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66. Effects of organic fertilizers on changes of functional traits in vegetative growth period of Leymus chinensis

Author

DAI Jingzhong, YAN Ruirui, ZHANG Xiaoying, and LÜ Guangping

Subjects
leymus chinensis, organic fertilizers, functional traits, correlation, driving factor, Biology (General), QH301-705.5, Botany, QK1-989
Abstract

Abstract [Objective] This paper studied the changes, response mechanism, and correlation of functional traits of Leymus chinensis during vegetative growth period under different levels of organic fertilizers, in an attempt to provide reference for the dynamic study of resources, environment, and plant functional characters, as well as restoration of the grassland ecosystems. [Methods] Selecting a grassland that had not been fertilized for more than 20 years as the experimental site, which was enclosed and divided into several small areas. Setting up four levels of organic fertilizer applications, with effective N amounts of 0, 63, 127, and 190 kg/hm2, respectively. Collecting samples before, during, and after the major vegetative growth of L. chinensis, and measuring functional traits. The changing trend, difference, correlation, and influence coefficient of functional characters of L. chinensis were analyzed by conic equation fitting, single factor multivariate analysis of variance, Pearson correlation analysis, and integral (stepwise) regression. [Results] (1) Organic fertilizers increased at beginning and then decreased the leaf length, leaf area, natural leaf width, unfolded leaf width, specific leaf area, stem length, stem mass, and single plant mass of L. chinensis sativus, while leaf dry matter and stem dry matter were decreased. (2) Organic fertilizers changed the correlation among functional characters of L. chinensis, enhancing the correlation between leaf area and specific leaf area, stem length and stem mass, and stem length and single plant mass, while decreasing the correlation between single leaf mass and single plant mass, and leaf width and plant height. (3) Organic fertilizers changed the contribution rate of phenotypic traits, including leaves, stems, and plant heights, to single plant mass. Increasing fertilization levels, the contribution of leaves, stems and plant heights tended to be balanced. The order of phenotypic traits affecting single plant mass of L. chinensis was: Leaves > plant heights > stems. [Conclusion] Short-term application of organic fertilizers had a significant effect on the functional characters of L. chinensis, which was mainly occurred in the later stage of vegetative growth, especially on the single plant mass. Among them, leaf length, unfolding leaf width, leaf area, and plant height had great influence on the single plant mass of L. chinensis, while plant height was the most stable factor driving the change of single plant mass. At the same time, the correlation between functional characters of L. chinensis was affected by the levels of fertilization. Fertilization enhanced the correlation of some functional traits while weakened the correlation among others, and thus there was a balance as a whole.

Published
2024
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67. Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples

Author

Xie, Haidong, Tan, Jia, Zhang, Xiaoying, Ji, Nan, Liao, Haihua, Yu, Zuguo, Xiang, Xueshuang, and Liu, Naijin

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Deep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficiency. This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform. Some existing works address this problem directly using the concept of adversarial examples in the image domain without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose a low-intercept waveform~(LIW) generation method that can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Our LIW exhibits significant low-interception performance even in the physical hardware experiment, decreasing the accuracy of the state of the art model to approximately $15\%$ with small perturbations., Comment: 4 pages, 4 figures, published in 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021

Published
2022
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68. Toward Self-learning End-to-End Task-Oriented Dialog Systems

Author

Zhang, Xiaoying, Peng, Baolin, Gao, Jianfeng, and Meng, Helen

Subjects
Computer Science - Computation and Language
Abstract

End-to-end task bots are typically learned over a static and usually limited-size corpus. However, when deployed in dynamic, changing, and open environments to interact with users, task bots tend to fail when confronted with data that deviate from the training corpus, i.e., out-of-distribution samples. In this paper, we study the problem of automatically adapting task bots to changing environments by learning from human-bot interactions with minimum or zero human annotations. We propose SL-AGENT, a novel self-learning framework for building end-to-end task bots. SL-AGENT consists of a dialog model and a pre-trained reward model to predict the quality of an agent response. It enables task bots to automatically adapt to changing environments by learning from the unlabeled human-bot dialog logs accumulated after deployment via reinforcement learning with the incorporated reward model. Experimental results on four well-studied dialog tasks show the effectiveness of SL-AGENT to automatically adapt to changing environments, using both automatic and human evaluations. We will release code and data for further research.

Published
2022

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