Prediction of traffic flow based on deep learning

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. Although existing DNN models can provide better performance than shallow models, it is still an open question to make full use of the spatio-temporal characteristics of traffic flows to improve performance. We propose a novel deep architecture combining CNN and LSTM for traffic flow (RCF) predictio. The model uses CNN to explore temporal correlation and LSTM to explore spatial correlation . Factors such as weather and historical period data are also added to the feature. Its advantage lies in making full use of the spatial-temporal correlation of traffic data and more comprehensively considered the impact of multiple related factors. Aiming at the difficult problem of obtaining spatial features, a feature selection method based on Random Forests is proposed. We use the gini score to represent the spatial connection between intersections to form a network graph constructed based on data.  The experimental results show that based on the random forest feature selection and RCF model, the accuracy of traffic prediction reaches 90%

Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks

Quantized Neural Networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, storage size and memory usage. In QNNs, parameters and activations are uniformly quantized, such that the multiplications and additions can be accelerated by bitwise operations. However, distributions of parameters in Neural Networks are often imbalanced, such that the uniform quantization determined from extremal values may under utilize available bitwidth. In this paper, we propose a novel quantization method that can ensure the balance of distributions of quantized values. Our method first recursively partitions the parameters by percentiles into balanced bins, and then applies uniform quantization. We also introduce computationally cheaper approximations of percentiles to reduce the computation overhead introduced. Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks. Experiments on standard datasets including ImageNet and Penn Treebank confirm the effectiveness of our method. On ImageNet, the top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is superior to the state-of-the-arts of QNNs

Molecular oxygen-assisted in defect-rich ZnO for catalytic depolymerization of polyethylene terephthalate

Polyethylene terephthalate (PET) is the most produced polyester plastic; its waste has a disruptive impact on the environment and ecosystem. Here, we report a catalytic depolymerization of PET into bis(2-hydroxyethyl) terephthalate (BHET) using molecule oxygen (O2)−assisted in defect-rich ZnO. At air, the PET conversion rate, the BHET yield, and the space-time yield are 3.5, 10.6, and 10.6 times higher than those in nitrogen, respectively. Combining structural characterization with the results of DFT calculations, we conclude that the (100) facet of defect-rich ZnO nanosheets conducive to the formation of reactive oxygen species (∗O2−) and Zn defect, promotes the PET breakage of the ester bond and thus complete the depolymerization processed. This approach demonstrates a sustainable route for PET depolymerization by molecule-assisted defect engineering.publishedVersio

Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model

Chemical reactions are the fundamental building blocks of drug design and organic chemistry research. In recent years, there has been a growing need for a large-scale deep-learning framework that can efficiently capture the basic rules of chemical reactions. In this paper, we have proposed a unified framework that addresses both the reaction representation learning and molecule generation tasks, which allows for a more holistic approach. Inspired by the organic chemistry mechanism, we develop a novel pretraining framework that enables us to incorporate inductive biases into the model. Our framework achieves state-of-the-art results on challenging downstream tasks. By possessing chemical knowledge, our generative framework overcome the limitations of current molecule generation models that rely on a small number of reaction templates. In the extensive experiments, our model generates synthesizable drug-like structures of high quality. Overall, our work presents a significant step toward a large-scale deep-learning framework for a variety of reaction-based applications

Preparation and Characterization of Solid Electrolyte Doped With Carbon Nanotubes and its Preliminary Application in NO2 Gas Sensors

In this work, a solid polymer electrolyte (SPE) doped with carbon nanotubes (CNTs) was used as a gas sensing material for a NO2 gas sensor. The electrolytes consisted of the ionic liquids (ILs) and CNTs, which were immobilized in a poly(vinylidene fluoride) (PVDF) matrix. The SPE membranes were characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), and cyclic voltammetry (CV). The experimental results show that the addition of an appropriate amount of CNTs can appropriately improve the electrochemical performance of the SPE membrane. It was shown that NO2 gas sensors with an appropriate amount of CNTs added to their SPEs had a higher gas sensitivity than those with SPE containing no CNTs. When the mass ratio of PVDF, N-methyl-2-pyrrolidone (NMP), IL, and CNT was 1:4:1:0.08, the SPE showed the best gas sensitivity, and its sensitivity is 0.00275 V/ppm