In this section, we study how the data augmentation carried out while training on local surface patches improves the robustness of Compass against self-occlusions and missing parts. To this end, we run an ablation experiment adopting the same training pipeline explained in the main paper at Section 3.2, without randomly removing points from the input cloud. As done in the main paper, we trained the model on 3DMatch and test it on 3DMatch, ETH, and Stanford Views. We compare Compass against its ablated version in terms of repeatability of the LRFs. Results for 3DMatch are shown in Table 1: the performance gain achieved by Compass when deploying the proposed data augmentation validates its importance.

Defining and reliably finding a canonical orientation for 3D surfaces is key to many Computer Vision and Robotics applications. This task is commonly addressed by handcrafted algorithms exploiting geometric cues deemed as distinctive and robust by the designer. Yet, one might conjecture that humans learn the notion of the inherent orientation of 3D objects from experience and that machines may do so alike. In this work, we show the feasibility of learning a robust canonical orientation for surfaces represented as point clouds. Based on the observation that the quintessential property of a canonical orientation is equivariance to 3D rotations, we propose to employ Spherical CNNs, a recently introduced machinery that can learn equivariant representations defined on the Special Orthogonal group SO(3). Specifically, spherical correlations compute feature maps whose elements define 3D rotations. Our method learns such feature maps from raw data by a self-supervised training procedure and robustly selects a rotation to transform the input point cloud into a learned canonical orientation. Thereby, we realize the first end-to-end learning approach to define and extract the canonical orientation of 3D shapes, which we aptly dub Compass. Experiments on several public datasets prove its effectiveness at orienting local surface patches as well as whole objects.

The UK government's proposed AI-powered crime prediction tool, designed to flag individuals deemed "high risk" for future violence based on personal data like mental health history and addiction, marks a provocative new frontier. Elsewhere, Argentina's new Artifical Intelligence Unit for Security intends to use machine learning for crime prediction and real-time surveillance. And in some US cities, AI facial recognition is paired with street surveillance to track suspects. The promise of anticipating violence Minority Report-style is compelling.

Breakthroughs, discoveries, and DIY tips sent every weekday. Poison dart frogs are hard to miss. They're bright, agile, and as their name suggests, toxic. But at least a few of these showy amphibians have gone under the radar, until now. Scientists surveying a difficult to reach area of the Brazilian Amazon report two new species in a set of recent papers. The first, published in April in the journal ZooKeys, describes the teal and black Ranitomeya aquamarina.

Video games have been simulating football since the 1970s, but they have rarely ever thought about simulating fandom. You can play a whole international tournament in the Fifa titles, but what they never show is the way the competition seeps into the everyday lives of supporters, how whole towns are overtaken, how a World Cup can become a national obsession. The way most of us experience the really big matches is through stolen moments of vicarious glory on televisions and giant pub screens, surrounded by friends and family and the sounds and images of real life. This is the territory of Despelote, a beautiful, utterly transportive game about childhood and memory, set during Ecuador's historic 2002 World Cup qualifying campaign. Football-mad eight-year-old Julián – a semi-autobiographical version of the game's co-designer Julián Cordero – has just watched the team beat Peru, but now four more matches stand between Ecuador and the World Cup finals in Japan and Korea.

Scientists have released the first X-ray images of a mysterious, sphere-shaped object recovered in Colombia, which locals claim is of alien origin. The so-called'UFO' was spotted in March over the town of Buga, zig-zagging through the sky in a way that defies the movement of conventional aircraft. The object was recovered shortly after it landed and has since been analyzed by scientists, who discovered it features three layers of metal-like material and 18 microspheres surrounding a central nucleus they are calling'a chip.' Dr Jose Luis Velazquez, a radiologist who examined the sphere, reported finding'no welds or joints,' which would typically indicate human fabrication. He and his team concluded: 'It is of artificial origin, in that it shows no evidence of welding, and its internal structure is composed of high-density elements. More testing is needed to establish its origin.'

Diffusion models have emerged as powerful generative tools with applications in computer vision and scientific machine learning (SciML), where they have been used to solve large-scale probabilistic inverse problems. Traditionally, these models have been derived using principles of variational inference, denoising, statistical signal processing, and stochastic differential equations. In contrast to the conventional presentation, in this study we derive diffusion models using ideas from linear partial differential equations and demonstrate that this approach has several benefits that include a constructive derivation of the forward and reverse processes, a unified derivation of multiple formulations and sampling strategies, and the discovery of a new class of models. We also apply the conditional version of these models to solving canonical conditional density estimation problems and challenging inverse problems. These problems help establish benchmarks for systematically quantifying the performance of different formulations and sampling strategies in this study, and for future studies. Finally, we identify and implement a mechanism through which a single diffusion model can be applied to measurements obtained from multiple measurement operators. Taken together, the contents of this manuscript provide a new understanding and several new directions in the application of diffusion models to solving physics-based inverse problems.

'The White Lotus' creator Mike White drops a hint about the Season 4 location Mashable Tech Science Life Social Good Entertainment Deals Shopping Games Search Cancel * * Search Result Tech Apps & Software Artificial Intelligence Cybersecurity Cryptocurrency Mobile Smart Home Social Media Tech Industry Transportation All Tech Science Space Climate Change Environment All Science Life Digital Culture Family & Parenting Health & Wellness Sex, Dating & Relationships Sleep Careers Mental Health All Life Social Good Activism Gender LGBTQ Racial Justice Sustainability Politics All Social Good Entertainment Games Movies Podcasts TV Shows Watch Guides All Entertainment SHOP THE BEST Laptops Budget Laptops Dating Apps Sexting Apps Hookup Apps VPNs Robot Vaccuums Robot Vaccum & Mop Headphones Speakers Kindles Gift Guides Mashable Choice Mashable Selects All Sex, Dating & Relationships All Laptops All Headphones All Robot Vacuums All VPN All Shopping Games Product Reviews Adult Friend Finder Bumble Premium Tinder Platinum Kindle Paperwhite PS5 vs PS5 Slim All Reviews All Shopping Deals Newsletters VIDEOS Mashable Shows All Videos Home Entertainment TV Shows'The White Lotus' creator Mike White drops a hint about the Season 4 location "I don't think we're gonna go South America." By Sam Haysom Sam Haysom Sam Haysom is the Deputy UK Editor for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Read Full Bio on April 9, 2025 Share on Facebook Share on Twitter Share on Flipboard Watch Next'The White Lotus' Season 3 trailer teases debauchery in Thailand'The White Lotus' Season 3 cast meeting Moo Deng is the crossover you didn't know you needed'The White Lotus' Season 3 star Natasha Rothwell shares BTS of meeting her lizard co-star'The White Lotus' Season 3, episode 6 trailer teases rising tension The White Lotus has so far taken place in Hawaii, Italy, and most recently Thailand -- but where might be a good spot for Season 4? Speaking to Howard Stern following the Season 3 finale, creator Mike White revealed that he's about to set off for Colombia to get out of LA. "Are you thinking maybe the next season will take place in Colombia, so you're going to do research?" asks Stern. "I don't think we're gonna go South America, I think probably not," responds White.

In recent years, the rise of cyber threats has emphasized the need for robust malware detection systems, especially on mobile devices. Malware, which targets vulnerabilities in devices and user data, represents a substantial security risk. A significant challenge in malware detection is the imbalance in datasets, where most applications are benign, with only a small fraction posing a threat. This study addresses the often-overlooked issue of class imbalance in malware detection by evaluating various machine learning strategies for detecting malware in Android applications. We assess monolithic classifiers and ensemble methods, focusing on dynamic selection algorithms, which have shown superior performance compared to traditional approaches. In contrast to balancing strategies performed on the whole dataset, we propose a balancing procedure that works individually for each classifier in the pool. Our empirical analysis demonstrates that the KNOP algorithm obtained the best results using a pool of Random Forest. Additionally, an instance hardness assessment revealed that balancing reduces the difficulty of the minority class and enhances the detection of the minority class (malware). The code used for the experiments is available at https://github.com/jvss2/Machine-Learning-Empirical-Evaluation.

The growing threat posed by deepfake videos, capable of manipulating realities and disseminating misinformation, drives the urgent need for effective detection methods. This work investigates and compares different approaches for identifying deepfakes, focusing on the GenConViT model and its performance relative to other architectures present in the DeepfakeBenchmark. To contextualize the research, the social and legal impacts of deepfakes are addressed, as well as the technical fundamentals of their creation and detection, including digital image processing, machine learning, and artificial neural networks, with emphasis on Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transformers. The performance evaluation of the models was conducted using relevant metrics and new datasets established in the literature, such as WildDeep-fake and DeepSpeak, aiming to identify the most effective tools in the battle against misinformation and media manipulation. The obtained results indicated that GenConViT, after fine-tuning, exhibited superior performance in terms of accuracy (93.82%) and generalization capacity, surpassing other architectures in the DeepfakeBenchmark on the DeepSpeak dataset. This study contributes to the advancement of deepfake detection techniques, offering contributions to the development of more robust and effective solutions against the dissemination of false information.