Researchers discovered how to control the anomalous Hall effect and Berry curvature to create flexible quantum magnets for use in computers, robotics, and sensors.

One of the key challenges in TDA is to distinguish between “signal”—meaningful structures underlying the data, and “noise”—features that arise from the local randomness and inaccuracies within the data15,16,17. The most prominent solution developed in TDA to address this issue is persistent homology. Briefly, it identifies structures such as holes and cavities (“air pockets”) formed by the data, and records the scales at which they are created and terminated (birth and death, respectively). The common practice in TDA has been to use this birth-death information to assess the statistical significance of topological features18,19,20,21. However, research so far has yet to provide an approach which is generic, robust, and theoretically justified. A parallel line of research has been the theoretical probabilistic analysis of persistent homology generated by random data, as means to establish a null-distribution. While this direction has been fruitful22,23,24,25, its use in practice has been limited. The main gap between theory and practice is that these studies indicate that the distribution of noise in persistent homology: (a) does not have a simple closed-form description, and (b) strongly depends on the model generating the point-cloud.
Our main goal in this paper is to refute the last premise, and to make the case that the distribution of noise in persistent homology of random point-clouds is in fact universal. Specifically, we claim that the limiting distribution of persistence values (measured using the death/birth ratio) is independent of the model generating the point-cloud. This result is loosely analogous to the central limit theorem, where sums of many different types of random variables always converge to the normal distribution. The emergence of such universal ity for persistence diagrams is highly surprising.
We support our universal ity statements by an extensive body of experiments, including point-clouds generated by different geometries, topologies, and probability distributions. These include simulated data as well as data from real-world applications (image processing, signal processing, and natural language processing). Our main goal here is to introduce the unexpected behavior of statistical universal ity in persistence diagrams, in order to initiate a shift of paradigm in stochastic topology that will lead to the development of a new theory. Developing this new theory, and proving the conjectures made here, is anticipated to be an exciting yet a challenging long journey, and is outside the scope of this paper. Based on our universal ity conjectures, we develop a powerful hypothesis testing framework for persistence diagrams, allowing us to compute numerical significance measures for individual features using very few assumptions on the underlying model.
Elon Musk has announced a new venture called xAI that plans to “understand the true nature of the universe”. Here’s what we know so far.
AI startup Stability AI continues to refine its generative AI models in the face of increasing competition — and ethical challenges.
Today, Stability AI announced the launch of Stable Diffusion XL 1.0, a text-to-image model that the company describes as its “most advanced” release to date. Available in open source on GitHub in addition to Stability’s API and consumer apps, ClipDrop and DreamStudio, Stable Diffusion XL 1.0 delivers “more vibrant” and “accurate” colors and better contrast, shadows and lighting compared to its predecessor, Stability claims.
In an interview with TechCrunch, Joe Penna, Stability AI’s head of applied machine learning, noted that Stable Diffusion XL 1.0, which contains 3.5 billion parameters, can yield full 1-megapixel resolution images “in seconds” in multiple aspect ratios. “Parameters” are the parts of a model learned from training data and essentially define the skill of the model on a problem, in this case generating images.
Suumit Shah, a 31-year-old CEO of an e-commerce platform called Dukaan based in India, is getting torn to shreds online for firing 90 percent of the company’s customer support staff after arguing that an AI chatbot had outperformed them.
It was an unusually callous announcement that clearly didn’t sit well with plenty of netizens, as Insider reports.
“We had to layoff [sic] 90 percent of our support team because of this AI chatbot,” he tweeted. “Tough? Yes. Necessary? Absolutely.”
British health tech startup Twinn Health recently emerged from stealth, boasting an AI-powered platform that analyzes MRI scans to detect preventable disease “earlier than ever before.” Starting with metabolic disease, the company’s AI platform leverages validated imaging biomarkers to improve diagnosis and treatment decisions.
With age-related frailty and liver disease also on its roadmap, Twinn Health is positioning itself squarely in the domain of longevity and preventive healthcare. The company is supported by WAED, a $500 million venture capital fund backed by Saudi Aramco, which invests in innovative tech-based startups.
Longevity. Technology: Magnetic resonance imaging (MRI) has been used in healthcare for decades and is widely used in hospitals and clinics for the diagnosis and follow-up of disease. In recent years, AI tools have appeared that help identify the presence of specific conditions within MRI scans, but the technology is not yet widely used in healthcare to support healthspan and longevity improvements. Twinn Health aims to change that, combining MRI and AI to enable the early detection and management of multiple age-related diseases. To learn more, we caught up with founder and CEO Dr Wareed Alenaini.
A new solar-powered high-altitude drone has successfully navigated a stratospheric test, opening the door to a new set of possibilities for unmanned vehicles, not least in modern warfare.
The PHASA-35 solar and battery-powered unmanned aerial system reached an altitude of 66,000 feet during a 24-hour test flight launched from New Mexico in June, British defense giant BAE Systems said in mid-July.
The stratospheric test, which comes after the system’s maiden flight back in 2020, “marks a significant milestone” in the development program started in 2018, BAE said in a press release.
Robot Rollin’ Justin is a pioneer in testing how astronauts can control a machine on another world. He just finished a test on simulated sands of a Mars-like world.
All navigations reported in Fig. 2 were performed autonomously within 150 s and without intraoperative imaging. Specifically, each navigation was performed according to the pre-determined optimal actuation fields and supervised in real time by intraoperative localization. Therefore, the set of complex navigations performed by the magnetic tentacle was possible without the need for exposure to radiation-based imaging. In all cases, the soft magnetic tentacle is shown to conform by design to the anatomy thanks to its low stiffness, optimal magnetization profile and full-shape control. Compared to a stiff catheter, the non-disruptive navigation achieved by the magnetic tentacle can improve the reliability of registration with pre-operative imaging to enhance both navigation and targeting. Moreover, compared to using multiple catheters with different pre-bent tips, the optimization approach used for the magnetic tentacle design determines a single magnetization profile specific to the patient’s anatomy that can navigate the full range of possible pathways illustrated in Fig. 2. Supplementary Movies S1 and S2 report all the experiments. Supplementary Movie S1 shows the online tracking capabilities of the proposed platform.
In Table 1, we report the results of the localization for four different scenarios. These cases highlight diverse navigations in the left and right bronchi. The error is referred to as the percentage of tentacles outside the anatomy. This was computed by intersecting the shape of the catheter, as predicted by the FBG sensor, and the anatomical mesh grid extracted from the CT scan. The portion of the tentacle within the anatomy was measured by using “inpolyhedron” function in MATLAB. In Supplementary Movie S1, this is highlighted in blue, while the section of the tentacle outside the anatomy is marked in red. The error in Table 1 was computed using the equation.