Pure functional epidemics

Agent-Based Simulation (ABS) is a methodology in which a system is simulated in a bottom-up approach by modelling the micro interactions of its constituting parts, called agents, out of which the global system behaviour emerges. So far mainly object-oriented techniques and languages have been used in ABS. Using the SIR model of epidemiology, which simulates the spreading of an infectious disease through a population, we demonstrate how to use pure Functional Reactive Programming to implement ABS. With our approach we can guarantee the reproducibility of the simulation at compile time and rule out specific classes of run-time bugs, something that is not possible with traditional object-oriented languages. Also, we found that the representation in a purely functional format is conceptually quite elegant and opens the way to formally reason about ABS

An embedded language framework for hardware compilation

Various languages have been proposed to describe synchronous hardware at an abstract, yet synthesisable level. We propose a uniform framework within which such languages can be developed, and combined together for simulation, synthesis, and verification. We do this by embedding the languages in Lava — a hardware description language (HDL), itself embedded in the functional programming language Haskell. The approach allows us to easily experiment with new formal languages and language features, and also provides easy access to formal verification tools aiding program verification.peer-reviewe

Semantic programming model-based design

For a generic flexible efficient array antenna receiver platform a hierarchical tiled architecture has been proposed, giving a heterogeneous multi-processor system-on-chip (MPSoC), multiple chips on a board (MCoB) and multiple boards in a system (MBiS). A wide range of MPSoCs are predicted to be used in the near future but how to efficiently apply these designs remains an issue. We will advocate a model-based design approach and propose a single semantic (programming) model for representing the specification, design and implementation and allowing for verification, simulation, architecture definition and design space exploration.\ud \ud A single model for specification, (formal or functional) verification, simulation and programming an MPSoC has obvious as well as some less obvious advantages. It allows for model-based design down to the implementation, especially for hierarchical MPSoC architectures. Partitioning and mapping of the functionality to an architecture is commonly done manually. Using the proposed approach the feasibility of (partly) automated design space exploration is discussed for determining either a partitioning and mapping for a given architecture or an optimal architecture based on set constraints.\ud \ud The proposed hierarchical tiled architecture provides a flexible reconfigurable solution, however partitioning, mapping, modeling and programming such systems remains an issue. The proposed approach tackles these problems at a higher conceptual level, thereby exploiting the inherent composability and parallelism available in the formalism. Design space explorations is facilitated by allowing transformations between different partitionings and mappings. However, the generic applicability and limitations of this approach will need to be researched further.\ud \u

Particular: A Functional Approach to 3D Particle Simulation

Simulating large bodies of entities in various environments is an old science that traces back decades in computer science. There are existing software frameworks with well built mathematical models for approximating various environments. These frameworks are however built on imperative programming fundamentals often following a object oriented paradigm. This thesis presents Particular a 3d particle simulator software library for simulating movements of independent entities on a time dependant three-dimensional vector field using a functional approach. Particular uses functional programming paradigms to create a quite customizable, flexible and maintainable library based on lambda functions with all relevant parameters encapsulated in closures. Particular uses a functional implementation of a Entity Component System software architecture usually found in game development to create a highly performant, flexible, data oriented design. Which uncouples the data with the aforementioned lambda functions that predicate particle behaviour. According to evaluations particular shows a significant performance increase with regards to execution time compared to comparison to other contemporary trajectory simulation frameworks such as opendrift. With some evaluations showing a 900% faster execution time under certain conditions

Accurate,robust and harmonized implementation of morpho-functional imaging in treatment planning for personalized radiotherapy

In this work we present a methodology able to use harmonized PET/CT imaging in dose painting by number (DPBN) approach by means of a robust and accurate treatment planning system. Image processing and treatment planning were performed by using a Matlab-based platform, called CARMEN, in which a full Monte Carlo simulation is included. Linear programming formulation was developed for a voxel-by-voxel robust optimization and a specific direct aperture optimization was designed for an efficient adaptive radiotherapy implementation. DPBN approach with our methodology was tested to reduce the uncertainties associated with both, the absolute value and the relative value of the information in the functional image. For the same H&N case, a single robust treatment was planned for dose prescription maps corresponding to standardized uptake value distributions from two different image reconstruction protocols: One to fulfill EARL accreditation for harmonization of [18F]FDG PET/CT image, and the other one to use the highest available spatial resolution. Also, a robust treatment was planned to fulfill dose prescription maps corresponding to both approaches, the dose painting by contour based on volumes and our voxel-by-voxel DPBN. Adaptive planning was also carried out to check the suitability of our proposal. Different plans showed robustness to cover a range of scenarios for implementation of harmonizing strategies by using the highest available resolution. Also, robustness associated to discretization level of dose prescription according to the use of contours or numbers was achieved. All plans showed excellent quality index histogram and quality factors below 2%. Efficient solution for adaptive radiotherapy based directly on changes in functional image was obtained. We proved that by using voxel-by-voxel DPBN approach it is possible to overcome typical drawbacks linked to PET/CT images, providing to the clinical specialist confidence enough for routinely implementation of functional imaging for personalized radiotherapy.Junta de Andalucía (FISEVI, reference project CTS 2482)European Regional Development Fund (FEDER

Functional Big-step Semantics

When doing an interactive proof about a piece of software, it is important that the underlying programming language’s semantics does not make the proof unnecessarily difficult or unwieldy. Both smallstep and big-step semantics are commonly used, and the latter is typically given by an inductively defined relation. In this paper, we consider an alternative: using a recursive function akin to an interpreter for the language. The advantages include a better induction theorem, less duplication, accessibility to ordinary functional programmers, and the ease of doing symbolic simulation in proofs via rewriting. We believe that this style of semantics is well suited for compiler verification, including proofs of divergence preservation. We do not claim the invention of this style of semantics: our contribution here is to clarify its value, and to explain how it supports several language features that might appear to require a relational or small-step approach. We illustrate the technique on a simple imperative language with C-like for-loops and a break statement, and compare it to a variety of other approaches. We also provide ML and lambda-calculus based examples to illustrate its generality

Evolutionary improvement of programs

Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues

An Experiment in Ping-Pong Protocol Verification by Nondeterministic Pushdown Automata

An experiment is described that confirms the security of a well-studied class of cryptographic protocols (Dolev-Yao intruder model) can be verified by two-way nondeterministic pushdown automata (2NPDA). A nondeterministic pushdown program checks whether the intersection of a regular language (the protocol to verify) and a given Dyck language containing all canceling words is empty. If it is not, an intruder can reveal secret messages sent between trusted users. The verification is guaranteed to terminate in cubic time at most on a 2NPDA-simulator. The interpretive approach used in this experiment simplifies the verification, by separating the nondeterministic pushdown logic and program control, and makes it more predictable. We describe the interpretive approach and the known transformational solutions, and show they share interesting features. Also noteworthy is how abstract results from automata theory can solve practical problems by programming language means.Comment: In Proceedings MARS/VPT 2018, arXiv:1803.0866