Is Argument Structure of Learner Chinese Understandable: A Corpus-Based Analysis

This paper presents a corpus-based analysis of argument structure errors in learner Chinese. The data for analysis includes sentences produced by language learners as well as their corrections by native speakers. We couple the data with semantic role labeling annotations that are manually created by two senior students whose majors are both Applied Linguistics. The annotation procedure is guided by the Chinese PropBank specification, which is originally developed to cover first language phenomena. Nevertheless, we find that it is quite comprehensive for handling second language phenomena. The inter-annotator agreement is rather high, suggesting the understandability of learner texts to native speakers. Based on our annotations, we present a preliminary analysis of competence errors related to argument structure. In particular, speech errors related to word order, word selection, lack of proposition, and argument-adjunct confounding are discussed.Comment: Proceedings of the 2018 International Conference on Bilingual Learning and Teaching (ICBLT-2018

A LiDAR-Inertial SLAM Tightly-Coupled with Dropout-Tolerant GNSS Fusion for Autonomous Mine Service Vehicles

Multi-modal sensor integration has become a crucial prerequisite for the real-world navigation systems. Recent studies have reported successful deployment of such system in many fields. However, it is still challenging for navigation tasks in mine scenes due to satellite signal dropouts, degraded perception, and observation degeneracy. To solve this problem, we propose a LiDAR-inertial odometry method in this paper, utilizing both Kalman filter and graph optimization. The front-end consists of multiple parallel running LiDAR-inertial odometries, where the laser points, IMU, and wheel odometer information are tightly fused in an error-state Kalman filter. Instead of the commonly used feature points, we employ surface elements for registration. The back-end construct a pose graph and jointly optimize the pose estimation results from inertial, LiDAR odometry, and global navigation satellite system (GNSS). Since the vehicle has a long operation time inside the tunnel, the largely accumulated drift may be not fully by the GNSS measurements. We hereby leverage a loop closure based re-initialization process to achieve full alignment. In addition, the system robustness is improved through handling data loss, stream consistency, and estimation error. The experimental results show that our system has a good tolerance to the long-period degeneracy with the cooperation different LiDARs and surfel registration, achieving meter-level accuracy even for tens of minutes running during GNSS dropouts

Hybrid Optimization Algorithm for Vehicle Routing Problem with Simultaneous Delivery-Pickup

In order to provide reasonable and effective decision support for logistics enterprises in vehicle distribu-tion path planning, this paper studies the vehicle routing problem with simultaneous delivery-pickup and time windows (VRPSDPTW) for single distribution center distribution mode, and establishes a mathematical model with the objective of minimizing the total distribution cost. According to the characteristics of the model, a hybrid optimization algorithm (SA-ALNS) based on the combination of simulated annealing (SA) and adaptive large-scale neighborhood search (ALNS) is proposed. An insertion heuristic algorithm based on time and distance weighting is used to construct the initial solution of the problem. A variety of delete and insert operators are introduced to optimize the path with adaptive selection strategy. Through the feedback mechanism, the probability of each operator being selected is gradually adjusted to make the algorithm more inclined to choose the operator with better optimization effect. The Metropolis criterion of simulated annealing mechanism is used to control the solution updating. In the simulation experiment, 56 large-scale examples are tested, and other intelligent optimization algori-thms such as p-SA algorithm, DCS algorithm and VNS-BSTS are compared and statistically analyzed. The results show that the algorithm is feasible and superior in solving the vehicle routing problem with simultaneous delivery-pickup and time windows. The research results greatly enrich the related research of vehicle routing problem (VRP)

Evaluation on substitution of energy transition—An empirical analysis based on factor elasticity

The study explores into the dynamic change features and technological differences in substitution between factors and energy sources for various types of China’s technological progresses from 1990 to 2020. The measurement for such a study is conducted from the perspective of factor substitution by employing the transcendental logarithmic production function. The results reveal that the sources of contribution to China’s economic development are mainly attributed to non-energy factors such as capital and labor, as capital and labor can effectively substitute energy, and non-fossil energy sources possess certain comparative advantages over fossil energy sources in terms of technology within energy factors. With such an increase in substitution, the trend of clean energy substitution for fossil energy is irreversible. Accordingly, it is proposed that the path for energy conservation and consumption reduction via energy transformation be achieved by increasing input into capital and labor to improve the utilization efficiency of these two factors from the perspective of factor substitution. Meanwhile, preferences should be delivered for the development of non-fossil energy sources in terms of technology bias and input scale

Heat stress affects tassel development and reduces the kernel number of summer maize

Maize grain yield is drastically reduced by heat stress (HTS) during anthesis and early grain filling. However, the mechanism of HTS in reproductive organs and kernel numbers remains poorly understood. From 2018 to 2020, two maize varieties (ND372, heat tolerant; and XY335, heat sensitive) and two temperature regimens (HTS, heat stress; and CK, natural control) were evaluated, resulting in four treatments (372CK, 372HTS, 335CK, and 335HTS). HTS was applied from the nine-leaf stage (V9) to the anthesis stage. Various morphological traits and physiological activities of the tassels, anthers, and pollen from the two varieties were evaluated to determine their correlation with kernel count. The results showed that HTS reduced the number of florets, tassel volume, and tassel length, but increased the number of tassel branches. HTS accelerates tassel degradation and reduces pollen weight, quantity, and viability. Deformation and reduction in length and volume due to HTS were observed in both the Nongda 372 (ND372) and Xianyu 335 (XY335) varieties, with the average reductions being 22.9% and 35.2%, respectively. The morphology of the anthers changed more conspicuously in XY335 maize. The number of kernels per spike was reduced in the HTS group compared with the CK group, with the ND372 and XY335 varieties showing reductions of 47.3% and 59.3%, respectively. The main factors underlying the decrease in yield caused by HTS were reductions in pollen quantity and weight, tassel rachis, and branch length. HTS had a greater effect on the anther shape, pollen viability, and phenotype of XY335 than on those of ND372. HTS had a greater impact on anther morphology, pollen viability, and the phenotype of XY335 but had no influence on the appearance or dissemination of pollen from tassel

Automated Extraction of Human Settlement Patterns From Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks

Information extraction from historical maps represents a persistent challenge due to inferior graphical quality and the large data volume of digital map archives, which can hold thousands of digitized map sheets. Traditional map processing techniques typically rely on manually collected templates of the symbol of interest, and thus are not suitable for large-scale information extraction. In order to digitally preserve such large amounts of valuable retrospective geographic information, high levels of automation are required. Herein, we propose an automated machine-learning based framework to extract human settlement symbols, such as buildings and urban areas from historical topographic maps in the absence of training data, employing contemporary geospatial data as ancillary data to guide the collection of training samples. These samples are then used to train a convolutional neural network for semantic image segmentation, allowing for the extraction of human settlement patterns in an analysis-ready geospatial vector data format. We test our method on United States Geological Survey historical topographic maps published between 1893 and 1954. The results are promising, indicating high degrees of completeness in the extracted settlement features (i.e., recall of up to 0.96, F-measure of up to 0.79) and will guide the next steps to provide a fully automated operational approach for large-scale geographic feature extraction from a variety of historical map series. Moreover, the proposed framework provides a robust approach for the recognition of objects which are small in size, generalizable to many kinds of visual documents.</div

Construction and Mechanism of Action of Gelatin/Sodium Hexametaphosphate/Glutamine Aminotransferase Based Composite Hydrogel System

In this study, a composite hydrogel system was constructed by cross-linking of primary network hydrogels of gelatin (GE) and sodium hexametaphosphate (SHMP) by transglutaminase (TGase) after addition of Lactobacillus plantarum in order to improve its viability and bioavailability. The experimental results showed that the modification by SHMP and TGase changed the gel strength, water distribution state, and gel network structure of gelatin, and reduced the gelation rate, so that the three-dimensional network structure of the gel was more stable, and the intermolecular forces of the composite hydrogel was stronger, contributing to the resistance of the encapsulated L. plantarum to adverse environments. The presence of L. plantarum was found to slightly disrupt the ordered structure of the hydrogel by scanning electron microscopy (SEM). Endogenous fluorescence spectroscopy analysis showed that addition of L. plantarum resulted in the exposure of the extended region containing tryptophan within the GE molecule to a more polar environment. The steric effect occurred during the gelling process, delaying the formation of covalent crosslinks and physical interactions between the biopolymer molecules, which led to changes in their microstructure. Simulated gastrointestinal digestion tests and storage tests showed that L. plantarum encapsulated in GE/SHMP/TGase gels had better survival rates and gastrointestinal release properties compared to single GE-based hydrogels. It was confirmed that GE/SHMP/TGase hydrogels had a better protective effect on L. plantarum. In conclusion, this study has explored a new method for preparing GE-based hydrogels as a delivery system for probiotics, which will provide a theoretical basis for the development of probiotic functional foods