The success of large language models (LLMs) in cognitive tasks prompts the question of whether their next-token prediction (NTP) paradigm can be adapted to model physiological signals from wearable devices. A key target for this adaptation is photoplethysmography (PPG), the most prevalent sensing modality in consumer wearables for non-invasive monitoring of diverse physiological conditions. Unlike in NLP, where NTP aligns with generative objectives, physiological signal analysis involves fundamentally different tasks, such as continuous parameter estimation (regression) and discrete state recognition (classification). This disparity creates a semantic mismatch between the pre-training paradigm and the downstream tasks. To bridge this gap, we propose PPGPT, the first foundation model that reformulates NTP into next-feature token prediction (NFTP), learning hierarchical feature transition probabilities to unify pre-training and downstream objectives. PPGPT features a novel dual-stream encoder that generates feature tokens by jointly modeling temporal dynamics and local-global morphological patterns. The model is developed using a two-stage training framework: it is first pre-trained on a large-scale mixed dataset of 1.6 billion data points and then validated on our newly released BioMTL benchmark, which includes data from 172 subjects over 285 days across seven different tasks. Extensive experiments show that PPGPT significantly outperforms competing methods, achieving a 16.5% improvement in F1-score and a 25.9% reduction in Mean Absolute Error (MAE). Furthermore, the model demonstrates robust few-shot learning capabilities.

Facial beautification removal presents a formidable inverse challenge due to the inherent diversity and unpredictability of beautification processes. Current methodologies often fall short in effectively restoring facial structural alterations and preserving texture features during makeup removal. This paper introduces DeBeauty, an innovative joint framework for facial beautification removal, comprising two primary workflows: Adversarial De-Makeup Flow (ADF) and Relocation Deformation Flow (RDF). ADF incorporates Multi-Level Perception Collaborative Discrimination (MLPCD) and Discriminator-Guided Spatial Adaptive Multi-Scale Attention (SAMA), which enhance the detection of subtle makeup and facilitate the comprehensive removal of extensive makeup while preserving facial texture. RDF introduces a Hyperplane Relocation Strategy that adjusts the latent code of the input face to align with the original structural distribution. Experimental evaluations on the newly proposed Multivariate Beautified Face dataset demonstrate that this approach effectively restores the original color and structural context of the face while preserving essential facial features, achieving state-of-the-art performance.

Deep autoencoder networks’ flexibility and advantages in learning hidden features have made them popular in unmixing hyperspectral images recently. Based on specific network structures, the effective learning of spatial-spectral features for unmixing is always crucial. This paper presents a novel unmixing method using spatial-spectral twin autoencoders. One autoencoder generates coarse abundances with strong local spatial similarity for pixels in hyperedges defined in homogeneous superpixels. The other autoencoder with identical modules further adopts the coarse abundances to build a spatial resemblance constraint in the loss function to produce refined abundances. Experiments with simulated and real hyperspectral remote sensing data have confirmed the validity of the proposed method.