnings. New approaches are needed to better understand the biologic processes that underlie the clinical phenotypic expression of complex disease [1] . Study of the associations between bioindicators and disease outcomes can provide evidence of early changes preceding disease, which leads to a better understanding of the mechanistic underpinnings of complex disease [2] .. Gene expression microarrays developed over the past decade represent powerful tools for investigating biological and disease processes [3] . For example, Sen [4] summarized the published literature on transcriptional profiles resulting from exposure of cells or organisms to complex environmental mixtures such as cigarette smoke, diesel emissions, urban air, motorcycle exhaust, carbon black, jet fuel, and metal ore fumes. Relevant to this study are studies using blood cell gene expression analyses to evaluate environmental chemical exposures [5] [6] [7] . Lobenhofer et al. [8] demonstrated the utility of performing gene expression profiling on whole blood samples as an effective surrogate for the target organ to classify compound exposures. They demonstrated the power to detect not only the presence of liver injury, but also the potential severity. A significant body of literature now provides the promise of gene expression in peripheral blood cells for developing biomarkers of disease [9] [10] [11] . Together, these studies suggest that gene expression in peripheral blood cells has a role in deciphering target tissue effects relevant to both exposure and clinical assessments. Gene expression studies with "phenotypic anchoring" are limited but provide more meaningful results than when either gene expression or phenotypic data is viewed alone [8, [12] [13] [14] . Few studies of childhood asthma use gene expression in peripheral blood cells; however, such data combined with clinical measurements may provide a more comprehensive understanding of asthma etiology and diagnosis.. Childhood asthma is known to involve both genetic and environmental components with low-level, ubiquitous environmental exposures contributing substantially [15, 16] . Evidence exists for genomic features contributing to pathogenesis, which in part relates to innate immunological characteristics such as variation in host defense genes [17] . Some scientists believe that, as the result of rapid urbanization over the last few decades, genes previously protecting humans from parasitic infection may now contribute to a 'misdirected' response to environmental agents [18] . Tools to characterize and unravel interacting genetic and environmental factors are clearly required [19, 20] . Understanding the contribution of environmental factors to disease susceptibility requires a more comprehensive view of exposure and biological response than has traditionally been applied.. Various methods have been proposed to identify and describe the mechanistic underpinning of asthma and complex airway disease endotypes. Several groups have defined asthma endotypes based on phenotypes and putative pathophysiology that was applied to asthma for use in clinical study design and drug development [21] [22] [23] [24] [25] . Cho et al. used clustering algorithms to better define airway disease, without specifically addressing asthma, by combining genetic and phenotypic variables with a combination of factor and cluster analysis [26] . However, with childhood asthma in particular, the phenotypes are variable, leading to difficulty in linking phenotype information to endotypes of asthma [27] .. Clustering was most successful when applied to severe asthmatics [28, 29] . Weatherall et al. defined distinct clinical phenotypes of airway disease (including asthma) by cluster analysis [30] . Kelley et al. suggested grouping patients based on questionnaire responses and skin prick tests followed by multivariate regression to determine whether demographic or potential risk factors varied between phenotypes and whether measures of severity varied by phenotypes [31] . Kelley's method was successful in defining endotypes but did not lead to greater mechanistic understanding. Results from the National Heart, Lung, and Blood Institute's Severe Asthma Research Program have shown clustering to be very effective generally [29] and specifically for children [28] . These methods rely heavily on existing clinical criteria and are clearly the best approach when determining diagnostic criteria for asthma endotypes. However, restricting focus to established clinical criteria is not a viable option when the primary goal is the discovery of potentially novel mechanisms.. Auffray et al. proposed the use of global genome, transcriptome, proteome, and metabolome data sets collected in cross-sectional patient cohorts and integrated with biologic and clinical data to develop predictive multi-scale models, which can be evaluated and expanded using systematic perturbations of carefully selected animal models of disease [1] . Many recent studies illustrate the feasibility of this approach [32] . Gene expression from airway epithelial cells was used to define potential asthma endotypes [33] , which had implications for corticosteroid treatment [34] and biomarker identification [35] . Similar studies using induced sputum samples [36] , and blood [37, 38] have also provided information about molecular mechanisms underlying different asthma endotypes.. Current methods have been most successful when characterizing disease using previously established biomarkers or looking at gene expression in the target tissue such as airway epithelial cells. In this paper, we present a new approach focused specifically on the identification of novel biomarkers and previously unreported mechanisms driving disease endotypes from blood. This method was developed using data from the Mechanistic Indicators of Childhood Asthma (MICA) study [39] . The new method was developed as a result of the lack of success with traditional methods such as Student's t-test, single-data domain clustering, and the more complex Modk-prototypes algorithm. We evaluate the results from each of the analysis methods based on segregation of known asthmatics from non-asthmatics, as well as information provided to support biological interpretation of the results. Secondly, we evaluate the impact on the analytical results of how and when each domain of data is incorporated in each analysis scheme. Insights of analysis methods with respect to complex disease are presented.

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


Improved Ordinary Measure and Image Entropy Theory based intelligent Copy Detection Method Nowadays, more and more multimedia websites appear in social network. It brings some security problems, such as privacy, piracy, disclosure of sensitive contents and so on. Aiming at copyright protection, the copy detection technology of multimedia contents becomes a hot topic. In our previous work, a new computer-based copyright control system used to detect the media has been proposed. Based on this system, this paper proposes an improved media feature matching measure and an entropy based copy detection method. The Levenshtein Distance was used to enhance the matching degree when using for feature matching measure in copy detection. For entropy based copy detection, we make a fusion of the two features of entropy matrix of the entropy feature we extracted. Firstly, we extract the entropy matrix of the image and normalize it. Then, we make a fusion of the eigenvalue feature and the transfer matrix feature of the entropy matrix. The fused features will be used for image copy detection. The experiments show that compared to use these two kinds of features for image detection singly, using feature fusion matching method is apparent robustness and effectiveness. The fused feature has a high detection for copy images which have been received some attacks such as noise, compression, zoom, rotation and so on. Comparing with referred methods, the method proposed is more intelligent and can be achieved good performance. Nowadays, internet technology and multimedia technology develop so fast. Many of us marvel at the novelty of internet sites that seem to appear from everywhere. Accompany with the technology, a new social network platform [1] which is used for media creating, publishing and sharing has become the hot spot. YouTube is a good example. YouTube, which founded in February 2005, is a typical of internet companies. It originated in a garage, followed quickly by significant funding from a venture capital company, in this case, Sequoia Capital. At first, users shared personal videos, but as the Web site's popularity grew rapidly, YouTube contracted with traditional content providers (television and movie studios and record labels) to load commercial clips. In less than a year, the video and user statistics were staggering. The company claims that 65,000 videos are uploaded daily, with consumers watching more than 100 million videos a day. Twenty million unique users, mainly in the 18-49 age range, view the Web site monthly [2] . In YouTube site, one can upload and share their videos conveniently and frequently. Due to the media content can be download and upload freely, the freedom also brings some serious content security problems, such as some harmful content's spread, the content may include some sensitive contents related to sex, terror, privacy and pirate and so on. Therefore, the manager of the website has the responsibility to check the media contents which were uploaded to the website to make sure whether there are some sensitive contents in it or not. In the website, someone may also upload the media that others had already uploaded, in order to protect the original author's copyright and save the space of the server disk, the website manager should prevent the same media's uploading. Nowadays, the dominant method of checking the media contents is manual operation. But when the numbers of the media increasing so fast, it is obvious that the manual method cannot complete this complex task efficiently. Therefore, a new Computer-based Control System used to detect the media has been proposed in our previous work [1] . In this paper, we continue do some research on the Computer-based Control System. An improved media feature matching measure using Levenshtein Distance [3] and a new image entropy theory [4, 5, 6] based feature extraction and matching measure are proposed in this paper. The rest of the chapter is arranged as follows. Section 2 reviews related works. The improved matching measure for ordinary measure is shown in section 3. The entropy theory based media features extraction and matching measure is proposed in section 4. Section 5 presents the experiments and makes a comparison between the two proposed methods and the related methods. Final conclusion is shown in section 6. This paper reviews the computer-based copyright control system and the media feature extraction and matching measure. Based on this system and the previous works, this paper presents an improved grayscale based ordinary measure and a new image entropy theory based feature extraction and matching measure. And we do experiments on this two proposed measure. At last, according to the experiments result, we compare the two measures and other related methods.. The experiments show that both the two measures have their own advantages. The first improved ordinary measure are robust and high efficient when detecting the noised, compress, and scale images. The second image entropy theory based measure are efficient with the images which attacked by noise, compress and rotation. But the efficiency of the second measure is lower than the ordinary measure, so in the future works, some useful method such as Neural Network or other machine learning method can be used to search for the optimal threshold to increase the successful matching rate. Copy detection is an important aspect of copyright protection. Many domestic and foreign scholars had done a lot of research in this field. In our previous work, we have proposed a computerbased copyright control system [1] and a simple grayscale-based ordinary measure of feature extraction method which is efficient for image copy detection. Paper [7] proposed a new framework called the extended feature set for detecting copies of images. Firstly, virtual prior attacks are applied to copyrighted images to generate novel features, which serve as training data. Then the copy-detection problem can be solved by learning classifiers from the training data. This approach can be integrated into existing copy detectors to further improve their performance, but low efficiency for unknown attack type. In [21] , an anisotropic wavelet-Based intelligent method was used for image nearness measuring but the detection results are fuzzy not definite decision. Paper [8] used the video and audio fingerprint method to resist the attacks. Among the features, Audio copy detection appears to be more robust due to relatively simple nature of attacks. However, as seen in the experiments visual features cannot withstand some of the attacks such as picture-inpicture attack. Furthermore, the author improved the result by joint usage of visual and audio features. In next part, several related works detail will be shown.

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 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 O O O O O O O O O O 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 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 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 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 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 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 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 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


Progressive encoding and compression of surfaces generated from point cloud data We present a new algorithm for compressing surfaces created from oriented points, sampled using a laser range scanner or created from polygonal surfaces. We first use the input data to build an octree whose nodes contain planes that are constructed as the least square fit of the data within that node. Then, given an error threshold, we prune this octree to remove redundant data while avoiding topological changes created by merging disjoint linear pieces. From this octree representation, we provide a progressive encoding technique that encodes the octree structure as well as the plane equations. We encode the planes using distances to three points and a single bit. To decode these planes, we solve a constrained optimization problem that has closed-form solution. We then reconstruct the surface from this representation by implicitizing the discontinuous linear pieces at the leaves of the octree and take a level set of this implicit representation. Our tests show that the proposed method compresses surfaces with higher accuracy and smaller file sizes than other methods. Many recent advances in 3D scanner technology bring new opportunities and possibilities for creating digital models of real world objects. These digital models have many applications ranging from visualization and preservation of artistic works, to navigation and reconstruction of large environments. To acquire these models, one typically uses a laser range scanner that generates 3D point samples on the surface of the object. These point samples need to be processed further since almost all applications require polygonal models with explicit connectivity. Most methods for creating polygonal models are implicit, that is, they build from a set of (possibly oriented) point samples an implicit function whose level set is the reconstructed surface. Applying a polygonalization method such as Marching Cubes [1] to that function creates the desired polygonal model.. One of the main difficulties that has hampered the performance of surface reconstruction methods in recent years is the sheer quantity of data. For example, researchers in the Digital Michelangelo project [2] scanned statues for the purpose of digital preservation of these works of art to millimeter or sub-millimeter accuracy, which resulted in data sets for each statue in the range of hundreds of millions to billions of point samples. Light Detection And Ranging (LIDAR) data collected from an aircraft could be of the order of billions to tens of billions of samples. While several out-of-core reconstruction methods have been built [3, 4] to handle this massive amount of data, simply storing and/or transmitting the data is a challenge. Therefore, new innovative approaches for addressing storage and transmission issues via data compression are needed.. There are many opportunities for compression in the surface reconstruction process. One solution is to simply compress the input points. Another is to generate and compress an intermediate data structure that represents these points. One could also compress either the implicit function generated from the point cloud or the polygonal model that is the result of the surface reconstruction process (see Figure 1) . Our main contribution is the development of a new method for surface compression that builds and encodes an adaptive octree representation of this surface. We decode the octree by solving a constrained optimization problem that has closed-form solution. To display the surface, we use wavelet rasterization [5] to compute an approximation to the indicator function of the solid body whose boundary is the surface and reconstruct the shape by extracting a level set of this function using Marching Cubes. The proposed algorithm is fast (small examples like the African statue take less than 0.7s for encoding and about 0.2s for decoding, while larger examples like Atlas take about 15s for encoding and 2s for decoding on an Intel Core i7 960 processor), resilient to noise, and outperforms the state of the art compression algorithms.. In the future, we would like to study the advantages and disadvantages of using functions other than lin- ear polynomial fits at the octree nodes. Higher order polynomials should allow better approximation, further pruning of the octree and hopefully better compression rates. However, their use comes with more complicated decoding strategies and dangers of overfitting.. While we performed a comparison of the accuracy of the reconstructed surfaces according to RMS error, we mainly see surfaces through the variation of lighting (and hence normals) on the surface. Therefore, good normal reproduction is also important, especially in the presence of sharp features [34] . We have not investigated the quality of normal approximation using our method. Clearly the choice of reconstruction method in Section 3.1 will have a significant effect on normal quality. It would be interesting to compare the different techniques to determine if one compression method has better normal reproduction, but potentially worse positional error, than others.. Another avenue we would like to pursue is the development of out-of-core compression algorithm similar in style to the out-of-core surface reconstruction algorithms from [3, 4] . Currently, the input datasets we use from the Digital Michelangelo project are randomly sub-sampled by a factor of 50 from the full point distribution. While our method could handle the full point distribution, the extra detail would be lost at the reconstruction step since we currently limit the maximum octree depth to 10 (1024 3 grid). Our current method is limited by the size of the octree in memory and not the size of the input data, and a streaming, out-of-core compression algorithm would allow us to create deeper trees and use larger datasets. Our compression technique has two main components: building the adaptive octree structure that represents the surface and encoding this structure and the planes in its nodes. One important step of the first component is the pruning phase, described in Section 4.1. We have found that pruning around 80% of the original octree nodes results in a lossy compression without any noticeable changes in visual quality. This effect is mainly due to the fact that we can prune regions with low curvature without much loss to the visual appearance of the shape since we use planes to represent the geometry. Figure 9 shows an example where using a very small value for the tolerance ǫ removes about 80% of the nodes without much, if any, loss in visual fidelity. Since the pruning phase acts like smoothing in the presence of noise, pruning can even improve the quality of the reconstructed surface for noisy input data. Figure 9 also illustrates the dependence of the quality of the surface on the values of the tolerance ǫ. If we continue to increase ǫ, the piecewise linear nature of the reconstructed surface becomes apparent and the surface quality degrades. However, even if we keep only 2% of the nodes from the original octree, the shape is still recognizable (Figure 9 , right). Next, we compare the results of our algorithm with other known compression techniques. Note, that this is not an easy task since different compression methods use various types of input data or produce different output. For example, Park and Lee [29] use point cloud as input and output an octree whose leaves contain planes (as we do), but only use this structure for visualization and do not create a surface. Lee et al. [26] take a grid of values from an implicit function as input and produce a compressed structure that can be used to generate a polygonal model of the surface. Note that in order to perform a fair comparison between the different compression approaches, we also need a notion of a ground truth to compute the error of the compressed results.. First, we compare our method to the compression algorithm described in Park and Lee [29] . Since their output is a compressed tree structure, we need to post process this tree (using wavelet rasterization and polygonal extraction, as described in Section 3.1) and generate a polygonal model of the surface. Using Metro [33] , we then compute the RMS error between the surfaces generated from the uncompressed and compressed octrees, respectively, obtained from both our method and Park and Lee [29] . In each case, we choose settings for both algorithms such that the RMS error reported by Metro is approximately the same for both methods. For all examples in the paper we use a maximum octree depth d = 10 for all examples in the paper except the African statue in Figure 13 and the bunny in Figure 14 , where d = 8. The maximum octree depth should be proportional to the minimum sampling density, and both the African statue and bunny have far fewer samples than the other models. All of our examples were created from real scanner data (laser range scanners for Figures 9, 12, 14, 15, 16 and a structured light scanner for Figure 13 ) and contain noise. Figure 10 shows that our method out-performs Park and Lee in all examples and, on average, provides about a factor of three better compression rates. Depending on the model, the improvement is between 20% and 400%. For example, the African statue does not compress as well because of the high amount of noise from the structured light scanner, but other models, such as Awakening, compress well because the surface has large, nearly planar faces. Figure 12 shows an example reconstruction of Atlas with both techniques at approximately the same file size. Since Park and Lee's method is not as efficient, there is significant loss of details in the model. Second, we compare our method to compression schemes at different stages of the reconstruction pipeline (see Figure 1) . Note that we cannot use real scanner data to perform a quality comparison versus a ground-truth model, since the models the data was obtained from are not available. Therefore, we use a polygonal model as input and modify our octree construction in Section 2 (and that of Park and Lee) to build centroids and covariance matrices by integrating over the polygons clipped to the current nodes. We then compress this octree with each of the respective techniques, perform wavelet rasterization [5] to create an implicit function and extract polygons. We also perform wavelet rasterization on the uncompressed octree to create an implicit function and provide this function to Lee et al [26] for their implicit surface encoder. For each of these methods, we use the compressed representation to construct polygons and compare the output surface to the original polygon model using the RMS error reported by Metro [33] . Figure 11 displays the results obtained from these methods when applied to the model of the Stanford bunny. Similar to our previous tests, our method outperforms Park and Lee, especially at lower error values. Our compression results for the same amount of error are also better than the results, obtained using the algorithm from Lee et al.. Finally, we would like to point out that our method encodes a progressive representation of the surface that can be decoded and displayed during transmission. Figures 13, 14, 15, and 16 , depict examples of decoded surfaces with the size of the data transmitted up to that point as well as the final surface generated from the fully transmitted data stream ( far right images).. A potential limitation of our algorithm is the fact that the pruning phase could perform undesirable collapses or prevent collapses in regions that should collapse. We heuristically estimate the Hausdorff distance during the pruning phase, and while our approach performs well in practice, we have no theoretical guarantees that the reconstructed surface will have the same geometry or topology as the real model. Another drawback comes from the progressive nature of our compression/decoding. While this type of encoding stores multiple resolutions that can provide a view of the reconstructed surface without receiving all of the encoded data, it does not allow for random access to arbitrary data in the model. Data compression is an extensively studied area. To narrow our discussion of previous works, we will only examine progressive compression algorithms for surfaces. Unlike single rate encoders, progressive encoders embed a low resolution representation of the surface inside of a higher resolution representation. While generally having less potential for compression, progressive encoders are ideal for streaming data in low bandwidth environments or when transmissions may be truncated prematurely.. Based on the reconstruction pipeline in Figure 1 , the first opportunity for compression is to compress the input points. Progressive point cloud compression methods rely on inserting points into a deep octree and encoding the connectivity of the resulting structure [6, 7, 8] . More precisely, a point is simply represented by the center of its leaf node (at high depths, this encoding is almost lossless). Note that if the point set is oriented, normals will also have to be compressed [9] .. Many surface reconstruction algorithms [10, 11, 4] use the input points to create an implicit function represented as values over a uniform or adaptive grid, as shown in the third image in Figure 1 . Another compression strategy is to compress this function instead of the input points. For example, Laney et al. [12] use wavelet compressed signed distance volumes and encode the wavelet coefficients using a zero tree encoder, while Chandrasekaran et al. [13] use a multiscale surflet representation to compress the implicit function.. The final opportunity for compression is to compress the polygonal model that represents the reconstructed surface (see Figure 1 , right). While it is possible to treat the surface as an unstructured mesh and apply compression algorithms developed for such meshes [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] , Lee et al. [26] showed that significant gain is possible if we specialize the compression to take advantage of how the surface was generated, namely as a level set extracted from a uniform/adaptive grid. The authors achieve significant compression results by using a uniform octree and an entropy encoder to encode inside/outside information as well as detail vectors at the leaves of that tree. Lewiner et al. [27] encode surfaces extracted from hierarchical tetrahedral grids. Saupe and Kuska [28] compress the extracted iso-surface by exploiting a grid based extraction method to predict intersection values.. We achieve our compression results by creating, encoding and decoding an intermediate data structure, which is an adaptive octree with a plane inside each node (see the second image of Figure 1 ) that represents the original point cloud, and is used to produce the underlying surface. A similar approach has also been explored by Park and Lee [29] , where the authors build an octree refined to a uniform depth wherever points intersect the tree. Each node (interior or leaf) stores a plane constructed using principal component analysis (PCA) on the points within that node. They encode these planes by storing the signed distance along the normal direction of the parent's plane from three points (in the parent plane) to the child's plane and perform a zero-tree encoding [30] of these coefficients. The authors essentially view every child's plane as a function over the domain of the parent's plane. However, this approach has two serious flaws. The first is that the signed distance is infinite when the child's plane is perpendicular to the parent's plane. In this case the encoding fails. If the distance to all three points is infinite, the child's plane will contain the normal of the parent and can be rotated in any way about this normal. Therefore, the child's plane will be impossible to reconstruct without additional information. Given the progressive nature of the encoding, all further data in the file will be corrupted. The second disadvantage of this approach is the fact that the distances to these three points cannot be bounded. In practice, the distances will be small if the parent's plane is a good prediction of the child's plane, but in general the distance can be unbounded (even infinite as just stated), which makes quantization impossible and leads to poor compression.. We also encode planes using signed distances to three points; however, we use minimum Euclidean distance instead of distance along the parent normal. The advantage of doing so is that we can bound these distances with respect to the depth of each node, which improves compression performance and eliminates the failure case from [29] . The disadvantage is that we cannot reconstruct the child plane by solving a linear system of equations. Instead, during reconstruction, we solve a constrained optimization problem that has a computationally efficient closed-form solution.

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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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Towards the Development of a Simulator for Investigating the Impact of People Management Practices on Retail Performance Often models for understanding the impact of management practices on retail performance are developed under the assumption of stability, equilibrium and linearity, whereas retail operations are considered in reality to be dynamic, non-linear and complex. Alternatively, discrete event and agent-based modelling are approaches that allow the development of simulation models of heterogeneous non-equilibrium systems for testing out different scenarios.When developing simulation models one has to abstract and simplify from the real world, which means that one has to try and capture the 'essence' of the system required for developing a representation of the mechanisms that drive the progression in the real system. Simulation models can be developed at different levels of abstraction. To know the appropriate level of abstraction for a specific application is often more of an art than a science. We have developed a retail branch simulation model to investigate which level of model accuracy is required for such a model to obtain meaningful results for practitioners. The retail sector has been identified as one of the biggest contributors to the productivity gap that persists between the UK and other countries, in particular France, Germany and the USA (Reynolds et al, 2005) . UK retail productivity measures paint a worrying picture, describing lower levels of efficiency than what we would expect (Department of Trade and Industry, 2003) , and in particular lower than the benchmark countries already stated. Researchers have so far failed to explain fully the reasons accounting for the productivity gap, and management practices provide a valid and thoughtful way of looking at the problem. The analysis of management practices across different contexts has attempted to explain differences in organisational productivity and performance (for a review see Wall and Wood, 2005) .. A recent report on UK productivity asserted that "... the key to productivity remains what happens inside the firm and this is something of a 'black box'…" (Delbridge et al, 2006) . Siebers and colleagues conducted a comprehensive literature review of this research area to assess linkages between management practices and organisational productivity (Siebers et al, 2008) . The authors concluded that management practices are multidimensional constructs that generally do not demonstrate a straightforward relationship with productivity variables. Empirical evidence affirms that both management practices and productivity measures must be context specific to be effective. Management practices need to be tailored to the particular organisation and the working environment, whereas productivity indices must also reflect a particular organisation's activities on a local level to be a valid indicator of performance.. It is challenging work to try and delineate the effects of management practices from other socially embedded factors. Most Operations Research (OR) methods can be applied as analytical tools once management practices have been implemented, however they are not very useful at revealing system-level effects prior to the introduction of specific management practices. This is most restricting when the focal interest is the development of the system over time, because dynamic behaviour is a fundamental feature of many interesting real-world phenomena. An alternative to these methods, which offers potential to overcome such limitations, is simulation. In particular Agent-Based Simulation (ABS) seems to be well suited to the investigation of people centric complex adaptive systems, of which a retail department is an example.. There has been a lot of modelling and simulation of operational management practices, but people management practices, for example training, empowerment and teamwork, have often been neglected. This seems a fertile area for research as recent findings suggest that people management practices significantly impact on a business' productivity (Birdi et al, 2008) . One reason for the paucity of modelling and simulation of people management practices relates to their key component, an organisation's people, who are often unpredictable in their individual behaviour.. Our overall aim is to investigate if we can understand the relationship between people management practices and retail performance better by employing simulation as the technology for evaluating different strategies, considering that the system of interest is dynamic, non-linear and complex. In pursuit of this aim, we studied the following two research questions: (1) Is an event driven simulation a suitable tool to better understand the relationship between people management practices and retail performance? (2) What level of abstraction should we use for our simulation model?. To answer these research questions we have adopted a case study approach using applied research methods to collect both qualitative and quantitative data. We have worked with a leading UK retail organisation where we have conducted some surveys and four weeks of informal participant observation in four departments across two retail branches. The approach has enabled us to acquire a valid and reliable understanding of how the real system operates, revealing insights into the working of the system as well as the behaviour of and interactions between the different individuals and their complementary roles within the retail department. Using the obtained knowledge and the data, we have developed conceptual models of the real system and the individuals within the system and implemented these in a simulation model.. The simulation model has been developed in two major steps, each related to one of the research questions defined above. First, we have focused on putting together a functional representation of the real system using a mixed process oriented Discrete Event Modelling (DEM) and individual oriented Agent-Based Modelling (ABM) approach and we have tested if such an approach is of use for investigating the impact of people management practices on productivity and customer satisfaction. After obtaining a positive answer to the first research question (Siebers et al, 2009 ) in this paper we are focusing on investigating the second research question, i.e. the level of model accuracy required to obtain some meaningful results for practitioners. For this purpose, we have added some more details to the model (in form of additional algorithms and empirical data) and tested the sensitivity of the simulation model towards these additions. We have also compared the system performance predicted by the simulation model (without using any form of calibration) to the system performance measured in the real system, in order to access the predictive capabilities (qualitative or quantitative) of our simulation model in its current form.. The investigations described in this paper help practitioners to decide how to best coordinate time and effort when having limited resources for conducting similar simulation studies.. In Section 2 we give an overview of the relevant literature, looking at the different modelling approaches used in OR and at previous attempts to model people centric systems in an OR context. Section 3 describes the model design and its implementation. Section 4 presents two experiments we have conducted to validate the final simulation model and to test the sensitivity of the model output towards some of the features we have added in the second development step of our simulation model. Section 5 concludes the paper with a summary and an outlook of further developments.. OR is applied to problems concerning the conduct and co-ordination of the operations within an organisation (Hillier and Lieberman, 2005 ). An OR study usually involves the development of a scientific model that attempts to abstract the essence of the real problem. When investigating the behaviour of complex systems the choice of an appropriate modelling technique is very important. To inform the choice of an appropriate modelling technique we have reviewed relevant literature from the fields of Economics, Social Sciences, Psychology, Retail Management, Supply Chain Management, Manufacturing, Marketing, OR, Artificial Intelligence, and Computer Science. In this paper, we have presented an alternative to the modelling approaches commonly used for investigating the impact of management practices on retail performance. Simulation modelling has proven to be a very useful tool for this kind of analysis. In particular, the combination of DEM for representing the system and ABM for representing the entities inside the system seems to be the formula to success for building simulation models of dynamic heterogeneous people-centric systems in an OR context.. Our investigations for this paper have focused on the question of what level of abstraction to use for this kind of simulation models. We have added several features to our initial functional representation of the real system. Our latest version now includes more complex operational features to make it a more realistic representation closer to the real retail environment that we observed and also we have developed our agents further, enhancing their heterogeneity and decision making capabilities. We have tested some of these new features here by conducting validation experiments as well as a sensitivity analysis. However, more tests are required to establish the usefulness of all the enhancements implemented in the latest simulation model.. After the first experiment, we realised that with the introduction of a finite population we also had to rethink the way in which we collect statistics about the satisfaction of customers. The existing measures did not reflect individuals' satisfaction with the current service experience any more but instead measured the satisfaction with the overall service experience during the lifetime of the agent. We have added some customer satisfaction measure to evaluate how the current service is perceived by each customer. These measures have shown to provide some useful information for analysing the service quality as perceived by the customer during each visit and results are much closer to what we would expect from the real system.. A big advance has been the implementation of a diverse population by introducing customer types. These allow a better representation of the different service needs that customers have in different departments and the different responses to the service provided which is also apparent in the real world customer population. Through introducing customer types, we have been able to better define the heterogeneous customer groups who visit the different case study departments and observe previously hidden differences in the impact of the people management practices on the different departments. In our second experiment, we have demonstrated that customer types exert a considerable impact on system performance. It is therefore important that practitioners invest time and effort in analysing the existing types of customers and their actual proportions and derive an effective way of characterising the differences between these groups.. We have used likelihoods to buy, wait and request help for defining our customer types. However, there are many more categories one could use to distinguish different customer types, e.g. how frequent customers come back or how many items they buy per visit. Much of the required data could be available from loyalty cards or are collected by the marketing department of the company. Building on this advance is the introduction of a finite population with an enduring memory of customers' previous shopping experiences for each individual agent. This allows the agents to change their behaviour (e.g. their patience level) according to their previous experiences. These two enhancements enable us for the first time to study long-term effects of managerial decisions on specific customer groups with our simulation model, which opens up a new range of problem scenarios that can be investigated. This is particularly important as many managerial decisions are developed for the long-term (to encourage trust and loyalty from both staff and customers) and are unlikely to demonstrate their full potential after just a single visit of an individual customer.. Overall, we can affirm that the upgrades we have introduced and tested so far are all useful assets to the simulation model. They allow us to obtain a broader understanding of the situation and to investigate many issues and questions we could not previously investigate as for example long-term effects of people management practices on specific customer groups.. A major limitation in ManPraSim v2 is the absence of consideration of staff proactiveness. What we have actually observed during our case study and what is also encouraged by the guidelines for staff of the case study company is that employees act proactively, for example by approaching customers within a set period of time, or opening tills when queues grow beyond a defined level. In addition, if we want to use real staffing data in order to enhance the predictive capabilities of our simulation model relative to the real system, we need to model how staff allocate their tasks between competing activities rather than focusing on one type of work. Considering these features will allow us to increase the grade of realism to match the behaviour of the actors in the model better to those in the real system. This will also allow us to achieve a better match when comparing the system performance measures (e.g. transactions or staff utilisation) between our simulation model and the real system. Luckily, ABM is a technique that supports the modelling of proactive agent behaviour. First however, we need to test our remaining enhancements by conducting more sensitivity analyses, to see if these are useful assets or not.. Once these limitations have been eradicated, we would like to conduct some more fundamental investigations. As we stated earlier the most interesting system outcomes evolve over time and many of the goals of a retail business (e.g. service standards) form part of a long-term strategy. It would be interesting to see under which circumstances the demand for services varies in an unplanned fashion and how well staff can cope with it. A common application area for ABM is modelling the diffusion of innovations in social networks (Bonabeau, 2002; Garcia, 2005; Janssen and Jager, 2002) . We would like to use the ideas developed here and transfer them into a service context to model diffusion of customer experiences in form of word-of mouth networks. With our enhanced agents, we are now in the position to investigate this kind of issues.. Finally, we are interested in exploring other ways of implementing the agent decision making process. It has been argued that modelling the autonomous internal decision making logic of customers is a crucial element for simulation models of consumer behaviour (Jager, 2007) . It would be interesting to compare such an approach to the probabilistic one we have currently in place, in particular as no such study has been found in the literature.. In conclusion, we can say that the multidisciplinary of our team has helped us to gain new insight into the behaviour of staff and customers retail organisations. The main benefit from adopting this approach is improved understanding of, and debate about, a problem domain. The very nature of the methods involved forces researchers to be explicit about the rules underlying behaviour and to think in new ways about them. As a result, we have brought work psychology and simulation modelling together to form a new and exciting research area. The descriptive statistics for ManPraSim v1 and v2 are shown in Table 4 and graphical representations of the results are presented in Figure 4 . Looking at the number of transactions for both departments, it is clear that both simulation model versions produce a highly similar pattern of results. The number of satisfied customers is higher across all conditions of both departments for ManPraSim v2. This is as predicted and interestingly very high levels of satisfaction can be seen in WW in particular. We attribute this to the higher transaction volumes in WW coupled with our expectations of v2 resulting in higher levels of customer satisfaction as customers visit the branch on multiple occasions and commit to polarised opinions. Examining the overall satisfaction level, our hypotheses hold; results for both departments clearly follow the same trends regardless of simulation model version. In summary, all results are as predicted. . A series of two-way between groups ANOVAs have been used to assess the impact of customer types on counts of customer leaving satisfied after a transaction with a cashier, whilst waiting for normal or expert help, whilst waiting to pay, or leaving without finding a suitable purchase. The descriptive statistics for the experiment are shown in Figure 5b ). In particular, the significantly higher count of customers leaving prematurely for (c) and (e) is very pronounced.. Results for customers leaving before receiving expert help revealed significant effects for both department [F(1, 266 Figure 5d and display consistent support for the hypotheses.. Results for customers leaving before finding anything to buy revealed significant effects for both department [F(1, 266 Figure 5e ).. To investigate the impact of customer types on satisfaction indices, the mean customer satisfaction ratings have been calculated for each configuration and have been displayed in 100% stacked bar charts. Figure 6 shows customer satisfaction considering the customer's previous experiences, and Figure 7 shows customer satisfaction considering the customer's experience per visit. There is much differentiation in satisfaction scores across the contrasting customer type configurations. Evidence supports all hypotheses. . customer type configuration % of customers % dissatisfied % neutral % satisfied Figure 6 : Results from experiment 2 -customer satisfaction (considering history, in % of total customers).. Overall, our results indicate that customer types exert a considerable impact on system performance as demonstrated by a number of complementary measures. In addition, we have shown that our new customer satisfaction measures are a useful asset for analysing the service quality as perceived by the customer at each individual visit. We have presented evidence demonstrating that our improved customer satisfaction measure produces results closer to what we would expect from the real system, and the increased polarisation of customers' satisfaction is a further reason to select carefully which customer types to implement in the simulation model.. customer type configuration % of customers % dissatisfied % neutral % satisfied Figure 7 : Results from experiment 2 -customer satisfaction (per visit, in % of total visits). Our conceptual models have been implemented in AnyLogic™ v5.5. This is a Java™ based multi-paradigm simulation software (XJTEK, 2005) which supports the development of mixed DE and AB models. It allows replacing passive objects from the DE model with active objects (agents), which in our case represent the actors of the real world system.. As mentioned in Section 1, the simulation model has been developed in two major steps, each related to one of the research questions. In a first step, we have developed a relatively simple functional representation of the real system. In this simulation model the agents are largely homogeneous and without memory. Therefore, it is impossible to study the long term effects of people management practices on individual customer's satisfaction. However, this simulation model turned out to be useful for studying certain aspects of branch operations and to carry out some experiments for investigating the impact of different people management practices on a strategic level. In a second step, we have added some more features to the simulation model. We have created a finite population of heterogeneous agents with memory and the capability to evolve over time and we have added more accuracy to our department representation by adding empirical footfall data, opening hours varying by day and a special customer egress procedure for the time when the store is closing. The simulation model has been validated and we have conducted a sensitivity analysis. In this section, we describe the functionality and features of the latest version of our simulation model, ManPraSim v2. As mentioned earlier our simulation model has been developed in two major steps. After finishing the first step, we have conducted a set of experiments to investigate the usefulness of the developed simulation model for studying the impact of people management practices on productivity and customer satisfaction (Siebers et al, 2007a; 2007b) . We investigated the following scenarios:. • Branch operation: (1) Varying the number of open tills and consequently the mix of staff roles (as we kept the overall staffing level constant) to assess the effect on department performance. (2) Testing the impact of expert staff availability on customer satisfaction.. • Empowerment: (1) Varying the extent to which a cashier can independently decide whether or not to make a customer refund. (2) Testing the impact of non-expert staff members choosing whether or not to stay with his or her customer when the customer requires advice from an expert. If they choose to stay, the original staff member can learn from the interaction.. • Training: Mimicking an evolutionary process, whereby normal staff members can progressively develop their product knowledge over a period of time and at an agreed level of competence will be promoted to expert status.. With ManPraSimMod v2 we have conducted a validation experiment to test our customer pool implementation and we have conducted a sensitivity analysis to investigate the impact of our customer types on simulation output. These experiments are not conducted to provide insight into the operations of the case study department. Instead, they are carried out to test the simulation model behaviour.

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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 O O O O O O 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 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 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 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 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 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 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 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 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 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 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Features and models for human activity recognition Human Activity Recognition (HAR) is aimed at identifying current subject task performed by a person as a result of analyzing data from wearable sensors. HAR is a very challenging task that has been applied in different areas such as rehabilitation and localization. During the past ten years, plenty of models, number of sensors and sensor placements, and feature transformations have been reported for this task. From this bunch of previous ideas, what seems to be clear is that the very specific applications drive to the selection of the best choices for each case.Present research is focused on early diagnosis of stroke, what involves reducing the feature space of gathered data and subsequent HAR, among other tasks. In this study, an Information Correlation Coefficient (ICC) analysis was carried out followed by a wrapper Feature Selection (FS) method on the reduced input space. Additionally, a novel HAR method is proposed for this specific problem of stroke early diagnosing, comprising an adaptation of the well-known Genetic Fuzzy Finite State Machine (GFFSM) method.To the best of the author's knowledge, this is the very first analysis of the feature space concerning all the previously published feature transformations on raw acceleration data. The main contributions of this study are the optimization of the sample rate, selection of the best feature subset, and learning of a suitable HAR method based on GFFSM to be applied to the HAR problem. This research aims at developing a solution for the early diagnosis of stroke and the rehabilitation of elder people after a disruptive event: an injury due to a falling, a seizure onset, etc. In this context, only a small subset of activities are to be identified among those that a human being can usually do and hence, the recognition of those activities is simplified. On the other hand, activity recognition devices help to improve the mobility of elder people during rehabilitation, so technology is enhancing the quality of life for both elder and injured people.. Every human being performs different activities during the day and Human Activity Recognition (HAR) targets their identification. Though walking recognition is nowadays a clear-cut task [1] , the recognition of other activities is not. It is difficult due to the fact that there are many different activities that a person may perform and some of them could even co-occur at the same time (talking to another person or eating a sandwich while walking, reading and being seated). Additionally, a wide spread of feature transformations and HAR methods have been applied up to now [2] [3] [4] . Many techniques of data gathering, including video-images, are being used but tridimensional accelerometers are the data sources for the majority of previous HAR studies.. The main problem to be solved in this research is the early diagnosis of stroke onsets. During such episodes, the upper limbs are the parts of the body that best reflect the differences regarding normal behavior. According to this idea, two triaxial accelerometers are usually placed on the subject's wrists. The hypothesis is that with these sensors we would be able to recognize an onset due to the differences in the movement patterns. However, these movement patterns will depend on the task that the subject is carrying out. Therefore, early diagnosis of stroke also includes HAR. Interesting enough to mention, the activities to be identified also depends on the focused population; generally speaking, the older you are the lower the amount of activities you perform during everyday life. Additionally, the quality of the movement may be rather different depending on the age of people; the younger the faster. Thus, the target population define the different activities that may be excluded for recognition and the age reduces the amount of movement; movement for the elderly is lower than that for younger people while performing the same activities.. A light wearable device might be enough for the data gathering, pre-processing and analyzing provided the number of activities to be recognize is kept small. Previous studies have analyzed the window size for pre-processing the continuous data flow coming from data sources [5] . Once the data is properly pre-processed, they must be analyzed.. To do so, one of the most interesting HAR techniques that can be deployed in embedded devices is the Genetic Fuzzy Finite State Machine (GFFSM) [6] , which also handles expert knowledge with high accuracy. To this end, this study is focused on enhancing the HAR by using 3DACC sensors in wearable devices. The aim of this research is two-fold: on the one hand, the selection of the 3DACC transformations is analyzed and a feature subset is chosen; on the other hand, the improvements on the GFFSM model obtained due to the reduced feature subset are explored. The study also makes use of this GFFSM model for HAR, but additionally it tackles several issues identified as relevant in the HAR literature, such as those concerning the diversity of transformations from the acceleration raw data and how to reduce the dimensionality and the different methods for cross validation used so far.. The remainder of the present paper is organized as follows. Next section deals with the challenging task of HAR, including an overview of the input feature domain, as well as an in-depth review of the HAR literature. Section 3 introduces the proposed method and its different stages: a two-step Feature Selection (FS) and a HAR modeling by means of the GFFSM. Subsequently, Section 4 is devoted to evaluate and discuss the experimentation carried out. Finally the main conclusions from the obtained results as well as the future work are drawn in Section 5. This study presents a Soft Computing method for modeling and validation in HAR, focusing on three different activities: being seated, standing up and walking. The main objective of this study is choosing the best combination of parameters to get the subset of features that best describes this HAR problem while the activity conditions are modeled at the same time. So, this study proposes a FS method to reduce the space dimensionality of features that differs from previous work.. In keeping with this idea, a Hybrid Artificial Intelligent Method has been designed an applied. In a first stage, a filter FS is deployed to reduce the dimensionality by evaluating each feature with ICC. Then, the most important features are selected for the wrapper stage. Since the features are chosen, a wrapper FS method using GFFSM is the responsible of searching for the best feature subset associated with each data set and for modeling the human activity. Finally, different schemes are used for validation of the best parameters for features subset and the model from the best data set with different runs.. From the obtained results some conclusions are drawn. Firstly, the use of a cross validation scheme for feature selection might improve the overall outcome. Perhaps a voting scheme may also improve the performance of the feature filtering stage. Secondly, the window size has been found problem specific, thus the different studies in the literature should have made such analysis without reporting it. Finally, it has been found a proper feature subset and the corresponding GFFSM model for HAR, which is to be deployed in further research concerning early stroke diagnosis.. Future work envisages the implementation of more stages in the Finite State Machine, easing the experts reutilization of the presented approach. Further research would be done to include others styles to tackle the learning for the fuzzy rule base of the GFFSM instead of predefining it. León (2002) . He has worked in several companies focused in different fields, from automation to electric power generation. He was a member of the Electronic and Engineering Department at University of León (1998 León ( -2004 . Currently, he is an Associate Professor with the Computer Science Department at University of Oviedo.. His research topics are based on Genetic Fuzzy Systems and Soft Computing applied in real world systems. He has received public funds for his research in fields like Electrical Distribution Optimization and Bioinformatics. Moreover, he has signed research projects with local enterprises for developing new products or for research transfer; several patents have been approved as a consequence. Currently, he has published more than 20 research studies in indexed journals and more than 70 conference papers.. Emilio Corchado is Associate Professor at University of Salamanca and Full Professor accredited by ANECA since 15th February 2012. He is the President of IEEE Spain Section (2014-2015), founder and Vicepresident of Spanish Chapter SMC of IEEE. Founder of the GICAP Research Group of University of Burgos, member of IBSAL, and member of BISITE Research Group (University of Salamanca). He is the Coordinator of European Project WELCOME and partner of European project SEP, both projects under the initiative H2020 from European Commission.. He is president of 4 international congresses, he has been president of Organizing Board of 10 Congress/ Workshop of highly recognized prestige and member of Editorial Board of the following magazines: KAIS, IJNS, IJCIA, Computational Intelligence, IJRIS. He is/has been IP of more than 12 competitive national and regional projects of I+D, coordinator of 1 European project, member of another in H2020 framework, and member of 10 European competitive projects of I+D.. Besides he has been Coordinator of the node of University of Burgos and Salamanca of 2 European and 5 national thematic networks. He is member of several COST Actions (IntelliCIS, APP for AAL, etc.). He is author of more than 70 published articles in magazines with impact factor, 38 books and chapter of books of scientific character and author/coauthor of more than 120 published articles in international scientific congresses.

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


Spatiotemporal reconstruction of list-mode PET data Abstract-We describe a method for computing a continuous time estimate of tracer density using list-mode positron emission tomography data. The rate function in each voxel is modeled as an inhomogeneous Poisson process whose rate function can be represented using a cubic B-spline basis. The rate functions are estimated by maximizing the likelihood of the arrival times of detected photon pairs over the control vertices of the spline, modified by quadratic spatial and temporal smoothness penalties and a penalty term to enforce nonnegativity. Randoms rate functions are estimated by assuming independence between the spatial and temporal randoms distributions. Similarly, scatter rate functions are estimated by assuming spatiotemporal independence and that the temporal distribution of the scatter is proportional to the temporal distribution of the trues. A quantitative evaluation was performed using simulated data and the method is also demonstrated in a human study using 11 C-raclopride.

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