128 research outputs found
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/
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
Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity
Electronic Health Record (EHR) data frequently exhibits sparse
characteristics, posing challenges for predictive modeling. Current direct
imputation such as matrix imputation approaches hinge on referencing analogous
rows or columns to complete raw missing data and do not differentiate between
imputed and actual values. As a result, models may inadvertently incorporate
irrelevant or deceptive information with respect to the prediction objective,
thereby compromising the efficacy of downstream performance. While some methods
strive to recalibrate or augment EHR embeddings after direct imputation, they
often mistakenly prioritize imputed features. This misprioritization can
introduce biases or inaccuracies into the model. To tackle these issues, our
work resorts to indirect imputation, where we leverage prototype
representations from similar patients to obtain a denser embedding. Recognizing
the limitation that missing features are typically treated the same as present
ones when measuring similar patients, our approach designs a feature confidence
learner module. This module is sensitive to the missing feature status,
enabling the model to better judge the reliability of each feature. Moreover,
we propose a novel patient similarity metric that takes feature confidence into
account, ensuring that evaluations are not based merely on potentially
inaccurate imputed values. Consequently, our work captures dense prototype
patient representations with feature-missing-aware calibration process.
Comprehensive experiments demonstrate that designed model surpasses established
EHR-focused models with a statistically significant improvement on MIMIC-III
and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code
is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure
the reproducibility
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