349 research outputs found
Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning
The two fields of urban planning and artificial intelligence (AI) arose and
developed separately. However, there is now cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In the
present paper, we introduce the importance of urban planning from the
sustainability, living, economic, disaster, and environmental perspectives. We
review the fundamental concepts of urban planning and relate these concepts to
crucial open problems of machine learning, including adversarial learning,
generative neural networks, deep encoder-decoder networks, conversational AI,
and geospatial and temporal machine learning, thereby assaying how AI can
contribute to modern urban planning. Thus, a central problem is automated
land-use configuration, which is formulated as the generation of land uses and
building configuration for a target area from surrounding geospatial, human
mobility, social media, environment, and economic activities. Finally, we
delineate some implications of AI for urban planning and propose key research
areas at the intersection of both topics.Comment: TSAS Submissio
Capital Structure and Abnormal Returns : An Empirical Study on the Chinese Market Considering Average Industry Leverage
This dissertation conducts a study based on an inefficient market, the Chinese market, to detect whether the capital structure has a significant influence on a firm’s performance, and whether the influence is positive or negative. A firm’s cumulative abnormal return is used to represent a firm’s performance. In this dissertation, a panel data including 907 companies across the time period from 2000 to 2013 are collected from Chinese market. Beside firm’s leverage, the price-earnings ratio, the price-to-book value, the firm size, the interest rate, and the beta coefficient are also collected as independent variables in our model. The fixed effect model is applied in processing these data. Due to the characteristic of Chinese market, the firm’s leverage has limited effects on firm’s abnormal returns. The empirical results also indicate that the industry factor plays a part when examining their relationships. The abnormal returns increase with leverage ratio for the overall sample, but decline with firm’s leverage for companies in unregulated industries such as warehousing and transportation industry. Moreover, it is of importance to separate the average gearing ratio of the industry that the firm allocated in from firm’s particular leverage. The firm’s abnormal return rises as the average industry leverage increases. It can be seen that the impact of average industry gearing ratio is more significant than firm’s leverage ratio
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition
In this study, we aim to reduce generation latency for Named Entity
Recognition (NER) with Large Language Models (LLMs). The main cause of high
latency in LLMs is the sequential decoding process, which autoregressively
generates all labels and mentions for NER, significantly increase the sequence
length. To this end, we introduce Parallel Decoding in LLM for NE}
(PaDeLLM-NER), a approach that integrates seamlessly into existing generative
model frameworks without necessitating additional modules or architectural
modifications. PaDeLLM-NER allows for the simultaneous decoding of all
mentions, thereby reducing generation latency. Experiments reveal that
PaDeLLM-NER significantly increases inference speed that is 1.76 to 10.22 times
faster than the autoregressive approach for both English and Chinese.
Simultaneously it maintains the quality of predictions as evidenced by the
performance that is on par with the state-of-the-art across various datasets
Bioactivity-guided fractionation of the triglyceride-lowering component and in vivo and in vitro evaluation of hypolipidemic effects of Calyx seu Fructus Physalis
<p>Abstract</p> <p>Background</p> <p>In folklore, some people take the decoction of <it>Calyx seu Fructus Physalis </it>(CSFP) for lowering blood lipids. The present study is designed to evaluate the lipid-lowering activities of CSFP, and search for its pharmacodynamical material.</p> <p>Methods</p> <p>CSFP was extracted by water and 75% ethanol, respectively. The extracts of CSFP for reducing serum lipid levels were evaluated on mouse model of hyperlipidemia. The optimized extract was subjected to the bioactivity-guided fractionation in which the liquid-liquid extraction, collumn chromatography, the <it>in vivo </it>and <it>in vitro </it>models of hyperlipidemia were utilized. The structure of active component was determined by <sup>13 </sup>C-NMR and <sup>1</sup>H-NMR.</p> <p>Results</p> <p>The 75% ethanol extract of CSFP decreased the serum total cholesterol (TC) and triglyceride (TG) levels in mouse model of hyperlipidemia. Followed a separation process for the 75% ethanol extract of CSFP, the fraction B was proved to be an active fraction for lowering lipid <it>in vivo </it>and <it>in vitro </it>experiments, which could significantly decrease the serum TC and TG levels in mouse model of hyperlipidemia, and remarkably decrease the increase of TG in primary mouse hepatocytes induced by high glucose and the increase of TG in HepG2 cells induced by oleic acid. The fraction B2, isolated from B on bioactivity-guided fractionation, could significantly decrease TG level in HepG2 cells. One compound with the highest content in B2 was isolated and determined as luteolin-7-O-beta-D-glucopyranoside by NMR spectra. It could significantly reduce the TG level in HepG2 cells, and inhibited the accumulation of lipids by oil red O stain.</p> <p>Conclusion</p> <p>Our results demonstrated that the 75% ethanol extract of CSFP could improve <it>in vitro </it>and <it>in vivo </it>lipid accumulation. Luteolin-7-O-beta-D-glucopyranoside might be a leading pharmacodynamical material of CSFP for lowering lipids.</p
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