899 resultados para INTERSECTION


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Two new notions of reduction for terms of the λ-calculus are introduced and the question of whether a λ-term is beta-strongly normalizing is reduced to the question of whether a λ-term is merely normalizing under one of the new notions of reduction. This leads to a new way to prove beta-strong normalization for typed λ-calculi. Instead of the usual semantic proof style based on Girard's "candidats de réductibilité'', termination can be proved using a decreasing metric over a well-founded ordering in a style more common in the field of term rewriting. This new proof method is applied to the simply-typed λ-calculus and the system of intersection types.

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Inferring types for polymorphic recursive function definitions (abbreviated to polymorphic recursion) is a recurring topic on the mailing lists of popular typed programming languages. This is despite the fact that type inference for polymorphic recursion using for all-types has been proved undecidable. This report presents several programming examples involving polymorphic recursion and determines their typability under various type systems, including the Hindley-Milner system, an intersection-type system, and extensions of these two. The goal of this report is to show that many of these examples are typable using a system of intersection types as an alternative form of polymorphism. By accomplishing this, we hope to lay the foundation for future research into a decidable intersection-type inference algorithm. We do not provide a comprehensive survey of type systems appropriate for polymorphic recursion, with or without type annotations inserted in the source language. Rather, we focus on examples for which types may be inferred without type annotations.

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We define a unification problem ^UP with the property that, given a pure lambda-term M, we can derive an instance Gamma(M) of ^UP from M such that Gamma(M) has a solution if and only if M is beta-strongly normalizable. There is a type discipline for pure lambda-terms that characterizes beta-strong normalization; this is the system of intersection types (without a "top" type that can be assigned to every lambda-term). In this report, we use a lean version LAMBDA of the usual system of intersection types. Hence, ^UP is also an appropriate unification problem to characterize typability of lambda-terms in LAMBDA. It also follows that ^UP is an undecidable problem, which can in turn be related to semi-unification and second-order unification (both known to be undecidable).

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If every lambda-abstraction in a lambda-term M binds at most one variable occurrence, then M is said to be "linear". Many questions about linear lambda-terms are relatively easy to answer, e.g. they all are beta-strongly normalizing and all are simply-typable. We extend the syntax of the standard lambda-calculus L to a non-standard lambda-calculus L^ satisfying a linearity condition generalizing the notion in the standard case. Specifically, in L^ a subterm Q of a term M can be applied to several subterms R1,...,Rk in parallel, which we write as (Q. R1 \wedge ... \wedge Rk). The appropriate notion of beta-reduction beta^ for the calculus L^ is such that, if Q is the lambda-abstraction (\lambda x.P) with m\geq 0 bound occurrences of x, the reduction can be carried out provided k = max(m,1). Every M in L^ is thus beta^-SN. We relate standard beta-reduction and non-standard beta^-reduction in several different ways, and draw several consequences, e.g. a new simple proof for the fact that a standard term M is beta-SN iff M can be assigned a so-called "intersection" type ("top" type disallowed).

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Recent work has shown equivalences between various type systems and flow logics. Ideally, the translations upon which such equivalences are based should be faithful in the sense that information is not lost in round-trip translations from flows to types and back or from types to flows and back. Building on the work of Nielson & Nielson and of Palsberg & Pavlopoulou, we present the first faithful translations between a class of finitary polyvariant flow analyses and a type system supporting polymorphism in the form of intersection and union types. Additionally, our flow/type correspondence solves several open problems posed by Palsberg & Pavlopoulou: (1) it expresses call-string based polyvariance (such as k-CFA) as well as argument based polyvariance; (2) it enjoys a subject reduction property for flows as well as for types; and (3) it supports a flow-oriented perspective rather than a type-oriented one.

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Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP synthesize fuzzy logic and ART networks by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic: intersection (∩) with the fuzzy intersection (∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric: theory in which the fuzzy inter:>ec:tion and the fuzzy union (∨), or component-wise maximum, play complementary roles. Complement coding preserves individual feature amplitudes while normalizing the input vector, and prevents a potential category proliferation problem. Adaptive weights :otart equal to one and can only decrease in time. A geometric interpretation of fuzzy AHT represents each category as a box that increases in size as weights decrease. A matching criterion controls search, determining how close an input and a learned representation must be for a category to accept the input as a new exemplar. A vigilance parameter (p) sets the matching criterion and determines how finely or coarsely an ART system will partition inputs. High vigilance creates fine categories, represented by small boxes. Learning stops when boxes cover the input space. With fast learning, fixed vigilance, and an arbitrary input set, learning stabilizes after just one presentation of each input. A fast-commit slow-recode option allows rapid learning of rare events yet buffers memories against recoding by noisy inputs. Fuzzy ARTMAP unites two fuzzy ART networks to solve supervised learning and prediction problems. A Minimax Learning Rule controls ARTMAP category structure, conjointly minimizing predictive error and maximizing code compression. Low vigilance maximizes compression but may therefore cause very different inputs to make the same prediction. When this coarse grouping strategy causes a predictive error, an internal match tracking control process increases vigilance just enough to correct the error. ARTMAP automatically constructs a minimal number of recognition categories, or "hidden units," to meet accuracy criteria. An ARTMAP voting strategy improves prediction by training the system several times using different orderings of the input set. Voting assigns confidence estimates to competing predictions given small, noisy, or incomplete training sets. ARPA benchmark simulations illustrate fuzzy ARTMAP dynamics. The chapter also compares fuzzy ARTMAP to Salzberg's Nested Generalized Exemplar (NGE) and to Simpson's Fuzzy Min-Max Classifier (FMMC); and concludes with a summary of ART and ARTMAP applications.

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Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART by exploiting the formal similarity between tile computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic intersection (∩) with the fuzzy intersection(∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric theory in which the fuzzy intersection and the fuzzy union (∨), or component-wise maximum, play complementary roles. A geometric interpretation of fuzzy ART represents each category as a box that increases in size as weights decrease. This paper analyzes fuzzy ART models that employ various choice functions for category selection. One such function minimizes total weight change during learning. Benchmark simulations compare peformance of fuzzy ARTMAP systems that use different choice functions.

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The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.

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A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

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How do human observers perceive a coherent pattern of motion from a disparate set of local motion measures? Our research has examined how ambiguous motion signals along straight contours are spatially integrated to obtain a globally coherent perception of motion. Observers viewed displays containing a large number of apertures, with each aperture containing one or more contours whose orientations and velocities could be independently specified. The total pattern of the contour trajectories across the individual apertures was manipulated to produce globally coherent motions, such as rotations, expansions, or translations. For displays containing only straight contours extending to the circumferences of the apertures, observers' reports of global motion direction were biased whenever the sampling of contour orientations was asymmetric relative to the direction of motion. Performance was improved by the presence of identifiable features, such as line ends or crossings, whose trajectories could be tracked over time. The reports of our observers were consistent with a pooling process involving a vector average of measures of the component of velocity normal to contour orientation, rather than with the predictions of the intersection-of-constraints analysis in velocity space.

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“History, Revolution and the British Popular Novel” takes as its focus the significant role which historical fiction played within the French Revolution debate and its aftermath. Examining the complex intersection of the genre with the political and historical dialogue generated by the French Revolution crisis, the thesis contends that contemporary fascination with the historical episode of the Revolution, and the fundamental importance of history to the disputes which raged about questions of tradition and change, and the meaning of the British national past, led to the emergence of increasingly complex forms of fictional historical narrative during the “war of ideas.” Considering the varying ways in which novelists such as Charlotte Smith, William Godwin, Mary Robinson, Helen Craik, Clara Reeve, John Moore, Edward Sayer, Mary Charlton, Ann Thomas, George Walker and Jane West engaged with the historical contexts of the Revolution debate, my discussion juxtaposes the manner in which English Jacobin novelists inserted the radical critique of the Jacobin novel into the wider arena of history with anti-Jacobin deployments of the historical to combat the revolutionary threat and internal moves for socio-political restructuring. I argue that the use of imaginative historical narrative to contribute to the ongoing dialogue surrounding the Revolution, and offer political and historical guidance to readers, represented a significant element within the literature of the Revolution crisis. The thesis also identifies the diverse body of historical fiction which materialised amidst the Revolution controversy as a key context within which to understand the emergence of Scott’s national historical novel in 1814, and the broader field of historical fiction in the era of Waterloo. Tracing the continued engagement with revolutionary and political concerns evident in the early Waverley novels, Frances Burney’s The Wanderer (1814), William Godwin’s Mandeville (1816), and Mary Shelley’s Valperga (1823), my discussion concludes by arguing that Godwin’s and Shelley’s extension of the mode of historical fiction initially envisioned by Godwin in the revolutionary decade, and their shared endeavour to retrieve the possibility enshrined within the republican past, appeared as a significant counter to the model of history and fiction developed by Walter Scott in the post-revolutionary epoch.

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This study found that natural community supports were comprised of two distinct groupings; firstly immediate families, friends and peer support groups; secondly neighbours and local community groups such as sporting and activity- based organisations and groups. The findings of this study indicate that living with acquired brain injury involves a process where the person moves from acute high intensity health services onto rehabilitative services and then onto re-establishing independent lives. It is evident that smooth transitions and interconnectivity of services are essential in facilitating this recovery process. Instrumental to the recovery is the support of immediate family and close friends, who form people’s immediate natural support network and go a long way towards facilitating individuals in rebuilding their lives. A key finding of this study is that broader natural community supports do not appear to play as central a role in supporting individuals to live independent lives when compared to the role of family and friends. The lack of involvement of broader community groups, in many ways, prompted individuals to contact formal support services. For the majority of participants, independence is facilitated through the combination of immediate natural community supports and formal services. The role of formal support services is key to developing broader community support networks. This study found a blurred division between formal services and broader community support networks. The authors recommended that the role of formal supports services in acting as a bridge between the needs of the individual and the development of meaningful community networks, be formally recognised and further developed. Additionally, they argued that the importance of the role of broader natural community, supports such as those provided by community and sporting groups must be enhanced. Greater awareness of the issues faced by people living with acquired brain injury and its often invisible nature is necessary in this endeavour. The authors stated it is important to recognise that there are multiple issues impacting on independent living and these issues intersect, for instance with age, gender, employment, qualifications and so on. A lack of public awareness of acquired brain injury was found to be a key barrier to independent living, along with issues relating to socialising, access to employment and finances. The findings of this study reflect the complexities of living with acquired brain injury and the need for holistic support that is cognisant of the factors which impact on integration. It is vital that flexible, personalised services are developed which are fit for purpose and meet the needs of not only people with acquired brain injury but also their immediate natural community support network. Recognition of the intersection between immediate/ broader natural community supports and formal services is also key to developing the comprehensive and practical supports required to achieve an independent life. This was a qualitative study and all participants were sourced through Headway, a community based service provider for people with ABI. Data collection was divided into two stages: firstly focus groups, followed by individual interviews. Four focus groups were convened in Cork (2), Dublin (1) and Limerick (1). Each focus group was facilitated by at least two members of the research team and a total of twenty-six individuals participated in the focus groups. Thematic analysis of the data was undertaken to guide and inform the second stage of the study; the individual interviews. Ten interviews were undertaken with individuals who presented with ABI in the Cork and Limerick regions.

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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.

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The intersection of the amyloid cascade hypothesis and the implication of metal ions in Alzheimer's disease progression has sparked an interest in using metal-binding compounds as potential therapeutic agents. In the present work, we describe a prochelator SWH that is enzymatically activated by beta-secretase to produce a high affinity copper chelator CP. Because beta-secretase is responsible for the amyloidogenic processing of the amyloid precursor protein, this prochelator strategy imparts disease specificity toward copper chelation not possible with general metal chelators. Furthermore, once activated, CP efficiently sequesters copper from amyloid-beta, prevents and disassembles copper-induced amyloid-beta aggregation, and diminishes copper-promoted reactive oxygen species formation.

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The size, shape, and connectivity of water bodies (lakes, ponds, and wetlands) can have important effects on ecological communities and ecosystem processes, but how these characteristics are influenced by land use and land cover change over broad spatial scales is not known. Intensive alteration of water bodies during urban development, including construction, burial, drainage, and reshaping, may select for certain morphometric characteristics and influence the types of water bodies present in cities. We used a database of over one million water bodies in 100 cities across the conterminous United States to compare the size distributions, connectivity (as intersection with surface flow lines), and shape (as measured by shoreline development factor) of water bodies in different land cover classes. Water bodies in all urban land covers were dominated by lakes and ponds, while reservoirs and wetlands comprised only a small fraction of the sample. In urban land covers, as compared to surrounding undeveloped land, water body size distributions converged on moderate sizes, shapes toward less tortuous shorelines, and the number and area of water bodies that intersected surface flow lines (i.e., streams and rivers) decreased. Potential mechanisms responsible for changing the characteristics of urban water bodies include: preferential removal, physical reshaping or addition of water bodies, and selection of locations for development. The relative contributions of each mechanism likely change as cities grow. The larger size and reduced surface connectivity of urban water bodies may affect the role of internal dynamics and sensitivity to catchment processes. More broadly, these results illustrate the complex nature of urban watersheds and highlight the need to develop a conceptual framework for urban water bodies.