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Learned denoising with simulated and experimental low-dose CT data
Get PDFLike in many other research fields, recent developments in computational imaging have focused on developing machine learning (ML) approaches to tackle its main challenges. To improve the performance of computational imaging algorithms, machine learning methods are used for image processing tasks such as noise reduction. Generally, these ML methods heavily rely on the availability of high-quality data on which they are trained. This work explores the application of ML methods, specifically convolutional neural networks (CNNs), in the context of noise reduction for computed tomography (CT) imaging. We utilize a large 2D computed tomography dataset for machine learning to carry out for the first time a comprehensive study on the differences between the observed performances of algorithms trained on simulated noisy data and on real-world experimental noisy data. The study compares the performance of two common CNN architectures, U-Net and MSD-Net, that are trained and evaluated on both simulated and experimental noisy data. The results show that while sinogram denoising performed better with simulated noisy data if evaluated in the sinogram domain, the performance did not carry over to the reconstruction domain where training on experimental noisy data shows a higher performance in denoising experimental noisy data. Training the algorithms with an optimization in the reconstruction domain mapping directly from sinogram to reconstruction significantly improved model performance, emphasizing the importance of matching raw measurement data to high-quality CT reconstructions. The study furthermore suggests the need for more sophisticated noise simulation approaches to bridge the gap between simulated and real-world data in CT image denoising applications and gives insights into the challenges and opportunities in leveraging simulated data for machine learning in computational imaging
OPEN-DASH-PC - SPIRIT Open Call
No full textDo you want to learn more about the SPIRIT Open Call innovators? We’ve gathered short video interviews where the winning experimenters present their projects, explain how they used the SPIRIT platform, and share the impact of their work
Non-clairvoyant scheduling with progress bars
Get PDFCitation:
@article{DBLP:journals/corr/abs-2509-19662,
author = {Ziyad Benomar and
Romain Cosson and
Alexander Lindermayr and
Jens Schl{\"{o}}ter},
title = {Non-Clairvoyant Scheduling with Progress Bars},
journal = {CoRR},
volume = {abs/2509.19662},
year = {2025},
url = {https://doi.org/10.48550/arXiv.2509.19662},
doi = {10.48550/ARXIV.2509.19662},
eprinttype = {arXiv},
eprint = {2509.19662},
}In non-clairvoyant scheduling, the goal is to minimize the total job completion time without prior knowledge of individual job processing times. This classical online optimization problem has recently gained attention through the framework of learning-augmented algorithms. We introduce a natural setting in which the scheduler receives continuous feedback in the form of progress bars: estimates of the fraction of each job completed over time. We design new algorithms for both adversarial and stochastic progress bars and prove strong competitive bounds. Our results in the adversarial case surprisingly induce improved guarantees for learning-augmented scheduling with job size predictions. We also introduce a general method for combining scheduling algorithms, yielding further insights in scheduling with predictions. Finally, we propose a stochastic model of progress bars as a more optimistic alternative to conventional worst-case models, and present an asymptotically optimal scheduling algorithm in this setting
Security analysis of covercrypt: A quantum-safe hybrid key encapsulation mechanism for hidden access policies
Get PDFThe ETSI Technical Specification 104 015 proposes a framework to build Key Encapsulation Mechanisms (KEMs) with access policies and attributes, in the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) vein. Several security guarantees and functionalities are claimed, such
as pre-quantum and post-quantum hybridization to achieve security against Chosen-Ciphertext At-
tacks (CCA), anonymity, and traceability.
In this paper, we present a formal security analysis of a more generic construction, with application
to the specific Covercrypt scheme, based on the pre-quantum ECDH and the post-quantum ML-
KEM KEMs. We additionally provide an open-source library that implements the ETSI standard,
in Rust, with high effiency
Optimal test statistics for anytime-valid hypothesis tests
Get PDFIn this dissertation, we study hypothesis testing: evaluating whether sample data support a claim regarding a broader population. The approach is to assume that the claim is false and to examine whether this assumption, called the null hypothesis, holds up in light of the data. For example, researchers in a clinical trial wish to use the data to refute the hypothesis that their medication does not work. Hypothesis tests help guide decision-making in all walks of life, from inance to agriculture, by offering a structured framework to evaluate the strength of evidence against hypotheses. This dissertation is a contribution to the theory of anytime-valid hypothesis tests, which are tools that are compatible with lexible experimental design. That is, anytime-valid methods allow researchers to stop or continue their experiment based on observed data, which is not possible with traditional methods. Anytime-valid methods work by keeping track of a numerical measure of evidence—the e-process—against the null hypothesis. In particular, we consider e-processes obtained through the combination of e-statistics, which measure the evidence that can be derived from each data point separately. The hypothesis can be refuted if the combined evidence against it grows too large. The goal is therefore to construct e-statistics that give as much evidence as possible if the hypothesis is not true. These are called log-optimal e- statistics. In Chapter 3, we discuss the abstract problem of finding log-optimal e-statistics, which leads to a general recipe for their construction. In Chapters 4–7, we use this recipe to find the log-optimal e-statistics for specific hypotheses f interest (exponential families, conditional independence and group invariance). A key assumption underlying the optimality results in these chapters is that we know (or can learn) what exactly happens if the null hypothesis is not true. In chapter 8, we take a different approach and consider the worst case over a set of possible alternative hypotheses. That is, we study a setting where it is possible to compute the e-statistic that maximizes the rate at which evidence is accumulated in the worst case over the alternative
A cb-Bohnenblust–Hille inequality with constant one and its applications in learning theory
Get PDFThe main result of this work shows that Bohnenblust–Hille inequality for m-homogeneous polynomials holds with constant one when the infinity norm is replaced by the completely bounded norm. Moreover, we show that this inequality finds some interesting consequences in quantum learning theory. In the second part of this paper, we broaden our investigation of the Bohnenblust–Hille inequality to other contexts. In particular, we extend recent results by Volberg and Zhang, demonstrating its applicability within a framework we have termed “Learning Low-Degree Quantum Objects”