Design of Painting Art Style Rendering System Based on Convolutional Neural Network

Convolutional Neural Network- (CNN-) based GAN models mainly suffer from problems such as data set limitation and rendering efficiency in the segmentation and rendering of painting art. In order to solve these problems, this paper uses the improved cycle generative adversarial network (CycleGAN) to render the current image style. This method replaces the deep residual network (ResNet) of the original network generator with a dense connected convolutional network (DenseNet) and uses the perceptual loss function for adversarial training. The painting art style rendering system built in this paper is based on perceptual adversarial network (PAN) for the improved CycleGAN that suppresses the limitation of the network model on paired samples. The proposed method also improves the quality of the image generated by the artistic style of painting and further improves the stability and speeds up the network convergence speed. Experiments were conducted on the painting art style rendering system based on the proposed model. Experimental results have shown that the image style rendering method based on the perceptual adversarial error to improve the CycleGAN + PAN model can achieve better results. The PSNR value of the generated image is increased by 6.27% on average, and the SSIM values are all increased by about 10%. Therefore, the improved CycleGAN + PAN image painting art style rendering method produces better painting art style images, which has strong application value.

Hidden network generating rules from partially observed complex networks

Complex biological, neuroscience, geoscience and social networks exhibit heterogeneous self-similar higher order topological structures that are usually characterized as being multifractal in nature. However, describing their topological complexity through a compact mathematical description and deciphering their topological governing rules has remained elusive and prevented a comprehensive understanding of networks. To overcome this challenge, we propose a weighted multifractal graph model capable of capturing the underlying generating rules of complex systems and characterizing their node heterogeneity and pairwise interactions. To infer the generating measure with hidden information, we introduce a variational expectation maximization framework. We demonstrate the robustness of the network generator reconstruction as a function of model properties, especially in noisy and partially observed scenarios. The proposed network generator inference framework is able to reproduce network properties, differentiate varying structures in brain networks and chromosomal interactions, and detect topologically associating domain regions in conformation maps of the human genome.

Stratisfimal Layout: A modular optimization model for laying out layered node-link network visualizations

Node-link visualizations are a familiar and powerful tool for displaying the relationships in a network. The readability of these visualizations highly depends on the spatial layout used for the nodes. In this paper, we focus on computing layered layouts, in which nodes are aligned on a set of parallel axes to better expose hierarchical or sequential relationships. Heuristic-based layouts are widely used as they scale well to larger networks and usually create readable, albeit sub-optimal, visualizations. We instead use a layout optimization model that prioritizes optimality— as compared to scalability— because an optimal solution not only represents the best attainable result, but can also serve as a baseline to evaluate the effectiveness of layout heuristics. We take an important step towards powerful and flexible network visualization by proposing STRATISFIMAL LAYOUT, a modular integer-linear-programming formulation that can consider several important readability criteria simultaneously— crossing reduction, edge bendiness, and nested and multi-layer groups. The layout can be adapted to diverse use cases through its modularity. Individual features can be enabled and customized depending on the application. We provide open-source and documented implementations of the layout, both for web-based and desktop visualizations. As a proof-of-concept, we apply it to the problem of visualizing complicated SQL queries, which have features that we believe cannot be addressed by existing layout optimization models. We also include a benchmark network generator and the results of an empirical evaluation to assess the performance trade-offs of our design choices. A full version of this paper with all appendices, data, and source code is available at osf.io/3vqmswith live examples at https://visdunneright.github.io/stratisfimal/.

Nine Mechanisms of Job-Searching and Job-Finding Through Contacts Among Young Adults

Since Granovetter’s seminal works, the influence of personal networks on the labour market has attracted widespread attention. This article analyses the role played by contacts in the context of the labour trajectories of young people in Spain, for whom the use of personal networks represents one of the most important job-searching methods. Using narrative data extracted from a life-history grid and ego-network generator, the analysis brings to light nine mechanisms in which personal contacts intervene in job-searching and job-finding in a sample of 90 young people living in the Barcelona Metropolitan Area. The article emphasizes that contacts play primarily three roles in these processes as informers, employers, or influencers. This distinction offers a renewed framework for the study of networks in the labour market, further complementing the debate on the strength of ties. Using this framework allows me to create a map of the mechanisms that shed light on personal networks as tools with which to deal with labour insecurity and unemployment among young people, thus providing resources that to a large extent reaffirm the objective character of class differences. The article offers innovative insights into how social capital operates in the labour market and helps understand how youth precarity, which is widespread in Spain, is experienced in a relational way.

A combined DFTB nanoreactor and reaction network generator approach for mechanism of hydrocarbon combustion

We explored the mechanism of ethylene combustion by combining density functional tight-binding based nanoreactor molecular dynamic method (DFTB-NMD) and a hidden Markov model (HMM) based reaction network generator approach. The...

Single Image Defogging Algorithm Based on Conditional Generative Adversarial Network

Outside the house, images taken using a phone in foggy weather are not suitable for automation due to low contrast. Usually, it is revised in the dark channel prior (DCP) method (K. He et al. 2009), but the non-sky bright area exists due to mistakes in the removal. In this paper, we propose an algorithm, defog-based generative adversarial network (DbGAN). We use generative adversarial network (GAN) for training and embed target map (TM) in the anti-network generator, only the part of bright area layer of image, in local attention model image training and testing in deep learning, and the effective processing of the wrong removal part is achieved, thus better restoring the defog image. Then, the DCP method obtains a good defog visual effect, and the evaluation index peak signal-to-noise ratio (PSNR) is used to make a judgment; the simulation result is consistent with the visual effect. We proved the DbGAN is a practical import of target map in the GAN. The algorithm is used defogging in the highlighted area is well realized, which makes up for the shortcomings of the DCP algorithm.

Loss Architecture Search for Few-Shot Object Recognition

Few-shot object recognition, which exploits a set of well-labeled data to build a classifier for new classes that have only several samples per class, has received extensive attention from the machine learning community. In this paper, we investigate the problem of designing an optimal loss function for few-shot object recognition and propose a novel few-shot object recognition system that includes the following three steps: (1) generate a loss function architecture using a recurrent neural network (generator); (2) train a base embedding network with the generated loss function on a training set; (3) fine-tune the base embedding network using the few-shot instances from a validation set to obtain the accuracy and use it as a reward signal to update the generator. This procedure is repeated and implemented in the reinforcement learning framework for finding the best loss architecture such that the embedding network yields the highest validation accuracy. Our key insight is to create a search space of the loss function architectures and evaluate the quality of a particular loss function on the dataset of interest. We conduct experiments on three popular datasets for few-shot learning. The results show that the proposed approach achieves better performance than state-of-the-art methods.

PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations

Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically consistent deep neural network architectures is an open issue. In the spirit of physics-informed neural networks (NNs), the PDE-NetGen package provides new means to automatically translate physical equations, given as partial differential equations (PDEs), into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The latter exploits NN-based implementations of PDE solvers using Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally efficient yet compact representations to address a variety of issues, including, among others, adjoint derivation, model calibration, forecasting and data assimilation as well as uncertainty quantification. As an illustration, the workflow is first presented for the 2D diffusion equation, then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.