Move Analysis of Conclusion Section of Aerospace Research Article

This paper reports on the move analysis of the conclusion section of the aerospace English research article RA The results are based on the identification of 50 pieces of aerospace conclusion section of RA published in two leading journals that were written by English native writers from 2018 to 2023 Yang Allison 2003 and Zhiqing Hu 2007 s model are the starting point for the analysis Then the major Moves and Steps were extracted and the frequencies were calculated which attempts to provide a modified model for aerospace research articles conclusion section The results reveal that the aerospace conclusion section has three major Moves which are Move 1 Summary of study Move 2 evaluation of study Move 3 suggestion for future research Most of the conclusions have Move 1 and Move 2 This study aims at improving the genre awareness of novice and non-native researchers in order to facilitate their disciplinary writing publishing and readin

The Implementation of Genre-Based Teaching Method in Aerospace English Academic Writing Class

Notwithstanding the voluminous literature devoted to academic writing especially using genre analysis more investigation needs to be done on academic writing pedagogy focusing on discipline-specific However English for Academic Purpose EAP has long been focusing on what students learn instead of how they learn In this regard this study provides an integrated genre-based academic writing teaching model by classroom observation and interview combining Rothery s 1994 genre-based teaching learning cycle Hommand 1994 s circular writing model with Swales 1990 move analysis to shed some light on aerospace English academic writing classroom setting This study also designs interviews to investigate the views of students toward genre-based teaching model and academic English writing Hopefully this integrated genre-based teaching model can develop students writing ability while improving their academic literac

Deep Sufficient Representation Learning via Mutual Information

We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approximation power of deep neural networks. MSRL learns a sufficient representation with the maximum mutual information with the response and a user-selected distribution. It can easily handle multi-dimensional continuous or categorical response variables. MSRL is shown to be consistent in the sense that the conditional probability density function of the response variable given the learned representation converges to the conditional probability density function of the response variable given the predictor. Non-asymptotic error bounds for MSRL are also established under suitable conditions. To establish the error bounds, we derive a generalized Dudley's inequality for an order-two U-process indexed by deep neural networks, which may be of independent interest. We discuss how to determine the intrinsic dimension of the underlying data distribution. Moreover, we evaluate the performance of MSRL via extensive numerical experiments and real data analysis and demonstrate that MSRL outperforms some existing nonlinear sufficient dimension reduction methods.Comment: 43 pages, 6 figures and 5 table

Multi-View Representation is What You Need for Point-Cloud Pre-Training

A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge. This paper proposes a novel approach to point-cloud pre-training that learns 3D representations by leveraging pre-trained 2D networks. Different from the popular practice of predicting 2D features first and then obtaining 3D features through dimensionality lifting, our approach directly uses a 3D network for feature extraction. We train the 3D feature extraction network with the help of the novel 2D knowledge transfer loss, which enforces the 2D projections of the 3D feature to be consistent with the output of pre-trained 2D networks. To prevent the feature from discarding 3D signals, we introduce the multi-view consistency loss that additionally encourages the projected 2D feature representations to capture pixel-wise correspondences across different views. Such correspondences induce 3D geometry and effectively retain 3D features in the projected 2D features. Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks, including 3D shape classification, part segmentation, 3D object detection, and semantic segmentation, achieving state-of-the-art performance.Comment: 14 pages, 6 figure

Linearly and Circularly Polarized Emission in Sagittarius A*

We perform general relativistic ray-tracing calculations of the transfer of polarized synchrotron radiation through the relativistic accretion flow in Sagittarius (Sgr) A*. Based on a two-temperature magneto-rotational-instability (MRI) induced accretion mode, the birefringence effects are treated self-consistently. By fitting the spectrum and polarization of Sgr A* from millimeter to near-infrared bands, we are able to not only constrain the basic parameters related to the MRI and the electron heating rate, but also limit the orientation of the accretion torus. These constraints lead to unique polarimetric images, which may be compared with future millimeter and sub-millimeter VLBI observations. In combination with general relativistic MHD simulations, the model has the potential to test the MRI with observations of Sgr A*.Comment: 12 pages, 2 figures, ApJL accepte

Air-Fuel Ratio Control of Spark Ignition Engines With Unknown System Dynamics Estimator: Theory and Experiments

This brief addresses the emission reduction of spark ignition engines by proposing a new control to regulate the air-fuel ratio (AFR) around the ideal value. After revisiting the engine dynamics, the AFR regulation is represented as a tracking control of the injected fuel amount. This allows to take the fuel film dynamics into consideration and simplify the control design. The lumped unknown engine dynamics in the new formulation are online estimated by suggesting a new effective unknown system dynamics estimator. The estimated variable can be superimposed on a commercially configured, well-calibrated gain scheduling like proportional-integral-differential (PID) control to achieve a better AFR response. The salient feature of this proposed control scheme lies in its simplicity and the small number of required measurements, that is, only the air mass flow rate, the pressure and temperature in the intake manifold, and the measured AFR value are used. Practical experiments on a Tata Motors Limited two-cylinder gasoline engine are carried out under a realistic driving cycle. The comparative results show that the proposed control can achieve an improved AFR control response and reduced emissions