Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data availability in multi-label learning (MLL) by leveraging unlabeled data to improve the model's performance. Due to the difficulty of estimating the reliable label correlation on minimal multi-labeled data, previous SSMLL methods fail to unlash the power of the correlation among multiple labels to improve the performance of the predictive model in SSMLL. To deal with this problem, we propose a novel SSMLL method named PCLP where the correlation-induced label prior is inferred to enhance the pseudo-labeling instead of dirtily estimating the correlation among labels. Specifically, we construct the correlated label prior probability distribution using structural causal model (SCM), constraining the correlations of generated pseudo-labels to conform to the prior, which can be integrated into a variational label enhancement framework optimized by both labeled and unlabeled instances in a unified manner. Theoretically, we demonstrate the accuracy of the generated pseudo-labels and guarantee the learning consistency of the proposed method. Comprehensive experiments on several benchmark datasets have validated the superiority of the proposed method.

Silicon photodetectors, such as photodiode (PD), are extensively used in optical sensing as their proper performance and low cost to achieve screen brightness adjustment, ambient light flicker detection, and other functions. With the rise of under-screen optical sensing, PD is no longer sufficient for low-light level light detection and real-time low light level flicker detection. While an emerging detector, single photon avalanche diode (SPAD), shows its advantage to ultralow light sensing. However, SPAD tends to saturate at high light levels, which limits its widespread application. To inspire ambient light sensor design and address these issues, this paper proposes a high sample per second (SPS), wide dynamic range (DR) and ultralow illuminance detection SPAD array system on chip (SoC). A$16\times 16$SPAD array architecture along with counter and charge-pump is designed in 180nm BCD process to sense 100μdux ambient light at 660nm. On-chip FFT processor of 2048 points with 32-bit accuracy is realized to process sampled optical waveform for flicker detection. Besides, an area-efficient integrated charge pump is implemented to regulate high-voltage (19~25V) to SPADs with on-chip digital auto-gain control (AGC), which improves the optical DR from 87db to 123db. This work extends the optical sensing range significantly and demonstrates the effectiveness of the proposed SoC with systematical measurements.