This work presents a review of attack methodologies targeting Pix, the instant payment system launched by the Central Bank of Brazil in 2020. The study aims to identify and classify the main types of fraud affecting users and financial institutions, highlighting the evolution and increasing sophistication of these techniques. The methodology combines a structured literature review with exploratory interviews conducted with professionals from the banking sector. The results show that fraud schemes have evolved from purely social engineering approaches to hybrid strategies that integrate human manipulation with technical exploitation. The study concludes that security measures must advance at the same pace as the growing complexity of attack methodologies, with particular emphasis on adaptive defenses and continuous user awareness.

Semantic understanding plays a crucial role in Dense Simultaneous Localization and Mapping (SLAM), facilitating comprehensive scene interpretation. Recent advancements that integrate Gaussian Splatting into SLAM systems have demonstrated its effectiveness in generating high-quality renderings through the use of explicit 3D Gaussian representations. Building on this progress, we propose SGS-SLAM, the first semantic dense visual SLAM system grounded in 3D Gaussians, which provides precise 3D semantic segmentation alongside high-fidelity reconstructions. Specifically, we propose to employ multi-channel optimization during the mapping process, integrating appearance, geometric, and semantic constraints with key-frame optimization to enhance reconstruction quality. Extensive experiments demonstrate that SGS-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, and semantic segmentation, outperforming existing methods meanwhile preserving real-time rendering ability.

Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addressed: (a) improving label efficiency and (b) enhancing robustness in realistic scenarios where modalities are missing at the test time. To address these challenges, we first propose a simple yet efficient multi-modal fusion mechanism Linear Fusion, that performs better than the state-of-the-art multi-modal models even with limited supervision. Second, we propose M3L: Multi-modal Teacher for Masked Modality Learning, a semi-supervised framework that not only improves the multi-modal performance but also makes the model robust to the realistic missing modality scenario using unlabeled data. We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities. Our proposal shows an absolute improvement of up to 10% on robust mIoU above the most competitive baselines. Our code is available at https://github.com/harshm121/M3L

High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labeling poses a major challenge: medical professionals who can expertly label the data are a scarce and expensive resource. Making matters worse, voxel-wise delineation of data (e.g. for segmentation tasks) is tedious and suffers from high inter-rater variance, thus dramatically limiting available training data. We propose a recursive training strategy to perform the task of semantic segmentation given only very few training samples with pixel-level annotations. We expand on this small training set having cheaper image-level annotations using a recursive training strategy. We apply this technique on the segmentation of intracranial hemorrhage (ICH) in CT (computed tomography) scans of the brain, where typically few annotated data is available.

Angelo Yu is not afraid of Donald Trump. This year, Yu's auto startup in southwestern China is set to deliver its first vehicle to a U.S. buyer, but he has not even bothered to check how much President Trump's tariffs on Chinese goods might affect him. After all, he has a workaround -- even if it sounds like the stuff of science fiction. Pix Moving, Yu's company, is using artificial intelligence to design cars and convert the blueprints into instructions for 3D printers. His vision is to upload the data to the cloud and let his team in the U.S. print all the components there.

An exhibition of an unattended vending car built by PIX, a start-up based in Guiyang, Southwest China's Guizhou Province Photo: Courtesy of PIX Imagine waking up and making a reservation for a robot-staffed vending car first thing in the morning so you can enjoy a cup of coffee on the way to work. After work, you get some exercise on the way home via a moving gym. Then a mobile grocery store stops right in front of your door when you arrive home. You select fresh vegetables and ingredients to cook a delicious dinner. PIX claims it will be the first in China to make this convenient future possible. The start-up was incubated in Silicon Valley and is based in the Guiyang Hi-tech Industrial Development Area in Southwest China's Guizhou Province.

SAN FRANCISCO: Tech giant Microsoft has updated its'Pix' camera application for iOS devices which now lets users take better scans of documents, whiteboards and business cards too. From the beginning, 'Pix' app used artificial intelligence (AI) technology to choose and optimise photographs and apply a range of filters. "We have data that shows people are taking a lot of whiteboard photos at work, they are doing a lot of document scanning," said Josh Weisberg, a principal programme manager in the Computational Photography Group within Microsoft's research organisation in Redmond, Washington. Once the shutter clicks, the app uses AI to improve the image, such as cropping edges, boosting colour and tone, sharpening focus and tweaking the angle to render the image in a straight-on perspective.

When you shoot with the iPhone's built-in Camera app, there are various ways to tweak focus and exposure, both before and after you hit the shutter. You might first tap and hold or swipe to adjust and lock focus and exposure. After the shot, you have options for adjusting saturation, contrast, sharpness, and more. Microsoft Pix (free on the iTunes Store), a photo and video shooting and editing app for iOS, doesn't want you to work that hard. It offers an effortless point-and-shoot alternative to the iPhone Camera app with a promise of superior results.