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)).     

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 Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.     

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.     

例谈物理核心素养视角下培养学生策略性学习的实践研究

文章结合自身教学实践,针对在物理核心素养背景下,从"借助项目设计,培养学习习惯,促进学生‘策略性学习’反应程序化""借助过程优化,积累学习方法,促进学生‘策略性学习’研究深度化""借助物理与生活互动,提高学生对学习理解,促进学生‘策略性学习’运用实践化"3个途径来提升学生思维水平,发展学生思维品质,进而提高学生物理核心素养.     

基于多域特征提取和深度学习的声源被动测距*

由于实际海洋环境中存在大量的非高斯噪声,一些基于高斯假设的传统去噪方法在实际海洋环境中性能下降甚至失效。针对非高斯噪声,如α稳定分布噪声、非平稳行船噪声下的脉冲信号的去噪与重构,该文提出一种基于深度学习的方法。去噪模型首先通过学习带噪信号短时傅里叶变换谱与残差谱之间的映射关系以去除环境噪声,之后对去噪信号的时频谱进行逆变换重构脉冲信号。仿真实验结果表明,深度学习模型在非高斯噪声环境下脉冲信号的去噪与重构任务中有着良好的表现,在实测样本上也表现出良好的泛化性,体现了一定的工程应用价值。     

Toward amino acid typing for proteins in FFLUX

Continuing the development of the FFLUX, a multipolar polarizable force field driven by machine learning, we present a modern approach to atom‐typing and building transferable models for predicting atomic properties in proteins. Amino acid atomic charges in a peptide chain respond to the substitution of a neighboring residue and this response can be categorized in a manner similar to atom‐typing. Using a machine learning method called kriging, we are able to build predictive models for an atom that is defined, not only by its local environment, but also by its neighboring residues, for a minimal additional computational cost. We found that prediction errors were up to 11 times lower when using a model specific to the correct group of neighboring residues, with a mean prediction of ∼0.0015 au. This finding suggests that atoms in a force field should be defined by more than just their immediate atomic neighbors. When comparing an atom in a single alanine to an analogous atom in a deca‐alanine helix, the mean difference in charge is 0.026 au. Meanwhile, the same difference between a trialanine and a deca‐alanine helix is only 0.012 au. When compared to deca‐alanine models, the transferable models are up to 20 times faster to train, and require significantly less ab initio calculation, providing a practical route to modeling large biological systems. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.     

THE PREPARATION OF CLOUD TEACHING MATERIALS OF ENGINEERING MECHANICS AND NEW ENGINEERING CONCEPT1)

新工科的人才培养目标使混合式教学模式成为高校教学改革的重要趋势。云教材是与混合式教学模式相契合的教学资源。工程力学云教材的建设贯彻以成果为导向的教育理念,明确学习目标,集成富媒体数字出版、云服务和移动学习三大领域的前沿技术,为工程力学混合式教学提供配套的教学资源。实践应用表明,工程力学云教材提高了学生的学习兴趣和自主学习能力,助力新工科的建设。     

ON THE CHANGES OF MEANING OF “LI” AND “LIXUE” (“FORCE” AND “MECHANICS” IN CHINESE)1)

在古代中国,“力”字和“力学”一词早就出现了。“力”的含义最初是对“人的力量”的直观描述,后来发展到对一般物体的重力的描述,再到对运动状态原因的描述。本文对“力”字含义的变迁进行了辨析。中国现代“力学”是西学东渐的结果,学科名字的确定过程也有不小的 曲折。通过分析确定“力学”为学科名字的过程,揭示了中国古人对“力学”学科理解的变迁。     

The Relevance of the Collaborative Effect in Determining the Performances of Photorefractive Polymer Materials

A derivative of 2‐methylindole, 3‐[2‐(4‐nitrophenyl)ethenyl]‐1‐allyl‐2‐methylindole, NPEMI‐A, is studied for its photoconductivity and photorefractivity behaviour. Its blends with the organic polymer poly‐(2,3‐dimethyl‐N‐vinylindole), PVDMI, are also investigated. Due to the expected and devised mutual solubility of the two components of the blends, it is possible to carry out measurements with the weight percent of the chromophore NPEMI‐A changing from zero to 100. Films were produced by a squeezing process between two ITO‐covered glass sheets. No opacity phenomena, that are so common for many other organic blends due to the segregation of the dissolved chromophore, are observed. The photorefractive optical gain Γ₂ is obtained as a function of the chromophore content. Differential scanning calorimetry measurements (DSC) are also carried out to obtain the whole change of the glass transition temperature T_g as a function of the amount of chromophore contained in the blends. From the experimental trend of T_g a meaningful quantitative estimate of the value of the electrostatic interactions acting in the studied blends, is obtained. The importance of the value of T_g, and of the electrostatic interactions, in determining the extent of the photorefractivity is clearly evident. The results are compared for NPEMI‐A (Γ₂=210 cm^?1) and for NPEMI‐E (Γ₂ ≈ 2000 cm^?1) that has a N‐2‐ethylhexyl group instead of a N‐allyl group. The Pockels and Kerr contributions and—for the first time—a “collaborative effect” of the photorefractivity of NPEMI‐A are distinguished and quantitatively evaluated.