An agent model of urban economics: Digging into emergence This paper presents an agent-based 'monocentric' model: assuming only a fixed location for firms, outcomes closely parallel those found in classical urban economic models, but emerge through 'bottom-up' interaction in an agent-based model. Agents make buying and movement decisions based on a set of simple costs they face from their current location. These spatial costs are reduced to two types: the costs of moving people and goods across geographical distances and the costs (and benefits) of 'being here' (the effects of being at a particular location such as land costs, amenities or disamenities). Two approaches to land cost are compared: landlords and a 'density cost' proxy. Emergent equilibrium outcomes are found to depend on the interaction of externalities and time. These findings are produced by looking at how agents react to changing four types of cost, two spatial and two non-spatial: commuting, wage, good cost and good delivery. The models explore equilibrium outcomes, the effect of changing costs and the impact of heterogeneous agents, before focusing in on one example to find the source of emergence in the externalities of agent choice. The paper finishes by emphasising the importance of thinking about emergence as a tool, not an end in itself. . In the 'monocentric' model tradition, a market, firm or central business district (CBD) is set at a central location surrounded by land. This tradition examines the choices economic agents make as they optimise between distance to the centre and the cost of land. As well as being useful for investigating real-world settlements with fixed central areas, it is also effective for thinking about the polarity and magnitude of spatial economic forces 1 .. The concept of spatial equilibrium is at the heart of traditional monocentric models (Fujita et al. 2001 p.17) . While originally a verbal argument (Thunen 1826) , modern variants of the model have taken Launhardt's initial mathematical approach (Launhardt 1885) to develop the modern version of spatial equilibrium (Blaug 1997 pp.600 ). This made its way into urban economics via Alonso (1964) , becoming in time the Alonso-Muth-Mills (AMM) monocentric framework, as outlined in Glaeser 2008 .. As Glaeser puts it (ibid. p.4), spatial equilibrium is "the bedrock on which everything else in the field stands... essentially, there must be no arbitrage across space". No agent can unilaterally make themselves better off by choosing a different location (Lemoy et al. 2010 p.7). The assumption that this arbitrage has already taken place provides a solid mathematical foundation -that all agents' utility has become equal and static across distance; δu/δd = 0 (where u is utility and d is distance) as one moves from d = 0 at the centre point to the edge of the settlement. All other deductions are built on top of this assumption. In the simplest (and most powerful) finding, spatial equilibrium is used to show that "rents must decline with distance to exactly offset the increase in transportation costs" (Glaeser 2008 p.20) .. In contrast, rather than assume equilibrium, Agent Based Modelling (ABM) asks how system-level properties emerge through the interaction of individual economic agents, each with their own behaviour. As Bonabeau argues (2002 p.7280) , ABM is "the canonical approach to modelling emergent phenomena". Emergence is as central to ABM as equilibrium is for analytic economics. So a natural ABM approach to the monocentric model is to ask whether spatial equilibrium is an emergent property. Can interacting agents create a stable, equal-utility settlement pattern?. A small number of theorists have applied ABM to monocentric models showing, in different ways, how agent interaction can create emergent settlement patterns (the work of Lemoy et al. does a particularly thorough job; see below). ABM, however, often suffers from a 'methodcentring' malady (Maslow 1966 p.15) where emergence becomes a goal in itself. This paper digs a little deeper into emergent spatial equilibrium, asking: why does spatial equilibrium emerge from agent interaction? What role is space actually playing in spatial equilibrium?. The answer proposed is this: space provides a medium for agent choice externalities (the effects of each agent's choices on others) to interact over time. These externalities will accrete into a settlement where all agents share the same utility. Time is equally important: the model provides agents with the ability to make independent, objectively valid decisions at their own time point. The spatial environment itself is a store for their decision, simply in the form of their location choice, so that subsequent agents face an altered landscape.. To do this, an agent-based monocentric model is presented with some additions to the traditional framework. These are kept simple. The spatial costs that agents face are broken down into two types. First, distance costs: the cost of moving people and goods across geographical distances (rather than just people as traditional monocentric models tend to focus on). Second, proximity costs: the cost of 'being here' -that is, the effects of being at a particular location such as land rent, amenities or disamenities.. The usual proximity cost in monocentric models is the rent paid to occupy a plot of land.. Land costs are the keystone of urban economic models: they provide the reason for distance to exist at all in a model trying to explain spatial morphology "as an endogenous outcome of the economic process" (Storper 2010, p.315) . A limited stock of land is an essential component of the traditional monocentric model, and its impact on morphological outcomes well-known in urban economics (land is a normal good; better-off people wish to consume more). In this paper, a land market is implemented in a way that avoids many of the complexities of bidding processes commonly faced in ABM market models.. A second proximity cost is also introduced -a 'density cost' derived solely from the proximity of other agents. This approach is a simple way to avoid the 'black hole' outcome where actors do away with space altogether if they can, collapsing into a single point (Fujita 1999 p.58) 2 . A land market is thus considered a specific case of the more general proximity cost.. This point is then used to make the case for the role of externalities working through proximity costs.. The following section looks at ABM and the idea of emergence, as well as agent-based approaches to the monocentric model. The model's structure is then explained in detail, before the results are presented -a series of models illustrating how spatial equilibrium is reached, as well as how agents react to key cost changes, heterogeneous wealth of various forms, and how they react if given heterogeneous preferences.. Both distance and proximity costs are necessary conditions for spatial equilibrium to emerge. Section 4.4 takes a closer look at the decision sets of two-and three-agent scenarios, in order to examine how spatial equilibrium comes about through these two types of spatial cost interacting with externalities.. 2 As Alonso put it, "if the only criteria for residential location are accessibility to the centre and the minimising of the costs of friction, and considerations of the size of the site are excluded, all residence would be clustered around the centre of the city at a very high density." Alonso 1964 p.9 In ABM, the 'agents' are distinct code objects, programmed to interact with their environment and each other. ABM's use ranges from the most abstract artificial life to 'autonomous' agents earning their keep controlling real-world infrastructure. ABM developed in tandem with Object-Oriented Programming (OOP); this approach has been the prime determinant of agent modelling theory and practice (Robinson and Sharp 2009 p.211 ). Wooldridge defines objects as "computational entities that encapsulate some state, are able to perform actions on this state, and communicate by message passing" (Wooldridge 2009 p.28) . Objects are created from classes; a real-world metaphor would be that classes are the blueprint and objects the physical form. Thus, a model may have a single 'Firm' class but many 'Firms' created from that blueprint, replicating a structure but each with their own internal state.. ABM's power as a method is rooted in its ability to investigate emergent outcomes -systemlevel properties that result from the interaction of many agents, but that are qualitatively different from those agents (Bonabeau 2002 p.7280) . A common physical analogy is that atoms do not have temperature and pressure -these are system-level properties of their interaction that cannot be deduced by examining atoms in isolation (Flake 1998 p.134) . Emergence is thus as central to ABM as equilibrium is to classical economic models.. The role of emergence as ABM's default focus has led to some rejecting system-level analyses -adopting honorary proto-ABM theorist (Vriend 2002 p.2; Miller and Page 2007) Friedrich Hayek's earlier insistence that "we must show how a solution is produced by the interactions of people each of whom possesses only partial knowledge" (Hayek 1945 p.530) , dismissing system-level descriptions as 'fallacies of misplaced concreteness' (Hoover 2001 p.108). It is perhaps unsurprising, then, that many see ABM as a rejection of equilibrium economics, where an assumed system-level end-state is essential to the whole approach. In this view, ABM offers a "a pioneering break from a moribund Newtonian worldview" (Manson 2001 p.412) that is "simply wrong: stability is not the norm in complex systems" (Colander and Rothschild 2010 p.286) .. The common rejection of equilibrium approaches seems to go hand in hand with an overly 'method-centred' (Maslow 1966 p.15 ) outlook: identifying emergent outcomes has dominated over using it to dig deeper into an understanding of the system being modelled. Epstein's claim that "if you didn't grow it, you didn't explain it" (Epstein 1996 p.xii) is a highly cited case of making emergence the goal rather than the tool. In this view, equilibrium outcomes, if they exist at all, can only be explained through emergence. Explanation is a heavy burden for emergence to bear -if 'growing' it explains it, there is nothing else to ask. The result, as Di Paolo et al. say, is often that any deeper understanding of why a system acts as it does is "brush[ed] under the carpet of emergence" (Di Paolo et al. 2000 p.8 ).. Users of the equilibrium assumption seem to suffer less from this method-centring; there is a general self-awareness of its limitations. As Glaeser says of spatial equilibrium, "no-one thinks that this assumption is a literal depiction of reality. Still, models based on the concept ... do a generally good job of actually explaining the real world." (Glaeser 2008 p.4) . Most importantly, the equilibrium approach, while acknowledged as limited, is put to use as a tool to answer questions. This is perhaps why equilibrium approaches have continued to dominate in spatial economics, despite ABM's enormous potential. In ABM, the fundamental spatial questions -"who produces what, where and why?" (Ohlin 1933 quoted in Brakman et al., p.81 While ACE mostly avoids space, there are plenty of spatial ABM models -but again, few tackling topics traditional to spatial economics, despite ABM tackling spatial issues since its earliest precursor models predating OOP. Thomas Schelling's segregation model, first presented in 1969 (Schelling 1969 (Schelling , 1978 was originally a pencil and paper exercise (Schelling 2006) . It still finds application in modern agent models (Crooks 2010; Feitosa et al. 2011; Jordan et al. 2014 ) and as an exemplar of how good economic explanation links micro to macro (Krugman 1996, p.15) . In other areas, ABM land use models with a cellular automata lineage (Batty 2000 ) loom large (see e.g. Torrens' survey of spatial ABM; 2010), many with highly complex layers of properties (e.g. Zellner et al. 2008) or increasingly high-resolution movement detail (Benenson et al. 2008; Malleson et al. 2010; Torrens et al. 2012) .. So while space and geography have been part of the ABM story from the start, other fields like geographical economics (Brakman et al. 2009 ) have had traditional spatial economic questions mostly to themselves. None of this is to question the power of emergence. The aim of this section is to suggest it needs to be downgraded from its common role as an end-goal in itself and re-focused as the ABM equivalent of the equilibrium assumption: the method's central tool for addressing research questions that dictates both its strengths and weaknesses.. ABM may then be in a better position to tackle spatial economic questions in novel ways.. There have been applications of ABM to monocentric models in recent years; this section gives a brief overview. In an early example, Sasaki and Box seek to 'verify' the classic von Thunen model with ABM (Sasaki and Box 2003) by showing how spatial equilibria can come about through individual-level action but, aside from referring to von Thunen himself, it sticks to the ABM literature and rejects utility in favour of a 'goal-based' approach (p.2).. More recently, ABM has been used in two quite different ways to examine urban economic ideas. First, Huang et al. (2013) build an extension to a social-environmental coupled land model with the aim of investigating the impact of agent heterogeneity. They impose this heterogeneity directly on, respectively, preferences for proximity to the centre and for land (or for 'open space amenity ', p.191 ) and on agents' budgets, leading to a mix between "more compact developments in the city centre but more sprawling developments in the suburbs" (ibid.. p.199). However, building a preference for proximity or land into the model makes it difficult to inspect the economic impact of those factors.. Lemoy et al. (Lemoy et al. 2010; Lemoy et al. 2012; Lemoy et al. 2013) have created a series of ABM models that successfully recreate the equilibrium outcomes of the traditional AMM approach. As with Fowler's approach to producing an ABM version of Krugman's coreperiphery model (Fowler 2007; Fowler 2011) , their models aim initially to take components from the analytic original and recreate an agent version. They then make an excellent case for the flexibility of ABM in urban economics, for example by analysing polycentric cities. Lemoy et al.' s models are referred to again in the results section below for comparison, where relevant, but it is worth noting some specific differences. This paper's categorisation of spatial costs into two types -distance and proximity costs -is built on adding extra components to the AMM framework (and Lemoy et al.' s extension of it). Rather than just commute costs, a delivery cost for goods is used. Rather than just landlords supplying a rented land market, a density cost is introduced. The addition of a delivery cost for goods (rather than distance only impacting via commute costs, as the AMM model and Lemoy et al. emplement) is used to make the point that distance costs have a more general impact on urban form: the morphological impact of changes in commute and delivery costs is the same.. In common with most ABM market modelling, Lemoy et al. use a form of bidding process for land rent. In contrast, the land market method presented here takes a quite different approach that builds on allowing buyers to have their objective demand met at their point of decision (see section 3.3 for more on this). The introduction of density as a second proximity cost makes it easier to see how agent choices interact through externalities. This then allows for a tight focus on exactly how micro-level choices are responsible for spatial equilibrium outcomes by looking at just two and three agent interaction (section 4.4).. In looking at wealth differences, Lemoy et al. impose higher transport costs for richer people, on the basis that their opportunity cost of time is higher. Rather than impose this, it is assumed here that this valuation should come about through agents' endogenous optimisation of their time. They also impose different preferences on rich and poor, giving each separate parameter values in their utility function, rather than using wealth differences alone. Doing so conflates two separate questions: how do agents react to heterogeneous preferences, and what is the impact of differences in wealth? Section 4.3 looks at these. Each graph is split into three sections at two key cross-over points where People are incentivised to move. Unbroken lines indicate the utility-maximising choice in each case. Again, increasing delivery costs is agglomerating: starting at lower values, People's best option is to spread out, one per region. Region three's resident has no incentive to move to region two: if two people reside there, its utility drops. They should stay put until 'two people in region one' becomes the better option. That has a knock-on effect: the existing resident is made worse off.. At the second cross-over, region two's new resident now finds region one better, increasing their utility but lowering that of the other two in the process.. For base good cost, the direction of agglomeration is different, but the outcome is the same:. the pressure for spatial equilibrium comes from the external effect of one Person's optimal choice. Note, it is also possible to read off who will be incentivised to move. In the case of increasing base good cost, the utility of '2 in 1' -higher for cheaper goods -drops until a Person in region one finds moving right out to the edge the best option.. Economically, the impact of Peoples' choices on proximity costs cause externalities for others: if 'my' location decision is before others, it will change land or density costs for them with no compension being made (Button 2010 p.161 ) and this will alter how they weigh them against distance costs. The externalites will be both positive and negative, decreasing proximity costs in the place a Person moves from while increasing them in their newly chosen spot. The accumulation of these external effects for many agents over time is the source of the model's emergent outcomes.. This paper has presented an agent-based 'monocentric' model of the emergence of urban form.. It has been argued that the minimum conditions for urban spatial equilibrium to emerge are two types of fundamental spatial cost -distance cost and proximity cost. Commuting costs and land markets, the most common form of spatial costs in AMM-style urban models, are special cases of each. This paper has added good delivery and density cost to make its case for the more general categories of distance and proximity.. The presented results have, firstly, confirmed the essential spatial equilibrium outcomes of previous models (both the classical AMM models and agent-based approaches such as the work of Lemoy et al.) . Secondly, they have demonstrated how spatial morphology of the settlement is determined by agents' reaction to costs, as well as differences in wealth and preferences and whether agents are 'transport-rich' (leading to locating on the outskirts) and 'good-cost rich'. (living near the centre). Direct wage differences, in the presence of Landlords, is shown to create 'cheek by jowl' rich and poor spread across the settlement, though this outcome is unstable, reliant on exactly homothetic utility. Heterogeneous preferences, in the form of variable 'love of variety', is shown to determine morphology as those with high elasticity of substitution rent less land, crowding into the centre.. These results have used a distributed market structure to show how spatially heterogeneous supply and demand can produce equilibria. Reactive Landlord agents that respond with Leijonhufvud-style 'laws of motion' to every tick of demand have been coupled with consumer agents whose consumption can input into the system at arbitrary times and places. As section 3.3 noted, spatial economic problems differ fundamentally from 'dimensionless' economicsspace breaks assumptions of centralised market mechanisms, such as auction processes, that are vital for traditional supply-demand approaches. A neoclassical solution to this spatial problem was first presented by Krugman in 1991 (see section 2) but it avoids the spatial market problem.. ABM has the potential to tackle this issue more directly, being especially suited to modelling distributed systems -this paper's market model is a contribution to that aim.. The addition of a density cost illustrates that spatial equilibrium does not need to be a market equilibrium; agents facing any kind of spatial cost, if they can choose their location, will push the system to equilibrium. The density cost has also provided a novel way to dig deeper into the origin of spatial equilibrium in simple two-and three-Person scenarios, identifying how opposite spatial outcomes depend on whether the cost is spatial or non-spatial. By showing the crossover points for where agents' optimal choices change, it is possible to see how proximity costs interact with distance costs through the externalities of each sequential agent choice, changing the environment on subsequent timesteps. Agents' ability to be cognisant of better utility options elsewhere in space (however limited their sampling) pushes the whole system to consistent equilibrium outcomes.. The model structure presented here is ideal for investigating a number of obvious extensions. ABM's flexibility and 'loose coupling' approach is perfect for doing this. Lemoy et al's 'polycentric city' models are a step in this direction, but there are still more permutations to explore once the monocentric assumption is dropped, allowing both goods and wages to be provided from heterogeneous locations. The 'partial' approach of the monocentric model could also be opened up further with ABM to examine fully open wage markets and firms making their own price and location decisions. Some steps in this direction are taken in Olner 2013, where the distributed market mechanism presented here is used to model Krugman-style economies of scale in a more 'general' model 17 .. There is further work to be done also on the dynamics of distributed spatial markets and how they differ from 'dimensionless' markets. As well as examining markets as distributed networks (see e.g. Ladley and Bullock 2008) , there are a range of other spatio-temporal ideas to investigate. For instance, in this paper, while agents' decisions are independent, they are still embedded within a timing structure where all agents decide 'daily'. What are the dynamics of more heterogenous timing decisions over longer periods?. These more complex modelling problems bring their own challenges, but ABM is wellpositioned to deal with them. This paper has argued, however, that ABM is often 'method-5. Conclusions centred' at the expense of 'problem-centring' (Maslow 1966 p.15) ; this has kept it away from many central spatial economic questions. To shift ABM back towards 'problem-centring', emergence needs demoting from its common position as the end-goal of agent models, or even in some cases as the 'explanation' for outcomes. An analogy has been made between equilibrium and emergence as the founding assumption of analytic economics and ABM respectively. The comparison is far from perfect: static equilibrium states are a fixed foundation on which models can be built; emergent results are dynamic, often surprising and -by definition -not predictable from examination of single agents. But this is precisely the source of ABM's strength. If used to move beyond 'what happens if' to 'why does it happen?', emergence will thrive as a tool for digging into a rich seam of spatial economics.

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A new model for context-aware transactions in mobile services Abstract With the ubiquity of handheld devices (such as smart phones and PDAs) and the availability of a wide range of mobile services (such as mobile banking, road traffic updates, and weather forecast), people can nowadays access information and conduct online transactions virtually anywhere and anytime. In such flexible, dynamic but less reliable environment, transaction management technology is believed to provide service reliability and data consistency. Indeed, in mobile and ubiquitous environments where devices as well as services can seamlessly join and leave the ubiquitous network; transaction management can be very helpful during the recovery of services from failure. Current transaction models and commit protocols do not take into account context information. However, in mobile environments, it is imperative to consider context information in the commit of a transaction-i.e., a transaction can be successfully completed if it meets the required context. In this paper, we propose a new model for context-aware transactions and their performance management in mobile environments. Unlike conventional transactions, context-aware transactions adapt to the required context. By context, we mean the service's context as well as the users' context that includes users' needs and preferences. This paper designs and develops the proposed transaction model and evaluates its performance in terms of time and message complexities as well as transaction's throughput. Recent developments in mobile technologies, such as wireless communication networks, handheld devices, and service standards enable people to access information and acquire services in a ubiquitous manner. For instance, Google Mobile provides users with access to a variety of services from their mobile phones, ranging from simple web page's browsing through to products' prices to driving directions. The service-oriented computing and web services technologies further facilitate the provision and consumption of mobile services [1] -i.e., find, select, and develop (or compose) new services from existing ones in order to provide enhanced functionalities, enable universal accessibility, and reduce operational and development costs through service reuse [2] . In the following discussion, we use the term mobile or M-services (as in [3, 4] ) to refer to web services in the mobile environment.. The work presented in this paper investigates into the issue of transaction management in M-services. A transaction represents an abstract view of a sequence of operations that are involved in the execution of an application. Transaction management has been used in a number of applications such as databases, e-commerce, m-commerce, and engineering applications [5, 6] . We believe that transaction management is important for M-services, since protecting and managing the integrity of M-services' outcomes is essential in a ubiquitous environments. Specifically, from the users' perspective, transaction management should guarantee that services obtained using mobile devices are consistent with what users request and what service providers offer. From the M-service providers' perspective, transaction management must ensure that transactions are correctly executed, the enterprise has correct information about the outcomes of those transactions, and information held in an enterprise's databases is maintained to provide a truthful and consistent record of the state of the enterprises.. Various models and protocols have been developed for mobile transactions. However, they are limited to the classical commit procedures that do not give attention to context-awareness. Consider an example where Sofia is using her iPhone while walking in the Broad Street in Oxford, UK. Sofia intends to book a table in a nearby Italian restaurant for a dinner with her friends. Using her iPhone, she browses mobile services in order to find appropriate restaurants, check the location of nearby restaurants, and also check the menus and prices of those restaurants. Current approaches will simply commit a transaction whether the required table is available in a restaurant. They do not take into account the ''context'' information such as ''a table should be booked in a restaurant which is located nearby''. Existing literature contains significant work on context-aware systems [7] [8] [9] [10] . But to our knowledge, context awareness is not addressed in the transaction management in general and mobile transaction management in particular. The philosophy of context awareness is that systems must automatically adapt their operations to the environment by taking into account context information such as current location, time, users' needs, and other environmental parameters. Thus, in M-services, it is desirable that a transaction reacts to the contextual information and adapts its behavior according to the changes in context. Furthermore, current approaches are generally based on the conventional ACID (atomicity, consistency, isolation, and durability) criteria or ETMs (extended transaction models) [11] . ACID criteria require that a transaction must not expose its intermediate results (isolation) and must be atomic (all its actions must be carried out or none is). ETMs relax the ACID criteria especially the isolation property. However, neither the ACID nor the ETMs take into account the context in transaction management.. The contributions of this paper are defined as follows.. • To develop a new model for context-aware transactions in mobile services environments. This paper proposes a new set of transaction correctness criteria, called RACCD (relaxed atomicity, consistency, context, and durability). RACCD criteria include ''context'' as one of the main properties of transactions, which has not been considered in the existing transaction models. The new criteria are believed to be more appropriate to the context aware, more volatile, dynamic, and open nature of transactions in M-services environment.. • To design and develop a protocol for the execution of context-aware transactions.. We design and develop a protocol in order to enforce the RACCD criteria during the execution of contextaware transactions. The protocol is developed in multiphases in order to collect service context, reserve required services, and acquire the required services by successfully completing (committing) a transaction.. • To implement the proposed model and evaluate its performance.. We implement the proposed model as a prototype system, called ''Smart Campus''. Analytical model is also developed in order to evaluate the performance of the proposed model in terms of message and time complexities and transaction's throughput. The remainder of the paper is organized as follows. Section 2 describes the basic definitions of M-services and analyses their characteristics. Section 3 reviews the related work and establishes its limitations with respect to the requirements of M-services transaction management. Section 4 presents the proposed context-aware transaction model. System architecture and the execution protocol are described in Sect. 5. Section 6 describes the evaluation and validation of the proposed model. Finally, the paper is concluded and directions for future work are identified in Sect. 7.

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


Statistical Properties of Phase-Decorrelation in Phase-Resolved Doppler Optical Coherence Tomography Abstract-Decorrelation noise limits the ability of phase-resolved Doppler optical coherence tomography systems to detect smaller vessels exhibiting slower flow velocities, which limits the utility of the technique in many clinical and biological settings. An understanding of the statistical properties of decorrelation noise can aid in the optimal design of these systems and guide the development of noise mitigating strategies. In this work, the statistical properties of decorrelation noise are derived from the underlying statistics of the coherent imaging system and validated through comparison with empirical results and Monte Carlo modeling. Expressions for the noise distribution and the noise variance as a function of relevant imaging system parameters are given, and the implications of these results on both system and algorithm design are discussed. The statistical distribution of decorrelation noise in PR-DOCT imaging was derived from the underlying statistical properties of OCT signals and using optical mode overlaps to quantify correlations. Expressions for the noise distribution and variance as a function of the fractional displacement between beams are given and validated through comparison with both measured and numerically modeled noise distributions. The utility of these noise properties in the design of imaging systems was illustrated in two examples. The improved performance that results can directly enhance the ability of PR-DOCT systems to visualize smaller vessels or to image more rapidly, both of which can lead to enhancements in the clinical or biological utility of the technology. As discussed earlier, knowledge of the specific properties of a dominate noise source can guide the optimal design of an imaging system and influence the selection of signal processing and noise filtering algorithms. In this section, we present two such examples along these lines. First, we use these statistics to guide the selection of the fractional displacement, which generally involves a compromise between the need for 1) rapid imaging enabled by larger fractional displacements and 2) high sensitivity that requires smaller fractional displacements. Using (10), we can quantify the magnitude of decorrelation noise as a function of the fractional displacement, see (12) at the bottom of the next page.This integral can be evaluated straightforwardly using numerical integration. We note that the noise magnitude predicted by (12) conflicts with a previously reported expression [19] but agrees both with measured and simulated noise magnitudes. To illustrate the utility of (12) in the design of an imaging system, we follow the method of [19] which examines the relative magnitude of decorrelation noise to that resulting from the SNR limit. In Fig. 6 , the fractional displacement (lower -axis) is plotted as a function of its resultant noise magnitude. Note that, for large displacements, the noise magnitude asymptotes to radians-the standard deviation of a uniform distribution over the range . Below fractional displacements of approximately 10%, the noise follows an approximately linear dependence on displacement. Also plotted in Fig. 6 is the SNR (upper -axis) required to achieve a given noise magnitude in the absence of decorrelation noise. This relationship [19] , [32] is approximated by . Although this expression is inexact because it neglects the circular nature of the phase difference, simulations indicate the error in its predicted rms noise magnitude is less than 3% for SNRs exceeding 10 dB. If we assume an SNR of light scattered from vessels of interest ranging from 15 to 30 dB, typical for vessels located at a depth of , one can see from Fig. 6 that fractional displacements less than 0.064 and 0.009, respectively, are required to maintain nearly SNR-limited performance. These fractional displacements along with the focused beam size and system A-line rate determine maximum imaging speeds such that decorrelation noise remains at or below SNR-limited performance levels.. As a second example, we use the specific distribution of decorrelation noise described by (10) to quantify the performance of three simple filtering approaches commonly applied in Doppler imaging to reduce noise. An examination of the distribution presented in Fig. 3 highlights the high kurtosis of this noise, suggesting that relatively few samples of large deviation from the mean disproportionately affect the overall noise performance, and that filtering approaches with outlier rejection properties are well matched to decorrelation noise. We analyzed the performance of straightforward mean and median filters (applied directly to the phase difference data), and an amplitude weighted mean filter that weights each phase difference by a power of the amplitude of one of the component signals,. . Using the numerical model, filters were applied to decorrelation noise samples of size , and the residual error of each was recorded for 250 000 independent sets for each set size. Fig. 7 plots the standard deviation of this error for each of the three filters as a function of the sample size. In Fig. 7 , the amplitude weighted filter used the squared magnitude as the weighting parameter. The dependence of the estimator error as a function of the weighting parameter for set size is shown in the inset and indicates that the error is minimized near this value of . As anticipated, the mean filter performs poorly relative to the median filter due to the poor outlier rejection properties of the former. Further, the amplitude-weighted filter outperforms both the mean and median filter. The origin of outlier rejection for the filter in this application results from its ability to de-emphasize those phase differences for which the magnitude of the first measurement, , is small relative to its ensemble average. A small magnitude increases the susceptibility of the measurement to phase perturbation by the (12) Fig. 6 . The fractional displacement and SNR are plotted versus their resulting rms phase noise. Using these relations, imaging systems can be designed to achieve the highest imaging speed (resulting from the highest fractional displacements) while limiting decorrelation noise levels at or below the level resulting from an anticipated SNR level. As an example, SNR levels of 15-30 dB require limiting fractional displacements to less than 0.064 and 0.009, respectively, to maintain near SNR limited Doppler performance. Fig. 7 . The performance of mean, median, and weighted mean filters applied to decorrelation noise is plotted for a series of sample counts (n = 3, 10, 20, 30, and 40). The estimator errors were calculated as the standard deviation of that estimator applied to 25 000 independent sets (of sample count n). These sets were generated using the Monte Carlo model of decorrelation noise. For the weighted mean, weights were given by j0 j . Both the median and weighted mean posses outlier rejection properties that reduce the impact of the large power in the tails of the decorrelation noise distribution function. The error for the median and weighted mean estimators as a fraction of the error of the mean estimator is given as the label for each point in their respective curves. The fractional displacement (x=! ) for this calculation was 0.12 corresponding to an unfiltered rms noise level of 0.31 rad. Inset: the estimator error for the weighted mean filter and sample count n = 20 is shown as a function of the weighting exponent, k. Optimal filtering is achieved near k = 2.. phasor [see Fig. 2(c)] , and, therefore, lower values of are characteristic of the outlier points (data not shown). We note that, although we have used the magnitude of the phasor as the weighting, the complimentary phasor could be used with the same results by symmetry, or the average of the two phasor magnitudes could be used. Further, it is interesting to note that amplitude weighting by the squared magnitude is additionally optimal if the system is SNR limited assuming Gaussian noise statistics, and the weighted mean filter can thus be applied efficiently across both of these noise limiting regimes.. The examples above serve both to describe specifically the implications of the derived statistical properties of decorrelation noise on the design of PR-DOCT systems as well as to illustrate generally the practical utility of these properties in the design of phase-sensitive OCT imaging systems.

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


Characterizing the memory of the GAL regulatory network in Saccharomyces cerevisiae Abstract Genetic regulatory networks respond dynamically to perturbations in the intracellular and extracellular environments of an organism. The GAL system in the yeast Saccharomyces cerevisiae has evolved to utilize galactose as an alternative carbon and energy source, in the absence of glucose in the environment. We present a dynamic model for GAL system in Saccharomyces cerevisiae, which includes a novel mechanism for Gal3p activation upon induction with galactose. The modification enables the model to simulate the experimental observation that in absence of galactose, oversynthesis of Gal3p can also induce the GAL system. We then characterize the memory of the GAL system as the domain of attraction of the steady states. Biological memory can be defined as a sustained cellular response to a transient stimulus (see Burrill and Silver 2010; Ajo-Franklin et al. 2007) . One way that cells exhibit memory is through transcriptional states, which involve populations of molecules regulating gene expression. If the transcriptional response is bistable, a chemical state becomes defined as on or off and, given certain parameters, this state can be inherited through DNA replication and cell division. A cell can thus produce a lasting memory of a biological response (see Alon 1999; Monod and Jacob 1961) . Such transcriptional responses are useful to synthetic biologists as well since much of the information processing in a cell is through transcription so that the transcription process enables a user with an useful set of characterized genetic units such as promoters, activators, and inhibitors that can be recombined to create new transcriptional circuits. The construction of synthetic memory circuits will improve our understanding of natural networks, further aiding the creation of useful, new biological tools. For example, a device capable of remembering a biological experience might be utilized in the long-term study of particular cells within a heterogeneous population following a defined event or applied in industry for the sustained production of desired proteins after induction by a brief stimulus (see Burrill and Silver 2010) . In this paper, we show how the domain of attraction analysis results (see, for example, Materassi and Salapaka 2009) can be used to impart desirable memory to synthetic biological constructs built using the galactose regulatory system (the GAL system) of the yeast Saccharomyces cerevisiae, i.e., the Baker's yeast. The domain of attraction analysis technique itself is quite generic and may be applied to analyze the memory, and robustness thereof, of a variety of biological systems.. Our model of the GAL system is built on the model derived in Ruhela et al. (2004) by hypothesizing new interactions in the galactose sensing mechanism so as to capture the constitutive expression of the GAL system when Gal3p is overexpressed. In addition, so as to be consistent with the observed Gal2p mutant behaviour, it includes non-facilitated diffusion of galactose as a galactose sensing mechanism over and above the Gal2p mediated transport mechanism. Our model includes the growth and substrate consumption dynamics and, in addition, establishes a relationship between the dilution of the various protein complexes and the nutritional status of the medium. This model successfully predicts various mutant and experimental scenarios such as effect of preculturing on the memory of the cells (see Acar et al. 2005 ) and the effect of regulation of permease on the sensitivity of the switch (see Hawkins and Smolke 2006) . We have derived an ODE model of the GAL regulatory network of Saccharomyces cerevisiae. We have shown that although the ODE model of Smidtas et al. (2006) gives an elegant explanation of the transient response of a subset of this network, it does not exhibit bistability, a key property of the GAL regulatory network. By including more chemical reactions in the approach of Smidtas et al. (2006) , we have proposed a 25 state variable quadratic model of the GAL regulatory network. For this model, we have established sufficiency conditions for the domain of attraction of an equilibrium point for the special case of full-state feedback control. This result is useful in characterizing the persistence of cellular memory. Unlike the existing literature on GAL regulatory systems, our approach is not limited to 2 state-variables or 2 parameters; our LMI conditions scale well to address more state-variables and parameters, as is the case in the GAL regulatory system, and can be easily implemented in software.

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


VEGAWES: variational segmentation on whole exome sequencing for copy number detection Background: Copy number variations are important in the detection and progression of significant tumors and diseases. Recently, Whole Exome Sequencing is gaining popularity with copy number variations detection due to low cost and better efficiency. In this work, we developed VEGAWES for accurate and robust detection of copy number variations on WES data. VEGAWES is an extension to a variational based segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has previously outperformed several algorithms on segmenting array comparative genomic hybridization data. Results: We tested this algorithm on synthetic data and 100 Glioblastoma Multiforme primary tumor samples. The results on the real data were analyzed with segmentation obtained from Single-nucleotide polymorphism data as ground truth. We compared our results with two other segmentation algorithms and assessed the performance based on accuracy and time. Conclusions: In terms of both accuracy and time, VEGAWES provided better results on the synthetic data and tumor samples demonstrating its potential in robust detection of aberrant regions in the genome. We have developed a segmentation algorithm VEGAWES, based on Mumford and Shah variational model, to perform copy number segmentation on whole exome sequencing data. In our pipeline, we preprocess paired tumor-normal tumor BAM samples and prepare it for CN segmentation using VEGAWES. In addition to the variational approach derived from VEGA, we have enhanced the original model to include a specific property in WES data related to the average distance between exons of adjacent regions. VEGA follows a pyramidal approach where smaller segments are merged to create larger ones. In VEGAWES, we have taken the distance between exons into consideration while merging adjacent segments in addition to the existing parameters.. To validate our approach, we experimented our pipeline on both synthetic and real data of 100 GBM primary tumor samples and compared the segmentation results in terms of time performance and accuracy with two other algorithms commonly used in WES segmentation -CBS and HSLM. We also compare the results of VEGAWES with the original VEGA model. We observed that on synthetic data, VEGAWES outperforms both CBS and HSLM in all amplification events, whereas with deletion, it segments shorter regions more accurately than both other algorithms. With experiments on real data, we noticed that while VEGAWES provided similar or better segmentation results as compared to CBS, the time difference between the two approaches were significant with CBS performing very slow. Both VEG-AWES and VEGA beat HSLM in terms of accuracy, although in terms of time, all three are comparable. We also reported that introducing the distance between the exons measure into the model reduced errors and detection of false positives in the segmentation results. However, we have included a weight parameter in the model that allows the usage of both the original VEGA model and the enhanced VEGAWES for segmentation purposes. Structural variants are genomic rearrangements larger than 50 bp accounting for around 1 % of the variation among human genomes. Deletions, duplications, triplications, insertions, and translocations can all result in copy number variations (CNVs) [1] . CNVs correspond to the regions of the genome that have been deleted or duplicated on certain chromosomes. Various biological studies have shown a close association between chromosomal regions aberrant in copy number (CN) and diseases like tumor [1, 2] , intellectual disability and autism [3, 4] . Due to its importance in the study of the molecular basis of tumor, several techniques have been recently implemented to detect CNVs. In the past, CNVs were detected on data collected using array comparative genomic Benevento, Italy Full list of author information is available at the end of the article gybridization (aCGH), Single-nucleotide polymorphism (SNP) and fluorescence in situ hybridization (FISH) techniques. However, the low resolution data in these preliminary methods prohibits the detection of short CNVs.. Recently, Next Generation Sequencing (NGS), a low cost and high throughput sequencing technique, has emerged as an effective alternative approach for DNA analysis including CNV detection [5] . NGS involves parallel sequencing of massive amounts of short DNA strands, also known as the reads, from randomly fragmented copies of a genome. Although, NGS gained popularity with the development of Whole Genome Sequencing (WGS) that enabled several large scale sequencing projects, the costs, computational complexity and effectiveness of WGS has consistently posed limitations. Researchers are typically interested in the coding regions of the genome, known as the exons, which comprise of 1 % of the whole human genome and is cost effective with respect to whole genome sequencing.. Detection of CNV from NGS data comprises of three steps: preprocessing of the data to remove biases in the data, segmentation of the data, and classification of amplification/deletion events. Segmentation is used to identify the breakpoints at which these aberrations are likely to occur along the genome. This step is important as it allows identification of critical regions in the genome which contributes towards the detection of various genetic diseases. Hence, there is a need of accurate, efficient, and robust segmentation algorithms.. Several tools have been implemented to detect CNVs detection on WGS data using state-of-the-art segmentation algorithms. ReadDepth [6] is one such commonly used software that primarily contributes towards the preprocessing of the WGS raw data, wherein a new model for data representation is introduced, and deploys the Circular Binary Segmentation (CBS) [7] algorithm for segmentation. SeqCNA [8] also focuses on preprocessing of the data and proposes a novel approach to remove GC bias, and then applies the Gain and Loss Analysis of DNA (GLAD) [9] segmentation algorithm. CNV-TV [10] uses a Total Variation (TV) approach to fit the data. It applies a piecewise constant function with the TV penalized least squares, and detects the plateau/basin in the signal that indicates a amplification/deletion event. The Schwarz information criterion (SIC) is used to find the optimal parameter to control the sensitivity and specificity.. Unlike the case of WGS, there are fewer publicly available tools that perform CNV detection on Whole Exome Sequencing (WES) data. ExomeCNV [11] is one of the earliest tools that processes WES data to detect CNV. It uses CBS algorithm and proposes an optimal computation of parameters to call out CNVs. EXCAVATOR [12] is a recently published tool which introduces three normalization steps based on median approach [13] . It then calls the heterogeneous shifting level model (HSLM), which is a modified version of the shiting level model (SLM) segmentation algorithm [14] to include the distance between exons, and applies the FastCall algorithm [15] for CNV classification. Control-FREEC [16] is another such tool that is flexible with calling CNVs on WGS and WES data with or without matched normal samples. VarScan2 [17] uses a heuristic approach to call CNVs and CBS algorithm for segmentation. In addition to these tools that require a paired sample for CNV detection, there are other set of tools that detect CNVs based on large samples cohorts. Copy Number Inference From Exome Reads (CONIFER) [18] and XHMM [19] are two commonly used tools for this purpose. While both are based on a similar idea, CONIFER exploits the singular value decomposition (SVD) model to detect rare CNVs, and XHMM uses the Hidden Markov model (HMM) model, based on the principal component analysis (PCA) approach, to find the principal sources of variation. However, both tools require sampling of large number of sequences for detection.. In this work, we focus on segmentation of WES data while considering the accuracy as well as the computational performance of the algorithm. There are very limited segmentation algorithms to specifically analyze WES data. In CBS, the data is represented as a sequence of random variables and the change points are the locations of copy number changes. These change points are iteratively localized until the copy numbers of adjacent regions are significantly different. CBS was initially developed to detect CNV on aCGH data and has been extensively used in the past for segmentation of WES data. However, it gives poor computational performance due to the recursive splits. HSLM, introduced in EXCAVATOR, specifically designed for WES data to include the distance between the exons, gives a higher precision rate due to the parameters used to determine the confidence levels in the selection of a CNV.. In recent years, variational-based approaches are being widely considered to solve segmentation. Most solutions are primarily based on the discontinuity-adaptive variational models proposed by Mumford and Shah [20] and Rudin et al. [21] , which have been applied to segmentation of piecewise constant images. Variational models include minimization of an energy functional that attempts to find a solution such that the similarity between the computed segmentation and the observed data is increased while penalizing complex or irregular solutions. The measure of regularity is taken into account by a penalty term in the energy function that typically weighs the variability of the solution. In the Mumford and Shah model, the regularity is measured with the length of the boundary between regions. A regularization parameter controls the balance between the interpolation term and the regularity term.. With their initial accomplishment in addressing several image processing problems [22, 23] , variational based models have been experimented and proved to be successful with segmentation on several kinds of DNA data [10, 24] . Besides CNV-TV, variational estimator for genomic aberrations (VEGA) [24] is one such segmentation algorithm that is based on the Mumford and Shah model and similar to the region growing segmentation algorithms introduced in Koepfler et al. [25] . It performs minimization of the energy function that allows the identification of breakpoints and penalizes the number of segments with a bottom-up approach. In this approach, it merges smaller regions into larger ones in a sequential manner. The regularization parameter is computed based on data-driven heuristics. This algorithm has been applied on CN segmentation of aCGH data and shown to perform robustly and accurately when compared with three other state-of-the-art segmentation algorithms (CBS, Ultrasome [26] , and SMAP: Segmental Maximum A Posteriori approach [27] ).. Here, we propose a variational segmentation algorithm, inspired by VEGA, to detect CNV on WES data: VEGAWES. We created a pipeline to apply variational based model on WES data and modified the segmentation algorithm to include WES properties. To preprocess the WES data, we remove bias using an approach similar to the one taken in EXCAVATOR tool. In addition, we enhance the original VEGA model by introducing the distance between exons as a feature in the computation of the parameters for the energy function minimization. In order to evaluate and validate our algorithm, we compare the performance of VEGAWES in terms of accuracy and robustness (time) with CBS and HSLM, and with the original model of VEGA. The experiments were conducted on synthetic data as well as real data, a 100 Glioblastoma Multiforme (GBM) paired primary tumor WES samples, downloaded from The Cancer Genome Atlas (TCGA) database. Read count (RC) is one of the commonly used approaches for CN detection on WES data [5, 28] . In this process, the WES data are first used to extract the RC for each exon. This is done by calculating the number of reads aligned to an exon. Once the average read coverage has been computed for each exon in the paired sample (tumor and normal), the data are preprocessed for bias removal. The basic idea behind RC approach is that the read count of the genomic region should be proportional to the CN profile of the region. However, the data contain several forms of bias that need to be removed. In this work, we apply a two-step bias removal approach -Mappability and GC Correction. The data are then represented as a log ratio (LR) signal of the tumor sample with respect to its matched normal. This list of LR values corresponding to each exon is the input to the segmentation algorithm, which uses these values to fragment the signal and eventually help in the identification of amplifications and deletions in the genome. In the following subsections, we will describe the details of the algorithm.

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


Computing

the

number

of

cubic

runs

in

standard

Sturmian

words

Abstract.

The

standard

Sturmian

words

are

extensively

studied

in

combinatorics

of

words.

They

are

enough

complicated

to

have

many

interesting

properties

and

at

the

same

time

they

are

highly

compressible.

In

this

paper

we

present

compact

formulas

for

the

number

ρ

of

cubic

runs

in

any

standard

word.

We

show

also

thatand

present

the

sequence

of

strictly

growing

standard

words

achieving

this

limit.

The

exact

asymptotic

ratio

is

here

irrational,

contrary

to

the

situation

of

squares

and

runs

in

the

same

class

of

words.

Furthermore

we

design

an

efficient

algorithm

computing

the

number

of

cubic

runs

in

standard

words

in

linear

time

with

respect

to

the

size

of

the

compressed

representation

(recurrences)

describing

the

word.

The

explicit

size

of

the

word

can

be

exponential

with

respect

to

this

representation.

This

is

yet

another

example

of

a

very

fast

computation

on

highly

compressible

texts.

Repetitions

in

strings

are

important

in

combinatorics

on

words

and

many

practical

applications,

see

for

instance

and

The

structure

of

repetitions

is

almost

completely

understood

for

the

class

of

Fibonacci

words,

see

however

it

is

not

well

understood

for

general

words..

Runs

are

repetitions

in

which

the

period

repeats

at

least

twice.

Highly

repetitive

segments,

in

which

the

repetitions

ratio

is

at

least

called

the

cubic

runs,

were

introduced

and

studied

in

We

say

that

a

number

i

is

a

period

of

the

word

w

if

w[j]

w[i

j]

for

all

i

with

i

j

|w|.

The

minimal

period

of

w

will

be

denoted

by

period(w).

We

say

that

a

word

w

is

periodic

if

period(w)

are

defined

in

the

same

way,

but

we

require

that

the

period

repeat

at

least

three

times

(k

A

run

α

can

be

properly

included

as

an

interval

in

another

run

β,

but

in

this

case

period(α)

period(β).

The

value

of

the

run

α

[i..j]

is

the

factor

val(α)

w[i..j]..

When

it

creates

no

ambiguity

we

identify

sometimes

run

with

its

value

and

the

period

of

the

run

α

[i..j]

with

the

subword

w[i..period(w)]

-called

also

the

generator

of

the

repetition.

The

meaning

will

be

clear

from

the

context.

Observe

that

two

different

runs

could

correspond

to

the

identical

subwords,

if

we

disregard

their

positions.

Hence

runs

are

also

called

the

maximal

positioned

repetitions.

All

together

we

have

runs

and

cubic

runs,

see

Figure

for

comparison..

Denote

by

ρ(w)

and

ρ

(w)

the

number

of

runs

and

cubic

runs

in

the

word

w,

and

by

ρ(n)

and

ρ

(n)

the

maximal

number

of

runs

and

cubic

runs

in

the

words

of

length

n

respectively.

The

most

interesting

and

open

conjecture

about

maximal

repetitions

is:.

In

Kolpakov

and

Kucherov

(see

showed

that

the

number

ρ(w)

of

runs

in

a

string

w

is

O(|w|),

but

the

exact

multiplicative

constant

coefficient

is

still

unknown.

The

best

known

results

related

to

the

value

of

ρ(n)

are

n

ρ(n)

n..

The

upper

bound

is

by

and

the

lower

bound

is

by

The

best

known

results

related

to

ρ

(n)

are

(due

to

For

the

class

S

of

standard

Sturmian

words

there

are

known

exact

formulas

for

the

number

of

runs

and

squares

and

their

asymptotic

behavior,

see

and

for

details..

In

this

case

we

have.

This

paper

is

devoted

to

the

investigation

of

the

structure

of

cubic

runs

in

standard

Sturmian

words.

We

present

the

exact

recurrence

formulas

for

the

number

ρ

(w).

Next

we

derive

the

algorithm

computing

ρ

(w)

for

any

word

w

S

in

linear

time

with

respect

to

the

compressed

representation

of

w,

hence

logarithmic

time

with

respect

to

the

length

of

the

whole

word

w.

We

show

also,

that

for

any

standard

word

w,

we

have.

and

construct

the

sequence

{w

k

of

strictly

growing

standard

words,

for

which

we

have.

Some

useful

applets

related

to

problems

considered

in

this

paper

can

be

found

on

the

web

site:

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


An Approach to Multi-modal Human-Machine Interaction for Intelligent Service Robots The paper describes a multi-modal scheme for human-robot interaction suited for a wide range of intelligent service robot applications. Operating in un-engineered, cluttered, and crowded environments, such robots have to be able to actively contact potential users in their surroundings and to offer their services in an appropriate manner. Starting from a real application scenario, the usage of a robot as mobile information kiosk in a home store, some reliable methods for vision-based interaction, sound analysis and speech output have been developed. These methods are integrated into a prototypical interaction cycle that can be assumed as a general approach to human-machine interaction. Experimental results demonstrate the strengths and weaknesses of the proposed methods. Intelligent service robots, a research field that became more and more popular over the last years, cover a wide range of application scenarios, from robotic assistance for disabled or elderly people up to climbing machines for cleaning large storefronts. Our specific scenario is aimed at the development of an intelligent interactive shopping assistant, working as a mobile information kiosk in a home store (see fig. 1 ). In contrast to the application of personalized robots, where robot and user can adopt to each other, such a robot has to be able to interact with anybody. Furthermore, these people typically know neither the scope of the robot nor its functional capabilities. People have no idea of how the robot works, if it has a name by which it may be called, or if it understands speech at all. In general, for robots working in public places, an intuitive interactive behavior is a necessary prerequisite for the acceptance of such robots by their potential users. When looking at stationary information terminals often placed in shopping centers, these terminals are almost always an eyesore. One major reason for that fact is that these terminals are not interactive in a natural sense. They cannot detect if there is anybody interested in the information provided, but repeat their information repertoire endlessly. To preserve service robots from the same fate, we suppose that a natural, intuitively understandable interaction scheme is urgently needed. Such an interaction scheme should contain components everybody is familiar with, during everyday human-tohuman interaction. Consequently, vision and acoustics should play the major role. During the past decade, a variety of approaches to intelligent human-robot interfaces has been proposed ( [3, 15, 7] ). Most of them argue, as we do, that the combined utilization of speech and vision channel seems the most appropriate way for building such interfaces. As stated above, we are particularly interested in a more general framework, whereas most of the previous approaches are very specific for a certain domain. First of all we want to summarize typical behavioral skills of an interactive service robot. The system has to contact potential users in its surroundings, to verify if the person is interested, to offer its services, and finally to keep continuous contact during the whole interaction process. This collection takes into account the necessities of our application scenario, but the mentioned skills are valid for service robot applications in general. In our proposed interaction scheme, the first step contains the generation of hypotheses concerning people in the surroundings of the robot and is called person detection throughout the paper. Here, a vision-based movement detection and an analysis of acoustic signals are combined into an attentional process, that results in a turning of the robot towards the most salient direction. Then, a person localization procedure rechecks if there really is a person and if the person could be interested in using the robot. For the case that an interested person approaches the robot, the robot welcomes and offers its services. This is realized by means of situation dependent speech output and a graphical user interface running on a touch-screen. As long as the current user remains in the (visible) surroundings of the robot, the robot tries to keep continuous contact to its user via person tracking. The remainder of the paper is structured as follows. After introducing the robot and its technical setup, section 2 describes the developed methods in detail. In section 3, experimental results are given and an exemplary interaction process is demonstrated. Section 4 contains ongoing and complementary work as well as some summarizing conclusions. Fig. 2 shows the robot PERSES, an extended version of a standard mobile robot B21 by RWI (IS Robotics). In addition to the common equipment of two sonar and one IR-layers, PERSES utilizes (i) an omnidirectional color camera with a 360 o panoramic view used for user localization and tracking, self localization and local navigation, (ii) a binocular 6 DoF active-vision head with 2 frontally aligned color cameras used for user localization and tracking, odometry correction and obstacle avoidance, (iii) a binaural auditory system for acoustic user localization and tracking, and (iv) a touch-screen for immediate user-robot interaction.

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