The Improvement of Neural Network Cascade-Correlation Algorithm and Its Application in Picking Seismic First Break

Neural Network is a kind of widely used seismic wave travel time auto-picking method. Most commercial software such as Promax often uses Back Propagation (BP) neural network. Here we introduce a cascade-correlation algorithm for constructing neural network. The algorithm’s convergence is faster than BP algorithm and can determine its own network architecture according to training samples, in addition, it can be able to expand network topology to learn new samples. The cascaded-correlation algorithm is improved. Different from the standard cascade-correlation algorithm, improved algorithm starts at an appropriate BP network architecture (exits hidden units), but the standard one’s initial network only includes input layer and output layer. In addition, in order to prevent weight-illgrowth, adding regularization term to the objective function when training candidate hidden units can decay weights. The simulation experiment demonstrates that improved cascade-correlation algorithm is faster convergence speed and stronger generalization ability. Analytically study five attributes, including instantaneous intensity ratio, amplitude, frequency, curve length ratio, adjacent seismic channel correlation. Intersection figure shows that these five attributes have distinctiveness of first break and stability. The neural network first break picking method of this paper has achieved good effect in testing actual seismic data.Key words: Neural network; Cascade-correlation algorithm; Picking seismic first brea

CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose

Animal pose estimation is challenging for existing image-based methods because of limited training data and large intra- and inter-species variances. Motivated by the progress of visual-language research, we propose that pre-trained language models (e.g., CLIP) can facilitate animal pose estimation by providing rich prior knowledge for describing animal keypoints in text. However, we found that building effective connections between pre-trained language models and visual animal keypoints is non-trivial since the gap between text-based descriptions and keypoint-based visual features about animal pose can be significant. To address this issue, we introduce a novel prompt-based Contrastive learning scheme for connecting Language and AniMal Pose (CLAMP) effectively. The CLAMP attempts to bridge the gap by adapting the text prompts to the animal keypoints during network training. The adaptation is decomposed into spatial-aware and feature-aware processes, and two novel contrastive losses are devised correspondingly. In practice, the CLAMP enables a new cross-modal animal pose estimation paradigm. Experimental results show that our method achieves state-of-the-art performance under the supervised, few-shot, and zero-shot settings, outperforming image-based methods by a large margin. The source code will be made publicly available

mer-Triaqua­(1,10-phenanthroline-κ2 N,N′)(sulfato-κO)magnesium(II)

In the title compound, [Mg(SO4)(C12H8N2)(H2O)3], the MgII centre exhibits a slightly distorted octa­hedral coordination environment defined by two N atoms from a 1,10-phenanthroline mol­ecule, one O atom from a sulfate dianion and three meridionally arranged O atoms from coordinated water mol­ecules. The crystal structure involves intra- and intermolecular O—H⋯O hydrogen bonds

Ultrastructure and Topochemistry of Plant Cell Wall by Transmission Electron Microscopy

Plant cell walls are typically described as complex macromolecular composites consisting of an ordered array of cellulose microfibrils embedded in a matrix of non-cellulosic polysaccharides and lignin. Generally, the plant cell wall can be divided into three major layers: middle lamella, primary cell wall, and secondary cell wall. Investigation of plant cell walls is complicated by the heterogeneous and complex hierarchical structure, as well as variable chemical composition between different sub-layers. Thus, a complete understanding of the ultrastructure of plant cell walls is necessary. Transmission electron microscopy (TEM) has proven to be a powerful tool in elucidating fine details of plant cell walls at nanoscale. The present chapter describes the layering structure and topochemistry of plant cell wall revealed by TEM

Monitoring Enzyme Reaction and Screening of Inhibitors of Acetylcholinesterase by Quantitative Matrix-Assisted Laser Desorption/Ionization Fourier Transform Mass Spectrometry

A matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS)–based assay was developed for kinetic measurements and inhibitor screening of acetylcholinesterase. Here, FTMS coupled to MALDI was applied to quantitative analysis of choline using the ratio of choline/acetylcholine without the use of additional internal standard, which simplified the experiment. The Michaelis constant (Km) of acetylcholinesterase (AChE) was determined to be 73.9 μmol L−1 by this approach. For Huperzine A, the linear mixed inhibition of AChE reflected the presence of competitive and noncompetitive components. The half maximal inhibitory concentration (IC50) value of galantamine obtained for AChE was 2.39 μmol L−1. Inhibitory potentials of Rhizoma Coptidis extracts were identified with the present method. In light of the results the referred extracts as a whole showed inhibitory action against AChE. The use of high-resolution FTMS largely eliminated the interference with the determination of ACh and Ch, produced by the low-mass compounds of chemical libraries for inhibitor screening. The excellent correlation with the reported kinetic parameters confirms that the MS-based assay is both accurate and precise for determining kinetic constants and for identifying enzyme inhibitors. The obvious advantages were demonstrated for quantitative analysis and also high-throughput characterization. This study offers a perspective into the utility of MALDI-FTMS as an alternate quantitative tool for inhibitor screening of AChE

Learning to Construct 3D Building Wireframes from 3D Line Clouds

Line clouds, though under-investigated in the previous work, potentially encode more compact structural information of buildings than point clouds extracted from multi-view images. In this work, we propose the first network to process line clouds for building wireframe abstraction. The network takes a line cloud as input , i.e., a nonstructural and unordered set of 3D line segments extracted from multi-view images, and outputs a 3D wireframe of the underlying building, which consists of a sparse set of 3D junctions connected by line segments. We observe that a line patch, i.e., a group of neighboring line segments, encodes sufficient contour information to predict the existence and even the 3D position of a potential junction, as well as the likelihood of connectivity between two query junctions. We therefore introduce a two-layer Line-Patch Transformer to extract junctions and connectivities from sampled line patches to form a 3D building wireframe model. We also introduce a synthetic dataset of multi-view images with ground-truth 3D wireframe. We extensively justify that our reconstructed 3D wireframe models significantly improve upon multiple baseline building reconstruction methods. The code and data can be found at https://github.com/Luo1Cheng/LC2WF.Comment: 10 pages, 6 figure

The Relationship between Gentle Tactile Stimulation on the Fetus and Its Temperament 3 Months after Birth

Objective. The aim of this study was to evaluate the effect of gentle tactile stimulation on the fetus in its temperament 3 months after birth. Method. A total of 302 mother-3-month-infant dyads enrolled the retrospective cohort study. 76 mothers had regular gentle tactile stimulation on the fetus in their pregnancy; 62 mothers had irregular tactile stimulation on the fetus, and the rest of 164 mothers who had no tactile stimulation served as nonexposure group. Temperament was assessed using the EITS (a ninedimensional scale of temperament). Results. Significant difference in temperament type was found among infants in 3 groups at 3 months of age. In the regular practice group, the babies with easy type temperament accounted for 73.7%, which was higher than that in irregular practice group (53.2%, = 0.012) and that in the control group (42.1%, < 0.001). Compared to infants in no practice group, the infants who had received regular gentle tactile stimulation before birth were lower in negative mood ( = 0.047) while higher in adaptability ( < 0.001), approach ( = 0.001), and persistence ( = 0.001), respectively. Conclusion. Regular gentle tactile stimulation on fetus may promote the formation of easy type infant temperament