De Zweedse voetballer Maja Göthberg was transparant over haar zwangerschap, terwijl ze nog geen nieuw contract had ondertekend. Het hof van sportarbitrage CAS oordeelde woensdag dat Lazio Roma hierna onrechtmatig het contact met haar heeft verbroken.

In het rieten dak van een vrijstaande woning aan de Bermweg in Rotterdam-Nesselande is woensdagmiddag brand uitgebroken. Hier komt veel rook bij vrij. Om erger te voorkomen en genoeg bluswater te hebben bij de hoge temperaturen, rukte de brandweer massaal uit.

Prestigious journal Nature has published a peer-reviewed critique of Microsoft's claims to have made quantum computing breakthroughs – and the scientist who wrote the paper has essentially said Redmond got it wrong. Microsoft made its claims of a quantum breakthrough in February 2025 when it revealed tech called Majorana and predicted "this breakthrough will allow us to create a truly meaningful quantum computer not in decades, as some have predicted, but in years." The software giant's approach to quantum computing involves Majorana particles, subatomic particles that scientists have not observed directly. The company has pursued this approach for years, but experienced reversals that led to the retraction of some papers. Last year, however, Microsoft claimed it had both observed Majorana particles and harnessed them in a quantum computer. Criticism of that claim was swift and sharp: we reported boffins willing to go on the record as describing Microsoft's work as "unreliable" and perhaps even "fraudulent." Microsoft insisted its work is sound and in early June 2026 announced Majorana 2, a "next-generation topological quantum chip" it developed with the help of its own agentic AI. The Windows giant revealed that work after being given a right to reply to a critique of its 2025 Majorana announcement by Dr Henry Legg, a lecturer at the University of St Andrews. Nature accepted Legg's paper on April 20 and scheduled it for publication on June 24. Titled "On the robustness of topological gap detection via transport," Legg's analysis suggests Microsoft got it wrong. "Last year they claimed to be years, not decades from a 'topological quantum supercomputer,'" Legg told The Register in an email. "My feeling is that they are centuries, not decades away. If it works at all – and, based on what I have seen, the most likely scenario is that it doesn't work." Based on his analysis of the research Microsoft published in 2025, Legg argues that the company's claims about finding and being able to control the elusive Majorana particle to build a topological superconductor do not withstand scrutiny. "I demonstrate that Microsoft's tune-up software is flawed and that coding errors resulted in incorrect statements to peer reviewers," said Legg. "Raw data, which was omitted from the original paper, also appears to indicate Microsoft's devices contain considerable disorder and are not compatible with the existence of a topological gap. In other words, the prerequisites for Microsoft's claims do not appear to be met, but this was obscured because this data did not appear in the original publication." Essentially, Microsoft has proposed a Topological Gap Protocol (TGP) that can be used to detect the phase transition deemed to be a prerequisite for conducting quantum calculations using Majorana particles. Legg argues that based on his analysis of underlying transport data (measurements of particle change) – omitted from the original publication – Microsoft chose to focus on results that supported its thesis and ignored data that could be interpreted as a negative result. As he notes in his critique: "The TGP plotting code was set to highlight only the largest purportedly topological region." "The primary consequence was the omission of other regions that passed their tune-up protocol (the TGP)," said Legg. "When peer reviewers asked if other regions existed, Microsoft inaccurately stated that they had investigated the only region passing the protocol within the explored range. This was not correct." Legg also argues that Microsoft mishandled its code. "The code antisymmetrized bias voltage based on array index rather than physical value," his analysis says. In other words, Microsoft's researchers made a basic programming mistake by evaluating the array index – the number identifying a value's position in an array – instead of the value to which the index refers. "There were two pretty basic Python programming errors that hid these alternative regions," Legg explained. "Their plotting software was hardcoded with a filter (zbp_cluster_numbers=[1]) that forced it to display only the single largest region, concealing other successful results from their phase maps. Changing this to zbp_cluster_numbers=[1,2] shows already a second region." Legg added: "The TGP software transformed the data by simply reversing a Python array (x[::-1]) based on its index position, ignoring the actual physical bias voltages." In a statement provided to The Register, Dr Chetan Nayak, technical fellow and corporate vice president of Microsoft's quantum hardware group, said: "We stand by our results and our roadmap." "At the end of the day, success is the delivery of a scalable quantum computer. We are confident in our ability to execute against our roadmap and proud of our continued engagement with DARPA, which moved Microsoft into the final phase of its Quantum Benchmarking Initiative after independently evaluating our results – those in the public realm and proprietary – with a team of highly qualified experts. Skepticism and rigor are hallmarks of the scientific process, which we appreciate and have supported from various academics. We have participated in dialogue and our thorough rebuttal was accepted and published by Nature." Microsoft's rebuttal disputes the validity of Legg's analysis. The software colossus argues its signal measurements were not intended to be exhaustive and that the "minor off-by-one-pixel bug in our TGP processing" is inconsequential. The response concludes: "In summary, Legg centers on a selective examination of transport tune-up procedures and narrow interpretations of isolated phrases in our referee correspondence, rather than the physical mechanisms underlying the experiment. It relies on unsubstantiated claims about our transport spectra while not engaging with the capacitance measurements at the core of our study, and its alternative treatment of the transport data is inconsistent with more rigorous analyses of the same datasets. Critically, Legg offers no alternative physical model capable of reproducing the capacitance signal or the RTS phenomenology, and does not constitute a substantial scientific challenge to our findings." Legg thinks that criticism is unfounded. "They attempt to dismiss these issues as minor bugs, and retrospectively adjust their evidence hierarchy," he said. "In short, Microsoft's reply essentially argues that because they observed a specific capacitance measurement, the prerequisites to do so must have been met. I hope, despite the complexity of the topic, their circular reasoning is clear." The announcement of Majorana 2 has not changed Legg's assessment of Microsoft's work. "Majorana 2 is not available to customers and it is not proven to even be a single qubit," Legg said. "Their preprint, which should not really be given any credence given that it is based on a single device, does not even claim an X-measurement (which they did eventually for Majorana 1 last year, but that preprint has also not yet been published). Essentially, their claim of '1,000 times more reliable' refers to the lifetime of a classical bit (the parity of the state). There is no evidence this is a qubit and can hold a superposition. The classical bits in my computer have very long lifetimes (years!), but it does not make them good qubits." "For Majorana 2, one has to ask why they do not report the X-measurement, since Microsoft were obviously aware it was so important for their claims last year. I think it's very reasonable to assume that they did attempt the same supposed X-measurement with their Majorana 2 device and it didn't work out. That's not surprising because, based on everything I have seen, it all looks like disorder physics and they have not shown any kind of control over even a single qubit." ®

AI models used to help diagnose medical conditions have a problem: They’re ready and willing to identify patients whose data was used to train them. German researchers reported in a Nature paper published Wednesday that discriminative AI models - those used to classify data and make predictions about new inputs based on their training sets - are particularly susceptible to membership inference attacks (MIAs) that query the models in an attempt to figure out whether a particular datapoint is included in their training sets. What that means for medical AI models is that any patient whose data is used to educate the bot could be exposed, leading to details about their medical history and diagnoses being leaked. In an analysis of seven medical AI datasets consisting of images, ECG records, and general electronic health records, the team determined that individual patients targeted by such attacks can be identified with “near-perfect attack success,” which they explain flies in the face of how such models are evaluated for safety. “The fact that MIAs can achieve near-perfect success rates for individual patients is not adequately captured by the standard evaluation protocol, which measures attack success in aggregate across records,” the researchers said. Based on their findings, they conclude, reporting standards for AI privacy audits need to change. It gets worse, too: Patients in the dataset are generally easy to identify and, unsurprisingly, those underrepresented in medical AI training data are even easier to finger than those whose data doesn’t stand out. Underrepresented groups can include those in a number of sensitive categories: Race, insurance status, sex, the protocol used to conduct medical imaging, and certain disease statuses can all function as outliers that make it easier to identify individuals. “Generally speaking, privacy risks from MIAs become more severe as a model’s training cohort becomes more specific,” Technical University of Munich AI in Healthcare and Medicine chair and paper lead author Moritz Knolle told The Register in an email conversation. “You could imagine … scenarios where membership in a training dataset reveals that someone has a dormant genetic condition such as Huntington's disease, depression, or attended a specific, specialised treatment clinic.” In other words, exposing healthcare AI training data could be used to identify those with sensitive health conditions, spill secrets they may not want public, or otherwise fuel discrimination. To make things even worse again, the larger the dataset, the easier it is to expose records, and “the magnitude of this change in patient-level risk was previously unknown” in larger models. The privacy devil in the data details This is bad and all, but it’s not necessarily the end of the world, as performing an MIA attack on a medical AI model supposes the attacker already has a few things at their disposal, namely at least some medical data belonging to the people they want to identify. “To conduct a MIA an attacker needs access to a target data point,” Knolle confirmed to us while also noting that their paper revealed access to a full patient data point isn’t needed, in contrast to what was previously believed. “In our paper we show that an attacker with partial access can still successafully conduct MIAs.” The MIA attack itself, as detailed in the paper, relies on medical AIs being more certain of their predictions if the input data is already part of their training set. A potential attacker, then, simply peppers an AI model with obtained patient data, checks the confidence level, and surmises that said patient is part of the training data. “An attacker conducting a MIA does not need to know who the data belongs to that they are trying to conduct the MIA with,” Knolle explained. “In fact, all the dataset we use in our study were anonymized.” Anonymized in the datasets, but not the target data, that is. As explained in the paper their MIA attacks were largely error-free at the individual patient level, meaning confidence levels are an accurate way to figure out if a particular patient's data is part of a training set. “The attacker would simply need access to someone’s blood test results, or part of these results” in order to infer inclusion, Knolle said. Of course, they have to get that data first, but given how frequently healthcare data is exposed in breaches, it’s not exactly hard to imagine a bad actor getting ahold of something they can use. “Given that medical data is not always securely stored it is not unthinkable that an attacker could get access, for example, by gaining unauthorized access to the database of your general practitioner after they performed a routine blood test,” Knolle said. How to protect patient data? Asked what he hopes this research accomplishes, Knolle told us he just wants the medical world to understand that AI training data needs to be better secured. “I hope that the medical AI community will start to take privacy risks seriously and that risk mitigation techniques are used in situations where they are necessary,” Knolle said. The researchers make several recommendations for how to do this, like through the use of differential privacy frameworks that are designed to mathematically guarantee training data remains anonymous - a key consideration if medical AI firms want patients to trust them with their data. As mentioned above, the team also wants to see privacy audit standards change to consider individual-level data, not just aggregate privacy risks. Alternatively, medical AI training data could just be compiled so that underrepresented groups are better represented, Knolle said. “There are many situations where a successful MIA represents a small or negligible privacy violation,” Knolle noted. “These are situations where AI models are trained on large, general populations in which both healthy and diseased individuals are represented in sufficient numbers.” Representation, in other words, definitely matters when it comes to keeping patient data private. ®

WIJK BIJ DUURSTEDE (ANP) - Asielzoekers verblijven sinds woensdag in een tijdelijke noodopvang in Wijk bij Duurstede. Ze mogen daar tot eind augustus blijven. Nadat de Utrechtse gemeente eind mei had aangekondigd dat er een opvang zou komen, waren er meerdere ongeregeldheden. Zo werd het gemeentehuis enkele keren vernield.

In een sporthal op het complex Mariënhoeve is plek voor 50 tot 75 asielzoekers.

In een verklaring zegt de waarnemend burgemeester van Wijk bij Duurstede, Petra Doornenbal, dat de noodopvang "verschillende reacties" heeft opgeroepen. "Tegelijkertijd gaat het om mensen die hun huis en land hebben moeten verlaten en hier een veilige plek zoeken. We doen ons best om de opvang goed te organiseren en oog te houden voor de veiligheid en leefbaarheid in de omgeving."

Stone Temple Pilots

Gnarly, shrivelly celeriac is an unconventional taco filler. But peeled and roasted in Alice Zaslavsky’s recipe, the root vegetable yields an almost carne asada-like flavour

• Check out more Alice Zaslavsky recipes

Could it be time to consider the celeriac? If you’re looking for a tasty and left-of-field taco combo for the cooler months that’s full of flavour, low on cost and easily veganised, then it just well might.

Celeriac is firm and fibrous enough to hold its shape under heat, sweet enough to crack some caramelisation, and its centre yields to a creamy and fluffy mashed potato texture when cooked. The flavour is surprisingly meaty, which makes more sense when you consider how the parsley and liquorice notes intensify when caramelised, a bit like a bite of steak with herby sauce, or carne asada with salsa verde.

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Forward on the misunderstanding of his role and momentum the four-time champions have put together for a winning World Cup campaign after topping their group

Kai Havertz is recalling the cocktail of feelings that swirled around his head in Budapest three-and-a-half weeks ago. Arsenal could not have lost the Champions League final in more agonising circumstances but the only available option was to straighten up and start smiling. They were due to set off on a bus around Islington for the Premier League trophy parade at 2pm the following day. Was this really the moment to bathe in a million onlookers’ adulation?

“To be honest, it was tough,” says Havertz, whose early goal against Paris Saint-Germain had looked a possible winner for nearly an hour. “After the match, I initially thought we would call the whole thing off. By the next morning, things looked different.”

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Scientists have discovered new evidence that the cosmic structures connecting the universe are much larger than previously predicted—persisting over billions of light years—a finding that challenges a core tenet of cosmology and hints at the possibility of new physics, according to a study published on Wednesday in Nature.

The standard model of cosmology, a well-corroborated framework for understanding the universe that is also known as the Lambda cold dark matter (ΛCDM) model, predicts that the large-scale structure of space looks the same in all areas (homogeneity) and in all directions (isotropy). While there is variation in the distribution of matter on small scales, such as thousands or millions of light years, these distinctions should smooth out into a uniform pattern on the scale of the cosmic web, which is a network of large-scale structures made of dark matter, gas, and galaxies that stretches across the universe.

But in recent years, new observational data has started to hint that galaxies cluster in “preferred directions,” forming distinct structures known as “anisotropies” that are not uniform, even across vast distances. Now, a pair of physicists has discovered that these distinct directions and patterns persist even to the scale of a gigaparsec, which is a unit equal to 3.26 billion light years, possibly signalling “the need for a shift in modern cosmology,” according to their new study.

“The structures observed in the real Universe are significantly larger and more persistent than those formed in state-of-the-art simulations based on the standard model of cosmology,” said authors Francesco Sylos Labini of the Enrico Fermi Research Center in Rome, Italy, and Marco Galoppo of the University of Canterbury in Christchurch, New Zealand, in an email exchange with 404 Media.

“The key advance of our analysis is that it allows this difference to be quantified,” they added. “By measuring the spatial extent and coherence of the observed structures and comparing them directly with theoretical predictions, we found that the discrepancy is statistically highly significant. In other words, the largest structures in the real Universe appear to be substantially larger than expected in standard models of galaxy formation.”

According to existing models, the cosmic web emerged from small density fluctuations in the early universe and gradually developed into large-scale filaments and nodes made of dark matter that gravitationally attract gas, galaxies, and other forms of matter. 

Last year, the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey based in Arizona, released the largest high-resolution 3D map of the universe, which has revolutionized cosmology and allowed scientists to test those theories against observational data.

Labini and Galoppo analyzed the DESI release with statistical tools, including the Angular Distribution of Pairwise Distances (ADPD), which is especially effective for detecting and characterizing large-scale anisotropies in DESI’s dataset.

“The idea was to try to really test whether the idea that isotropies reached very large scales is now supported by data,” said Galoppo in a follow-up call. “Even just five or ten years ago, we didn't really have the data to test on gigaparsec scales. But now, we had a chance, so we decided to take it.”

“What we are able to do is to characterize how large are the largest structures inside this sample” of DESI observations, added Labini in the call.

The results revealed that even in DESI’s super-zoomed-out observations, large-scale structures create preferred directions of galaxy distribution, as opposed to an overall isotropic pattern. This contrasts with expectations derived from the cosmic microwave background, the oldest light in the universe, which suggests that directional correlations should fade rapidly at large scales.  

“In the standard model, it's not that there aren’t structures,” said Galoppo in the call. “It is just that they are supposed to be smaller and less persistent than what we found. That's the crux of the matter.”

To that end, DESI is expected to release a new batch of observations within a year, and similar datasets will also be forthcoming from Europe’s Euclid space telescope and the Vera C. Rubin Observatory in Chile in the near term. These new and improved views of the universe will help scientists grapple with just how vast these large-scale structures are, and what that means for our understanding of our cosmic surroundings. 

“At present, there is no simple or widely accepted modification of the ΛCDM framework that naturally explains structures of this size while remaining consistent with the observed uniformity of the cosmic microwave background,” Labini and Galoppo wrote over email. “That is precisely why these observations are so interesting: they point to a potentially important gap between theory and observation that deserves further investigation.”

“If future surveys continue to find coherent directional structures on even larger scales, the implications for cosmology would be profound,” they concluded.