Tootfinder

Goed interview over rekencentra, cloud, digitale autonomie, soevereiniteit ed van de TU-Delft.
https://delta.tudelft.nl/en/article/digital-autonomy-is-unavoidable-but-demands-more-than-a-quick-fix

Digital autonomy is unavoidable, but demands more than a quick fix - Delta
Digital autonomy is not a luxury, it is essential. This was the message communicated this summer by researchers from the Rathenau Institute to Dutch knowledge institutions in a report about digital dependence on foreign technology companies. Delta asked ICT director Erik Scherff about the university’s position on this. His reply: “The current geopolitics forces us to negotiate.”

This from IPPR on the greatly increased costs of UK government debt servicing seems reasonable from IPPR.
1) Tax the big 4 banks on the windfall profits that the government is, effectively, funding.
2) Slow down the QE buy-back.
I would also think that the government/bank could, as an exceptional measure, buy back some of the long term gilts using 'monetary financing' aka 'printing money'.
Not something to be done willy-nilly, but here it could get govt ou…

Fixing the leak: How to end the £22 billion annual taxpayer losses at the Bank of England
The Institute for Public Policy Research (IPPR) is an independent charity working towards a fairer, greener, and more prosperous society.

On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study
Jacqueline J. Vallon, William Overman, Wanqiao Xu, Neil Panjwani, Xi Ling, Sushmita Vij, Hilary P. Bagshaw, John T. Leppert, Sumit Shah, Geoffrey Sonn, Sandy Srinivas, Erqi Pollom, Mark K. Buyyounouski, Mohsen Bayati
https://arxiv.org/abs/2509.0…

On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study
Over the past decade, the use of machine learning (ML) models in healthcare applications has rapidly increased. Despite high performance, modern ML models do not always capture patterns the end user requires. For example, a model may predict a non-monotonically decreasing relationship between cancer stage and survival, keeping all other features fixed. In this paper, we present a reproducible framework for investigating this misalignment between model behavior and clinical experiential learning…

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
https://arxiv.org/abs/2508.15031

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
Machine learning (ML) models have significantly grown in complexity and utility, driving advances across multiple domains. However, substantial computational resources and specialized expertise have historically restricted their wide adoption. Machine-Learning-as-a-Service (MLaaS) platforms have addressed these barriers by providing scalable, convenient, and affordable access to sophisticated ML models through user-friendly APIs. While this accessibility promotes widespread use of advanced ML c…

Calibrated and uncertain? Evaluating uncertainty estimates in binary classification models
Aurora Grefsrud, Nello Blaser, Trygve Buanes
https://arxiv.org/abs/2508.11460 https://…

Calibrated and uncertain? Evaluating uncertainty estimates in binary classification models
Rigorous statistical methods, including parameter estimation with accompanying uncertainties, underpin the validity of scientific discovery, especially in the natural sciences. With increasingly complex data models such as deep learning techniques, uncertainty quantification has become exceedingly difficult and a plethora of techniques have been proposed. In this case study, we use the unifying framework of approximate Bayesian inference combined with empirical tests on carefully created synthe…

Change point detection in ERA5 ground temperature time series
Fatemeh Aghaei A., Ewan T. Phillips, Holger Kantz
https://arxiv.org/abs/2507.15045 https://…

Change point detection in ERA5 ground temperature time series
We analyze the ERA5 reanalysis 2-meter temperature time series on all land grid points using change point analysis. We fit two linear slopes to the data with the constraint that they merge at the point in time where the slope changes. We compare such fits to a standard linear regression in two ways: We use Akaike's and the Bayesian information criteria for model selection, and we test against the null hypothesis of no change of the trend value. For those grid points where the …