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Tissue P systems provide distributed parallel devices inspired by actual biological reality, where communication rules are used for object exchange between cells (or between cells and the environment). In such systems, the environment continuously provides energy to cells, so the cells are very dependent on the objects in the environment. In biology, there is a mechanism called homeostasis, that is, an internal organism is independent from the external conditions, thus keeping itself relatively stable. Inspired by this biological fact, in this paper, we assume that the environment no longer provides energy for cells, introducing multiset rewriting rules into tissue P systems, thereby constructing a novel computational model called homeostasis tissue-like P systems. Based on the model, we construct two uniform solutions in feasible time. One solution is constructed to solve the 3-coloring problem in linear time in standard time, and the other solution is constructed to solve the SAT problem with communication rules and multiset rewriting rules of the length at most 3 in time-free mode. Moreover, we prove that the constructed system can generate any Turing computable set of numbers using communication rules and multiset rewriting rules with a maximal length 3, working in the mode of standard time and time-free, respectively. The results show that our constructed system does not rely on the environment and reflects the phenomenon of biological homeostasis. https://www.selleckchem.com/products/nsc-663284.html In addition, although the system runs in time-free way, it not only has Turing university, but also can effectively solve NP-complete problem.Relating small-scale structures to large-scale appearance is a key element in material appearance design. Bi-scale material design requires finding small-scale structures that produce a desired large-scale appearance expressed as a macro-scale BRDF. The adjustment of small-scale geometry and reflectances to achieve a desired appearance can become a tedious trial-and-error process. We present a learning-based solution to fit a target macro-scale BRDF with a combination of a meso-scale geometry and micro-scale BRDF. We confront challenges in representation at both scales. At the large scale we need macro-scale BRDFs that are both compact and expressive. At the small scale we need diverse combinations of geometric patterns and potentially spatially varying micro-BRDFs. For large-scale macro-BRDFs, we propose a novel 2D subset of a tabular BRDF representation that well preserves important appearance features for learning. For small-scale details, we represent geometries and BRDFs in different categories with different physical parameters to define multiple independent continuous search spaces. To build the mapping between large-scale macro-BRDFs and small-scale details, we propose an end-to-end model that takes the subset BRDF as input and performs classification and parameter estimation on small-scale details to find an accurate reconstruction. Compared with other fitting methods, our learning-based solution provides higher reconstruction accuracy and covers a wider gamut of appearance.Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to learn an automatic photo enhancer for paired data, which consists of low-quality photos and corresponding expert-retouched versions. However, the style and characteristics of photos retouched by experts may not meet the needs or preferences of general users. In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images. The proposed model is based on single deep GAN which embeds the modulation and attention mechanisms to capture richer global and local features. Based on the proposed model, we introduce two losses to deal with the unsupervised image enhancement (1) fidelity loss, which is defined as a l2 regularization in the feature domain of a pre-trained VGG network to ensure the content between the enhanced image and the input image is the same, and (2) quality loss that is formulated as a relativistic hinge adversarial loss to endow the input image the desired characteristics. Both quantitative and qualitative results show that the proposed model effectively improves the aesthetic quality of images.Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimization in conditional parameter spaces are usually built on trees. In this work, we generalize the additive assumption to tree-structured functions and propose an additive tree-structured covariance function, showing improved sample-efficiency, wider applicability and greater flexibility. Furthermore, by incorporating the structure information of parameter spaces and the additive assumption in the BO loop, we develop a parallel algorithm to optimize the acquisition function and this optimization can be performed in a low dimensional space. We demonstrate our method on an optimization benchmark function, on a neural network compression problem and on pruning pre-trained VGG16 and ResNet50 models. Experimental results show our approach significantly outperforms the current state of the art for conditional parameter optimization including SMAC, TPE and Jenatton et al. (2017).Light field essentially represents rays in space. The epipolar geometry between two light fields is an important relationship that captures ray-ray correspondences and relative configuration of two views. Unfortunately, so far little work has been done in deriving a formal epipolar geometry model that is specifically tailored for light field cameras. This is primarily due to the high-dimensional nature of the ray sampling process with a light field camera. This paper fills in this gap by developing a novel ray-space epipolar geometry which intrinsically encapsulates the complete projective relationship between two light fields, while the generalized epipolar geometry which describes relationship of normalized light fields is the specialization of the proposed model to calibrated cameras. With Plecker parameterization, we propose the ray-space projection model involving a 6 6 ray-space intrinsic matrix for ray sampling of light field camera. Ray-space fundamental matrix and its properties are then derived to constrain ray-ray correspondences for general and special motions.
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