From the traditional national style to the artistic development of contemporary traditional art

Senior Project submitted to The Division of Arts of Bard College

Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models

This paper presents a comprehensive survey of ChatGPT and GPT-4, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledge across the entire world wide web, instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF) have played significant roles in enhancing LLMs' adaptability and performance. We performed an in-depth analysis of 194 relevant papers on arXiv, encompassing trend analysis, word cloud representation, and distribution analysis across various application domains. The findings reveal a significant and increasing interest in ChatGPT/GPT-4 research, predominantly centered on direct natural language processing applications, while also demonstrating considerable potential in areas ranging from education and history to mathematics, medicine, and physics. This study endeavors to furnish insights into ChatGPT's capabilities, potential implications, ethical concerns, and offer direction for future advancements in this field.Comment: 35 pages, 3 figure

Microwave Power Absorption in Materials for Ferrous Metallurgy

© 2016, The Minerals, Metals & Materials Society. The characteristics of microwave power absorption in materials for ferrous metallurgy, including iron oxides (Fe2O3, Fe3O4 and Fe0.925O) and bitumite, were explored by evaluating their dielectric loss (QE) and/or magnetic loss (QH) distributions in the 0.05-m-thick slabs of the corresponding materials exposed to 1.2-kW and 2.45-GHz microwave radiation at temperatures below 1100°C. It is revealed that the dielectric loss contributes primarily to the power absorption in Fe2O3, Fe0.925O and the bitumite at all of the examined temperatures. Their QE values at room temperature and slab surface are 9.1311 × 103 W m−3, 23.7025 × 103 W m−3, and 49.5999 × 103 W m−3, respectively, showing that the materials have the following heating rate initially under microwave irradiation: bitumite \u3e Fe0.925O \u3e Fe2O3. Compared with the other materials, Fe3O4 has much stronger power absorption, primarily originated from its magnetic loss (e.g., QH = 1.0615 × 106 W m−3, QH/QE = 2.4185 at 24°C and slab surface), below its Curie point, above which the magnetic susceptibility approaches to zero, thereby causing a very small QH value at even the surface (QH = 1.0416 × 105 W m−3 at 880°C). It is also demonstrated that inhomogeneous power distributions occur in all the slabs and become more pronounced with increasing temperature mainly due to rapid increase in permittivity. Characterizing power absorption in the oxides and the coal is expected to offer a strategic guide for improving use of microwave energy in ferrous metallurgy

Stacked dense networks for single-image snow removal

Single image snow removal is important since snowy images usually degrade the performance of computer vision systems. In this paper, we deduce a physics-based snow model and propose a novel snow removal method based on the snow model and deep neural networks. Our model decomposes a snowy image into a nonlinear combination of a snow-free image and dynamic snowflakes. Inspired by our model and DenseNet connectivity pattern, we design a novel Multi-scale Stacked Densely Connected Convolutional Network (MS-SDN) to simultaneously detect and remove snowflakes in an image. The MS-SDN is composed of a multi-scale convolutional sub-net for extracting feature maps and two stacked modified DenseNets for snowflakes detection and removal. The snowflake detection sub-net guides snow removal through forward transmission, and the snowflake removal sub-net adjusts snow detection through back transmission. In this way, snowflake detection and removal mutually improve the final results. For training and testing our method, we constructed a large-scale benchmark synthesis dataset which contains 3000 triplets of snowy images, snowflakes, and snow-free images. Specifically, the snow-free images are captured from snow scenes, and the snowy images are synthesized by using our deduced snow model. Our extensive quantitative and qualitative experimental results show that our MS-SDN performs better than several state-of-the-art methods, and the stacked structure is better than multi-branch structures in terms of snow removal. (C) 2019 Elsevier B.V. All rights reserved

Hydrochemical and Isotopic Characterization of the Impact of Water Diversion on Water in Drainage Channels, Groundwater, and Lake Ulansuhai in China

Lakes are important natural water reservoirs that connect other water bodies and play essential roles in water supply, ecological preservation, and climate regulation. Because of global climate change and human activities, many lakes worldwide are facing severe challenges, such as ecological degradation and reductions in their water storage, levels, surface areas, and quality. Water diversion into lakes is considered an effective measure to address these challenges and has attracted much attention. Water has been diverted into Lake Ulansuhai through drainage channels from the Yellow River since 2013. This shallow lake is located in arid northern China and is greatly affected by high salinity and eutrophication. The lake is the lowest area in the Hetao basin and is a sink for terrestrial water in this region. High salinity in lake water, drainage channels, and groundwater caused by NaCl is an ongoing problem; however, water diversion has played an important role in dilution. The main hydrochemical type in the lake water is Cl·HCO3–Na·Mg, while those in the drainage channels and the groundwater show more diversity because of spatial differences. The main source of water in the lake (52–60%) is that diverted through six drainage channels on the west bank, followed by meteoric precipitation (36–38%). Groundwater recharge to the lake is minimal (west bank: 2–7%, and east bank: 1–5%). Extensive evaporation occurs in the lake before the lake water is discharged into the Yellow River through a waste canal. The hydrochemical evolution and salinization of the lake are dominated by the six drainage channels, followed by evaporation from the lake surface. Thus, resolution of soil salinization in the Hetao irrigation area is key to addressing salinity issues in the lake. This study will be helpful for the planning of future water diversion and ecological restoration