Elena Williams | Searcharxiv

arXiv2026

Pair-Breaking and Dimensionality in Spin-Orbit Coupled Superconductors

The response of ultra-thin superconducting materials under parallel magnetic fields is often leveraged to obtain insight into the nature of the condensate, including features attributable to unconventional forms of pairing. Despite there being multiple competing mechanisms responsible for suppressing superconductivity, it is common for these analyses to overlook certain depairing channels. Here we report an analysis of thickness dependent superconductivity in thin films of \ce{LaBi2} using the multi-mechanism Kharitonov-Feigel'man framework . By resolving field-enhanced superconductivity in the thin-limit, we obtain an estimate the role of spin exchange scattering, in addition to paramagnetic and orbital effects. Our analyses offer insight into how fundamental quantities such as the critical temperature as well as Pauli limit are defined, recasting the landscape for how scattering times in two-dimensional superconductors can be interpreted.

cond-mat.supr-con↗

arXiv2021

Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows

Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology. However, inaccuracies, mainly due to the limited availability of clinical samples for training these algorithms, hamper their wider applicability, acceptance, and recognition amongst clinicians. We present an analysis of state-of-the-art automatic quality control (QC) approaches that can be implemented within these algorithms to estimate the certainty of their outputs. We validated the most promising approaches on a brain image segmentation task identifying white matter hyperintensities (WMH) in magnetic resonance imaging data. WMH are a correlate of small vessel disease common in mid-to-late adulthood and are particularly challenging to segment due to their varied size, and distributional patterns. Our results show that the aggregation of uncertainty and Dice prediction were most effective in failure detection for this task. Both methods independently improved mean Dice from 0.82 to 0.84. Our work reveals how QC methods can help to detect failed segmentation cases and therefore make automatic segmentation more reliable and suitable for clinical practice.

eess.IV↗