Inferring Intersection Typings that are Equivalent to Call-by-Name and Call-by-Value Evaluations

We present a procedure to infer a typing for an arbitrary λ-term M in an intersection-type system that translates into exactly the call-by-name (resp., call-by-value) evaluation of M. Our framework is the recently developed System E which augments intersection types with expansion variables. The inferred typing for M is obtained by setting up a unification problem involving both type variables and expansion variables, which we solve with a confluent rewrite system. The inference procedure is compositional in the sense that typings for different program components can be inferred in any order, and without knowledge of the definition of other program components. Using expansion variables lets us achieve a compositional inference procedure easily. Termination of the procedure is generally undecidable. The procedure terminates and returns a typing if the input M is normalizing according to call-by-name (resp., call-by-value). The inferred typing is exact in the sense that the exact call-by-name (resp., call-by-value) behaviour of M can be obtained by a (polynomial) transformation of the typing. The inferred typing is also principal in the sense that any other typing that translates the call-by-name (resp., call-by-value) evaluation of M can be obtained from the inferred typing for M using a substitution-based transformation.

Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation

Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.

Effect of solder volume on joint shape with variable chip-to-board contact pad ratio

The objective of this paper is to investigate the effect of the pad size ratio between the chip and board end of a solder joint on the shape of that solder joint in combination with the solder volume available. The shape of the solder joint is correlated to its reliability and thus of importance. For low density chip bond pad applications Flip Chip (FC) manufacturing costs can be kept down by using larger size board pads suitable for solder application. By using “Surface Evolver” software package the solder joint shapes associated with different size/shape solder preforms and chip/board pad ratios are predicted. In this case a so called Flip-Chip Over Hole (FCOH) assembly format has been used. Assembly trials involved the deposition of lead-free 99.3Sn0.7Cu solder on the board side, followed by reflow, an underfill process and back die encapsulation. During the assembly work pad off-sets occurred that have been taken into account for the Surface Evolver solder joint shape prediction and accurately matched the real assembly. Overall, good correlation was found between the simulated solder joint shape and the actual fabricated solder joint shapes. Solder preforms were found to exhibit better control over the solder volume. Reflow simulation of commercially available solder preform volumes suggests that for a fixed stand-off height and chip-board pad ratio, the solder volume value and the surface tension determines the shape of the joint.

Specification of optimum holistic building environmental and energy performance information to support informed decision making

Political drivers such as the Kyoto protocol, the EU Energy Performance of Buildings Directive and the Energy end use and Services Directive have been implemented in response to an identified need for a reduction in human related CO2 emissions. Buildings account for a significant portion of global CO2 emissions, approximately 25-30%, and it is widely acknowledged by industry and research organisations that they operate inefficiently. In parallel, unsatisfactory indoor environmental conditions have proven to negatively impact occupant productivity. Legislative drivers and client education are seen as the key motivating factors for an improvement in the holistic environmental and energy performance of a building. A symbiotic relationship exists between building indoor environmental conditions and building energy consumption. However traditional Building Management Systems and Energy Management Systems treat these separately. Conventional performance analysis compares building energy consumption with a previously recorded value or with the consumption of a similar building and does not recognise the fact that all buildings are unique. Therefore what is required is a new framework which incorporates performance comparison against a theoretical building specific ideal benchmark. Traditionally Energy Managers, who work at the operational level of organisations with respect to building performance, do not have access to ideal performance benchmark information and as a result cannot optimally operate buildings. This thesis systematically defines Holistic Environmental and Energy Management and specifies the Scenario Modelling Technique which in turn uses an ideal performance benchmark. The holistic technique uses quantified expressions of building performance and by doing so enables the profiled Energy Manager to visualise his actions and the downstream consequences of his actions in the context of overall building operation. The Ideal Building Framework facilitates the use of this technique by acting as a Building Life Cycle (BLC) data repository through which ideal building performance benchmarks are systematically structured and stored in parallel with actual performance data. The Ideal Building Framework utilises transformed data in the form of the Ideal Set of Performance Objectives and Metrics which are capable of defining the performance of any building at any stage of the BLC. It is proposed that the union of Scenario Models for an individual building would result in a building specific Combination of Performance Metrics which would in turn be stored in the BLC data repository. The Ideal Data Set underpins the Ideal Set of Performance Objectives and Metrics and is the set of measurements required to monitor the performance of the Ideal Building. A Model View describes the unique building specific data relevant to a particular project stakeholder. The energy management data and information exchange requirements that underlie a Model View implementation are detailed and incorporate traditional and proposed energy management. This thesis also specifies the Model View Methodology which complements the Ideal Building Framework. The developed Model View and Rule Set methodology process utilises stakeholder specific rule sets to define stakeholder pertinent environmental and energy performance data. This generic process further enables each stakeholder to define the resolution of data desired. For example, basic, intermediate or detailed. The Model View methodology is applicable for all project stakeholders, each requiring its own customised rule set. Two rule sets are defined in detail, the Energy Manager rule set and the LEED Accreditor rule set. This particular measurement generation process accompanied by defined View would filter and expedite data access for all stakeholders involved in building performance. Information presentation is critical for effective use of the data provided by the Ideal Building Framework and the Energy Management View definition. The specifications for a customised Information Delivery Tool account for the established profile of Energy Managers and best practice user interface design. Components of the developed tool could also be used by Facility Managers working at the tactical and strategic levels of organisations. Informed decision making is made possible through specified decision assistance processes which incorporate the Scenario Modelling and Benchmarking techniques, the Ideal Building Framework, the Energy Manager Model View, the Information Delivery Tool and the established profile of Energy Managers. The Model View and Rule Set Methodology is effectively demonstrated on an appropriate mixed use existing ‘green’ building, the Environmental Research Institute at University College Cork, using the Energy Management and LEED rule sets. Informed Decision Making is also demonstrated using a prototype scenario for the demonstration building.

Designing tools that enhance communication and understanding between multiple stakeholders: the case of mobile payment value networks

The pervasive use of mobile technologies has provided new opportunities for organisations to achieve competitive advantage by using a value network of partners to create value for multiple users. The delivery of a mobile payment (m-payment) system is an example of a value network as it requires the collaboration of multiple partners from diverse industries, each bringing their own expertise, motivations and expectations. Consequently, managing partnerships has been identified as a core competence required by organisations to form viable partnerships in an m-payment value network and an important factor in determining the sustainability of an m-payment business model. However, there is evidence that organisations lack this competence which has been witnessed in the m-payment domain where it has been attributed as an influencing factor in a number of failed m-payment initiatives since 2000. In response to this organisational deficiency, this research project leverages the use of design thinking and visualisation tools to enhance communication and understanding between managers who are responsible for managing partnerships within the m-payment domain. By adopting a design science research approach, which is a problem solving paradigm, the research builds and evaluates a visualisation tool in the form of a Partnership Management Canvas. In doing so, this study demonstrates that when organisations encourage their managers to adopt design thinking, as a way to balance their analytical thinking and intuitive thinking, communication and understanding between the partners increases. This can lead to a shared understanding and a shared commitment between the partners. In addition, the research identifies a number of key business model design issues that need to be considered by researchers and practitioners when designing an m-payment business model. As an applied research project, the study makes valuable contributions to the knowledge base and to the practice of management.

Quantifying the value of improved wind energy forecasts in a pool-based electricity market

This work illustrates the influence of wind forecast errors on system costs, wind curtailment and generator dispatch in a system with high wind penetration. Realistic wind forecasts of different specified accuracy levels are created using an auto-regressive moving average model and these are then used in the creation of day-ahead unit commitment schedules. The schedules are generated for a model of the 2020 Irish electricity system with 33% wind penetration using both stochastic and deterministic approaches. Improvements in wind forecast accuracy are demonstrated to deliver: (i) clear savings in total system costs for deterministic and, to a lesser extent, stochastic scheduling; (ii) a decrease in the level of wind curtailment, with close agreement between stochastic and deterministic scheduling; and (iii) a decrease in the dispatch of open cycle gas turbine generation, evident with deterministic, and to a lesser extent, with stochastic scheduling.

Design principles for an enterprise systems chartering method

Our research follows a design science approach to develop a method that supports the initialization of ES implementation projects – the chartering phase. This project phase is highly relevant for implementation success, but is understudied in IS research. In this paper, we derive design principles for a chartering method based on a systematic review of ES implementation literature and semi-structured expert interviews. Our analysis identifies differences in the importance of certain success factors depending on the system type. The proposed design principles are built on these factors and are linked to chartering key activities. We specifically consider system-type-specific chartering aspects for process-centric Business Intelligence & Analytics (BI&A) systems, which are an emerging class of systems at the intersection of BI&A and business process management. In summary, this paper proposes design principles for a chartering method – considering specifics of process-centric BI&A.

Coffee Filters and Creativity: The Value of Multiple Filters in the Creative Process

Creativity is often defined as developing something novel or new, that fits its context, and has value. To achieve this, the creative process itself has gained increasing attention as organizational leaders seek competitive advantages through developing new products, services, process, or business models. In this paper, we explore the notion of the creative process as including a series of “filters” or ways to process information as being a critical component of the creative process. We use the metaphor of coffee making and filters because many of our examples come from Vietnam, which is one of the world’s top coffee exporters and which has created a coffee culture rivaling many other countries. We begin with a brief review of the creative process its connection to information processing, propose a tentative framework for integrating the two ideas, and provide examples of how it might work. We close with implications for further practical and theoretical directions for this idea.

Why do nominal characteristics acquire status value? A minimal explanation for status construction.

Why do beliefs that attach different amounts of status to different categories of people become consensually held by the members of a society? We show that two microlevel mechanisms, in combination, imply a system-level tendency toward consensual status beliefs about a nominal characteristic. (1) Status belief diffusion: a person who has no status belief about a characteristic can acquire a status belief about that characteristic from interacting with one or more people who have that status belief. (2) Status belief loss: a person who has a status belief about a characteristic can lose that belief from interacting with one or more people who have the opposite status belief. These mechanisms imply that opposite status beliefs will tend to be lost at equal rates and will tend to be acquired at rates proportional to their prevalence. Therefore, if a status belief ever becomes more prevalent than its opposite, it will increase in prevalence until every person holds it.

Tom Sawyer and the construction of value

This paper challenges the common assumption that economic agents know their tastes. After reviewing previous research showing that valuation of ordinary products and experiences can be manipulated by non-normative cues, we present three studies showing that in some cases people do not have a pre-existing sense of whether an experience is good or bad-even when they have experienced a sample of it. © 2005 Elsevier B.V. All rights reserved.

Attentional Biases in Value-Based Decision-Making

Humans make decisions in highly complex physical, economic and social environments. In order to adaptively choose, the human brain has to learn about- and attend to- sensory cues that provide information about the potential outcome of different courses of action. Here I present three event-related potential (ERP) studies, in which I evaluated the role of the interactions between attention and reward learning in economic decision-making. I focused my analyses on three ERP components (Chap. 1): (1) the N2pc, an early lateralized ERP response reflecting the lateralized focus of visual; (2) the feedback-related negativity (FRN), which reflects the process by which the brain extracts utility from feedback; and (3) the P300 (P3), which reflects the amount of attention devoted to feedback-processing. I found that learned stimulus-reward associations can influence the rapid allocation of attention (N2pc) towards outcome-predicting cues, and that differences in this attention allocation process are associated with individual differences in economic decision performance (Chap. 2). Such individual differences were also linked to differences in neural responses reflecting the amount of attention devoted to processing monetary outcomes (P3) (Chap. 3). Finally, the relative amount of attention devoted to processing rewards for oneself versus others (as reflected by the P3) predicted both charitable giving and self-reported engagement in real-life altruistic behaviors across individuals (Chap. 4). Overall, these findings indicate that attention and reward processing interact and can influence each other in the brain. Moreover, they indicate that individual differences in economic choice behavior are associated both with biases in the manner in which attention is drawn towards sensory cues that inform subsequent choices, and with biases in the way that attention is allocated to learn from the outcomes of recent choices.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Global value chains in a post-Washington Consensus world

Contemporary globalization has been marked by significant shifts in the organization and governance of global industries. In the 1970s and 1980s, one such shift was characterized by the emergence of buyer-driven and producer-driven commodity chains. In the early 2000s, a more differentiated typology of governance structures was introduced, which focused on new types of coordination in global value chains (GVCs). Today the organization of the global economy is entering another phase, with transformations that are reshaping the governance structures of both GVCs and global capitalism at various levels: (1) the end of the Washington Consensus and the rise of contending centers of economic and political power; (2) a combination of geographic consolidation and value chain concentration in the global supply base, which, in some cases, is shifting bargaining power from lead firms in GVCs to large suppliers in developing economies; (3) new patterns of strategic coordination among value chain actors; (4) a shift in the end markets of many GVCs accelerated by the economic crisis of 2008-09, which is redefining regional geographies of investment and trade; and (5) a diffusion of the GVC approach to major international donor agencies, which is prompting a reformulation of established development paradigms. © 2013 © 2013 Taylor & Francis.