Formalizing and Verifying a Modern Build Language

CLOUDMAKE is a software utility that automatically builds executable programs and libraries from source code—a modern MAKE utility. Its design gives rise to a number of possible optimizations, like cached builds, and the exe-cutables to be built are described using a functional programming language. This paper formally and mechanically verifies the correctness of central CLOUDMAKE algorithms. The paper defines the CLOUDMAKE language using an operational semantics, but with a twist: the central operation exec is defined axiomatically, making it pluggable so that it can be replaced by calls to compilers, linkers, and other tools. The formalization and proofs of the central CLOUDMAKE algorithms are done entirely in DAFNY, the proof engine of which is an SMT-based program verifier

Engagement in a virtual learning environment predicts academic achievement in research methods modules: A longitudinal study combining behavioral and self-reported data

The use of virtual learning environments (VLE) has grown exponentially in the past years. Research indicates that students’ online learning behavior predicts their academic performance and that students’ academic emotions can play a key role in this process. However, few studies have attempted to investigate the effectiveness of VLE activities in learning achievement within psychology education. In this longitudinal study, we analyzed the relationship between students’ activity in a VLE, their attendance, academic emotions, and module grades at a face-to-face-based university in the United Kingdom. Data were collected over 1 year across two research methods modules, each of which is compulsory for a psychology degree. VLE and attendance data from 210 students were gathered for the first-year module, with 152 students continuing to the second year. The data were cross-referenced with students’ module grades, alongside self-reported emotion data for a subset of students. The results showed that overall VLE activity and the use of specific online tools such as optional online tests and lecture recording were important predictors of academic achievement. While some significant relationships between emotions and student’s learning behavior and achievement were found, these correlations were relatively small and not consistent throughout the year. These findings have potential implications for curriculum design, particularly by making psychology educators aware of the usefulness of VLE activities and tools from the onset of students’ research methods learning journey

ValoMC: a Monte Carlo software and MATLAB toolbox for simulating light transport in biological tissue

A Monte Carlo method for photon transport has gained wide popularity in biomedical optics for studying light behaviour in tissue. Nowadays, typical computation times range from a few minutes to hours. Although various implementations of the Monte Carlo algorithm exist, there is only a limited number of free software available. In addition, these packages may require substantial learning efforts. To address these issues, we present a new Monte Carlo software with a user-friendly interface. The simulation geometry is defined using an unstructured (triangular or tetrahedral) mesh. The program solves the photon fluence in the computation domain and the exitance at the domain boundary. It is capable of simulating complex measurement geometries with spatially varying optical parameter distributions and supports several types of light sources as well as intensity modulated light. Furthermore, attention is given to ease of use and fast problem set up with a MATLAB (The MathWorks Inc., Natick, MA) interface. The simulation code is written in C++ and parallelized using OpenMP. The simulation code has been validated against analytical and numerical solutions of radiative transfer equation and other Monte Carlo software in good agreement. The software is available for download from the homepage https://inverselight.github.io/ValoMC/ and the source code from GitHub https://github.com/InverseLight/ValoMC

Stepwise refinement of heap-manipulating code in Chalice

Stepwise refinement is a well-studied technique for developing a program from an abstract description to a concrete implementation. This paper describes a system with automated tool support for refinement, powered by a state-of-the-art verification engine that uses an SMT solver. Unlike previous refinement systems, users of the presented system interact only via declarations in the programming language. Another aspect of the system is that it accounts for dynamically allocated objects in the heap, so that data representations in an abstract program can be refined into ones that use more objects. Finally, the system uses a language with familiar imperative features, including sequential composition, loops, and recursive calls, offers a syntax with skeletons for describing program changes between refinements, and provides a mechanism for supplying witnesses when refining non-deterministic programs

Flexible Invariants Through Semantic Collaboration

Modular reasoning about class invariants is challenging in the presence of dependencies among collaborating objects that need to maintain global consistency. This paper presents semantic collaboration: a novel methodology to specify and reason about class invariants of sequential object-oriented programs, which models dependencies between collaborating objects by semantic means. Combined with a simple ownership mechanism and useful default schemes, semantic collaboration achieves the flexibility necessary to reason about complicated inter-object dependencies but requires limited annotation burden when applied to standard specification patterns. The methodology is implemented in AutoProof, our program verifier for the Eiffel programming language (but it is applicable to any language supporting some form of representation invariants). An evaluation on several challenge problems proposed in the literature demonstrates that it can handle a variety of idiomatic collaboration patterns, and is more widely applicable than the existing invariant methodologies.Comment: 22 page

Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues

Specification and verification challenges for sequential object-oriented programs

The state of knowledge in how to specify sequential programs in object-oriented languages such as Java and C# and the state of the art in automated verification tools for such programs have made measurable progress in the last several years. This paper describes several remaining challenges and approaches to their solution

Comment on ``Structure of exotic nuclei and superheavy elements in a relativistic shell model''

A recent paper [M. Rashdan, Phys. Rev. C 63, 044303 (2001)] introduces the new parameterization NL-RA1 of the relativistic mean-field model which is claimed to give a better description of nuclear properties than earlier ones. Using this model ^{298}114 is predicted to be a doubly-magic nucleus. As will be shown in this comment these findings are to be doubted as they are obtained with an unrealistic parameterization of the pairing interaction and neglecting ground-state deformation.Comment: 2 pages REVTEX, 3 figures, submitted to comment section of Phys. Rev. C. shortened and revised versio