LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous detection and navigation tasks. Though many 3D deep architectures have been developed, efficient collection and annotation of large amounts of point clouds remain one major challenge in the analytics and understanding of point cloud data. This paper presents PolarMix, a point cloud augmentation technique that is simple and generic but can mitigate the data constraint effectively across various perception tasks and scenarios. PolarMix enriches point cloud distributions and preserves point cloud fidelity via two cross-scan augmentation strategies that cut, edit, and mix point clouds along the scanning direction. The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the LiDAR scanning direction. The second is instance-level rotation and paste which crops point instances from one LiDAR scan, rotates them by multiple angles (to create multiple copies), and paste the rotated point instances into other scans. Extensive experiments show that PolarMix achieves superior performance consistently across different perception tasks and scenarios. In addition, it can work as a plug-and-play for various 3D deep architectures and also performs well for unsupervised domain adaptation.

But her passion is for paper--with no scissors. Today, she's a tessellation expert who teaches, invents new designs, and writes papers on the underlying math. Madonna Yoder '17 photographed in her studio Ross Mantle When Madonna Yoder '17 was eight years old, she learned how to fold a square piece of paper over and over and over again. After about 16 folds, she held a bird in her hands. The first time she pulled the tail of a flapping crane, she says, she realized: . That first piece was an origami classic, folded by kids at summer camp for generations and many people's first foray into the art form.

Manually editing pasted code is a long-standing developer pain point. In internal software development at Google, we observe that code is pasted 4 times more often than it is manually typed. These paste actions frequently require follow-up edits, ranging from simple reformatting and renaming to more complex style adjustments and cross-language translations. Prior work has shown deep learning can be used to predict these edits. In this work, we show how to iteratively develop and scale Smart Paste, an IDE feature for post-paste edit suggestions, to Google's development environment. This experience can serve as a guide for AI practitioners on a holistic approach to feature development, covering user experience, system integration, and model capabilities. Since deployment, Smart Paste has had overwhelmingly positive feedback with a 45% acceptance rate. At Google's enterprise scale, these accepted suggestions account substantially for over 1% of all code written company-wide.

The threat from AI-powered online scams is on the rise. You can be fooled by realistic-looking fake emails and messages pretending to be from your bank or other companies, that convince you to unwittingly install malware on your device. How can everyday people have a chance against these hugely sophisticated schemes? Avast has the answer – fight fire with fire. It has launched a new AI assistant that can answer security-related questions about communications you may suspect are fraudulent, as well as check URLs to see if websites really are what they report to be.

Breakthroughs, discoveries, and DIY tips sent every weekday. A new type of 3D printer could help households do their part to reduce food waste while also producing some nifty household accessories. In 2019 alone, the US generated 66 million tons of food waste. The majority of that waste (60 percent) ended up in landfills. According to one EPA report, the carbon dioxide generated from food waste is equivalent to the emissions of 42 coal-fired power plants.

In recent years, there has been tremendous progress in object detection performance. However, despite these advances, the detection performance for small objects is significantly inferior to that of large objects. Detecting small objects is one of the most challenging and important problems in computer vision. To improve the detection performance for small objects, we propose an optimal data augmentation method using Fast AutoAugment. Through our proposed method, we can quickly find optimal augmentation policies that can overcome degradation when detecting small objects, and we achieve a 20% performance improvement on the DOTA dataset.

Haywood Talcove, CEO of LexisNexis Risk Solutions' Government Group, tells Fox News Digital that criminal groups, mostly in other countries, are advertising on social media to market their AI capabilities for fraud and other crimes. I was having dinner with my husband in Paris. We got the wine menu and all the names, of course, were in French. Barry wanted something equivalent to a Napa cabernet, so I took a picture of the menu and asked ChatGPT. In seconds, it recommended a wine.

There are all kinds of reasons why you might want to extract text out of images. Maybe you've taken photos of restaurant bills and you want to make a record of what you've eaten; or perhaps you've got a bunch of screenshots that you need to get product names out of; or you could have scanned in some important documents that need sorting. Whatever the reason, Windows comes with built-in tools for picking out text from image files (technically known as OCR, or Optical Character Recognition)--in fact, there are several different ways, so you can pick the one that suits you best. Here's how to get started, assuming you already have your images saved somewhere. The Snipping Tool is the easiest way to extract text from images on Windows.

Chat GPT is not the only AI service in town. Google Gemini is a similar service where you can ask questions and get answers in plain text–no commands required. You can "converse" just as if the AI robot were a real person. If you're familiar with Chat GPT, you'll recognize it because the layout is similar. You're greeted by a stripped-down screen with a text input field at the bottom.

Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A key advantage of VAD is its unsupervised nature, which eliminates the need for costly and time-consuming labeled data collection. However, despite its potential for real-world applications, the literature has given limited focus to resource-efficient VAD, particularly for deployment on edge devices. This work addresses this gap by leveraging lightweight neural networks to reduce memory and computation requirements, enabling VAD deployment on resource-constrained edge devices. We benchmark the major VAD algorithms within this framework and demonstrate the feasibility of edge-based VAD using the well-known MVTec dataset. Furthermore, we introduce a novel algorithm, Partially Shared Teacher-student (PaSTe), designed to address the high resource demands of the existing Student Teacher Feature Pyramid Matching (STFPM) approach. Our results show that PaSTe decreases the inference time by 25%, while reducing the training time by 33% and peak RAM usage during training by 76%. These improvements make the VAD process significantly more efficient, laying a solid foundation for real-world deployment on edge devices.