The new Ferrari Luce, the brainchild of iPhone designer Sir Jony Ive, is unlike anything the Italian carmaker has ever created - so is the backlash it is facing. Its launch was such a big deal that Italian President Sergio Mattarella and Pope Leo were invited to view the luxury brand's first electric vehicle (EV). But internet critics, investors and even politicians have hit out at the Luce - which is Italian for light. The firm's shares fell 8% the day after the unveiling, as a host of memes mocked the $640,000 (£475,625) car, which is also its first five-seater. It comes as the global motor industry faces a number of major challenges, including fierce competition from Chinese carmakers.

Is the Ferrari Luce's Design Really That Bad? 3 Italian Auto Experts Weigh In The first electric Ferrari is already this year's most divisive car. We asked three Italian auto industry professionals to explain where the EV's design makes sense, and where it doesn't add up. The Ferrari Luce, the first electric vehicle in the brand's history, has generated heated discussion online, as comments and opinions about the design continue to bounce around the web. The Luce, an electric sedan with a $650,000 price tag that Ferrari presented with pomp and circumstance at the Quirinale in Rome on Monday, has paid dearly for its coming out from behind the curtain. Since Monday, the automaker has been suffering an avalanche of complaints and skepticism about the Luce.

Best Power Banks Best Smart Rings Routers vs. Modems Choose the Right Laptop Smart Sprinklers Deals Delivered But This Is Why It's Heartbreaking Designed by Jony Ive and a host of ex-Cupertino colleagues, the Luce shows us what might have been had Apple made good on its $10 billion bet. You know things are bad when the Pope gets involved . No doubt reeling from a launch that somehow went down even worse than Ferrari itself anticipated, the Italian carmaker sought to get the endorsement of none other than His Holiness Pope Leo XIV for its first EV, the Luce. Guided by Ferrari chairman John Elkann and senior Ferrari executives, in a hillside town about 15 miles southeast of Rome, the pontiff sat in the driver's seat and listened patiently as test driver Raffaele De Simone explained the vehicle's controls and driving modes as if he really was speaking to a man clearly in the market for a 1,000-horsepower electric car capable of hitting 62 mph in 2.5 seconds. Meanwhile, as Pope Leo was no doubt pondering how the Luce could boast one of the largest batteries in any production EV yet still only manage a maximum 329 miles, or how an accelerometer on the rear axle somehow worked like a guitar pickup to create in-cabin sound like an "instrument," the market was speaking.

Thus, any advancements in segmentation and localization algorithm are automatically transferred to the performance of high level tasks (17).

This is useful for a variety of applications like sports analysis (e.g.

Valve's Steam Machine: Everything we know Representatives want a record of all related communications. Several hundred protesters had gathered near the Broadview ICE center, chanting against immigration enforcement policies. The House Judiciary Committee wants the US Department of Justice to turn over all its communications with both Apple and Google regarding the companies' decisions to remove apps that shared information about sightings of US Immigration and Customs Enforcement officers. Several apps were removed from both Apple's App Store and Google's Play Store in October. "The coercion and censorship campaign, which ultimately targets the users of ICE-monitoring applications, is a clear effort to silence this Administration's critics and suppress any evidence that would expose the Administration's lies, including its Orwellian attempts to cover up the murders of Renee and Alex," Raskin wrote to Bondi.

Existing methods employ the influence function to achieve feature unlearning, which is impractical for FL as it necessitates the participation of other clients,if not all, in the unlearning process. Furthermore, current research lacks an evaluation of the effectiveness of feature unlearning. Toaddress these limitations, we define feature sensitivity in evaluating feature unlearning according to Lipschitz continuity. Thismetric characterizes themodel output'srateofchange or sensitivity to perturbations in the input feature. We then propose an effective federated feature unlearning framework called Ferrari, which minimizes feature sensitivity. Extensive experimental results and theoretical analysis demonstrate the effectiveness of Ferrari across various feature unlearning scenarios, including sensitive, backdoor, and biased features.

Ferrari's New Jony Ive-Designed EV Is Swathed in Glass and Aluminum We got a peek at the interior of Ferrari's new Luce electric car, which was dreamed up by famed ex-Apple designer and his firm, LoveFrom. It looks and feels a whole lot like an Apple product. Despite Ferrari dramatically scaling back its EV plans at the end of 2025, it's no exaggeration to say that the reveal of the Italian automaker's first full electric car is going to be automotive event of 2026. While the exterior is still under wraps, Ferrari has unveiled the interior of its upcoming electric vehicle designed by LoveFrom, the creative firm of Apple's former chief designer, Jony Ive. It may not turn out quite like the Project Titan car Apple worked on for a decade then killed in 2024, but it sure does look like it has similar DNA. "We are entering a new era in Ferrari," the company's CEO Benedetto Vigna said at the unveiling, which took place last week at San Francisco's pyramid-shaped Transamerica building.

The advent of Federated Learning (FL) highlights the practical necessity for the'right to be forgotten' for all clients, allowing them to request data deletion from the machine learning model's service provider. This necessity has spurred a growing demand for Federated Unlearning (FU). Feature unlearning has gained considerable attention due to its applications in unlearning sensitive, backdoor, and biased features. Existing methods employ the influence function to achieve feature unlearning, which is impractical for FL as it necessitates the participation of other clients, if not all, in the unlearning process. Furthermore, current research lacks an evaluation of the effectiveness of feature unlearning. To address these limitations, we define feature sensitivity in evaluating feature unlearning according to Lipschitz continuity. This metric characterizes the model output's rate of change or sensitivity to perturbations in the input feature. We then propose an effective federated feature unlearning framework called Ferrari, which minimizes feature sensitivity. Extensive experimental results and theoretical analysis demonstrate the effectiveness of Ferrari across various feature unlearning scenarios, including sensitive, backdoor, and biased features.

[Context and motivation] Large language models (LLMs) show notable results in natural language processing (NLP) tasks for requirements engineering (RE). However, their use is compromised by high computational cost, data sharing risks, and dependence on external services. In contrast, small language models (SLMs) offer a lightweight, locally deployable alternative. [Question/problem] It remains unclear how well SLMs perform compared to LLMs in RE tasks in terms of accuracy. [Results] Our preliminary study compares eight models, including three LLMs and five SLMs, on requirements classification tasks using the PROMISE, PROMISE Reclass, and SecReq datasets. Our results show that although LLMs achieve an average F1 score of 2% higher than SLMs, this difference is not statistically significant. SLMs almost reach LLMs performance across all datasets and even outperform them in recall on the PROMISE Reclass dataset, despite being up to 300 times smaller. We also found that dataset characteristics play a more significant role in performance than model size. [Contribution] Our study contributes with evidence that SLMs are a valid alternative to LLMs for requirements classification, offering advantages in privacy, cost, and local deployability.