Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization

Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type of filter. In this work, we propose a co-training model for visual object recognition using deep neural networks by adding layers of self-supervised neural networks as intermediate inputs to the views, where the views are diversified through the cross-entropy regularization of their outputs. Since the model merges the concepts of co-training and self-supervised learning by considering the differentiation of outputs, we called it Differential Self-Supervised Co-Training (DSSCo-Training). This paper presents some experiments using the DSSCo-Training model to well-known image datasets such as MNIST, CIFAR-100, and SVHN. The results indicate that the proposed model is competitive with the state-of-art models and shows an average relative improvement of 5% in accuracy for several datasets, despite its greater simplicity with respect to more recent approaches.     

Enhanced headspace single drop microextraction method using deep eutectic solvent based magnetic bucky gels: Application to the determination of volatile aromatic hydrocarbons in water and urine samples

A facile headspace single drop microextraction method was developed using deep eutectic solvent‐based magnetic bucky gel as the extraction solvent for the first time. The hydrophobic magnetic bucky gel was formed by combining choline chloride/chlorophenol deep eutectic solvent and magnetic multiwalled carbon nanotube nanocomposite. Magnetic susceptibility, high viscosity, high sorbing ability, and tunable extractability of organic analytes are the desirable advantages of the prepared gel. Using a rod magnet as a suspensor in combination with the magnetic susceptibility of the prepared gel resulted in a highly stable droplet. This stable droplet eliminated the possibility of drop dislodgement. The prepared droplet made it possible to complete the extraction process in high temperatures and elevated agitation rates. Furthermore, using larger micro‐droplet volumes without any operational problems became possible. These facts resulted in shorter sample preparation time, higher sensitivity of the method, and lower detection limits. Under the optimized conditions, an enrichment factor of 520–587, limit of detection of 0.05–0.90 ng/mL, and linearity range of 0.2–2000 ng/mL (coefficient of determination = 0.9982–0.9995) were obtained. Relative standard deviations were < 10%. This method was successfully coupled with gas chromatography and used for the determination of benzene, toluene, ethylbenzene, and xylene isomers as harmful volatile organic compounds in water and urine samples.     

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.     

A review of computational modeling and deep brain stimulation: applications to Parkinson's disease

Biophysical computational models are complementary to experiments and theories, providing powerful tools for the study of neurological diseases. The focus of this review is the dynamic modeling and control strategies of Parkinson's disease (PD). In previous studies, the development of parkinsonian network dynamics modeling has made great progress. Modeling mainly focuses on the cortex-thalamus-basal ganglia (CTBG) circuit and its sub-circuits, which helps to explore the dynamic behavior of the parkinsonian network, such as synchronization. Deep brain stimulation (DBS) is an effective strategy for the treatment of PD. At present, many studies are based on the side effects of the DBS. However, the translation from modeling results to clinical disease mitigation therapy still faces huge challenges. Here, we introduce the progress of DBS improvement. Its specific purpose is to develop novel DBS treatment methods, optimize the treatment effect of DBS for each patient, and focus on the study in closed-loop DBS. Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.     

基于分子工程的方法设计首例具有Sr2Be2B2O7 (SBBO)结构的深紫外氟碳酸盐双折射晶体AMgLi2(CO3)2F (A=K,Rb)※

分子结构设计是开发新化合物和通过原子尺度操纵优化晶体结构的一种引人注目的策略. 在这个工作中, 利用分子工程的思想, 基于SBBO结构, 成功设计并合成两个新型氟碳酸盐KMgLi₂(CO₃)₂F和RbMgLi₂(CO₃)₂F. 在两个结构中, a-b平面是由CO₃和LiO₃F阴离子基团组成的无限[Li₃C₃O₆F₃]_∞层, 进一步相邻的层通过F原子连接形成一个独特的[Li₆C₆O₁₂F₃]_∞双层. 这种结构特征对改善晶体的层状生长习性和消除晶体的多晶性有很大的帮助. 光学测试表明, 该系列晶体具有大的双折射和短的紫外截止边, 是深紫外双折射晶体良好的候选材料.     

虚拟震源测深法及其在INDEPTH-3台阵中的应用

基于中美合作项目INDEPTH第3期在青藏高原布设的台站，使用虚拟震源测深法研究青藏高原中部的地壳厚度。结果显示，拉萨地体和羌塘地体的地壳结构存在巨大差异。拉萨地体的地壳厚度大约为57 km，与艾里均衡说预测的地壳厚度基本一致，说明拉萨地体的地壳结构比较简单。羌塘地体的地壳厚度为60~75 km，向北有增厚趋势，明显较艾里均衡说预测的地壳厚，说明羌塘地体地壳结构比较复杂，原因有可能是羌塘地体下存在高温流体和低速带，或者与印度板块岩石圈在班公湖-怒江缝合带以北向下俯冲有关。     

Evaluation of green and efficient deep eutectic solvents as media for extracting alkaloids from lotus leaf

Deep eutectic solvents (DESs) were applied as eco-friendly solvents in this study for the extraction of alkaloids from lotus leaf, including O-nornuciferine, N-nornuciferine, nuciferine and roemerine. A series of hydrophilic and hydrophobic DESs with different hydrogen bond donors and a acceptors were synthesized and screened for a suitable DESs for extraction of alkaloids from lotus leaf. The study results showed that the hydrophilic DES with choline chloride and propanediol had the highest extraction yield. The main factors affecting the extraction efficiency—choline chloride–propanediol ratio, water content in deep eutectic solvents, solid–liquid ratio and extraction time—were investigated via a single-factor experiment. The optimized extraction conditions were 30% of water in choline chloride–propanediol (1:4) for heated extraction for 30 min and solid–liquid ratio 1:100 g/ml. Under optimum conditions, the extraction yields of O-nornuciferine, N-nornuciferine, nuciferine and roemerine were 0.069, 0.152, 0.334 and 0.041 g/100 g respectively, which were higher than those of methanol in acidified aqueous solution. This study suggests considerable potential for DESs as promising materials for the green and efficient extraction solvents for bioactive alkaloids from natural sources.     

Magnetic graphene oxide modified with choline chloride-based deep eutectic solvent for the solid-phase extraction of protein

Four kinds of green deep eutectic solvents (DESs) based on choline chloride (ChCl) have been synthesized and coated on the surface of magnetic graphene oxide (Fe₃O₄@GO) to form Fe₃O₄@GO-DES for the magnetic solid-phase extraction of protein. X-ray diffraction (XRD), vibrating sample magnetometer (VSM), Fourier transform infrared spectrometry (FTIR), field emission scanning electron microscopy (FESEM) and thermal gravimetric analysis (TGA) were employed to characterize Fe₃O₄@GO-DES, and the results indicated the successful preparation of Fe₃O₄@GO-DES. The UV–vis spectrophotometer was used to measure the concentration of protein after extraction. Single factor experiments proved that the extraction amount was influenced by the types of DESs, solution temperature, solution ionic strength, extraction time, protein concentration and the amount of Fe₃O₄@GO-DES. Comparison of Fe₃O₄@GO and Fe₃O₄@GO-DES was carried out by extracting bovine serum albumin, ovalbumin, bovine hemoglobin and lysozyme. The experimental results showed that the proposed Fe₃O₄@GO-DES performs better than Fe₃O₄@GO in the extraction of acidic protein. Desorption of protein was carried out by eluting the solid extractant with 0.005 mol L⁻¹ Na₂HPO₄ contained 1 mol L⁻¹ NaCl. The obtained elution efficiency was about 90.9%. Attributed to the convenient magnetic separation, the solid extractant could be easily recycled.     

Electrodeposition and characterization of Zn–Sn alloy coatings from a deep eutectic solvent based on choline chloride for corrosion protection

With the aim of obtaining high corrosion resistant Zn–Sn alloy coatings from an ionic liquid, the effects of electrodeposition potential and electrolyte composition on the electrodeposition behavior, film composition, morphology and corrosion performance were investigated. Cyclic voltammograms indicate that Zn and Sn were co‐deposited at distinct reduction potentials as pure Zn and Sn elements. In addition, the phase composition analysis also showed that the obtained Zn–Sn alloy deposits (8 wt.%–45 wt.% Zn) consist of a two‐phase mechanical mixture of small aggregates of Zn and Sn metals. The Zn content of the alloy significantly increases as the electrodeposition potential and electrolyte Zn (II)/Sn (II) ratio increase. The corrosion performance study of the obtained Zn–Sn coatings showed that they have a passivation behavior and their corrosion resistance increases as the alloy‐Sn content increases. To improve their morphological properties, ethylene diamine tetraacetic acid additive was introduced into the electrolyte and greatly improved the morphology and corrosion resistance of the deposits. For the first time, it was shown that high corrosion resistance Zn–Sn coatings can be obtained from ionic liquids. Copyright © 2014 John Wiley & Sons, Ltd.     

基于DCGANs的半片光伏组件电致发光图像增强技术

针对半片光伏组件电致发光（electroluminescence,EL）缺陷自动识别过程中训练用样本不足导致模型过拟合的问题,采用深度卷积生成对抗网络（deep convolutional generative adversarial networks,DCGANs）生成可控制属性的半片光伏组件EL图像,再采用多尺度结构相似性（multiscale structural similarity,MS-SSIM）指标对生成的EL图像与拍摄的EL图像之间的相似程度进行了评估。评估结果得到,使用DCGANs生成的所有类型半片光伏组件的EL图像与拍摄的EL图像的MS-SSIM指标都大于0.55,大部分的MS-SSIM值在0.7附近。在分类模型的训练过程中,测试集准确率随着训练集中生成图像数量的增加而升高,当生成图像数量达到6 000张时,测试集准确率达到97.92%。实验结果表明,采用DCGANs能够生成高质量且可控制属性的半片光伏组件EL图像,较好地解决因缺少训练样本而导致的模型过拟合问题。