Exploration of Integrating Ideological and Political Education into the Curriculum of “Road Survey and Design” from the Perspective of Moral and Intellectual Education in the New Era

In the context of the new era and the goal of moral and intellectual education, the design of ideological and political education in curriculum holds significant importance for civil engineering majors. By incorporating ideological and political education into the curriculum, students’ political awareness, comprehensive qualities, innovative spirit, social responsibility, environmental consciousness, attention to social development, and technological innovation can be cultivated. After integrating ideological and political content into the “Road Survey and Design” course, there have been significant improvements in students’ political awareness, comprehensive qualities, innovative spirit, social responsibility, environmental consciousness, career development awareness, and teachers’ instructional capabilities

Exploration and Innovative Research on Ideological and Political Education in Architectural Mechanics Course

The emphasis and implementation of ideological and political education in courses lie within the courses themselves, ensuring effective education through the process of course implementation and playing a role in nurturing students. This paper explores the elements of ideological and political education in the architectural mechanics course. It provides a detailed design of ideological and political education in accordance with the course content and characteristics. Additionally, innovative designs incorporating ideological elements are introduced for parts of the course. Finally, an application exploration is conducted within the sections of the course, specifically focusing on the subtopic of “rigid frames”, with the hope of providing reference value to related courses

Exploration of Teaching Reform in the Course of Concrete Structure Design Principles in the Context of the New Era

In response to the requirements for cultivating applied talents in civil engineering under the background of the new era, and addressing the practical problems existing in the teaching of the course on concrete structure design principles in higher education institutions, this paper conducts an analysis of the current situation and challenges of the course teaching. It focuses on issues such as the abstract difficulty of teaching content, the singularity of teaching methods, and the lack of practical teaching. Combining the characteristics of the course and the industry’s demand for talent capabilities, the paper explores and outlines reform measures, including optimizing course content, transforming the roles of teachers and students, and integrating theory with practice. The aim is to provide insights and inspiration for the teaching of related courses

In-Domain GAN Inversion for Faithful Reconstruction and Editability

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution. Code, models, and demo are available at the project page: https://genforce.github.io/idinvert/

LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their large number of parameters and computational loads, making them unsuitable for mobile health applications. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as competitive alternatives to CNN and Transformer architectures. Building upon this, we employ Mamba as a lightweight substitute for CNN and Transformer within UNet, aiming at tackling challenges stemming from computational resource limitations in real medical settings. To this end, we introduce the Lightweight Mamba UNet (LightM-UNet) that integrates Mamba and UNet in a lightweight framework. Specifically, LightM-UNet leverages the Residual Vision Mamba Layer in a pure Mamba fashion to extract deep semantic features and model long-range spatial dependencies, with linear computational complexity. Extensive experiments conducted on two real-world 2D/3D datasets demonstrate that LightM-UNet surpasses existing state-of-the-art literature. Notably, when compared to the renowned nnU-Net, LightM-UNet achieves superior segmentation performance while drastically reducing parameter and computation costs by 116x and 21x, respectively. This highlights the potential of Mamba in facilitating model lightweighting. Our code implementation is publicly available at https://github.com/MrBlankness/LightM-UNet

A comprehensive review on the ferroelectric orthochromates: Synthesis, property, and application

Multiferroics represent a class of advanced materials for promising applications and stand at the forefront of modern science for the special feature possessing both charge polar and magnetic order. Previous studies indicate that the family of RECrO3 (RE = rare earth) compounds is likely another rare candidate system holding both ferroelectricity and magnetism. However, many issues remain unsolved, casting hot disputes about whether RECrO3 is multiferroic or not. For example, an incompatibility exists between reported structural models and observed ferroelectric behaviors, and it is not easy to determine the spin canting degree. To address these questions, one key step is to grow single crystals because they can provide more reliable information than other forms of matter do. In this review, the parent and doped ferroelectric YCrO3 compounds are comprehensively reviewed based on scientific and patent literatures from 1954 to 2022. The materials syntheses with different methods, including poly-, nano-, and single-crystalline samples and thin films, are summarized. The structural, magnetic, ferroelectric and dielectric, optical, and chemical-pressure (on Y and Cr sites by doping) dependent chemical and physical properties and the corresponding phase diagrams, are discussed. Diverse (potential) applications, including anti-corrosion, magnetohydrodynamic electrode, catalyst, negative-temperature-coefficient thermistor, magnetic refrigeration, protective coating, and solid oxide fuel cell, are present. To conclude, we summarize general results, reached consensuses, and existing controversies of the past nearly 69 years of intensive studies and highlight future research opportunities and emerging challenges to address existing issues.Comment: 69 pages, 35 figures, accepted by Coordination Chemistry Review

A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care

The COVID-19 pandemic has posed a heavy burden to the healthcare system worldwide and caused huge social disruption and economic loss. Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data. Despite their initial success in certain clinical applications, there is currently a lack of benchmarking results to achieve a fair comparison so that we can select the optimal model for clinical use. Furthermore, there is a discrepancy between the formulation of traditional prediction tasks and real-world clinical practice in intensive care. To fill these gaps, we propose two clinical prediction tasks, Outcome-specific length-of-stay prediction and Early mortality prediction for COVID-19 patients in intensive care units. The two tasks are adapted from the naive length-of-stay and mortality prediction tasks to accommodate the clinical practice for COVID-19 patients. We propose fair, detailed, open-source data-preprocessing pipelines and evaluate 17 state-of-the-art predictive models on two tasks, including 5 machine learning models, 6 basic deep learning models and 6 deep learning predictive models specifically designed for EHR data. We provide benchmarking results using data from two real-world COVID-19 EHR datasets. One dataset is publicly available without needing any inquiry and another dataset can be accessed on request. We provide fair, reproducible benchmarking results for two tasks. We deploy all experiment results and models on an online platform. We also allow clinicians and researchers to upload their data to the platform and get quick prediction results using our trained models. We hope our efforts can further facilitate deep learning and machine learning research for COVID-19 predictive modeling.Comment: Junyi Gao, Yinghao Zhu and Wenqing Wang contributed equall

Stereoscopic video quality assessment based on 3D convolutional neural networks

The research of stereoscopic video quality assessment (SVQA) plays an important role for promoting the development of stereoscopic video system. Existing SVQA metrics rely on hand-crafted features, which is inaccurate and time-consuming because of the diversity and complexity of stereoscopic video distortion. This paper introduces a 3D convolutional neural networks (CNN) based SVQA framework that can model not only local spatio-temporal information but also global temporal information with cubic difference video patches as input. First, instead of using hand-crafted features, we design a 3D CNN architecture to automatically and effectively capture local spatio-temporal features. Then we employ a quality score fusion strategy considering global temporal clues to obtain final video-level predicted score. Extensive experiments conducted on two public stereoscopic video quality datasets show that the proposed method correlates highly with human perception and outperforms state-of-the-art methods by a large margin. We also show that our 3D CNN features have more desirable property for SVQA than hand-crafted features in previous methods, and our 3D CNN features together with support vector regression (SVR) can further boost the performance. In addition, with no complex preprocessing and GPU acceleration, our proposed method is demonstrated computationally efficient and easy to use