Fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactions

We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimise data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximises parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie (J. Phys. Chem, Vol. 81, p. 2340-2361, 1977). In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system’s size

Learning to Infer Graphics Programs from Hand-Drawn Images

We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input

Implementation of computer assisted assessment: lessons from the literature

This paper draws attention to literature surrounding the subject of computer-assisted assessment (CAA). A brief overview of traditional methods of assessment is presented, highlighting areas of concern in existing techniques. CAA is then defined, and instances of its introduction in various educational spheres are identified, with the main focus of the paper concerning the implementation of CAA. Through referenced articles, evidence is offered to inform practitioners, and direct further research into CAA from a technological and pedagogical perspective. This includes issues relating to interoperability of questions, security, test construction and testing higher cognitive skills. The paper concludes by suggesting that an institutional strategy for CAA coupled with staff development in test construction for a CAA environment can increase the chances of successful implementation