The ability to sense and reconstruct 3D appearance and geometry is critical to applications in vision, graphics, and beyond.

Medical decision-making makes frequent use of algorithms that combine risk equations with rules, providing clear and standardized treatment pathways. Symbolic regression (SR) traditionally limits its search space to continuous function forms and their parameters, making it difficult to model this decision-making. However, due to its ability to derive data-driven, interpretable models, SR holds promise for developing data-driven clinical risk scores. To that end we introduce Brush, an SR algorithm that combines decision-tree-like splitting algorithms with non-linear constant optimization, allowing for seamless integration of rule-based logic into symbolic regression and classification models. Brush achieves Pareto-optimal performance on SRBench, and was applied to recapitulate two widely used clinical scoring systems, achieving high accuracy and interpretable models. Compared to decision trees, random forests, and other SR methods, Brush achieves comparable or superior predictive performance while producing simpler models.

Training and deploying multiple vision transformer (ViT) models for different resource constraints is costly and inefficient. To address this, we propose transforming a pre-trained ViT into a stratified knowledge-density super-network, where knowledge is hierarchically organized across weights. This enables flexible extraction of sub-networks that retain maximal knowledge for varying model sizes. We introduce \textbf{W}eighted \textbf{P}CA for \textbf{A}ttention \textbf{C}ontraction (WPAC), which concentrates knowledge into a compact set of critical weights. WPAC applies token-wise weighted principal component analysis to intermediate features and injects the resulting transformation and inverse matrices into adjacent layers, preserving the original network function while enhancing knowledge compactness. To further promote stratified knowledge organization, we propose \textbf{P}rogressive \textbf{I}mportance-\textbf{A}ware \textbf{D}ropout (PIAD). PIAD progressively evaluates the importance of weight groups, updates an importance-aware dropout list, and trains the super-network under this dropout regime to promote knowledge stratification. Experiments demonstrate that WPAC outperforms existing pruning criteria in knowledge concentration, and the combination with PIAD offers a strong alternative to state-of-the-art model compression and model expansion methods.

A1.1 What is the rationale for constructing a dataset for mHealth signal imputation from equivalent signals connected in the clinical setting? We can mimic real-world mHealth settings by applying realistic patterns of mHealth missingness. A1.2 What are the differences in how the ECG/PPG sensors collect pulsative signals across both settings? An ECG signal is a recording of the electrical activity of the heart. In clinical hospital settings, the pulse oximeter device is clipped to a stationary patient's finger, so the A1.3 How do the populations differ in these two settings?

We summarize the SD module in Algorithm 1. We omit some algorithmic details and state the SD module in Algorithm 1 for an easy understanding. Here, we continue to elaborate our mechanism in Algorithm 2. The main supplement is the step of ASR is already higher than 90%. However, it doesn't work under clean-label attacks (shown in Figure 1(c,f)) since poisoned samples are mixed up with clean samples. Then, we reuse the SD module and find that clean and poisoned samples can be well separated.

We retrieve top 10 passages and use them as input to mGEN. Gettysburg College, where he was a member of the Lambda Chi Alpha fraternity. We further subsample 50% of the synthetically generated questions. For our multilingual retriever, we split each article into 100-token chunks (Karpukhin et al., 2020), The original passage text file is 29GB, and the total index size is around 129 GB. Both two datasets are under the MIT licence.

Knee osteoarthritis (KOA) diagnosis from radiographs remains challenging due to the subtle morphological details that standard deep learning models struggle to capture effectively. We propose a novel multimodal framework that combines anatomical structure with radiographic features by integrating a morphological graph representation - derived from Segment Anything Model (SAM) segmentations - with a vision encoder. Our approach enforces alignment between geometry-informed graph embeddings and radiographic features through mutual information maximization, significantly improving KOA classification accuracy. By constructing graphs from anatomical features, we introduce explicit morphological priors that mirror clinical assessment criteria, enriching the feature space and enhancing the model's inductive bias. Experiments on the Osteoarthritis Initiative dataset demonstrate that our approach surpasses single-modality baselines by up to 10\% in accuracy (reaching nearly 80\%), while outperforming existing state-of-the-art methods by 8\% in accuracy and 11\% in F1 score. These results underscore the critical importance of incorporating anatomical structure into radiographic analysis for accurate KOA severity grading.