MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality

Image quality in modern imaging systems emerges from the coupled effects of the sensor, optics, and computational reconstruction. Ultra-thin metalenses offer a path toward substantial miniaturization of optical modules, but practical designs often exhibit pronounced chromatic and field-dependent aberrations that necessitate computational reconstruction. In current metalens pipelines, reconstruction models are commonly trained and selected using distortion-based fidelity objectives, such as PSNR, yet these proxies can be weakly correlated with human preference and downstream utility, reflecting the well-known perception--distortion trade-off. We introduce MetaRanker, a human-in-the-loop active ranking framework that formalizes metalens image quality in terms of semantic interpretability, defined as the degree to which humans can reliably recognize objects and structures in the presence of optical artifacts. MetaRanker combines a probabilistic preference model with uncertainty-aware query selection, and leverages vision--language models to provide lightweight semantic priors. Importantly, these priors are used only to guide the sampling of informative comparisons; human judgments remain the primary supervision signal throughout. Across real-world and synthetic metalens datasets with distinct degradation profiles, MetaRanker produces rankings that align most closely with human assessments, while reducing the number of pairwise annotations required by approximately 80% relative to exhaustive pairwise evaluation. Finally, we show that standard image quality assessment metrics exhibit limited alignment with human interpretability in the metalens domain, positioning MetaRanker as a practical step toward perceptually grounded metalens evaluation and co-design.

Neural Adjoint Method for Meta-optics: Accelerating Volumetric Inverse Design via Fourier Neural Operators

Meta-optics promises compact, high-performance imaging and color routing. However, designing high-performance structures is a high-dimensional optimization problem: mapping a desired optical output back to a physical 3D structure requires solving computationally expensive Maxwell's equations iteratively. Even with adjoint optimization, broadband design can require thousands of Maxwell solves, making industrial-scale optimization slow and costly. To overcome this challenge, we propose the Neural Adjoint Method, a solver-supervised surrogate that predicts 3D adjoint gradient fields from a voxelized permittivity volume using a Fourier Neural Operator (FNO). By learning the dense, per-voxel sensitivity field that drives gradient-based updates, our method can replace per-iteration adjoint solves with fast predictions, greatly reducing the computational cost of full-wave simulations required during iterative refinement. To better preserve sensitivity peaks, we introduce a stage-wise FNO that progressively refines residual errors with increasing emphasis on higher-frequency components. We curate a meta-optics dataset from paired forward/adjoint FDTD simulations and evaluate it across three tasks: spectral sorting (color routers), achromatic focusing (metalenses), and waveguide mode conversion. Our method reduces design time from hours to seconds. These results suggest a practical route toward fast, large-scale volumetric meta-optical design enabled by AI-accelerated scientific computing.

Dodgersort: Uncertainty-Aware VLM-Guided Human-in-the-Loop Pairwise Ranking

Pairwise comparison labeling is emerging as it yields higher inter-rater reliability than conventional classification labeling, but exhaustive comparisons require quadratic cost. We propose Dodgersort, which leverages CLIP-based hierarchical pre-ordering, a neural ranking head and probabilistic ensemble (Elo, BTL, GP), epistemic--aleatoric uncertainty decomposition, and information-theoretic pair selection. It reduces human comparisons while improving the reliability of the rankings. In visual ranking tasks in medical imaging, historical dating, and aesthetics, Dodgersort achieves a 11--16\% annotation reduction while improving inter-rater reliability. Cross-domain ablations across four datasets show that neural adaptation and ensemble uncertainty are key to this gain. In FG-NET with ground-truth ages, the framework extracts 5--20$\times$ more ranking information per comparison than baselines, yielding Pareto-optimal accuracy--efficiency trade-offs.

EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting

Pairwise comparison is often favored over absolute rating or ordinal classification in subjective or difficult annotation tasks due to its improved reliability. However, exhaustive comparisons require a massive number of annotations (O(n^2)). Recent work has greatly reduced the annotation burden (O(n log n)) by actively sampling pairwise comparisons using a sorting algorithm. We further improve annotation efficiency by (1) roughly pre-ordering items using the Contrastive Language-Image Pre-training (CLIP) model hierarchically without training, and (2) replacing easy, obvious human comparisons with automated comparisons. The proposed EZ-Sort first produces a CLIP-based zero-shot pre-ordering, then initializes bucket-aware Elo scores, and finally runs an uncertainty-guided human-in-the-loop MergeSort. Validation was conducted using various datasets: face-age estimation (FGNET), historical image chronology (DHCI), and retinal image quality assessment (EyePACS). It showed that EZ-Sort reduced human annotation cost by 90.5% compared to exhaustive pairwise comparisons and by 19.8% compared to prior work (when n = 100), while improving or maintaining inter-rater reliability. These results demonstrate that combining CLIP-based priors with uncertainty-aware sampling yields an efficient and scalable solution for pairwise ranking.

Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models

Designing free-form photonic devices is fundamentally challenging due to the vast number of possible geometries and the complex requirements of fabrication constraints. Traditional inverse-design approaches--whether driven by human intuition, global optimization, or adjoint-based gradient methods--often involve intricate binarization and filtering steps, while recent deep learning strategies demand prohibitively large numbers of simulations (10^5 to 10^6). To overcome these limitations, we present AdjointDiffusion, a physics-guided framework that integrates adjoint sensitivity gradients into the sampling process of diffusion models. AdjointDiffusion begins by training a diffusion network on a synthetic, fabrication-aware dataset of binary masks. During inference, we compute the adjoint gradient of a candidate structure and inject this physics-based guidance at each denoising step, steering the generative process toward high figure-of-merit (FoM) solutions without additional post-processing. We demonstrate our method on two canonical photonic design problems--a bent waveguide and a CMOS image sensor color router--and show that our method consistently outperforms state-of-the-art nonlinear optimizers (such as MMA and SLSQP) in both efficiency and manufacturability, while using orders of magnitude fewer simulations (approximately 2 x 10^2) than pure deep learning approaches (approximately 10^5 to 10^6). By eliminating complex binarization schedules and minimizing simulation overhead, AdjointDiffusion offers a streamlined, simulation-efficient, and fabrication-aware pipeline for next-generation photonic device design. Our open-source implementation is available at https://github.com/dongjin-seo2020/AdjointDiffusion.

BOLDSimNet: Examining Brain Network Similarity between Task and Resting-State fMRI

Traditional causal connectivity methods in task-based and resting-state functional magnetic resonance imaging (fMRI) face challenges in accurately capturing directed information flow due to their sensitivity to noise and inability to model multivariate dependencies. These limitations hinder the effective comparison of brain networks between cognitive states, making it difficult to analyze network reconfiguration during task and resting states. To address these issues, we propose BOLDSimNet, a novel framework utilizing Multivariate Transfer Entropy (MTE) to measure causal connectivity and network similarity across different cognitive states. Our method groups functionally similar regions of interest (ROIs) rather than spatially adjacent nodes, improving accuracy in network alignment. We applied BOLDSimNet to fMRI data from 40 healthy controls and found that children exhibited higher similarity scores between task and resting states compared to adolescents, indicating reduced variability in attention shifts. In contrast, adolescents showed more differences between task and resting states in the Dorsal Attention Network (DAN) and the Default Mode Network (DMN), reflecting enhanced network adaptability. These findings emphasize developmental variations in the reconfiguration of the causal brain network, showcasing BOLDSimNet's ability to quantify network similarity and identify attentional fluctuations between different cognitive states.

Uncertainty-Weighted Mutual Distillation for Multi-View Fusion

Multi-view learning often faces challenges in effectively leveraging images captured from different angles and locations. This challenge is particularly pronounced when addressing inconsistencies and uncertainties between views. In this paper, we propose a novel Multi-View Uncertainty-Weighted Mutual Distillation (MV-UWMD) method. Our method enhances prediction consistency by performing hierarchical mutual distillation across all possible view combinations, including single-view, partial multi-view, and full multi-view predictions. This introduces an uncertainty-based weighting mechanism through mutual distillation, allowing effective exploitation of unique information from each view while mitigating the impact of uncertain predictions. We extend a CNN-Transformer hybrid architecture to facilitate robust feature learning and integration across multiple view combinations. We conducted extensive experiments using a large, unstructured dataset captured from diverse, non-fixed viewpoints. The results demonstrate that MV-UWMD improves prediction accuracy and consistency compared to existing multi-view learning approaches.

Calibration of ordinal regression networks

Recent studies have shown that deep neural networks are not well-calibrated and produce over-confident predictions. The miscalibration issue primarily stems from the minimization of cross-entropy, which aims to align predicted softmax probabilities with one-hot labels. In ordinal regression tasks, this problem is compounded by an additional challenge: the expectation that softmax probabilities should exhibit unimodal distribution is not met with cross-entropy. Rather, the ordinal regression literature has focused on unimodality and overlooked calibration. To address these issues, we propose a novel loss function that introduces order-aware calibration, ensuring that prediction confidence adheres to ordinal relationships between classes. It incorporates soft ordinal encoding and label-smoothing-based regularization to enforce both calibration and unimodality. Extensive experiments across three popular ordinal regression benchmarks demonstrate that our approach achieves state-of-the-art calibration without compromising accuracy.

Facial Wrinkle Segmentation for Cosmetic Dermatology: Pretraining with Texture Map-Based Weak Supervision

Facial wrinkle detection plays a crucial role in cosmetic dermatology. Precise manual segmentation of facial wrinkles is challenging and time-consuming, with inherent subjectivity leading to inconsistent results among graders. To address this issue, we propose two solutions. First, we build and release the first public facial wrinkle dataset, `FFHQ-Wrinkle', an extension of the NVIDIA FFHQ dataset. This dataset includes 1,000 images with human labels and 50,000 images with automatically generated weak labels. This dataset can foster the research community to develop advanced wrinkle detection algorithms. Second, we introduce a training strategy for U-Net-like encoder-decoder models to detect wrinkles across the face automatically. Our method employs a two-stage training strategy: texture map pretraining and finetuning on human-labeled data. Initially, we pretrain models on a large dataset with weak labels (N=50k) or masked texture maps generated through computer vision techniques, without human intervention. Subsequently, we finetune the models using human-labeled data (N=1k), which consists of manually labeled wrinkle masks. During finetuning, the network inputs a combination of RGB and masked texture maps, comprising four channels. We effectively combine labels from multiple annotators to minimize subjectivity in manual labeling. Our strategies demonstrate improved segmentation performance in facial wrinkle segmentation both quantitatively and visually compared to existing pretraining methods.

Wave Interpolation Neural Operator: Interpolated Prediction of Electric Fields Across Untrained Wavelengths

Designing photonic structures requires electromagnetic simulations, which often require high computational costs. Researchers have developed surrogate solvers for predicting electric fields to alleviate the computational issues. However, existing surrogate solvers are limited to performing inference at fixed simulation conditions and require retraining for different conditions. To address this, we propose Wave Interpolation Neural Operator (WINO), a novel surrogate solver enabling simulation condition interpolation across a continuous spectrum of broadband wavelengths. WINO introduces the Fourier Group Convolution Shuffling operator and a new conditioning method to efficiently predict electric fields from both trained and untrained wavelength data, achieving significant improvements in parameter efficiency and spectral interpolation performance. Our model demonstrates approximately 100 times faster performance than traditional finite-difference frequency-domain simulations. Moreover, compared to the state-of-the-art model, we achieve a 74% reduction in parameters and 80.5% improvements in prediction accuracy for untrained wavelengths, and 13.2% improvements for trained wavelengths.