Social network, intra-network education spillover effect and rural–urban migrants\u27 wages: Evidence from China

This study examines the determinants of rural–urban migrant wages, paying special attention to the intra-network education spillover effect of the migrants\u27 social network in China. Using the new migrant sample of Rural Urban Migration in China (RUMiC) 2009 survey data, we find that the migrants\u27 social network does have a significant impact on their own earnings. In particular, we find evidence that there exists an education spillover effect of the migrants\u27 social network, which indicates that the education level of the migrants\u27 social network has a significant positive effect on their earnings. We also find that the education spillover effects differ with gender. The results are robust after considering the potential problem of endogeneity

Numerical analyses of the flow past a short rotating cylinder

This work studies the three-dimensional flow dynamics around a rotating circular cylinder of finite length, whose axis is positioned perpendicular to the streamwise direction. Direct numerical simulations and global stability analyses are performed within a parameter range of Reynolds number $Re=DU_\infty/\nu<500$ (based on cylinder diameter $D$, uniform incoming flow velocity $U_\infty$), length-to-diameter ratio $AR=L/D\leq2$ and dimensionless rotation rate $\alpha=D\Omega/2U_\infty\leq2$ (where $\Omega$ is rotation rate). By solving Nav\-ier--Sto\-kes equations, we investigated the wake patterns and explored the phase diagrams of the lift and drag coefficients. For a cylinder with $AR=1$, we found that when the rotation effect is weak ($0\leq\alpha\lesssim0.3$), the wake pattern is similar to the unsteady wake past the non-rotating finite-length cylinder, but with a new linear unstable mode competing to dominate the saturation state of the wake. The flow becomes stable for $0.3\lesssim\alpha\lesssim0.9$ when $Re<360$. When the rotation effect is strong ($\alpha\gtrsim0.9$), new low-frequency wake patterns with stronger oscillations emerge. Furthermore, the stability analyses based on the time-averaged flows and on the steady solutions demonstrate the existence of multiple unstable modes undergoing Hopf bifurcation, greatly influenced by the rotation effect. The shapes of these global eigenmodes are presented and compared, as well as their structural sensitivity, visualising the flow region important for the disturbance development with rotation. This research contributes to our understanding of the complex bluff-body wake dynamics past this critical configuration.Comment: 35 pages, 29 figures, the version of record of this article is accepted in Journal of Fluid Mechanic

Prompting GPT-3 To Be Reliable

Large language models (LLMs) show impressive abilities via few-shot prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world language applications. However, the crucial problem of how to improve the reliability of GPT-3 is still under-explored. While reliability is a broad and vaguely defined term, we decompose reliability into four main facets that correspond to the existing framework of ML safety and are well-recognized to be important: generalizability, social biases, calibration, and factuality. Our core contribution is to establish simple and effective prompts that improve GPT-3's reliability as it: 1) generalizes out-of-distribution, 2) balances demographic distribution and uses natural language instructions to reduce social biases, 3) calibrates output probabilities, and 4) updates the LLM's factual knowledge and reasoning chains. With appropriate prompts, GPT-3 is more reliable than smaller-scale supervised models on all these facets. We release all processed datasets, evaluation scripts, and model predictions. Our systematic empirical study not only sheds new insights on the reliability of prompting LLMs, but more importantly, our prompting strategies can help practitioners more reliably use LLMs like GPT-3.Comment: ICLR 202

READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises

For many real-world applications, the user-generated inputs usually contain various noises due to speech recognition errors caused by linguistic variations1 or typographical errors (typos). Thus, it is crucial to test model performance on data with realistic input noises to ensure robustness and fairness. However, little study has been done to construct such benchmarks for Chinese, where various language-specific input noises happen in the real world. In order to fill this important gap, we construct READIN: a Chinese multi-task benchmark with REalistic And Diverse Input Noises. READIN contains four diverse tasks and requests annotators to re-enter the original test data with two commonly used Chinese input methods: Pinyin input and speech input. We designed our annotation pipeline to maximize diversity, for example by instructing the annotators to use diverse input method editors (IMEs) for keyboard noises and recruiting speakers from diverse dialectical groups for speech noises. We experiment with a series of strong pretrained language models as well as robust training methods, we find that these models often suffer significant performance drops on READIN even with robustness methods like data augmentation. As the first large-scale attempt in creating a benchmark with noises geared towards user-generated inputs, we believe that READIN serves as an important complement to existing Chinese NLP benchmarks. The source code and dataset can be obtained from https://github.com/thunlp/READIN.Comment: Preprin

The Art of Self-Control – Autoregulation of Plant–Microbe Symbioses

Plants interact with diverse microbes including those that result in nutrient-acquiring symbioses. In order to balance the energy cost with the benefit gained, plants employ a systemic negative feedback loop to control the formation of these symbioses. This is particularly well-understood in nodulation, the symbiosis between legumes and nitrogen-fixing rhizobia, and is known as autoregulation of nodulation (AON). However, much less is understood about the autoregulation of the ancient arbuscular mycorrhizal symbioses that form between Glomeromycota fungi and the majority of land plants. Elegant physiological studies in legumes have indicated there is at least some overlap in the genes and signals that regulate these two symbioses but there are major gaps in our understanding. In this paper we examine the hypothesis that the autoregulation of mycorrhizae (AOM) pathway shares some elements with AON but that there are also some important differences. By reviewing the current knowledge of the AON pathway, we have identified important directions for future AOM studies. We also provide the first genetic evidence that CLV2 (an important element of the AON pathway) influences mycorrhizal development in a non-legume, tomato and review the interaction of the autoregulation pathway with plant hormones and nutrient status. Finally, we discuss whether autoregulation may play a role in the relationships plants form with other microbes