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Onderzoekers ontwikkelen AI-model om erfelijke cholesterolziekte FH op te sporen - ArtsenApotheker - 09-01-2025
No full textPtyGenography: Using generative models for regularization of the phase retrieval problem
Get PDFIn phase retrieval and similar inverse problems, the stability of solutions across different noise levels is crucial for applications. One approach to promote it is using signal priors in a form of a generative model as a regularization, at the expense of introducing a bias in the reconstruction. In this paper, we explore and compare the reconstruction properties of classical and generative inverse problem formulations. We propose a new unified reconstruction approach that mitigates overfitting to the generative model for varying noise levels
Total completion time scheduling under scenarios
Get PDFScheduling jobs with given processing times on identical parallel machines so as to minimize their total completion time is one of the most basic scheduling problems. We study this classical problem under uncertainty, in which the uncertainty is modeled by a set of scenarios. In our model, a scenario is defined as a subset of a predefined and fully specified set of jobs. The aim is to find an assignment of the whole set of jobs to identical parallel machines such that the schedule, obtained for the given scenarios by simply skipping the jobs not in the scenario, optimizes a function of the total completion times over all scenarios. While the underlying scheduling problem without scenarios can be solved efficiently by a simple greedy procedure (SPT rule), scenarios, in general, make the problem NP-hard. We paint an almost complete picture of the evolving complexity landscape, drawing the line between easy and hard. One of our main algorithmic contributions relies on a deep structural result on the maximum imbalance of an optimal schedule, based on a subtle connection to Hilbert bases of a related convex cone
Stable self-adaptive timestepping for Reduced Order Models for incompressible flows
Get PDFThis work introduces RedEigCD, the first self-adaptive timestepping technique specifically tailored for reduced-order models (ROMs) of the incompressible Navier-Stokes equations. Building upon linear stability concepts, the method adapts the timestep by directly bounding the stability function of the employed time integration scheme using exact spectral information of matrices related to the reduced operators. Unlike traditional error-based adaptive methods, RedEigCD relies on the eigenbounds of the convective and diffusive ROM operators, whose computation is feasible at reduced scale and fully preserves the online efficiency of the ROM. A central theoretical contribution of this work is the proof, based on the combined theorems of Bendixson and Rao, that, under linearized assumptions, the maximum stable timestep for projection-based ROMs is shown to be larger than or equal to that of their corresponding full-order models (FOMs). Numerical experiments for both periodic and non-homogeneous boundary conditions demonstrate that RedEigCD yields stable timestep increases up to a factor 40 compared to the FOM, without compromising accuracy. The methodology thus establishes a new link between linear stability theory and reduced-order modeling, offering a systematic path towards efficient, self-regulating ROM integration in incompressible flow simulations
The story of Python and how it took over the world | Python: the documentary
No full textThis is the story of the world's most beloved programming language: Python. What began as a side project in Amsterdam during the 1990s became the software powering artificial intelligence, data science and some of the world’s biggest companies. But Python's future wasn't certain; at one point it almost disappeared.
This 90-minute documentary features Guido van Rossum, Travis Oliphant, Barry Warsaw, and many more, and they tell the story of Python’s rise, its community-driven evolution, the conflicts that almost tore it apart, and the language’s impact on... well… everything
Public accountability and regulatory expectations for AI in journalism: qualitative evidence from focus groups with Dutch citizens
Get PDFAs artificial intelligence (AI) continues to reshape society, its integration into journalism raises critical questions about transparency, accountability, and public trust. Existing AI regulations have largely been developed without meaningful public input, prompting concerns about whether current governance approaches adequately address societal expectations. This study investigates the expectations and concerns of Dutch citizens regarding mandatory AI disclosures in journalism through three focus groups (N = 21). We aimed for a broad sample of participants to ensure diversity in terms of age, gender, and education level. Key questions measured the main concerns about AI-generated content, why participants want to know if they are interacting with AI-generated content and which rights individuals would like to have in this context. The results reveal a preference for participatory regulatory processes and standardized transparency measures, such as the disclosure of sources. The results further underscore the wish to be able to hold news organizations and individual AI users accountable when regulations are breached. The findings can inform news professionals and regulators alike, for example, in the context of the implementation of the AI transparency obligations in the European AI Act
PMSM geometrical parameters and torque signals
No full textGeometrical data of a permanent magnet synchronous machine (PMSM) and corresponding torque signals from paper: Partovizadeh, Aylar, Sebastian Schöps, and Dimitrios Loukrezis. "Fourier-enhanced reduced-order surrogate modeling for uncertainty quantification in electric machine design." Engineering with Computers 41, 2619–2639 (2025)
On the impossibility of actively secure distributed samplers
No full textOne-round secure computation is generally believed impossible due to the residual function attack: any honest-but-curious participant can replay the protocol in their head changing their input, and learn, in this way, a new output. Inputless functionalities are among the few that are immune to this problem.
This paper studies one-round, multi-party computation protocols (MPC) that implement the most natural inputless functionality: one that generates a random sample from a fixed distribution. These are called distributed samplers. At Eurocrypt 2022, Abram, Scholl and Yakoubov showed how to build this primitive in the semi-honest model with dishonest majority. In this work, we give a lower bound for constructing distributed samplers with a malicious adversary in the standard model. More in detail, we show that for any construction in the stand-alone model with black-box simulation, even with a CRS and honest majority, the output of the sampling protocol must have low entropy. This essentially implies that this type of construction is useless in applications. Our proof is based on an entropic argument, drawing a new connection between computationally secure MPC, information theory and learning theory
A Fast and Robust Reformulation of the UVN-Flash Problem via Direct Entropy Maximization
Get PDFWe investigate the phase equilibrium problem for multicomponent mixtures under specified internal energy (U), volume (V), and mole numbers (N1,N2, . . . ,Nn), commonly known as the UVN-flash problem. While conventional phase equilibrium calculations typically use pressure-temperature-mole number (PTN) specifications, the UVN formulation is essential for dynamic simulations of closed systems and energy balance computations. Existing approaches, including those based on iterative pressure-temperature updates and direct entropy maximization, suffer from computational inefficiencies due to nested iterations and reliance on inner Newton solvers. In this work, we present a novel reformulation of the UVN-flash problem as a direct entropy maximization problem that eliminates the need for inner Newton iterations, addressing key computational bottlenecks. We derive two new novel formulations: 1) a formulation based on entropy and internal energy and (2) an alternative formulation based on Helmholtz free energy. We begin with a stability analysis framework, followed by a reformulation of the UVN flash problem in natural variables. We then introduce our novel approach and discuss the numerical methods used, including gradient and Hessian computations. The proposed method is validated against benchmark cases, demonstrating improved efficiency and robustness
On circuit diameter bounds via circuit imbalances
No full textWe study the circuit diameter of polyhedra, introduced by Borgwardt, Finhold, and Hemmecke (SIDMA 2015) as a relaxation of the combinatorial diameter. We show that the circuit diameter of a system
for is bounded by , where is the circuit imbalance measure of the constraint matrix. This yields a strongly polynomial circuit diameter bound if e.g., all entries of A have polynomially bounded encoding length in n. Further, we present circuit augmentation algorithms for LPs using the minimum-ratio circuit cancelling rule. Even though the standard minimum-ratio circuit cancelling algorithm is not finite in general, our variant can solve an LP in augmentation steps