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Blind date: ‘It felt like taking part in Blind Date was a lifelong thing she wanted to do’
Laurine, who works in forensics, meets Theo, a financial adviser. They are both 27What were you hoping for? Or someone new, great conversation, a free dinner and feature in my favourite Guardian column.
'Labubu economics': Game-theoretic model explains why blind box strategies benefit suppliers, retailers, and consumers
'Labubu economics': Game-theoretic model explains why blind box strategies benefit suppliers, retailers, and consumers Gaby Clark Scientific Editor Andrew Zinin Lead Editor The billion-dollar Labubu phenomenon broke a cardinal rule of retail: Consumers need to know what they're buying before they open their wallet. Most new Labubu sales took the form of "blind boxes," where purchasers found out which type of doll they'd purchased only after the fact. Zhechao Yang, assistant professor of...
Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference
arXiv:2509.25269v3 Announce Type: replace-cross Abstract: In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval without any knowledge of scan positions, which then must be recovered jointly with the image. The motivation for this problem comes from single-particle diffractive X-ray imaging, where particles in random orientations are illuminated and a set of diffraction patterns is collected. If one uses a...
Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference
arXiv:2509.25269v3 Announce Type: replace-cross Abstract: In this work, we present and investigate the novel blind inverse problem of position-blind ptychography, i.e., ptychographic phase retrieval without any knowledge of scan positions, which then must be recovered jointly with the image. The motivation for this problem comes from single-particle diffractive X-ray imaging, where particles in random orientations are illuminated and a set of diffraction patterns is collected. If one uses a...
Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack
arXiv:2506.01318v4 Announce Type: replace Abstract: Machine unlearning (MU) aims to expunge a designated forget set from a trained model without costly retraining, yet the existing techniques overlook two critical blind spots: "over-unlearning" that deteriorates retained data near the forget set, and post-hoc "relearning" attacks that aim to resurrect the forgotten knowledge. Focusing on class-level unlearning, we first derive an over-unlearning metric, OU@epsilon, which quantifies...
Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training
Announce Type: replace Abstract: Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit: geometric blindness. This failure to ground outputs in objective geometric constraints leads to plausible yet factually incorrect hallucinations, rooted in training paradigms that prioritize linguistic fluency over geometric fidelity. This paper introduces Med-Scout, a novel framework...
Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors
arXiv:2511.17126v4 Announce Type: replace-cross Abstract: Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes FoundCAC, a universal foundational framework that resolves two challenges hindering the generalization of existing pipelines: the difficulty of scaling training...
Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors
arXiv:2511.17126v4 Announce Type: replace-cross Abstract: Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes FoundCAC, a universal foundational framework that resolves two challenges hindering the generalization of existing pipelines: the difficulty of scaling training...
A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models
arXiv:2605.31080v1 Announce Type: new Abstract: Blind and low-vision (BLV) audiences remain underserved by visual art descriptions, particularly across languages and in museum settings where privacy and intellectual-property constraints may favour small on-premise vision-language models (VLMs). This pilot study investigates curator-guided multilingual art description with Qwen2.5-VL-3B-Instruct for German, Romanian, and Serbian. We construct a parallel BLV-oriented caption corpus from...
Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems
Announce Type: replace Abstract: The rapid growth of AI-driven data centers and large-scale energy storage systems is increasing the reliance of power system operation on real-time measurement data and automated decision-making. However, many existing detection methods rely on statistical or data-driven analysis of measurements and can fail when attackers exploit the same data structure to craft stealthy perturbations. To illustrate this limitation, we demonstrate a blind False Data...