A Review of Researches on Return Migration

Driven by factors related to economic development, return migration has become a topic of increasing academic interest. There are several mainstream theoretical interpretations of the phenomenon of return migration, and the existing literature focuses on the causes of return, employment choice and return effects. Through literature review, it is found that both economic factors and non-economic factors will have an impact on the decision to return. Compared with non-migrant group, returned migrants are more likely to engage in self-employed. Returned migrants may bring back advanced ideas and technologies, which will have a positive impact on local economic and social development, but the driving effect on employment is limited. In developing countries, “entrepreneurship” means vulnerability. Entrepreneurship is a choice made when all other labor market opportunities are not satisfactory or individuals have no employment opportunities, which belongs to necessity-based entrepreneurship. This paper discusses the findings based on a summary of the review and provides the prospects for future research

Bayesian modeling of ChIP-chip data using latent variables

<p>Abstract</p> <p>Background</p> <p>The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.</p> <p>Results</p> <p>In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length.</p> <p>Conclusion</p> <p>The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.</p

The Evolution of the Phase Lags Associated with the Type-C Quasi-periodic Oscillation in GX 339--4 during the 2006/2007 Outburst

We present the evolution of the phase lags associated with the type-C QPO in GX 339--4 during the rising phase of the 2006/2007 outburst. We find that the phase lags at the QPO frequency are always positive (hard), and show very different behavior between QPOs with frequencies below and above $\sim1.7$ Hz: when the QPO frequency is below $\sim1.7$ Hz, the phase lags increase both with QPO frequency and energy, while when the QPO frequency is above $\sim1.7$ Hz, the phase lags remain more or less constant. When the QPO frequency is higher than $\sim1.7$ Hz, a broad feature is always present in the lag-energy spectra at around 6.5 keV, suggesting that the reflection component may have a significant contribution to the phase lags. Below $\sim1.7$ Hz, the QPO rms first decreases with energy and then turns to almost flat, while above $\sim1.7$ Hz, the QPO rms increases with energy. During the transition from the low-hard state to the hard-intermediate state, the second harmonic and subharmonic of this QPO appear in the power density spectra. The second-harmonic and subharmonic phase lags show very similar evolution with their centroid frequencies. However, the energy dependence of the second-harmonic and subharmonic phase lags are quite different. Our results suggest that, at different phases of the outburst, different mechanisms may be responsible for the phase lags of the QPO. We briefly discuss the possible scenarios for producing the lags.Comment: 15 pages, 12 figures, accepted for publication in Ap

A framework of human–robot coordination based on game theory and policy iteration

In this paper, we propose a framework to analyze the interactive behaviors of human and robot in physical interactions. Game theory is employed to describe the system under study, and policy iteration is adopted to provide a solution of Nash equilibrium. The human’s control objective is estimated based on the measured interaction force, and it is used to adapt the robot’s objective such that human-robot coordination can be achieved. The validity of the proposed method is verified through a rigorous proof and experimental studies

Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models

Most open-domain dialogue systems suffer from forgetting important information, especially in a long-term conversation. Existing works usually train the specific retriever or summarizer to obtain key information from the past, which is time-consuming and highly depends on the quality of labeled data. To alleviate this problem, we propose to recursively generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability. Specifically, our method first stimulates LLMs to memorize small dialogue contexts and then recursively produce new memory using previous memory and following contexts. Finally, the LLM can easily generate a highly consistent response with the help of the latest memory. We evaluate our method using ChatGPT and text-davinci-003, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation. Notably, our method is a potential solution to enable the LLM to model the extremely long context. Code and scripts will be released later

Model Predictive Control for Connected Hybrid Electric Vehicles

This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc.) are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method