Deep learning neural networks for the third-order nonlinear Schrödinger equation: bright solitons,breathers, and rogue waves

The dimensionless third-order nonlinear Schrödinger equation (alias the Hirota equation) is investigated via deep leaning neural networks. In this paper, we use the physics-informed neural networks (PINNs) deep learning method to explore the data-driven solutions (e.g. bright soliton, breather, and rogue waves) of the Hirota equation when the two types of the unperturbated and perturbated (a 2% noise) training data are considered. Moreover, we use the PINNs deep learning to study the data-driven discovery of parameters appearing in the Hirota equation with the aid of bright solitons.     

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).     

From a Point Cloud to a Simulation Model—Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D Modelling

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.     

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.     

Detection of Anomalous Diffusion with Deep Residual Networks

Identification of the diffusion type of molecules in living cells is crucial to deduct their driving forces and hence to get insight into the characteristics of the cells. In this paper, deep residual networks have been used to classify the trajectories of molecules. We started from the well known ResNet architecture, developed for image classification, and carried out a series of numerical experiments to adapt it to detection of diffusion modes. We managed to find a model that has a better accuracy than the initial network, but contains only a small fraction of its parameters. The reduced size significantly shortened the training time of the model. Moreover, the resulting network has less tendency to overfitting and generalizes better to unseen data.     

Deep eutectic solvents and glycerol: a simple,environmentally benign and efficient catalyst/reaction media for synthesis of N-aryl phthalimide derivatives

Abstract

Deep eutectic solvents (DES) and glycerol have been successfully employed as efficient catalysts/reaction media in the synthesis of N-aryl phthalimide derivatives from phthalic anhydride and primary aromatic amines. The DES prepared from choline chloride and malonic acid proved to be an efficient catalyst whereas glycerol and the DES of choline chloride and urea played a dual role of catalyst and solvent. These mixtures are biodegradable, nontoxic, and cost-effective thereby providing a good industrial alternative to conventional methods. These methods gave products in moderate to high yields with good recyclability of catalyst/solvent at least up to five consecutive runs.     

Low‐Transition‐Temperature Mixtures (LTTMs): A New Generation of Designer Solvents

A new generation of designer solvents emerged in the last decade as promising green media for multiple applications, including separation processes: the low‐transition‐temperature mixtures (LTTMs). They can be prepared by mixing natural high‐melting‐point starting materials, which form a liquid by hydrogen‐bond interactions. Among them, deep‐eutectic solvents (DESs) were presented as promising alternatives to conventional ionic liquids (ILs). Some limitations of ILs are overcome by LTTMs, which are cheap and easy to prepare from natural and readily available starting materials, biodegradable, and renewable.     

Photo‐capacitance spectroscopy investigation of deep‐level defects in AlGaN/GaN hetero‐structures with different current collapses

We have investigated electronic deep levels in two AlGaN/GaN hetero‐structures with different current collapses grown at 1150 and 1100 °C by a photo‐capacitance spectroscopy technique, using Schottky barrier diodes. Three specific deep levels located at ～2.07, ～2.80, and ～3.23 eV below the conduction band were found to be significantly enhanced for severe current collapse, being in reasonable agreement with photoluminescence and capacitance–voltage characteristics. These levels probably originate in Ga vacancies and residual C impurities and are probably responsible for the current collapse phenomena of the AlGaN/GaN hetero‐structures. (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

堪为物理学第三分支的计算物理

本文是作者1982年在中国访问时所作的讲演,论述物理学各领域的前沿工作,用许多事例说明计算物理对推动物理理论的重大意义。文中还指出计算物理在其性质、方法及需要等方面不同于和独立于解析的理论物理和实验物理,而成为物理学的第三分支。     

纺锤形BiVO_4微米管:低温离子熔盐合成及光催化性能

以Bi(NO_3)_3·5H_2O和NH_4VO_3为原料,以氯化胆碱和尿素组成的低温离子熔盐为反应介质,采用离子热合成法成功制备出了具有纺锤状外形的BiVO_4微米管。利用XRD,SEM,UV-Vis DRS,光催化测试等手段考察了BiVO_4颗粒的物相、形貌和光催化性能。结果表明,在离子熔盐环境下可以制备出结晶良好的BiVO_4纺锤形微米管,该BiVO_4微米管长10～15μm,直径为1.5μm左右,管壁厚约为200 nm。同时,研究了pH值对BiVO_4颗粒物相与形貌的影响,发现随着pH值的变化可分别合成出具有柱状、纺锤形微米管、柱状微米管和针柱状单斜相BiVO_4颗粒。光催化测试结果表明,这些单斜白钨矿BiVO_4颗粒在可见光范围都具有一定的光催化活性,其中纺锤状微米管对罗丹明B的降解效果最佳,可见光照射4.5h后罗丹明B的降解率可达到93%。