Vertical selection in the presence of unlabeled verticals Vertical aggregation is the task of incorporating results from specialized search engines or verticals (e.g., images, video, news) into Web search results. Vertical selection is the subtask of deciding, given a query, which verticals, if any, are relevant. State of the art approaches use machine learned models to predict which verticals are relevant to a query. When trained using a large set of labeled data, a machine learned vertical selection model outperforms baselines which require no training data. Unfortunately, whenever a new vertical is introduced, a costly new set of editorial data must be gathered. In this paper, we propose methods for reusing training data from a set of existing (source) verticals to learn a predictive model for a new (target) vertical. We study methods for learning robust, portable, and adaptive crossvertical models. Experiments show the need to focus on different types of features when maximizing portability (the ability for a single model to make accurate predictions across multiple verticals) than when maximizing adaptability (the ability for a single model to make accurate predictions for a specific vertical ). We demonstrate the efficacy of our methods through extensive experimentation for 11 verticals. Many web search engines provide their users with access to specialized search services, or verticals, that focus on a specific type of media (e.g., blogs, images, video) or domain (e.g., health, music, travel). In some cases, if a user is aware of a relevant vertical, the query can be issued directly to a vertical-specific search engine. However, to improve userexperience, portal search engines have started embedding relevant vertical content in Web search results. This has been referred to as aggregated search. A necessary part of an aggregated search system is vertical selection, the task of deciding, given a query, which vertical back-ends, if any, should be presented alongside Web search results.. Vertical selection can be viewed as a type of resource selection, a well-studied task in distributed information retrieval. Recent work shows that a supervised machine learning approach to vertical selection outperforms traditional resource selection methods [2, 1] . Most of these traditional methods derive evidence exclusively from vertical content and do not require extensive training data [4, 15, 18, 17, 16, 19] .. While a supervised approach outperforms state-of-the-art methods, one of its drawbacks is that it requires training data. As is shown in Arguello et al. [2] , suitable training data (e.g., vertical relevance judgments for a set of queries) can originate from human annotations. Human annotation is resource intensive in terms of time and money. An annotation effort may be sensible as a one-time investment. However, in practice, the aggregated search environment is often dynamic. Verticals can be added to or removed from the set of candidate verticals. Documents can be added to or removed from a vertical. The interests of the user population may drift, in effect, changing the set of vertical documents most likely to be requested by users. It may not be feasible to annotate a new set of queries every time the environment undergoes a significant change.. In this paper, our goal is to improve a system's return on editorial investment. We are interested in the following scenario. Suppose we have a set of verticals for which we have collected training data in the form of human vertical relevance judgements. We refer to these verticals as the source verticals. Then, suppose we have a new vertical associated with no training data. We refer to this vertical as the target vertical. Our objective is to learn a predictive model for the target vertical using only source-vertical training data.. Our solution to this problem focuses on two model properties: portability and adaptability. A portable model is one that can be effectively applied to any target vertical with no additional training data. If a model is portable, it can be used as a 'black box' for any new vertical. An adaptable model is one that can be tailored to a specific new vertical with no additional training data. If a model is adaptable, its parameters can be automatically adjusted to suit the new vertical at no editorial cost. We will present models which exhibit these properties and evaluate their performance across a set of 11 target verticals. Our approach is to use signals shown to be useful for supervised vertical selection in previous work. We describe them briefly below. Maximizing the return on editorial investment is an important aspect of any system requiring training data. We presented an ensemble of approaches which significantly improve prediction of a new target vertical using only sourcevertical training data. Our results demonstrate that model portability, the ability of a model to generalize across different target verticals, requires careful attention to feature portability, the ability of a feature to correlate with vertical relevance across different target verticals. We found that those features which seemed to be the most portable-and hence most important for a portable model-were queryvertical features as opposed those that are independent of the candidate vertical. Conversely, when we tried to adapt a model for a specific target, the least portable features appeared to be those most important for the adapted model to consider.. Furthermore, we showed that a portable solution can be used to build a target-specific one at no additional editorial cost. Our best approach adapted a portable model using its own target-vertical predictions. This approach consistently outperformed both the base model and a competitive alternative which does not require adaptation. Results also showed that, given available resources, human-annotation on the new target vertical remains the best alternative.. This work could be extended in several directions. In terms of portability, vertical balancing may be improved by modeling the similarity (in terms of predictive evidence) between source verticals. In terms of adaptability, further improvements may be achieved by modeling the similarity between each source vertical and the target vertical. In the previous section, we demonstrated that the performance improvement for both portable and adaptive models requires measuring individual feature portability. In order to investigate precisely which features were being selected, we plotted the value of π havg AP in Figure 1 . As it turns out, the same five features were consistently chosen as the most portable for all target verticals (i.e., for all sets of source verticals). Interestingly, these correspond to our five queryvertical features (Section 4.1.1). Conversely, those features which were the least portable were consistently our remaining query features (Section 4.1.2). In hindsight, this observation makes sense. Many existing resource selection methods score resources using a single metric and focus exclusively on query-vertical evidence [4, 15, 18, 17, 16, 19] . Despite this observation, we recommend future experiments continue to measure feature portability since this behavior may not generalize to different sets of verticals and different tasks.. Vertical balancing significantly improved the basic model only across 4/11 verticals based on AP and 3/11 based on NDCG. Recall that we introduced balancing in order to discourage examples from source verticals with high priors dominating the training set. However, this does not address cases where several sources have the same non-portable features correlated with relevance. For example, video and images tend to be relevant to queries that mention a celebrity name; travel and local tend to be relevant to queries that mention a geographic entity; and finance and jobs tend to be relevant to queries that contain a company name. Even though vertical balancing addresses a single vertical's dominance in the training set, it does not address a small coalition of related verticals causing the model to use nonportable features. We believe that a more robust averaging technique-for example, the harmonic average used for feature portability-may result in superior performance in the presence of similar source verticals.. In the previous section, TRADA improves considerably when restricted access to only the non-portable features for adaptation. We believe this is due to the following. TRADA was pseudo-trained using predictions from the base model. This model (our best portable model) used only the most portable features. The set of all features is a superset of these. When TRADA is given access to the same features used by the base model, the adapted model tends to focus on these features in order to better fit the original base-model predictions. Therefore, the non-portable features (purposely inaccessible to the base model) were ignored. As it turns out, our set of non-portable features are highly effective given target-vertical training data. We compared a set of targettrained models using only the portable features (excluding the non-portable ones) with our target-trained models using all features (Table 2) . When given access to non-portable features performance improved significantly across all verticals for AP and all but one vertical for NDCG (results suppressed due to space limitations). When restricted access to only the non-portable features for adaption, TRADA is forced to focus on these highly effective features. This mechanism can be seen as a sort of regularization ensuring that both portable and non-portable features are used for prediction.. With respect to pseudo-training data parameter N , we observe that the optimal value of N seems to be verticaldependent. There may be two reasons for this. First, the base model performance (column 5 in Table 3 ) is also verticaldependent. Given a fixed value of N across verticals, pseudolabels from some verticals may be noisier than others. Second, the optimal value of N for a given vertical may correlate with the vertical's prior. We present portability results in Table 3 . Across metrics, both vertical balancing (VB) and feature weighting (FW), that is, using only the most portable features, improves the performance of the basic model. Performance across verticals was either statistically indistinguishable or better. Compared to each other, feature weighting significantly improves the basic model across more verticals (8/11 for both metrics). Compared to Soft.ReDDE, the only method that does noticeably better is the basic model with feature weighting. Performance was significantly better for 4 verticals based on AP and 5 based on NDCG. Performance was significantly worse for only one vertical based on AP and no vertical based on NDCG. We present adaptability results in Table 4 . TRADA adapts a basic model using its target-vertical predictions as pseudotraining data. Given its superior performance (Table 3) , we used the unbalanced basic model with only portable features (basic+FW). We refer to this as the base model. TRADA was tested under two conditions. In the first condition, TRADA is given access to all features for adaptation. In the second condition, it is restricted access to only the nonportable features (those purposely ignored to improve the portability of the base model). Table 4 presents several meaningful results. First, TRADA performs poorly when the adapted model is given access to all features (columns 4-6). In contrast, when restricted access to only the non-portable features (TRADA+FW), results improve (columns 7-9).. TRADA+FW performs either equal to or better than the base model in all but one case for AP and in all cases for NDCG. Similarly, across metrics, TRADA+FW significantly outperforms Soft.ReDDE across most verticals for all values of N . It performs significantly worse than Soft.ReDDE in three cases in terms of AP and none in terms of NDCG. Overall, we interpret this as a positive result in favor of adaptability. TRADA succeeds at adapting a portable model to a specific target vertical at no additional editorial cost. In distributed IR, most traditional approaches to resource selection cast the problem as resource ranking and score collections using functions tuned manually on a few training queries. Most of these approaches focus exclusively on the similarity between the query and the collection content, possibly using sampled documents [4, 15, 18, 17, 16, 19] . Of these, so-called large document models treat each collection (or its document samples) as a single monolithic document and adapt document ranking functions to rank collections [4, 18] . In contrast, small document models focus on those (sampled) collection documents most relevant to the query [15, 17, 16, 19] . ReDDE [17] , for example, scores collections by their expected number of relevant documents. This expectation is based on the number of collection samples predicted relevant, scaled by a factor proportional to the collection size.. Other approaches cast resource selection as a supervised machine learned classification [2, 1] or rank-learning task [20] . In contrast to the single-evidence resource-scoring methods described above, these approaches have the advantage of easily incorporating multiple types of evidence as input features, for example, resource-specific query-logs [2] or clickthrough data [1] . This type of evidence integration is critical when dealing with text-impoverished verticals (e.g., images, video), which are problematic for methods that focus exclusively on content-based evidence.. In machine learning, domain adaptation is the task of using training data from one or more source domains to learn a predictive model for a target domain, typically associated with little or no training data. While domain adaptation has not been studied in the context of supervised vertical selection, it has been widely studied in other applications. The domain adaptation problem arises from the fact that the source and target data originate from different distributions.. One line of work performs instance weighting to downweight the influence of "misleading" source-domain training instances. Jiang and Zhai [11] do this by discarding sourcedomain training instances that are misclassified by a model trained on whatever target-domain training data is available. This is slightly different than our problem setting since we assume that we have no training data in the target domain.. A different approach is to perform feature weighing to learn a model that generalizes better to the target domain. Saptal and Sarawagi [14] perform feature subset selection by minimizing a distance function between a single source domain and target domain data distribution. The target data distribution is estimated using predicted labels from a source-trained model. This is different from our work since we want to learn from multiple source domains. Jiang [10] proposes an approach that uses training data from multiple domains. First, a generalizable subset of features is identified based on their predictiveness across source domains. Then, a model that focuses heavily on these features is used to produce predictions on the target domain. Finally, a target-domain classifier with access to all available features is trained on these predictions. This approach assumes binary features and requires using linear classifiers. The framework we propose can handle real-valued features and models that can explore complex features interactions.. Feature subset selection can be viewed as a type of representation change aimed to make a model more generalizable to the target domain. A similar technique is feature augmentation. Given access to some target-domain training data, Daumé III [6] augments the feature space by making three versions of each feature: a source-domain, target-domain, and general version. A model is then trained on the union of the source and target training data. This allows the model to effectively weight a source of evidence differently when it is predictive of the target class in only the source domain, only the target domain, or both. Blitzer et al. [3] propose an approach that does this without requiring target-domain training data. The goal is to identify the correspondence of non-pivot features, which may have a different correlation with the target class across domains, based on their predictiveness of pivot features, manually identified as similarly correlated with the target class across domains. The assumption is that the cross-domain correspondence between pairs of non-pivot features is encoded in the weights assigned to each when training linear classifiers to predict the presence of each pivot feature using non-pivot features.. A different approach is to train a model on the source domain and then to only fine-tune it using a few target-domain training examples. In the context of web search, Chen et al. [5] propose several ways to fine-tune a model trained using Gradient Boosting Decision Trees, which combines weak models into a more complex model. The approach of appending trees (based on target-domain training data) to a source-trained model has the advantage of accommodating features only present in the target domain.

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Delayed password disclosure We present a new authentication protocol called Delayed Password Disclosure. Based on the traditional username and password paradigm, the protocol's goal is aimed at reducing the effectiveness of phishing/spoofing attacks that are becoming increasingly problematic for Internet users. This is done by providing the user with dynamic feedback while password entry occurs. While this is a process that would normally be frowned upon by the cryptographic community, we argue that it may result in more effective security than that offered by currently proposed "cryptographically acceptable" alternatives. While the protocol cannot prevent partial disclosure of one's password to the phisher, it does provide a user with the tools necessary to recognize an ongoing phishing attack, and prevent the disclosure of his/her entire password, providing graceful security degradation. Phishing is a form of online fraud that is doing increasing damage to the financial industry. In its traditional form, a victim is sent a fraudulent email message directing the victim to fraudulent website, normally hosted on a hacked machine, with some urgent call to action. The purpose of the call to action is typically to incite users to follow a link, by suggesting that they will receive some form of reward for following the link, suffer a penalty for failing to follow, or some combination of the two. The website that the victims are directed to is designed to mimic the appearance of a legitimate site, such as an online bank, vendor or payment system. The goal here is to continue the confidence game, initiated in the call to action, so that the victim remains convinced that she is interacting with the legitimate site. The fraudulent website will request from the vicim a number of credentials and other information of interest, such as personally identifying information. The goal of the attacker, termed a phisher, is to trick the victim into releasing his credentials to the fraudulent website, so that later they may be used to perform some form of fraud. In general, the credentials of interest are credit or banking related, the most common examples of which include credit-card numbers, or pairings of usernames and passwords for online banking sites. Phishers try to obtain as much information as they can from their victims. Phishers generally sell off the information that they collect to other individuals, called cashers, who specialize in monetizing the information, and the more information that a phisher can acquire on a given victim the more value it will fetch, as this information aids the cashers in their goal.. Apart from the social aspects of phishing, there are also technical components. These are often aimed at circumventing phishing countermeasures (see, e.g., [32, 37] ) or spam filters (see, e.g. [35] ), or at mining user-specific data of relevance to an attack (see, e.g., [26, 29, 31] .) Thus, phishing is a complex threat, consisting of both a technical component and a social/psychological component. Its very complexity is also what makes it so difficult to defend against. As an example of why it is difficult to defend against, we can recall how security experts, until quite recently, argued that once the public became aware of the threat, then technical security features such as SSL would eradicate the problem. This is not happening. One reason is that while most people understand that there is an association between security and the presence of an SSL lock, many do not know how to interpret security indicators (see, e.g., [22, 14] ) and few know that phishers can also use SSL [39] or simply add locks in the content portion of a webpage [33] . Second, it is known that there is a tremendous discrepancy between what typical users know and what they practice. An example of this is illustrated by recent studies involving eyeball tracking [46] , in which it was concluded that most users rarely look for SSL indicators, much less choose to interact with these. While educating users is necessary to some degree, it is also difficult [43] . Moreover, it is not sufficient to explain the problems to the target audience, but one must also change their behavior. It is hard to untangle the technical aspect from the social aspect when we address the problem of phishing, and an understanding of both may be necessary to make significant progress. The rapid rise of phishing attacks and their potential to have large negative effects on e-commerce has resulted in a significant number of researchers trying to solve the phishing problem. The approaches have varied widely, which is appropriate given the fact that phishing is at heart a social engineering attack, and thus can take on many different guises. We briefly review some of the main works in this area.. Chou et al. [12] use a system that evaluates a given web page and comes up with a "phishiness" index to indicate the likelihood that the web page in question is that of a phisher. The index is computed based on factors including, but not limited to: similarity of URL to known phishers' sites, the inclusion of official logos from official sites and requests for passwords and credit cards.. Several commercial efforts, among them those by Microsoft and eBay, involve browser extensions to flag blacklisted sites (where an updated blacklist is frequently made available for automated download). A related but academic effort is the Trust-Bar construction by Herzberg and Gbara [25] , which associates logos with the public keys of the certificates of visited sites. The hope is that displaying the logos will create a better conceptual connection between the organizations certifying that a site can be trusted and the trusted site. Further, it is postulated that the use of logos allows for the development of corporate branding for certificate authorities, creating a valuable asset for which they will have incentive to protect, by not improperly issuing certificates in improper situations.. When a user can knowingly trust the user interface he or she is using, then traditional cryptographic PAKE protocols can be used to ensure security, as the phisher cannot simply bypass them. Therefore, a key issue in overcoming doppelganger attacks is to produce a trusted path from the user to the server, when possible. An excellent review of trusted paths is available in [16] . A way to circumvent the traditional problem of trusted path was recently suggested by Parno, Perrig and Kuo [40] ; their approach involved the use of an auxiliary device (a cell phone) to maintain trusted state. Dhamija and Tygar [15] also approach the phishing problem by trying to address phishing by creating a trusted path between the user interface and the user. In their proposal, to establish trust in the user interface, a user must select a specific image which will be superimposed onto all dialog boxes presented by the browser. Since the selected image is known only to the user (and possibly provided by the user), the phisher cannot duplicate such a dialog box. For this solution to be used, the user must have a pre-established secret (the user's image) with the browser, and so this technique does not allow for promiscuous browser use, the model we address.. Clearly, the notion of trusted path is very relevant to defend against phishing and doppelganger attacks. The trusted path problem is closely related to the trusted computing problem (see [42] for a comprehensive discussion). While trusted paths are typically concerned with how to secure data input (e.g., by standardized and secure interfaces) trusted computing instead is concerned with controlling what processes are running on a given machine, and what resources they have access to. Typical trusted computing efforts do not directly address the doppelganger problem, since an attacker may try to deceive a user to perform some action (such as inputting his password) using a web browser window. Trusted computing controls what applications are run, and must for practical reasons allow the execution of web browsers; in typical trusted computing scenarios the contents of webpages viewed by the browser are not verified or validated. Note that it is nontrivial to automate the interpretation of content, as can be seen by the failure of commercial efforts to block spam.. Passmark TM (also branded as SiteKey) is a product of RSA Security (EMC), and has recently been deployed by the Bank of America. This product attempts to fight phishing by building on the traditional username/password interface by having the site authenticate back to the user. It does so by first recognizing client machines by means of previously saved cookies, and by requesting the user to enter his username. If the user and his machine are recognized then Passmark causes a user-specific image to be displayed, after which the user can enter his password. Users are trained not to enter their password unless they recognize the image displayed to them. The product also has a server-side component that analyzes login attempts and usage patterns, looking for fraudulent activity.. While Passmark is a promising approach, the client-side portion has some drawbacks. First, in the presence of cookies, Passmark protected sites display an image to the user, but this constitutes placing a secret on the user's computer; in such cases stronger cryptographic mechanisms surely could be used. Second, given that cookies are not resilient to pharming (see, e.g., [28] ), their techniques do not seem to provide any protection against such attacks. Finally, alternative identity verification methods are needed when users do not accept cookies or when users migrate between machines (i.e., the case of promiscuous user). In such cases, the user is not presented with the image, but is instead authenticated through other independent means. User studies by Schecter et al. [41] have shown that the approach may not succeed in bridging the security gap between users and their machines: Subjects in a user study were found not to react to the absence of Passmark images. Further, the subjects in the study were aware of participating in a phishing study, which is known (see, e.g., [1] ) to introduce a bias. However, a similar observation was made in the context of the "eBay user greeting" in [30] , where subjects were not aware of participating in a study, much less a phishing study.. A similar technique to Passmark is that of Yahoo!'s Site seal; this is a technique by which users can upload text or images to a server which then displays these when the user returns to the site. Like Passmark, this is a technique reliant on cookies and elements stored in the user cache [48] , but instead of identifying the user (which requires asking for the user name), it only recognizes the browser installed on a given machine. While the interface of Site Seal is similar to that of Passmark it is not identical, and it is unclear, although likely, that it would suffer from the same lack of user recognition that current Passmark interfaces suffered, as previously discussed.. While it remains possible that users of our proposed DPD system would also fail to notice the absence of their correct feedback images, we note that such issues are critically dependent on the user interface that is used to interface with the protocol, and that an appropriately designed user interface can address this problem. We note that the design of such an interface is the purview of HCI experts, and therefore out of the scope of this presentation. Our goal is to design a secure protocol upon which such an interface can be draped. Our emphasis is on the design of a cryptographic protocol that meets realistic efficiency requirements and which supports a user interface that helps defend against doppelganger attacks, without the need for local storage such as is necessary for cookies.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B 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 B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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


Multiresolution approximation scale and time-shift subspaces Abstract Multiresolution Approximation subspaces are L 2 (R)-subspaces defined for each scale over all time shifts, i.e., "scale subspaces", while with respect to a given wavelet, the signal space L 2 (R) not only admits orthogonal scale subspaces basis, but orthogonal "time shift subspaces" basis as well. It is therefore natural to expect both scale subspaces and time shift subspaces to play a role in Wavelet Theory and, in particular, in Multiresolution Approximation as well. This is what will be discussed in the paper.

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Exponentially Weighted Moving Average Charts for Detecting Concept Drift .Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted Moving Average (EWMA) chart to monitor the misclassification rate of an streaming classifier. Our approach is modular and can hence be run in parallel with any underlying classifier to provide an additional layer of concept drift detection. Moreover our method is computationally efficient with overhead O(1) and works in a fully online manner with no need to store data points in memory. Unlike many existing approaches to concept drift detection, our method allows the rate of false positive detections to be controlled and kept constant over time. In many situations it is necessary to analyze data streams consisting of time ordered data points which are being received at too high a rate, and in too large volumes, for conventional statistical methods to be deployed. In this case streaming (online) methods must be developed which are more computationally efficient. Streaming methods are usually required to meet the following criteria [7] :. 1. Single pass: points from the data stream should be processed only once and discarded rather than stored in memory. It may be acceptable to store a small number of points for repeated processing, but the maximum number stored should be small and constant rather than increasing indefinitely over time. 2. Computationally efficient updates: the time required to process each point should be small and constant over time. The computational complexity should be O(1).. Classification is a common task in the analysis of data streams, where objects must be assigned to one of several classes based on their observed features [11] . The streaming version of the classification problem has the following form: at time t, the classifier is presented with the feature vector f t of a single object belonging to an unknown class. It is required to predict the class of this object. The task is to incrementally learn a classification rule which assigns objects to classes. In the literature on streaming classification, it is usual [3, 8, 15] to assume that the true class of the object is revealed immediately after the prediction is made so that the classifier receives immediate feedback on whether the classification was accurate.. The key difference between the streaming classification problem and the conventional offline version is that, in the streaming case, the optimal classification rule may change over time due to changes in the stream dynamics, a phenomena known as concept drift [24] . A distinction can be made between cases where the optimal rule is gradually changing, and cases where the change is abrupt. For the remainder of the paper we assume that changes are abrupt, although we will briefly consider gradual drift in our experimental analysis.. In classification tasks where concept drift may occur, it is important to design classifiers which can adapt to changes in the stream so that they do not incur a significant decrease in performance. Many methods which have been proposed to deal with concept drift fall into one of two categories. The first is to design classifiers which automatically adapt their behavior to stay up-to-date with the stream dynamics [24, 14, 16] . Alternatively, classification and concept drift can be treated as separate problems and concept drift detectors are designed to flag when changes occur, and allow some action to be taken [8, 3, 15] . Methods of the second kind are useful in situations where it is not only necessary to adapt to concept drift, but also to give some indication that it has occurred; for example, if classification techniques are used to detect credit card fraud [23] it may be necessary to take further investigative action of the behavior of fraudsters is thought to have changed. In this paper we are concerned only with with the second type of method, and our goal is to detect the change points at which concept drift occurs.. Most existing approaches to concept drift detection have two main limitations. First, many of them are not single-pass, and have a computational complexity which grows with the number of observations. This makes them unsuitable forstreaming classification problems where large numbers of observations are received frequently. Second, there is generally no way to control the false positive rate, where a false positive is defined as the detector flagging that concept drift has occurred, when in fact there is none. This is a serious problem in cases where it is desirable to know whether concept drift has really occurred. Suppose that a standard concept drift detection method such as [8, 2] is used on a stream containing 500 observations. Suppose also that 5 different change points are detected, at which abrupt concept drift occurs. Now, we wish to ask whether these are genuine change points, or simply false positives that have been flagged by the detector due to statistical fluctuation. Because with most existing concept drift detectors there is no way of knowing the rate at which false positives are occurring, we do not know whether these change points are likely to be significant: if the detector generates a false positive every 100 observations, then it is quite likely that there is actually no concept drift, and all the detected instances are false positives. However if we had a way to control the false positive rate so that the detector generates one false positives roughly every (e.g.) 5000 observations for any data stream, then we could conclude with some degree of certainty that the change points are likely to be genuine instance of concept drift.. In this paper we present an alternative approach to concept drift detection which is both single pass and computationally efficient with only O(1) overhead, and which allows the rate of false positive detections to be hence controlled. We consider the two-class classification problem, although our method could be extended to the multi-class case. Suppose we have a streaming classifier which predicts class labels for the objects f 1 . . . f n . Assuming that feedback is received on whether the prediction is correct, we can form the error stream {X t } where X t = 0 if the prediction for the class of point f t is correct and X t = 1 if it is incorrect. {X t } can then be viewed as a sequence of observations from a Bernoulli distribution, with the Bernoulli parameter p corresponding to the probability of misclassifying a point. Detecting concept drift then becomes the problem of detecting an increase in p, beyond that associated with sampling variability.. Since our method uses only this error stream, it treats the underlying classifier as a black-box and does not make use of any of its intrinsic properties. Therefore, it is able to be deployed alongside any classifier (decision trees, neural networks, support vector machines, etc) to provide a modular layer of concept drift detection. This is in contrast to concept drift detectors which are designed only to work with (eg) linear discriminant classifiers [16] , or support vector machines [13] .. Several schemes for detecting a change in a Bernoulli parameter have been proposed; however much of this is either not single pass [18, 5] or assumes the pre-change value of p to be known [19] . We choose to adapt the Exponentially Weighted Average (EWMA) chart recently developed in [25] so that it can func-tion in concept drift detection.. This paper proceeds as follows: Section 2 presents some general background about the EWMA chart, and Section 2.1 shows how it is applied to the Bernoulli distribution. The standard formulation of the EWMA chart assumes that various parameters of the stream being monitored are known. Section 3 explains how the EWMA can be adapted for practical situations where these are unknown. Section 3.1 presents a method for keeping the rate of false positives constant over time. Section 4 describes how to incorporate this EWMA chart into a concept drift detector, and we name our algorithm ECDD (EWMA for Concept Drift Detection). Section 5 analyses the performance of our approach on several real data sets, and compares it to several other recently proposed methods. We presented ECDD, a method for detecting concept drift in streaming classification problems based on the Exponentially Weighted Moving Average chart. Since our method uses only the classification error stream, it can be incorporated into any streaming classifier assuming that feedback is received regarding whether predictions are correct. Our approach does not require any data to be stored in memory, and only adds O(1) overhead to the classifier. Additionally, it allows the rate of false positive concept drift detections to be controlled in a manner which other approaches do not. Experimental analysis showed that the performance of our approach is competitive with other state of the art methods. One possible direction of future research is to extend our methodology to classification problems which have more than two classes, perhaps by monitoring each entry of the confusion matrix separately. Exponentially Weighted Moving Average (EWMA) charts were originally proposed in [20] for detecting an increase in the mean of a sequence of random variables. Suppose we observe the independent random variables X 1 , . . . , X n which have a common mean µ 0 before the change point and µ 1 after. We write µ t for the mean at time t, noting that this quantity only has two possible values. For now it is assumed that both µ 0 and σ X , the standard deviation of the stream, are known. In Section 3 we will show how to proceed when this is not the case. Define the EWMA estimator of µ t as:. This EWMA estimator is essentially a way of forming a 'recent' estimate of µ t , with older data being progressively downweighted. The parameter λ controls how much weight is given to more recent data compared with older data. It can be shown [20] that, independent of the distribution of the X t variables, the mean and standard deviation of Z t are:. Before the change point we know that µ t = µ 0 , and the EWMA estimator Z t will fluctuate around this value. When a change occurs, the value of µ t changes to µ 1 , and Z t will react to this by diverging away from µ 0 and towards µ 1 . This can be used for change detection by flagging that a change has occurred when:. 4. The parameter L is called the control limit and determines how far Z t must diverge from µ 0 before a change is flagged. The value of L is normally chosen to ensure that the detector achieves some predefined level of performance. A common performance measure is the expected time between false positive detections, denoted ARL 0 (for Average Run Length). A false positive here is defined as the EWMA chart flagging that a change has occurred when µ t has not changed. L is chosen so that the expected time between false positives is equal to some desired value for ARL 0 . Determining which value of L corresponds to a desired ARL 0 is non-trivial, and will be discussed later in Section 3.1. We now assess the performance of the ECDD and ECDD-WT algorithms on several synthetic and real world data sets, and compare it to two alternative algorithms for concept drift detection which can also be deployed alongside any base classifier: the Paired Classifier (PC) method described in [2] , and the Sequential Probability Ratio Test (SPRT) method described in [15] .. We evaluate performance using two different base classifiers: the streaming Linear Discriminant Analysis (LDA) classifier described in [1] , and K-Nearest Neighbours (KNN),with k = 3. LDA is chosen since it is computationally inexpensive, and can be written in a recursive form which makes it very suitable for streaming classification. We chose KNN as a simple example of a classifier which utilizes more complex decision boundaries. Note that our implementation of KNN is not recursive, and the class of the t th observation is predicted using the previous t − 1 observations in the usual way. There are adaptations of KNN which make it more suitable to data streams [17] , but since we are only concerned with comparing the performance of concept drift detectors, we will not explore this further. For both ECDD and SPRT, we discarded all old data and reinitialized the classifiers whenever a change was detected. We note that the PC algorithm has a memory facility which allows a small number of observations to be retained after the change, with the rest discarded. It is hence comparable to our ECDD-WT approach from Section 4.1.. Both the PC and SPRT detectors have tunable parameters which must be set by the user. Given a particular data set, the approach taken in [2] is to evaluate the detector on the data set using many different sets of parameters, and then choose only the set which give the best performance. We feel this is a slightly unrealistic approach to take, and prefer to find a small set of parameter values which gives acceptable performance on a wide range of data sets.. As we have done throughout the paper, we view the change detection problem in terms of the mean time between false positives (ARL 0 ). If we choose a set of parameters which gives false positives every 100 observations on average, then it will generally detect concept drift faster than one which gives false positives every 600 observations. However the increased number of false positives will have a negative effect on performance; whenever the detector flags for a change, most old data from the stream will be discarded and the classifier reinitialized. This will cause performance to drop until enough new observations have been seen to allow the classification rule to be relearned.. To investigate this, we decided to use two versions of each concept drift detector with ARL 0 's of 100 and 600 respectively, in order to test how classification performance is affected. These particular values were chosen since (as seen below) they are equal to 2 × T , where T is the location of the change point in the artificial data sets which we consider. We would expect detectors using an ARL 0 of 100 to give better performance on streams where changes occur early, and the detectors with an ARL 0 of 600 to give better performance when changes occur after many observations. For our ECDD algorithm, we used the appropriate polynomial from Table 1 to select the control limit for ECDD with λ = 0.2. We stress again that due to the way this Table has been constructed, the same polynomial can be used for each data set and will give the target ARL 0 .. Tuning the SPRT and PC methods to give a required ARL 0 is a more difficult problem, since these approaches do not contain any way of adapting their parameters online in order to control the false positive rate. The PC algorithm has two parameters, w which defines the size of a window, and h which acts as a threshold. Similarly the SPRT algorithm has two parameters α and β, which roughly correspond to the probability of making a Type I and Type II error.. However there is no obvious way to choose values for these parameters without knowing features of the data stream in advance. For a fair comparison with our algorithm, we chose values which gave an ARL 0 of 100 and 600. However unlike with our approach, the parameters which give these values for the false positive rate vary depending on the data set and base classifier. For example we found that using the PC detector, the parameter values which give an ARL 0 of 600 on the SINE dataset using the LDA classifier only give an ARL 0 of 285 on the GAUSS dataset using the same classifier. Because there is hence no way to control the false positive rate in advance, it is difficult to assess the statistical signifiance of any change points found using these approaches, as discussed in Section 1.. Finally, we note that both our ECDD, ECDD-WT and the SPRT algorithms have a very low computational overhead, with only a small number of calculations being performed at each time step. The computational overhead of the PC algorithm is much higher, unless the underlying classification algorithm can be written in a special form [2] , which limits the situations in which it can be deployed.

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O O O O O O O O O O O O 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 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 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 B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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Wind tunnel experiments of tracer dispersion downwind from a small-scale physical model of a landfill Wind tunnel experiments have been carried out on a small-scale physical model of a municipal waste landfill (MWL) in the CRIACIV (Research Centre of Building Aerodynamics and Wind Engineering) "environmental" wind tunnel in Prato (Italy). The MWL model simulates a landfill whose surface is higher than the surrounding surface, applying a 1:200 scaling factor. Modelling an area source such as landfill is a difficult task for numerical models due to turbulence phenomena that modifies the flow near the source increasing ground level concentration (GLC). For the specific task a new set-up of the wind tunnel has been developed, with respect to previous studies carried out on line and point sources physical models. The tracer used in the experiments was ethylene, suitable for non-buoyant plume conditions, typical for MWL emissions. A detailed result data base has been obtained in terms of GLC and concentration profiles as well as flow turbulence and velocity field characterisation. Both numerical and physical models can be valid instruments for assessing pollutants dispersion in the atmosphere. Models have many advantages with respect to full-scale measurements and can be used to support monitoring networks in the evaluation of pollution levels, as established by the European legislation (96/92/EC).. A large amount of commercial and research numerical models are currently available for a wide range of different applications, depending on the specific case study characteristics (terrain configuration, meteorological data, source and pollutant typology). Evaluation of numerical code result accuracy and definition of the optimal set up are very important tasks.. Small-scale physical models are very useful for developing, improving or testing numerical codes, which mainly rely on empirical parameters and on field data sets affected by large uncertainties (Schatzmann et al., 1997) .. In this application the dispersion of a tracer from a municipal waste landfill (MWL) was modelled, in order to evaluate the diffusional path of emitted pollutants (mainly odours and hydrocarbons).. Pollutant dispersion from MWL is strongly influenced by fully turbulent, localised flows around the plant (specifically referring to a relief one). These conditions represent a challenging problem for numerical models in order to have a sufficient accuracy of the results.. Starting from these considerations the aim of this study was to build an experimental data set, useful for characterising dispersion phenomena with the aim of numerical codes calibration. A set of experiments concerning the dispersion of a non-buoyant tracer emitted from a landfill, using a small-scale physical model, was carried out in a boundary layer wind tunnel facility.. Results can be useful for characterisation of atmospheric dispersion in similar situations (at fullscale) and for validation and/or comparison of mathematical models.. Despite the difficulties of measuring low concentrations, and of modelling the complex flow near the landfill, the experiments showed good reproducibility, with relative variation less than 10%, confirming the experimental set-up validity.. The results analysis allowed the evaluation of the dispersion parameters, which showed how the flow downwind of the landfill, due to induced turbulence, increases the ground level concentration (GLC) with a contemporaneous high variability of GLC near the source.. A phenomenon similar to the "downwash effect" was foregrounded too, with the analysis of the mean plume height (<z>), together with a high ground reflection effect due to both low emission height and the absence of buoyant phenomena.. Higher GLC data obtained in the model scale field appear to be largely influenced by these nearthe-source effects, being located immediately close to the landfill.

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


Waiting time estimates in symmetric ATM-oriented rings with the destination release of used slots Abstract-This paper considers the basic access mechanism in asynchronous transfer mode (ATM)-oriented rings which, like Orwell, ATM ring, and ALine, apply destination release of used slots. The released slots may be reused by the destination station, or in some protocols, they must be given to the next station. Such a mechanism can be modeled by a multiple-server multiqueue system, where switchover times between consecutive polling instants of the queues are nonzero. The server polls the queues according to a certain polling discipline, which is dependent on the service time. This paper presents approximate mean minipacket waiting times in such systems. The approximation is based on a polling queueing model with Markovian server routing. Poisson arrivals and a symmetric workload model for minipackets having a full and partial traffic matrix are assumed. The performance characteristics are compared between the alternative options. T HIS PAPER deals with a slotted-ring basic-access mechanism in high-speed local area networks (LAN's), which are asynchronous transfer mode (ATM)-oriented. In particular, only mechanisms which apply the destination release of used slots, like ATM rings (ATMR 's) [1] , Orwell [2] , and ALine [10] , are considered.. The basic access mechanism in such rings is as follows. The ring is partitioned into equal-length slots, as illustrated in Fig. 1 . Slots circulate around the ring and can be empty or full. A full slot is occupied by a minipacket, i.e., a medium access control protocol data unit (MAC PDU), which may include an ATM cell. A full slot circulating around the ring reaches the destination station, which then reads it and passes it on to a higher layer. We assume that each station is capable of using every empty slot that arrives, and reading each slot destined to itself.. There are two variants regarding the slot reuse. Case : The destination station releases a slot that was full as an empty slot and the next downstream station may use it (Case , denoting no slot reuse). Case : The destination station may reuse the slot that was full for its own transmission (Case , Manuscript received March 24, 1997; revised June 9, 1998 ; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Editor P. O'Reilly.. M. Zafirovic-Vukotic is with the M. Pupin Intitute, Telecommunications Department, University of Belgrade, 11000 Beograd, Yugoslavia (e-mail: ezafirov@ubbg.etf.bg.ac.yu).. I. G. M. M. Niemegeers is with CTIT, University of Twente, 7500 AE Enschede, The Netherlands (e-mail: niemegee@cs.utwente.nl).. Publisher Item Identifier S 1063-6692(99)03822-4. denoting yes for the slot reuse). Note that protocols [1] , [2] , and [10] apply slot reuse by destination (Case ). The older variant of the Orwell protocol corresponded to Case [7] . Modeling of the basic access mechanism in ATM-oriented rings with the destination release of used slots in Case has been done in [7] as follows. It has been shown that such a mechanism can be modeled by a multiple-server multiqueue system, where switchover times between consecutive polling instants of the queues are nonzero. The service strategy at a queue is 1-limited. The server polls the queues according to a certain polling discipline, which is dependent on the service time. The Markovian server polling model to approximate such a system was stated in [7] , and the exact stability conditions for a symmetric case derived from it. However, no attempt to solve this model as a polling model was made. Instead, a service preemptive model with minipacket batch arrivals leads to the delay estimates. Another effort to estimate the delays in the ATM-oriented ring with the destination release of used slots was done in [9] , and an evaluation of the estimate is given in Section VI-C.. The waiting time approximation in this paper is based on a polling model with Markovian server routing, 1-limited service discipline and Poisson arrival process. The pseudoconservation law for this case is derived in [6] . It gives a weighted sum of the expected customer waiting times, and for a symmetric case, it gives the expected waiting times. A possibility of using the Markovian server routing model as solved in [6] for approximate Orwell modeling Case is mentioned in a sentence in [6] , even though the idea was not elaborated on in any way.. We follow the Markovian server polling model approach, and that is the contribution of [7] to this paper. The idea of using a polling model with Markovian server routing to represent Case is new. We apply the solution of the model given in [6] which has not been done up to now, and we obtain the delay estimates and stability conditions for the ATMR in diverse cases. Two traffic cases are considered: 1) a full (Case ) and 2) a partial traffic matrix case (Case ). In the full traffic matrix case, a station sends minipackets to itself, whereas in the partial traffic matrix case, it does not.. In addition, a cyclic and a random polling model are used in this paper. The cyclic model evaluates the lower boundary for the expected delays and provides an estimate of the expected delays under a low load. Mean waiting times in single-server cyclic polling systems have been obtained for symmetric and some asymmetric cases, e.g., see [3] and [5] . An analysis of single-server random polling systems, where each queue is polled with a certain probability, has been done in [4] . Formulas for mean waiting times have been derived for a fully symmetric case, and a unique service strategy is used at all queues. The random polling model could provide estimates of the expected delays under high loads in ATMRs for some cases.. The organization of the rest of the paper is as follows. The general model of the basic access mechanism in the ATMoriented rings is presented in Section II. Section III presents approximate modeling for a single-slot ring. The exact stability condition is stated, and approximate mean waiting time models for high-load and low-load intensity are derived in Section IV. Approximate minipacket waiting time modeling for a multipleslot case is done in Section V. The model is tested and the performance characteristics between the protocol options are compared in Section VI. The conclusions are presented in Section VII. An approximate model which estimates the expected minipacket delay in the ATM-oriented ring with the destination release of used slots, has been developed in this paper. It is based on the multiqueue single-server Markovian server routing model.. The ATM-oriented ring is modeled in two protocol variants (Cases and ): with and without the possibility of slot reuse by the destination station. The workload is fully symmetric, i.e., equal for all stations. Two traffic matrices are considered (Cases and ): a full traffic matrix where each station sends to every station including itself, and a partial traffic matrix where each station sends to all stations but itself.. The approximate analytic model provides estimates for the expected minipacket waiting times in a single-slot ring, (37)-(41). However, it can be applied to the multiple-slot ring as well, as the numerical examples show (up to 6% difference in the delays in a single-and a 30-slot ring under any load). This effect is due to clustering of the servers resulting in a virtually single-server system with a powerful server. Interestingly, the expected minipacket waiting times can be much smaller than the mean minipacket sojourn times in a multiple-slot ring, due to propagation from the source to the destination station.. The analytic model was tested by comparing it to simulations. The expected minipacket waiting time estimates are very accurate for relative loads of up to 0.60, leading to a difference of less than 0.24-slot duration, and the estimate deviates from the simulation results by less than 10%, i.e., 0.7 s. For relative loads of 0.80 the difference is less than 1.5-slot duration and the estimate deviates from the simulation results by less than 21%, i.e., 4.2 s. The model is not intended to be used for relative loads higher than 0.80; e.g., for relative loads of 0.95, the difference is up to 40%.. At very high loads, the simulations show very high delays, which are even higher than the analytic results obtained from the Markovian server routing model can provide ( in Case ). To significantly improve the analytic results, one has to include the dependence between the polling and service processes, i.e., to remove the major approximation of this paper when modeling a single-slot ring.. The exact stability conditions derived for ATMR's in (49)-(53) are independent of the number of slots in the ring. They lead to the expression for the maximum load a ring can carry (55). The ring with an option has a higher maximum load than the one with an option. For each relative load the option is more efficient carrying a higher load. However, the maximum load converges to the same value (276 Mb/s in the numerical examples) as all the parameters being the same and the number of slots arbitrary, and therefore the maximum load the ring can carry would not be much different for a large number of stations in all cases: and and .

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


Behavioral experiments in networked trade We report on an extensive series of highly controlled human subject experiments in networked trade. Our point of departure is a simple and well-studied bipartite network exchange model, for which previous work has established a detailed equilibrium theory relating wealth to network topology. A notable feature of this theory is its prediction that there may be significant local variation in equilibrium wealths and prices purely as a result of structural asymmetries in the network.Our experiments mix recent lines of thought from algorithmic game theory, behavioral economics and social network theory, and are among the first and largest behavioral economics experiments on network effects conducted to date. They continue a line of human subject experiments on networked games and optimization allowing only local interactions. In recent years there has been much research on networkbased models in game theory, in both the computer science and economics communities. Topics receiving considerable attention include the effects of network topology on equilibrium properties [6, 9, 11, 7, 14] , price of anarchy analyses of selfish routing and other networking problems [16] , gametheoretic models of network formation (see [17] and citations therein), equilibrium computation in networked settings (see [10, 15] and citations therein), and many others. This large and growing literature has been almost [3, 5] exclusively theoretical, with essentially no accompanying empirical or behavioral studies examining the relevance of the mathematical models to actual behavior.. In this paper, we report on an extensive series of highly controlled human subject experiments in networked trade. Our point of departure is a simple and well-studied bipartite network exchange model, for which previous work has established a detailed equilibrium theory relating wealth to network topology [9] . A notable feature of this theory is its prediction that there may be significant local variation in equilibrium wealths and prices purely as a result of structural asymmetries in the network. A (centralized) efficient algorithm for computing such equilibria in our model was discovered only recently [4] , and it uses linear programming as a subroutine. One can view our experiments as a test of human subjects' ability to solve this challenging problem in a distributed setting using only local information.. Our methodology and experiments mix recent lines of thought from algorithmic game theory, behavioral economics and social network theory, and are among the first and largest behavioral experiments on network effects conducted to date. We adopt many of the practices of behavioral game theory [2] , which has tended to focus on two-player or small-population games rather than larger networked settings. The work described here continues a broader line of research in behavioral games on networks [12] .. In each of our experiments, three dozen human subjects simultaneously engage in trade over an exogenously imposed bipartite network. Trade is permitted only between network neighbors and takes place via a limit order mechanism; communication thus occurs only through local price discovery throughout the network. Subjects have real financial incentives to trade their initial endowments for as much neighboring commodity as possible.. Our results include a detailed examination of how network structure influences collective and individual outcomes (wealths), as well as a study of the behavioral relevance of the equilibrium theory for the model. The networks imposed are drawn from models common in social network theory, including preferential attachment graphs, random (ErdosRenyi) networks, and some carefully designed structures. Among our most striking findings are the following:. • The behavioral collective performance, as measured by social welfare, is generally strong, with market efficiency close to 90% across all experiments.. • Different network topologies can be strongly distin-guished statistically by individual wealth levels across experiments and other measures.. • When equilibrium theory predicts that greater disparity in individual wealths is required to clear the market, the collective performance (social welfare) of the subjects degrades. However, greater equilibrium wealth disparity does in fact lead to greater behavioral wealth disparity.. • The equilibrium theory appears highly relevant to the behavioral outcomes, outperforming plausible alternatives in predicting individual wealth on an experimentby-experiment basis. However, our best predictor of behavioral individual wealth nudges the equilibrium theory towards equality (uniform wealth distribution); this can be viewed as a networked form of inequality aversion, a bias extensively documented and studied in the behavioral game theory literature on two-player games [2] .. We also provide analyses of the behavioral dynamics within the experiments, including the phenomenon of early trading and the fragmentation of liquidity due to trading. All of the above results and almost all others reported are highly statistically significant.

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On the Static and Dynamic Extents of Delimited Continuations We show that breadth-first traversal exploits the difference between the static delimited-control operator shift (alias S) and the dynamic delimited-control operator control (alias F). For the last 15 years, this difference has been repeatedly mentioned in the literature but it has only been illustrated with one-line toy examples. Breadth-first traversal fills this vacuum.We also point out where static delimited continuations naturally give rise to the notion of control stack whereas dynamic delimited continuations can be made to account for a notion of 'control queue.' To distinguish between the static extent and the dynamic extent of delimited continuations, we first need to review the notions of continuation and of delimited continuation. In Section 2, we present an environment-based abstract machine that specifies the behaviors of shift and control, and we show how the extent of a shift-abstracted delimited continuation is static whereas that of a control-abstracted delimited continuation is dynamic. We show how shift can be trivially simulated in terms of 1 control and prompt, which is a well-known result [11] , and we review recently discovered simulations of control and prompt in terms of shift and reset [12, 47, 68] . In Section 3, we present a roadmap of Sections 4 and 5, where we show how the static extent of a delimited continuation is compatible with a control stack and depth-first traversal, and how the dynamic extent of a delimited continuation can be made to account for a 'control queue' and breadth-first traversal. Since control, even more dynamic delimited-control operators (some of which generate control delimiters dynamically) have been proposed [27, 39, 42, 56, 58, 64] , all of which go beyond CPS but only two of which, to the best of our knowledge, come with motivating examples illustrating their specificity:. • In his PhD thesis [2] , Balat uses the extra expressive power of Gunter, Rémy, and Riecke's control operators set and cupto over that of shift and reset to prototype a type-directed partial evaluator for the lambda-calculus with sums [3, 4] .. • In his PhD thesis [58] , Nanevski introduces two new dynamic delimited-control operators, mark and recall, and illustrates them with a function partitioning a natural number into the lists of natural numbers that add to it. He considers both depth-first and breadth-first generation strategies, and conjectures that the latter cannot be written using shift and reset. As such, his is our closest related work. These applications are rare and so far they tend to be daunting. Dynamic delimited continuations need simpler examples, more reasoning tools, and more program transformations. Continuation-passing style (CPS) is a time-honored and logic-based format for functional programs where all intermediate results are named, all calls are tail calls, and programs are evaluation-order independent [38, 54, 61, 65, 73] . While this format has been an active topic of study [5, 6, 9, 28, 35, 37, 40, 49, 51, 57, 59, 62, 66, 69, 70, 76] , it also has been felt as a straightjacket both from a semantics point of view [28, 29, 31, 32, 44, 45, 71] and from a programming point of view [18, 19, 21, 22] , where one would like to relax the tail-call constraint and compose continuations.. In direct style, continuations are accessed with a variety of control operators such as Landin's J [50] , Reynolds's escape [65] , Scheme's call/cc [17, 46] , and Standard ML of New Jersey's callcc and throw [26] . These control operators give access to the current continuation as a first-class value. Activating such a first-class continuation has the effect of resuming the computation at the point where this continuation was captured; the then-current continuation is abandoned. Such first-class continuations do not return to the point of their activation-they model jumps, i.e., tail calls [73, 74] .. In direct style, delimited continuations are also accessed with control operators such as Felleisen et al.'s control (alias F ) [28, 31, 32, 71] and Danvy and Filinski's shift (alias S) [21] [22] [23] . These control operators also give access to the current continuation as a first-class value; activating such a first-class continuation also has the effect of resuming the computation at the point where this continuation was captured; the then-current continuation, however, is then resumed. Such first-class continuations return to the point of their activation-they model non-tail calls.. For a first-class continuation to return to the point of its activation, one must declare its point of completion, since this point is no longer at the very end of the overall computation, as with traditional, undelimited first-class continuations. In direct style, this declaration is achieved with a new kind of operator, due to Felleisen [28, 29] : a control delimiter. The control delimiter corresponding to control is called prompt (alias #). The control delimiter corresponding to shift is called reset (alias · ) and its continuation-passing counterpart is a classical backtracking idiom in functional programming [1, 14, 16, 53, 67, 75] , one that is currently enjoying a renewal of interest [10, 24, 43, 48, 72, 78, 82] . Other, more advanced, delimited-control operators exist [27, 39, 42, 56, 58, 64] ; we return to them in the conclusion.. In the present work, we focus on shift and control.

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Numerical investigation of a laminar pulsating flow in a rectangular duct A pulsating laminar flow of a viscous, incompressible liquid in a rectangular duct has been studied. The motion is induced under an imposed pulsating pressure difference. The problem is solved numerically. Different flow regimes are characterized by a non-dimensional parameter based on the frequency () of the imposed pressure gradient oscillations and the width of the duct (h). This, in fact, is the Reynolds number of the problem at hand. The induced velocity has a phase lag (shift) with respect to the imposed pressure oscillations, which varies from zero at very slow oscillations, to 90°at fast oscillations. The influence of the aspect ratio of the rectangular duct and the pulsating pressure gradient frequency on the phase lag, the amplitude of the induced oscillating velocity, and the wall shear were analyzed. Copyright The study of unsteady pulsating flows is of practical engineering importance. High speed (turbulent) pulsating flows occur in turbomachinery, rotor blade aerodynamics, reciprocating piston-driven flows, etc. Numerous experimental investigations were focused on fundamental studies of fully developed periodic pipe flows with sinusoidally varying pressure gradients (or flow rates). Low speed (laminar) pulsating flows were studied in order to analyze the flows through small pipes or in the blood circulation systems. Laminar flows are relatively simple for analytical (or numerical) analysis and are a natural choice to provide basic studies of fundamental hydrodynamic effects in pulsating flows.. An incompressible viscous fluid, which is forced to move under a pulsating pressure difference in a duct, has a number of characteristic properties. Some features of such a flow are similar to those occurring in the boundary layer on a body performing reciprocating harmonic oscillations. This is an extension of the problem of the boundary layer when a viscous fluid is bounded by an infinite plane surface that executes a simple harmonic oscillation, with frequency , in its own plane. The latter is the classic problem of the flow near an oscillating flat plate, which was first studied by Stokes [1] . For this type of flow, transverse waves occur in a viscous fluid, with the velocity perpendicular to the direction of propagation. Stokes introduced a new length scale, the depth of penetration of the viscous wave, l= (2w/) 1/2 . The amplitude of the transverse waves is rapidly damped by the factor exp( − y/l) as one moves away from the solid surface whose oscillations generate the waves. In addition, an oscillating fluid layer has a phase lag, =y/l, with respect to the motion of the wall, which depends on a distance y, from the wall.. In the present study, the authors consider a pulsating laminar flow of a viscous, incompressible liquid in a rectangular duct. The influence of the duct aspect ratio, the imposed frequency and the amplitude on the phase lag and the amplitude of the induced oscillating flow are analyzed. A pulsating laminar flow of a viscous incompressible liquid in a rectangular duct was considered. The motion is induced by imposing a pulsating pressure difference. The problem was solved numerically. For the problem at hand, a high-order-accurate numerical scheme was used. The truncation error for the suggested scheme is of sixth-order O(h 6 ) on a square mesh (h x =h y = h) and of fourth-order O(h x 4 , h x 2 h y 2 , h y 4 ) on an unequally spaced mesh. Different flow regimes are characterized by a non-dimensional parameter based on the frequency of the imposed pressure gradient oscillations and the width of the duct. This is the Reynolds number of the problem at hand, which is defined as hh =(/2w) 1/2 h. Calculations for low and high frequency regimes (15 hh520) in a rectangular duct with different aspect ratios (a/h = 1 and 10) were performed. The induced velocity has a phase lag (shift) with respect to the imposed pressure oscillations, which varies from zero at very slow oscillations, to 90°at fast oscillations. The influences of the aspect ratio of the rectangular duct and the imposed pulsating pressure gradient frequency on the phase lag and the amplitude of the induced oscillating velocity and wall frictional force were analyzed.

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