arXiv subjects

Jiangtao Yu

Publications and source records attributed to Jiangtao Yu.

5 recordsLinked to original sources

arXiv2026

Hybrid-parity sliding multiferroics

In this work, we introduce a class of hybrid-parity sliding multiferroics in which the spontaneous ferroelectric polarization is coupled to certain nonrelativistic spin splitting components through interlayer sliding, allowing these components to be reversibly switched in an electrical way. Symmetry analysis identifies coplanar magnets as natural platforms for realizing this form of sliding multiferroicity. First-principles calculations establish bilayer VBr$_2$ as a representative example, demonstrating the coupled reversal of the out-of-plane ferroelectric polarization and the signs of both even- and odd-parity nonrelativistic spin splitting components via an interlayer-sliding pathway. The signs of these nonrelativistic spin splitting components are locked to the sliding-switchable ferroelectric polarization and encoded in the spin-current responses, providing a signature of the coupled ferroic switching. Our findings expand the scope of sliding multiferroics and the functionality of sliding ferroelectrics for low-energy, nonvolatile logic devices.

cond-mat.mtrl-sci↗

arXiv2023

Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals

Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, considering the practicality in real scenarios, latency and recognizable vocabulary size are two critical factors that cannot be overlooked. In this paper, we propose Radio2Text, the first mmWave-based system for streaming automatic speech recognition (ASR) with a vocabulary size exceeding 13,000 words. Radio2Text is based on a tailored streaming Transformer that is capable of effectively learning representations of speech-related features, paving the way for streaming ASR with a large vocabulary. To alleviate the deficiency of streaming networks unable to access entire future inputs, we propose the Guidance Initialization that facilitates the transfer of feature knowledge related to the global context from the non-streaming Transformer to the tailored streaming Transformer through weight inheritance. Further, we propose a cross-modal structure based on knowledge distillation (KD), named cross-modal KD, to mitigate the negative effect of low quality mmWave signals on recognition performance. In the cross-modal KD, the audio streaming Transformer provides feature and response guidance that inherit fruitful and accurate speech information to supervise the training of the tailored radio streaming Transformer. The experimental results show that our Radio2Text can achieve a character error rate of 5.7% and a word error rate of 9.4% for the recognition of a vocabulary consisting of over 13,000 words.

cs.SD↗

arXiv2022

Convexity of 2-convex translating and expanding solitons to the mean curvature flow in $\mathbb{R}^{n+1}$

In this paper, inspired by the work of Spruck-Xiao [27] and based partly on a result of Derdzi\'nski [11], we prove the convexity of complete 2-convex translating and expanding solitons to the mean curvature flow in $\mathbb{R}^{n+1}$. More precisely, for $n\geq 3$, we show that any $n$-dimensional complete 2-convex translating solitons are convex, and any $n$-dimensional complete 2-convex self-expanders asymptotic to (strictly) mean convex cones are convex.

math.DG↗

arXiv2022

Radio2Speech: High Quality Speech Recovery from Radio Frequency Signals

Considering the microphone is easily affected by noise and soundproof materials, the radio frequency (RF) signal is a promising candidate to recover audio as it is immune to noise and can traverse many soundproof objects. In this paper, we introduce Radio2Speech, a system that uses RF signals to recover high quality speech from the loudspeaker. Radio2Speech can recover speech comparable to the quality of the microphone, advancing from recovering only single tone music or incomprehensible speech in existing approaches. We use Radio UNet to accurately recover speech in time-frequency domain from RF signals with limited frequency band. Also, we incorporate the neural vocoder to synthesize the speech waveform from the estimated time-frequency representation without using the contaminated phase. Quantitative and qualitative evaluations show that in quiet, noisy and soundproof scenarios, Radio2Speech achieves state-of-the-art performance and is on par with the microphone that works in quiet scenarios.

cs.SD↗

arXiv2020

On Complete Gradient Steady Ricci Solitons with Vanishing D-tensor

In this paper, we extend the work of Cao-Chen [9] on Bach-flat gradient Ricci solitons to classify $n$-dimensional ($n\ge 5$) complete $D$-flat gradient steady Ricci solitons. More precisely, we prove that any $n$-dimensional complete noncompact gradient steady Ricci soliton with vanishing $D$-tensor is either Ricci-flat, or isometric to the Bryant soliton. Furthermore, the proof extends to the shrinking case and the expanding case as well.

math.DG↗