The paper presents a spectral hierarchy for classifying the cosmic web by applying scale-weighting kernels to the density field before using eigenvalue-based methods. This framework unifies existing web definitions—potential/tidal, curvature-based, and higher-derivative levels—into a single, interpretable hierarchy that captures structure from large to small scales. The authors quantify the hierarchy’s information content by correlating a web contrast field with halo distributions, showing that it retains significant tracer-relevant information across scales, especially at nonlinear levels.
By Francisco-Shu Kitaura, Francesco Sinigaglia
arXiv:2609.37114v1 Announce Type: new
Abstract: DANCo (Dimensionality from Angle and Norm Concentration) jointly calibrates nearest-neighbor distance and angular statistics and consistently reaches s...
By Chih-Hsuan Huang, Chih-Wei Chen, Szu-Chi Chung
DANCo (Dimensionality from Angle and Norm Concentration) jointly calibrates nearest-neighbor distance and angular statistics and consistently reaches state-of-the-art accuracy on clean intrinsic-dimen...
arXiv:2606. 08652v1 Announce Type: cross Abstract: Routine full-disk EUV imaging has been available only since the modern era, such as SOHO and SDO.
By Marco Marena, Qin Li, Haimin Wang, Haodi Jiang, Prajwal Shah, Bo Shen
arXiv:2610.22474v1 Announce Type: new
Abstract: Figures are essential for scientific communication, and the field of Visual Question Answering (VQA) aims to test the figure understanding capabilities...
By J. P. Naiman
arXiv:2609.35966v1 Announce Type: cross
Abstract: Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, compl...
By Shunyuan Mao, Andrea Isella, Paris Perdikaris, Li-Ta Lo, Hui Li
arXiv:2508. 12450v2 Announce Type: replace Abstract: This article presents an adaptive mean shift algorithm in which every parameter used at a point is derived from that point's own distance distribution.
By \'Etienne Pepin
The paper presents a closed‑form system of image‑scaling operators <K_s> that faithfully reproduce optical zoom by leveraging the camera’s point spread function (PSF). These operators compose multiplicatively, form a Moore‑Penrose pseudoinverse pair, and provide a generalized, bidirectional scale‑space that extends beyond conventional Gaussian kernels. The authors validate the model with real captures and simulations, showing improved stability for tasks such as super‑resolution and classification.
By Assaf Shocher
arXiv:2610.05518v1 Announce Type: cross
Abstract: The Fr\'echet distance is a de facto standard for evaluating generative models across domains, appearing as FID for images and FVD for videos. It sum...
By Yunghee Lee, Jaeyeon Kim
arXiv:2610.08533v1 Announce Type: cross
Abstract: Geometric alignment scores based on Gram determinants provide a compact way to model higher-order consistency among modalities, yet how such scores r...
By Yongsheng Luo, Wengan He, Yu Li, Rouying Wu, Wei Lv
The study audits the AION-1 foundation model, a 39‑modality transformer trained on over 200 million astronomical objects, and finds that its reliance on a survey detection channel—specifically the segmentation map—introduces a severe systematic bias. By keeping image tokens unchanged and editing only the segmentation map, all model outputs (flux, size, ellipticity, redshift) shift by factors of 110–4400 compared to a placebo, revealing that the model’s predictions are driven more by detection gating than by the actual light distribution. This bias propagates into cosmological analyses, shifting tomographic mean redshifts by a median 0.71 × the LSST DESC requirement and exceeding it in multiple assignments, while removing the detection channel eliminates the effect without measurable cost.
whyItMatters":"The bias in the detection channel directly inflates errors in key astronomical measurements, potentially compromising the precision of cosmological studies that rely on accurate redshift estimates."
By Ihor Kendiukhov
arXiv:2606. 07882v1 Announce Type: cross Abstract: Different vision neural networks -- trained to classify, contrast, reconstruct, or match images to text -- should have correspondingly different internal representations.
By Yousef Radwan