Text mining for bone biology Osteoporosis, which is characterized by reduced bone mass and debilitating fractures, may reach epidemic proportions with the aging of the US population. The intensity of research in this field of study is reflected by the facts that The American Society of Bone and Mineral Research has a membership of nearly 4,000 physicians, clinical investigators, and basic research scientists from over fifty countries and that NIH is expected to spend over 200 million dollars on osteoporosis research alone in 2010. Bone biologists may be overwhelmed by the amount of literature constantly being generated, thus the identification and extraction of existing and novel relationships among biological entities or terms appearing in the biological literature is an ongoing problem. The problem has become more pressing with the development of large online publicly available databases of biological literature. Extraction and visualization of relationships between biological entities appearing in these databases offers the opportunity of keeping researchers up-to-date in their research domain. This may be achieved through helping them visualize possible biological pathways and by generating likely new hypotheses concerning novel interactions through methods such as transitive closure network flow. All generated predictions can be verified against already existing data, and possible new relationships can be verified against experiment. This paper presents a method for the extraction and visualization of potentially meaningful relationships. Bone diseases affect tens of millions of people and include bone cysts, osteoarthritis, fibrous dysplasia, and osteoperosis among others. With osteoperosis, the density of bone mineral is reduced, the proteins of the bone are altered, and the microarchitecture of the bone is disrupted. Bioinformatics, the application of information technology and computer science to the field of molecular biology, has seen a great amount of development since the term was first coined in 1979. (Hogeweg et al., 1979) The field is varied and includes databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the massive amounts of data. The field has been of particular interest for informaticists and biologists to develop automatic methods to extract embedded knowledge from literature data. This particular problem of relationship extraction has been studied by numerous researchers in the field. Natural language processing (NLP), which many times is used to translate information from computer databases into readable human language, also finds an application in converting samples of human language into more formal representations which many times include parse trees, first-order logic structures, or other data structures. Statistical natural-language processing uses stochastic, probabilistic and statistical methods to resolve difficulties. One such statistical method is Markov models, where it is assumed that there are purely random processes and that future states can be inferred from the current state. NLP has overlap with the Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. computational linguistics, and is very closely related to the field of artificial intelligence within the subject of computer science.. Artificial intelligence, which was coined in the middle 1950's by John McCarthy, is typically defined as "the study and design of intelligent agents" where intelligent agents try to maximize their reward within a well defined environment. (Poole et al., 1998) Machine learning and unsupervised learning without class labels are very typical within many artificial intelligence research problems. Artificial intelligence has found broader user in the field of computer science in a wide variety of problems, including graph searching.. The biological literature is a huge and constantly increasing source of information which the biologist may consult for information about their field, but the vast amount of data can sometimes become overwhelming. It is thus important for science and new technologies to help discover new relationships and increase the efficiency of biological information workers. Medline, which makes a great amount of biological journal data available online, makes the development of automated text mining systems and hence "data-driven discovery" possible. A method known as text mining, which will draw on elements of natural language processing and artificial intelligence, allows for the extraction of knowledge contained in the literature, and holds promising developments. Teriparatide is the only FDA-approved drug that replaces bone lost to osteoporosis. Helping bone biologists visualize possible biological pathways and generate likely new hypotheses concerning novel interactions through methods such as transitive closure and maximal network flow offer a new method to help find a cost-effective treatment to bone diseases such as osteoporosis.. Bioinformatics, the application of information technology and computer science to the field of molecular biology, has seen a vast development since 1979. Natural language processing (NLP), which many times is used to translate information from computer databases into readable human language, and artificial intelligence have both played an important role in the growth of the field. In particular, text mining allows for the extraction of knowledge contained in the literature and increased efficiency of biological information workers.. Of particular interest in our examination was the association pairs where the transitive testing results in Table 4 and the expert provided rankings in Table 5 disagree. If text mining results predict the existence of an association where the expert has not provided the prediction of an association, this could point to some novel transitive hypotheses for the expert to further study and verify against experiment. On the other hand, if text mining indicates that there is no association and the expert indicates that there is an association, it signifies that there is an association according to evidence given by the expert but that there is not an association according to the evidence given by the model of the literature.. The direct association matrix generated in our work suggests that there is a strong relationship in the literature between NMP4 and PTH, p130Cas, zyxin, actin, and beta-catenin in decreasing order. Our work with maximum network flow suggests that there may be a more buried and weak relationship between NMP4 and almost all of the terms.. The thesaurus based method presented in this paper obtains a significant improvement over random guessing. Future work on this problem would be very likely to include an extended set of vocabulary terms and extended work on the development of visualizations which are more meaningful to a bone biologist information expert. Work on extending the examination in to associations between multiple proteins or terms could also be conducted in an effort to further improve the accuracy and obtain more meaningful results.. A text mining system, such as the one described in this paper, has great relevance to biomedical researchers with diverse background and interest. The direct associations, although somewhat limited in its ability to reproduce an expert's knowledge of associations in a given domain, may still be very useful to a relatively inexperienced researcher, or someone changing their research project to a new one. Further, even for an expert, a system to automatically extract associations from literature may be useful in the presence of dynamic information sources, e.g., the PubMed. This is because the user in such a case will not have to spend time and effort in the manual survey of the literature periodically in order to keep up-to-date with the changing literature data. However, perhaps the greatest benefit for such a system will be its ability to predict novel association hypotheses through transitivity, which can be used as a biological discovery aid by biomedical researchers, both experts and novices. In the given domain of bone biology described in this paper, for example, relatively strong transitive associations were discovered between PTH and p130Cas (terms 4 and 3) and ECM and p130Cas (terms 6 and 3) through transitivity, even though both the direct association mining as well as the expert data failed to predict these two associations. It is now up to the biomedical experts to study the transitive paths involved in hypothesizing these novel associations to validate or negate these postulated associations. The results of these in-depth studies of specific transitive associations will be reported elsewhere. The 2009 paper "Support tools for literature-based information access in molecular biology" by Fabio Rinaldi and Dietrich Rebholz-Schuhmann describes how the rapid growth of information in molecular biology, which is driven by high throughput experiments, creates a very high demand for the integration of the literature into the knowledge discovery channels of the biomedical research domain. The paper shows different tools developed by the authors to support professional biologists in accessing information which is already existing in publically available scientific literature. The paper compares the accuracy and performance of a variety of the tools against different proscribed "gold standards" and note that they are still finalizing some scores high performance on gold standard data does not necessarily translate into high performance for database annotation.. "Accomplishments and challenges in literature data mining for biology", a paper by Hirschman et al., reviewed recent results in literature data mining for biology through 2002. Hirschman et al. trace literature data mining from its recognition of protein interactions to its solutions to a range of problems such as improving homology search and identifying cellular location, and note that the field has progressed from simple term recognition to the actual extraction of much more complex interactions between degrees of entities. The paper examines successful work from the natural language processing perspective and notes that templates may now be used to increase sensitivity. The paper also examines progress in biomedical applications, specifically in organizing a challenge evaluation, the extraction of biological pathways, and automated database curation and ontology development. Park, Hyunchul Jang, and Sunhee Park discusses how information retrieval is to supporting biomedical researches but that it retrieves greatly varying literatures for the target relations. The authors also note that synonyms of the biomedical terms make the information retrieval task more difficult. The paper proposes a workbench composed of searching/collecting, literature mining, relation probing, and statistics analysis to help solve the problem. The presented method searches and collects literatures of Pubmed articles and USPTO patents and mines using text mining techniques such as named entity recognition, gene/protein name normalization, and relation extraction. The workbench also provides statistical information of literature meta data such as authors, organizations, publication years and so on. Some weaknesses of the method are that it does not support statistics for extracted biomedical substances and relations, and the relation types and frequencies are also not displayed.. In the 1999 paper "Automatic Term Identification and Classification in Biology Texts", Collier, Nobata, and Tsujii discuss the rapid growth of literature databases and the difficulty that they pose to academics wishing to efficiently access relevant data. Collier et al. examine information extraction methods for the identification and classification of terms which appear in biological abstracts from the online database MEDLINE. The paper makes use of a decision tree for classification and term candidate identification, and uses a variant of shallow parsing for identification. The paper conducted experiments against a corpus of 100 expert tagged abstracts. Collier et al. ' s results indicate that while identifying term boundaries is non-trivial, a high success rate can eventually be obtained in term classification.. The paper "Selecting an Ontology for Biomedical Text Mining", written by He Tan and Patrick Lambrix in 2009, writes that the developers of text mining applications specifically look for ontologies that can be integrated into their systems over developing new ontologies from scratch, but that there is often a lack of documentation of the qualities of the ontologies. The paper examines a number of methodologies for evaluating ontologies that have been developed, but the authors argue that these methods all make it hard for the developers to choose a proper ontology. The paper proposes a framework for selecting the most appropriate ontology based off of three components which consider different aspects of the requirements of text mining applications on ontologies. An experiment based on the framework choosing an ontology for a gene normalization system is also presented. The paper shows initially promising results, but there is no service to support ontology selection for biomedical text mining and it is only tested with limited applications and ontologies.. In the 2001 paper "Predicting Emerging Technologies with the Aid of Text-Based Data Mining: A Micro Approach", N. R. Smalheiser outlines how text mining can connect complementary pieces of information across domains of scientific literature. Smalheiser's paper again attempted to predict genetic engineering technologies that may impact on viral warfare in the future. The paper's analysis was carried out using a combination of conventional Medline searches. Smalheiser's findings strongly indicated genetic packaging technologies as plausible candidates for study that had not previously been examined. The method of the paper was to define two fields that are hypothesized to contain complementary information, to identify common factors that bridge the two disciplines, and to progressively shape the query once initial findings were obtained. Thus the process was somewhat manual and involved a great amount of feedback from domain experts.. "Literature mining in molecular biology", a 2002 paper by Bruijn and Martin, examines a variety of literature mining in Medline abstracts or full text articles. It divides the process in to text categorization, named entity tagging, fact extraction, and collection-wide analysis. Text categorization is defined to divide a collection of documents into disjoint subsets. The goal of the named entity tagging is to identify what entities or objects the article mentions. Fact extraction aims to grasp the interactions or relationships between those entities. Finally, collection wide analysis opens the door to knowledge discovery, where combined facts form the basis of a novel insight. The paper finds that the scalability of algorithms becomes a more urgent issue as the size of the data grows, but that literature mining systems will move closer towards the human reader.. The 2009 paper "TX Task: Automatic Detection of Focus Organisms in Biomedical Publications" by Thomas Kappeler, Kaarel Kaljurand, Fabio Rinaldi outlines how in biomedical information extraction the disambiguation of ambiguous names for domain specific entities is a central problem. The authors present an approach to the detection and disambiguation of the focus organisms., or the organisms that are the subject of research in the associated papers. The paper suggests that its method may be useful within a curation environment and may be a baseline for more complex approaches. One weakness of the method is that it presents only a semi-automatic method which can be used in a curation environment, and that a fully automated disambiguation process is not placed within immediate reach.. In the 2007 paper, "An application of bioinformatics and text mining to the discovery of novel genes related to bone biology", Gajendran, Lin, and Fyhrie report the results of text mining for a bone biology pathway including SMAD genes. They also proposed a ranking systems for the genes based on text mining. In contrast, the current paper focuses on the Nmp4 gene pathways in Bone biology which is the expertise of Dr. Bidwell, a co-author of the current paper.. Transminer is a system developed by Narayanasamy et . The contributions of the current paper with respect to the state-ofthe-art in this field can be summarized as follows:. (i) In terms of the computational methodologies, for the first time, a maximal network flow based algorithm is presented to determine, in a theoretically sound manner, a confidence score for the derived transitive associations. (ii) In terms of the application domain, a specific pathway in bone biology consisting of a number of important proteins is subjected to the text mining approach. (iii) In terms of the experimental results, this paper reports for the first time (to the authors' knowledge) that a significant higher agreement with an expert's knowledge can be obtained with transitive mining than that with only direct associations. Further, both direct as well as the transitive associations were in much better agreement with the expert's knowledge than a random association matrix. These results demonstrate the usefulness of such text mining methodologies in general, and the transitive mining methods in particular.

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


Measuring and adapting behavior during product interaction to influence affect Abstract Sometimes, the way in which we interact with products implicitly communicates how we feel. Based on previous studies on how emotions can be detected and communicated via product interaction, we discuss how an interactive product could influence affect by responding and changing behaviors expressing affect. We discuss the proposal of the affective feedback loop in product interaction by its implementation in the prototype of a pen that senses two implicit behaviors related to restlessness, rock and roll. Furthermore, the pen provides inherent feedback, focusing on the perceptual motor skills, as a means to reflect on these behaviors. The pen was evaluated in an experiment, by which we explored whether this type of feedback would influence the emotional experience. Two participant groups were compared, and participants that used the pen with feedback showed a lower heart rate throughout the whole experiment. Because these participants were not aware that the pen provided any feedback, we propose the concept of unaware interaction and discuss what its implications are for design. Over the last years, the context of design has started to include products that are highly interactive and able to respond and adapt to our behavior. However, the miniaturization and increased amount of computing technology implemented in these products will eventually lead to enormous amounts of data that need to be processed. Therefore, research is necessary to determine what behavioral data offer the most interesting insight into these processes, and designers have the responsibility to design feedback that is tailored to these situations. Thus, not only data representation is moving toward the background as described in calm technology [1] but products are also starting to measure all kinds of sensory input in the background.. Products with embedded sensors to measure behavior may address functional properties such as a closet that supports in choosing clothes in different occasions [2] , or it may be applied to communicate or to measure concrete values, such as taken steps by a pedometer. Behavioral biometric profiling has also been investigated in security applications as people have very specific means of behavior [3] . Research is also being conducted on how products could measure implicit meanings underlying these functional behaviors, for example to predict emotions. Wensveen et al. [4] for example developed an alarm clock that measures emotions while people move the twelve sliders that are used to set the wake-up time.. While behaviors may be used as a means to measure affect, the way that these behaviors or interpretations need to be fed back to the user remains unclear. In terms of emotions, the affective loop is defined as a feedback mechanism that continuously adapts to the behavioral expression of the user [5] . Höök et al. [6] have explored the affective loop in various interactive systems such as eMoto, which explored how bodily expressions could add emotional loading to mobile messages. Another exploration of the affective loop was in a personal logging system [7] , in which day-to-day experiences were logged in a diary on which users could reflect.. The vision behind the affective loop is that the user first expresses an emotion through physical interaction involving the body, for example, through gestures or manipulations of an artifact. Consequently, the system (or another user through the system) responds by generating an affective expression, using for example, colors, animations, or haptics, which in turn affects the user (both mind and body) to respond and step-by-step feel more and more involved with the system [6] . In this vision, the first step reflects the expression of the user toward the product. The second describes the feedback mechanism, in which the product invites the user to perform a new action to keep her engaged in the interaction. This presentation of information can be defined as a means of feed forward. In the affective loop, the feedback appears to gradually evolve into feed forward to achieve the third step, in which the user is engaged in a feedback and feed forward process of interaction that gradually changes her expression and thus the dynamics of her movement. The product continuously adapts to the input of the user.. The affective loop provides a clear vision on how the product could respond to emotional interaction, such as nervous behaviors with a pen that could be considered as implicit expressions of stress [8] . However, all projects exploring the affective loop rely on the cognitive skills of the user and do not present feedback that addresses the perceptual motor skills. According to Djajadiningrat et al. [9] , products should exploit the sensory richness and action potential of physical objects in relationship to perceptual motor skills for which the affective loop could also address the perceptual motor skills of the user. In the current paper, we propose a new means of feedback for the affective loop that does not necessarily rely on the cognitive skills of the user. In this paper, we explored how products could respond to behavioral expression of emotion by building on the concept of the affective loop described by Höök [5] as well as on the concept of inherent feedback in tangible interaction [9] . It combined these two concepts, with the aim of affecting the experience of an emotional situation through product interaction. The concept of the affective feedback loop in product interaction was explored in the context of stress by means of a pen prototype that measured how the user manipulated it to determine her level of affect. First, a behavioral response of the user toward the pen was expected; secondly, a shift in how the user interacted with In this result section, the effect of the picture presentation and the memory task will be evaluated first. Consequently, the experimental results are presented in three steps. First, the question will be addressed whether it is possible to differentiate emotional situations based on how the prototype is manipulated. Secondly, the way that the prototype stimulates the user to change her behavior through inherent feedback will be assessed. And finally, it will explore whether inherent feedback to implicit behavior by means of the affective pen succeeded in affecting the emotional experience. The emotional event that was selected to affect the experience is a stressor. As mentioned before, stress is an emotion that generally provokes nervous behaviors resulting in object manipulations [12] . To evaluate how the affective feedback loop can be integrated in product interaction, the six steps were implemented in a prototype after a series of design iterations (e.g., [14] ). A pen was selected to illustrate the concept because a study on nervous habits by Hansen et al. [15] found that 63% of college students ''play with pens and pencils.'' Furthermore, rocking and rolling movements with a pen were previously defined as indicators of stress [8] . In addition, the pen appeared to be an inconspicuous object that is of general use and fits the context as it can unobtrusively sense implicit behaviors.

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


Discretization of the multiscale semiconductor Boltzmann equation by diffusive relaxation schemes In this paper we derive diffusive relaxation schemes for the linear semiconductor Boltzmann equation that work in both the kinetic and diffusive regimes. Similar to our earlier approach for multiscale transport equations, we use the even-and oddparity formulation of the kinetic equation, and then reformulate it into the diffusive relaxation system (DRS). In order to handle the implicit anisotropic collision term efficiently, we utilize a suitable power series expansion based on the Wild sum, which yields a time discretization uniformly stable with any desired order of accuracy, yet is explicitly solvable with the correct drift-diffusion limit. The velocity discretization is done with the Gauss-Hermite quadrature rule equivalent to a moment expansion method. Asymptotic analysis and numerical experiments show that the schemes have the usual advantages of a diffusive relaxation scheme for multiscale transport equations and are asymptotic-preserving. Kinetic equations for highly integrated semiconductor devices have a diffusive scaling, measured by the mean free path, that lead asymptotically to the drift-diffusion equations as the mean free path goes to zero [Pou, MRS] . In practical applications, there is necessity to conduct numerical simulations in both resolved (when spatial grid size is smaller than the mean free path) and the underresolved (when spatial grid size is larger than the mean free path) regimes. In the former case, standard numerical methods for kinetic equations work effectively. In the latter case, one expects that the best possible numerical resolution is the approximation of the drift diffusion equation. However, even to achieve this goal is nontrivial for standard numerical methods applicable to the kinetic equations. In addition to the difficulty of numerical stiffness arising due to the small scaling, improper underresolved numerical solution often fail to capture the hydrodynamic drift-diffusion limit. Earlier study on numerical methods for transport or kinetic equations indicates that, in order for the underresolved numerical approximation to capture the correct diffusive behavior, the scheme should be asymptotic preserving (AP), in the sense that the asymptotic limit that leads from the transport or kinetic equations to the diffusion equations should be preserved at the discrete level [Ada, Jin, JL1, JL2, JPT1, JPT2, Kl1, Kl2, LMM, LM, Mil, NP1, NP2] .. In an earlier work on discrete-velocity models [JPT] we handled this challenging numerical problem by reformulating the system into a form commonly used for a relaxation scheme for conservation laws [JX] . Such a reformulation allows us to use the splitting technique for relaxation schemes to design a class of implicit, yet explicitly implementable, schemes that work with high resolution uniformly with respect to the mean free path. Such a numerical technique was extended to general transport equations with isotropic collision kernels, by using parity formulation of the transport equation [JPT2] .. Let be the dimensionless mean free path. The diffusive relaxation schemes, developed in [JPT1, JPT2] for kinetic and transport equations, have have the following features:. • they have an -independent (ε 1), parabolic CFL condition t ≈ ( x) 2 ; • the schemes, though implicit in the collision terms, can be implemented explicitly;. • they are asymptotic preserving (AP);. • they have a uniform accuracy with respect to .. In this paper, we will derive diffusive relaxation schemes for the linear semiconductor Boltzmann equation. Such a problem differs from those studied in [JPT2] in two aspects. First, this equation involves a derivative in the velocity, which requires a suitable velocity discretization. Here, as was previously done, we will use the Hermite quadrature rule [Ch, Kl2] which is equivalent to a moment expansion method [SZ, RSZ] . The main obstacle, however, is due to the anisotropic collision kernel. Since the diffusive relaxation schemes are implicit in the collision term, in this case, it becomes challenging to invert numerically the anisotropic collision term in an efficient way.. To this aim we utilize a power series expansion (a generalized Wild sum [Wil] ), which has been used for nonlinear kinetic equation in the Euler scaling by Gabetta et al. [GPT] . A class of numerical schemes is derived by replacing the high order terms in the Wild sum by a Maxwellian, i.e., relaxing them to equilibrium.. Applying that to the diffusive relaxation system allows us to have an implicit time discretization for anisotropic collision that is uniformly stable with respect to the mean free path, is asymptotic-preserving, yet can be solved explicitly.. We mention here other approaches to the numerical solution of the semiconductor Boltzmann equation based on hydrodynamic models [ARR] , spherical harmonics expansions [VGBO] , and particle simulations [DD] .. The rest of the paper is organized as follows. In Section 2 we present the kinetic semiconductor equation and its drift-diffusion limit. In Section 3 we formulate it into the parity systems and then the diffusive relaxation system. We then use the standard splitting method for such problem, which decouples the explicit convection step from the implicit relaxation step. In order to efficiently solve the implicit anisotropic collision term, in Section 4 we use the Wild sum and show that it has the desired properties. Section 5 discusses the moment expansion method with the Hermite quadrature rule and the spatial discretization. In Section 6 some numerical examples are presented. We conclude in Section 7. In this paper we derived a class of diffusive relaxation schemes for time-dependent multiscale kinetic semiconductor equations in any space dimension, based on the even-and odd-parity formulation of the kinetic equation. Key in the scheme is the use of a suitable implicit time discretization for the collision operator utilizing the Wild sum, which yields asymptotic-preserving diffusive relaxation schemes with mean-free-path-independent stability, yet can be implemented explicitly. Our scheme has a uniform accuracy with respect to the relaxation parameter, from the kinetic regime to the drift-diffusive regime, thus allowing the capturing of the hydrodynamic behavior with an underresolved numerical discretization, a property highly desirable for semiconductor device simulation using kinetic models.

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


An efficient key point quantization algorithm for large scale image retrieval We focus on the problem of large-scale near duplicate image retrieval. Recent studies have shown that local image features, often referred to as key points, are effective for near duplicate image retrieval. The most popular approach for key point based image matching is the clustering-based bagof-words model. It maps each key point to a visual word in a code-book that is constructed by a clustering algorithm, and represents each image by a histogram of visual words. Despite its success, there are two main shortcomings of the clustering-based bag-of-words model: (i) it is computationally expensive to cluster millions of key points into thousands of visual words; (ii) there is no theoretical analysis on the performance of the bag-of-words model. We propose a new scheme for key point quantization that addresses these shortcomings. Instead of clustering, the proposed scheme quantizes each key point into a binary vector using a collection of randomly generated hyper-spheres, and a bag-ofwords model is constructed based on such randomized quantization. Our theoretical analysis shows that the resulting image similarity provides an upper bound for the similarity based on the optimal partial matching between two sets of key points. Empirical study on a database of 100, 000 images shows that the proposed scheme is not only more efficient but also more effective than the clustering-based approach for near duplicate image retrieval. Although content-based image retrieval (CBIR) has been studied for years, the challenge of semantic gap, i.e. the gap between visual similarity and conceptual/perceptual relevance, has made it a much harder problem than most researchers originally expected [23] . However, recent studies have shown that near duplicate image retrieval [10] , whose objective is to identify images with high visual similarity, can be solved effectively. In particular, studies [24, 29, 22, 13, 26, 10] have shown that local image features, e.g. SIFT descriptors [17] , often referred to as key points, are effective for near duplicate image retrieval and visual object recognition than global image features. The key idea is to extract salient local patches from an image, and represent each local patch by a multi-dimensional vector. As a result, each image is represented by a bag-of-features [4] . A number of algorithms [8, 18, 2] have been proposed to measure the similarity between two images based on their bag-of-features representations, including similarity based on the optimal partial matching between two sets of key points [30, 18, 2] , pyramid kernel similarity [8] , similarity based on the principal angle between two sets of key points [32] , and similarity measure based on the match between two distributions [11, 20] . It has been shown [8, 18 , 2] that despite its simplicity, the similarity based on the optimal partial matching performs well in comparison to the other similarity measurements. However, these optimal partial matching based approaches suffers from high computational complexity: given a query image, a linear scan is required to compute the similarity between the query image and every image in the database, which does not scale well to a large database with millions of images.. Among various approaches that have been proposed to improve the computational efficiency of optimal partial matching, the bag-of-words model [29] is the most popular and probably the most successful one. The key idea is to quantize the continuous high-dimensional space of SIFT features to a vocabulary of "visual words", which is typically achieved by a clustering algorithm. By treating each cluster center as a word in a codebook, this approach maps each image feature onto its closest visual word, and represent each image by a histogram of visual words. A number of studies have shown promising performance of this approach for image retrieval [24, 29, 22, 13, 27, 26, 10] .. Despite its success, most studies on the bag-of-words model suffer from the following drawbacks: (i) High computational cost of clustering when the number of clusters is very large. In our application, we need to cluster ten million key points into a million clusters. Although a number of algorithms [21, 24, 13, 27, 5, 15, 28] have been proposed for large-scale data clustering, they are still computationally expensive when handling millions or even billions of key points. (ii) Most of the studies on the bag-of-words model are focused on its empirical performance, and lack theoretical analysis.. In this work, we propose a new quantization scheme that explicitly addresses the shortcomings of the clustering-based approaches. The main idea is to quantize each key point by a set of randomly generated hyper-spheres. Each hyper-sphere is analogous to a cluster. Each key point is quantized by a binary bit, 1 when the key point is within the hyper-sphere and 0 otherwise. The bag-of-words representation for each image I is computed as a histogram over the hyper-spheres. To distinguish from the clustering based approach for key point quantization, we refer to the proposed scheme as a random seeding approach for key point quantization. Compared to the clustering-based method, the main advantage of the proposed random seeding approach is its computational efficiency, which is clearly demonstrated in our empirical study. In addition, we present a theoretical analysis that reveals the relationship between the image similarity based on the optimal partial matching and the bag-of-words model generated by the proposed scheme.. Based on the proposed scheme for key point quantization, we present a two-stage framework for large-scale image retrieval. In such a system, a bag-of-words model is first used to identify a small subset of images that are likely to be similar to a query image. A more precise similarity measure is then applied to re-rank the image subset to further improve the accuracy. We demonstrate the proposed system in the application domain of tattoo image retrieval. We emphasize that although the empirical study is carried out in a specific application domain, the proposed system is generic in that (1) it uses SIFT features for image representation, and (2) the quantization method is designed for the general purpose image indexing.. The rest of the paper is organized as follows: Section 2 reviews the related work on the bag-of-words model and methods for key point quantization; Section 3 describes the proposed scheme for key point quantization, together with a theoretical analysis that validates the proposed approach; Section 4 presents the empirical study of the proposed scheme in the application domain of tattoo image retrieval; Section 5 concludes this work and sets out future research directions. We have proposed an efficient algorithm for key point quantization. The main idea is to quantize each key point into a sparse vector of real numbers by a set of randomly generated hyper-spheres. The proposed algorithm is computationally more efficient compared to the clustering-based key point quantization in that (i) the center of each hypersphere is randomly determined, avoiding an iterative search for cluster centers, and (ii) the radius of each hyper-sphere is fixed to be constant, avoiding nearest neighbor search. In addition, our theoretical analysis shows that the similarity based on the proposed quantization algorithm provides an upper bound for the optimal partial matching based similarity. Empirical study on a data set of 100, 000 images with more than ten million key points shows that (i) our approach yields better retrieval performance than the clustering-based approach, and (ii) it reduces the running time of key point quantization by a factor of 8. In the future, we plan to investigate other approaches for key point quantization, such as random projection. The CMC curves of the two-stage retrieval system using the proposed random seed algorithm and the clustering algorithm for key point quantization. The number of visual words is set to be 1 million. The number of candidate images re-ranked in the second stage is set to be 100 in (a) and 500 in (b). One of the major challenges in exploring the bag-of-words model for image retrieval, as pointed out in [24] , is how to efficiently construct a vocabulary with hundreds of visual words for a large image database. This requires developing efficient algorithms for large-scale data clustering. Various approaches have been explored for large-scale data clustering, including flat K-means clustering [29] , hierarchical clustering [22] , and clustering based on approximate nearest neighbor search [21, 24, 13, 27, 10, 16] . However, these approaches are still computationally expensive when handling tens of millions key points, as will be revealed by our study.. In addition to the clustering approach, several random algorithms have been proposed for key point quantization [6, 3] . The key idea of these approaches is to extend Locality Sensitive Hashing [5] to encode the key points by a series of hashing functions. The proposed approach differs from the existing randomized algorithms for key point quantization in its quantization procedure. Furthermore, unlike the existing key point quantization methods that are mainly focused on empirical investigation, we present in detail a theoretical analysis that verifies the proposed randomized approach. Finally, our empirical study shows that the proposed approach is not only more efficient but also more effective for image retrieval than the clustering approach. To generate the bag-of-words model h(X) in (13), the first step is to sample zi within a hyper-sphere. In practice, sampling zi uniformly within a hyper sphere is a nontrivial problem. In our implementation, we acquire the samples z1, . . . , zN by first sampling N points from all the key points in the image database D, and then scale the sampled key points by a factor γ. This practice essentially assumes that all the key points are uniformly distributed within a hyper-sphere. Although this assumption may not be true, it significantly reduces the computational cost of the proposed algorithm, and also yields good performance. We set γ = 1 in our experiment. Although our theoretical result requires γ to be large, γ = 1 does yield desirable performance in our empirical study.. The second step of generating the bag-of-words model is to compute element h k (X) in the bag-of-words model h(X). Note that h k (X) defined in (14) is a real number, making it difficult to implement it by a typical text search engine that requires integer elements in a bag-of-words model. We thus simplify the function max(0, exp(−µ|z k − yj| 2 2 ) − η) to be I(exp(−µ|z k − yj| 2 2 ) ≥ η), where I(z) is an indicator function that outputs 1 if z is true and 0 otherwise. Note that function I(exp(−µ|z k − yj| 2 2 ) ≥ η) is equivalent to I(|z k − yj |2 ≤ r), where r = p [ln(1/η)]/µ. In practice, we set r = 0.75r , wherer is the average distance between any two key points in image collection D. We estimater by randomly sampling 10, 000 key point pairs from the entire collection.. To efficiently compute the bag-of-words model, we need to efficiently compute I(|z k − yj|2 ≤ r), which requires identifying the subset of key points in the image collection D that are within the range r of each center z k . To this end, we use the Fast Library for Approximate Nearest Neighbors (FLANN) 1 , which implements the randomized kd-trees [28] , for efficient range search. We aim to verify both the efficiency and the efficacy of the proposed scheme for key point quantization in the domain of large-scale tattoo image retrieval. We choose the tattoo image retrieval for evaluation because the relevance judgments of retrieved images can be determined objectively. In particular, a retrieved tattoo image is judged as relevant to a query tattoo image only when both images contain the same tattoo symbol. In addition, the large size of the tattoo image database used in our study, which contains more than 100K images, allows us to evaluate both the efficiency and efficacy of proposed approach for large-scale image retrieval.. Below we first describe the task of tattoo image retrieval, which is an application of near duplicate image retrieval in law enforcement. We then present the experimental setup, including the image data set and the evaluation protocol used in our study. We finally present the comparative study using both the clustering-based method and the proposed scheme for key point quantization. Data set.. The database used here consists of 101, 745 images, among which 61, 745 are tattoo images that were provided by the authors of [12] and the remaining 40, 000 images were randomly selected from the ESP game data set 3 . The purpose of adding images from the ESP game data set is to verify the capability of the developed system in distinguishing tattoo images from non-tattoo images. On average, about 100 key points are detected from each image. So we have more than 10 million key points for the entire image collection.

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


High-order Implicit Large Eddy Simulation of gaseous fuel injection and mixing of a bluff body burner Implicit Large Eddy Simulation (ILES) with high-resolution and high-order computational modelling was applied to a turbulent mixing fuel injector flow. In the ILES calculation, the governing equations for three dimensional, nonreactive, multi-species compressible flows were solved using a finite volume Godunov-type method. Up to ninth-order spatial accurate reconstruction methods were examined with a second order explicit Runge-Kutta time integration. Mean and root mean square velocity and mixture fraction profiles showed good agreement with experimental data, which demonstrated that ILES using high-order methods successfully captured complex turbulent flow structure without using an explicit subgrid scale model. The effects of grid resolution and the influence of order of spatial accuracy on the resolution of the kinetic energy spectrum were investigated. An k −5/3 decay of energy could be seen in a certain range and the cut-off wavenumbers increased with grid resolution or order of spatial accuracy. The effective cut-off wavenumbers are shown to be larger than the maximum wavenumbers appearing on the given grid for all test cases, implying that the numerical dissipation represents sufficiently the energy transport between resolved and unresolved eddies. The fifth-order limiter with a 0.6 million grid points was found to be optimal in terms of the resolution of kinetic energy and reasonable computational time. Fuel jet injection and mixing with air has a major impact on overall combustor performance and the flow field contains many complex fluid mechanical phenomena: transition of the fuel jet from laminar to turbulent, forming of the recirculation zones, vortex breakdown and turbulent mixing and hence, numerous experiments and computations have been conducted. In the context of numerical research, with the aid of recent developments in computational power, Large Eddy Simulation (LES) has been applied for these flows since LES has been recognised as a more promising approach for time dependent flows than the Reynolds Averaged Navier-Stokes Simulation approach in which only temporally averaged values are available.. In this decade, extensive research has been carried out on several specific configurations of reactive and non-reactive fuel jet flows in the framework of a series of the International Workshop on Measurement and Computation of Turbulent Nonpremixed Flames (TNF) which is summarised in [1, 2] . In this research, all of three dimensional non reactive computations used a conventional LES approach to deal with turbulence, and solved the filtered governing equations together with explicit turbulence models, e.g., a Smagorinsky's eddy viscosity model. Grid resolutions varied for each method from 1×10 6 to 4 × 10 6 grid points [1] . Though grid convergence was examined for a method up to 4.0 × 10 7 grid points [2] grid dependancy still can be seen near the central jet region. The sampling periods varied from 10ms to 30ms, which are relatively short time compared to reactive test cases. The computational costs also varied from the order of 100hr to 1000hr depending on the grid resolutions and numerical methods. In a comparative work of two numerical methods [6] , authors combined different chemical reaction models to fluid dynamics by the flamelet concept however, the turbulent subgrid scales were the same Smagorinsky model. Both methods applied second-order spatial discretisation. The inflow boundary condition is one of the key points for accurate simulation and hence, each simulation set them carefully; one applied upstream boundaries with turbulent generation and the other put random fluctuations. Other results of the TNF non reactive test cases can be found in, e.g., [3, 4] .. These results showed a reasonable agreement with experimental data but at the same time showed some limitations. As described in [2] , the simulation is sensitive to the subgrid scale model coefficient which effects both velocity and scalar fields. Moreover, the constant turbulent Schmidt number often used in the conventional LES [5, 6] is involved in the subgrid scale model and this may cause the deviation in the scalar field. Therefore, to preset and tune up these coefficients can be a difficult task. A well known solution is to apply the dynamic procedure [7] , which can avoid an explicit setting of the model coefficient, however, this results in strong dependence of the turbulent viscosity on grid resolution and, hence, the conventional LES requires sufficiently large grid size. In the conventional LES, the filtered equations are derived assuming the commutation between differentiated and filtered variables but this assumption is valid only with equal filter length, which is not likely in many cases of complex geometry. A number of other issues are reported in past reviews (see, for example, [8] ). These problems are basically unavoidable as long as filtering is explicitly operated to the governing equations.. Implicit Large Eddy Simulation (ILES) techniques (see reviews [9, 10, 11] ) together with high-resolution methods [12, 13] have been developed to overcome the principal disadvantages of the conventional LES approach described above. In ILES computation it is assumed that the numerical discretisation itself accounts for unresolved scales, in other words implicitly separates scales, and hence setting explicit filters for determination of subgrid scale is unnecessary. Furthermore, the use of appropriate limiters in the reconstruction process of high-resolution methods, which are employed in ILES, accomplishes non-linear dissipation to be selectively added to the computational cells. The numerical dissipation is a function of the eigenstructure of the non-linear (advective) terms of the Navier-Stokes equations and acts locally with the flow field depending on the flow gradients.. There are a number of recent and past studies, which have demonstrated the accuracy of ILES in simulations of both basic and complex flows, e.g., [9, 10, 32, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29] , as well as studies dealing with theoretical aspects of ILES and their associated numerical methods [9, 10, 30, 31] . ILES methods have also been validated in the past with respect to gas turbine combustor flows [15, 16] . In [16] two LES models, a second-order ILES model with 1 and 2 step Arrhenius chemistry, and a fractal flame-wrinkling LES model coupled to a conventional one-equation eddy-viscosity subgrid model, were used. The authors showed reasonable agreement when comparing their predictions with experimental data and with other LES computations of the same case.. The aim of the present paper is assess the accuracy of higher-order ILES methods for non reactive, multi-species fuel jet flows. The paper is organised as follows. Section 2 presents the governing equations and the ILES models. Section 3 presents the test case geometry of a fuel jet injector and the numerical setup. The ILES results, including instantaneous and mean flow structures, mean velocity and mixture fraction profiles, and comparisons with the experimental data are presented in Section 4. Finally, Section 5 summarises the main conclusions drawn from the present study. An ILES technique based on high-resolution and high-order methods was used to investigate a turbulent mixing flow generated by a generic geometry injector. In the ILES simulation, the governing equations for compressible, single phase, multi-species, non reactive flow were solved without explicit modelling of the subgrid scales using a finite volume Godunov-type method. Up to ninth-order spatial accuracy was used with a second-order explicit Runge-Kutta method for time integration. Temporally and circumferentially averaged flow profiles were compared with the experimental data. The axial and the radial velocity and rms velocity profiles showed good agreement in the strong turbulent region, recirculation region and the downstream region of the flow fields. Also, the mixture fraction profiles demonstrated a good match to the experimental data in multi-species flow simulations.. The one dimensional kinetic energy spectrum was examined to evaluate the effects of grid resolution and limiter on energy dissipation in the ILES computation method. An ideal k −5/3 decay of energy could be seen in a certain range which increased with grid resolution. In all computations with the fifth-order accurate limiter the cut-off wavenumbers are larger than the estimated maximum wavenumbers appearing on the given computational grid. This implies that the numerical dissipation accounted for the energy transportation between large and small eddies sufficiently up to the effective cut-off.. The second, fifth and ninth-order limiter were examined on the medium grid. The fifth-order limiter showed good resolution of the kinetic energy dissipation with reasonable computational time compared to the second-order limiter. The ninth-order limiter required substantially larger computational power.

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


Specifying memory consistency of write buffer multiprocessors Write buffering is one of many successful mechanisms that improves the performance and scalability of multiprocessors. However, it leads to more complex memory system behavior, which cannot be described using intuitive consistency models, such as Sequential Consistency. It is crucial to provide programmers with a specification of the exact behavior of such complex memories. This article presents a uniform framework for describing systems at different levels of abstraction and proving their equivalence. The framework is used to derive and prove correct simple specifications in terms of program-level instructions of the SPARC total store order and partial store order memories.The framework is also used to examine the SPARC relaxed memory order. We show that it is not a memory consistency model that corresponds to any implementation on a multiprocessor that uses write-buffers, even though we suspect that the SPARC version 9 specification of relaxed memory order was intended to capture a general write-buffer architecture. The same technique is used to show that Coherence does not correspond to a write-buffer architecture. A corollary, which follows from the relationship between Coherence and Alpha, is that any implementation of Alpha consistency using write-buffers cannot produce all possible Alpha computations. That is, there are some computations that satisfy the Alpha specification but cannot occur in the given write-buffer implementation. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) Distributed algorithm designers typically assume strong consistency models such as Sequential Consistency and Linearizability [Lamport 1979a; Herlihy and Wing 1990] . Multiprocessor architectures, however, incorporate mechanisms such as write-buffers to improve the performance and scalability. Such mechanisms lead to the relaxation of the resulting memory consistency model. These weakened consistency guarantees make it challenging for a programmer to be certain of the possible program outcomes and to implement correct and efficient distributed algorithms. Some attempted specifications of the behavior of such complex memory systems resulted in incomplete or incorrect conclusions. Thus, such specifications must be shown to exactly capture the operational behavior of the underlying memory architecture.. This article presents a framework that allows us to describe systems at various levels of abstraction, ranging from operational or architectural hardware functionality, to object-oriented or transaction-based abstractions. The uniform framework used for such descriptions facilitates the comparison of different models of consistency, so that claims of the relative strength of models can be stated precisely and proven correct. In particular, it provides the setting in which to prove that a nonoperational description of a multiprocessor system captures exactly the operational behavior of that system. The uniformity of the descriptions at various levels of abstraction means that proofs of correct implementations of abstract systems on systems with finer granularity of description can be composed. Starting from a highly abstract description, a sequence of provably correct implementations onto successively lower levels of abstraction can be constructed within the framework, eventually arriving at the actual operational machine. Thus, the framework can be seen as a generalization of the well-established notions of levels of abstraction and proofs of correctness of implementations, to include arbitrary memory consistency models.. The framework is used to describe, at an operational level, the execution of programs on possible write-buffer multiprocessor architectures. Three different write-buffer machines are considered. One represents the "bare minimum" write-buffer machine capturing the very basic behavior of write-buffers. The other two successively stronger machines capture the operational behavior of SPARC's partial and total store order machines [SPARC International, Inc. 1992; Weaver and Germond 2000] . Using the framework, three sets of increasingly stringent constraints on outcomes of program executions on these machines are specified.. The same framework is used to provide two more abstract (nonoperational) memory consistency models, corresponding to the SPARC's total and partial store order machines. These higher level models constitute the programmer's view of the behavior of multiprocess programs on the respective machines. For each of the two pairs, the operational and nonoperational descriptions are proven to be equivalent.. The SPARC version 9 manual [Weaver and Germond 2000] specifies a third memory consistency model called relaxed memory order, which seems to be intended to capture the executions of a still more permissive write-buffer architecture. Exploiting our framework, we show, however, that relaxed memory order does not correspond to any implementation on a write-buffer multiprocessor. In fact, we prove that Coherence [Dubois et al. 1986; Goodman 1989 ] or any weaker model does not correspond to a write-buffer architecture. We also prove that Alpha consistency [Compaq Computer Corporation 1998 ], which is Coherence combined with some dependencies [Attiya and Friedman 1994] that arise from processors' private registers, admits computations that cannot arise on any write-buffer machine. Thus, any implementation of Alpha consistency using write-buffers must produce a memory consistency model strictly stronger than that specified by the Alpha documentation [Compaq Computer Corporation 1998 ], even though this documentation suggests such an implementation.. Software system designers require an unambiguous description of the memory consistency conditions at the level of the operations used in their programs. This applies to the SPARC architecture, with which the UltraSPARC I, II, III, and IV processors are all compliant [Sun Microsystems 2004] , since it continues to be one of the commercial choices for server machines. Typically, hardware architecture manuals use natural language or axiomatic specifications to describe how the hardware operates [SPARC International, Inc. 1992; Weaver and Germond 2000; Intel Corporation 2002; International Business Machines Corporation 1997] . These descriptions are not programmer centric and may contain ambiguities. They can lead to incorrect or inefficient programs and they are complex to work with. Frameworks have been defined to provide a way to formalize and unify descriptions of memory consistency models, and to help us reason about them. The literature contains many examples of such frameworks [Hoare 1972; Owicki and Gries 1976; Misra 1986; Lamport 1979b Lamport , 1997 Lamport , 1986a Lamport , 1986b Anger 1989; Attiya et al. 1998; Attiya and Friedman 1992; Friedman 1995; Ahamad et al. 1993 Ahamad et al. , 1995 Kohli et al. 1993; Gibbons and Merritt 1992; Lynch and Tuttle 1989; Lynch 1996; Herlihy and Wing 1990; Adir et al. 2003 ]. A comparison of our framework to the closely related ones is provided in Subsection 2.3 after ours is defined.. Capturing the consistency of a multiprocessor architecture simply and precisely at a programmer oriented level is crucial for the development of correct and efficient programs targeted to these architectures. But this has proven to be surprisingly tricky for several machines. Erroneous definitions may lead to incorrect programs. Complex definitions lead programmers to unnecessarily and aggressively use expensive synchronization primitives, which negatively impact program performance. For example, one earlier attempt to define the semantics of the SPARC memory consistency model called total store order ] resulted in a definition that is stronger than what the machine level architecture description in the SUN Microsystems manuals [SPARC International, Inc. 1992; Weaver and Germond 2000] actually provides. In fact, any program using only read and write operations on variables that is correct for Sequential Consistency can be compiled into an equivalent program with only read/write operations that is correct for this strong definition [Higham and Kawash 2000] . Thus using this definition, there is a solution to the mutual exclusion problem that uses only read-write variables. In earlier work [Higham and Kawash 2000; Kawash 2000 ], we exploit the definitions derived and proven correct in this article to prove, to the contrary, that read-write variables are insufficient to solve the mutual exclusion problem on a SPARC total store order machine. The total and partial store order definitions derived in this article are used in another work [Higham and Kawash 2005] , to determine under what conditions wait-free producer-consumer coordination is possible or impossible in various SPARC models without resorting to expensive synchronization primitives, in spite of the impossibility of mutual exclusion.. The simplicity of the SPARC memory consistency descriptions provided in this article gives programmers an improved tool for reasoning about the outcomes of their programs. It also facilitates the comparison of the SPARC models to each other and to proposed consistency models including Processor Consistency [Goodman 1989 ], Causal Consistency [Ahamad et al. 1995] , and Java consistency [Gontmakher and Schuster 2000; Higham and Kawash 1998 ], and aids the development of verification tools [Park and Dill 1999] .. The rest of the article is organized as follows. Section 2 describes the framework that is used throughout the article. In Section 3, the framework is used to describe two write-buffer machines that capture the familiar total store order and partial store order semantics of a multiprocessor with write-buffers. Section 4 defines two nonoperational memory consistency specifications. It is then proved that the total store order and partial store order machines implement exactly these specifications, establishing that these two nonoperational specifications are correct. Section 5 establishes that these operational and nonoperational models are equivalent to the corresponding consistency models described in the SPARC manuals. A computation is given in Section 6 that is possible on any of the memory consistency models defined by the SPARC relaxed memory order, Alpha consistency, and Coherence yet cannot arise from any write-buffer machine-even one substantially more permissive than a partial store order write buffer machine. Thus, these consistency models are not equivalent to any write buffer model. Section 7 summarizes and concludes. A list object, l , is the set of sequences over operations of the type APPEND(l ,·), DELETE(l ,·), and ←LAST(l ) that satisfy the validity condition:. For any LAST operation on list object, l , it returns a value ρ =⊥ if and only if the most recent preceding undeleted APPEND operation is APPEND(l ,ρ). Otherwise, it returns ⊥.

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


Distributed Cognition at the Crime Scene


Exploring the effects of group size and display configuration on visual search Visual search is the subject of countless psychology studies in which people search for target items within a scene. The bulk of this literature focuses on the individual with the goal of understanding the human perceptual system. In life, visual search is performed not only by individuals, but also by groups -a team of doctors may study an x-ray and a team of analysts may study a satellite photograph. In this paper, we examine the issues one should consider when searching as a group. We present the details of an experiment designed to investigate the impact of group size on visual search performance, and how different display configurations affected that performance. We asked individuals, pairs, and groups of four people to participate in a baggage screening task in which these teams searched simulated x-rays for prohibited items. Teams conducted these searches on single monitors, a row of four monitors, and on a single horizontal display. Our findings suggest that groups commit far fewer errors in visual search tasks, although they may perform slower than individuals under certain conditions. The interaction between group size and display configuration turned out to be an important factor as well. Visually searching images for target objects is a critical task in a variety of domains. These include screening for prohibited items in x-ray imagery of luggage in airports, identifying anomalies in medical imagery, and looking for unusual activity and changes over time in satellite imagery. Unlike many other human-machine interface tasks where a small error rate is quite acceptable, a crucial element of visual search tasks in these application areas is that a single mistake can have catastrophic consequences. Of particular concern is recent research [19] which demonstrates that when the occurrence of a target is rare -as is typical in the airport screening and medical imaging examples -observers' ability to detect those targets are disturbingly poor. In other words, unless observers see what they are looking for fairly frequently, they will often miss it when it does appear. Thus, any improvements -in technology or human processes -that can be made to aid visual search would be invaluable in these safety critical areas.. Although visual search has been studied extensively [18] , most of this literature focuses on understanding an individual's performance in a search task and the underlying human perceptual processes. In real-life applications, however, visual search may be conducted by groups of people rather than an individual -several doctors often study an x-ray, teams of security personnel work at screening checkpoints, and many analysts may pore over satellite imagery. When groups of people work together in this manner, it is plausible that their performance is a function of both their individual perceptual capabilities and the dynamics of the group, with factors such as competition and discussion amongst group members possibly playing a significant role. Whether or not these group dynamics help or hinder the search task is an important question that has not been explored in the literature.. From a technology standpoint, most visual search studies have been conducted with an individual observer viewing a single scene on a single vertical display. In practice, however, visual search need not be restricted to vertical displays and could benefit from new form factors such as horizontal displays and multiple monitors. While much recent research has explored using these new displays, their benefits, if any, are not fully understood either in general or in the context of visual search per se. For example, it is unclear whether using the increased pixel count of multiple displays to provide multiple, simultaneous orientations of an image, without degradation in quality, adds any value to visual search tasks. Similarly, it is unclear if and how the horizontal orientation of a tabletop display might impact visual search. Perhaps most interesting is the question of how different display factors might affect the behavior and performance of groups of people performing search tasks.. In this paper, we present an experiment designed to probe these questions. In particular, we consider the impact of group sizes of 1, 2, and 4 people and three display configurations -a single vertical display, a horizontal tabletop display, and four vertical displays each showing the same image at one of four orientations -on the performance of a visual search task where target occurrence is rare. The display configurations chosen are ones that could be deployed in practical settings, while nonetheless providing sufficient variability in several dimensions to be interesting from a theoretical/experimental perspective. Our results show the value of having groups perform visual search, and can guide the design of future displays for such tasks. While our results should be of interest to anyone involved in designing systems for group searching tasks, important differences exist between the simulated baggage screening task that our study's participants completed and other visual searching tasks that small groups perform. For example, a team of doctors examining a medial image bring a huge amount of contextual knowledge to the table. Information about the patient's medical history and symptoms, as well as preconceived diagnoses must influence the group's search. Similarly, intelligence analysts looking at satellite images will often view a series of images of the same location, leading to a high correlation of features among the images. The searching of images presented later in the sequence is certainly influenced by previously viewed images. While recruiting expert participants for these types of experiments is difficult, a study designed around the specific constraints of the tasks described above would be a valuable piece of future work. One interesting question that arose during the design of this experiment was that of adding interaction to the search. A baggage screener wishing to get another view of an x-ray might benefit from being able to rotate the picture by 90 degrees, and invert or otherwise filter the image to better examine objects of interest. Similarly, mammograms are often taken from both the top and side providing a doctor with multiple views with which to make a diagnosis. An analyst working with a high-resolution photograph may choose to enlarge an interesting portion of a image or may filter it digitally. The question remains, how might collaboration and performance be affected by the addition of interaction? While our design included a horizontal display condition, most research into tabletop interfaces have examined larger display areas than that used in our study. Our aim in using identical monitors in every display configuration was to reduce the complexity of the user study design. Display size and resolution will likely affect group performance. It is interesting to note the similarity of performance for individuals working with the horizontal and single vertical display conditions. In this case, the orientation of the display itself made little difference to perception. It would be a valuable piece of work to investigate larger display sizes used in both a horizontal and vertical orientation. A large horizontal workspace may emphasize the foreshortening and distortion of images that has been shown to reduce performance in visual searching tasks [10] .. As part of our ongoing research, we plan to address these and other issues as we continue to explore small group collaboration. We computed a repeated measures ANOVA for both reaction time and error rate using display configuration, and number of distracters as within-participant factors, and group size as a between-participants factor. Because of the different number of trials in the target-present and target-absent conditions (given the 10% target prevalence), we analyzed the two target presence conditions independently. Our findings support hypotheses H2, H3, and H4, while they allow the rejection of hypotheses H1, H5, and H6.

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