URBAN service diversity and labor mobility — Analysis Based on "meituan.com" big data and micro survey of floating population

In the context of the new era, people's pursuit of a better life is becoming more and more prominent. The diversity and welfare of urban services will become an important support for attracting labor and optimizing talent structure. This paper uses the "meituan.com" life service classification and the 2017 China floating population dynamic monitoring survey (CMDS) data to study the impact of urban service diversity on labor mobility. The results show that the diversity of urban services will significantly reduce the willingness of migrant population to move out. For every 1% increase in the diversity of service categories, the average probability of labor migration will be reduced by about 3.5% 23%; The impact of urban service diversity has group differences. Younger and highly skilled groups are more sensitive, and the marginal effect can reach 4.5% 62% and 4 03%. Considering the adjustment effect and regional heterogeneity, the expansion analysis further found that the level of urban informatization and marketization has a positive amplification effect on the diversity of service categories to attract and retain talents, especially in the eastern region and large cities with a population of more than 5 million. This study provides policy enlightenment for urban talent attraction and labor competition

Weakly Labelled AudioSet Tagging with Attention Neural Networks

Audio tagging is the task of predicting the presence or absence of sound classes within an audio clip. Previous work in audio tagging focused on relatively small datasets limited to recognising a small number of sound classes. We investigate audio tagging on AudioSet, which is a dataset consisting of over 2 million audio clips and 527 classes. AudioSet is weakly labelled, in that only the presence or absence of sound classes is known for each clip, while the onset and offset times are unknown. To address the weakly-labelled audio tagging problem, we propose attention neural networks as a way to attend the most salient parts of an audio clip. We bridge the connection between attention neural networks and multiple instance learning (MIL) methods, and propose decision-level and feature-level attention neural networks for audio tagging. We investigate attention neural networks modeled by different functions, depths and widths. Experiments on AudioSet show that the feature-level attention neural network achieves a state-of-the-art mean average precision (mAP) of 0.369, outperforming the best multiple instance learning (MIL) method of 0.317 and Google's deep neural network baseline of 0.314. In addition, we discover that the audio tagging performance on AudioSet embedding features has a weak correlation with the number of training samples and the quality of labels of each sound class.Comment: 13 page

A minimal sensing and communication control strategy for adaptive platooning

Several cooperative driving strategies proposed in literature, sometimes known as cooperative adaptive cruise control strategies, assume that both relative spacing and relative velocity with preceding vehicle are available from on-board sensors (laser or radar). Alternatively, these strategies assume communication of both velocity states and acceleration inputs from preceding vehicle. However, in practice, on-board sensors can only measure relative spacing with preceding vehicle (since getting relative velocity requires additional filtering algorithms); also, reducing the number of variables communicated from preceding vehicle is crucial to save bandwidth. In this work we show that, after framing the cooperative driving task as a distributed model reference adaptive control problem, the platooning task can be achieved in a minimal sensing and communication scenario, that is, by removing relative velocity measurements with preceding vehicle and by removing communication from preceding vehicle of velocity states. In the framework we propose, vehicle parametric uncertainty is taken into account by appropriately designed adaptive laws. The proposed framework is illustrated and shown to be flexible to several standard architectures used in cooperative driving (one-vehicle look-ahead topology, leader-to-all topology, multivehicle look-ahead topology)

Research on SLM Algorithm for PAPR reduction in MB-OFDM UWB Systems

AbstractMultiband orthogonal frequency division multiplexing (MB-OFDM) is one of the key techniques of ultra wideband (UWB) systems. A major drawback of MB-OFDM technique is the high peak-to-average power ratio (PAPR) of the transmit signal. In this paper, a novel phase sequence of selected mapping algorithm which makes the side information not needed is designed to lower the PAPR of MB-OFDM UWB signals. It is also shown that comparable PAPR reduction performance with the original SLM algorithm can be achieved with a small increase in signal power. Simulation results show that there must be equilibriums between SLM computational complexity and PAPR performance. The objective of the new algorithm is to lower PAPR close to ordinary SLM technique with reduced computational complexity with little performance degradation and achieves better system resource utilization

The Error Performance and Fairness of CUWB Correlated Channels

AbstractThe symbol period becomes smaller compared to the channel delay in multiband orthogonal frequency division multiplexing (MB-OFDM) cognitive ultra wideband (CUWB) wireless communications, the transmitted signals experiences frequency-selective fading and leads to performance degradation. In this paper, a new design method for space-time trellis codes in MB-OFDM systems with correlated Rayleigh fading channels is introduced. This method converts the single output code symbol into several STTC code symbols, which are to be transmitted simultaneously from multiple transmitter-antennas. By using Viterbi optimal soft decision decoding algorithm, we investigate both quasi-static and interleaved channels and demonstrate how the spatial fading correlation affects the performance of space–time codes over these two different MB-OFDM wireless channel models. Simulation results show that the performance of space–time code is to be robust to spatial correlation. When the system bandwidth increases, the long term fairness quality will gradually become better and finally converges to 1

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations