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From Global to Local: A Scalable Benchmark for Local Posterior Sampling
Rohan Hitchcock, Jesse Hoogland
https://arxiv.org/abs/2507.21449 https://arxiv.org/…

From Global to Local: A Scalable Benchmark for Local Posterior Sampling
Degeneracy is an inherent feature of the loss landscape of neural networks, but it is not well understood how stochastic gradient MCMC (SGMCMC) algorithms interact with this degeneracy. In particular, current global convergence guarantees for common SGMCMC algorithms rely on assumptions which are likely incompatible with degenerate loss landscapes. In this paper, we argue that this gap requires a shift in focus from global to local posterior sampling, and, as a first step, we introduce a novel …

Evaluation of facial landmark localization performance in a surgical setting
Ines Frajtag, Marko \v{S}vaco, Filip \v{S}uligoj
https://arxiv.org/abs/2507.18248 https://

Evaluation of facial landmark localization performance in a surgical setting
The use of robotics, computer vision, and their applications is becoming increasingly widespread in various fields, including medicine. Many face detection algorithms have found applications in neurosurgery, ophthalmology, and plastic surgery. A common challenge in using these algorithms is variable lighting conditions and the flexibility of detection positions to identify and precisely localize patients. The proposed experiment tests the MediaPipe algorithm for detecting facial landmarks in a …

Real-time CBCT reconstructions using Krylov solvers in repeated scanning procedures
Fred Vickers Hastings, S M Ragib Shahriar Islam, Malena Sabat\'e Landman, Sepideh Hatamikia, Carola-Bibiane Sch\"onlieb, Ander Biguri
https://arxiv.org/abs/2509.08574

Real-time CBCT reconstructions using Krylov solvers in repeated scanning procedures
This work introduces a new efficient iterative solver for the reconstruction of real-time cone-beam computed tomography (CBCT), which is based on the Prior Image Constrained Compressed Sensing (PICCS) regularization and leverages the efficiency of Krylov subspace methods. In particular, we focus on the setting where a sequence of under-sampled CT scans are taken on the same object with only local changes (e.g. changes in a tumour size or the introduction of a surgical tool). This is very common…