Terahertz (THz) communication is a key enabling technology for achieving high-capacity, long-distance inter-satellite and satellite-ground communications in the next-generation wireless systems. Photonic-assisted upconversion provides a cost-effective approach to realizing ultra-wideband THz communication systems. To explore the potential of quantum neural network for the photonic-assisted THz communication systems, this work is the first to design the hybrid quantum-classical neural network for quadrature amplitude modulation (QAM) symbol recovery over tens of Gbit/s THz kilometer-level wireless transmission link, which can significantly enhance the receiver performance and reduce the computational complexity. For the proof of concept, we create the 10 Gbaud sub-THz prototype to validate the proposed scheme over 4.6km wireless long-distance. The results demonstrate that the proposed scheme achieves a 0.7dB improvement in receiver sensitivity and a one-hundred-times reduction in real-valued multiplications per symbol (RMps) compared to the classical ones.

With the rapid development of the Internet, social media platforms are filled with a large amount of article information, so the task of topic generation, which can greatly improve browsing and reading efficiency, has gradually developed. Currently, manually organizing and generating topic sentences requires a lot of human resources, and the previous researches on automatic topic generation suffer from poor effectiveness, poor readability and high redundancy due to the generation of topic words only. To address these problems, our work introduces the maturing text summarization technology into the automatic topic sentence generation task to improve the coherence and readability of the generated topic sentences. We present a two-stage summary-based news topic sentence generation model, TTSG, which acquires summaries from each article, clusters and sorts the summaries, and then performs a secondary summary to obtain the final topic sentence. Experimental results show that TTSG outperforms the baseline model and generates more coherent and readable topic sentences. Our code is available at https://github.com/sutaoyu/TTSG.

With the increasing complexity of the field-programmable gate array (FPGA) architecture, heterogeneity and clock constraints have greatly challenged FPGA placement. In this paper, we present a high-performance placement algorithm for large-scale heterogeneous FPGAs with clock constraints. We first propose a connectivity-aware and type-balanced clustering method to construct the hierarchy and improve the scalability. In each hierarchy level, we develop a novel hybrid penalty and augmented Lagrangian method to formulate the heterogeneous and clock-aware placement as a sequence of unconstrained optimization subproblems and adopt the Adam method to solve each unconstrained optimization subproblem. Then, we present a matching-based IP blocks legalization to legalize the RAMs and DSPs, and a multi-stage packing technique is proposed to cluster FFs and LUTs into HCLBs. Finally, history-based legalization is developed to legalize CLBs in an FPGA. Based on the ISPD 2017 clock-aware FPGA placement contest benchmarks, experimental results show that our algorithm achieves the smallest routed wirelength for all the benchmarks among all published works in a reasonable runtime.