Nonparametric Detection of Geometric Structures over Networks

Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exist, all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary and unknown. The goal is to design statistically consistent tests with probability of errors converging to zero as the network size becomes asymptotically large. Kernel-based tests are proposed based on maximum mean discrepancy that measures the distance between mean embeddings of distributions into a reproducing kernel Hilbert space. Detection of an anomalous interval over a line network is first studied. Sufficient conditions on minimum and maximum sizes of candidate anomalous intervals are characterized in order to guarantee the proposed test to be consistent. It is also shown that certain necessary conditions must hold to guarantee any test to be universally consistent. Comparison of sufficient and necessary conditions yields that the proposed test is order-level optimal and nearly optimal respectively in terms of minimum and maximum sizes of candidate anomalous intervals. Generalization of the results to other networks is further developed. Numerical results are provided to demonstrate the performance of the proposed tests.Comment: Submitted for journal publication in November 2015. arXiv admin note: text overlap with arXiv:1404.029

Nonparametric Detection of Anomalous Data Streams

A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence contains m i.i.d. samples drawn from a distribution q that is distinct from p. The distributions p and q are assumed to be unknown in advance. Distribution-free tests are constructed using maximum mean discrepancy as the metric, which is based on mean embeddings of distributions into a reproducing kernel Hilbert space. The probability of error is bounded as a function of the sample size m, the number s of anomalous sequences and the number n of sequences. It is then shown that with s known, the constructed test is exponentially consistent if m is greater than a constant factor of log n, for any p and q, whereas with s unknown, m should has an order strictly greater than log n. Furthermore, it is shown that no test can be consistent for arbitrary p and q if m is less than a constant factor of log n, thus the order-level optimality of the proposed test is established. Numerical results are provided to demonstrate that our tests outperform (or perform as well as) the tests based on other competitive approaches under various cases.Comment: Submitted to IEEE Transactions on Signal Processing, 201

Progress in ideotype breeding to increase rice yield potential

The ideotype approach has been used in breeding programs at the International Rice Research Institute (IRRI) and in China to improve rice yield potential. First-generation new plant type (NPT) lines developed from tropical japonica at IRRI did not yield well because of limited biomass production and poor grain filling. Progress has been made in second-generation NPT lines developed by crossing elite indica with improved tropical japonica. Several second-generation NPT lines outyielded the first-generation NPT lines and indica check varieties. China's "super"rice breeding project has developed many F1 hybrid varieties using a combination of the ideotype approach and intersubspecific heterosis. These hybrid varieties produced grain yield of 12 t ha-1 in on-farm demonstration fields, 8-15% higher than the hybrid check varieties. The success of China's "super" hybrid rice was partially the result of assembling the good components of IRRI's NPT design in addition to the use of intersubspecific heterosis. For example, both designs focused on large panicle size, reduced tillering capacity, and improved lodging resistance. More importantly, improvement in plant type design was achieved in China's "super" hybrid rice by emphasizing the top three leaves and panicle position within a canopy in order to meet the demand of heavy panicles for a large source supply. The success of "super"hybrid rice breeding in China and progress in NPT breeding at IRRI suggest that the ideotype approach is effective for breaking the yield ceiling of an irrigated rice crop

Comparison on Grain Quality Between Super Hybrid and Popular Inbred Rice Cultivars Under Two Nitrogen Management Practices

This study was conducted to determine the differences in grain quality traits between super hybrid and popular inbred rice cultivars grown under two nitrogen (N) management practices. Field experiments were done at the Experimental Farm of Guangxi University, Guangxi Province, China in early and late rice-growing seasons in 2014. Two representative super hybrid cultivars Liangyoupeijiu (LYPJ) and Y-liangyou 1 (YLY1) and a popular inbred rice cultivar Huanghuazhan (HHZ) were grown under fixed-time N management (FTNM) and site-specific N management (SSNM) practices in each season. Grain quality traits and N uptake were measured for each cultivar. LYPJ and YLY1 had higher milling efficiency, poorer appearance and palatability, and equal nutritional value than HHZ. The higher milling efficiency and poorer appearance in LYPJ and YLY1 were associated with their higher rice width compared with HHZ. Total N application rate was reduced by 15–20% under SSNM than under FTNM, whereas there was nearly no significant difference in grain quality between SSNM and FTNM. Our results suggest that (1) strategies for grain quality improvement in super hybrid rice should be focused on appearance and palatability, and (2) replacing FTNM with SSNM can reduce N input without sacrificing grain quality in rice production

Cover crops and chicken grazing in a winter fallow field improve soil carbon and nitrogen contents and decrease methane emissions

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zheng, H., Zhou, L., Wei, J., Tang, Q., Zou, Y., Tang, J., & Xu, H. Cover crops and chicken grazing in a winter fallow field improve soil carbon and nitrogen contents and decrease methane emissions. Scientific Reports, 10(1), (2020): 12607, doi:10.1038/s41598-020-69407-y.Using symbiotic farming methods [cover crops and chicken grazing (+ C)] in a winter fallow field, we found that the soil organic matter and total nitrogen of the + C treatment were 5.2% and 26.6% higher, respectively, than those of a treatment with cover crops and no chicken grazing (− C). The annual rice grain yield of the + C treatment was 3.8% higher than that of the − C treatment and 12.3% higher than that of the bare fallow field (CK), while the annual CH4 emissions of the + C treatment were 26.9% lower than those of the − C treatment and 10.6% lower than those of the CK treatment. The 100-year global warming potential of the + C treatment was 6.2% lower than that of the − C treatment. Therefore, the use of winter cover crops and chicken grazing in a winter fallow field was effective at reducing CH4 emissions and significantly improving soil nutrients and rice yield.This study was supported by the Earmarked Fund for China Agriculture Research System (CARS-01-26), the China-UK joint Red Soil Critical Zone project from the National Natural Science Foundation of China (Grant No. 41571130053), and Hunan “A Hundred Scholars” Program