Feng Liu

Wenjie Wang, Biwei Huang, Feng Liu et al.

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

Tianning Zhang, Feng Liu, Yuming Yuan et al.

Existing EEW approaches often treat phase picking, location estimation, and magnitude estimation as separate tasks, lacking a unified framework. Additionally, most deep learning models in seismology rely on full three-component waveforms and are not suitable for real-time streaming data. To address these limitations, we propose a novel unified seismic neural network called Fast Information Streaming Handler (FisH). FisH is designed to process real-time streaming seismic data and generate simultaneous results for phase picking, location estimation, and magnitude estimation in an end-to-end fashion. By integrating these tasks within a single model, FisH simplifies the overall process and leverages the nonlinear relationships between tasks for improved performance. The FisH model utilizes RetNet as its backbone, enabling parallel processing during training and recurrent handling during inference. This capability makes FisH suitable for real-time applications, reducing latency in EEW systems. Extensive experiments conducted on the STEAD benchmark dataset provide strong validation for the effectiveness of our proposed FisH model. The results demonstrate that FisH achieves impressive performance across multiple seismic event detection and characterization tasks. Specifically, it achieves an F1 score of 0.99/0.96. Also, FisH demonstrates precise earthquake location estimation, with location error of only 6.0km, a distance error of 2.6km, and a back-azimuth error of 19°. The model also exhibits accurate earthquake magnitude estimation, with a magnitude error of just 0.14. Additionally, FisH is capable of generating real-time estimations, providing location and magnitude estimations with a location error of 8.06km and a magnitude error of 0.18 within a mere 3 seconds after the P-wave arrives.

Shuochen Gao, Shun Lei, Fan Zhuo et al.

The Song Generation task aims to synthesize music composed of vocals and accompaniment from given lyrics. While the existing method, Jukebox, has explored this task, its constrained control over the generations often leads to deficiency in music performance. To mitigate the issue, we introduce an important concept from music composition, namely chords, to song generation networks. Chords form the foundation of accompaniment and provide vocal melody with associated harmony. Given the inaccuracy of automatic chord extractors, we devise a robust cross-attention mechanism augmented with dynamic weight sequence to integrate extracted chord information into song generations and reduce frame-level flaws, and propose a novel model termed Chord-Conditioned Song Generator (CSG) based on it. Experimental evidence demonstrates our proposed method outperforms other approaches in terms of musical performance and control precision of generated songs.