The Case of Chu

Die östliche Zhou- (770–221 v. Chr.) und die Han-Dynastie (206 v. Chr.–220 n. Chr.) waren Perioden sozialer, kultureller und politischer Umwälzungen in China. In dieser Übergangszeit hat sich China von einem durch rivalisierende Staaten beherrschten zu einem unter einem einzigen Herrscher vereinten Land gewandelt. Archäologische Funde aus dem 7. und 6. Jh. v. Chr. legen nahe, dass sich die rivalisierenden Staaten größtenteils der musikalischen Tradition des Zhou-Staats angepasst haben. Die Fülle an Glocken und Klangsteinspielen, die bisher mit Zhou-staatlichen Zeremonien und Ahnenritualen verbunden werden, zeugen von diesem Einfluss. Jedoch implizieren materielle Belege aus dem 5. und 4. Jh. v. Chr. einen weitgreifenden Wandel auf der kulturellen und musikalischen Ebene, vor allem im zunehmend an Macht gewinnenden Chu-Staat. Trotz des politischen Niedergangs der Chu im 3. Jh. v. Chr. hielten sich die musikalischen und kulturellen Einflüsse bis in die Han-Dynastie

Design and Validation of a Software Defined Radio Testbed for DVB-T Transmission

This paper describes the design and validation of a Software Defined Radio (SDR) testbed, which can be used for Digital Television transmission using the Digital Video Broadcasting - Terrestrial (DVB-T) standard. In order to generate a DVB-T-compliant signal with low computational complexity, we design an SDR architecture that uses the C/C++ language and exploits multithreading and vectorized instructions. Then, we transmit the generated DVB-T signal in real time, using a common PC equipped with multicore central processing units (CPUs) and a commercially available SDR modem board. The proposed SDR architecture has been validated using fixed TV sets, and portable receivers. Our results show that the proposed SDR architecture for DVB-T transmission is a low-cost low-complexity solution that, in the worst case, only requires less than 22% of CPU load and less than 170 MB of memory usage, on a 3.0 GHz Core i7 processor. In addition, using the same SDR modem board, we design an off-line software receiver that also performs time synchronization and carrier frequency offset estimation and compensation

Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System

Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner poses a burden for the expanding transportation network companies. Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) with convolutional recurrent neural network (CRNN), convolutional neural network (CNN), and recurrent neural network (RNN) for simultaneously forecasting spatio-temporal tasks in a city as well as across different cities. Furthermore, a task adaptation layer is integrated with the architecture for learning joint representation in multi-task learning and revealing the contribution of the input features utilized in prediction. The proposed architecture is tested with data from Didi Chuxing for: (i) simultaneously forecasting demand and supply-demand gap in Beijing, and (ii) simultaneously forecasting demand across Chengdu and Xian. In both scenarios, models from our proposed architecture outperformed the single-task and multi-task deep learning benchmarks and ensemble-based machine learning algorithms.Comment: arXiv admin note: text overlap with arXiv:2012.0886