Analysis of Research Papers on the Use of English Movies in Chinese Senior High School English Teaching in the Past Three Years

Much attention has been paid to using English movies in senior high school English teaching in China since the release of 2017 edition of Senior High School English Curriculum Standards that lists English Movie Appreciation as an optional compulsory course. Senior high school teachers have conducted substantial research over the past three years regarding the practical application of English movies in their teaching, yet there has been a lack of thorough and systematic of analysis of the large number of relevant papers published, which, more or less, limits the understanding and further advancements of this study area. This paper attempts, in light of the emerging systematic review methodology, to present an overview of the research papers spanning the period 2019 to 2022 on the use of English movies in Chinese senior high school from the four perspectives: language ability, cultural awareness, thinking quality, and learning ability based on the concept of core literacy of English as an academic discipline in the new curriculum standard. With focus on the discussion of the common problems and corresponding countermeasures that have been sorted out from the works of front-line teachers, the paper also puts forward suggestions and prospects for further studies

AMPose: Alternatively Mixed Global-Local Attention Model for 3D Human Pose Estimation

The graph convolutional networks (GCNs) have been applied to model the physically connected and non-local relations among human joints for 3D human pose estimation (HPE). In addition, the purely Transformer-based models recently show promising results in video-based 3D HPE. However, the single-frame method still needs to model the physically connected relations among joints because the feature representations transformed only by global relations via the Transformer neglect information on the human skeleton. To deal with this problem, we propose a novel method in which the Transformer encoder and GCN blocks are alternately stacked, namely AMPose, to combine the global and physically connected relations among joints towards HPE. In the AMPose, the Transformer encoder is applied to connect each joint with all the other joints, while GCNs are applied to capture information on physically connected relations. The effectiveness of our proposed method is evaluated on the Human3.6M dataset. Our model also shows better generalization ability by testing on the MPI-INF-3DHP dataset. Code can be retrieved at https://github.com/erikervalid/AMPose.Comment: ICASSP 2023 Accepted Pape

USimAgent: Large Language Models for Simulating Search Users

Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. Nonetheless, accurately simulating user search behaviors has long been a challenge, because users' actions in search are highly complex and driven by intricate cognitive processes such as learning, reasoning, and planning. Recently, Large Language Models (LLMs) have demonstrated remarked potential in simulating human-level intelligence and have been used in building autonomous agents for various tasks. However, the potential of using LLMs in simulating search behaviors has not yet been fully explored. In this paper, we introduce a LLM-based user search behavior simulator, USimAgent. The proposed simulator can simulate users' querying, clicking, and stopping behaviors during search, and thus, is capable of generating complete search sessions for specific search tasks. Empirical investigation on a real user behavior dataset shows that the proposed simulator outperforms existing methods in query generation and is comparable to traditional methods in predicting user clicks and stopping behaviors. These results not only validate the effectiveness of using LLMs for user simulation but also shed light on the development of a more robust and generic user simulators

Maximum Likelihood Estimation of Model Uncertainty in Predicting Soil Nail Loads Using Default and Modified FHWA Simplified Methods

Accuracy evaluation of the default Federal Highway Administration (FHWA) simplified equation for prediction of maximum soil nail loads under working conditions is presented in this study using the maximum likelihood method and a large amount of measured lower and upper bound nail load data reported in the literature. Accuracy was quantitatively expressed as model bias where model bias is defined as the ratio of measured to predicted nail load. The maximum likelihood estimation was carried out assuming normal and lognormal distributions of bias. Analysis outcomes showed that, based on the collected data, the default FHWA simplified nail load equation is satisfactorily accurate on average and the spread in prediction accuracy expressed as the coefficient of variation of bias is about 30%, regardless of the distribution type. Empirical calibrations were proposed to the default FHWA simplified nail load equation for accuracy improvement. The Bayesian Information Criterion was adopted to perform a comparison of suitability between the competing normal and lognormal statistical models that were intended for description of model bias. Example of reliability-based design of soil nail walls against internal pullout limit state of nails is provided in the end to demonstrate the benefit of performing model calibration and using calibrated model for design of soil nails

A New Approach of Waveform Interpretation Applied in Nondestructive Testing of Defects in Rock Bolts Based on Mode Identification

Due to the characteristics of dispersion of guided waves, the waveforms recorded in ultrasonic nondestructive testing (NDT) of rock bolts are complicated to interpret. With a goal to increase the inspection sensitivity and accuracy in NDT of rock bolts, an approach of waveform interpretation based on wave modes identification is developed. The numerical simulation of full rock bolt and rock bolts with grout defect by Finite Element Method (FEM) is applied to illustrate the approach; it is found that the sensitive and low attenuation wave modes exist. Laboratory tests on full rock bolt and rock bolt with grout loss using NDT are conducted to evaluate the efficacy of the approach of waveform interpretation. In addition to that, a parametric study was conducted on rock bolt models with different sectional defect size. Based on the waveform interpretation, the mode-based reflection coefficient R is proposed to evaluate the sensitivity of wave modes to the defect size of sectional area. It is found that the sensitivity of the wave mode does not change with the defect sectional area, and the amplitude depends on the size of the defect

The diagnostic analysis of the planet bearing faults using the torsional vibration signal

© 2019 Elsevier Ltd This paper aims to investigate the effectiveness of using the torsional vibration signal as a diagnostic tool for planet bearing fault detection. The inner race of the planet bearing is connected to the planet carrier and its outer race is connected to the planet gear bore hole. When moving, the planet bearing not only spins around the planet gear axis, but also revolves about the sun gear axis. This rotating mechanism poses a challenge for the condition monitoring of the planet bearing because of the variant vibration transfer paths. The transducer mounted on the carrier arm measuring the torsional vibration is theoretically free from this modulation effect and it is used in this research to extract the diagnostic information from the torsional vibration. A 34 degrees of freedom planetary gear lumped-parameter model with detailed planet bearing model was developed to obtain the dynamic response. The planet bearing was modelled by 5 degrees of freedom, with 2 degrees of freedom from the outer race, 2 degrees of freedom from the inner race and one degree of freedom from the sprung-mass. The variations of the sun-planet and ring-planet mesh stiffnesses were evaluated by the finite element method and the variation of the planet bearing stiffness was evaluated by the Hertzian contact theory. The localized faults on the planet bearing inner race, outer race and the rolling element were created mathematically and then these faults were incorporated into the planetary gear model to obtain the faulted vibration signal. The linear prediction method and the minimum entropy deconvolution method were used to enhance the planet bearing signal and then the amplitude demodulation results were analysed. It was found that the carrier arm instantaneous angular speed was an effective alternative approach for planet gear condition monitoring

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function