Accurate short-term solar irradiance forecasting is critical for grid stability, yet existing deep learning models often struggle to capture complex dynamics over longer horizons. In this paper, we propose the Attention-Contextual U-Net (AC U-Net), a new architecture for multi-step solar irradiance map prediction using GK-2A satellite imagery over the Korean Peninsula. The proposed model enhances a standard U-Net by integrating spatiotemporal attention and a Feature-wise Linear Modulation (FiLM) layer. This layer injects high-level contextual information directly into the model’s bottleneck, allowing for dynamic feature adaptation. Experiments demonstrate that AC U-Net outperforms not only the persistence baseline but also deep learning-based competitors such as ConvLSTM and HRNet. Our work demonstrates that conditioning on contextual features is a powerful strategy for improving long-range forecasting accuracy.

We propose a new calibration method for survival models based on the Kolmogorov–Smirnov (KS) metric. Existing approaches—including conformal prediction, D-calibration, and Kaplan–Meier (KM)-based methods—often rely on heuristic binning or additional nonparametric estimators, which undermine their adaptability to continuous-time settings and complex model outputs. To address these limitations, we introduce a streamlined $\textit{KS metric-based post-processing}$ framework (KSP) that calibrates survival predictions without relying on discretization or KM estimation. This design enhances flexibility and broad applicability. We conduct extensive experiments on diverse real-world datasets using a variety of survival models. Empirical results demonstrate that our method consistently improves calibration performance over existing methods while maintaining high predictive accuracy. We also provide a theoretical analysis of the KS metric and discuss extensions to in-processing settings.

Active learning, a paradigm within machine learning, aims to select and query unlabeled data to enhance model performance strategically. A crucial selection strategy leverages the model's predictive uncertainty, reflecting the informativeness of a data point. While the sample's distance to the decision boundary intuitively measures predictive uncertainty, its computation becomes intractable for complex decision boundaries formed in multiclass classification tasks. This paper introduces the *least disagree metric* (LDM), the smallest probability of predicted label disagreement. We propose an asymptotically consistent estimator for LDM under mild assumptions. The estimator boasts computational efficiency and straightforward implementation for deep learning models using parameter perturbation. The LDM-based active learning algorithm queries unlabeled data with the smallest LDM, achieving state-of-the-art *overall* performance across various datasets and deep architectures, as demonstrated by the experimental results.

This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between the dimensionality-reduced conditional distributions of different protected classes. The incorporation of MMD naturally leads to an exact and tractable mathematical formulation of fairness with good statistical properties. We formulate the problem of fair PCA subject to MMD constraints as a non-convex optimization over the Stiefel manifold and solve it using the Riemannian Exact Penalty Method with Smoothing (REPMS). Importantly, we provide a local optimality guarantee and explicitly show the theoretical effect of each hyperparameter in practical settings, extending previous results. Experimental comparisons based on synthetic and UCI datasets show that our approach outperforms prior work in explained variance, fairness, and runtime.

Neurons in the primary visual cortex (V1) are often classified as simple or complex cells, but it is debated whether they are discrete hierarchical classes of neurons or if they represent a continuum of variation within a single class of cells. Herein, we show that simple and complex cells may arise commonly from the feedforward projections from the retina. From analysis of the cortical receptive fields in cats, we show evidence that simple and complex cells originate from the periodic variation of ON-OFF segregation in the feedforward projection of retinal mosaics, by which they organize into periodic clusters in V1. From data in cats, we observed that clusters of simple and complex receptive fields correlate topographically with orientation maps, which supports our model prediction. Our results suggest that simple and complex cells are not two distinct neural populations but arise from common retinal afferents, simultaneous with orientation tuning.

This paper considers a surface-normal learning algorithm referred to as few-shot kernel associative domain adaptation (FS-KADA) that reduces the domain shift between abundant synthetic source normals and a few real target normals. The FS-KADA takes an unpaired source and target samples as input and captures invariant representations. However, models trained on synthetically rendered normals do not perform well when accurately predicting real environmental normals due to the domain shift. To address this issue, a contextual weighting is considered for learning FS-KADA on the neighborhood of target ground truth, with kernel association in latent spaces and smoothing at predictions. FS-KADA is evaluated on both a real outdoor target dataset (SNOW) and real indoor datasets (NYUv2) using a synthetic indoor dataset (MLT). The state-of-the-art performance was observed on the SNOW dataset. The performance of FS-KADA using a single ground truth of a randomly selected pixel in each image of the NYUv2 is compared with others using the full ground truth.