Understanding the cost of capital of logistics service providers: an empirical investigation of multiple contingency variables Abstract The article analyzes the influence of company-, industry-and market-related variables on the cost of capital of logistics service providers, as well as on their systematic risk. Financial information has become more and more important in strategic decision making (especially in the international context); in addition of being a measure of performance, the cost of capital is an important variable for logistics service providers in decisions about investing capital and developing the appropriate strategy. In total, financial data on over 700 logistics service providers for a period of 10 years were analyzed applying multiple regression analysis. Our results show that the logistics service industry is rather nonvolatile from an investor's viewpoint. Microeconomic variables significantly influence the cost of capital of logistics service providers, whereas systematic risk is influenced by macroeconomic variables. In both cases, significance is strongly dependent on the services offered and financial structure of the companies, although the headquarters location is irrelevant. Main implication of our study underlines the specific interdependencies of strategic decision making and cost of capital of logistics service providers. As recent research made only little efforts in linking the fields of business logistics and corporate finance, we follow a broad research approach to give a first comprehensive overview on this interdisciplinary topic. While logistics has become more and more critical for the success of manufacturing or retail companies [1] , the market for logistics from a provider's perspective is highly competitive. In order to compete, logistics service providers (LSP) have to understand their customers (shippers), general economic developments, and their main competitors. By being aware of their internal resources and capabilities, LSPs can then make several strategic decisions within the competitive environment in order to achieve business objectives such as profitability, organizational success, and growth [2] .. A strategy and its affiliated investments should aim at achieving returns over the cost of resources or capital, respectively. The success of an LSP's strategic decisions is hence largely dependent on its capability to make a profit that exceeds its cost of capital (CoC) [3] . CoC refers to the cost of a company's fund (both debt and equity). It may be seen as the required rate of return on capital from an investor's point of view (shareholders), in which the expected return on (invested) capital under a certain risk must be greater than the CoC. Apergis et al. [4] observed that ''one of the key decisions a firm has to reach is the fundamental determination of its cost of capital. This has substantial impact on both the composition of the firm's operations and its profitability.'' CoC supports company valuation and strategy formulation [5] and allows for an integrated consideration of yield expectations and risks.. With regard to the challenges of LSPs, the consideration of CoC becomes more and more important [6] as it offers valuable information when pursing appropriate strategies and investments. The CoC of LSPs' competitors is also of major relevance for strategic decisions that are, e.g., concerned with mergers and acquisitions (M&A) or cooperation [7] , as it allows for the assessment of potential takeover targets or network partners, respectively. For example, CoC can be gathered to determine the monetary performance of LSPs (and their competitors) in terms of the economic value added (EVA).. The analysis of the influence of company, industry, and market characteristics, especially on stock price and systematic risk in general, has a long tradition in research. These factors, among others, were analyzed by Daugherty et al. [8] , Elyasiani et al. [9] , Driesprong et al. [10] , Huang et al. [11] , Abugri [12] , Rapach et al. [13] , Cavaglia et al. [14] , Sadorsky [15] , Kavussanos and Marcoulis [16] , Kaneko and Lee [17] , Fama and French [18] , Ross [19] , and King [20] . At present, it is unknown whether the appropriate findings of previous studies are also valid for LSPs or whether specific patterns can be observed. Based on recent findings in financial logistics research, which indicate significant differences in the financial structure of LSP industries [21] [22] [23] [24] [25] [26] and also a higher exposure of LSPs to (financial) risks than other industry companies [27] , we expect significant differences for the analyses of the leverage of company, industry, and market characteristics on the CoC and the systematic risk of LSPs.. Despite the relevance of CoC, recent research has made little effort in the financial analysis of LSPs. Initial approaches were followed by Hofmann and Lampe [21] , who analyzed the financial structure of LSPs, and Liu and Lyons [22] , who investigated the relationship between the financial performance and service capabilities of LSPs. Comparable analyses were made by Töyli et al. [23] , Panayides [24] , Panayides and So [25] , and Ellinger et al. [26] . The analyses revealed that the performance (e.g., in terms of profitability) of LSPs is largely dependent on the industry in which an LSP operates and that appropriate LSPs also show a heterogeneous financial structure that is affected by both macroeconomic developments (e.g., oil price shocks), and microeconomic attributes (e.g., selffinancing power). However, CoC has not yet been specifically researched in the context of logistics. Thus, we consider the absence of appropriate studies to underpin our contribution on elaborating contingency variables concerning the CoC of LSPs.. Based on the practical importance of the CoC for LSPs and the identified research gap, this article aims to answer the following research question:. To what extent are the cost of capital and the systematic risk of LSPs dependent on company, industry, and market characteristics?. As recent research has shown, on the one hand, LSPs' financial structure is very heterogeneous, which justifies the analysis of company characteristics (microeconomic variables in terms of resource-based considerations). On the other hand, the profitability, but also the financial structure of LSPs, is largely dependent on the industry in which they operate, which is in turn embedded in an overall economic context. This highlights the importance of external characteristics (macroeconomic variables in terms of market-based considerations) when examining the CoC of LSPs.. In order to explore the CoC of LSPs, an appropriate operationalization of this ''unit of analysis'' is needed. A common conceptualization of CoC is the weighted average cost of capital (WACC). It represents ''the average cost of each dollar of financing'' [28] . Frequently, it is considered as a performance benchmark [29] and includes both the cost of debt and the cost of equity. Whereas the determination of the cost of debt is rather simple because interest rates in the financial market are easily obtained [30] , the calculation of the cost of equity presents some challenges as ''neither the rate of return nor the risk of a risky asset can normally be observed'' [31, 32] . By referring to the systematic risk (beta, b), which describes the relation of stock price to market index volatility and the relation of the assumed market risk to an investment or financing measure, the cost of equity can be determined in general, and also for LSPs in particular.. As only little effort has been made in recent research, we follow a broad, overview approach. The investigation of a variety of influencing factors will provide a first comprehensive overview of the determinants of LSPs' CoC and systematic risk. For that purpose, financial data on over 700 LSPs for a period of 10 years are analyzed. An initial analysis of the stock price development of quoted LSPs offers implications for the development of hypotheses concerned with the influencing factors of LSPs' CoC and systematic risk. The influence of company, industry, and market characteristics is then investigated by conducting multiple linear regression analyses. From a managerial perspective, our results should explain how external developments, but also internal characteristics of an LSP, influence its CoC and systematic risk. The findings are intended to provide further insights for the strategic decision making of LSPs from a corporate finance perspective. This paper is structured as follows. The following Section 2 reveals the background of the research on the CoC of LSPs and gives an overview on the literature concerned with determinants of stock price, systematic risk, and CoC, from a general and logistical point of view. Based on this review, the hypotheses and the analytical model are derived (Section 3). Section 4 describes the methodology and data collection. Section 5 presents the results of the analysis, which are discussed in Section 6, in addition to the limitations of the research. Section 7 summarizes the results, discusses the managerial implications, and makes recommendations for future research. To date, research has not sufficiently considered LSPs from a corporate finance perspective. Relevant characteristics and determinants of CoC and systematic risk have been ignored:. • First, LSPs do not form a homogeneous group of companies, 1 even when considering both their activity portfolios and their business risks. For example, aircraft carriers are more dependent on the oil price or global economic developments than railway companies.. • Second, LSPs show completely different financial structures [21] . For example, asset intensity (ratio of non-current assets to current assets) and the affiliated fiscal structure vary regarding both the general strategic orientation (e.g., ''asset-light'' vs. ''asset-based'' approaches) and the form of corporate funding (e.g., debt-vs. equity-based funding).. To underscore our assumptions, we primarily analyze the stock price development of quoted LSPs, according to industry (on the Standard Industry Classification [SIC] basis), and company characteristics (e.g., asset turnover). Graphs (a) and (b) in Fig. 1 show obvious differences in the stock price developments of LSPs since the year 2000, according to cluster aggregation (e.g., SIC codes and level of asset turnover). All graphs have the same underlying values and differ only in categorizing the LSPs to different groups (''firm clusters''). The descriptive results of the analysis of the stock prices of LSPs indicate that the financial performance of an LSP depends on the industry sector in which the LSP operates as well as its capital structure. In contrast, the country in which the LSP's headquarters is located seems to be less important. 2 These initial insights reaffirm our efforts to shed more light on the CoC of LSPs and its influencing contingency variables. [33] found that the industry in which a company operates has more explanatory power with regard to stock price returns than does the country where the company is located. They investigated ten industries, but not specifically logistics. Similar investigations were also made by Baca et al. [34] and Cavaglia et al. [14] . In a logistical context, Kavussanos and Marcoulis [16] investigated the stock market perception of different LSP industries (on the SIC basis) based on an analysis of microeconomic variables like the equity ratio. As a key result, they found that the influence of microeconomic variables on stock returns varies among the LSP industries. The influence of an LSP's industry (on the SIC basis) on its financial statement was analyzed by Hofmann and Lampe [21] . They revealed that the industry largely influences an LSP's financial structure, which is also closely linked to its stock price and CoC. The influence of macroeconomic variables (market characteristics) on stock price is considered in various disciplines, especially finance. The influence of macroeconomic variables (e.g., exchange rate, interest rate, money supply, industrial production, unemployment rate, and oil price) was addressed by Elyasiani et al. [9] , who analyzed the influence of oil price shocks on different industry sectors (on the SIC basis), including the transportation industry. In most industries, they observed a significant relationship between oil-future return and industry returns. Comparable analyses were conducted by Driesprong et al. [10] and Sadorsky [15] . However, the results of previous analyses were not homogeneous. The results varied or were even contradictory, depending on the analyzed period and industry. Huang et al. [35] , for example, identified a positive correlation between oil price development and stock returns for companies in the transportation sector, but not for S&P 500 companies in general. b. Determinants of systematic risk: The systematic risk represents ''the percentage performance of the stock which has historically accompanied a one percent move in the market'' [36] . Regarding the determinants of systematic risk, recent research has primarily focused on microeconomic variables (company characteristics). Iqbal and Shah [37] identified a negative correlation between liquidity, leverage, operating efficiency, dividend payout, market value of equity, and systematic risk and a positive correlation between profitability, firm size, growth, and systematic risk of companies from the non-financial sector. Hong and Sarkar [38] focused on the correlation between systematic risk and leverage ratio, earnings volatility, market price of risk, and growth options (positive correlation) as well as earnings growth rate, tax rate, and investments in expansion (negative correlation) in general, without differentiating between industries. Other analyses regarding both micro-and macroeconomic variables were conducted by Arfaoui and Abaoub [39] and Martikainen [40] , also revealing the influence of both set of variables on systematic risk. In a logistical context, Houmes et al. [41] analyzed the influence of the financial structure of trucking companies on their systematic risk and showed among other things, a positive influence of operating leverage on [47] . He showed that company characteristics like capital intensity (which was also considered on an aggregated level on an industry basis) significantly influence the systematic risk and the CoC of companies. In general, the determinants-interpreted as contingency variables-can be distinguished by (1) company, (2) industry, and (3) market characteristics [14, 16, 20] . As our analysis shows, most research has considered the influence of industry, company, and market characteristics on stock price development and systematic risk, primarily on a general level. Despite the close link between systematic risk and CoC, few investigations considering its determinants have been made in recent research. In logistics and transportation-oriented literature in particular, this aspect has been almost completely neglected. [34] stated that country factors have lost importance and explanatory power, while industry factors are becoming more and more important in explaining the differences in financial performance. Referring first to findings in financial logistics research that revealed significant differences in the financial structure of LSP industries [21] [22] [23] [24] [25] [26] , we expect that the industry in which an LSP operates-especially the main mode of transport used-has a significant influence on its CoC. Such industry-specific impacts have been worked out by a variety of authors. For example, Gebhardt et al. [49] showed that ''a firm's implied cost of capital is a function of its industry membership.'' Ghoul et al. [50] found that ''[…] firms with better corporate social responsibility scores exhibit cheaper equity financing,'' whereas companies in the ''sin industries'' (tobacco and nuclear power) revealed reverse developments. Similarly, Rajan and Zingales [51] found that industrial sectors have different needs for external finance. Moreover, Fama and French [52] highlighted the influence of industry characteristics on the cost of equity. Finally, our introductory analysis of the stock prices of LSPs (Fig. 1a) indicates a strong influence of the ''industry'' in which a service provider operates (expressed by the predominant mode of transport used).. Based on this, hypothesis H1a states:. H1a The cost of capital of LSPs is significantly influenced by the predominant mode of transport.. Due to the close relationship between the CoC and the systematic risk of LSPs, we formulate hypothesis H1b as follows:. H1b The systematic risk of LSPs is significantly influenced by the predominant mode of transport.. It is obvious that microeconomic variables determine the total capital costs of a firm. The corporate discount rate (WACC) even depends largely on specific company characteristics (expressed by key financial figures of the firm), highlighted in a wide range of studies. For example, the analyses of Sudarsanam [47] showed a general influence of company characteristics on their CoC. Moreover, microeconomic variables are captured by various works on the ''capital structure choice'' [53] . In addition to the analysis of a firm's capital structure and the influence of microeconomic variables, Balakrishnan and Fox [54] showed a close relationship between CoC and a firm's strategy.. Based on these coherences and our introductory analysis on the influence of microeconomic variables-the asset turnover-on stock prices of LSPs (Fig. 1b) , we propose a second hypothesis H2a as follows:. H2a Microeconomic variables influence the cost of capital of LSPs.. We assume that the influencing microeconomic variables differ among the industries in which LSPs operates (=coherence to H1a and H1b). Moreover, the close relationship between systematic risk and CoC leads to the assumption that company characteristics also influence the systematic risk of LSPs, which was also shown in recent research results [37, 38] .. CoC is calculated as the weighted sum of the cost of debt and cost of equity. Because the cost of debt and equity of LSPs are closely correlated with their financial structures, we suppose that microeconomic variables have a higher influence on CoC than on the systematic risk of LSPs. The minor influence of microeconomic variables on systematic risk was shown by Iqbal and Alisha [37] , Rapack et al. [13] , and Martikainen [40] . Based on this assumption, hypothesis H2b states:. H2b The influence of microeconomic variables on cost of capital is more significant than on systematic risk.. Several studies have shown the influence of market characteristics on the systematic risk of LSPs [39, 40, 42, 45] , but a comparative analysis of all kinds of LSPs has not been conducted. Also investigating the differences between industry sectors, hypothesis H3a states:. H3a Macroeconomic variables influence the systematic risk of LSPs.. Additionally, the close relationship between CoC and systematic risk leads to the assumption that market characteristics also influence the systematic risk of LSPs, which was also shown in recent research results [46] .. Beta is calculated as the covariance of a company's stock price (R i ) and market index (R m ) divided by the variance of a market index [55] . Based on the direct influence of a market index, we suppose a higher influence of macroeconomic variables on the systematic risk than on the CoC of LSPs. Furthermore, Abugri [12] found that variables, such as exchange, interest rates, or money supply, significantly influence market returns, which are closely related to systematic risk. Similar results were presented by Chen et al. [56] . We therefore state hypothesis H3b as:. H3b The influence of macroeconomic variables on systematic risk is more significant than on cost of capital.

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Six strategies for generalizing software engineering theories General theories of software engineering must balance between providing full understanding of a single case and providing partial understanding of many cases. In this paper we argue that for theories to be useful in practice, they should give sufficient understanding of a sufficiently large class of cases, without having to be universal or complete. We provide six strategies for developing such theories of the middle range. In lab-to-lab strategies, theories of laboratory phenomena are developed and generalized to other laboratory phenomena. This is a characteristic strategy for basic science. In labto-field strategies, theories are developed of artifacts that first operate under idealized laboratory conditions, which are then scaled up until they can operate under uncontrolled field conditions. This is the characteristic strategy for the engineering sciences. In case-based strategies, we generalize about components of real-world cases, that are supposed to exhibit less variation than the cases as a whole. In sample-based strategies, we generalize about the aggregate behavior of samples of cases, which can exhibit patterns not visible at the case level. We discuss three examples of sample-based strategies. Throughout the paper, we use examples of theories and generalization strategies from software engineering to illustrate our analysis. The paper concludes with a discussion of related work and implications for empirical software engineering research. Scientific theories consist of a conceptual framework, and usually contain generalizations and/or models of phenomena. They can be used, among others, to describe, analyze, explain, and predict phenomena. Explanations may be causal or architectural. A causal explanation explains a change in a variable by an earlier change in another variable. An architectural explanation explains a phenomenon in terms of the interactions among components that produced it.. Theories are useful for engineers because they can allow them to describe, analyze, explain and predict the behavior of artifacts in a context. Not every theory may be usable in all of these ways at the same time.. Software engineering researchers must balance between the extremes of idealization and practice. By making too many idealizations their theories would lose relevance for practice; by including too many conditions of practice they would lose the ability to generalize [49] . In the next section we look at different strategies to choose a balance between the two extremes.. To summarize, generalization strategies can be classified according to their source and target, and according to whether they are about sample phenomena or about case phenomena. Field-to-lab generalizations are possible too, but we have not discussed them. Basic science usually generalizes from lab to lab, requiring the researcher to eliminate in the laboratory much of the variability of the real world, to approximate the idealizations required by fundamental laws of nature. The engineering sciences usually generalize from lab to field. They sacrifice universality by incorporating conditions of practice in their generalizations.. In each of these strategies, we can study samples or cases. Sample-based research deals with the variability of the real world by studying aggregate phenomena of samples, in which individual random variation cancels out. There are various ways to do that, using randomization, quasi-experimentation, or computer-intensive methods. In case-based research, the variability of the real world is reduced by decomposing a case into components, that can produce case phenomena by their interactions. Generalization should be based on architectural similarity, and generalizations can be made more robust by analytical induction. This paper has three contributions. Our first contribution is to emphasize the utility of middle-range theories that balance generality with practicality (Fig. 2) .. Our second contribution is that we recognize models as theories. This opens the road to accepting architectural models as theories. Architectural models supplement causal explanations of phenomena by showing how a causal influence is produced by an underlying mechanism. Architectural models are also useful to define the relevant similarity relationship when generalizing from a case to a theoretical population, or from a study population to a theoretical population.. Third, we have identified six generalization strategies useful for theories of the middle-range. The characteristic strategy of basic science is lab-to-lab generalization, and the characteristic strategy for engineering sciences is lab-to-field generalization. Each of these strategies can be done in a case-based and a sample-based way. For case-based generalization we have argued that architectural models allow better support for judging similarity between cases than variable-based theories do. Finally, we have reviewed three sample-based strategies, namely randomized difference-making experiments, quasi-experiments, and statistical learning strategies. Our view of theories is similar to that of Sjoberg et al. [46] , who present a theory of UML-based development consisting of (1) a conceptual framework, (2) propositions, (3) explanations, and (4) an indication of scope. Their propositions and explanations seem to be similar to our causal and architectural explanations, respectively. Comparison of our analysis with Gregor's [98] analysis of theories in information systems research (Fig. 10 ) reveals that we agree on the importance of constructs, generalizations and scope. However, Gregor ignores the role of models in theories and does not recognize architectural explanations. We do not regard notations to be part of a theory, and we do not require a theory to give causal explanations. Testable propositions may follow from a theory in particular cases, but we do not regard them as part of a theory. And we do not think that prescriptive theories can exist. Theories can be used to justify design decisions, but do not prescribe them. We can use a scientific theory to predict the effect of a decision, but not to tell us what we must do.. Gregor and Jones [99] outline a structure for design theories, in which testable propositions seem to have taken the place of generalizations (Fig. 11) . The elements in the lower part of the table are not required to be part of a theory in our approach. In the table they are briefly explained.. Seddon and Scheepers [100] provide a roadmap for generalizations in information systems research, that is relevant for software engineering research too. They identify analytical induction (called "analytical generalization" by them, just as Yin [95] does) as one of the important routes to theoretical generalization from samples or from cases, as we do too. They emphasize combination of research findings with prior knowledge, with which we agree.. Seddon and Scheepers provide a more elaborate analysis of different kinds of statistical generalization than we do. They emphasize that statistical inference based on the Central-Limit Theorem, using e.g. p-values or confidence intervals, should satisfy the assumption of that theorem, namely random samples. They provide a useful analysis of strategies to follow some kind of statistical inference for non-random sampling, that complements ours. In particular they discuss the possibility to use Bayesian statistics, which we do not discuss. Seddon and Scheepers do not consider the possibility of architectural explanations and do not emphasize the need for middle-range theories as much as we do.. Lee and Baskerville [101] identify four kinds of generalization, which they call EE, TT, ET and TE. Their type EE is, in our terms, generalizing from case to case. Their type TT is, in our terms, the extension of a conceptual framework with a causal explanation. Their type ET is, in our terms, the causal explanation of a phenomenon, and their type TE is the application of a prior causal theory to a phenomenon.

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


Specifying and proving properties of timed I/O automata using Tempo Abstract Timed I/O automata (TIOA) is a mathematical framework for modeling and verification of distributed systems that involve discrete and continuous dynamics. TIOA can be used for example, to model a real-time software component controlling a physical process. The TIOA model is sufficiently general to subsume other models in use for timed systems. The Tempo Toolset, currently under development, is aimed at supporting system development based on TIOA specifications. The Tempo Toolset is an extension of the IOA toolkit, which provides a specification simulator, a code generator, and both model checking and theorem proving support for analyzing specifications. This paper focuses on the modeling of timed systems and their properties with TIOA and on the use of TAME4TIOA, the TAME 1 (Timed Automata Modeling Environment) based theorem proving support provided in Tempo, for proving system properties, including timing properties. Several examples are provided by way of illustration. For the development of high assurance complex systems, an appropriate development framework supporting system specification, implementation, and analysis is essential. To be generally usable, the support provided by the framework should apply not only to those systems that can be modeled as finite state machines but also to those that cannot, such as many real-time embedded or hybrid systems involving software and/or continuous behavior. Thus an ideal general development framework should provide:. 1. A mathematical model capable of capturing the range of discrete and continuous phenomena that arise in typical systems, 2. A well defined notion in the model of external (visible) behavior, and a definition of implementation of one component by another, or equivalence of two components, in terms of their visible behavior, 3. Compositionality-i.e, the ability to build larger systems by composing smaller components in a manner that respects the notion of implementation, 4. User-friendly tool support for proving the commonly encountered types of properties for the models, such as invariant properties, implementation relations, and stability, and 5. A basis supporting the use of automatic analysis and other software development and analysis tools to the extent possible.. The Tempo Toolset, based on the Timed Input/Output Automaton (TIOA) specification language and tools [10, 16, 27] , provides just such a framework. The TIOA model [17] is especially suited to the specification and analysis of real-time, embedded systems.. The Tempo Toolset provides a spectrum of complementary analysis tools, including support for simulation of specifications and analysis of specification properties using model checking and theorem proving. The focus of this paper is on Tempo's support for theorem proving. With a set of small examples, we illustrate how one can use Tempo to model timed systems and specify their properties in the TIOA language, and then verify the specified properties using the theorem prover PVS [34] through the interface TAME4TIOA, an extension of TAME [3] . This paper is an expanded version of [6] . It makes at least three contributions: First, it demonstrates the use of the Tempo Toolset by showing how it can be used to specify three example systems and their properties of interest and to verify these properties using PVS through TAME4TIOA. Second, it describes how TAME has been extended to TAME4TIOA by adding new strategies for reasoning about the trajectories in a TIOA specification, and illustrates how these new strategies can be used in mechanized proofs of invariant properties. Third, it illustrates by example the improved translation scheme that supports the new strategies.. The paper is organized as follows. Section 2 gives an overview of the Timed I/O Automaton (TIOA) model and the Tempo Toolset that supports its use. Section 3 describes how one can specify and prove properties of TIOA models and how Tempo supports verifying (or proof checking) the properties mechanically in PVS. Section 4 presents our example TIOA specifications of automata and their properties, and shows how the properties can be proved in a "natural", high-level fashion in PVS using the toolkit's TAME4TIOA support. Finally, Sect. 5 discusses some lessons learned from these and other examples, Sect. 6 mentions some related work, and Sect. 7 describes our future plans and presents some conclusions.. Except where the distinction is significant, in the remainder of this paper, we will refer to TAME4TIOA simply as TAME. Tempo is ultimately intended to support all phases of system development from specification, through verification and validation, to implementation. In this paper, we have focused on the usability of Tempo for modeling and mechanical verification of properties of timed systems with both discrete and continuous transitions. We have described the theorem proving support provided, and illustrated how it is used in examples where the properties of interest are invariant properties or simulation properties, and where the models involve nontrivial data types.. Our plan for the future is to use experimentation with more complex examples, such as SATS or the Dynamic Host Configuration Protocol DHCP (using models based on the work described in [14] ), to explore extensions and improvements to our proof support. Previous work has been performed to develop tools to translate specifications written in the IOA language to the language of various theorem provers, for example, Larch [7, 11] , PVS [8] , and Isabelle [33, 38] . Our implementation of the TIOA-to-PVS translator described in [22] builds upon [7] . The target PVS specifications of this translator strongly resemble TAME specifications. In addition, an early version of TAME's deadline_reason strategy was implemented as the PVS strategy deadline_check described in [22] . The TIOA-to-TAME translator is essentially a version of the TIOA-to-PVS translator of [22] with modifications that allow the straightforward implementation of new TAME strategies for TIOA and the most effective use of existing TAME strategies. A more complete description of the recent improvements made to the translation scheme and strategies described in [22] can be found in [21] . In [13] , a slightly different approach using urgency predicates instead of stopping conditions or invariants to limit trajectories is used to describe timed I/O automata. An approach to proving invariant properties of timed I/O automata using urgency predicates is described, but no tool support. A proposed design for supporting urgency predicates in the Tempo toolset is given in [4] .. A hybrid model of the SATS system has been verified by encoding it as a discrete system and then applying a combination of explicit state model checking (implemented as a PVS strategy) and automated theorem proving in PVS [31, 32] . The argument for the correctness of the encoding is given in natural language. By contrast, the "encoding correctness" argument in the TIOA approach is done in terms of a forward simulation that can be verified mechanically. There is much more automation in the proof process described in [31, 32] than in the TIOA approach using TAME; however, this proof process involves first defining and implementing several application-specific techniques and strategies, and the extent to which these techniques and strategies can be reused in other applications remains to be clarified.

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


MILD: Multiple-Instance Learning via Disambiguation Abstract-In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set are associated with bags rather than instances. A bag is labeled positive if at least one of its instances is positive; otherwise, the bag is labeled negative. Since a positive bag may contain some negative instances in addition to one or more positive instances, the true labels for the instances in a positive bag may or may not be the same as the corresponding bag label and, consequently, the instance labels are inherently ambiguous. In this paper, we propose a very efficient and robust MIL method, called Multiple-Instance Learning via Disambiguation (MILD), for general MIL problems. First, we propose a novel disambiguation method to identify the true positive instances in the positive bags. Second, we propose two feature representation schemes, one for instance-level classification and the other for bag-level classification, to convert the MIL problem into a standard single-instance learning (SIL) problem that can be solved by well-known SIL algorithms, such as support vector machine. Third, an inductive semi-supervised learning method is proposed for MIL. We evaluate our methods extensively on several challenging MIL applications to demonstrate their promising efficiency, robustness, and accuracy. The inherent ambiguity of instance labels in MIL makes MIL problems difficult to solve. In this paper, we propose a very efficient and robust method for MIL via disambiguation. Our method is based on an instance label disambiguation method which exploits the distinguishing properties of true positive instances to identify the true positive instances in the positive bags. Based on the disambiguation method, two feature representation schemes, one for instance classification and the other for bag classification, are proposed to represent the bags from two different views. Moreover, the two views can be integrated into a multiview learning framework for semi-supervised MIL. We have conducted extensive experimental evaluation on a number of applications to show that our method delivers very promising performance and compares favorably with stateof-the-art MIL methods.. The underlying principle of our method is to try to bridge the gap between multiple-instance learning and single-instance learning. Moreover, our method can be used to perform either instance-level or bag-level classification. In addition, unlike MISSL [5] which is only transductive in nature, our semi-supervised MIL method is inductive, which, to the best of our knowledge, is the first inductive semi-supervised MIL method proposed thus far.. The most attractive properties of our method are highlighted as follows:. . Unlike most existing MIL methods, our method can answer the essential question giving rise to the gap between MIL and standard single-instance learning. . The classification accuracy of our method is at least comparable with the state-of-the-art MIL methods in diverse applications. . Our method is much more efficient than existing methods. . Our method is very robust. Once we have converted an MIL problem into a singleinstance problem, many standard single-instance methods can be applied for MIL based on our feature representation schemes. Besides the co-training method, many sophisticated semi-supervised learning methods can effectively utilize both labeled and unlabeled data to build better models. Currently, very few MIL methods, except [5] , exploit the unlabeled data during model training. With our feature representation schemes, other semi-supervised learning methods such as graph-based methods based on graph Laplacians [40] can also be extended for MIL. This will be our future pursuit. He is a student member of the IEEE.. Dit-Yan Yeung received the BEng degree in electrical engineering and the MPhil degree in computer science from the University of Hong Kong, and the PhD degree in computer science from the University of Southern California. He started his academic career as an assistant professor at Illinois Institute of Technology. He then joined the Hong Kong University of Science and Technology where he is now a professor of computer science and engineering. His research interests are in computational and statistical approaches to machine learning, pattern recognition, and data mining. He is a member of the IEEE.. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib. Since labels are not available for the training instances, some methods [2] , [6] simply assume that all instances in a bag have the same label as the bag label. However, this assumption can be very unreasonable for positive bags because a positive bag may contain as few as only one true positive instance. If the majority of the negative instances in a positive bag are mislabeled this way, the features learned for distinguishing positive instances from negative instances may end up being very misleading. Other methods [7] , [8] , [9] try to extend some standard single-instance learning (SIL) methods for multi-instance data by adding some constraints. Unfortunately, the resulting methods typically require solving nonconvex optimization problems which suffer from the local minima problem and have high computation cost.. Considering the limitations of some previous methods, we advocate here the necessity of instance label disambiguation as a way to eventually improve the prediction accuracy of the bag labels. In the context of MIL, disambiguation essentially refers to identifying the true positive instances in the positive bags.. However, existing disambiguation methods cannot achieve promising performance. The axis-parallel rectangle (APR) method [1] tries to find an axis-parallel rectangle (or, more generally, hyper-rectangle) in the feature space to represent the area of the true positive instances. This rectangle should include at least one instance from each positive bag but exclude all instances from the negative bags. Although APR works quite well for the drug activity prediction problem, it is highly possible that no APR can be found for some other applications, such as image or text categorization, to satisfy the requirement that at least one instance from each positive bag is included while all instances from the negative bags are excluded. Moreover, APR is very sensitive to labeling noise. Suppose only one negative bag is mislabeled as a positive one. In order to include at least one instance from this mislabeled negative bag, the computed APR may contain so many negative instances that it cannot represent the area of true positive instances at all (cf. Section 3.2.3). Diverse density (DD) value [10] is proposed to measure how many different positive bags have instances near a point in the feature space and how far the negative instances are from that point. The DD method tries to find the point with the highest DD value as the target concept. The DD method is very sensitive to labeling noise too since the DD value at a point will be exponentially reduced if an instance from some negative bag is close to that point (cf. Section 3.2.3). This phenomenon has been validated empirically by Chen et al. [11] . Moreover, since the DD landscape contains local maxima, searching for the point with the highest DD value generally requires multiple restarts and hence incurs high computation cost. Other methods, such as mi-SVM [7] , adopt some heuristic methods to solve the optimization problem, which may lead to local minima and incur high computation cost. Over the past few years, many applications have been formulated as MIL problems. These include drug activity prediction [1] , [13] , [14] , [15] , image classification [4] , [11] , [16] , [17] , [18] , text classification [2] , [7] , stock selection [7] , [10] , protein family modeling [19] , computer aided diagnosis [20] , [21] , and security applications [22] . To solve these problems, many MIL methods have been proposed. The first MIL method is APR [1] , which represents the target concept by an axis-parallel rectangle (or hyper-rectangle) in the feature space. The rectangle includes at least one instance from each positive bag but excludes all instances from the negative bags. Maron and Lozano-Pérez [10] proposed a measure called DD, which essentially measures how many different positive bags have instances near a point in the feature space and how far the negative instances are from that point. A DD-based method tries to find the point with the highest DD value as the target concept. EM-DD [23] , [24] combines expectation-maximization (EM) [25] with the DD formulation by using EM to search for the most likely concept.. Several other methods try to modify standard singleinstance learning methods for MIL by introducing constraints derived from the MIL formulation. Andrews et al. [7] proposed two methods based on SVM, one (mi-SVM) for instance-level classification and the other (MI-SVM) for baglevel classification. Both methods are formulated as mixed integer quadratic programming problems. Gehler and Chapelle [26] applied deterministic annealing to the SVM formulation to find better local mimima compared to the heuristic methods. Gärtner et al. [6] proposed a kernel function directly for bags. With this multi-instance (MI) kernel, in principle any kernel-based classifier can be trained for classifying the bags. Kwok and Cheung [27] extended this work by proposing a marginalized MI kernel to convert the MIL problem from an incomplete data problem to a complete data problem. Bunescu and Mooney [28] proposed an SVMbased method for sparse positive bags by directly enforcing the desired constraint that at least one of the instances in a positive bag is positive. Zhou and Xu [9] proposed to apply transductive SVM for solving MIL problems.. Recently, some methods try to convert MIL into a standard single-instance problem and then solve it with conventional methods by representing the bags as feature vectors. Chen and Wang [4] proposed the DD-SVM method through a feature mapping defined on some instance prototypes learned by DD. Chen et al. [11] proposed another method, called Multiple Instance Learning via Embedded instance Selection (MILES), based on a novel instance-based feature mapping and the 1-norm SVM model for simultaneous feature selection and classification.. However, most of the existing methods cannot answer the essential question giving rise to the gap between multiple-instance learning and single-instance learning: which are true positive instances and what property do the true positive instances have? This motivates the work in this paper.

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


An automated land subdivision tool for urban and regional planning: Concepts, implementation and testing Simulation of the land subdivision process is useful in many applied and research areas. Planners use such tools to understand potential impacts of planning regulations prior to their implementation. While the credibility of both land-use change and urban growth models would be enhanced by integrating capabilities to simulate land subdivision, such research is lacking in the published literature. Of the few subdivision tools that exist, most are either not fully-automated or are unable to generate realistic subdivision layouts. This limits their applicability, particularly for high resolution land-use change models. In this paper, we present a fully-automated land subdivision tool that uses vector data and is capable of generating layouts with both lot and street arrangements for land parcels of any shape. When the new streets are generally parallel to each other and lots are of approximately the same size, the simulation's output very closely resembles observed subdivision patterns in our southeastern Australia study area. From this, we identify opportunities to improve the subdivision tool for a next version. Future research will also explore how this subdivision tool could be used in conjunction with a land-use change model for urban and regional planning. Land subdivision is a standard practice in land surveying that aims to partition a tract of land into smaller sized 'lots' (Easa, 2008) . It is normally conducted using expert knowledge and skills, rigorous field work, both spatial and non-spatial data, and through incorporating a range of zoning and development rules (Chen and Jiang, 2000; Wakchaure, 2001 ).. Simulation of the land subdivision process is useful in many applied and research areas. For example, exploring the effects of various subdivision layouts on a given parcel is an integral component of 'build out' analysis, which is performed by planners who seek to understand potential impacts of planning regulations prior to their implementation (Wakchaure, 2001) .. Similarly, land subdivision plays a vital role in urban growth (Stevens and Dragicevic, 2007, Vanegas et al., 2009; Morgan and O'Sullivan, 2009 ) and landuse change modelling (Alexandridis and Pijanowski, 2007) . Stevens and Dragicevic (2007) in their study of urban growth modelling emphasize the importance of incorporating automatic land subdivision capability in theirs and similar models to maximize their effectiveness. In summary, models and tools capable of simulating the land subdivision process are in demand by planners and researchers alike.. Any simulation model should meet certain criteria to be considered well designed and practical. For example, Jakeman et al. (2006) argue that a model developed to influence decision-making should be tested against criteria such as 'fitness for purpose' and transparency. In this vein, Parker et al. (2002) , suggest that a model's ability to approximate reality is an important evaluation criterion -as a model's behaviour approaches reality, its fitness for purpose increases. For example, Rietveld et al. (2010) and Piuleac et al. (2010) evaluated the performance of their models by comparing predicted values with observed or known values, and claimed that the models have achieved the design goals as the predicted values approximated the observed values well. Further criteria for evaluating a simulation model include the level of automation and the computational complexity (Kohn et al., 2009 ).. Keeping the tool computationally simple is vital. It is easy for a complex tool to exceed current computational power when the tool needs to simulate subdivision of a large area.. Following from these issues, there are several sub-criteria against which the tool should be evaluated in order to assess the extent to which it meets the fitness for purpose criterion.. These are: (1) Can the tool generate both lots and streets? (2) Are all lots given access to the street network? (3) Can parcel boundaries and shapes be realistically portrayed? (4) Can the tool generate cul-de-sacs and multiple direction streets? (5) Is the tool able to make the optimum use of space by intelligently choosing an appropriate subdivision layout for a given parcel? Adding the level of automation and the computation efficiency to this list yields seven criteria against which to assess a given model. Meeting all of these criteria is not necessarily our aim at this stage. The development of a tool is an incremental process -any given version of a particular tool will inevitably involve some compromises. We argue that the tool presented here represents an improvement on previous tools and we assess it and existing tools against the aforementioned criteria in subsequent sections.. Information on attempts to simulate land subdivision is limited. Few studies have modelled land subdivision either as an integrated module in a landuse model, or as a stand alone application. One notable exception is, Alexandridis and Pijanowski (2007) , who used a parcelization algorithm in an agent-based landscape model. Further, Ko et al. (2006) developed a model called 'FLOSS' to simulate ownership fragmentation. Recently, Morgan and O'Sullivan (2009) derived a binary space partitioning algorithm to generate cadastral spatial patterns of cities. However, all of these land subdivision automations are developed for raster-based landscape models, and are performed at relatively coarse resolutions. Street generation, which should accompany a realistic land subdivision simulation, is missing in all three studies. On the whole, subdivision layouts generated by these raster-based tools do not sufficiently resemble the observed subdivision layouts produced by traditional methods. A major reason for this inferiority of raster-based approaches is the inability of regular tessellation of cells of the same size and shape to simulate irregular and heterogeneous realworld phenomena (Moreno et al., 2008) . Hence, raster-based tools can not realistically portray parcel boundaries and shapes. The computational simplicity of the raster versus vector-based approaches explains why land subdivision more commonly uses the former. We are aware of only two studies that do use a vector based approach and incorporate street generation. Wakchaure (2001) used a semi-automated tool to generate realistic subdivision layouts. This tool is capable of generating large-scale subdivision layouts (with both lot and street arrangement). However, its semi-automated nature is itself a limitation, particularly when the tool is to be used as an integral component in a simulation model. Vanegas et al. (2009) developed a fully automated parcel subdivision module which is capable of generating both lot and street arrangements. Their module uses recursive binary division on an initial parcel until lots of desired size are achieved. However, the model is computationally complex, and does not aim to optimize the use of space in a given parcel when lots and streets are generated.. Hence, our primary objective in this study was to develop a fully automated vector-based land subdivision tool to generate complete subdivision layouts, showing both lot and street arrangements for a given parcel of any shape and its existing street arrangements, all while remaining relatively simple computationally. Such a tool should be capable of selecting a subdivision layout that generates the highest number of lots and the lowest number of streets for any given parcel. Subdividing an irregularly-shaped land parcel is more challenging than subdividing a regularly-shaped parcel. Thus, the other objective was to identify areas where further development of the tool is warranted, particularly to improve its ability to generate a variety of realistic subdivision layouts that can handle many situations involved in subdividing irregular-shaped land parcels. The land subdivision tool presented in this paper generates subdivision layouts for a given parcel and maximizes the number of lots while minimizing the number of new streets generated under the constraints of minimum lot width, lot length and street width. The tool also ensures that all lots in the layout have access to at least one street, despite the minimum number of streets it generates. The extended tool is capable of subdividing parcels of any shape. Subdivision layouts produced by the tool resemble the observed layouts very closely, when a parent parcel is subdivided to produce more or less parallel new streets, and when lots are oriented mostly in one direction (type A subdivision). As the observed layouts contain lots and new streets of various sizes oriented in many directions (type B subdivision), the layouts generated by the tool clearly deviate from observed. However, the majority of the observed subdivisions in our study area follow the type A scheme. Moreover, type A subdivision is also common in many urban, suburban and rural areas of Australia, areas of the United States where rectangular land survey systems is adopted (Brown, 2003 ; Donnelly and Evans, 2008) , and virtually in any properly planned city in the world. Thus, the subdivision tool presented here has a broad, global-level applicability even at this initial development stage.. The accuracy of model-generated layouts for irregular parcels can be improved in several ways. For example, sliver polygons (under-sized lots) created during the subdivision of an irregular parcel can be joined to adjacent, proper-sized lots. Alternatively, adjacent sliver polygons can be joined together to ensure a minimum lot size. Further, it would be helpful to allowing the most exterior lots to change their orientation if needed. A limitation of the current version of the tool is the fixed lot size used within a subdivision layout per parceladdressing this would be helpful as well. While the aforementioned improvements to the model could enhance the accuracy of subdivision layouts generated for both type A and type B schemes, allowing the tool to generate streets that do not completely traverse a parcel (i.e.. cul-de-sacs) could augment its ability to generate more close matches for type B subdivision layouts in particular. Though all of the improvements noted above are technically challenging, we plan to incorporate them into future versions of the subdivision automation tool. Finally, though we do not address it here, the frontage of lots generated by the subdivision tool compared to that of the observed lots could be an important assessment criterion, particularly in places like North America. Therefore, potential users of this tool are advised to test how the tool behaves in relation to lot frontage, prior to using it in areas where lot frontage is equally important as the lot size.. Subdivision layouts generated by the tool discussed in this paper are not intended to replace layouts produced by a surveyor based on field measurements. This tool should instead be used for initial exploratory work prior to actual field survey. Aside from direct land subdivision tasks, many other application areas may benefit from the subdivision tool presented. For example, landuse change models could use this tool to simulate subdivision of land parcels, where modelling land subdivision phenomenon is an integral component of the overall model. Environmental planners and policy makers could use this subdivision tool to analyse habitat fragmentation due to land development, gaining an estimate and a means to visualize how habitats are fragmented and lost due to differing subdivision schemes. This would assist governing bodies to set appropriate zoning regulations to minimize the negative effects of land subdivision on biodiversity. As used here, the term 'parcel' describes a land area that is managed as a single, relatively large unit and which is the target of the subdivision process. The term 'lot' refers to a relatively small, single subdivided unit of land resulting from the subdivision of a much larger parcel.. The subdivision tool described in this paper handles parcels that are either rectangular/square or irregular in shape. This subdivision tool is an extension of a tool we previously developed to handle situations where all parent parcels for subdivision are either rectangular or square in shape (Wickramasuriya et al., 2010) . Here, we not only extend the subdivision tool, but also validate the tool's performance against observed data. Before going into the details of extension and validation, we first briefly revisit the concepts and methods used in the original tool. Both rectangular and irregular subdivision automations are implemented as macros in ArcGIS software using ArcObjects and VBA programming. The main user input to the tool is a polygon shapefile which includes both parcel and street polygon features. The attribute Figure 1) , and selects the best layout. It then generates lots and new streets according to the selected layout to match automatically calculated parameters (based on user specified constraints such as minimum lot width, lot length and street width). These parameters are the final number of 'blocks of lots', new streets, 'rows of lots' and connecting streets. If the subdivision is carried out for an irregularly-shaped parcel, new lots and streets are clipped using original parent parcel as a mask. Subsequently, the original parcel is replaced with the newly generated lots and streets.. Then the tool begins to subdivide the next parcel if requested.

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


Dimension-independent likelihood-informed MCMC Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. First, we introduce a general class of operator-weighted proposal distributions that are well defined on function space, such that the performance of the resulting MCMC samplers is independent of the discretization of the function. Second, by exploiting local Hessian information and any associated low-dimensional structure in the change from prior to posterior distributions, we develop an inhomogeneous discretization scheme for the Langevin stochastic differential equation that yields operator-weighted proposals adapted to the non-Gaussian structure of the posterior. The resulting dimension-independent and likelihood-informed (DILI) MCMC samplers may be useful for a large class of high-dimensional problems where the target probability measure has a density with respect to a Gaussian reference measure. Two nonlinear inverse problems are used to demonstrate the efficiency of these DILI samplers: an elliptic PDE coefficient inverse problem and path reconstruction in a conditioned diffusion. Many Bayesian inference problems require sampling a posterior distribution of high-dimensional parameters that represent the discretization of some underlying function. Examples include inverse problems governed by partial differential equations (PDEs) [1, 2, 3] and path reconstructions of stochastic differential equations [4, 5] . Yet sampling the posterior with standard Markov chain Monte Carlo (MCMC) algorithms can become arbitrarily slow as the representation of the unknown function is refined; the convergence rates of such algorithms typically degrade with increasing parameter dimension [6, 7, 8, 9] . Here we present a dimension-independent and likelihood-informed MCMC sampling framework that enables efficient exploration of posterior distributions over functions. Our framework builds on recent work in dimension-independent MCMC sampling by identifying changes in the local curvature of the log-posterior, relative to the log-prior, and using this information to design more effective proposal distributions. We cast these proposals within a general class of operator-weighted proposals that guarantee dimension independence while allowing flexibility in the design of structure-exploiting MCMC algorithms.. Previous work [10, 11] has shown that it is possible to overcome degeneration of the convergence rates of standard MCMC algorithms by formulating them in function space. In particular, [10, 11] consider a posterior that is absolutely continuous with respect to a Gaussian reference measure, typically chosen to be the prior. By deriving proposals that yield valid Markov transition kernels on a function space, these efforts yield algorithms with performance that is dimension independent, i.e., robust under mesh refinement. Recent work has begun to integrate this function space perspective with other state-of-the-art sampling approaches such as sequential Monte Carlo [12, 13] and particle MCMC [14] (which were originally designed for finite-dimensional parameters) along with multilevel Monte Carlo [15] . Examples include [16] , which uses function space proposals inspired by those in [10, 11] as building blocks within a sequential Monte Carlo scheme, and [17, 18] , which employ function space algorithms within a multi-level MCMC scheme for variance reduction. An important takeaway from these efforts is that although proposals that are well-defined on function space provide mixing rates that are independent of discretization, they do not guarantee good mixing in absolute terms; additional analysis and exploitation of the structure of the posterior is essential.. To improve MCMC mixing in finite-dimensional settings, another important line of research has focused on using information about the local geometry of the posterior distribution to scale and guide proposals. Examples include the stochastic Newton method [19] , which uses low-rank approximations of the Hessian of the log-posterior to build Gaussian proposals, and Riemannian manifold MCMC [20] , which uses the Fisher information matrix (and the prior precision matrix) to construct a local metric for both Langevin proposals and Hamiltonian dynamics. Implicit in the lowrank approximations of [19] is the idea that the Hessian of the log-likelihood can be compared with the prior covariance to identify directions in parameter space along which the posterior distribution differs most strongly from the prior. This idea is formalized for linear inverse problems in [21] ; at its heart lies the solution of a generalized eigenproblem involving both operators. In linearGaussian problems, a transformation of the resulting dominant eigendirections reveals the directions of greatest variance reduction, relative to the prior. Solutions of the eigenproblem can also be used to construct optimal approximations of the posterior covariance, in the form of low-rank negative semidefinite updates of the prior covariance [21] . The form of these approximations is crucial, as it suggests the following tie to dimension-independent algorithms: The proposals used in [10, 11] rely on sampling from the Gaussian prior measure on an infinite-dimensional space. It is natural to alter these proposals only in a finite number of dimensions-where the posterior differs most strongly from the prior-to achieve better mixing while preserving dimension independence.. A first attempt in this direction is the operator-weighted proposal of [22] , which employs a relaxed version of the log-likelihood Hessian, evaluated at a single point near the posterior mode, to rescale the preconditioned Crank-Nicolson proposal of [11] . Of course, in inverse problems with nonlinear forward operators or non-Gaussian noise, the log-likelihood Hessian varies over the parameter space. Nonetheless, if local Hessians have low rank and dominant eigenspaces that are somewhat aligned, changes from the prior to the posterior will be confined to a relatively low-dimensional subspace of the parameter space. We will call this subspace the "likelihood-informed" subspace (LIS). [23] introduced the notion of the LIS and constructed it from the dominant eigenspace of the posterior expectation of a preconditioned log-likelihood Hessian. There, the LIS was used to approx-imate the posterior distribution in finite-dimensional nonlinear inverse problems, by replacing the posterior in complementary directions with the prior. In inverse problems with limited observations and smoothing forward operators, the LIS is relatively low dimensional and such approximations can be quite accurate.. We will use the notion of an LIS in the present work, but extend it to the infinite-dimensional setting and seek exact sampling; in this context, the LIS can be seen as means of convergence acceleration that captures key features of the posterior. The goal of this paper is to design MCMC algorithms that are dimension independent, that sample from the exact posterior distribution, and that exploit a posterior structure wherein departures from the prior-including non-Gaussianityare concentrated on a finite number of directions. To realize these goals, we introduce a general class of proposal distributions that simultaneously guarantee dimension-independent MCMC sampling and allow the essential structure of the posterior (i.e., departures from the Gaussian prior) to be captured in a flexible manner, via both local gradient information and a set of global covariancelike (i.e., bounded and self-adjoint) operators. We then discuss particular instances of this class for which the operators are derived from the LIS. The proposals themselves can be obtained from an underlying Langevin SDE by applying an inhomogeneous time discretization scheme on the LIS and its complement. We then incorporate these proposals into MCMC either as Metropolis or Metropolis-within-Gibbs updates.. The rest of this paper is organized as follows. Section 2 reviews some theoretical background on the infinite-dimensional Bayesian formulation of inverse problems and function-space MCMC methods, as well as previous work aimed at improving the efficiency of these samplers. Section 3 introduces a general class of operator-weighted proposals that enables the design of dimensionindependent and likelihood-informed (DILI) samplers. We then discuss the construction of a global LIS and describe several different DILI MCMC samplers based on the LIS. We also recall a few non-DILI algorithms that will subsequently be used for benchmarking. In Section 4, an elliptic PDE inverse problem is used to evaluate the performance of the various samplers introduced in Section 3. In Section 5, we apply the same samplers to the problem of path reconstruction in a conditioned diffusion and highlight key differences in performance. Section 6 offers some brief discussion and concluding remarks. This paper has introduced a general class of operator-weighted MCMC proposals that are well defined on function space. In Bayesian inference problems that require exploring a posterior distribution over functions, these proposals yield MCMC sampling performance that is invariant under refinement of the discretization of these functions, and hence dimension-independent. While this class includes earlier dimension-independent algorithms [10, 11, 22] as particular cases, we use the versatility offered by our new family of proposals to design samplers that exploit posterior structure wherein departures from the prior-including non-Gaussianity-are concentrated on a finite number of directions, captured by a global likelihood-informed subspace (LIS). The global LIS is identified by approximating the posterior expectation of the Hessian of a preconditioned data-misfit functional. A further adaptive strategy based on this decomposition yields computationally affordable approximations of the posterior covariance. All of this geometric information is used to construct dimension-independent and likelihood-informed (DILI) proposal distributions; the four variations studied here (Proposals 3.1-3.4) also make use of local gradient information and Metropolis-withinGibbs updates. Numerical experiments for two nonlinear inverse problems suggest that these DILI samplers offer significant gains in sampling efficiency over current state-of-the-art algorithms.. This work can be extended in many directions. First, one can certainly consider alternative constructions of the LIS. Of particular interest are parallel methods for constructing the global LIS, updates based on incremental SVD algorithms [45] , strategies for batch-updating of the LIS that are compatible with adaptive MCMC, and even directed strategies for optimally exploring the variation of the preconditioned data-misfit Hessian. If only gradients of the data-misfit function are available, one might resort to so-called active subspace methods [46, 47] as an alternative way of building up LIS-like information. If gradients are also not available, then a different approach might be taken, for example adaptively building covariance information from samples and using regularized estimates to build an approximate LIS.. Second, many other operator-weighted proposal constructions are possible. Finally, while this work has focused on the design of global operators (e.g., A, B, and G that do not depend on the local parameter value), there is much room to combine global and local operators. As an example of the latter, [48] uses a semi-implicit discretization of a locally-preconditioned Langevin equation to derive a proposal that, under appropriate assumptions, yields a dimensionindependent MCMC algorithm. The present work may provide a rather general way of constructing local preconditioners that satisfy these assumptions. In particular, the partition of parameter space induced by the LIS provides an opportunity to introduce a variety of efficient proposals on the LIS (e.g., locally-preconditioned manifold MALA, even RMHMC) while preserving dimension independence through a suitable discretization of a Langevin equation on the CS. Combining proposals in this manner extends beyond the notion of local preconditioning. We leave a fuller development of these ideas to future work. * − I is Hilbert-Schmidt.

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


Integrating evidence into clinical information systems for nursing decision support Purpose-To illustrate approaches for providing decision support for evidence-based nursing practice through integration of evidence into clinical information systems (CISs) with examples from our experience at Columbia University Medical Center.Organizing Construct-Examples are organized according to three types of decision support functions: information management, focusing attention, and patient-specific consultation.Methods-Three decision support tools that are integrated into three types of CISs are discussed: 1) infobuttons that provide context-specific access to digital sources of evidence; 2) automated FallInjury Risk Assessment; and 3) personal digital assistant-based screening reminders, screening assessments, and tailored documentation templates for the identification and management of obesity, depression, and tobacco cessation. The informatics infrastructure for implementing these decision support tools is described from the perspective of components identified in the published literature.Conclusions-Efforts to facilitate application of evidence into nursing practice are unlikely to be successful unless the approaches used are integrated into the clinical workflow. Our approaches use a variety of informatics methods to integrate evidence into CISs as a mechanism for providing decision support for evidence-based practice in a manner consistent with nursing workflow.In recent years, there has been increased attention to evidence-based nursing practice. The number of systematic reviews and resources for evidence-based practice has also risen. Despite these factors, application of evidence to practice remains challenging for nurses as well as for other clinicians. The findings of Tanner, Pierce, and Pravikoff's [1] national survey indicate Corresponding Efforts to facilitate application of evidence into nursing practice are unlikely to be successful unless the approaches used are integrated into the clinical workflow. The approaches that we have described use components of an informatics infrastructure to integrate evidence into CISs to provide decision support for evidence-based practice in a manner consistent with the nursing workflow. It is the responsibility of the tool developer to ensure the safety, usability, and usefulness of such tools. In order to gain maximum benefit from such tools, nurses also must possess basic competencies in the areas of evidence-based practice and informatics.. Toward the goal of informing development by others, our CUMC examples illustrated implementation of three types of decision support functions (information management, focusing attention, and patient-specific consultation) to evidence-based nursing practice. We are currently evaluating the impact of the applications used in our examples. Additional research is needed to implement and test integrated decision support functions for evidencebased nursing practice in other CISs and to further develop the necessary information infrastructure. [61] Screen shots from PDA-based decision support system for tobacco cessation. Ask, Assess, Assist, and Arrange are components of the 5 A's of smoking cessation. The fifth A, Advise, in which the smoker is advised of the importance of quitting prior to Assessing willingness to quit is not explicitly documented in the application.

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