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.     

Tunable and highly sensitive temperature sensor based on graphene photonic crystal fiber

Optical fiber temperature sensors have been widely employed in enormous areas ranging from electric power industry, medical treatment, ocean dynamics to aerospace. Recently, graphene optical fiber temperature sensors attract tremendous attention for their merits of simple structure and direct power detecting ability. However, these sensors based on transfer techniques still have limitations in the relatively low sensitivity or distortion of the transmission characteristics, due to the unsuitable Fermi level of graphene and the destruction of fiber structure, respectively. Here, we propose a tunable and highly sensitive temperature sensor based on graphene photonic crystal fiber (Gr-PCF) with the non-destructive integration of graphene into the holes of PCF. This hybrid structure promises the intact fiber structure and transmission mode, which efficiently enhances the temperature detection ability of graphene. From our simulation, we find that the temperature sensitivity can be electrically tuned over four orders of magnitude and achieve up to ～ 3.34×10^-3 dB/(cm·℃) when the graphene Fermi level is ～ 35 meV higher than half the incident photon energy. Additionally, this sensitivity can be further improved by ～ 10 times through optimizing the PCF structure (such as the fiber hole diameter) to enhance the light-matter interaction. Our results provide a new way for the design of the highly sensitive temperature sensors and broaden applications in all-fiber optoelectronic devices.     

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.     

Zn or O? An Atomic Level Comparison on Antibacterial Activities of Zinc Oxides

For the first time, the influence of different types of atoms (Zn and O) on the antibacterial activities of nanosized ZnO was quantitatively evaluated with the aid of a 3D‐printing‐manufactured evaluation system. Two different outermost atomic layers were manufactured separately by using an ALD (atomic layer deposition) method. Interestingly, we found that each outermost atomic layer exhibited certain differences against gram‐positive or gram‐negative bacterial species. Zinc atoms as outermost layer (ZnO?Zn) showed a more pronounced antibacterial effect towards gram‐negative E. coli (Escherichia coli), whereas oxygen atoms (ZnO?O) showed a stronger antibacterial activity against gram‐positive S. aureus (Staphylococcus aureus). A possible antibacterial mechanism has been comprehensively discussed from different perspectives, including Zn²⁺ concentrations, oxygen vacancies, photocatalytic activities and the DNA structural characteristics of different bacterial species.     

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.     

The impact of a conceptual model-based mathematics computer tutor on multiplicative reasoning and problem-solving of students with learning disabilities

The study explored the impact of Please Go Bring Me-COnceptual Model-based Problem Solving (PGBM-COMPS) computer tutoring system on multiplicative reasoning and problem solving of students with learning disabilities. The PGBM-COMPS program focused on enhancing the multiplicative reasoning and problem solving through nurturing fundamental mathematical ideas and moving students above and beyond the concrete level of operation. This is achieved by taking advantages of the constructivist approach from mathematics education and explicit conceptual model-based problem solving approach from special education. Participants were three elementary students with learning disabilities (LD). A mixed method design was employed to investigate the effect of the PGBM-COMPS program on enhancing students’ multiplicative reasoning and problem solving. It was found that the PGBM-COMPS program significantly improved participating students’ problem solving performance not only on researcher developed criterion tests but also on a norm-referenced standardized test. Qualitative and quantities data from this study indicate that, in addition to nurturing fundamental concept of composite units, it is necessary to help students to understand underlying problem structures and move toward mathematical model-based problem representation and solving for generalized problem solving skills.     

Multi-guasiparticle excitations of ~(91)Ru

The level structure in neutron-deficient nucleus 91Ru was investigated via the 58Ni(36Ar,2 plnγ)Ru reaction at a beam energy of 111 MeV.Charged particles,neutrons,and y-rays were emitted in this reaction and detected by the DIAMANT CsI ball,Neutron Wall,and the EXOGAM Ge clover array,respectively.In addition to the previously reported levels in 91Ru,new low-to-medium spin states were observed.Angular correlation and linear polarization measurements were performed to unambiguously determine spins and parities of the excited states in 91 Ru.The low-spin states of 91 Ru exhibit a scheme of multi-quasiparticle excitations,which is very similar to that of the neighboring N=47 isotone.These excitations have been interpreted in terms of the shell model.The calculations performed in the configuration space(p3/2,f5/2,p1/2,g9/2)reproduce the experimental excitation energies reasonably well,supporting the interpretation of the newly assigned positive-parity states in terms of the three quasiparticle configurationsπ(g9/2)^-2v(g9/2^-1 and v(g9/2)^-3.     

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。