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Spin matters: A multidisciplinary roadmap to understanding spin effects in oxygen evolution reaction during water electrolysis
No full textA central challenge in water electrolysis lies with the oxygen evolution reaction (OER) where the formation of molecular oxygen (O2) is hindered by the constraint of angular momentum conservation. While the reactants OH− or H2O are diamagnetic (DM), the O2 product has a paramagnetic (PM) triplet ground state, requiring a change in spin configuration when being formed. This constraint has prompted interest in spin-selective catalysts as a means to facilitate OER. In this context, the roles of magnetism and chirality-induced spin selectivity (CISS) in promoting the OER reaction have recently been investigated through both theoretical and experimental studies. However, pinpointing the key principles and their relative contribution in mediating spin-enhancement remains a significant challenge. This roadmap offers a forward-looking perspective on current experimental trends and theoretical developments in spin-enhanced OER electrocatalysis and outlines strategic directions for integrating incisive experiments and operando approaches with computational modeling to disentangle key mechanisms. By providing a conceptual framework and identifying critical knowledge gaps, this perspective aims to guide researchers toward dedicated experimental and computational studies that will deepen the understanding of spin-induced OER enhancement and accelerate the development of next-generation catalysts
Nationale supercomputer kwam er mede door Purmerender Walter Lioen: 'We helpen bij vraagstukken als de opwarming van de aarde' - Noordhollands Dagblad - 19-06-2025
No full textMicrosoft trekt na 22 jaar stekker uit Skype (en gaat verder met Teams) - Nieuws.Marketing - 03-03-2025
No full textGame theoretic mixed experts for combinational adversarial machine learning
Get PDFRecent advances in adversarial machine learning have shown that defenses previously considered robust are actually susceptible to adversarial attacks which are specifically customized to target their weaknesses. However, whether the adversarial examples generated by customized attacks, are effective on other defenses, is an open question. In this work we seek to explore three important security questions: First, do different defense strategies exhibit the same low transferability properties as different model architectures and, if so, how can this low transferability be utilized to improve robustness? Second, how can a white-box adversary design attacks to specifically thwart multi-defense based setups? Last, how can game theoretic analysis further improve the robustness against an adversary capable of implementing multiple state-of-the-art attacks? To this end we provide multiple contributions, including the first transferability study between multiple defense strategies, three new attack algorithms designed to break random transform and ensemble defenses, and two game theoretic frameworks for analyzing and optimizing robustness over a combination of adversarial attacks and defenses. Empirically, we show our framework is 18% more robust on CIFAR-10 and is 27% more robust on Tiny-ImageNet than the best single state-of-the-art defense that we analzye
Van 200 naar 50 dagen: dit systeem moet wachtlijst verpleeghuizen verkorten - NU - 13-05-2025
No full textNeuromorphic computing: inspiratie uit de natuur voor energiezuinige AI - TW - 01-05-2025
No full textOFFICIEEL: Nederlandse regering lanceert staatsobligatieplatform - Stichting JAS - 23-07-2025
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The Galileo Ferraris contest: A benchmark initiative for data-driven multi-physics modeling of traction electric motors
Get PDFThe Galileo Ferraris Contest is a benchmark initiative aimed at evaluating data-driven surrogate modeling methodologies for multi-physics simulation of traction electric motors. The design of such motors traditionally relies on high-fidelity finite-element simulations, which are accurate but introduce severe computational bottlenecks for large-scale design exploration. The use of emerging surrogate modeling strategies offers a promising path to overcome these limitations. Yet, a systematic and fair comparison of their capabilities for realistic electric motor design is still lacking. Built upon an open-access dataset including electromagnetic, thermal, and structural results for three families of V-shaped interior permanent magnet motors, the contest provided a standardized testbed to assess interpolation, extrapolation, and innovation capabilities of surrogate models. A uniform multi-objective optimization and FEM validation pipeline was applied to all participant models, ensuring fair comparison across different machine learning strategies. The results show that different approaches successfully reproduced the input–output relationships of the reference motors, achieving accurate predictions even in unseen design regions. Moreover, when integrated into the optimization loop, surrogate models identified Pareto-optimal configurations beyond the resolution of the original dataset, enabling physically-consistent design exploration at negligible computational cost compared to direct finite element analysis. By establishing a reproducible framework, the Galileo Ferraris Contest preliminarily validated surrogate models as essential tools for electric motor optimization and advanced the standardization of data-driven multi-physics design workflows for future research
Tackling the transparency puzzle: Five perspectives from AI disclosure research in news
No full textThe question of whether and how to disclose the use of AI in news and journalism remains a complex and unresolved issue. While transparency is often seen as a key principle of journalism, the effectiveness of AI disclosures is far from clear. Research on disclosure effectiveness shows mixed results and remains inconclusive to a certain extent. Some studies indicate that audiences appreciate transparency, while others show that disclosures may have little impact on trust or may even reduce credibility if users perceive AI-generated content as less reliable. Furthermore, different stakeholders such as news organizations, policymakers, or audiences, may have conflicting expectations.
This raises some fundamental questions:
What do we know about AI disclosures and the research conducted about them?
How do AI disclosures in news articles influence audience perceptions of credibility and trust?
Which types of audiences notice these AI disclosures, which groups does it benefit, and in what ways?
Without clear evidence of their effectiveness, AI disclosures risk becoming symbolic gestures rather than meaningful interventions. Complicating matters further, existing knowledge about AI disclosures in journalism is fragmented across multiple fields, leading to knowledge silos. For example, regulatory frameworks focus on legal obligations and ethical standards, while user experience (UX) research examines how design elements influence reader perception. At the same time, studies on audience preferences explore whether users actually want AI disclosures and how they interpret them.
In short, these often-siloed areas of research often operate independently, leading to gaps in our understanding of AI disclosures. In this article, we want to highlight AI disclosures in news organizations from the different disciplines and expertise present in the AI, Media & Democracy Lab where we work. In doing so, we hope to contribute to solving the inherent complex puzzle that AI disclosures pose for news organizations and society. In addition, we offer some recommendations and considerations for further research