• Motivation mainly focuses on enhancing understanding of abstract concepts. • Most research focuses on transformations, leaving other topics underexplored. • Unity-based mobile tools with marker tracking are widely used. • Evidence shows improved performance, but comparisons with traditional methods are mixed. • Mixed evidence on cognitive load and recurring usability challenges.

The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with simple networks and incrementally evolving both their topologies and weights. Although its capability across various challenges is evident, the algorithm’s computational efficiency remains an impediment, limiting its scalability potential. To address these limitations, this article introduces TensorNEAT, a GPU-accelerated library that applies tensorization to the NEAT algorithm. Tensorization reformulates NEAT’s diverse network topologies and operations into uniformly shaped tensors, enabling efficient parallel execution across entire populations. TensorNEAT is built upon JAX, leveraging automatic function vectorization and hardware acceleration to significantly enhance computational efficiency. In addition to NEAT, the library supports variants such as CPPN and HyperNEAT and integrates with benchmark environments like Gym, Brax, and gymnax. Experimental evaluations across various robotic control environments in Brax demonstrate that TensorNEAT delivers up to 500× speedups compared to existing implementations, such as NEAT-Python. The source code for TensorNEAT is publicly available at: https://github.com/EMI-Group/tensorneat .

The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with simple networks and incrementally evolving both their topologies and weights. Although its capability across various challenges is evident, the algorithm's computational efficiency remains an impediment, limiting its scalability potential. In response, this paper introduces a tensorization method for the NEAT algorithm, enabling the transformation of its diverse network topologies and associated operations into uniformly shaped tensors for computation. This advancement facilitates the execution of the NEAT algorithm in a parallelized manner across the entire population. Furthermore, we develop TensorNEAT, a library that implements the tensorized NEAT algorithm and its variants, such as CPPN and HyperNEAT. Building upon JAX, TensorNEAT promotes efficient parallel computations via automated function vectorization and hardware acceleration. Moreover, the TensorNEAT library supports various benchmark environments including Gym, Brax, and gymnax. Through evaluations across a spectrum of robotics control environments in Brax, TensorNEAT achieves up to 500x speedups compared to the existing implementations such as NEAT-Python. Source codes are available at: https://github.com/EMI-Group/tensorneat.

The paper proposes a new single image dehazing method based on a convolutional neural network. Our method directly learns an end to end mapping between the haze images and their corresponding haze layers (i.e. residual images between haze images and non-haze images). A convolutional neural network takes the haze image as an input and the residual image as an output. Then, a recovered dehazed image can be obtained by removing the residual from the haze image. Residual learning allows the network to directly estimate the initial haze layer with relatively high learning rates, which reduce computational complexity and speed-up the convergence process. Since the initial haze layer is only approximate, we use a guided filter to refine this layer to avoid halos and block artefacts, which makes the recovered image more similar to a real scene. The algorithms are tested on fog images with different fog densities. Comparisons are provided with other dehazing algorithms. Experiments demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world images, qualitatively and quantitatively.

An improved dehazing algorithm based on dark channel theory is proposed, in order to solve the problems of colour distortion and halo effect which still exists in dark channel prior algorithm. The dark channel prior theory may lead to colour distortion in sky region. Firstly, the guided filter is used to refine the segmentation of the sky region, and the atmospheric light is estimated accurately. Then, the median filter is used to obtain the detailed edge information. So a more clear transmission can be gotten which effectively suppress the halo problem. Finally, the gamma correction is applied to enhance image lightness with an empirically selected gamma parameter. The experimental results show that the proposed algorithm can effectively remove the haze. It can correct the colour distortion of the sky area and eliminate the halo effect at the edge of the scene.