Enabling community-driven information integration through clustering Abstract It has become widely recognized that user feedback can play a fundamental role in facilitating information integration tasks, e.g., the construction of integration schema and the specification of schema mappings. While promising, existing proposals make the assumption that the users providing feedback expect the same results from the integration system. In practice, however, different users may anticipate different results, due, e.g., to their preferences or application of interest, in which case the feedback they provide may be conflicting, thereby deteriorating the quality of the services provided by the integration system. In this paper, we present clustering strategies for grouping information integration users into groups of users with similar expectations as to the results delivered by the integration system. As well as grouping information integration users, we show that clustering results can be used as inputs to a wide range of functionalities that are relevant in the context of crowd-driven information integration. Specifically, we show that clustering can be used to identify feedback of relevance to a given user by exploiting the feedback provided by other users in the same cluster. We report on evaluation exercises that assess the effectiveness of the clustering strategies we propose, and showcase the benefits communityand crowd-driven information integration can derive from clustering. Information integration is difficult. Essentially, the difficulty lies in devising schema mappings that populate the elements of the integration schema using given data sources. To face this challenge, there has recently been growing interest in guiding the specification of schema mappings using feedback solicited from end users [4, 8, 9, 19, 28] . For example, Jeffery et al. [19] developed a decision-theoretic framework for specifying the order in which candidate mappings can be confirmed by soliciting feedback from users, with the objective of providing the most benefit to a data integration system.. As existing proposals demonstrate [4, 8, 9, 19, 22, 28] , feedback can play a key role in facilitating the construction of information integration systems. However, a user may need to provide a sizable number of feedback instances before the quality of the services provided by the integration system reaches a satisfactory level. To address the problem of feedback scarcity, a handful of researchers investigated the use of feedback solicited from communities of users [22] , or more generally through the crowd [19, 22, 31, 32] . For example, McCann et al. [22] developed a community-based approach that solicits feedback on matches from the multitude of community members. The idea is to use collectively the feedback provided by the members of given community, thereby ensuring that the quality of the services provided by the information integration system quickly reaches an acceptable level. Wang et al. [31] and Whang et al. [32] explored the use of crowdsourcing techniques for leveraging entity resolution. Specifically, the crowd members are presented with a set of record pairs with the objective of identifying the pairs of records that are semantically equivalent.. While promising, the above proposals make the assumption that the users providing feedback have the same expectations as to the results that should be delivered by the integration system. In practice, however, different users may have different expectations from the integration, in which case the quality of the services provided by the integration system may deteriorate due to conflicts in feedback. By user expectations, we mean the results that the user anticipates from the integration system. The difference in expectations between users may be due to differences in their preferences, or to their domain and application of interest.. The difference in users' expectations can be captured by analyzing metadata that describe, e.g., their fields and applications of interest. However, such metadata are not always available, and when they are, they may be misleading. For example, two scientists who work on the same field, e.g., proteomics, may be judged as having the same expectations. Yet, one may be interested in using the integration system to profile proteins functionalities of a given species, whereas the other is interested in identifying protein interactions of a different species. In these cases, user feedback seems to be a better source of information. For example, to deal with the low precision of wrappers of web resources that are generated based on metadata, Crescenzi et al. [10] showed that the quality of the wrappers Fig. 1 Examples of tuples of the FavoriteCit y relation annotated with feedback from two users generated using crowd feedback is better than the wrappers generated based on generic metadata.. To illustrate the case of users with different expectations, consider an information integration system providing integrated access to information about geographical areas. In particular, the integration system contains a relation, FavoriteCit y, that provides information about favorite cities in the world. Figure 1 illustrates example tuples of the FavoriteCit y relation. Different users have different preferences as to the criteria a city must meet to be listed among his/her favorites. For example a given user, user 1 , may prefer cities that are located near ski resorts, whereas another user, user 2 , would prefer cities located near a beach. Because of this, some of the feedback instances supplied by the two users (see Fig. 1 ) are conflicting: the tuples t 1 , t 3 and t 6 are true positives for user 1 and are false positives for user 2 , and the tuples t 2 and t 5 are true positives for user 2 and false positives for user 1 . Tuple t 4 refers to the city of Manchester, which is a false positive for both users as it is not located near a ski resort or a beach. Using the feedback instances provided by the two users, e.g., to identify the mapping to be used to populate the FavoriteCit y relation, may deteriorate the quality of the integration system over time, e.g., the mappings constructed or selected in the light of the feedback provided by the two users may miss a large number of true positives for both users. This simple example shows the need to identify groups of users with similar expectations as to the results that should be provided by the integration system, e.g., user 1 and user 2 should belong to different groups. Grouping users ensures that if used collectively, the feedback instances the users provide can improve the quality of the integration system.. We consider for the purposes of this paper feedback of the above form, that comments on the membership of tuples to relations in the integration schema. We have shown in previous work [3, 25] , that schema mappings can be selected, refined and annotated with metrics specifying their fitness based on this kind of feedback. We have also showed through empirical exercises that feedback scarcity impacts negatively on the quality of the data integration system. For example, we have shown that using a few feedback instances, e.g. 10, commenting on tuple membership to a relation in the integration schema, the precision estimates computed for assessing the quality of the schema mappings that are candidates to populate such a relation, suffer from a high error [3] .. In this paper, we show how clustering [18] can be used to identify groups of users with similar expectations as to the results delivered by the integration system. Specifically, given a set of users, we use clustering to identify groups of users with similar expectations. Users can be clustered by comparing the feedback instances they provide, i.e., by comparing the feedback given by different users on the same tuples. The more overlapping and less conflicting the feedback provided by two users, the more likely they are to belong to the same cluster.. Because users provide feedback only on a subset of the tuples presented to them, and because the number of tuples that can be used to populate a given relation in the integration schema can be large, the chance that the same tuple is annotated by more than one user may be small. As a result the above clustering strategy performs poorly, as the experimental results that we will report on later in the paper show, due to the sparsity of the feedback provided by users. To overcome this problem, we devised a second clustering strategy that measures similarity between users as to the results they expect from the integration by using mapping precision estimates that are computed based on the feedback provided by users. Such annotations are estimates that assess the fitness of schema mappings in the information integration system to the expectations of a given user based on the feedback supplied by that user. Given a schema mapping, the closer the respective precision estimates computed based on the feedback supplied by two users, the more similar are their expectations from the integration. Unlike the first strategy, users do not have to provide feedback annotating the same tuples. We show that this clustering strategy produces good quality clustering with only a small amount of feedback.. Clustering results can be used to leverage the process by which user expectations from the integration are ascertained. In particular, the expectations of individual users from the integration system can be learned using the feedback supplied by the cluster to which they belong. We empirically investigate the amount of feedback gained by using clustering results to learn the expectations of individual users from the integration system. We also present a method to accelerate the process by which the expectations of new users of the integration system are ascertained. To do so, we cast this problem as a classification problem whereby new users are assigned to existing clusters.. Clustering can be used to improve the quality of information integration systems. For example, it can be used to improve the quality of schema mappings by refining them to meet the expectations of the users within a cluster [3] . In this paper, we report on the results of two applications of clustering. The first shows that the feedback supplied by a cluster can be used to select the schema mappings that fit best the expectations of the users within the cluster. We also show that clustering can be used to identify malicious users, i.e., users who provide misleading feedback with the objective of hampering the process by which the quality of the information integration system is improved.. The contributions we make in this paper enable crowd-based information integration using clustering. While in a general setting the workers in the crowd are different from the users who exploit the integration system, in our setting, the users of the information integration system are enlisted to be the workers who, through their feedback, improve the quality of the information integration system. Specifically, we make the following contributions:. 1. Strategies for clustering information integration users: we present and examine distance functions that can be used to group information integration users (in Sect. 3). 2. Methods for learning feedback for users, as to their expectations from the integration system, based on clustering: we estimate the benefit in terms of feedback that individual users gain, i.e. do not have to provide, by using clustering results (in Sect. 4). We also present a method whereby new users are assigned to the (existing) cluster that best meets their expectations (in Sect. 5). 3. Clustering applications: we show that clustering can be used to improve the quality of an information integration system. Specifically, we show that it can be used to identify the mappings that best meet the expectations of users, and to identify malicious users (in Sect. 6).. Additionally, we present the feedback model we consider in this paper, and show how user feedback can be used to annotate schema mappings with precision estimates in Sect. 2. We analyze and compare existing works to ours in Sect. 7, and conclude the paper in Sect. 8. Crowd computing provides access to a new and extensive resource for use in a wide range of applications. In information management, the crowd has been used to support a range of tasks, for example in information extraction [10] , matching [22] , mapping [25] and entity resolution [31, 32] , demonstrating its potentially wide utility. However, by their nature, crowds are populated by individuals, who may have different perspectives and requirements. As such, it is potentially important when obtaining information from crowds to identify different groups of crowd users, if maximum use is to be of the information.. In this paper, we have presented clustering strategies that can be used to organize information integration users into groups with similar expectations based on the feedback they provide. Revisiting the contributions claimed in the introduction:. 1. Strategies for clustering information integration users: we have described and evaluated distance functions that have been used with hierarchical clustering to support dependable clustering even when there is little or no feedback on identical data items. This enables users to provide feedback on whichever data items they choose, both allowing the use of unobtrusive information gathering and reducing the requirement for users to answer rather specific questions for which they may not have the necessary knowledge. 2. Methods for sharing feedback between users: given the clusters of users from (1), we have: (i) explored the circumstances in which, and the extent to which, benefits can be derived from sharing feedback within clusters; and (ii) described and evaluated a technique for that for assigning new users to existing clusters efficiently in terms of the amount of feedback required. 3. Clustering applications: given the clusters of users from (1), we have: (i) described and evaluated how feedback associated with the users in a cluster can be used to select schema mappings that return results of relevance to individual cluster members; and (ii) shown how the clustering process can itself be used to identify users who maliciously provide incorrect information.. As such, we complement existing work on crowdsourcing for information management, which generally assumes that information obtained from the crowd is agnostic to the perspectives and requirements of individuals, by showing how clustering can be used to identify similarities between groups of users that can be built upon to support the sharing of feedback between similar users. In this section, we compare and analyze existing work to ours. In doing so, we review proposals that tackle the problem of clustering of users, and those exploiting feedback given by the crowd (e.g., a user community) to drive the construction and improve the quality of information integration systems.

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


Automated Marsh-like classification of celiac disease in children using local texture operators Automated classification of duodenal texture patches with histological ground truth in case of pediatric celiac disease is proposed. The classical focus of classification in this context is a two-class problem: mucosa affected by celiac disease and unaffected duodenal tissue. We extend this focus and apply classification according to a modified Marsh scheme into four classes. In addition to other techniques used previously for classification of endoscopic imagery, we apply Local Binary Patterns (LBP) operators and propose two new operator types, one of which adapts to the different properties of Wavelet transform subbands. The achieved results are promising in that operators based on LBP turn out to achieve better results compared to many other texture classification techniques as used in earlier work. Specifically, the proposed wavelet-based LBP scheme achieved the best overall accuracy of all feature extraction techniques considered in the two-class case and was among the best in the four-class scheme. Results also show that a classification into four classes is feasible in principle, however, when compared to the two-class case we note that there is still room for improvement due to various reasons discussed. Celiac disease is a complex autoimmune disorder in genetically predisposed individuals of all age groups after introduction of gluten containing food. Commonly known as gluten intolerance, this disease has several other names in literature, including coeliac disease, c(o)eliac sprue, non-tropical sprue, endemic sprue, gluten enteropathy or gluten-sensitive enteropathy. The gastrointestinal manifestations invariably comprise an inflammatory reaction within the mucosa of the small intestine caused by a dysregulated immune response triggered by ingested gluten proteins of certain cereals (wheat, rye, and barley), especially against gliadine. During the course of the disease, hyperplasia of the enteric crypts occurs and the mucosa eventually looses its absorptive villi thus leading to a diminished ability to absorb nutrients. The real prevalence of the disease has not been fully clarified yet. This is due to the fact that most patients with celiac disease suffer from no or atypical symptoms and only a minority develops the classical form of the disease. Since several years, prevalence data have been continuously adjusted upwards. Fasano et al. (2003) state that more than 2 million people in the United States, this is about one in 133, have the disease. People with untreated celiac disease, even if asymptomatic, are at risk for developing various complications like osteoporosis, infertility and other autoimmune diseases including type 1 diabetes, autoimmune thyroid disease and autoimmune liver disease.. Endoscopy with biopsy is currently considered the gold standard for the diagnosis of celiac disease. Besides standard upper endoscopy, several new endoscopic approaches for diagnosing celiac disease have been applied (Chand and Mihas, 2006) . The modified immersion technique described in Cammarota et al. (2006) is based on the instillation of water into the duodenal lumen for better visualization of the villi. Furthermore, magnifying endoscopy (standard endoscopy with additional magnification) has been investigated (Cammarota et al., 2004) . For conducting capsule endoscopy (see Petroniene et al. (2005) ) the patient swallows a small capsule equipped with a camera that takes images of the duodenal mucosa during its passage through the intestine. All these techniques aim to detect total or partial villous atrophy and other specific markers that show a high specificity for celiac disease in patients. These markers include scalloping of the small bowel folds, reduction in the number or loss of Kerkring's folds, mosaic patterns and visualization of the underlying blood vessels (Niveloni et al., 1998) . During endoscopy at least four duodenal biopsies are taken. Microscopic changes within these specimen are classified by a histological analysis according to a classification scheme by Oberhuber et al. (1999) which is based on Marsh (1992) .. Automated classification as a support tool is an emerging option for endoscopic diagnosis and treatments (e.g. Karkanis (2003) ; Ameling et al. (2009) ; Alexandre et al. (2008) ; Iakovidis et al. (2006) ; Liedlgruber and Uhl (2009) ). Systems are being developed that support physicians during surgery or highlight malignant areas during endoscopy for further inspection. Such systems could also be used for training purposes. In the context of celiac disease, an automated system identifying duodenal areas affected by the disease would offer the following benefits (among other):. • Methods that help indicating specific areas for biopsy might improve the reliability of celiac disease diagnosis. As biopsying is invasive and the number of biopsy samples should be kept small, optimal targeting is desirable. This targeting can be supported by an automated system for identification of areas affected by celiac disease.. • The whole diagnostic work-up of celiac disease, including duodenoscopy with biopsies, is time-consuming and cost-intensive. To save costs, time, and manpower and simultaneously increase the safety of the procedure it would be desirable to develop a less invasive approach avoiding biopsies. Recent studies by Cammarota et al. (2006 Cammarota et al. ( , 2007 investigating such endoscopic techniques report reliable results. These could be further improved by analysis of the acquired visual data (digital images and video sequences) with the assistance of computers.. • The (human) interpretation of the video material captured during capsule endoscopy (Petroniene et al., 2005 ) is an extremely time consuming process. Automated identification of suspicious areas in the video would significantly enhance the applicability and reduce the costs of this technique for the diagnosis of celiac disease.. In a prior study, Vécsei et al. (2008) suggest using histogram-based and Wavelet-based features for classification. Subsequent work optimizes Fourier features used for classification by applying an evolutionary process already delivering competitive classification results. In recent work (Hegenbart et al., 2009) , we have systematically compared the classification performance of two different image capturing techniques (i.e. conventional imaging vs. the modified immersion technique) and various pre-processing schemes using a set of different feature extraction and classification methods. Ciaccio et al. (2010) measure the mean and standard deviation in brightness over 10 × 10 pixel subimages to identify areas affected by celiac disease in capsule endoscopy, and also apply spectral analysis over sequential images to identify abnormal bowel motility.. Contributions In this work, we describe for the first time a system aimed at performing automated classification of duodenal texture patches according to a reduced 4-class Marsh-like classification system. Corresponding results are requested for a staging of the observed mucosa defects with impact on clinical practice regarding treatment. Local Binary Patterns (LBP) based feature extraction is applied to the problem of automated celiac disease diagnosis for the first time and turns out to outperform techniques previously applied. In particular, we propose two new operator types, one of which adapts to the different properties of Wavelet transform subbands and results in the best overall classification accuracy in the two-class scheme of all feature extraction schemes considered. In the four-class scheme the proposed method was still among the best methods. Moreover, we contribute in providing explicit strategies for threshold selection and quantization in operators proposed in earlier work.. Structure In section 2, we describe image acquisition and the establishment of ground truth information according to a modified Marsh classification. Section 3 covers LBP operators where we also propose two new operator types, among them a new Wavelet-based operator that combines two LBP-based operators to adapt to Wavelet subband properties. In section 4 we present experimental results where we compare the classification results of the proposed methods to techniques applied previously to classify endoscopic image material. Section 5 concludes the paper. We have found statistically significant differences in classification accuracy among different settings, especially between the two and four-classes case. The performance of the used methods builds a solid basis for future work in case of the two-class scheme for classification. In case of the fourclasses case however we saw that the used features fail to discriminate between the Marsh-3 subtypes. Overall classification rates in the range of 60 to 65 percent requires more effort to justify a clinical deployment. We saw that using information about how well certain methods generalize an improved ensemble yielding robust features that improved the classification rates in the four-class case could be found. Although comparing this result to the result of the other methods lacks fairness to some degree, it indicates that there is room for further improvement. Ensari (2010) states that the Marsh classification, as modified by Oberhuber et al. (1999) , might lead to increased intraobserver and interobserver variations. Ensari suggests to use a new classification scheme based on Corazza and Villanacci (2005) using only 3 classes by combining Marsh type 3A and 3B. By using this scheme, automated classification might be improved. Also more advanced techniques using feature subset construction such as suggested byŠajn and Kukar (2010) in combination with a more realistic leave-one-patient-out cross validation to increase feature reliability should be considered towards the improvement of classification accuracy. Considering the discriminative power visible features among the Marsh type-3 subclasses, advanced techniques used in endoscopy such as narrow band imaging (NBI, Gross and Wallace (2006) ) could possibly be beneficial to automated classification accuracy. For the two-class problem (distinguishing areas affected by celiac disease and unaffected areas) the obtained classification accuracy builds a solid basis for future work towards employment in a clinical study.. The results show that the LBP-operator family exhibited better result accuracy compared to a wide range of other feature extraction techniques. The proposed Wavelet-based operator (WT-LBP), combining the LTP operator using an adaptive threshold and the LBP/C operator using an empirical distribution function for quantization of the contrast values, was among the best operators in all experiments. We saw that combining the first derivativeand second derivative information based operators using the Wavelet transform is beneficial to the feature discrimination and is able to improve the classification results. In this section we present the results of the conducted experiments. We present two result tables for each classification task (i.e. two-class and fourclass). One result table displays the classification results estimated by a leaveone-out cross validation performed on the training set. The second table presents the results of classifying the evaluation set based on the previously optimized parameters and trained classifiers. Authors in a related field might not be in the position to use distinct datasets to evaluate presented methods due to a limited amount of available data. We therefore study both evaluation methods to be able to give a comprehensive view of how well certain methods generalize on an independent dataset and of how significantly methods tend to (over)-fit the extracted features and parameters towards the data.. Within the result tables we use the abbreviations "Spec." for specificity (the percentage of correctly classified images actually showing a normal mucosal state) and "Sens." to indicate the methods sensitivity (the percentage of correctly classified images showing villous atrophy). To improve the readability the results are rounded to one decimal position in the discussion. In case of the four-class scheme we use the abbreviations 0, 3A, 3B or 3C to indicate the specific Marsh class.. We display the best overall classification results among all LBP-based methods as well as the other methods (except for the Ensemble classifiers) in bold face. In case of one or more methods with the same classification accuracy we display the method with the highest sensitivity in bold face. The "k"-column indicates the number of neighbors that was used for the nearest neighbor classification. The column labeled as "Int." indicates the number of intervals used for the contrast values in case of the two dimensional LBP/C based histograms. We present the two ensembles of single methods with a corresponding superscript to unambiguously identify the specific ensembles.. In addition to the result tables we also show the results of the statistical tests for significance we performed. The check sign indicates that a statistical significant difference between two results according to McNemar's test (McNemar, 1947) between two classification results. The null hypothesis of marginal homogeneity states that the marginal outcomes of two considered experiments are the same. This means, considering two experiments, that the probabilities of experiment one being correct for an image while experiment two being incorrect and vice versa (experiment two being correct while experiment one being incorrect for that same image) are equal. If McNemar's test statistic is significant (the significance level used in McNemar's test is used to evaluate whether the test statistic is likely in terms of a chi-squared distribution) there is evidence to reject the null hypothesis. This implies that the difference between two classification results are considered to be statistically significant. At a significance level of 2.5 percent (α = 0.025) there is a confidence level of 97.5 percent that the differences between two classification results were not caused by random variation. Tables 3 and 4 present the results of the experiments based on the twoclass scheme for classification. Comparing the results using a leave-one-out cross validation and the classification of the evaluation set, we see that the classification accuracy drops by an average of 8.6 percentage points in case of the LBP-based methods as well as the non-LBP-based methods. This is interesting as the LBP-based methods all use feature subset selection as compared to the other methods were only FFT-Evolved applies an additional process of feature optimization. This indicates that the selected feature subsets generalize well on an independent dataset. The decrease in classification rate is assumed to be caused by a bias within the training data caused by possibly multiple texture patches of a single patient in combination with the leave-one-out cross validation. In general the LBP-based methods performed better on the evaluation set (85.9%) as compared to the non-LBP-based methods (81.8%). The better overall accuracy of the LBP-based methods is explained by a higher average sensitivity of approximately 9.3 percentage points. The best result of a single method based on the evaluation set is achieved by the WT-LBP operator with 88.0 percentage points overall accuracy. Compared to the classification accuracy of the LOOCV this method's accuracy drops by 7.8 percentage points which is below the average decrease. By using the Ensemble classifier the result could be slightly improved to 88.7 percentage points. Interestingly the manually combined ensemble could not further improve the classification rates. Table 5 displays the outcomes of the conducted statistical significance tests based on the classification results of the evaluation set. We see that there is no statistically significant difference between the WT-LBP operator and the other LBP-based operators at a significance level of 0.025. Considering a significance level of 0.05 there is a significant difference between the results of the WT-LBP and the basic LBP operator. Compared to the non-LBP-based methods the differences are all statistically significant. As a consequence of the single methods selected, there are no statistically significant differences between the Ensemble classifiers 1 2 and the LBP-based methods except for the basic LBP-operator. Statistically significant differences to the non-LBP-based methods can be seen at both significance levels. The standard deviation of the LBP/C method among all evaluated interval numbers (2 to 22) during the training of was 1.2 percentage points with a mean classification accuracy of 86.6 percent. The mean accuracy and standard deviation of the WT-LBP method was 87.3 percentage points and 1.6 percentage points respectively. To be able to assess the performance of the proposed extensions and the WT-LBP operator and to gain insight into the possible performance of the four-class modified Marsh classification scheme, we applied as a comparison a set of several different feature extraction methods that provided promising results in classification of endoscopic image data in earlier work. The abbreviations of the techniques used throughout this work are shown in bold. We used the following feature extraction methods (given in alphabetical order):. • DT-CWT Correlation Signatures: We have extended the Wavelet Correlation Signatures approach of de Wouwer et al. (1997) to work with the Dual-Tree Complex Wavelet Transform in Häfner et al. (2008) . The correlation between subbands of different (and equal) color channels is computed based on the mean and standard deviation of coefficient magnitudes. The DT-CWT decomposition depth is set to four levels and the color space is RGB. The resulting features vectors have 144 elements.. • DT-CWT-Weibull: The Dual-Tree Complex Wavelet Transform is used to decompose the images into 6 scales and the empirical his-togram of the detail subband coefficient magnitudes is modeled by two-parameter Weibull distributions. The Weibull parameters are then arranged into a feature vector (Kwitt and Uhl, 2007) . In case of color images (which applies here) a feature vector has 216 elements.. • ELBP: Extended Local Binary Patterns (Huang et al., 2004) are used with an 8-neighborhood and LBP-scales ranging from 1 to 3. The image is gradient filtered by applying a Sobel filter using a horizontal, a vertical and a diagonal orientation. The optimal filter directions and LBP-scales are determined by using the SFS algorithm. The histogram dimensionality is 58.. • ELTP: The approach is applied as introduced in section 3.1. The ELTP operator is used with an 8-neighborhood and LBP-scales ranging from 1 to 3. The used α value was 0.1. In analogy to the ELBP operator, the filter orientations as well as color channels and LBPscales are optimized using the SFS approach. The dimensionality of a each histogram is 116.. • FFT-Evolved: By using the FFT an image is transformed into the respective power spectrum. Multiple ring-shaped filters are then applied to the spectrum of each color channel of the RGB color model to concentrate on discriminative frequency subbands only. Since the number of possible ring filters is quite large, an evolutionary algorithm is used to find an optimal set of filters for each color channel ) (sets are denoted by F 1 , F 2 , and F 3 ). For each of these ring filters the mean of the coefficient magnitudes within such a ring is used as a feature. This results in a feature vector for each color channel having a length equal to the number of rings used. By concatenating the feature vectors of all color channels of an image, the final feature vector is obtained having a length of |F 1 | + |F 2 | + |F 3 | (restricted to less then 20 elements).. • Gabor, Classic: The Gabor Wavelet Transform is used with 4 scales and 6 orientations, the mean and standard deviation of the coefficient magnitudes within a subband are used as features (Manjunath and Ma, 1996; Häfner et al., 2009c) . The resulting feature vectors have 144 elements in case of color images.. • LBP: The Local Binary Pattern operator (Ojala et al., 1996) is used in an 8-neighborhood to compute histograms for each LBP-scale employed (in the range 1 -3). The optimal combination of LBP-scales and color channels is found by the optimization routine (SFS) as described in section 3.4.. • LBP/C: The Local Binary Pattern operator combined with a contrast measure (Ojala et al., 1996) is used in an 8-neighborhood to compute histograms for each LBP-scale employed (in the range 1 -3). The optimal combination of LBP-scales and color channels is found by the SFS algorithm. The optimal number of quantization intervals used for the contrast measure is optimized from 2 to 22 by an exhaustive search during each training. Let c n be the number of used contrast values. The dimensionality of a single histogram is 58 · c n .. • LTP: The Local Ternary Pattern operator (Tan and Triggs, 2007) is used in an 8-neighborhood to compute histograms for each LBP-scale employed (in the range 1 -3). The adaptive thresholds are computed using an α value of 0.1. The optimal combination of LBP-scales, color channels and filter orientations is found by using the SFS algorithm. The dimensionality of a single histogram is 116.. • WT-BBC: The Best Basis Centroids method (Liedlgruber and Uhl, 2007) uses the Best-Basis algorithm to find an optimal basis for each image in a training set and computes a centroid over all resulting Wavelet packet decomposition structures (maximal decomposition depth 3). After transforming all images into this basis, the most informative subset of the resulting subbands (with respect to a cost function) is used to compute the energy over all coefficients within a subband. These values are concatenated to form the feature vector for an image. We use all color channels of the RGB color model and end up with a final feature vector length of 3 · S with S being the number of selected subbands.. • WT-LBP: The approach is used as introduced in section 3.3 by applying a three stage dyadic Wavelet transform of the image data. The optimal combination of Wavelet-scales, color channels and LBP-scales is found by applying the SFS algorithm. In parallel to the LBP/C operator the quantized contrast values are found by an exhaustive search within the range of 2 to 22. The used α value was 0.1. For c n contrast values, the approximation subband based histograms have a dimensionality of 58 · c n , while the detail subband based histograms have a dimensionality of 116.. • WT-GMRF: This method (Häfner et al., 2009a) first transforms an image to the Wavelet domain using the pyramidal discrete Wavelet transform (two stages) resulting in 3 · 3 · 2 = 18 detail subbands since we use each color channel of the RGB color model. For each of these detail subbands the Markov parameters of a Gaussian Markov Random Field are estimated. The number of parameters resulting from one detail subband depends on the neighborhood order (neighborhoods used are of Geman type (Geman and Geman, 1984) ). In addition to the Markov parameters we use the approximation error for each subband as a feature too. Hence, when assuming a neighborhood consisting of n neighbors, we have n 2 + 1 features per subband (the neighborhoods are symmetric). Since we estimate these parameters for each subband in each color channel, the final feature vector length equals to 18( n 2 + 1).. • WT-LDB: The Local Discriminant Basis algorithm is employed to find an optimal Wavelet packet decomposition basis (maximal number of stages is 3) with respect to discrimination between the image classes into which all images are transformed to. Based on the resulting decompositions we use the energy contained within a subband as feature, where only the S most discriminative subbands are used for feature extraction. The most discriminative subbands are found by computing the discriminative information for every respective subband following Saito and Coifman (1994) . Since we use all color channels of the RGB color model we end up with feature vectors having a length of 3 · S (Liedlgruber and Uhl, 2007) .. In case of the methods FFT-Evolved, WT-BBC, WT-GMRF, and WT-LDB the images were always pre-processed by applying CLAHE (Zuiderveld, 1994) followed by a Laplace Sharpening with a kernel size of 9 × 9 (Gonzalez and Woods, 2002) . For the other techniques no image pre-processing has been applied.. For classification we apply a k-nearest neighbors (k-nn) classifier to the extracted features. In the classifier, all methods except for the LBP-based ones use the Euclidean distance metric for the k-nn classification. The LBPbased methods use the histogram intersection as distance metric. While each of the employed techniques has been published with a certain specific classifier (often leading to better results compared to k-nn classification), we want to give more emphasis to the features used by applying a common classifier. The optimal k-value was determined by an exhaustive search through the admissible corresponding parameter range. Based on previous experiments with the different techniques, the parameter range is specified as follows. For all methods k (the number of neighbors considered) is chosen from 1 to 15 except for the FFT-Evolved Method. The results of the FFT-Evolved methods are optimized by an evolutionary process, which either assigns k=1 or k=2 depending on the used chromosomes. On a tied decision among classes the a priori probability (class frequencies) is used for the final classification decision.. To evaluate how well the methods and estimated parameters perform on an independent dataset we constructed two disjoint sets of texture patches as explained in section 2. Parameter and feature optimization (including the k-value of the k-nn classifier) was based on using a leave-one-out cross validation (LOOCV, (Fukunaga, 1990) ) on the training set. The evaluation of the methods accuracy was then performed by applying the trained classifiers to the evaluation set. No prior knowledge was used concerning the classification of the evaluation set.. To improve the results obtained by the k-nearest neighbor classifier, we use an Ensemble classifier as described in Häfner et al. (2009b) ; Vécsei et al. (2009) . This classifier aims at achieving a higher overall classification accuracy and more stable results across different image classes by combining different methods. The performance of the Ensemble classifier is dependant on single methods with high accuracy and a high measure of diversity among each other. Therefore the selection algorithm starts by selecting the method with the highest accuracy based on a cross validation on the training data. Then the best method in terms of classification accuracy with a significant different outcome to the previously picked method (at a significance level of 5%) is selected. This process is repeated until no more methods are found. The optimization of the k-value as well as the reliability measure used by the Ensemble classifier was entirely based on the training set of images (denoted as Ensemble 1 and Ensemble 3 ). We additionally combined a set of single classifiers by using knowledge of how well these methods generalize based on the performed experiments on the evaluation set. These results however have to be considered with care as the manual combination of methods prevents a fair comparison to the other methods and are only used to assess how much room for improvement exists for the Ensemble of classifiers. We denote these Ensembles as Ensemble 2 and Ensemble 4 in the corresponding tables.

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Topology and evolution of the network of western classical music composers The expanding availability of high-quality, large-scale data from the realm of culture and the arts promises novel opportunities for understanding and harnessing the dynamics of the creation, collaboration, and dissemination processes -fundamentally network phenomena -of artistic works and styles. To this end, in this paper we explore the complex network of western classical composers constructed from a comprehensive CD (Compact Disc) recordings data that represent the centuries-old musical tradition using modern data analysis and modeling techniques. We start with the fundamental properties of the network such as the degree distribution and various centralities, and find how they correlate with composer attributes such as artistic styles and active periods, indicating their significance in the formation and evolution of the network. We also investigate the growth dynamics of the network, identifying superlinear preferential attachment as a major growth mechanism that implies a future of the musical landscape where an increasing concentration of recordings onto highly-recorded composers coexists with the diversity represented by the growth in the sheer number of recorded composers. Our work shows how the network framework married with data can be utilized to advance our understanding of the underlying principles of complexities in cultural systems. Networks have been used extensively in recent years for characterizing and modeling intricate patterns found in various social, technological, and biological complex systems originating from the functional and dynamical interdependence between their components. The methodology is expanding its horizon, being eagerly adopted in new fields such as culture for exploring novel answers to new and long-standing issues [-]. In broad terms, the potential of the network framework for understanding culture originates from the observation that the creation and transmission of cultural products are essentially network phenomena. Networks, therefore, may lead to a new fundamental understanding of the complex nature of culture.. There are several interesting questions one can explore regarding such 'network in culture' -e.g., How does it evolve and change over time? Who are the most prominent or popular artists, and how do we measure their importance? How do the different styles combine to produce cultural products? How does information flow over network ties? [] -the answers to which would contribute to deepening our understanding of culture and the arts.. There have been several notable works that highlight the importance of networks on understanding various issues in culture. Suárez, Sancho and de la Rosa [] analyzed the linkage patterns from the data of , artworks from Spain and Latin America with respect to genre and theme, identifying religious theme to be a dominant factor connecting the paintings. Gleiser and Danon [] studied the topology and the community structure of the collaboration network of Jazz musicians, uncovering the presence of communities based on the locations of the bands correlated with the racial segregation between the musicians. Park et al. [] compared two networks of contemporary popular musiciansone representing the collaboration history of the musicians, and the other representing musical similarities as judged by human experts -finding that the significant topological differences are related closely to the nature of the connections. Salganik et al. [] studied the role of social influence and success of cultural products. They found that social influence has a significant effect, elevating the inequality and the unpredictability of success.. Uzzi and Spiro [] investigated the small-world property of the network of the creators of original Broadway musicals that reflect the level of their cohesion and its impact on the success of the musicals. They found that cohesion had a positive impact up to some point, but began to impede the creativity when it becomes too large. The characteristics of a system and growth or evolutionary dynamics are deeply intertwined [] . This is likely to be the case for cultural system as well, and grasping the underlying growth principles may In this paper we analyze the co-occurrence network of western classical composers constructed from the comprehensive data set of CD recordings. Using the network framework, we try to shed light on the following specific questions in order: Who are the prominent composers? What are the driving force behind composers being co-featured on a common CD? Can we characterize the temporal growth of the network and the composers? We start by measuring the fundamental network properties that give us a bird'seye view of the general features of the network, including the size, clustering coefficient, assortativity, degree distribution, centralities, and community structure. The centralities identify influential composers with varying artistic styles, while the positive assortativity shows that period designations and artistic styles are the main driving force behind cofeatured composers. This is explored further using the community structure of the composers. We characterize the growth of the composer-CD network by way of the temporal evolution of the bipartite degree distribution, and how it can be mapped to a superlinear preferential attachment model. It also allows us to forecast the future of the landscape of the composer network, where the growths of the degrees of prominent composers are accelerating and accordingly the recordings are becoming increasingly concentrated on those already well established, an effect similar to the so-called urban-scaling laws, where existing cultural centers win out over new centers [, ] . The network representation of ArkivMusic data. The association between the CDs and composers visualized as a bipartite network (left). One-mode projection of the bipartite network onto the set of composers by connecting composers when they are associated with a common CD (right). (B) The composer-composer network backbone, projected from the CD-composer network, reveals the major component of the network. The node sizes represent the composers' degrees, and the colors represent their active periods (violetBaroque, pink -Classical, red -Romantic, orange -Modern). Serrano's [15] algorithm was used to extract the backbone. (C) The cumulative distributions for the bipartite degree q, the number of CDs in which a composer was featured (blue), and the projected degree k, the number of composers with whom a composer was co-featured on at least one CD (red). Both exhibit a right-skewed behavior. In this paper, we studied the network of classical music composers constructed from the comprehensive recording data from ArkivMusic. We presented the basic properties of the CD-composer and composer-composer networks, finding that they exhibit characteristics common to many real-world networks, including the small-world property, the existence of a giant component, high clustering, and heavy-tailed degree distributions. We also explored the global association patterns of composers via centrality, assortative mixing, community structure analyses, which suggest an intriguing interplay between the networks of musicians and our musicological understanding of the western musical tradition in which both are undoubtedly continuously influencing each other. We then examined the growth of the CD-composer bipartite network over time. We identified superlinear preferential attachment as a strong candidate for explaining the increasing concentration of edges around top-degree nodes and the power-law degree distributions. The growth of edges and composer degrees exhibits a quadratic growth, allowing us to forecast the future of several prominent composers. If this behavior persists further into the future, it would suggest an interesting future research direction regarding the growth dynamics of the network.. An analytical investigation as ours on new, large-scale data can provide either new lessons or more rigorous answers to questions that are only partially understood on a subject matter (in our case classical music), although the boundary between the two can often be fuzzy: New findings challenge us to find rigorous answers to already known issues, which in turn can bring about novel discoveries about a system. In our paper we have quantified the overlap (correlation) between manually designated periods and the computationally identified communities; found the superlinear preferential attachment behavior and a quadratic growth of network edges; and showed that the growth of the composer pool is sublinear against the network size, leading to a concentration of edge weights onto specific composer pairs. These findings pose interesting possibilities and opportunities for the type of commercial databases such as the one on classical music that we have used here which, we believe, will play an increasingly important role as a source of many more academic findings than those presented here. We are living in an era where technology is greatly facilitating the consumption of culture by the public, evidenced by the increasing adoption of technology by artistic institutions worldwide for public outreach. This will lead to larger and higher-quality data that can allow us to learn more about culture and art. Classical music as presented here is merely one of many cultural subjects for this kind of investigation, and we believe that it would be interesting to apply our quantitative methodology to other subjects, e.g. visual arts and literature, and explore their nature in novel ways. Going further, it would also be interesting to compare different systems and find universal characteristics of cultural systems as well as those unique to each. We believe that our work highlights the potential of network science coupled with well-curated large-scale data in answering many pertinent questions.

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


Time Course of Retrieval and Movement Preparation in Music Performance ABSTRACT: Music performance requires that musicians represent many different kinds of sequence structure: musicians must remember which pitch to produce, when to produce it, and how to produce it (with what movements). The time course of item retrieval and movement preparation processes during music performance are considered. Serially ordered stage models of retrieval, in which item retrieval ends before movement preparation begins, are compared with interactive cascade models, in which the time course of both processes overlap, permitting interaction. Evidence from transfer of learning paradigms, production errors, and anticipatory movements, as measured in motion capture, are described. This early evidence suggests different time courses for item retrieval (slower, earlier) than for movement preparation (faster, later) with significant temporal overlap during music performance. KEYWORDS: performance; motor planning; memory retrieval; stage models; cascade models Most research on the brain functions that support music focuses on perception. Although live music performances are the stimulus for most music perception, our understanding of the neural mechanisms underlying music performance is just beginning. The research reported here addresses two lines of study on the time course of brain processes during music performance: one on memory retrieval, and one on movement preparation. Music performance requires that musicians represent many different kinds of sequence structure: musicians must remember which pitch to produce, when to produce it, and how to produce it (with what movements). Music contains pitches that repeat within a melody in different contexts; the mapping of musical pitches to the movements that produce them is not one-to-one. FIGURE 1 shows the pitch sequence of a simple melody and the right-hand finger sequence that a pianist might use to produce it. In this example, the pitch E is produced with finger 3 as well as with finger 1; likewise, finger 3 produces the pitch G as well as the pitch E. When one adds rhythmic and timbral complexity to the process, music performance becomes a complex sequencing task. 1 The objective of this paper is to discuss how memory retrieval functions and movement preparation combine in music performance. Evidence from production errors and motion capture in music performance indicated temporal overlap in the time course of item retrieval and movement preparation, with item retrieval (three to four events, 400-700 ms prior to an event) preceding movement preparation (one to three events, 150-500 ms prior). Although more evidence is necessary to address the roles of important performance factors, such as individual differences in working memory, performer expertise, and instrument differences, this early evidence is most consistent with the class of cascade models. At the least, the evidence argues against serially ordered processes in which movement preparation does not begin until item retrieval is complete.. Cascade models are popular in modeling many psychological tasks. Behavioral evidence consistent with cascade models suggests that stimulus identification does not need to be complete before response preparation can begin, 24, 25 and that perceptual object identification is not completed before semantic representations can be formed. 26 Cascade models are also prominent in neurophysiological modeling, 27, 28 based on evidence from single-cell studies that indicates significant temporal overlap of target-related spike activity at different neural sites. 29, 30 What are the advantages of a cascade model? One advantage may be to accommodate different timescales of neural processes. Item retrieval in word recognition, speech production, and music performance tasks is relatively slow (300 ms) compared with movement preparation (150 ms); reflexive muscle stiffening (30 ms) is even faster. If faster processes have to wait for slower processes to finish, as in stage models, then fast and accurate performance typified by music performance might not be possible. Cascade models also make it possible that more easily executed items may be more easily remembered, if information from process B informs process A.. Study of the time course of mental processes in music performance is in its infancy, in part because of the recent development of time-sensitive measurement techniques that do not disrupt performance. This early evidence suggests that item retrieval is an early, slow process, whereas movement preparation is later and faster. The time course of these processes suggests significant overlap. Further evidence is necessary to examine possible feedback among the cascaded processes. Interaction among processes may be evidenced in production errors and the movements that accompany them. When the wrong item is retrieved, how early does the finger producing that event alter its trajectory, compared with production of the correct event? Can incorrect trajectories be adjusted after item retrieval? Techniques, such as motion capture and imaging with musical instruments, make it possible to address memory retrieval and movement preparation in music performance, a domain in which brain processes are pushed to their temporal limits. Yet performers excel.

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


Network utility maximization over partially observable Markovian channels Abstract-This paper considers maximizing throughput utility in a multi-user network with partially observable Markov ON/OFF channels. Instantaneous channel states are never known, and all control decisions are based on information provided by ACK/NACK feedback from past transmissions. This system can be viewed as a restless multi-armed bandit problem with a concave objective function of the time average reward vector. Such problems are generally intractable. However, we provide an approximate solution by optimizing the concave objective over a non-trivial inner bound on the network performance region, where the inner bound is constructed by randomizing welldesigned stationary policies. Using a new frame-based Lyapunov drift argument, we design a policy of admission control and channel selection that stabilizes the network with throughput utility that can be made arbitrarily close to the optimal in the inner performance region. Our problem has applications in limited channel probing in wireless networks, dynamic spectrum access in cognitive radio networks, and target tracking of unmanned aerial vehicles. Our analysis generalizes the MaxWeight-type scheduling policies in stochastic network optimization theory from time-slotted systems to frame-based systems that have policy-dependent frame sizes. This paper studies a multi-user wireless scheduling problem in a partially observable environment. We consider a base station serving N users via N independent Markov ON/OFF channels (see Fig. 1 ). Time is slotted with normalized slots ON (1) OFF (0) P n,10 P n,11 P n,00 P n,01 t ∈ Z + . Channel states are fixed in every slot, and can only change at slot boundaries. Suppose the base station has unlimited data to send for all users. In every slot, the channel states are unknown, and the base station selects at most one user to which it blindly transmits a packet. The transmission succeeds if the used channel is ON, and fails otherwise. At the end of a slot, an error-free ACK is fed back from the Chih-ping Li (web: http://www-scf.usc.edu/∼chihpinl) and Michael J. Neely (web: http://www-rcf.usc.edu/∼mjneely) are with the Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA.. This material is supported in part by one or more of the following: the DARPA IT-MANET grant W911NF-07-0028, the NSF Career grant CCF-0747525, and the Network Science Collaborative Technology Alliance sponsored by the U.S. Army Research Laboratory. served user to the base station over an independent control channel (absence of an ACK is considered as a NACK). Since channels are ON/OFF and correlated over time, the ACK/NACK feedback provides partial information of future channel states, and can improve future scheduling decisions for better performance. The goal is to design a control policy that maximizes a concave utility function of the throughput vector from all channels. Specifically, let y n (t) be the number of packets delivered to user n ∈ {1, . . . , N } in slot t. Define y n lim t→∞. τ =0 E [y n (τ )] as the throughput of user n. Let Λ denote the network capacity region, defined as the closure of the set of all achievable throughput vectors y (y n ) N n=1 . The goal is to solve: maximize: g(y), subject to: y ∈ Λ,. where g(·) is a concave, continuous, nonnegative, and nondecreasing function.. The interest in the above problem comes from its many applications. One application is limited channel probing over wireless networks. Consider the same wireless downlink as stated above, except that at most one channel is explicitly probed in every slot. A packet is served over the probed channel if the state is ON. This setup is essentially the same as our original problem, except that channels are probed differently (implicit probing by ACK/NACK feedback versus probing by explicit signaling). The motivation for studying limited channel probing is that, in a fast-changing environment where full channel probing may be infeasible due to timing overhead, we shall probe channels wisely and exploit channel memory to improve network performance. As an example of (1), we may additionally provide fairness to all users, such as a variant of rate proportional fairness [1] , [2] by solving:. In cognitive radio networks [3] , [4] , a secondary user has access to a collection of Markov ON/OFF channels. Every channel reflects the occupancy of a spectrum by a primary user, and the secondary user opportunistically transmits data over unused spectrums for better spectrum efficiency. In target tracking of unmanned aerial vehicles (UAVs) [5] , a UAV detects one of the many targets in every slot. Every Markov channel reflects the movement of a target; a channel is ON if its associated target moves to a hotspot, and OFF otherwise. Delivering a packet over a channel represents gaining a reward by locating a target at its hotspot. In the above two applications, possible goals include maximizing a weighted 2011 International Symposium of Modeling and Optimization of Mobile, Ad Hoc, and Wireless Networks sum g(y) = N n=1 c n y n of throughputs/time-average rewards, where c n are given constants, or providing fairness to different spectrums/targets by solving (2) .. The problem (1) is challenging because the current information available for each channel depends on the past transmission decisions. This problem belongs to the class of restless multi-armed bandit (RMAB) problems [6] , which are generally intractable [7] . In addition, the network capacity region Λ in (1) does not seem to have a closed form expression (see [8] for more discussions). Therefore we must resort to approximation methods to solve (1) . In this paper, we propose an achievable region approach to construct an approximate solution to (1) . There are two steps: (i) we construct a good inner performance region Λ int ⊂ Λ for the original problem, then (ii) we solve the constrained problem: maximize: g(y), subject to:. which serves as an approximation to the original problem (1). In previous work [8] , we have constructed a non-trivial inner performance region Λ int using the rich structure of Markov channels (see Section III for details). The inner performance region Λ int is rendered as a convex hull of performance vectors of a well-designed collection of round robin policies. The tightness of the inner region Λ int (see Fig. 2 for an example) is analyzed in [8] when channels are statistically identical. In this special case we show that the gap between the boundary of the inner region Λ int and that of the full performance region Λ decreases to zero geometrically fast as the reference direction moves closer to the 45-degree angle. 1 The main contribution of this paper is with respect to the second step of the achievable region approach: Given an inner performance region Λ int , we construct a policy that solves (3) using Lyapunov drift theory. Lyapunov drift theory is originally developed for throughput optimal control over time-slotted wireless networks [9] , [10] , later extended to optimize various performance objectives such as average power [11] or rate utility functions [1] , [12] , [13] in wireless networks, and recently generalized to optimize dynamic systems that have a renewal structure [14] - [16] . The intuition is the following. Since the performance region Λ int is a convex hull of performance vectors of the round robin policies we design, the problem (3) is solved by some random mixture of these policies. Using Lyapunov drift theory (see more details in Section IV), we greedily construct a sequence of round robin policies whose long-term time sharing can approximate the optimal solution as close as desired, with some tradeoffs discussed later.. Our control policy that solves (3) has two components. To facilitate the solution to (3), we keep an infinite-capacity queue for every user at the base station, and design an admission 1 We remark that the tightness of the inner region Λ int is difficult to check in general cases, although the region is intuitively large by the nature of its construction. The bottomline is, constructing an intuitively good and easily achievable inner performance region is of practical interest, because satisfying performance outside the inner region may inevitably involve solving much more complicated partially observable Markov decision processes. From this view, in intractable RMAB problems, we may regard an inner performance region as an operational performance region, which shall be gradually improved by a deeper investigation into the problem structure.. control algorithm that admits data into the queues for eventual transmissions. In every slot, the amount of data admitted for every user is decided by solving a simple convex program. 2 In addition, the base station deploys a sequence of round robin policies implemented frame by frame, where every frame is one round of execution by a round robin policy. The round robin policy used in every frame is selected by maximizing an average "drift minus reward" ratio over the average frame size (c.f. (18)). We emphasize that this new ratio rule generalizes the MaxWeight-type policies [1] , [10] for stochastic network optimization from time-slotted wireless networks to framebased systems in which the distribution of the random frame size is policy-dependent. We prove that the above policy of admission control and channel scheduling yields a throughput vector y satisfying. where g(y * ) is the optimal objective of problem (3), B > 0 is a finite constant, V > 0 is a predefined control parameter, and we temporarily assume that all limits exist. By choosing V sufficiently large in (4), the performance utility g(y) can be made arbitrarily close to the optimal g(y * ), with the tradeoff that the average queue size at the base station grows linearly with V . We remark that the proof of (4) does not require the knowledge of the optimal utility g(y * ). In the literature, stochastic utility maximization over wireless networks is solved in [1] , [12] , assuming that channel states are i.i.d. over slots and are known perfectly and instantly. Limited channel probing in wireless networks is studied in different contexts in [17] - [22] , also assuming that channel states are i.i.d. over time. This paper generalizes the framework in [1] to wireless networks with limited channel probing and time-correlated channels, and uses channel memory to improve performance.. RMAB problems with Markov ON/OFF projects are previously studied in [23] - [28] for the maximization of sum of time average or discounted rewards. In particular, work [23] - [25] shows that greedy round robin policies are optimal in some special cases; we modify these policies in [8] for the construction of a tractable inner performance region Λ int . Index policies such Whittle's index [6] are constructed in [26] , [27] , and are shown to have good performance by simulations. A (2 + )-approximate algorithm is derived in [28] based on duality methods.. This paper provides a new mathematical programming method for optimizing nonlinear objective functions of time average rewards in RMAB problems. In the literature RMAB problems are mostly studied with linear objective functions. The two popular methods for linear RMABs -Whittle's index [6] and (partially observable) Markov decision theory [29] -seem difficult to apply to nonlinear RMABs because they are based on dynamic programming ideas. Extensions of our new method in this paper to other RMAB problems with general project state space are left for future research.. In the rest of the paper, the detailed network model is in the next section. Section III introduces the inner performance region Λ int constructed in [8] . Our dynamic control policy is motivated and given in Section IV, followed by performance analysis. This paper provides a theoretical framework for utility maximization over a wireless network with partially observable Markov ON/OFF channels. The performance and control in this network are constrained by limiting channel probing and delayed/uncertain channel state information, but can be improved by exploiting channel memory. Overall, attacking such problems requires solving (at least approximately) highdimensional restless multi-armed bandit problems with nonlinear objective functions of time average rewards, which are difficult to solve by existing tools such as Whittle's index or Markov decision theory. This paper provides a new achievable region method for these problems. The idea is to first identify a good inner performance region rendered by randomizing stationary policies, and then solve the problem over the inner region, serving as an approximation to the original problem. In this paper, with an inner performance region constructed in [8] , we provide a novel frame-based Lyapunov drift argument that solves the approximation problem with provably near-optimal performance. We generalize the classical MaxWeight policies from time-slotted wireless networks to frame-based ones that have policy-dependent random frame sizes. Extensions of this new achievable region method to other open stochastic optimization problems are interesting future research.. where (c) uses the nonnegativity of g(·) and t ≥ t K(t) , and (d) follows (28) . Dividing (29) by t, taking a lim inf as t → ∞, and noting t − E t K(t) ≤ E T K(t)+1 < ∞ yields lim inf. Using Jensen's inequality and the concavity of g(·), we get lim inf. where r . Combining (31) and (32) finishes the proof.

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