Monads are by now well-established as programming construct in functional languages. Recently, the notion of “Arrow ” was introduced by Hughes as an extension, not with one, but with two type parameters. At first, these Arrows may look somewhat arbitrary. Here we show that they are categorically fairly civilised, by showing that they correspond to monoids in suitable subcategories of bifunctors C op ×C → C. This shows that, at a suitable level of abstraction, arrows are like monads — which are monoids in categories of functors C → C. Freyd categories have been introduced by Power and Robinson to model computational effects, well before Hughes ’ Arrows appeared. It is often claimed (informally) that Arrows are simply Freyd categories. We shall make this claim precise by showing how monoids in categories of bifunctors exactly correspond to Freyd categories.

Arrows are a popular form of abstract computation. Being more general than monads, they are more broadly applicable, and in particular are a good abstraction for signal processing and dataflow computations. Most notably, arrows form the basis for a domain specific language called Yampa, which has been used in a variety of concrete applications, including animation, robotics, sound synthesis, control systems, and graphical user interfaces. computations captured by Yampa. Unfortunately, arrows are not concrete enough to do this with precision. To remedy this situation we introduce the concept of commutative arrows that capture a kind of non-interference property of concurrent computations. We also add an init operator, and identify a crucial law that captures the causal nature of arrow effects. We call the resulting computational model causal commutative arrows. To study this class of computations in more detail, we define an extension to the simply typed lambda calculus called causal commutative arrows (CCA), and study its properties. Our key contribution is the identification of a normal form for CCA called causal commutative normal form (CCNF). By defining a normalization procedure we have developed an optimization strategy that yields dramatic improvements in performance over conventional implementations of arrows. We have implemented this technique in Haskell, and conducted benchmarks that validate the effectiveness of our approach. When combined with stream fusion, the overall methodology can result in speed-ups of greater than two orders of magnitude.

The creation of engaging, interactive virtual environments is a difficult task, but one that can be eased with the development of better software support. This paper proposes that a better understanding of the problem of building Dynamic, Interactive Virtual Environments must be developed. Equipped with an understanding of the design space of Dynamics, Dynamic Interaction, and Interactive Dynamics, the requirements for such a support system can be established. Finally, a system that supports the development of such environments is briefly presented, Functional Reactive Virtual Reality.

Abstract: Functional Reactive Programming (FRP) is an approach to reactive programming where systems are structured as networks of functions operating on signals. FRP is based on the synchronous data-flow paradigm and supports both continuous-time and discrete-time signals (hybrid systems). What sets FRP apart from most other languages for similar applications is its support for systems with dynamic structure and for higher-order data-flow constructs. This raises a range of implementation challenges. This paper contributes towards advancing the state of the art of FRP implementation by studying the notion of signal change and change propagation in a setting of hybrid signal function networks with dynamic structure. To sidestep some problems of certain previous FRP implementations that are structured using arrows, we suggest working with a notion of composable, multi-input and multi-output signal functions. A clear conceptual distinction is also made between continuous-time and discrete-time signals. We then show how establishing change-related properties of the signal functions in a network allows such networks to be simplified (static optimisation) and can help reducing the amount of computation needed for executing the networks (dynamic optimisation). Interestingly, distinguishing between continuous-time and discrete-time signals allows us to characterise the change-related properties of signal functions more precisely than what we otherwise would have been able to, which is helpful for optimisation.

Functional Reactive Programming (FRP) is an approach to reactive programming where systems are structured as networks of functions operating on time-varying values (signals). FRP is based on the synchronous data-flow paradigm and supports both continuous-time and discretetime signals (hybrid systems). What sets FRP apart from most other reactive languages is its support for systems with highly dynamic structure (dynamism) and higher-order reactive constructs (higher-order data-flow). However, the price paid for these features has been the loss of the safety and performance guarantees provided by other, less expressive, reactive languages. Statically guaranteeing safety properties of programs is an attractive proposition. This is true in particular for typical application domains for reactive programming such as embedded systems. To that end, many existing reactive languages have type systems or other static checksthatguaranteedomain-specificconstraints, suchasfeedbackbeingwell-formed(causality analysis). However, comparedwithFRP,theyarelimitedintheircapacitytosupportdynamism andhigher-orderdata-flow. Ontheotherhand, asestablishedstatictechniquesdonotsufficefor highly structurally dynamic systems, FRP generally enforces few domain-specific constraints, leaving the FRP programmer to manually check that the constraints are respected. Thus, there

In this paper we introduce a VR system extension that focuses on the creation of interactive, dynamic Virtual Environments. The extension uses the recently developed programming concept, Functional Reactive Programming. This paradigm focuses on an explicit and more natural concept of time in the modeling of dynamics, without sacrificing interactivity. We present an implementation that embeds the Functional Reactive Programming concept

We describe a denotational model of higher-order functional reactive programming using ultrametric spaces, which provide a natural Cartesian closed generalization of causal stream functions. We define a domain-specific language corresponding to the model. We then show how reactive programs written in this language may be implemented efficiently using an imperatively updated dataflow graph and give a higher-order separation logic proof that this lowlevel implementation is correct with respect to the high-level semantics.

We begin with a functional reactive programming (FRP) model in which every program is viewed as a signal function that converts a stream of input values into a stream of output values. We observe that objects in the real world – such as a keyboard or sound card – can be thought of as signal functions as well. This leads us to a radically different approach to I/O – instead of treating real-world objects as being external to the program, we expand the sphere of influence of program execution to include them within the program. We call this virtualizing real-world objects. We explore how even virtual objects, such as GUI widgets, and non-local effects, such as are needed for debugging (using something that we call a “wormhole”) and random number generation, can be handled in the same way. Our methodology may at first seem naïve – one may ask how we prevent a virtualized device from being copied, thus potentially introducing non-determinism as one part of a program competes for the same resource as another. To solve this problem, we introduce the notion of a resource type that assures that a virtualized object is not duplicated and that I/Oandnon-localeffectsaresafe. Resourcetypesalsoprovideadeeperleveloftransparency: