Experiments on Risk Attitude: The Case of Chinese Students

This paper examines Chinese students' risk attitude using buying and selling experiments with lotteries. We found that subjects were more risk averse in the buying experiment than in the selling experiment, suggesting the endowment effect. In the selling experiment, subjects were risk loving when there was a low win probability and risk averse with a high win probability, whereas they were risk averse in the buying experiment. Using the prize money won during the experiment as a measure of wealth, we found decreasing absolute risk aversion. Subjects' risk attitude as revealed in the experiments explains their risky asset holding behavior.

Time Discounting: The Delay Effect and Procrastinating Behavior

The delay effect, that people discount the near future more than the distant future, has not been verified rigorously. An experiment conducted by us in China confirms that, by separating the delay from the interval, the delay effect exists only within a short delay. The results are reliable, because the rewards paid were very large, in order to elicit the subjects' true preferences. The interval and magnitude effects are also confirmed. Finally, subjects' procrastinating behavior, as reported in the questionnaire conducted at the end of the experiment, is explained by the time discount rates and the degree of the delay effect revealed in the experiment.

CEO Power and Financial Reporting Quality: An Examination from a Restatement Perspective

This study investigates the impact of CEO power on the quality of financial reporting measured by financial restatements. After reviewing the current research on corporate governance and financial reporting quality, this paper extends prior research by combining three streams of research on CEO power, including CEO tenure, CEO duality, and CEO ownership. I primarily hypothesize that CEO power is positively or negatively related to the financial reporting quality measured by restatements. Using 422 US companies during 2011 and 2015, it is concluded that CEOs who have more vested interests in the company are more likely to restate their financial reporting. CEO tenure and CEO duality may have no significant association with financial reporting quality, while the companies audited by Big4 firms and with high financial leverage are more likely to restate their financial information. Robustness tests using the control variable “loss” rather than “ROA” generate similar results. Keyword: Corporate governance; CEO power; Financial Reporting Quality; Restatement

Animal waste use and implications to agricultural greenhouse gas emissions in the United States

Acknowledgements: Z. Q. and S. D. have been partially supported by the National Basic Research Program of China (2016YFA0602701), the National Natural Science Foundation of China (41975113), and the Guangdong Provincial Department of Science and Technology (2019ZT08G090). The input of P. S. contributed to the following projects: DEVIL (NE/M021327/1) and Soils-R-GRREAT (NE/P019455/1). Data availability: The data that support the findings of this study are openly available at the following URL/DOI: https://greet.es.anl.gov/. Publisher Copyright: © 2021 The Author(s). Published by IOP Publishing Ltd. Creative Commons Attribution 4.0 license, Original content from this work may be used under the terms of the . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Peer reviewedPublisher PD

Perceptual Image Compression with Cooperative Cross-Modal Side Information

The explosion of data has resulted in more and more associated text being transmitted along with images. Inspired by from distributed source coding, many works utilize image side information to enhance image compression. However, existing methods generally do not consider using text as side information to enhance perceptual compression of images, even though the benefits of multimodal synergy have been widely demonstrated in research. This begs the following question: How can we effectively transfer text-level semantic dependencies to help image compression, which is only available to the decoder? In this work, we propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features. This is done by predicting a semantic mask to guide the learned text-adaptive affine transformation at the pixel level. Furthermore, we design a text-conditional generative adversarial networks to improve the perceptual quality of reconstructed images. Extensive experiments involving four datasets and ten image quality assessment metrics demonstrate that the proposed approach achieves superior results in terms of rate-perception trade-off and semantic distortion

Chemical Fractions and Availability of Zinc in Winter Wheat Soil in Response to Nitrogen and Zinc Combinations

Nitrogen (N) is critical for zinc (Zn) accumulation in winter wheat grain via enhancing Zn absorption into plant roots. This paper explored a possible mechanism for enhanced absorption of Zn in winter wheat by N combined with Zn application based on the Zn bio-availability in soil. A pot experiment with three N application rates (0.05, 0.2, and 0.4 g kg-1), two Zn application rates (0 and 10 mg kg-1), without and with plants was conducted. The results showed that high N (N0.2 and N0.4) combined with Zn (Zn10) application significantly increased the yield, yield components and Zn and N concentrations in winter wheat shoots and grain. The available Zn concentration in soil with and without plants was increased by N0.2Zn10 and N0.4Zn10 treatment at each growth stage. N0.2Zn10 and N0.4Zn10 treatment significantly decreased the pH in soil without plants but had different influences on the pH in soil with plants, which depended on the different N application rates and growth stages. Meanwhile, N0.2Zn10 and N0.4Zn10 treatment decreased the exchangeable Zn but increased loose organic-, carbonate- and Fe-Mn oxides-bound Zn concentrations in soil without plants. The exchangeable, loose organic- and carbonate-bound Zn concentrations in soil with plants was increased by N0.2Zn10 and N0.4Zn10 treatment at different growth stages. Different rates of N combined with Zn application influenced the proportion of Zn in different fractions in soil with and without plants at different growth stages. At Zn10, N0.4 treatment showed higher yield, N and Zn concentrations in shoot and grain, and available Zn concentration in soil, but lower pH in soil than N0.2 treatment. In addition, soil without plants had higher available Zn concentrations and lower pH than did the soil with plants. There were significant differences in Zn chemical fractions concentrations and proportions between the soils with and without plants at each growth stage. Therefore, combined influence of roots and the combination of N and Zn (especially N0.4Zn10 treatment) improved the bio-availability of Zn in soil via changing the soil pH and promoting the transformation and distribution of Zn in different fractions

Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression

In this paper, we propose a progressive learning paradigm for transformer-based variable-rate image compression. Our approach covers a wide range of compression rates with the assistance of the Layer-adaptive Prompt Module (LPM). Inspired by visual prompt tuning, we use LPM to extract prompts for input images and hidden features at the encoder side and decoder side, respectively, which are fed as additional information into the Swin Transformer layer of a pre-trained transformer-based image compression model to affect the allocation of attention region and the bits, which in turn changes the target compression ratio of the model. To ensure the network is more lightweight, we involves the integration of prompt networks with less convolutional layers. Exhaustive experiments show that compared to methods based on multiple models, which are optimized separately for different target rates, the proposed method arrives at the same performance with 80% savings in parameter storage and 90% savings in datasets. Meanwhile, our model outperforms all current variable bitrate image methods in terms of rate-distortion performance and approaches the state-of-the-art fixed bitrate image compression methods trained from scratch