Analysis of Statistical Question Classification for Fact-Based Questions Abstract. Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome. Question classification is the process by which a system analyzes a question and labels the question based on its expected answer type. For example, the question "Who was the first Prime Minister of Canada?" expects a person's name as an answer. Given a finite set of possible expected answer types, known as a question ontology, the goal of a question classification system is to learn a mapping from questions to answer types. Although this task may sound simple, there are many factors that determine how well such systems perform and how robust they are. This paper highlights and analyzes these factors in a statistical machine learning framework.. We focus our attention on fact-based questions. These questions are typically pointed, trivia-like questions where a short, factual answer is expected. Examples of such questions are: "Where is the Orinoco River?", "What type of currency is used in Australia?", and "What is the speed of light?". Although interesting, other types of questions, such as task-oriented questions, are not explored.. Question classification systems are primarily used as components of question answering (QA) systems. QA is the task of retrieving answers to questions posed in natural language from a collection of documents, where an answer is generally a short fragment of text drawn from the corpus. QA systems are a shift away from classical document retrieval towards information retrieval. This saves the user valuable time by eliminating the need to search through a long ranked list of documents for an answer to their question.. There are many kinds of QA systems, all with different underlying architectures. Although each system varies in the way it produces an answer to a given question, most systems follow a general framework (Voorhees 2001) . Given a question, most systems first analyze the question and use a question classification system to determine the most likely expected answer type or types. Next, some form of document or passage-level retrieval is done to retrieve candidate answers from the corpus. Finally, the named entities within the retrieved documents/passages are tagged. This allows the system to prune possible answers based on the expected answer type(s) returned by the question classification system. For instance, if the expected answer type is most likely a person, then only those documents/passages that contain person entity tags are considered possible answers. From this list of candidates, the system determines the best answer or list of answers to present to the user. If the original classification of the question is incorrect there is little hope of correctly answering the question. Although question classification plays a vital role in most QA systems, many factors influence the overall ability of a system to produce the correct answer to a given question. It has been shown that parallel improvements in question classification accuracy, retrieval of candidate answer, named entity tagging, and answer extraction are needed to improve the overall performance of a QA system (Ittycheriah et al. 2000) .. Online digital reference services (Pomerantz et al. to appear) represent another domain may make use of a question classification system. Here, question classification can be used as a component of a query triage system that determines whether a question is best answered automatically by a QA system or by a human expert based on the expected answer type. For example, a question expecting a simple result, such as a person's name, can be routed to an automatic QA system, whereas a question seeking a technical definition or a detailed explanation should likely be routed to human expert. The expected answer type may also be used by the system to choose which human expert to route the question to.. Question classification systems can be used as parts of many other applications related to information retrieval and natural language processing. This paper tries to give a domain independent overview of the subject from a machine learning perspective so as to not limit applicability to only QA systems. In this paper, we presented an overview of statistical question classification applied to factbased questions. Many past approaches resorted to building specialized hand-crafted rules for each question type. Although such rules prove effective, they do not scale well and are tedious to create. Statistical classifiers provide a more robust framework for exploring question classification. Our statistical classifier is based on SVMs and uses prior knowledge about correlations between question words and types to learn question word specific classifiers. Under such a statistical framework, any data set, question ontology, or set of features can be used.. We showed empirically that statistical classifiers are robust in handling different types of features. In general, semantic features are more powerful than syntactic features. They endow the statistical classifier with a certain understanding of a question's meaning via the use of WordNet hypernyms. Furthermore, combining both syntactic and semantic features allows for the most flexibility and generally achieves better performance, increasing precision significantly on three of the data sets.. The analysis of common misclassifications gives insight into possible improvements to our system and other statistical classifiers. First, data sets require expressive yet unambiguous question ontologies to guard against mislabeling errors or ambiguities. Next, more sophisticated labeling methods, such as allowing questions to be associated with one or more question types, are necessary to overcome the problem of questions with ambiguous question types. Although this may solve some problems, difficult questions will always exist that escape being correctly classified with only a limited understanding of the question. Also, inaccurate POS taggers often cause errors to propagate through to the final result. Finally, WordNet's ability to abstract common concepts can lead to problems when it is used to extract features in a system, as we showed in the case of cereal. Therefore, natural language processing techniques, such as word sense disambiguation, could prove to be beneficial.. There are many avenues of future work left to explore. Our results show that simple semantic features can improve system performance more than syntactic features. Unfortunately, these features are not as well studied or understood compared to their syntactic counterparts. Therefore, more advanced methods of including semantic information need to be explored. Also, better question ontologies need to be developed to be both expressive enough to cover most question types and as unambiguous as possible. If the ontology is being designed for a QA system, we must also take into account whether or not a named entity tagger can extract candidate answers for each question type. Finally, a larger realistic data set like the MadSci data should be created as a standard test collection that would allow comparison across different classification systems and help further advance the state of the art. As the baseline results for the three data sets show, our statistical approach to question classification is robust. Each data set discussed has very different qualities, yet our system was able to achieve relatively good performance on each using simple features. From the results we see that statistical classifiers can be robust across multiple data sets with varying characteristics. The same generally cannot be said of traditional hand-crafted rules. For each data set a different collection of rules would have to be manually created, which is a timely, expensive process. For supervised statistical classifiers, such as the one presented here, only a set of labeled questions is necessary. Creating such a set is often less time consuming and requires less domain knowledge than hand crafting rules. Furthermore, it is often possible to obtain labeled training data without any human intervention (Davidov et al. 2004 ). Thus, statistical classifiers, such as our SVM-based system, may be applied to a wide range of data sets and question ontologies with very little or no hand tuning and manual effort necessary.. From these results we also see that a great deal of information can be learned by looking at more than just the answer type associated with the highest classification score. For example, for the TREC QA questions, only considering the top answer type results in an accuracy of 77.59%. However, if the second most likely answer type is also considered the accuracy jumps to 86.42%. As discussed in Section 3, the P ≤n metric should be considered along with other system properties and requirements to determine how to best use the question type list returned by the question classifier. In the case of the TREC QA questions, it may be beneficial to make use of the top two types returned by the classifier.. Finally, we present results from combining a number of features discussed above. Table 9 shows results for our system using the following combination of features: unigrams, bigrams, WordNet hypernyms, and proper noun phrase presence.. The combined feature set outperforms the baseline results in terms of MRR and P ≤1 . For all data sets, excluding MadSci, precision is increased significantly. Furthermore, for both the TREC QA track and UIUC fine grained questions the MRR is increased significantly. This feature set represents the best performance achieved by our system on the TREC QA and UIUC coarse and fine grained question sets. However, for the MadSci data, using unigram and WordNet features yielded the best results. Such poor generalization is largely due to the small training set and large number of features. Thus, for larger data sets combining both syntactic and semantic features can lead to significantly better system performance. One of the largest QA evaluations is the Text REtrieval Conference's (TREC) QA track. Over the years this forum has introduced many approaches to QA and fostered a great deal of research in the field. Many of the systems use the general QA framework described above and thus make use of some form of question classification. A majority of systems use hand-crafted rules to identify expected answer types (Hull 1999 , Lee et al. 1998 , Prager et al. 1999 . The following are examples of such rules from (Pasca and Harabagiu 2001): What {is | are} < phrase to define >? What is the definition of < phrase to define >? Who {is | was | are | were} < person name(s) >?. The first two rules detect definition questions and the last detects biographical questions. These rules have the potential to be very powerful. However, they are cumbersome to create and often do not generalize well. Hand-crafted rules that work well on a specific set of questions may give poor results when applied to another set. Rules created for a specific question ontology must be re-tailored before being applied to different ontologies. In addition to TREC QA track systems, several web-based QA systems have relied on such rules with limited success (Radev et al. 2002) . Therefore, there is a need for more robust systems that can easily be adapted to handle new data sets and question ontologies.. To overcome these problems, machine learning techniques for question classification have been researched and successfully applied. Several systems make use of statistical approaches. Since it is not possible to list all such systems, we briefly describe several. Among these are IBM's TREC-9 system (Ittycheriah et al. 2000) that utilizes maximum entropy models (Della Pietra et al. 1997) . It uses a mix of syntactic and semantic features (see Section 5). The authors use a data set of 1,900 questions specifically created and labeled for the task in addition to a set of 1,400 questions from a trivia database. The questions are labeled according to the MUC categories (Chinchor and Robinson 1998) . On a heldout portion of the data, the system yields an accuracy of 90.95%.. Roth and Li developed a question classification system based on the Sparse Network of Winnows (SNoW) architecture (Li and Roth 2002) . The system also makes use of a collection of syntactic and semantic features. The data set and question ontology they use is discussed in detail in Section 4. The system achieves 91.00% accuracy on general, coarse grained question types, and 84.20% on more specific, fine grained types.. Finally, Zhang and Lee's question classification system (Zhang and Lee 2003 ) is based on Support Vector Machines (SVMs) (Vapnick 1998) . The system uses a tree kernel (Collins and Duffy 2002) and simple syntactic features. It is trained and tested on the same data set and question ontology used by Roth and Li. The system achieves 90.0% accuracy on the coarse grained question types.. It should also be mentioned that some work has made use of natural language processing techniques to automatically construct grammars to match question types against (Hovy et al. 2001 , Nyberg et al. 2003 . These systems typically make use of some underlying statistical methods, but are susceptible to poor question type coverage. The Javelin system (Nyberg et al. 2003) combines automatically learned parsers augmented with hand built rules to achieve 92.00% accuracy on a test set of TREC questions. Such parser-based approaches often perform comparably to discriminative classifiers, which is the focus of this work.. Each of these systems takes a unique statistical approach to the question classification and achieves good results (typically above 80% accuracy) on their respective data sets. Unfortunately, most past studies only present results for a single data set and provide very little in the way of error analysis. Therefore, in the remainder of this paper we explore how well statistical methods perform across several data sets. Each data set has different characteristics, such as the expressiveness of its question ontology and its source. This allows for a broad empirical evaluation. We also examine the role different types of features have on system performance. Finally, we identify factors that hinder classification accuracy by providing an analysis and explanation of common causes of misclassification. All experiments in this paper make use of Joachims' SVM light (Joachims 1998 ) software, a one-versus-all approach to multi-class classification, RBF kernels for the SVM (Burges 1998) , and 10-fold cross-validation for test set evaluation unless otherwise noted. The parameter for the RBF kernel (the variance) is set to a value that gave good performance in the past on similar classification tasks. No stopword removal or stemming is performed. Although our system makes use of SVMs, it should be noted that any multi-class statistical method can be used. Machine learning techniques applied successfully to text classification are particularly well suited for this task and include methods such as Naive Bayes (McCallum and Nigam 1998) , maximum entropy models (Nigam et al. 1999) , and k-nearest neighbor (Yang and Liu 1999) .. This section provided a brief overview of our statistical question classification framework. Not all machine learning methods are created equal. As a result, system performance depends on the underlying learning paradigm. Regardless of this, many system-independent issues must still be resolved, such as what question ontology and set of features to use. After explaining how system performance is measured, we will explore these issues using the experimental framework developed in this section.

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A privacy preserving web recommender system In this paper we propose a recommender system that helps users to navigate though the Web by providing dynamically generated links to pages that have not yet been visited and are of potential interest. To this end, traditional recommender systems use Web Usage Mining (WUM) techniques in order to automatically extract knowledge from Web usage data. Thanks to WUM techniques we are able to classify users and adaptively provide useful recommendations. The drawback of a user classification approach is that it makes the system prone to privacy breaches.Our contribution here is πSUGGEST, a privacy enhanced recommender system that allows for creating serendipity recommendations without breaching users privacy. We will show that our system does not provide malicious users with any mean to track or detect users activity or preferences. The continuous and rapid growth of the Web has led to the development of new methods and tools in the Web recommender or personalization domain. In [6] the goal of the Web personalization is defined as "provide users with the information they want or need, without expecting from them to ask for it explicitly".. Web Mining has shown to be a viable technique to discover information "hidden" into Web-related data [4] . In particular, Web Usage Mining (WUM) is the process of extracting knowledge from Web users access data (or click-stream) by exploiting Data Mining (DM) technologies [5] . It can be used for different purposes such as personalization, system improvement and site modification.. The knowledge extracted is actually a classification model for users in different groups with different interests. Obviously the presence of such classification system can introduce privacy breaches, by either disclosing personal information or allowing malicious queries capable of reconstructing the knowledge collected by the system. In this work we mainly focus on this last aspect, and present a WUM system, called πSUGGEST, which is designed to dynamically generate personalized contents of potential interest for users of a Web Site, without providing any privacy breaches to malicious users. The architecture of πSUGGEST is based on a twotier structure. One of them has to be plugged-in into the browser at the client-side. The other tier is based on an incremental personalization procedure, tightly coupled with the Web server. Its knowledge base is incrementally updated by monitoring usage data, and then notified to the client, which will be able to use it to personalize on-the-fly the requested HTML page, by appending a list of page links (suggestions).. Eventually, we define a measure of privacy in order to evaluate with which confidence a malicious user can infer users activities from the provided suggestions. The quality of suggestions was evaluated by adopting the metric introduced in [2] . This metric tries to estimate the effectiveness of a recommendation system as the capacity of anticipating users requests that will be made farther in the future.. Summarizing, the main contributions of this work are: an algorithm to incrementally generate users profiles in a privacy preserving way. A general privacy measure for classification -based Web recommender systems. Finally we will show that πSUGGEST successfully preserves users privacy w.r.t. the measure we introduced.. The rest of the paper is organized as follow. In Section 2 we show some works related to this paper. Section 3 presents the architecture and the algorithms used by πSUGGEST. Section 4 presents a framework for analyzing privacy in cluster-based recommender systems in general and we adopt it for the analysis of πSUGGEST's privacy. Finally in Section 5 we conclude the paper by presenting some forthcoming work. In this work, we presented a privacy enhanced web recommender system. State-of-the-art algorithms require users to be classified in order to provide them with interesting suggestions. This classification-based approach has been shown to be a privacy breach itself. It reveals which pages a group of users have actually visited. This information may be used by potential competitors to, for instance, restructure their own sites according to the usage patterns "stolen" from a site which uses privacy-disclosing Web recommender systems. According to this framework of user classification based systems, we define a new privacy measure. This metric models the chance for a malicious user to recover the real behavior of a group or a single user, on the basis of the information revealed by the system. Finally we introduced πSUGGEST, a two-tier system which works both at client and server side. On the server side, a knowledge base is updated on-line. On the client side, a plugin create a list of links to pages of interest. Our recommender system is shown to be privacy safe. No significant additional information, i.e. that could be used by malicious users, is needed to create a knowledge base. From this knowledge base, a set of web page clusters is extracted and used to build recommendations. More importantly, we show that the probability to guess whether a user has visited a set of pages U on the basis of the extracted clusters, decrease exponentially with the cardinality of |U |. This probability is the same both for any third party user and for the server providing this service as well. This means, that according to our framework, the server which collects information to build the knowledge base, can not breach users' privacy. A set of experiments assess the quality of the recommendations. As the a future work, we want to tighten the bounds we provided in this article, and to study the evolution of the system from a privacy point of view. We had to evaluate the soundness of the system using historical data, but we are much more interested in how this scenario can change due to user interaction, i.e. when users actually use links provided by the system.

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


On modelling mobility We present a model of distributed computation which is based on a fragment of the -calculus relying on asynchronous point-to-point communication. We enrich the model with the following features: the explicit distribution of processes to locations, the routing of messages, the mobility of processes, and the failure of locations and their detection. Our contributions are two folds. At the speci cation level, we give a synthetic and exible formalization of the features mentioned above. At the veri cation level, we provide original methods to reason about the bisimilarity of processes. Traditional process calculi such as CCS and CSP lie their foundations on a reduced set of concepts and therefore do not provide direct support for the modelling of certain relevant aspects of systems such as the distribution of resources on di erent locations, the routing of messages, the mobility of processes, the impact of failures on the behaviour of the system, and the detection of failures. The exact meaning of these terms will become clearer, as we progress in our discussion. Developments in network and software technology have brought these aspects of computing to the limelight. Mobility in particular plays a prominent role and therefore we will shortly refer to the models considered in this article as`models of mobile computation'. We pursue a research line initiated in 6] and continued in 3], in which an explicit modelling of the features mentioned above is presented, and then a reduction to a more basic model is sought. In developing a model for mobile computation, a preliminary question is whether we should assume the notions of dynamic generation of names and processes and static scope of names. We believe that the answer to this question should be positive (as in 21, 22, 15] ) for the same reason that, say, a model of functional computation should take for granted -renaming and substitution. On these assumptions, it is natural to consider the -calculus 18, 34] or related formalisms such as Chocs 39] , as the backbone of a model of mobile computation. In rst approximation, the -calculus models systems of asynchronous processes which interact by message passing. The calculus models dynamic process creation, dynamic channel creation, transmission of channel names, and a static scoping discipline. The blending of these features has led to a calculus which is quite expressive and close to programming issues, while having a tractable semantic theory. We select a variety of -calculus as the basic model on which additional features are added. The advantage of this approach, is that notions and results can be inherited and stated, respectively, within the theory of the -calculus.. The variety of -calculus which we consider is a fragment of the asynchronous -calculus 23, 12] . In this calculus, the sending of a message is non-blocking, that is a process can deliver a message without waiting for a receiving process (think of e-mail). This communication schema implicitly relies on a nonbounded bu er in which messages can be stocked. Messages in the bu er can be reordered in arbitrary ways (the bu er does not obey a FIFO discipline). Moreover, we assume that every channel name is associated with a unique process which receives messages addressed to that name (communication becomes point-to-point). To emphasize the unicity of the receptor, we will refer to the calculus as the 1 -calculus. We note that asynchronous point-to-point communication does not require synchronizations between possibly distant processes and therefore it makes minimal assumptions on the capabilities of the distributed system. Technically, the 1 -calculus is formalized by means of a simple typing discipline which enjoys a suitable subject reduction property. We will spend sometime to illustrate the expressive power and the speci c properties of the 1 -calculus. Indeed, the 1 -calculus has some distinctive characters which make it worth of study independently from the speci c application to mobile computation. Taking the 1 -calculus as the basic formalism, we specify in an incremental way some aspects of mobile computation. As a rst step, we explicitly distribute processes to locations. Locations are our unit of distribution and they can be generated dynamically. Next we consider three orthogonal aspects of a mobile system: (i) the routing of messages to their destination, (ii) the mobility of processes, and (iii) the failure of locations and the detection of failure. The speci cation of these features relies on suitable typing and rewriting rules. A number of alternative speci cations arise by combinations of explicit or transparent routing, static or migrating processes, halting or transient failures, and various types of failures detectors. We will not try to cover all possible combinations of these choices, instead we will study in depth a simple model while hinting to possible variations and highlight the most interesting or challenging combinations. Thus, we will consider a system of asynchronous processes which are distributed to locations and interact by (asynchronously) exchanging messages which are transparently routed to their destination. Processes can spawn processes at remote locations. Locations can stop (halting failure) and their state can be tested.. The last part of the article is devoted to semantic issues. Our goal is to develop techniques to prove the bisimilarity of processes. In particular, we characterize a contextual equivalence for the 1 -calculus (barbed equivalence) using a recently introduced notion of asynchronous bisimulation 4], and we propose translations from the`located' 1 -calculus ( 1l ) to the 1 -calculus which preserve and re ect these bisimulations.. The rest of the article is organized as follows. In section 2, we present the 1 -calculus, and study its typing system. In section 3, we illustrate the expressive power of the 1 -calculus. In section 4, we de ne the 1l -calculus as an enrichment of the 1 -calculus. In section 5, we study two translations of the 1l -calculus in the the 1 -calculus and characterize the contextual bisimulation for the 1 -calculus. Finally in section 6, we consider related work and summarize our main achievements.

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


A Simplified Implicit Maxwell Solver We apply the second-order formulation of Maxwell's equations proposed by Jiang et al. (1996, J. Comput. Phys. 125, 104) to the solution of the implicit formulation of the three-dimensional, time-dependent Vlasov-Maxwell's system. An implicit finite difference algorithm is developed to solve the Maxwell's equations in a bounded domain with physical boundary conditions comprising electrically conducting walls (perfect conductors) and constant magnetic flux walls. We formulate the boundary conditions for Maxwell's equations to satisfy Poisson's equation throughout the domain by solving it only on the boundary. This eliminates the need for a separate projection step. We compare numerical results with analytical solutions for electromagnetic waves in vacuo, and using the implicit particle-in-cell code CELESTE3D, we test the new solver on the geospace environment modeling magnetic reconnection challenge problem. c 2002 Elsevier Science (USA) By computing the self-consistent motion of a large number of charged particles in the fields they produce, one can simulate the full complexities of a plasma from first principles [6, 25] . Such simulations have made important contributions to magnetic confinement [55] , laser fusion [30] , and space plasmas [61] .. This paper is about the application of the second-order formulation of Maxwell's equations proposed by Jiang et al. [32] to the solution of the full Vlasov-Maxwell system.. The numerical solution of Maxwell's equations is a topic widely discussed in the literature. The basic difficulty in solving the typical, first-order Maxwell's equations is related to the fact that Maxwell's equations comprise a system of eight equations for six unknowns (assuming charge and current densities are given). Nevertheless, it is incorrect to consider the divergence equations redundant, on the basis that the divergence conditions will be satisfied if they are initially, as is often done [24] . It has been shown that ignoring the divergence conditions leads to incorrect solutions of Maxwell's equations [32, 50] .. The second-order formulation of Maxwell's equations has also been considered for the implementation of a numerical algorithm and there are many reports regarding numerical solutions which do not satisfy the divergence constraints [5, 49, 53, 60] . The secondorder Maxwell's equations are derived from the first-order equations by applying the curl operator: The equations so obtained admit more solutions than do their progenitors. The spurious solutions, in particular, do not satisfy the divergence laws [32] . In this context, it is shown that boundary conditions play a crucial role in satisfying the constraints on the solution of Maxwell's equations [1, 32] . Jiang et al. show that if one satisfies Gauss's law on a part of the boundary at all times, the solution of Faraday's and Ampère's laws will satisfy Gauss's law throughout the volume [32] .. Within this context, in their paper Jiang et al. describe a node-based least-squares finite element method able to achieve the satisfaction of the divergence conditions [32] .. Here, we propose a finite difference algorithm, based on the second-order formulation of Maxwell's equations and its boundary conditions, as proposed by Jiang et al., applied in the context of plasma simulation using an implicit method for the solution of the VlasovMaxwell system. Implicit simulation methods are useful for the study of low-frequency phenomena and realistic electron-ion mass ratios. Predictions of self-generated magnetic fields in laserplasma interactions [19] were confirmed by experiments in Ref. [62] . Implicit simulations of quasi-perpendicular [20] and quasi-parallel shocks [46] reveal complex behavior that is the subject of continuing study [52] . Simulations of the lower-hybrid-drift instability give estimates of the nonlinear saturation mechanism [10] . A more recent reexamination of this problem led to the discovery of the drift-kink instability [26] and the nonlinear development of the Kelvin-Helmholtz instability [37] . Results for beam filamentation and expansion into a vacuum are reported in Ref. [14] . Studies of magnetic reconnection are reported in Refs. [17, 22, 23, 37, 57] . Comparisons of implicit and hybrid solutions for switch-off shocks [13] and contact discontinuities [35, 39] help to define the range of validity of hybrid methods. The implicit moment method is also applied to plasma opening switch modeling in Ref. [43] .. We remark that in the context of the plasma simulations, additional difficulties arise in solving Maxwell's equations. Implicit plasma simulations require the solution of the Maxwell equations in the presence of a permittivity in the form of a nonsymmetric tensor. Also, in the context of plasma simulations, it is pointed out that the continuity equation may be not satisfied [22] . Consistent with this observation, projection is used to assure that Poisson's equation is satisfied. (Charge-conserving algorithms have also been developed that eliminate the need to solve Poisson's equation [18, 58] , but these have not yet been employed in implicit simulations.) In a common projection method, a scalar potential is introduced as a correction to the electric field and is solved to satisfy Gauss's law, thus bringing into correspondence the number of boundary conditions and unknowns for the electric field [22, 33] . However, in implicit formulations, projection is only approximately correct because of a coupling of longitudinal and transverse components of the electric field by the plasma permittivity [12] .. The main point of this paper is that Poisson's equation can be satisfied in the volume without the need for projection through a new formulation of the boundary conditions for Maxwell's equations following Jiang et al. [32] .. The paper is organized as follows. First, we discuss Jiang et al.'s second-order Maxwell's equations, focusing on the boundary conditions in the context of the Vlasov-Maxwell system. These boundary conditions take an especially simple form for perfectly conducting and magnetic flux surfaces. Next, we describe the numerical implementation of consistent boundary conditions in CELESTE3D, a particle-in-cell (PIC) code based on the implicitmoment plasma simulation method [9, 59] . Finally, we present the results of calculations for electromagnetic wave propagation in vacuo and for the geospace environment modeling (GEM) magnetic reconnection challenge [7] . Within the implicit-moment method for plasma simulations, we have presented a Maxwell solver that eliminates the need to solve an implicit Poisson's equation (40) in the volume through a careful formulation of the boundary conditions following Jiang et al. [32] . (A standard projection, Eq. (35), corrects for inconsistencies between the particle and moment data each cycle.) Because implicit formulations of Eq. (40) couple transverse and longitudinal parts of the electric field, normal projection methods are only approximately accurate, and consistent boundary conditions are difficult to formulate [12] .. The Maxwell solver presented here leads to the solution of Eq. (44) for boundary conditions (47) and (48) , which correspond to perfectly conducting and magnetic mirror boundaries, respectively. It is shown that the solution of Maxwell's equations with these boundary conditions does not present spurious solutions and is unique.. Numerical results, including an electromagnetic wave in vacuo and an ion tearing mode (GEM challenge), demonstrate the accuracy of the solutions and good agreement with those provided by explicit PIC methods.

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


Evaluating Temporal Robustness of Mobile Networks Abstract-The application of complex network models to communication systems has led to several important results: nonetheless, previous research has often neglected to take into account their temporal properties, which in many real scenarios play a pivotal role. At the same time, network robustness has come extensively under scrutiny. Understanding whether networked systems can undergo structural damage and yet perform efficiently is crucial to both their protection against failures and to the design of new applications. In spite of this, it is still unclear what type of resilience we may expect in a network which continuously changes over time. In this work, we present the first attempt to define the concept of temporal network robustness: we describe a measure of network robustness for timevarying networks and we show how it performs on different classes of random models by means of analytical and numerical evaluation. Finally, we report a case study on a real-world scenario, an opportunistic vehicular system of about 500 taxicabs, highlighting the importance of time in the evaluation of robustness. Particularly, we show how static approximation can wrongly indicate high robustness of fragile networks when adopted in mobile time-varying networks, while a temporal approach captures more accurately the system performance. T HE study of real-world communication systems by means of complex network models has provided insightful results and has vastly expanded our knowledge on how single entities create connections and how these connections are used for communication or, more generally, interaction [1] . Statistical network models such as the Erdö s Rényi (ER) random graph [2] and the scale-free networks generated by preferential-attachment mechanisms [3] have been largely exploited to study and understand real-world systems. While the former model embodies networks whose nodes are statistically indistinguishable, resulting in fairly homogeneous properties, scale-free networks exhibit heterogeneous node properties, with the vast majority of nodes having a few connections and a small minority of hubs having a disproportionately large number of links. The different properties present in these two families of models are able to capture a vast range of real-world systems. In particular, technological networks such as the Internet and the World Wide Web have been under scrutiny in terms of structure and dynamic behavior [4] , [5] . More recently, with the widespread adoption of mobile and opportunistic networks, it has become important to develop new analytical tools to keep into account network dynamics over time [6] , [7] and how this affects phenomena such as information propagation [8] , [9] . Results have shown that time correlations and relative temporal ordering of connection events among nodes cannot be neglected, otherwise the performance of a given system can be greatly overestimated [10] , [11] .. At the same time, the problem of understanding whether real systems can sustain substantial damage and still maintain acceptable performance has been extensively addressed [12] , [13] . Various measures of network robustness have been defined and investigated for several classes of networks, evaluating how different system can be more or less resilient against random errors or targeted attacks thanks to their underlying structural properties [14] , [15] .. Nonetheless, it is still unclear how to approach the study of robustness of networks by taking into account their timevarying nature: by adopting a static representation of a temporal network, important features which impact the actual performance might be missed. Thus, it becomes important to develop a robustness metric which takes into account the temporal dimension and gives insights on how a mobile network is affected by damage or change. Particularly, the fact that links are not always active means that information spreading can be delayed or even stopped and that relative ordering in time of connection events may affect the creation of temporal paths in mobile networks.. Our main goal is to design a novel framework for the analysis of robustness in mobile time-varying networks. We adopt temporal network metrics [10] to quantify network performance and define a measure of robustness against generic network damages. At first, we study its performance on random network models to understand its properties; then we apply our method to study a real mobile network, describing how temporal robustness gives a more accurate evaluation of system resilience than static approaches.. Our contributions can be summarized as follows:. . We describe the use of temporal network metrics such as temporal distance to estimate the current network connectivity taking temporal variability into account. We define temporal network robustness (Section 2), a novel measure which quantifies how the communication of a given time-varying network is affected by damage. . We provide an analytical computation of temporal metrics on a temporal version of the Erdös Rényi random graph model (Section 3) and we evaluate through numerical simulation both a Markov-based link connectivity model, which provides timecorrelations, and two random mobility models, which introduce space-correlations (Section 4). . We show how, unlike what has been demonstrated with static measures, temporal networks do not exhibit sharp breakdowns but instead fail gracefully when they are subjected to failures. The temporal dimension is able to capture the evolution dynamics, exposing the fact that time allows to create temporal paths across otherwise disconnected portions of the network. . We investigate the usefulness of our approach to characterize a real communication system, an opportunistic vehicular network simulated with real mobility traces of about 500 taxis in the San Francisco Area (Section 5): we show that the robustness evaluation based on static network analysis is too optimistic in real scenarios, since it greatly overestimates how resilient a mobile system really is, without giving reliable measures. We discuss some implications of our findings for the design of new systems and applications (Section 6). Finally, we review related results on this topic (Section 8) and we conclude the paper (Section 9). This paper has presented a study of temporal robustness in time-varying network: we adopt temporal network metrics to assess network performance in presence of increasingly larger random failures. We have investigated the performance of our method both analytically on a random temporal network model and via simulations in a Markov-based and in two mobility-based models, exploring how the temporal dimension provides more redundancy to communication systems compared to static evaluation. Finally, we have shown how temporal robustness gives a more realistic estimation of the resilience of a real-world temporal network than standard static approaches. We plan to extend our work by taking into account attack strategies, where network damages might be correlated across different nodes, investigating whether random models and real networks react differently to this type of damage. Another interesting research direction would be to extend the theoretical analysis of the temporal network model to analyze more properties, such as the settling time of the system after a damage. Cecilia Mascolo is a reader in mobile systems in the Computer Laboratory, University of Cambridge, United Kingdom. She has published extensively in the areas of mobile computing, mobility, and social network modeling. She is and has been PI of various national and international projects on social network analysis, sensing, and mobility modeling. She has served as a program committee member of many mobile, sensor systems, and networking conferences and workshops. She has taught various courses on mobile and sensor systems and a master's course on social and technological network analysis. More information is available at http://www.cl.cam.ac.uk/users/cm542. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib. We study the reaction of the Cabspotting temporal network to random failures and compare it to our findings on random models. We adopt numerical simulation, but since the temporal dynamics of this network is not stationary, we cannot compare the temporal efficiency E G before and after a certain error, because the two temporal window will likely have already different properties. Instead, we fail nodes according to P ERR at the very first time step of the temporal sequence of graphs: in this way, we can compare the average temporal efficiency over all the time for the original network and for the damaged one. We adopt a value of ¼ 3;600, which allows us to consider temporal paths up to 1 hour, even if such longer paths cannot contribute much to temporal efficiency.. The first comparison that we show in Fig. 9a is between static robustness and temporal robustness for the Cabspotting temporal network. In this case, static robustness is computed on the static graph obtained by aggregating all the contacts in the trace and adopting static global efficiency as performance measure. Since the resulting static graph contains more than 100,000 edges it is clearly an overestimation of the communication properties of the real Fig. 8 . Temporal robustness R G as a function of probability of error P ERR and for different values of P ON for the different random models.. system, as not all these links are continuously available over time and some paths cannot be used due to temporal ordering constraints. Indeed, static robustness appears much larger than the temporal counterparts: only temporal robustness is able to capture the realistic communication capabilities of the system and how they are affected by random failures.. Then, we attempt to understand if the various random temporal network models we have studied can be used to approximate the robustness properties of the real scenario. For each model, we compute the temporal robustness as a function of P ERR for a network with the same number of nodes N and the same P ON measured in the Cabspotting temporal network (about 0.005), using the same simulation parameters as in the real scenario. As reported in Fig. 9b , all temporal networks present the same trend in network robustness, albeit random models have higher values of temporal robustness than the real network. Interestingly, the closest match is the Markov-based temporal model, while the mobility-based models are closer to the ER model than to the Cabspotting network, even if this is actually a mobility-based contact network. However, the assumptions used in mobility models require homogeneity of space and absolute freedom to move continuously and independently in a boundless area, while in reality taxis are usually constrained to move on streets and bridges and they often move together along the same direction or stop together in a particular place to wait for customers (i.e., airport or stations). The Markov model, instead, introduces the type of time correlations that appear to better mimic the real scenario. In fact, the most important aspect that needs to be captured is time ordering of events: in random mobility models connections do not follow particular time patterns, whereas real traces do (rush hour, working hours, human sleeping cycles). Only temporal robustness can take into account these unique characteristics.. These two results provide evidence that temporal robustness is a more accurate measure to be used on mobile networks instead of standard static approaches. Therefore, when testing protocols and applications to be deployed in mobile networks, a temporal study is more meaningful and should not be substituted by a static approximation. Previous related works lie in the two broad areas of temporal network analysis and network robustness studies.. One of the first attempts to generalize static network models to handle temporal information was to adopt time labels on edges to express temporal constraints on their presence [7] : this mainly algorithmic approach does not handle temporally disconnected nodes and, thus, is less suitable to investigate temporal networks arising from communication systems. Instead, some first attempts to investigate the properties of human contact networks reported on the temporal correlations and periodicities in these systems that arise at peculiar time scales [8] , and on the impact of the frequency distribution of intercontact time on delivery properties of opportunistic protocols [9] . More recently, the concept of network distance for temporal graphs has been formalized and explored [10] , [11] . We build up on these results, by adopting these temporal measures in the definition of temporal network robustness.. The study of network robustness initially focused on how different classes of random networks exhibit different behavior when affected by random errors or targeted attacks [12] . In particular, exponential random graphs appear equally robust against both errors and attacks, while scale-free networks have higher resilience against random failures while being easily disrupted by intelligent attacks. Another approach to analytically study this problem is to exploit percolation models and explore how the network behaves as edges or nodes are removed [13] . Several extension on this topic have provided both extensive analysis on what type of attacks can be more disruptive in real networks [14] and dynamic models of failure which take into account how errors modify not only the structural properties of a network but also its dynamic properties [15] . Nonetheless, there have been no attempts to address the problem of network robustness in time-varying graphs. To our knowledge, our work presents the first method to quantitatively evaluate network robustness by taking into account temporal properties.

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


A new vision-based approach to differential spraying in precision agriculture One of the objectives of precision agriculture is to minimize the volume of herbicides by using site-specific weed management systems. To reach this goal, two major factors need to be considered: (1) the similarity of spectral signatures, shapes, and textures between weeds and crops and (2) irregular distribution of weeds within the crop. This paper outlines an automatic computer vision method for detecting Avena sterilis, a noxious weed growing in cereal crops, and differential spraying to control the weed. The proposed method determines the quantity and distribution of weeds in the crop fields and applies a decisionmaking strategy for selective spraying, which forms the main focus of the paper. The method consists of two stages: image segmentation and decision-making. The image segmentation process extracts cells from the image as the low-level units. The quantity and distribution of weeds in the cell are mapped as area and structural based attributes, respectively. From these attributes, a multicriteria decision-making approach under a fuzzy context allows us to decide whether any given cell needs to be sprayed. The method was compared with other existing strategies.© 2007 Elsevier B.V. All rights reserved.Nowadays, there is a clear preference to reducing the use of chemicals in agriculture. Numerous technologies have been developed to make agricultural products safer and to lower their adverse impacts on the environment, and precision agriculture is a valuable component of the framework to achieve this goal (Kropff et al., 1997; Zhang et al., 2002; Stafford, 2006) . Within that general framework, weeds can be managed site-specifically using available geospatial and information technologies (Gerhards and Christensen, 2006) . Initial efforts * Corresponding author. Tel.: +34 1 3 94 75 46; fax: +34 1 3 94 75 47. E-mail address: pajares@fdi.ucm.es (G. Pajares).to detect weed seedlings by machine vision focused on geometrical measurements such as shape factor, aspect ratio, and length/area (Pérez et al., 2000) . Later, colour images were successfully used to detect weeds and other types of pests (Søgaard and Olsen, 2003) . Yang et al. (2003) estimated weed coverage and weed patchiness based on digital images, using a fuzzy algorithm for planning site-specific application of herbicides. Recently, Gerhards and Oebel (2006) Thorp and Tian, 2004; Ribeiro et al., 2005) . Bacher (2001) estimated weed density in a field of spring barley by image binarization and morphology followed by the identification of crop rows using information on distances between rows within the crop to decide on spraying. This process serves to make weed plants appear isolated from the crop.Avena sterilis L. ("winter wild oat") is one of the most widely distributed and abundant weeds of cereals in Spain and other regions with Mediterranean climate, causing substantial losses in these crops (Barroso et al., 2004a; Radics et al., 2004) . Although some A. sterilis plants may be found growing singly or in small patches, the majority of them are aggregated in relatively large patches (Ruiz et al., 2006) , and those in early spring, after broad-leaved weeds have been controlled by early postemergence treatments, are practically pure stands (Fernandez-Quintanilla, personal observation). Due to these two features, it is relatively easy for an experienced farmer or a technical consultant to detect patches of A. sterilis visually in the early stages of crop growth. In fields of cereals (barley or wheat), the cereal plants grow along the furrows: the plants growing between furrows can only be weeds. But weeds may also grow mixed with the cereal. We sought to detect weeds by differences in appearances: isolated plants, small or large patches, or mixed with the crop. Three main problems arise during detection, namely (1) irregular shapes and different sizes of the patches, (2) spectral signature and texture similar to those of the cereal plants, and (3) irregular distribution of the weeds in the field. This means that methods using only absolute sizes, shapes, textures, or spectral signatures are not applicable to our experiments (Aitkenhead et al., 2003; Onyango and Marchant, 2003; Granitto et al., 2005) . The total proportion of weeds in the field is important because it indicates the extent of competition between weeds and the crop (Tian et al., 1999; Ribeiro et al., 2005) , but distribution has not been considered in vision-based systems to our knowledge. Barroso et al. (2004b) studied the economic benefits of using site-specific weed management systems for large patches and numerous small patches of weeds. The damage from large patches to the crops is clear; they lower the yield substantially in the current year. When numerous small weeds patches appear during the cereal's growth phase, they tend to compete with the crop aggressively. Moreover, because weeds are more prolific in producing seeds and the seeds persist longer in soil, a failure to control weeds creates serious problems not only in the current year but also for the following 2-3 years (see Appendix A for details of weed density).Hence, we propose a new method with two objectives: (1) to determine the quantity and distribution of weeds present in the crop and (2) to decide, based on that knowledge, whether to undertake selective spraying to control the weeds. The method consists of an image segmentation process and a decision-making approach. The segmentation process extracts cells from the image as the low-level units. The quantity and distribution of weeds in the cell are mapped as area and structural based attributes, respectively. From these attributes, a multicriteria decision-making approach under a fuzzy context allows us to decide whether any given cell needs to be sprayed. Nowadays, there is a clear preference to reducing the use of chemicals in agriculture. Numerous technologies have been developed to make agricultural products safer and to lower their adverse impacts on the environment, and precision agriculture is a valuable component of the framework to achieve this goal (Kropff et al., 1997; Zhang et al., 2002; Stafford, 2006) . Within that general framework, weeds can be managed site-specifically using available geospatial and information technologies (Gerhards and Christensen, 2006) . Initial efforts * Corresponding author. Tel.: +34 1 3 94 75 46; fax: +34 1 3 94 75 47. E-mail address: pajares@fdi.ucm.es (G. Pajares).. to detect weed seedlings by machine vision focused on geometrical measurements such as shape factor, aspect ratio, and length/area (Pérez et al., 2000) . Later, colour images were successfully used to detect weeds and other types of pests (Søgaard and Olsen, 2003) . Yang et al. (2003) estimated weed coverage and weed patchiness based on digital images, using a fuzzy algorithm for planning site-specific application of herbicides. Recently, Gerhards and Oebel (2006) background (Thorp and Tian, 2004; Ribeiro et al., 2005) . Bacher (2001) estimated weed density in a field of spring barley by image binarization and morphology followed by the identification of crop rows using information on distances between rows within the crop to decide on spraying. This process serves to make weed plants appear isolated from the crop.. Avena sterilis L. ("winter wild oat") is one of the most widely distributed and abundant weeds of cereals in Spain and other regions with Mediterranean climate, causing substantial losses in these crops (Barroso et al., 2004a; Radics et al., 2004) . Although some A. sterilis plants may be found growing singly or in small patches, the majority of them are aggregated in relatively large patches (Ruiz et al., 2006) , and those in early spring, after broad-leaved weeds have been controlled by early postemergence treatments, are practically pure stands (Fernandez-Quintanilla, personal observation). Due to these two features, it is relatively easy for an experienced farmer or a technical consultant to detect patches of A. sterilis visually in the early stages of crop growth. In fields of cereals (barley or wheat), the cereal plants grow along the furrows: the plants growing between furrows can only be weeds. But weeds may also grow mixed with the cereal. We sought to detect weeds by differences in appearances: isolated plants, small or large patches, or mixed with the crop. Three main problems arise during detection, namely (1) irregular shapes and different sizes of the patches, (2) spectral signature and texture similar to those of the cereal plants, and (3) irregular distribution of the weeds in the field. This means that methods using only absolute sizes, shapes, textures, or spectral signatures are not applicable to our experiments (Aitkenhead et al., 2003; Onyango and Marchant, 2003; Granitto et al., 2005) . The total proportion of weeds in the field is important because it indicates the extent of competition between weeds and the crop (Tian et al., 1999; Ribeiro et al., 2005) , but distribution has not been considered in vision-based systems to our knowledge. Barroso et al. (2004b) studied the economic benefits of using site-specific weed management systems for large patches and numerous small patches of weeds. The damage from large patches to the crops is clear; they lower the yield substantially in the current year. When numerous small weeds patches appear during the cereal's growth phase, they tend to compete with the crop aggressively. Moreover, because weeds are more prolific in producing seeds and the seeds persist longer in soil, a failure to control weeds creates serious problems not only in the current year but also for the following 2-3 years (see Appendix A for details of weed density).. Hence, we propose a new method with two objectives: (1) to determine the quantity and distribution of weeds present in the crop and (2) to decide, based on that knowledge, whether to undertake selective spraying to control the weeds. The method consists of an image segmentation process and a decision-making approach. The segmentation process extracts cells from the image as the low-level units. The quantity and distribution of weeds in the cell are mapped as area and structural based attributes, respectively. From these attributes, a multicriteria decision-making approach under a fuzzy context allows us to decide whether any given cell needs to be sprayed. We propose a new approach to detecting weeds in row crops for selective spraying in precision agriculture. Although this approach has proved its value for Avena sterilis growing in wide-row cereal crops, it can be used in many other situations as well, e.g. maize. We have designed the method based on two subprocesses: (1) segmentation to separate weeds and crops from the rest and (2) decision-making to determine where the herbicide should be selectively applied. The segmentation is based on a combination of basic processing techniques. The decision-making is carried out by combining both structural and area-based measurements under a fuzzy context through MCDM. Although area-based measurements have been used before, we have established that the use of structural measurements improves the results obtained when area-based attributes are the only attributes used. This is because the distribution of weed patches in this kind of fields must be considered. The occluded weeds must be studied in greater depth in the future to increase the percentage of success.. An important issue to be addressed in the future is the robustness of the proposed approach, considering that light conditions outdoors vary a great deal. One approach to account for such variation is to apply homomorphic filtering (Gonzalez et al., 2004) , which separates the illumination and the reflectance components, thereby allowing reflectance alone to be considered and illumination effects to be discarded. Thus, only the reflectance of weeds, crops, and soil can be considered. Automatic learning of the weights attached to the benefit and cost criteria used during the decision-making process should also be considered in future research. Christensen and Heisel, 1998) have used stratified weed mapping approaches from historical weed maps (obtained with a low resolution) to divide the field into weed zones. Thereafter, these zones are assessed with a higher resolution using real-time detection technologies. To assess the validity and the performance of the proposed approach we used a set of 146 digital images, about half of which were taken on sunny days and the rest on cloudy days. Because the interval between any two members of the two subsets (images taken on sunny days and on cloudy days) was always less than 3 days, we can assume that both samples corresponded to a similar growth stage of weeds and the crop. At this stage, in which the herbicide must be applied, the weeds and the crop plants display similar spectral signatures and textures, which is one of the problems mentioned in the introduction. Under these circumstances, the digital images represented fundamentally different natural lighting conditions.

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


Optimal Design of the Power-Delivery Network for Multiple Voltage-Island System-on-Chips Abstract-This paper introduces techniques for power-efficient design of power-delivery network (PDN) in multiple voltage-island system-on-chip (SoC) designs. The first technique is targeted to SoC designs with static-voltage assignment, while the second technique is pertinent to SoC designs with dynamic-voltage scaling (DVS) capability. Conventionally, a single-level configuration of dc-dc converters, where exactly one converter resides between the power source and each load, is used to deliver currents at appropriate voltage levels to different loads on the chip. In the presence of DVS capability, each dc-dc converter in this network should be able to adjust its output voltage. In the first part of this paper, it is shown that, in a SoC design with static-voltage assignment, a multilevel tree topology of suitably chosen dc-dc converters between the power source and loads can result in higher power efficiency in the PDN. The problem is formulated as a combinatorial problem and is efficiently solved by dynamic programming. In the second part of this paper, a new technique is presented to design the PDN for a SoC design to support DVS. In this technique, the PDN is composed of two layers. In the first layer, dc-dc converters with fixed output voltages are used to generate all voltage levels that are needed by different loads in the SoC design. In the second layer of the PDN, a power-switch network is used to dynamically connect the power-supply terminals of each load to the appropriate dc-dc converter output in the first layer. Experimental results demonstrate the efficacy of both techniques.Index Terms-DC-DC converter, energy efficiency, low-power design, power-delivery network (PDN), system-on-chip (SoC), voltage island, voltage regulator. T HE power-delivery network (PDN) is a critical design component in system-on-chip (SoC) designs. A robust PDN is required to achieve a high level of power-supply integrity. If improperly designed, this network could be a major source of noise, such as IR-drop, ground bounce, and electromagnetic interference [1] . With careful design, the PDN can tolerate large variations in load currents while maintaining the supply-voltage level across the chip within a desired range [2] .. Emerging low-power design techniques have made the design of PDN an even more challenging task. More precisely, multiple voltage domains are being introduced on the SoC in order to minimize the overall power dissipation of the system while meeting a performance constraint. This means that it is possible to have multiple relatively small logic blocks operating at different voltages. The voltage of each logic block may be fixed or change dynamically based on workload monitoring. This is also known as the multiple voltage-island approach [3] . In these systems, it is required that the PDN delivers power at appropriate voltage levels to different functional blocks (FBs) while incurring the minimum power loss. A typical PDN design methodology for a high-performance SoC comprises of three steps: 1) establishing a target impedance to be met across a broad frequency range for the PDN; 2) designing a proper system-level decoupling network, i.e., specifying components to meet that impedance; 3) selecting the right voltage-regulator modules (VRMs). 1 A target impedance value of several tens or hundreds of milliohms is usually established. Decoupling capacitors (decaps) are used to try to achieve this target impedance value up to frequency that is at least several times the clock frequency. Finally, VRMs are selected such that design requirement for each FB is met while the power efficiency of the system is maximized.. This paper is mainly concerned about the third step in the design of PDN for a high-performance SoC, i.e., selecting VRMs in the PDN. We propose the following techniques to reduce power dissipation in a PDN: 1) using a two-level VRM tree to deliver different voltages in a multiple voltage-island SoC design with fixed voltages; 2) using a one-level VRM tree with an additional powerswitch network (PSN) to deliver different voltages in a multiple voltage-island SoC design with dynamicvoltage-scaled islands. The remainder of this paper is organized as follows. Section II provides some background on PDN design. In Section III, the problem of VRM tree optimization (RMTO) for a static-voltage-island SoC for minimum power dissipation is discussed, and an efficient algorithm is proposed to optimally select the best set of regulators in the VRM tree. In Section IV, a new architecture for the design of PDN on a SoC with dynamicvoltage-scaling (DVS) option is proposed, and an algorithm is presented to optimally select the best set of VRMs in this network. Section V is dedicated to the experimental results, while Section VI concludes this paper. Preliminary results of this paper have appeared in [4] and [5] . We presented two new techniques for optimal design of PDN for multiple voltage-island SoCs. First, we showed that by using a tree topology of suitably chosen voltage regulators between the power source and loads, one can achieve higher power efficiency in the PDN of static-voltage-island designs. We formulated the problem of optimizing the VRM tree as a dynamic program and solved it efficiently. Second, we presented a technique to design an efficient PDN for systems with DVS capability. In this technique, the PDN is composed of two layers: PCN and PSN. In PCN, fixed-V out VRMs are used to generate all voltage levels that may be needed by different FBs in the system. PSN is subsequently used to dynamically connect the power-supply terminals of each FB to the appropriate VRM output in the PCN. We showed that this technique not only reduces the cost of the PCN but also results in a more powerefficient PDN. We further described an algorithm to select the best VRMs to achieve a design target in the new PDN. Experimental results have demonstrated the efficacy of proposed problem formulations and solutions.

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Efficient 1-out-of-n oblivious transfer schemes with universally usable parameters Abstract-In this paper, we propose efficient and secure (string) oblivious transfer (OT 1 n ) schemes for any n ! 2. We build our OT 1 n scheme from fundamental cryptographic techniques directly. The receiver's choice is unconditionally secure and the secrecy of the unchosen secrets is based on the hardness of the decisional Diffie-Hellman problem. Some schemes achieve optimal efficiency in terms of the number of rounds and the total number of exchanged messages for the case that the receiver's choice is unconditionally secure. The distinct feature of our scheme is that the system-wide parameters are independent of n and universally usable, that is, all possible receivers and senders use the same parameters and need no trapdoors specific to each of them. We extend our OT 1 n schemes to distributed oblivious transfer schemes. Our distributed OT 1 n schemes take full advantage of the research results of secret sharing. For applications, we present a method of transforming any (single-database) PIR protocol into a symmetric PIR protocol by slightly increasing the communication cost only. We have presented efficient string 1-out-of-n oblivious transfer schemes and extended them to threshold and access-structure oblivious transfer schemes for any n ! 2.. We have also presented its application on private information retrieval. It is interesting to find more applications of this construction.. For the schemes with security against the malicious receiver, three approaches are mentioned. One is based on WIPK, another is based on NIZKPK, and the other is based on the random oracle model. The one based on WIPK needs more rounds. The one based on NIZKPK needs a shared random string between the sender and the receiver. The one based on the random oracle model, though efficient and adopted in security analysis of cryptography widely, is not technically sound. It may be possible to replace the cryptographically strong hash function with a universal family of hash functions such that the random oracle model assumption is removed and the round efficiency is maintained.

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