Synthesizing controllable, photo-realistic images and videos is one of the fundamental goals of computer graphics. Neural rendering is a rapidly emerging field in image synthesis that allows a compact representation of scenes, and by utilizing neural networks, rendering can be learned from existing observations. Neural Radiance Fields (NeRF) implement an effective combination of Neural Fields and the graphics component Volume rendering. It achieves the first photo-level view synthesis effect using an implicit representation. Unlike previous approaches, NeRF chooses Volume as an intermediate representation to reconstruct an implicit Volume. Although the advantages of NeRF are apparent, there are many drawbacks in the original version of NeRF: it is slow to train and render, requires a large number of perspectives, can only represent static scenes, and the trained NeRF representation does not generalize to other scenes. This report focuses on optimizing the shortcomings mentioned above of NeRF by scholars in the last three years and analyzes the solutions to the problems of NeRF from several perspectives.

The Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K) is a well-known research endeavor in the field of child development. In this research, some special education services are offered to those students who need supplementary support in some aspects. In this paper, our study aims to estimate the average treatment effect on students’ fifth grade math scores and assesses the effectiveness of these special education services based on the ECLS-K dataset, through both machine learning methods and traditional methods. We introduce Donald Rubin’s causal model and Propensity Score Analysis in the part of traditional methods, and Ordinary Least Squares (OLS), Targeted Maximum Likelihood Estimation (TMLE), Bayesian Additive Regression Trees (BART), Generalized Random Forests (GRF) and Double Machine Learning (DML) in the part of machine learning methods. Finally, we employ Propensity Score Matching, OLS and BART to estimate the ATE. All estimated ATEs are significantly different from zero. The estimated ATEs are found to be minus, suggesting that these special education services may have a negative effect on students’ fifth grade math scores. Obviously, this conclusion is inconsistent with the original intent of these services, which aimed to have a positive impact.

Today, medical and rehabilitation exoskeletons are chosen by more therapists to treat individuals with lower limb injuries. A flexible lower limb exoskeleton (FLLE) is a new exoskeleton robot for rehabilitation. Compared with rigid lower extremity exoskeleton (RLLE), FLLE has the advantages of lower weight, better compliance, lower energy consumption, and higher safety. This paper reviews the development and innovation of FLLE in recent years from the aspects of driving mode, design requirements and critical technologies. The characteristics of existing FLLE products are analyzed and summarized, and the challenges and future development directions of the research, such as the construction of a general, flexible exoskeleton power system model, motion intention and fast processing of pattern recognition, are discussed.

The live-streaming industry has seen significant growth in recent years, driven by advances in virtual reality technology and the growing popularity of online content consumption. This article explores the potential of virtual reality (VR) to revolutionize the live streaming industry, and analyzes VR's impact on user experience and content creation. After discussing the potential impact of VR technology on broadcasters, content creators and viewers, the article also elaborates on the main challenges and opportunities related to VR and live streaming platforms. The findings of this study provide valuable insights for researchers, practitioners, and industry stakeholders interested in understanding the role of VR in shaping the future of live streaming.

This paper describes an intelligent health monitoring system developed for detecting the health of the elderly. This paper introduces an intelligent health monitoring system used to detect the health status of the elderly. With the progress of mobile communication technology and the increasing demand for personal intelligence, wearable devices have become more and more popular products. The system can provide personalized services for the elderly, and also provide more comprehensive and accurate data support for medical staff, which is expected to play an important role in the future care of the elderly.

In this article, I propose four model classifications to summarize the characteristics and analyze the advantages and disadvantages of text generation models that have emerged in recent years, so as to give researchers an overall overview. The models based on the decoder only use the decoder for text extraction, and its output only depends on the previous output. The models based on the encoder-decoder, on the other hand, refer to both the encoder's output and the previous prediction. I've deliberately categorized prefix models and ensemble models to highlight their differences. I also present the current state of the text generation field and compare the advantages and disadvantages of several of these models. Finally, I summarize the difficulties encountered in the field of text generation and provide a research direction for the field. In the module Challenges, I focused on the problem of scarcity regarding datasets. The current solutions are given, as well as the efforts made by relevant workers on domain-specific datasets.

A personalized medical recommendation system is an intelligent system that utilizes medical data to provide targeted medical advice and services to individuals. With the lack of accumulation and development of medical data, personalized medical recommendation systems have great potential in improving medical effectiveness and saving medical resources. This article aims to explore the principles, methods, and applications of personalized medical recommendation systems based on medical data.

Artificial intelligence has played an important role in the overseas study industry. It provides students with more convenient ways to inquire and apply for overseas study information, and helps students to better choose their study abroad goals and schools through intelligent recommendation systems and personalized counseling services. At the same time, artificial intelligence is also applied to language learning and document writing, providing more efficient learning tools and writing AIDS. However, AI also presents some challenges, such as information security and privacy protection issues, as well as vicarious impacts on human professional advisors.

The study used the deep learning method to achieve the natural scene classification of remote sensing images, which were taken by the satellite Tiangong-2. Because of the diversity of remote sensing images, a Convolutional Neural Network (CNN) model that can complete the task of classifying natural scenes of remote sensing images was constructed using the variant ResNeSt based on the Residual Neural Network (ResNet). The NaSC-TG2 remote sensing image dataset released by the Space Application Engineering and Technology Center of the Chinese Academy of Sciences was used in this work. The dataset consists of 20,000 photos that are grouped into ten scene groups on average, with 2,000 images per scene category. And nine models including ResNet50, ResNet101, ResNet200, SE-ResNet50, SE-ResNeXt50, SE-ResNeXt101, SE-ResNeXt152, ResNeSt50, ResNeSt101 and ResNeSt200 were compared and tested on the NaSC-TG2 dataset. After training and testing on the dataset, ResNeSt101 achieved better results than other comparative models in the end, with the highest accuracy of 98.52% on the testing sets. This study offers a technique for categorizing remote sensing picture scenes and has made some significant contributions to space geoscience and application research.

Traffic congestion is one of the serious problems facing modern cities, posing a huge challenge to people’s travel and urban development. And it has been proved that traffic lights should be responsible for the congestion instead of too many cars on the road occasionally. So it is very important to find the optimal control of the traffic light signal. The purpose of this experiment is to explore ways to optimally control traffic light signals in order to reduce the average waiting time for people and vehicles at intersections. This paper used a stochastic simulation approach, based on an assumed Poisson process and Gamma distribution, to simulate the specific time for vehicles and pedestrians to arrive at the intersection over the course of an hour, and used this to calculate the average waiting time. We investigated the effect of the duration of red and green lights on the average waiting time and wrote the corresponding code for simulation and calculation through MATLAB. The experimental results show that shorter red light duration and moderate green light duration can achieve optimal results when there are more cars. On the contrary, shorter green light duration and moderate red light duration can achieve optimal results when there are more pedestrians. We reached this conclusion through experiments and evaluated the hypotheses in the experiments.