A differential oligopoly game with differentiated goods and sticky prices We investigate a dynamic oligopoly game where goods are differentiated and prices are sticky. We study the openloop and the closed-loop memoryless Nash equilibrium, and show that the latter equilibrium entails a larger level of steady state production as compared to the former; both equilibria entail a larger level of production in steady state than the static game. We also study the effects of price stickiness and product differentiation upon the steady state equilibrium allocation and profits. The per-firm equilibrium output is increasing in both product differentiation and price stickiness, while profits are increasing in both product differentiation and the speed of price adjustment. The steady state social welfare monotonically increases in the speed of price adjustment, and the overproduction entailed by dynamic competition has beneficial effect from a social standpoint. The aim of this paper consists in studying the properties of the equilibria in a dynamic oligopoly model with price stickiness, along the lines first introduced by Simaan and Takayama (1978) and then extended by Fershtman and Kamien (1987) and Cellini and Lambertini (2004) . 2 The novelty of this paper rests on the fact that we analyse an oligopoly where goods are differentiated, and we study how good differentiation and price stickiness interact in shaping the equilibrium allocation.. We take into consideration both the open-loop and the closed-loop memoryless Nash equilibrium. In both cases, an economically meaningful symmetric steady state exists; this equilibrium is a saddle. We focus on the steady state equilibrium allocation, and study its determinants. The already known properties of the differential game involving homogenous goods are confirmed; in particular, (i) the static game entails a lower level of production as compared to the steady-state equilibrium production levels of the differential game, and the steady-state Nash equilibrium production under the open-loop information structure is smaller than under the closed-loop rule; (ii) the stickier are the prices, the higher the steady state Nash equilibrium production levels. This consistency is not surprising, provided that the homogenous oligopoly case can be interpreted as a particular case of the present model.. In addition, we show that the degree of differentiation among goods is effective in determining both the production levels, and the responsiveness of quantities and profits to price stickiness. In particular, we show that the higher is the (symmetric) degree of differentiation among goods, the lower is the steady state level of production. Moreover, the higher is the degree of differentiation, the lower is the sensitivity of the steady state level of production to the price stickiness. This means that the degree of differentiation and the degree of price stickiness affect the steady state equilibrium level of production in much the same way. Concerning equilibrium profits, we show that they are affected in the same way by price flexibility and product differentiation.. Finally, we proceed to investigate the first best allocation, where a planner controls firmsÕ output decisions so as to maximise social welfare. We show that the welfare distortion caused by the strategic interaction of profit-maximising Cournot firms is smaller at the steady state under the closed-loop memoryless Nash equilibrium than in the steady state under the open-loop solution. Both steady states are associated to a lower welfare loss, as compared to the equilibrium of the corresponding static game. Thus, the welfare assessment usually drawn from the static analysis is too pessimistic.. The outline of the paper is as follows. Section 2 introduces the basics of the model. Section 3 develops the differential game under the open-loop information structure, while Section 4 solves the closed-loop game. Both sections focus on the steady-state level of production, showing that the steady state is a saddle, and presenting comparative statics exercises on the production levels. The socially optimal production plan is characterised in Section 5. Section 6 concludes the paper. In this paper we have investigated the properties of a dynamic oligopoly game with sticky prices and differentiated products. It is important to stress that the rigidities we have dealt here with, are real rather than nominal, provided that we have taken a partial equilibrium approach, with sticky relative prices.. We have shown that the dynamic rule governing the price motion (and in particular the degree of price stickiness) affects the final allocation, i.e., the steady state under the Nash equilibrium of the dynamic game. In particular two properties are worth mentioning: (i) in the (subgame perfect) closed-loop memoryless Nash equilibrium a steady state exists, which is stable in the saddle point sense, where the production is larger and the price is lower as compared to the open-loop steady state solution; (ii) irrespective of the equilibrium concept one adopts, in the dynamic framework, the steady state output levels and price levels are, respectively, higher and lower then their counterparts in the static game. Property (i) can be reformulated by saying that, if firms are unable to initially commit to a given output plan for the whole time horizon, then subgame perfection entails overproduction (for analogous results see Spence, 1979; Reynolds, 1987) . Property (ii) suggests that the dynamic nature of interaction leads forms to over-produce, as compared to the Nash equilibrium of a static interaction.. The above mentioned results are analogous with the findings from the homogenous oligopoly model. In the present paper, additional results have been found, concerning the effects of the differentiation among the goods produced by firms upon steady state allocations. Under both the open-loop and the closed-loop solution concepts, the higher is the substitutability among goods, the lower is the steady state level of production; the tougher is the price stickiness, the higher the steady state level of production. Under this respect, the degree of price stickiness and the degree of product differentiation exert the same qualitative effects on the steady state output level. Moreover, the degree of product differentiation interacts with the sensitivity of steady state profits to price stickiness. In particular, if the number of firms is low enough and products are sufficiently differentiated, an increase in substitutability enhances the positive effects of a higher speed of price adjustment on profits.. The welfare analysis has shown that the distortion associated with the dynamic oligopoly game is always lower than it appears when judging from the static version of the same model.

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


ANFIS approach to the scour depth prediction at a bridge abutment An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour counter-measure to be implemented against excessive scouring.Despite analysis of innumerable prototype and hydraulic model studies in the past, the scour depth prediction at the bridge abutments has remained inconclusive. This paper presents an alternative to the conventional regression model (RM) in the form of an adaptive network-based fuzzy inference system (ANFIS) modelling. The performance of ANFIS over RM and artificial neural networks (ANNs) is assessed here. It was found that the ANFIS model performed best among of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form. An attempt was made to assess the performance of the various predictions of RM, ANN and ANFIS using an adequate volume of laboratory data for scour depth at the bridge abutments.. In the case of ANN models, FFBP training algorithm was found to be the best among the other training algorithms. The performance of ANN with all three training algorithms (FFBP, FFCC and RBF) was found to be better than that of the regression method.. As far as the performance of the ANFIS model is considered, it was found to be best between the regression model and the artificial neural networks model. These results indicate that the ANFIS-based treatment to nonlinearity in the scour data worked much better than other approaches. As such, the scour data of the bridge abutments appear to be more amenable to fuzzy if-then rules rather than crisp processing. Further, it was found that the ANFIS approach predicts scour depth better when trained with raw data rather than grouped data.

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Self-sustaining non-repetitive activity in a large scale neuronal-level model of the hippocampal circuit The mammalian hippocampus is involved in spatial representation and memory storage and retrieval, and much research is ongoing to elucidate the cellular and systemlevel mechanisms underlying these cognitive tasks. Modeling may be useful to link network-level activity patterns to the relevant features of hippocampal anatomy and electrophysiology. Investigating the effects of circuit connectivity requires simulations of a number of neurons close to real scale. Here, we construct a model of the hippocampus with 16 distinct neuronal classes [1, 2] (including both local and projection cells) and 200,000 individual McCulloch-Pitts neurons [3] . The number of neurons in each class and their interconnectivity are drawn from rat anatomy [1] .

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Does Offline Political Segregation Affect the Filter Bubble? An Empirical Analysis of Information Diversity for Dutch and Turkish Twitter Users From a liberal perspective, pluralism and viewpoint diversity are seen as a necessary condition for a well-functioning democracy. Recently, there have been claims that viewpoint diversity is diminishing in online social networks, putting users in a "bubble", where they receive political information which they agree with. The contributions from our investigations are fivefold: (1) we introduce different dimensions of the highly complex value viewpoint diversity using political theory; (2) we provide an overview of the metrics used in the literature of viewpoint diversity analysis; (3) we operationalize new metrics using the theory and provide a framework to analyze viewpoint diversity in Twitter for different political cultures; (4) we share our results for a case study on minorities we performed for Turkish and Dutch Twitter users; (5) we show that minority users cannot reach a large percentage of Turkish Twitter users. With the last of these contributions, using theory from communication scholars and philosophers, we show how minority access is missing from the typical dimensions of viewpoint diversity studied by computer scientists and the impact it has on viewpoint diversity analysis. It is well known that traditional media have a bias in selecting what to report and in choosing a perspective on a particular topic. Individual factors such as personal judgment can play a role during the selection of news for a newspaper. Selection bias, organizational factors, advertiser and government influences can all affect which items will become news (Bozdag, 2013) . About 37% of Americans see a great deal of political bias in news coverage and 68% percent prefer to get political news from sources that have no particular point of view (Pew Research, 2012) . Similarly, in a survey performed before the general elections in the UK, 96% of the population said they believe they have seen clear bias within the UK media (Wei et al., 2013) . Evidence of bias ranges from the topic choice of the New York Times to the choice of think-tanks that the media refer to (DellaVigna & Kaplan, 2007) .. Many democracy theorists claim that modern deliberative democracy requires citizens to have socially validated and justifiable preferences. Citizens must be exposed to opposed preferences and viewpoints and should be able to defend their views (Held, 2006; Offe & Preuss, 1990; Dryzek, 1994) . Exposure to biased news information can foster intolerance to opposing viewpoints, lead to ideological segregation and antagonisms in major political and social issues (Glynn et al., 2004; An et al., 2012; SaezTrumper et al., 2013) . Being aware of and overcoming bias in news reporting is essential for a fair society, as media has the power to shape voting behavior (Saez-Trumper et al., 2013) .. Social information streams, i.e., status updates from social networking sites, have emerged as a popular means of information sharing. Political discussions on these platforms are becoming an increasingly relevant source of political information, often also used as a source of quotes for media outlets (Jürgens et al., 2011) . Traditional media are declining in their gatekeeping role to determine the agenda and select which issues and viewpoints reach their audiences (Bruns, 2011) . Internet users have moved from scanning traditional media such as newspapers and television to using the Internet, in particular social networking sites . Social networking sites are thus now acting as gatekeepers (Bozdag, 2013) .. Communication theorists argue that the traditional media are declining in their gatekeeping role to determine what is "newsworthy" and select which issues and viewpoints will reach their audiences Bruns (2011) . It is often argued that the Internet, by promoting equal access to diverging preferences and opinions in society, actually in-creases information diversity. Many scholars characterize the online media landscape as the "age of plenty, with an almost infinite choice and unparalleled pluralization of voices that have access to the public sphere (Karppinen, 2009) . Some argue that social media will disrupt the traditional elite control of media and amplify the political voice of non-elites and minorities (Castells, 2011) .Still others claim that tools such as Twitter are neutral spaces for collaborative news coverage operated by third parties outside the journalism industry. As a result, the information curated through collaborative action on such social media platforms should be expected to be drawn from a diverse, multi-perspectival range of sources (Bruns, 2011) . Some further claim that platforms such as Twitter are neutral communication spaces, and offer a unique environment in which journalists are free to communicate virtually anything to anyone, beyond many of the natural constraints posed by organizational norms that are existing in traditional media (Lasorsa et al., 2012) .. On the other hand, there are skeptical voices that argue that the Internet has not fundamentally changed the concentrated structure typical of mass media, but reflects the previously recognized inequalities (Karppinen, 2009) . It is also argued that it has brought about new forms of exclusion and hierarchy (Suoranta & Vadén, 2009) . While it has increased some sort of political participation, it has empowered a small set of elites and they still strongly shape how political material is presented and accessed (Hindman, 2008) . Others have pointed out the danger of "cyberbalkanization"" caused by the Internet (Sunstein, 2002; Pariser, 2011) . They argue that the filters we choose on the Internet, or the filters that are imposed upon us will weaken the democratic process. This is because it will allow citizens to join into groups that share their own views and values, and cut themselves off from any information that might challenge their beliefs. Group deliberation among like-minded people can create polarization; individuals may lead each other in the direction of error and falsehood, simply because of the limited argument pool and the operation of social influences.. It is thus very important to verify whether viewpoint diversity is diminishing in social media and whether cyberbalkanization indeed occurs. There are empirical studies that have observed a high level of information diversity in Twitter and Facebook, mainly due to retweets and weakties (Bakshy et al., 2012; An et al., 2011; Sun et al., 2013) . While being very valuable contributions to the literature, these studies often focus on American users and they define information diversity either as "novelty", or "source diversity". However, as we will show below, novel information does not necessarily contribute to information diversity and highly competitive media markets with many sources may still result in excessive sameness of media contents. As we will argue, marginalized members of segregated groups, structurally underprivileged actors and minorities must receive special attention and just measuring number of available sources will not guarantee viewpoint diversity.. In this paper, we contribute with a framework to analyze and understand the impact of political culture in Twitter. Rather than reducing the concept viewpoint diversity to a single quantity or metric, we introduce different dimensions of viewpoint diversity, based on previous studies and the theory from communication studies and political philosophy. In addition, we provide a set of new metrics and operationalize them. Finally, we present the result of a case study we performed for Dutch and Turkish Twitter users using this framework. We show that minority users cannot reach a large percentage of the studied Turkish Twitter users and political culture is making a difference. In short, The Netherlands and Turkey are two different countries in terms of political landscape and diversity policy. The Dutch society is less polarized than it was half a century ago, while the Turkish society is thought to be heavily polarized. The Dutch Parliament contains many 2 Cumhuriyet Halk Partisi political parties, no party has absolute power to govern alone. Turkey, on the other hand has few political parties represented in the government and the ruling party has almost 60% of all the seats. Further, the Dutch media is regulated with a diversity policy. While Turkey has a similar institution, it acts more as a censor board and does not employ an active diversity policy. If the social networking platforms mirror the society, then we can expect the Dutch users to receive more diverse content, while the Turkish users to be more polarized and have a less diverse newsfeed. In this paper, we have introduced a framework that lists metrics used in the previous social media analytics studies and added new ones using the theory from other fields. As one of the outcomes of the results from the previous section, we showed how minority access is missing from the typical dimensions of viewpoint diversity studied by computer scientists and the impact it has on viewpoint diversity analysis. To our knowledge, this is the first work that provides an overview of all the used metrics in social media analytics literature and the first study to apply an "openness" metric. Building on this framework, new studies can be performed for different countries and political cultures. For instance, Belgium, a country where different languages are spoken in different regions is an an interesting case to apply our framework.. In the recent months, Turkey experienced several political protests that spontaneously erupted against the destruction of trees and the building of a shopping mall at Gezi Park in Taksim Square and large scale corruptions within the government. Twitter and Facebook played a vital role during these movements and became the only communication medium when traditional media performed selfcensorship (Dorsey, 2013; Hammond & Angell, 2013; Oktem, 2013) . It would be very useful to see whether the political stance of our observed users have changed. It is also challenging to identify the opinion leaders during these movements and find whether they communicate with each other or form their own "bubbles". It is further valuable to see if minorities were able to reach a wider public during those protests. A hashtag based political communication or an extension of our methodology could bring new insights.. Our study was focused on Twitter and studied whether users have put themselves in bubbles by following individuals from only one end of the political spectrum and showed a biased sharing behavior. Twitter itself does not employ a personalization algorithm in a user's timeline. However other social networking platforms, such as Facebook, do use a personalization algorithm and filter certain information on user's behalf (Bozdag, 2013) . Future studies can perform black-box testing techniques to determine whether filters used by these platforms lead to bubbles (See Jürgens (2013) ). Creating multiple profiles while modifying certain factors, such as political affiliation, age, location, etc. can help us detect bubbles, if they exist. In this study we have shown different dimensions of diversity and discussed an additional dimension, namely minority access. This dimension has not been previously been subjected to large-scale quantitative validation. However, as many communication scholars and philosophers have argued, while the media should reflect the preferences present in the society, it should also allow equal access to everyone, including those whose common social location tends to exclude them from political participation. Public life needs to include differently situated voices, not just the majority.. We have shown that different definitions of diversity can be operationalized in different metrics and the question whether "the filter bubble exists in social media" will have different answers depending on the metric and political segregation of the observed country. For instance, according to the results of our study, source diversity does not differ much for Turkish and Dutch users and we certainly cannot observe a bubble. However, if we consider output, then we see that the diversity is much lower. Further, if we consider the minority access as a diversity metric, we see that minorities cannot reach a large percentage of the Turkish population.. Twitter, with its 140 character limitation, is not the ideal platform for deliberation. However, having a diverse information stream in Twitter is still important, as it can serve as an input for deliberation elsewhere. Information intermediaries such as Twitter could have a considerable influence in nudging people towards more valuable and diverse choices (Helberger, 2011) . Media diversity has been an important policy objective for the regulation of traditional media (van Cuilenburg & McQuail, 2003) . Journalism ethics requires the newspapers to have a balanced and fair coverage of news and opinions and editors and journalists to minimize bias in their filtering decisions. In the abundance of digital online information and algorithmic filters to deal with information overload, bias should also be minimized and ideas and opinions of minorities should not be lost.. Design choices in software codes and other forms of information politics still largely determine the way information is made available and who can speak to whom under what condition (Karppinen, 2009 ). According to Karppinen (2009) , it is important to make decisions about standards, because those "can have lasting influence on media pluralism, even if they are not necessarily recognized as sites of media policy as such". However, making minority voices reach a wider public is no easy matter. While identifying minorities and their valuable tweets is no easy task, showing these items to "challenge averse" users is a real challenge (Munson & Resnick, 2010) . For instance Munson et al.(2013) provided people with feedback about the political lean of their reading behaviors and found that such feedback had only a small effect on nudging people to read more diversely. More research is needed to understand how users' reading behavior change and to determine the conditions that would allow such a change.. Further, normative questions arise while making design decisions. When designing diverse recommendation systems, it is definitely a challenge to determine which view is "valid". For instance, should a recommendation system show all viewpoints in the climate change debate, if some viewpoints are not empirically validated or simply seen as false by the majority of the experts? Should a viewpoint get equal attention even if it provides no information or only contains arguments with fallacies? These questions would need to be addressed by a good ethical analysis before design decisions of such systems are made. This section shows the results for the defined metrics. We tested statistical significance of our results with a twotailed t-Test where the significance level was set to α = 0.01 unless otherwise noted.. Figures 6 and 7 show the distribution of the seed users for both countries. Figures 8 and 9 show the distribution of regular users. We see that our selection of popular users covers the political spectrum and it is not concentrated on a single political category. We have used several sources to do the seed user categorization (Krouwel, 2008; van der Eijk, 2000; Trendlight, 2012; Krouwel, 2012 Krouwel, , 2008 Baris, 2007) . We used the retweet behavior of the users to assign them to a political category to identify their political stance. In this section we provide our method of data collection, present our research model and the metrics we operationalized to measure information diversity.

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


Range image segmentation by an effective jump-diffusion method Abstract-This paper presents an effective jump-diffusion method for segmenting a range image and its associated reflectance image in the Bayesian framework. The algorithm works on complex real-world scenes (indoor and outdoor), which consist of an unknown number of objects (or surfaces) of various sizes and types, such as planes, conics, smooth surfaces, and cluttered objects (like trees and bushes). Formulated in the Bayesian framework, the posterior probability is distributed over a solution space with a countable number of subspaces of varying dimensions. The algorithm simulates Markov chains with both reversible jumps and stochastic diffusions to traverse the solution space. The reversible jumps realize the moves between subspaces of different dimensions, such as switching surface models and changing the number of objects. The stochastic Langevin equation realizes diffusions within each subspace. To achieve effective computation, the algorithm precomputes some importance proposal probabilities over multiple scales through Hough transforms, edge detection, and data clustering. The latter are used by the Markov chains for fast mixing. The algorithm is tested on 100 1D simulated data sets for performance analysis on both accuracy and speed. Then, the algorithm is applied to three data sets of range images under the same parameter setting. The results are satisfactory in comparison with manual segmentations. R ESEARCH on range images in the past was mostly motivated by robotics applications for recognizing industry parts on assembly lines, and most of the work was focused on simple polyhedra objects. Recently, long-range laser scanners have become accessible to many users. These scanners can capture accurate 3D range images of real-world scenes at large scales. For example, Fig. 1 displays four panoramic range images of indoor and outdoor scenes scanned in cylindric coordinates. Other examples are the 3D Lidar images in aerospace imaging which can capture depth maps of terrain and city scenes with accuracy up to 1 centimeter. As these range images provide much more accurate depth information than conventional vision cues, such as motion, shading, and binocular stereo, they are important for a wide variety of applications, for example, visualization of city scenes and historic sites [33] , spatiotemporal databases of 3D urban and suburban developments, 3D scene modeling, and natural scene statistics analysis [22] .. These new data sets pose much more serious challenges for range image analysis than the traditional polyhedra world. The main difficulties lie in the following aspects.. First, natural scenes contain many types of objects, for example, man-made objects (buildings, desks), animate objects (human and animals), and free-form objects (trees and terrain). These objects should be represented by various families of surface models which have different parameterization dimensions. Formulated in the Bayesian framework, the posterior probability is distributed over a solution (state) space with a countable number of subspaces of varying dimensions. Each subspace is for a certain combination of surface models. Thus, an algorithm must accommodate multiple surface models in representation and explore the possible combinations of these models during computation.. Second, objects and surfaces in natural scenes appear at multiple scales. For example, the office scenes in Fig. 1 contain large surfaces such as walls, ceilings, and floors; middle size objects such as people, chairs and tables; and small objects such as books and cups on the desktop. This is in contrast with the block world (see Figs. 9 and 10) where objects are of similar sizes. In the presence of such a broad range of perceptual scales, it becomes difficult to segment small objects without oversegmenting large surfaces. Conventional model complexity criteria like MDL and AIC [34] , [1] do not easily deal with such cases as they are based on image coding, not perception. Therefore, one should seek prior models to ensure proper segmentation of surfaces at all sizes.. Third, although range data is accurate in general, it is rather noisy around object boundaries. It becomes rather messy for objects like trees and bushes. Also, depth data are missing for objects at infinite distance like the sky and at metal, glass, and ceramic surfaces where the laser rays never return to the scanner.. In the literature, range image segmentation methods are mostly adapted from algorithms developed for optical images. For example, edge detection [24] , region-based methods [2] , [5] , surface fitting [19] , clustering [20] , [12] , and the generalized Hough transform [3] for detecting parametric surfaces of low dimension. We refer to Hoover et al. [21] for a survey of range segmentation algorithms and a good empirical comparison. Generally speaking, algorithms for range segmentation are not as advanced as those for intensity image segmentation. For example, there is no algorithm, to our knowledge, which can satisfactorily segment complex scenes such as those displayed in Fig. 1 . This paper presents a stochastic jump-diffusion algorithm for segmenting 3D scenes and reconstructing object surfaces from range images and their reflectance images. This work is an extension of our recent image segmentation work on datadriven Markov chain Monte Carlo [36] . It makes the following contributions to the range segmentation literature. We adopt a Bayesian formulation to integrate two cues, many families of generative models, and a number of prior models. We use a jump-diffusion process as a general tool for designing Markov chains traversing the complex solution space so that the solution no longer depends on initialization. The computational speed is largely expedited by bottom-up heuristics.. Some remaining problems that need to be resolved in future research include:. 1. The algorithm is still time consuming and takes about 1 hour on a Pentium IV PC to segment a scene (usually having 300 Â 300 pixels) with arbitrary initial conditions. Recent developments using the Swendsen-Wang cut [4] suggest considerable improvements in speed. 2. The experiments reveal that when the models are not sufficient, the segmentation is not good. For example, the cable in the air in Fig. 11 is missing because of being a 1D structure, not a region. 3. Better prior models for 3D objects are needed to group surfaces into objects and, therefore, to complete surfaces behind the occluding objects. 4. We acknowledge that the manual segmentations used for evaluation may not correspond to the optimal solutions given by the limited models and it remains an open question for measuring the goodness of a segmentation solution. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib.

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


Brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials Abstract Brain fingerprinting (BF) detects concealed information stored in the brain by measuring brainwaves. A specific EEG event-related potential, a P300-MERMER, is elicited by stimuli that are significant in the present context. BF detects P300-MERMER responses to words/ pictures relevant to a crime scene, terrorist training, bombmaking knowledge, etc. BF detects information by measuring cognitive information processing. BF does not detect lies, stress, or emotion. BF computes a determination of ''information present'' or ''information absent'' and a statistical confidence for each individual determination. Laboratory and field tests at the FBI, CIA, US Navy and elsewhere have resulted in 0% errors: no false positives and no false negatives. 100% of determinations made were correct. 3% of results have been ''indeterminate.'' BF has been applied in criminal cases and ruled admissible in court. Scientific standards for BF tests are discussed. Meeting the BF scientific standards is necessary for accuracy and validity. Alternative techniques that failed to meet the BF scientific standards produced low accuracy and susceptibility to countermeasures. BF is highly resistant to countermeasures. No one has beaten a BF test with countermeasures, despite a $100,000 reward for doing so. Principles of applying BF in the laboratory and the field are discussed. In our view, in order to be viable for field use or any application with non-trivial consequences, a technique must have an error rate of less than 1% overall, and less than 5% in each and every individual study. As discussed in the section below on common errors in research, failure to meet the brain fingerprinting scientific standards generally produces error rates ten times higher than this standard.. Brain fingerprinting exceeds this standard. In all laboratory and field research and field applications to date, brain fingerprinting has had an error rate of less than 1%. In each individual study, brain fingerprinting has also had an error rate of less than 1%. In fact, to date brain fingerprinting has never produced an error, neither a false positive nor a false negative, in any research or field applications.. Accuracy is 100% minus the error rate. In reporting results, it is important to report the error rate directly, or to report the accuracy rate in such a way that the true error rate can be computed 2 (see the section entitled ''Is Brain Fingerprinting 100% Accurate?''). The role of brain fingerprinting in forensic science is to bring within the realm of scientific scrutiny the record of a crime, terrorist act, terrorist training, specific crime-or terrorism-related knowledge or expertise, or other relevant information stored in the brain of a suspect or other person of interest, and to develop reliable forensic science evidence based on the accurate detection of such information.. Brain fingerprinting is a scientific technique to detect concealed information stored in the brain by measuring event-related potential (ERP) brainwave responses. Brain fingerprinting laboratory and field tests at the CIA, the FBI, the US Navy, and elsewhere have detected the presence or absence of information regarding the following:. • real-life events including felony crimes;. • real crimes with substantial consequences, including judicial outcomes such as the death penalty or life in prison; • concealed information in real-world cases where subjects were offered a $100,000 reward for beating the test; • knowledge unique to FBI agents;. • knowledge unique to explosives (EOD/IED) experts;. • knowledge unique to US Navy medical military personnel; • pictorially represented knowledge;. • mock crimes and mock espionage scenarios; • other laboratory tests and real-world applications.. Brain fingerprinting has been successfully applied in field settings, including actual criminal cases wherein the brain fingerprinting test was ruled admissible in court and/or contributed to bringing the perpetrator to justice or exonerating the innocent. Brain fingerprinting helped to bring a serial killer to justice and to exonerate an innocent man falsely convicted and imprisoned for murder. A brain fingerprinting test measures the subject's brain responses to specific information. The information is embodied in stimuli consisting of words, phrases, or pictures presented on a computer screen. Some of the stimuli are probe stimuli. Probes contain information that is relevant to the crime or situation under investigation and that the subject has no way of knowing outside of having participated in the crime. When the subject recognizes the relevant information contained in the probes as significant in the context of the crime, the brain emits an ''Aha!'' response. Brain fingerprinting measures and analyses the brainwaves and detects the corresponding P300-MERMER brain response.. Brain fingerprinting computes a determination of ''information present'' (the subject possesses the specific information tested) or ''information absent'' (he does not) for each individual subject. The brain fingerprinting bootstrapping algorithm also computes a statistical confidence for each individual determination.. In data analysis, responses to probes are compared with two standards. Target stimuli provide a standard for the subject's brain response to known crime-relevant information, information which is provided to all subjects. Irrelevant stimuli provide a standard for the subject's response to irrelevant information consisting of plausible but incorrect features of the crime. Data analysis determines whether the response to the probes is more similar to the response to the targets or to the response to the irrelevants, and provides a statistical confidence for this determination.. In our view, in order to be viable for field use, a technique must have less than 1% error rate overall, and less than 5% error rate in every individual study. Brain fingerprinting exceeds this standard. In over 200 cases including all field and laboratory research so far, brain fingerprinting has not produced a single error, neither a false negative nor a false positive. Error rate has been 0%. 100% of determinations have been correct. (Note, however, that this is simply a report of the actual data to date; no science can be generally characterized as ''100% accurate'' without qualification or reference to a specific data set.) In brain fingerprinting using the P300-MERMER, all tests have resulted in a definite determination with a high statistical confidence. There have been no indeterminates. In brain fingerprinting using the P300 alone, in less than 3% of cases, the data analysis algorithm has concluded that insufficient information is available to make a determination in either direction with a high statistical confidence, resulting in an indeterminate outcome (not an error).. Brain fingerprinting specific issue tests detect information regarding a specific event at a particular time and place, such as a crime or terrorist act. Brain fingerprinting specific screening tests detect a specific type of knowledge or expertise, such as knowledge unique to FBI agents, bomb makers, or Al-Qaeda-trained terrorists. Brain fingerprinting is not applicable for general screening, when the investigators have no idea what information is being sought.. Brain fingerprinting is highly resistant to countermeasures. Subjects have been taught the same countermeasures that have proven effective against other, non-brain fingerprinting techniques. Countermeasures had no effect on brain fingerprinting, despite the $100,000 reward offered for beating the test with countermeasures and the motivation to beat the test inherent in real-world applications. All subjects, whether practicing countermeasures or not, have been correctly detected.. The results of original research by the author and independent replications in other laboratories show that accuracy and validity can be reliably attained by following the established brain fingerprinting scientific standards outlined herein. Studies that have failed to meet the brain fingerprinting scientific standards show that such failure can result in low accuracy, susceptibility to countermeasures, and in some cases unreliable results and invalid procedures.. Studies in the original laboratory and independent replications elsewhere that meet the brain fingerprinting scientific standards consistently report extremely accurate results. Studies that fail to meet the brain fingerprinting standards, particularly certain critical standards, have resulted in inconsistent and lower accuracy rates, in some cases no better than chance. In some cases such failure also produced results that are uninterpretable and/or invalid. We have outlined herein the most common errors in brainwave-based concealed information tests, and the errors that have produced the greatest decrements in accuracy, reliability, and validity. This paper reviews all relevant research previously published in English. In view of the published data, some caveats are necessary with respect to reliability, accuracy, practical usefulness in the field, and generalizations regarding results. The published results indicate that following the established brain fingerprinting scientific standards outlined herein is sufficient to ensure accurate, reliable, and valid results in laboratory studies and field applications. However, the available evidence does not support the notion that any attempt that fails to meet the established brain fingerprinting scientific standards could be expected to obtain accurate, reliable, or valid results. Techniques that fail to meet at least the most essential of the brain fingerprinting scientific standards have generally produced error rates at least ten times higher than the error rates that in our view are necessary for viable field use. To be meaningful and practically useful, generalizations about brainwave-based concealed information tests must distinguish between the studies that meet the brain fingerprinting standards and those that fail to meet the standards. Generalizations that fail to recognize this distinction are inadequate to present a meaningful interpretation of the available data, and can result in drawing erroneous conclusions about brain fingerprinting that in fact apply only to non-brain fingerprinting tests that fail to meet the standards. For example, the low accuracy and susceptibility to countermeasures characteristic of several non-brain fingerprinting techniques has sometimes been erroneously generalized to apply to brain fingerprinting, whereas in fact the actual data directly contradict this generalization.. In addition to conducting the science according to established brain fingerprinting standards, it is also vitally important to restrict the interpretation of brain fingerprinting results to what the science actually shows. Brain fingerprinting detects the presence or absence of specific information stored in the brain. It does not detect guilt, lies, emotions, intentions, or any action, including participation in a crime.. Prior to a brain fingerprinting test, a criminal investigator develops his account of the crime, based not on science but on his skill and judgment as an investigator. He determines that, in his non-scientific judgment, information contained in specific probe stimuli is relevant to the crime.. The brain fingerprinting scientist tests scientifically whether or not this specific information is stored in a specific subject's brain. Scientific reports of brain fingerprinting results, and testimony by brain fingerprinting expert witnesses in court, must be confined to an explanation of the science and a report of what the science actually shows. The only legitimate scientific conclusion to be reported is that the brain fingerprinting evidence shows, with a particular statistical confidence, that the subject either does or does not know the information contained in the probe stimuli in the context of the crime. Any interpretation of the results in terms of the subject's guilt or innocence goes beyond the science and is outside the legitimate purview of testimony by a brain fingerprinting scientist. Brain fingerprinting scientists whose testimony on brain fingerprinting has been admitted as scientific evidence in court have adhered closely to this requirement.. As discussed herein, brain fingerprinting science does not evaluate whether the criminal investigator's account of the crime, and the probe stimuli included therein, accurately represent the crime, or whether the suspect committed the crime. Brain fingerprinting, and a brain fingerprinting scientist's testimony, do not address what the suspect should know, could know, or would know about a crime under what circumstances (e.g., if he is innocent or guilty). Brain fingerprinting only detects what the subject actually does know about the crime. It is up to the criminal investigator to come up with an account of the crime and the knowledge relevant thereto (the probe stimuli). It is up to the prosecuting and defense attorneys to debate, and the judge and jury to decide, what all the evidence, including the brain fingerprinting evidence, means with respect to what happened, whether a crime was committed, and if so what was the crime, who committed it, and who is guilty or not.. Brain fingerprinting is similar to other forensic sciences in this regard. DNA testing, for example, concludes only that Sample A (ostensibly from the crime scene) matches sample B (ostensibly from the subject). DNA science and DNA expert witnesses do not determine or opine on whether the subject is guilty of a murder. That is up to the judge and jury to decide based on all the evidence. Brain fingerprinting does not present a conclusion regarding the subject's guilt or innocence of the crime. Like other forensic sciences, it simply provides evidence that is useful for the the judge and jury in their determinations regarding what took place and who is guilty or not.. In weighing the evidence and extrapolating from witness testimony and scientific evidence to the question of whether a suspect committed a crime, judges and juries must use common sense and take into account the well known limitations of human memory. This consideration applies equally to witness testimony, which is a subjective (and not always truthful) account of the contents of memory, and to brain fingerprinting, which provides an objective, scientific account of the contents of human memory. Such considerations are evaluated by the judge and jury on the basis of their common sense and life experience, and are outside the realm of the scientific testimony of a brain fingerprinting scientist.. The results of published laboratory research and field applications indicate that brain fingerprinting testing consistently provides accurate, reliable, and valid scientific evidence in the detection of concealed information, provided that the science is conducted strictly according to the established brain fingerprinting scientific standards and interpreted according to these same standards such that the scientific interpretation stays strictly within the boundaries of what the science actually demonstrates. Brain fingerprinting evidence, when based on science so conducted and interpreted, has proven to be of value in real-world criminal justice and national security applications.. Brain fingerprinting has been successfully applied in real-world cases and ruled admissible as scientific evidence in court. Scientists in the United States currently continue to apply it successfully in the field. The results reviewed herein suggest that brain fingerprinting provides a new scientific method to accurately and reliably detect the presence or absence of concealed information that can generate useful forensic evidence in real-world applications in criminal justice and national security.

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


Bioavailability of iron, vitamin A, zinc, and folic acid when added to condiments and seasonings Seasonings and condiments can be candidate vehicles for micronutrient fortification if consumed consistently and if dietary practices ensure bioavailability of the nutrient. In this review, we identify factors that may affect the bioavailability of iron, vitamin A, zinc, and folic acid when added to seasonings and condiments and evaluate their effects on micronutrient status. We take into consideration the chemical and physical properties of different forms of the micronutrients, the influence of the physical and chemical properties of foods and meals to which fortified seasonings and condiments are typically added, and interactions between micronutrients and the physiological and nutritional status of the target population. Bioavailable fortificants of iron have been developed for use in dry or fluid vehicles. For example, sodium iron ethylenediaminetetraacetic acid (NaFeEDTA) and ferrous sulfate with citric acid are options for iron fortification of fish and soy sauce. Furthermore, NaFeEDTA, microencapsulated ferrous fumarate, and micronized elemental iron are potential fortificants in curry powder and salt. Dry forms of retinyl acetate or palmitate are bioavailable fortificants of vitamin A in dry candidate vehicles, but there are no published studies of these fortificants in fluid vehicles. Studies of zinc and folic acid bioavailability in seasonings and condiments are also lacking. Food fortification strategies involving the addition of micronutrients are designed to reduce deficiencies within defined populations. Identifying which food to fortify (known as the vehicle) is a multidisciplinary task, as there are many requirements of the candidate vehicle that need to be fulfilled. 1 For example, the vehicle is required to be a food that is regularly consumed by the target population. Spices, herbs, seasonings, and condiments are used with the intention of enhancing the aroma and taste of food and, for this purpose, are widely and regularly consumed. Hence, they are candidate vehicles for micronutrient fortification. There is no overall agreement about the exact definition of a seasoning or condiment, and some candidate vehicles may be regarded as both. We may refer to them as powdered and fluid mixtures of dried aromatic plants or animals, such as bouillon powder and cubes, curry powder, and soy and fish sauce. Salt and monosodium glutamate (MSG) are also used for conserving and flavoring foods. Powdered mixes of vitamins and minerals (sprinkles) added immediately before consumption are generally neutral in taste, but may be used for flavoring if sweetened.. Another crucial requirement is that the fortified micronutrient is made available for use by the body after the vehicle is ingested, a process known as bioavailability. The aim of this review is to assess the bioavailability of iron, vitamin A, zinc, and folic acid when added to condiments and seasonings, taking into consideration the chemical and physical properties of both the foods and the fortificants. during storage and preparation of the food, digestion and absorption in the gastrointestinal tract, and when the nutrient is inside the host. After ingestion of the meal containing the fortified vehicle, an important determinant of bioavailability is the food matrix, which constitutes the vehicle itself and co-ingested foods. Nutrients can be encapsulated and confined within other components, including plant and human cells and tissues. Before absorption in the small intestine, adequate digestion in the mouth, stomach, and intestines is needed to release the nutrients from encapsulation. Additionally, the food matrix contains other constituents that may bind to or change the chemical structure of the nutrient and thereby influence absorption. As an example, phytate chelates metal ions, such as iron and zinc, forming insoluble complexes in the gastrointestinal tract. 2 The human gastrointestinal tract lacks phytase activity and therefore the phytatebound minerals will be excreted in the stool. Phytate is primarily found in cereals, legumes, and oil seeds. In most cereals, phytate is concentrated in the bran, resulting in high amounts in whole-grain flour and low amounts in refined products. In legumes, phytate is uniformly distributed throughout the cotyledon, meaning that dehulling actually increases the concentration of phytate. 2 In addition to phytic acid from cereals, polyphenols from chocolate drinks and calcium and casein from dairy also chelate iron and zinc. 3 Bioavailability can be defined as the proportion or fraction of a nutrient in the diet that is ultimately available for utilization or storage in the body after digestion, absorption, and distribution. 4 In order to address the individual components within this broad encompassing definition, we expanded the terminology with complementary terms (Fig. 1) . 5, 6 Bioaccessibility is defined as the fraction of an ingested nutrient that is released from the food matrix and made available for absorption in the gut. 6 This term takes into account the effect of digestion, encapsulation, and binding. Bioavailability is defined as the amount of an ingested nutrient that is absorbed, thereby including the steps of bioaccessibility and absorption. When applicable, bioconversion describes the fraction of a bioavailable nutrient that is converted to the active form, and bioefficacy describes the sum of bioavailability and bioconversion. 7 Finally, functional bioefficacy refers to the fraction of an ingested nutrient that performs a certain metabolic function, 8 thereby including factors relevant to bioavailability and bioefficacy, as well as host factors that influence distribution, excretion, and utilization of the nutrient.

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


MODELING THE SINOATRIAL NODE BY CELLULAR AUTOMATA WITH IRREGULAR TOPOLOGY The role of irregularity in intercellular connections is studied in the first natural human pacemaker called the sinoatrial node by modeling with the Greenberg-Hastings cellular automata. Facts from modern physiology about the sinoatrial node drive modeling. Heterogeneity between cell connections is reproduced by a rewiring procedure applied to a square lattice. The Greenberg-Hastings rule, representing the intrinsic cellular dynamics, is modified to imitate self-excitation of each pacemaker cell. Moreover, interactions with nearest neighbors are changed to heterogeneous ones by enhancing horizontal connections. Stationary states of the modeled system emerge as self-organized robust oscillatory states. Since the sinoatrial node role relies on a single cell cyclic activity, properties of single cells are studied. It appears that the strength and diversity of cellular oscillations depend directly on properties of intrinsic cellular dynamics. But these oscillations also depend on the underlying topology. Moderate nonuniformity of intercellular connections are found vital for proper function of the sinoatrial node, namely, for producing robust oscillatory states that are able to respond effectively to the autonomic system control. The regular pulses which result in rhythmic contractions of the heart begin in the area of the cardiac tissue called sinoatrial node. 1 The activity of the sinoatrial node spreads throughout the atria causing atrial contraction. In parallel, activity is passed to the atrioventricular node -the secondary cardiac pacemaker, and then the specialized conduction pathways -a His bundle and Purkinje fibers, conduct the impulse through the ventricles causing their contraction in unison. In the following we propose a cellular model for the sinoatrial node that produces robust oscillatory pulses.. Physiological properties of the heart are extensively studied. Reference 2 collects the widely accepted latest results. Some of the electrophysiological properties of the sinoatrial node are known due to investigations on various species such as dogs, 3 mice 4 or rabbits. 5, 6 Fortunately, the basic observations found in these species are also valid for humans. 7 The sinoatrial node consists of a relatively small number of cells: 70 000 in the case of humans. The electrophysiology of a single cell is reasonably well understood. But it is not yet clear how the pacemaker cells maintain the final function: successful pace making of the heart. The main difficulty concerns the arrangement of cellshow rather poorly connected cells can drive the heart contraction. There are two basic approaches to the organization of the sinoatrial node cells: the mosaic 4 and gradient 5 models. The first one considers coexistence of two types of cells: nodal and atrial. The second one assumes the gradual change of properties of individuals cells when moving from the central part of the sinoatrial node to its border. Our main objective is to find whether the observed gradual changes can result from heterogeneity of intercellular links and not from the differentiation of individual cells. We propose a simple model that arises from physiological facts related to the topology of cell connections.. Cellular automata have been used to model biological systems of different types.

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


Management Implications in Information Systems Research: The Untold Story In this essay, we take a fresh look at the IS academic community's enduring concern with the management implications of its research. We examine in particular what we call the "variables-centered" research paradigm, which focuses its attention on covariance among independent and dependent variables. As the predominant research tradition in the field, the variables-centered paradigm ought to constitute a major platform from which our community can speak to issues of managerial interest. Unfortunately, the variables-centered paradigm appears to distance researchers from the organizational actors, such as managers, to whom they would give advice and counsel. Particularly disturbing is the systematic erasure of those very actors from the domain of inquiry. Erased, too, are their actions and means of acting. Thus, when it comes time to offer useful prescriptions for action, our community attempts to do so on the basis of research in which, ironically, neither actors nor action directly appear. We offer some recommendations that may help to rectify this problem and, thereby, enrich the capacity of variables-centered research to speak in an informative and useful way to issues of practice. On October 20, 2006, Michigan State University launched a project to replace its major administrative systems, including accounting, finance, human resources, and research administration. 1 This project (the Enterprise Business Systems Project, or EBSP) has the support of the Trustees, President, Provost, and whole executive management team; even the football coach participated in the university-wide "kick-off" meeting. Office space has been leased near campus, contracts have been signed with the vendors, consultants have been hired, and implementation is under way. The first modules are scheduled to golive in summer 2010. Decisions about scope, architecture, security, workflow, training, and change management are being made on an ongoing basis as the implementation proceeds.. These kinds of systems and these kinds of decisions are mainstream topics for information systems research and are of enormous interest for both researchers and practitioners. In spite of these shared interests, there is a perceived gap between scholarly publications and practitioner needs (Applegate 1999; Benbesat and Zmud 1999; Lee, 1999; Baskerville and Myers, 2003) . While it has lately been argued that this gap is unproven (Straub and Ang, 2008) , we share with others (e.g., Rosemann and Vessey, 2008; Klein and Rowe, 2008 ) the view that the gap is real and does not seem to be getting narrower. For example, to aid in their planning, the project leaders at Michigan State did not rely on research findings from academic research. Rather, they visited peer universities that had recently replaced similar systems to learn from their experiences. 2 We argue that the gap between research and practice arises because we study and write about variables, rather than focusing directly on actors, their actions, and the artifacts they use to accomplish those actions. For example, we know that perceived ease of use and usefulness correlate with technology acceptance. But Michigan State University cannot implement "ease of use" or "usefulness" -it has to implement specific artifacts (such as Kuali Financial Systems), for use by specific actors (such as the manager of the MSU Dairy Store), for specific actions (such as purchasing cream, sugar, and other ingredients, as well as keeping track of inventory and sales). In the analysis that follows, we show that actors, actions, and artifacts are systematically erased from the world portrayed in variables-centered research. As a result, when it comes to offering useful prescriptions for action to the actors in question, we attempt to do so on the basis of research in which, ironically, neither actors nor action actually appear.. A crucial point we wish to make is that this is not simply a deficiency in how our research is "written up." While we agree with others (e.g., see Robey and Markus, 1998; Straub and Ang, 2008 ) that improving the writing in our field would help make communications based on research more accessible, the fundamental problem will not be fixed through clearer or more lively writing. 3 Instead, the trouble originates back at the beginning, when our choice of methodology sets us on a path that shapes the very conceptualization of our research subject. In this essay, we have taken another look at the IS academic community's enduring concern with drawing management implications from research. We have explored how the conceptualization of research problems in terms of variables brings about a transformation in the subject matter that makes it difficult, subsequently, to frame compelling advice based on the findings of the research. As a step toward rectifying this problem, we have recommended that researchers hoping to speak to practice move toward more fully multi-modal inquiry, and thereby ground their findings, based on variables, in conceptualizations and observations that are much closer to the world in which actors act. We believe that this approach will almost certainly help make for better science as well. The problem with control-room advice ("just increase X") and micro-process reasoning is that you can't make something from nothing. Where actors, actions, and artifacts have been taken out, it is not so easy to put them back in. Variables-centered texts often acknowledge their limited ability to reach practical conclusions, but then beg off on drawing management implications until more research can be carried out ─ within the variables-centered frame, of course. The promise is that the larger research stream will pay off when enough knowledge is finally accumulated ─when enough independent variables, interaction terms, and contingent factors are added, and enough data points collected ─ so that something useful can at last be said. But this just amounts to a call for the future installation of more gauges and levers in the control room.. In contrast, we think it's fair to ask whether enough can ever be known without considering actors acting. Can the chosen manner of knowing produce the kind of insight that is needed? Our framework for characterizing the articles was based on the core concept of story. Combining characterizations of narrative analysis offered previously by Pentland (1999) and Ramiller (2001) , which were based, in turn, on classic work on narrative by such scholars as Bal, Barthes, Burke, Bruner, Polkinghorne, Ricoeur, and White, we regarded the story in the following way: A story involves actors undertaking actions intended to accomplish certain goals by certain means, within specific settings, leading to particular outcomes.. Of primary interest was the way in which the article told a story about the organizational, managerial, or technical phenomenon that was the focus of the study. For example, if the research concerned adoption of technology by end users, we asked, "What story does the article actually tell us about end users adopting technology?" Depending on the level of analysis, the phenomenon might involve the organization as a whole or some constituent part (e.g., individual members, groups or teams, organizational projects, etc.). In conducting this analysis, we took care to distinguish the story about the phenomenon from the story of the research process, and from other stories that might be present.. The coding for each article involved three main parts: (1) identifying the story elements suggested by the definition above; (2) characterizing the "factualism" of the story; and (3) determining the plot outline of the story that described the phenomenon.. Identifying narrative elements. We focused on the elements that provide the essential content of the narrative: actors, actions, and artifacts.

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


Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images.The sensitivity rises to 78.20% for detection of blue veil in those cases where it is a primary feature for melanoma recognition. Malignant melanoma, the most deadly form of skin cancer, is one of the most rapidly increasing cancers in the world, with an estimated incidence of 59,940 and an estimated total of 8,110 deaths in the United States in 2007 alone [1] .. Dermoscopy is a non-invasive skin imaging technique which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. Practiced by experienced observers, this imaging modality offers higher diagnostic accuracy than observation without magnification [2] [3] [4] [5] . Dermoscopy allows the identification of dozens of morphological features one of which is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film) [6] . This feature is one of the most significant dermoscopic indicator of invasive malignant melanoma, with a sensitivity of 51% and a specificity of 97% [7] . Figure 1 shows a melanoma with blue-white veil. In this article, a machine learning approach to the detection of blue-white veil in dermoscopy images was described.. The method is comprised of several steps including preprocessing, feature extraction, decision tree induction, rule application, and postprocessing. The detected blue-white areas were characterized using a numerical feature, which in conjunction with an ellipticity measure yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images. The presented blue-white veil detector takes a fraction of a second for a 768 x 512 image on an Intel Pentium D 2.66GHz. computer. ; ;

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