arXiv subjects

Ruyi Huang

Publications and source records attributed to Ruyi Huang.

3 recordsLinked to original sources

arXiv2026

Decision Support under Prediction-Induced Censoring

In many data-driven online decision systems, actions determine not only operational costs but also the data availability for future learning -- a phenomenon termed Prediction-Induced Censoring (PIC). This challenge is particularly acute in large-scale resource allocation for generative AI (GenAI) serving: insufficient capacity triggers shortages but hides the true demand, leaving the system with only a "greater-than" constraint. Standard decision-making approaches that rely on uncensored data suffer from selection bias, often locking the system into a self-reinforcing low-provisioning trap. To break this loop, this paper proposes an adaptive approach named PIC-Reinforcement Learning (PIC-RL), a closed-loop framework that transforms censoring from a data quality problem into a decision signal. PIC-RL integrates (1) Uncertainty-Aware Demand Prediction to manage the information-cost trade-off, (2) Pessimistic Surrogate Inference to construct decision-aligned conservative feedback from shortage events, and (3) Dual-Timescale Adaptation to stabilize online learning against distribution drift. The analysis provides theoretical guarantees that the feedback design corrects the selection bias inherent in naive learning. Experiments on production Alibaba GenAI traces demonstrate that PIC-RL consistently outperforms state-of-the-art baselines, reducing service degradation by up to 50% while maintaining cost efficiency.

eess.SY↗

arXiv2025

FaultGPT: Industrial Fault Diagnosis Question Answering System by Vision Language Models

Recently, employing single-modality large language models based on mechanical vibration signals as Tuning Predictors has introduced new perspectives in intelligent fault diagnosis. However, the potential of these methods to leverage multimodal data remains underexploited, particularly in complex mechanical systems where relying on a single data source often fails to capture comprehensive fault information. In this paper, we present FaultGPT, a novel model that generates fault diagnosis reports directly from raw vibration signals. By leveraging large vision-language models (LVLM) and text-based supervision, FaultGPT performs end-to-end fault diagnosis question answering (FDQA), distinguishing itself from traditional classification or regression approaches. Specifically, we construct a large-scale FDQA instruction dataset for instruction tuning of LVLM. This dataset includes vibration time-frequency image-text label pairs and human instruction-ground truth pairs. To enhance the capability in generating high-quality fault diagnosis reports, we design a multi-scale cross-modal image decoder to extract fine-grained fault semantics and conducted instruction tuning without introducing additional training parameters into the LVLM. Extensive experiments, including fault diagnosis report generation, few-shot and zero-shot evaluation across multiple datasets, validate the superior performance and adaptability of FaultGPT in diverse industrial scenarios.

cs.ET↗

arXiv2025

Flexible radiofrequency carbon nanotube transistors operating at frequencies above 100 GHz

The development of the sixth generation of wireless communications technology (6G) requires terminals that can operate at frequencies above 100 GHz. For human-centric applications, these terminals should also be flexible and have low power. However, current flexible radiofrequency transistors typically have lower maximum frequencies, in part due to the poor thermal conductivity of flexible substrates. Here, we report radiofrequency transistors that are based on aligned carbon nanotube arrays on flexible substrates and have current gain cutoff frequencies ($f_{\text{T}}$) and power gain cutoff frequencies ($f_{\text{max}}$) above 100 GHz. This is achieved by using electro-thermal co-design to improve the heat dissipation and radiofrequency performance of the devices. The transistors exhibit an on-state current of 0.947 mA $\mu$m$^{-1}$, a transconductance of 0.728 mS $\mu$m$^{-1}$, a peak extrinsic $f_{\text{T}}$ of 152 GHz, a peak extrinsic $f_{\text{max}}$ of 102 GHz, and a power consumption under 200 mW mm$^{-1}$. We also show that the devices can be used to create flexible radiofrequency amplifiers with an output power of 64 mW mm$^{-1}$ and a 11 dB power gain in the K-band.

physics.app-ph↗