Abstract-- LiDAR super-resolution addresses the challenge of achieving high-quality 3D perception from cost-effective, low-resolution sensors. While recent transformer-based approaches like TULIP show promise, they remain limited to spatial-domain processing with restricted receptive fields. We introduce FLASH (Frequency-aware LiDAR Adaptive Super-resolution with Hierarchical fusion), a novel framework that overcomes these limitations through dual-domain processing. FLASH integrates two key innovations: (i) Frequency-A ware Window Attention that combines local spatial attention with global frequency-domain analysis via FFT, capturing both fine-grained geometry and periodic scanning patterns at log-linear complexity. Extensive experiments on KITTI demonstrate that FLASH achieves state-of-the-art performance across all evaluation metrics, surpassing even uncertainty-enhanced baselines that require multiple forward passes. Notably, FLASH outperforms TULIP with Monte Carlo Dropout while maintaining single-pass efficiency, which enables real-time deployment. The consistent superiority across all distance ranges validates that our dual-domain approach effectively handles uncertainty through architectural design rather than computationally expensive stochastic inference, making it practical for autonomous systems. The high cost of high-resolution LiDAR sensors presents a fundamental challenge for autonomous systems.

A reliable uncertainty estimation method is the foundation of many modern out-of-distribution (OOD) detectors, which are critical for safe deployments of deep learning models in the open world. In this work, we propose TULiP, a theoretically-driven post-hoc uncertainty estimator for OOD detection. Our approach considers a hypothetical perturbation applied to the network before convergence. Based on linearized training dynamics, we bound the effect of such perturbation, resulting in an uncertainty score computable by perturbing model parameters. Ultimately, our approach computes uncertainty from a set of sampled predictions. We visualize our bound on synthetic regression and classification datasets. Furthermore, we demonstrate the effectiveness of TULiP using large-scale OOD detection benchmarks for image classification. Our method exhibits state-of-the-art performance, particularly for near-distribution samples.

Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand high-fidelity image understanding, such as counting, depth estimation, and fine-grained object recognition. These models, by performing language alignment, tend to prioritize high-level semantics over visual understanding, weakening their image understanding. On the other hand, vision-focused models are great at processing visual information but struggle to understand language, limiting their flexibility for language-driven tasks. In this work, we introduce TULIP, an open-source, drop-in replacement for existing CLIP-like models. Our method leverages generative data augmentation, enhanced image-image and text-text contrastive learning, and image/text reconstruction regularization to learn fine-grained visual features while preserving global semantic alignment. Our approach, scaling to over 1B parameters, outperforms existing state-of-the-art (SOTA) models across multiple benchmarks, establishing a new SOTA zero-shot performance on ImageNet-1K, delivering up to a $2\times$ enhancement over SigLIP on RxRx1 in linear probing for few-shot classification, and improving vision-language models, achieving over $3\times$ higher scores than SigLIP on MMVP. Our code/checkpoints are available at https://tulip-berkeley.github.io

An IoT startup focuses on devices connected to the internet which can be accessed remotely through the internet or a connected application. Research what is important to the end users to start and build a successful IoT. Leverage what is available to make the application easy to use. Then, create working prototypes, test, and build the application around the clear key purpose. Check out our full list of success stories.

Since humans invented art, sometime in the Paleolithic era, they've produced lots of pictures--"The Starry Night," some memes, that photo of Donald Trump staring at the eclipse. What does it all add up to? A few years ago, a company called OpenAI fed a good deal of those images, along with text descriptions, into the neural network of an artificial intelligence named DALL-E. DALL-E was being trained to create original art of its own, in any style, depicting in uncanny detail almost anything desired, based on written prompts. But a mastery of the entire universe of human imagery makes for difficult choices.

Predictive maintenance is one of the most exciting applications of digital technology in manufacturing. Simply put, predictive maintenance is the use of new and historical machine data to understand and, ideally, anticipate performance problems before they happen. Using sophisticated machine learning and AI techniques to analyze the data generated in the modern factory, predictive analytics can decrease downtime, optimize asset performance, and increase the lifespan of machines. The promises made on behalf of predictive maintenance (PdM) are big. Smart machines that flag performance issues before they happen.

Technology has played a key role in retail for decades, from early innovations like barcode scanning and digital point of sale devices, to the global frontier of modern logistics. Through it all, however, the fundamentals remain the same: retailers generate huge quantities of data, face unpredictable environments, and need to continually adapt to the ever-evolving needs of the customer. Throw in the chaos of Black Friday and Cyber Monday, and you've got one of the most complex enterprise challenges in the world. It's also a challenge tailor-made for AI: a technology that thrives on big data, adapts to change fluidly, and can deliver personalized experiences at scale. With the holiday rush upon us, let's take a look at how two Cloud AI customers--3PM for online shoppers and Tulip for in-store--are helping make retail more efficient, more personal, and more trustworthy.

The following is an excerpt of You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place by Janelle Shane. Listen to a radio interview with Janelle Shane about the mistakes artificial intelligence can make. Will the music, movies, and novels of the future be written by AI? Maybe at least partially. AI-generated art can be striking, weird, and unsettling: infinitely morphing tulips; glitchy humans with half-melted faces; skies full of hallucinated dogs. AT. rex may turn into flowers or fruit; the Mona Lisa may take on a goofy grin; a piano riff may turn into an electric guitar solo.

For so many of us, artificial intelligence (AI) is a completely intangible thing – often drawing us in towards the realm of science fiction. In a professional sense, it is hard to see yet filters into our everyday lives. For us marketers, its lack of visibility can leave us struggling to fully get to grips with what is means for our business decisions. So, when I saw that the Barbican Centre in London had curated an exhibition on artificial intelligence, AI: more than human, I was keen to go along and immerse myself in the experience. In spite of the fact it isn't specifically about AI in the workplace, I thought, whilst getting up close and personal with its history as well as some of the latest technology, there might be transferrable ideas and concepts that would inspire and give me a better grasp of what it offers.

Reaching a fever-pitch in the 1630s, "tulipmania" -- a Dutch Golden Age obsession with the rare and exotic flowers responsible, supposedly, for driving overzealous buyers to financial ruin -- has long been considered the first economic bubble. The tulip craze served as a convenient analogy for stories of our desire to monetize the natural world and our tendency towards speculative absurdity. While the extent of this botanical craze has been vastly exaggerated in books, blockbuster movies, and principles in economics, the idea that flowers might control markets continues to captivate social scientists as well as artists. In her latest work, London-based artist Anna Ridler brings this historic phenomenon into the future, using AI to produce thousands of invented "impossible" tulips, slowly developing the features that early modern collectors considered valuable -- their unpredictable stripes and stipples -- along with the price of bitcoin. Ridler's video installation, "Mosaic Virus," is named for the plant virus that creates the strange variations in color that catapulted the price of some tulips far beyond others for 17th century collectors.