Visual text design plays a critical role in conveying themes, emotions, and atmospheres in multimodal formats such as film posters and album covers. Translating these visual and textual elements across languages extends the concept of translation beyond mere text, requiring the adaptation of aesthetic and stylistic features. To address this, we introduce a novel task of Multimodal Style Translation (MuST-Bench), a benchmark designed to evaluate the ability of visual text generation models to perform translation across different writing systems while preserving design intent.Our initial experiments on MuST-Bench reveal that existing visual text generation models struggle with the proposed task due to the inadequacy of textual descriptions in conveying visual design.In response, we introduce SIGIL, a framework for multimodal style translation that eliminates the need for style descriptions.SIGIL enhances image generation models through three innovations: glyph latent for multilingual settings, pre-trained VAEs for stable style guidance, and an OCR model with reinforcement learning feedback for optimizing readable character generation. SIGIL outperforms existing baselines by achieving superior style consistency and legibility while maintaining visual fidelity, setting itself apart from traditional description-based approaches.

When you purchase through links in our articles, we may earn a small commission. If you're using long and overly specific prompts with ChatGPT's latest model, you're doing it wrong. OpenAI's latest and most powerful model, GPT-5.5, has been topping benchmark charts and impressing users with its coding and reasoning abilities, not to mention the sheer quantity of facts at its fingertips. But while ChatGPT's latest model doesn't require the hand-holding that older models did, it also gets fussy with the longer, highly detailed prompts that might have worked well in the past. If you're seeing worse performance with GPT-5.5 than you had with previous models, it might be your prompt constructions.

How to Do It I've Been Having the Time of My Life Sexting. But I Have a Shameful Secret About Who It's With. For the last few months, I've been trying out roleplaying online. It's with an AI bot, and I've been having a lot of fun doing it, exploring kinks and gender stuff. At the same time, I find myself feeling bad about it because I know the environmental impact of AI as well as how it feels like it's making me less creative in my other (unsexy) writing.

This supplementary material provides further elaboration and discussion on our work, including additional details that support our findings.

Say hello (Moto) to price hikes on all three of Motorola's latest Razr flip phones. Like clockwork, Motorola is back with a new set of Razr folding flip phones . The formula is the same as last year, with three phones differing in specs and price: the Razr Ultra, Razr+, and Razr. But alongside these models, Motorola is finally launching its first-ever book-style folding phone, the Razr Fold, which it first teased at CES 2026 . The company announced the new handsets at an event in Los Angeles, where it also revealed a new pair of Moto Buds 2 Plus wireless earbuds that look eerily like Apple's AirPods Pro, but in blue; these will retail for $150 and will be available on April 30.

We consider a non-convex constrained optimization problem, where the objective function is weakly convex and the constraint function is either convex or weakly convex. To solve this problem, we consider the classical switching subgradient method, which is an intuitive and easily implementable first-order method whose oracle complexity was only known for convex problems. This paper provides the first analysis on the oracle complexity of the switching subgradient method for finding a nearly stationary point of non-convex problems. Our results are derived separately for convex and weakly convex constraints. Compared to existing approaches, especially the double-loop methods, the switching gradient method can be applied to non-smooth problems and achieves the same complexity using only a single loop, which saves the effort on tuning the number of inner iterations.

The weaponry highlights a coastal Belgian city's longtime strategic location. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. Renovations on government buildings in the coastal Belgian town of Nieuwpoort are currently on hold after surveyors discovered an impressive archaeological trove: dozens of carefully crafted stone cannonballs dating as far back as the 14th century. However, the medieval ammunition backstock wasn't the only weaponry buried roughly 70 miles west of Brussels.

We study the problem of multi-agent reinforcement learning (multi-agent RL) with differential privacy (DP) constraints. This is well-motivated by various real-world applications involving sensitive data, where it is critical to protect users' private information. We first extend the definitions of Joint DP (JDP) and Local DP (LDP) to two-player zero-sum episodic Markov Games, where both definitions ensure trajectory-wise privacy protection. Then we design a provably efficient algorithm based on optimistic Nash value iteration and privatization of Bernstein-type bonuses. The algorithm is able to satisfy JDP and LDP requirements when instantiated with appropriate privacy mechanisms. Furthermore, for both notions of DP, our regret bound generalizes the best known result under the single-agent RL case, while our regret could also reduce to the best known result for multi-agent RL without privacy constraints. To the best of our knowledge, these are the first results towards understanding trajectory-wise privacy protection in multi-agent RL.

To help small aerial robots navigate in the dark and other low-visibility environments, my colleagues and I developed an ultrasound-based perception system inspired by bat echolocation. Current robots rely heavily on cameras or light detection and ranging, known as lidar, or both. But these sensors fail in visually challenging conditions, such as smoke, fog, dust, snow or complete darkness. I'm a scientific engineer who develops bio-inspired microrobots. To solve this challenge, my research team looked at nature's experts at navigating in poor visibility: bats.

However, this dance is an older male's game. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Attwater's prairie chickens live in coastal marshes. Breakthroughs, discoveries, and DIY tips sent six days a week. An exclusive dance party is raging in the coastal marshes along southern Texas--and it's coming to an end.