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Yingjie Song

Publications and source records attributed to Yingjie Song.

3 recordsLinked to original sources

Sub-50 Picosecond exceptionally Bright Perovskite Scintillation by Unlocking Giant Oscillator Strength

Ultrafast scintillators are indispensable for precise timing in high-energy physics and medical diagnostics. Fundamentally constrained by the trade-off between emission rate and light yield, conventional scintillators remain kinetically trapped in the sub-nanosecond regime, failing to break 50-picosecond limit. Here, we demonstrate a strategy to bypass this limitation by harnessing the coherent radiative acceleration in weakly confined CsPbCl3 perovskite nanocrystals to generate an ultrafast photon burst. This effect originates from the giant oscillator strength, which we unlock by suppressing exciton-phonon scattering at mild cryogenic temperatures. Consequently, our scintillator achieves an unprecedented dominant lifetime of 13.11 ps alongside a high light yield of 21,851 ph/MeV. The resulting prompt photon emission rate more than 100 times higher than that of state-of-the-art ultrafast scintillators. We validate this breakthrough in realistic detection scenarios, achieving a coincidence time resolution of 30.8 ps and accurately resolving 13.5 ps electron bunches and 16.6 ps single-shot gamma-ray pulses. Our findings establish a robust coherent framework for next-generation ultrafast scintillators, pushing extreme radiation diagnostics into the picosecond frontier.

cond-mat.mes-hall

Singing Voice Synthesis with Vibrato Modeling and Latent Energy Representation

This paper proposes an expressive singing voice synthesis system by introducing explicit vibrato modeling and latent energy representation. Vibrato is essential to the naturalness of synthesized sound, due to the inherent characteristics of human singing. Hence, a deep learning-based vibrato model is introduced in this paper to control the vibrato's likeliness, rate, depth and phase in singing, where the vibrato likeliness represents the existence probability of vibrato and it would help improve the singing voice's naturalness. Actually, there is no annotated label about vibrato likeliness in existing singing corpus. We adopt a novel vibrato likeliness labeling method to label the vibrato likeliness automatically. Meanwhile, the power spectrogram of audio contains rich information that can improve the expressiveness of singing. An autoencoder-based latent energy bottleneck feature is proposed for expressive singing voice synthesis. Experimental results on the open dataset NUS48E show that both the vibrato modeling and the latent energy representation could significantly improve the expressiveness of singing voice. The audio samples are shown in the demo website.

cs.SD

Two-stage optimization of urban rail transit formation and real-time station control at comprehensive transportation hub

This paper tries to discuss two strategies of dealing with this complex passenger demand from two aspects: transit train formation and real-time holding control. The genetic algorithm (GA) is designed to solve the integrated two-stage model of optimizing the number, timetable and real-time holding control of the multi-marshalling operated trains. The numerical results show that the combined two-stage model of multi-marshalling operation and holding control at stations can better deal with the demand fluctuation of urban rail transit connecting with the comprehensive transportation hub.

cs.OH