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Many industrial robotics applications still rely on 2D lidar for simple zone monitoring, which limits the field of view and vertical resolution required to detect complex or overhanging obstacles Ouster Firmware 3.2 embeds native 3D zone monitoring directly onto our digital lidar sensors, including the OS0, OS1, and OSDome

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🚨 SCIENTISTS JUST PRINTED A FULLY FUNCTIONAL ELECTRONIC SENSOR DIRECTLY ONTO A LIVING LEAF. Researchers have developed a technique to print electronics onto living biological surfaces including plant leaves, animal bones, and potentially human tissue without damaging them. In one striking demonstration, they printed a wireless humidity sensor directly onto a living leaf. The printed silver spiral antenna and circuitry remained functional while the leaf stayed alive. Why this matters: Traditional electronics are rigid and separate from biology. This new approach allows electronics to be directly integrated with living systems. Potential applications include smart medical implants that grow with tissue, real-time health monitoring devices printed onto bone or skin, and even “smart plants” that can report environmental data. It moves far beyond printing plastic prototypes this is functional electronics on living matter. The technique represents a major step in bio-integrated electronics and additive manufacturing. Instead of inserting devices into the body or environment, researchers can now print them onto living surfaces with high precision. This opens the door to entirely new classes of devices: living sensors, bio-hybrid robots, and medical implants that interface more naturally with the body. How do you think printing electronics onto living tissue will change medicine or environmental monitoring in the next decade?
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🚨 MACHINES MAY SOON SEE THE WORLD LIKE HUMANS A US company has unveiled what it calls the world’s first “native color lidar” system giving machines the ability to perceive depth, distance, and color simultaneously. Unlike traditional lidar systems that need separate cameras, this new technology captures full 3D color information directly at the point of detection. In simple terms: Machines may soon understand the world more like human vision instead of just measuring shapes and distance. Why this matters: This could dramatically improve: • self-driving cars • robotics • drones • factory automation • AI navigation • autonomous machines The system can reportedly process over 10 million points every second and detect objects nearly 1,640 feet away. Researchers say this could become one of the key technologies powering the next generation of “Physical AI” where machines don’t just calculate the world… They visually understand it. We may be watching the birth of machine perception in real time. Follow for more future technology and AI discoveries.
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Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."
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Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."

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Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians paper page: Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups. In this paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other, thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore, we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.
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Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians paper page: Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups. In this paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other, thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore, we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.

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3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

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633,674 Aufrufe • vor 3 Jahren

Are you safer with LIDAR, or are you safer with vision? This is a false dichotomy. The more pertinent question today is "do you have something, or do you have nothing?" As you can see from the clips below, vision based systems avoid countless potential collisions every day. The difference between a crash and no crash isn't what sensor suite you chose — it's whether you have any AI on your car at all. Even if we concede that LIDAR may help prevent some additional crashes, we are really debating whether it is 1% of crashes or 0.00001% of crashes. Not all crashes are super complex and require lasers to detect. Most are simple, routine, and can easily be prevented by today's vision based AI. In fact, evidence is mounting that computer vision based systems can actually outperform more traditional approaches to self-driving. Why? Because the low cost of cameras enables you to create a much larger, more varied, and more diverse dataset. If you want to have expensive custom cars that's fine, but you're going to get fewer vehicles for the same budget. Seeing what's in front of you now is actually less important than predicting what's going to happen next — and the large scale datasets used to train pure vision systems are the best for predicting what's next. Counter-intuitively, the simpler and lower cost sensor actually has properties that make it better suited for training advanced AI. Computer vision based self-driving is often framed by LIDAR proponents as "cheaping out" on the sensor suite to save money. But it's not about being cheap, it's about bringing the technology to everyone. 1.2 million people die on the road every year around the world. That's around 39 million people who've died on the roads around the world since I was born — equivalent to a city the size of Tokyo or New Delhi getting wiped off the map. The status quo is simply unacceptable, and something has to be done to fix it as soon as possible. Of the 1.2 million people that will die on the roads this year, about 40,000 will be Americans. That's about 3%. So if we moved entirely to self-driving cars in America and brought crashes down to 0, 97% of the world's crash fatalities would still be taking place as usual. Deploying a $200,000+ retrofitted self-driving car may work in a few American cities, but it is not going to make sense in most places around the world where fares are much cheaper. Most often, the choice is not between LIDAR and vision. It's between vision or nothing. The best system is the system that's there running on my car when I need it to save my life. To say that all self-driving cars must have LIDAR is to sentence most of the world to death. We can't write off computer vision if we want to make a serious dent in this problem. It's going to be a key piece of the solution. Let LIDAR based players build the best self-driving car they can, and let vision based players do the same. We need to be trying everything
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Are you safer with LIDAR, or are you safer with vision? This is a false dichotomy. The more pertinent question today is "do you have something, or do you have nothing?" As you can see from the clips below, vision based systems avoid countless potential collisions every day. The difference between a crash and no crash isn't what sensor suite you chose — it's whether you have any AI on your car at all. Even if we concede that LIDAR may help prevent some additional crashes, we are really debating whether it is 1% of crashes or 0.00001% of crashes. Not all crashes are super complex and require lasers to detect. Most are simple, routine, and can easily be prevented by today's vision based AI. In fact, evidence is mounting that computer vision based systems can actually outperform more traditional approaches to self-driving. Why? Because the low cost of cameras enables you to create a much larger, more varied, and more diverse dataset. If you want to have expensive custom cars that's fine, but you're going to get fewer vehicles for the same budget. Seeing what's in front of you now is actually less important than predicting what's going to happen next — and the large scale datasets used to train pure vision systems are the best for predicting what's next. Counter-intuitively, the simpler and lower cost sensor actually has properties that make it better suited for training advanced AI. Computer vision based self-driving is often framed by LIDAR proponents as "cheaping out" on the sensor suite to save money. But it's not about being cheap, it's about bringing the technology to everyone. 1.2 million people die on the road every year around the world. That's around 39 million people who've died on the roads around the world since I was born — equivalent to a city the size of Tokyo or New Delhi getting wiped off the map. The status quo is simply unacceptable, and something has to be done to fix it as soon as possible. Of the 1.2 million people that will die on the roads this year, about 40,000 will be Americans. That's about 3%. So if we moved entirely to self-driving cars in America and brought crashes down to 0, 97% of the world's crash fatalities would still be taking place as usual. Deploying a $200,000+ retrofitted self-driving car may work in a few American cities, but it is not going to make sense in most places around the world where fares are much cheaper. Most often, the choice is not between LIDAR and vision. It's between vision or nothing. The best system is the system that's there running on my car when I need it to save my life. To say that all self-driving cars must have LIDAR is to sentence most of the world to death. We can't write off computer vision if we want to make a serious dent in this problem. It's going to be a key piece of the solution. Let LIDAR based players build the best self-driving car they can, and let vision based players do the same. We need to be trying everything

Whole Mars Catalog

45,801 Aufrufe • vor 1 Jahr

NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions paper page: present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.
0:15

NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions paper page: present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.

AK

194,469 Aufrufe • vor 2 Jahren

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📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.

Yunzhu Li

25,279 Aufrufe • vor 1 Jahr

In the summer of 2023, I cold emailed Jensen Huang and asked to capture a NeRF of him at SIGGRAPH. He responded in about an hour and said yes. A radiance field is, in the simplest terms, akin to a 3D photograph. A moment in time, so completely reconstructed that you can move through it and see it from angles the original cameras never occupied. NeRFs were the original method. Gaussian splatting, which debuted at that same SIGGRAPH, has since become the dominant form of radiance field. I called my late friend James, who told me we needed to begin practicing immediately. We ran capture after capture for weeks until we consistently got the capture time down to ~30 seconds with one camera. Later, in a hallway at the LA Convention Center during SIGGRAPH, I captured the portrait you're seeing now, a full 360° gaussian splat of Jensen, rendered here as a 2D flythrough. Afterward, I continued the conversation with him and members of his team to make the case for radiance fields as a foundational representation for imaging. To my surprise, they listened. Three years later, NVIDIA has several works, including NuRec, fVDB, 3DGRUT, and gsplat all utilizing radiance fields. The landscape has evolved enough that the reasoning is obvious. Gaussian splatting has begun to ship across some of the world’s largest industries, including autonomous vehicles, AEC, geospatial, media and entertainment, robotics, e-commerce, hospitality. It’s become clear that lifelike 3D is here to stay. And yet I think we will look back and be disappointed by how late we started taking 3D portraits of the people around us, just like how we have sparse 2D photos of our grandparents and great grandparents. We have billions of photographs of the people we know and love, but almost no radiance fields of them. I'll be returning to SIGGRAPH in LA where this was initially captured three years ago, with the landscape looking significantly different. Radiance fields are more under deployed than ever relative to what they can do. I'm excited for the future of imaging, and for 2D to transition into 3D. I have a few things up my sleeve that I think will make that case plainly.
0:18

In the summer of 2023, I cold emailed Jensen Huang and asked to capture a NeRF of him at SIGGRAPH. He responded in about an hour and said yes. A radiance field is, in the simplest terms, akin to a 3D photograph. A moment in time, so completely reconstructed that you can move through it and see it from angles the original cameras never occupied. NeRFs were the original method. Gaussian splatting, which debuted at that same SIGGRAPH, has since become the dominant form of radiance field. I called my late friend James, who told me we needed to begin practicing immediately. We ran capture after capture for weeks until we consistently got the capture time down to ~30 seconds with one camera. Later, in a hallway at the LA Convention Center during SIGGRAPH, I captured the portrait you're seeing now, a full 360° gaussian splat of Jensen, rendered here as a 2D flythrough. Afterward, I continued the conversation with him and members of his team to make the case for radiance fields as a foundational representation for imaging. To my surprise, they listened. Three years later, NVIDIA has several works, including NuRec, fVDB, 3DGRUT, and gsplat all utilizing radiance fields. The landscape has evolved enough that the reasoning is obvious. Gaussian splatting has begun to ship across some of the world’s largest industries, including autonomous vehicles, AEC, geospatial, media and entertainment, robotics, e-commerce, hospitality. It’s become clear that lifelike 3D is here to stay. And yet I think we will look back and be disappointed by how late we started taking 3D portraits of the people around us, just like how we have sparse 2D photos of our grandparents and great grandparents. We have billions of photographs of the people we know and love, but almost no radiance fields of them. I'll be returning to SIGGRAPH in LA where this was initially captured three years ago, with the landscape looking significantly different. Radiance fields are more under deployed than ever relative to what they can do. I'm excited for the future of imaging, and for 2D to transition into 3D. I have a few things up my sleeve that I think will make that case plainly.

Radiance Fields

18,025 Aufrufe • vor 2 Monaten

🚨 CHINESE SCIENTISTS JUST INVENTED 3D PRINTING THAT CREATES OBJECTS IN 0.6 SECONDS USING ONLY LIGHT. Researchers at Tsinghua University have developed a new method called DISH (Digital Incoherent Synthesis of Holographic light fields) that can print complex millimeter-scale objects almost instantly. Instead of slowly building layer by layer, the system fires thousands of precisely patterned light images from multiple angles into a still vat of liquid resin. Where the light overlaps, the resin instantly hardens into a solid 3D object. The entire process takes just 0.6 seconds. Why this matters: • It’s currently the fastest volumetric 3D printing method ever demonstrated • Achieves extremely fine detail features thinner than a human hair • The resin stays completely still, so there’s no vibration or distortion • It can work with watery (low-viscosity) resins, making it suitable for biological applications • The team has already printed complex structures like blood vessel-like tubes and even a tiny bust of a historical figure The deeper implication: Traditional 3D printing has always been limited by speed and the need to move either the print head or the resin. This approach removes both constraints by using light itself as the sculptor. Because it can print directly into still liquid (and potentially onto living tissue), it opens new possibilities in bioprinting, medical devices, and rapid manufacturing. If the technology can be scaled beyond millimeter sizes, it could fundamentally change how we think about making physical objects turning “print” from a slow process into something closer to instantaneous fabrication. We’re moving from “layer by layer” to “all at once.” How do you think instant volumetric 3D printing like this could change medicine, manufacturing, or everyday life if it becomes widely available? Follow for more frontier manufacturing and materials science breakthroughs.
0:10

🚨 CHINESE SCIENTISTS JUST INVENTED 3D PRINTING THAT CREATES OBJECTS IN 0.6 SECONDS USING ONLY LIGHT. Researchers at Tsinghua University have developed a new method called DISH (Digital Incoherent Synthesis of Holographic light fields) that can print complex millimeter-scale objects almost instantly. Instead of slowly building layer by layer, the system fires thousands of precisely patterned light images from multiple angles into a still vat of liquid resin. Where the light overlaps, the resin instantly hardens into a solid 3D object. The entire process takes just 0.6 seconds. Why this matters: • It’s currently the fastest volumetric 3D printing method ever demonstrated • Achieves extremely fine detail features thinner than a human hair • The resin stays completely still, so there’s no vibration or distortion • It can work with watery (low-viscosity) resins, making it suitable for biological applications • The team has already printed complex structures like blood vessel-like tubes and even a tiny bust of a historical figure The deeper implication: Traditional 3D printing has always been limited by speed and the need to move either the print head or the resin. This approach removes both constraints by using light itself as the sculptor. Because it can print directly into still liquid (and potentially onto living tissue), it opens new possibilities in bioprinting, medical devices, and rapid manufacturing. If the technology can be scaled beyond millimeter sizes, it could fundamentally change how we think about making physical objects turning “print” from a slow process into something closer to instantaneous fabrication. We’re moving from “layer by layer” to “all at once.” How do you think instant volumetric 3D printing like this could change medicine, manufacturing, or everyday life if it becomes widely available? Follow for more frontier manufacturing and materials science breakthroughs.

TheNewPhysics

347,458 Aufrufe • vor 1 Monat

SIGNIFICANT SNOWFALL POTENTIAL FRIDAY NIGHT ******1ST CALL MAP****** 🎄 Merry Christmas to YOU!!! A pretty sizable snowstorm is expected across the region Friday night, with snow developing as early as Friday evening. I am very confident in a widespread 6–10" snowfall for a large portion of the Tri-State area, with lower confidence closer to NYC, Long Island, and the Jersey Shore where mixing remains a concern. At this time, I am keeping some mixing in play in central NJ/Long Island, but I’m not totally sold on how aggressive it becomes. While 4–6" looks like a very solid floor, several areas could easily boom to 6"+ if colder air locks in faster and holds longer than currently modeled. ZONE 1 – 6–10" OF SNOW This is the zone where cold air damming will be most prevalent, allowing snow to remain all snow for the duration of the event. Expect higher snow ratios, which could allow many areas to push toward double-digit totals, especially under any persistent banding. I could easily pull this zone into NYC and Nassau County as we see more model data. ZONE 2 – 4–6" OF SNOW This zone will likely start with heavy, wet snow, before transitioning to a lighter, fluffier snowfall as colder air works in. Some compaction is expected with lower snow ratios, but additional snow and mesoscale banding could still drive totals toward or above 6" in spots. ZONE 3 – 2–4" WITH MIXING This zone remains VERY uncertain. I could make a strong argument that cold air advection dominates, keeping this mostly snow and allowing totals to reach 4–6". However, low-level warm air transport on an easterly wind may introduce sleet, especially along the Jersey Shore (east of GSP) and into Suffolk County, which would eat into accumulations. I’ll be watching trends closely as this is shaping up to be a sharp cutoff storm where small shifts could have big impacts. 📺 On TV now with the latest on PIX11 News
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SIGNIFICANT SNOWFALL POTENTIAL FRIDAY NIGHT ******1ST CALL MAP****** 🎄 Merry Christmas to YOU!!! A pretty sizable snowstorm is expected across the region Friday night, with snow developing as early as Friday evening. I am very confident in a widespread 6–10" snowfall for a large portion of the Tri-State area, with lower confidence closer to NYC, Long Island, and the Jersey Shore where mixing remains a concern. At this time, I am keeping some mixing in play in central NJ/Long Island, but I’m not totally sold on how aggressive it becomes. While 4–6" looks like a very solid floor, several areas could easily boom to 6"+ if colder air locks in faster and holds longer than currently modeled. ZONE 1 – 6–10" OF SNOW This is the zone where cold air damming will be most prevalent, allowing snow to remain all snow for the duration of the event. Expect higher snow ratios, which could allow many areas to push toward double-digit totals, especially under any persistent banding. I could easily pull this zone into NYC and Nassau County as we see more model data. ZONE 2 – 4–6" OF SNOW This zone will likely start with heavy, wet snow, before transitioning to a lighter, fluffier snowfall as colder air works in. Some compaction is expected with lower snow ratios, but additional snow and mesoscale banding could still drive totals toward or above 6" in spots. ZONE 3 – 2–4" WITH MIXING This zone remains VERY uncertain. I could make a strong argument that cold air advection dominates, keeping this mostly snow and allowing totals to reach 4–6". However, low-level warm air transport on an easterly wind may introduce sleet, especially along the Jersey Shore (east of GSP) and into Suffolk County, which would eat into accumulations. I’ll be watching trends closely as this is shaping up to be a sharp cutoff storm where small shifts could have big impacts. 📺 On TV now with the latest on PIX11 News

Mike Masco

242,183 Aufrufe • vor 7 Monaten

A Look Into OpSec Cloudverse 👇 OpSec Cloudverse is a comprehensive platform designed to bring the formidable capabilities of blockchain technology to your fingertips. It's the engine that powers seamless access and management of blockchain nodes, enabling users to harness the full spectrum of web3 functionalities with unprecedented ease. How Cloudverse Works At its core, Cloudverse acts as a control panel for various blockchain-related services. It offers a user-friendly dashboard that allows you to remotely access, monitor, and manage nodes or servers, which are the fundamental building blocks of blockchain networks. These nodes operate round the clock, validating transactions, securing the network, and ensuring that your digital assets are always under your command. Node Operation Simplified Running nodes is often a technical and complex task, but Cloudverse streamlines the process. With its innovative 'one-click' setup, you can deploy a node in seconds, bypassing the intricate configuration typically required. This not only opens the door for non-technical users to contribute to blockchain networks but also significantly reduces the time and effort for developers and miners. Potential Rewards for Users Participation in the Cloudverse ecosystem is incentivized. As you contribute to the network by hosting nodes or validating transactions, you can earn rewards. These rewards vary based on the blockchain you support and the demand for transaction processing on the network. While specific figures can fluctuate, the potential for earning is tied directly to the vitality and activity of the blockchain ecosystem you choose to support. Beyond Nodes: A Diverse Web3 Toolkit Cloudverse also serves as a versatile suite of tools for traders, gamers, and developers. Whether it's hosting decentralized applications (DApps), enjoying high-speed gaming servers, or trading with the utmost performance, Cloudverse equips you with the resources to excel. Continuous Evolution The OpSec Cloudverse is a living ecosystem, continuously expanding with new features and products. The roadmap is dynamic, with regular updates enhancing your experience and capabilities within the digital realm. Your Role in the Digital Future By engaging with Cloudverse, you're not only utilising a service. You are actively participating in the foundation of a decentralized, secure digital landscape. It's a call to action for those who envision a future where digital trust is paramount, and everyone has a role to play in safeguarding the integrity of our online world. Join OpSec Cloudverse, and be at the forefront of the blockchain revolution. Let's build a stronger, more secure digital future, together! - End of the post, Beware of fake accounts -
0:15

A Look Into OpSec Cloudverse 👇 OpSec Cloudverse is a comprehensive platform designed to bring the formidable capabilities of blockchain technology to your fingertips. It's the engine that powers seamless access and management of blockchain nodes, enabling users to harness the full spectrum of web3 functionalities with unprecedented ease. How Cloudverse Works At its core, Cloudverse acts as a control panel for various blockchain-related services. It offers a user-friendly dashboard that allows you to remotely access, monitor, and manage nodes or servers, which are the fundamental building blocks of blockchain networks. These nodes operate round the clock, validating transactions, securing the network, and ensuring that your digital assets are always under your command. Node Operation Simplified Running nodes is often a technical and complex task, but Cloudverse streamlines the process. With its innovative 'one-click' setup, you can deploy a node in seconds, bypassing the intricate configuration typically required. This not only opens the door for non-technical users to contribute to blockchain networks but also significantly reduces the time and effort for developers and miners. Potential Rewards for Users Participation in the Cloudverse ecosystem is incentivized. As you contribute to the network by hosting nodes or validating transactions, you can earn rewards. These rewards vary based on the blockchain you support and the demand for transaction processing on the network. While specific figures can fluctuate, the potential for earning is tied directly to the vitality and activity of the blockchain ecosystem you choose to support. Beyond Nodes: A Diverse Web3 Toolkit Cloudverse also serves as a versatile suite of tools for traders, gamers, and developers. Whether it's hosting decentralized applications (DApps), enjoying high-speed gaming servers, or trading with the utmost performance, Cloudverse equips you with the resources to excel. Continuous Evolution The OpSec Cloudverse is a living ecosystem, continuously expanding with new features and products. The roadmap is dynamic, with regular updates enhancing your experience and capabilities within the digital realm. Your Role in the Digital Future By engaging with Cloudverse, you're not only utilising a service. You are actively participating in the foundation of a decentralized, secure digital landscape. It's a call to action for those who envision a future where digital trust is paramount, and everyone has a role to play in safeguarding the integrity of our online world. Join OpSec Cloudverse, and be at the forefront of the blockchain revolution. Let's build a stronger, more secure digital future, together! - End of the post, Beware of fake accounts -

OpSec

31,170 Aufrufe • vor 2 Jahren

We continue to analyze the technical details of the war with Iran, and we would like to note the Iranian novelty - subsonic barraging anti-aircraft missiles Missile 358, equipped with compact turbojet engines. To some extent unexpectedly, they showed good effectiveness against Israeli reconnaissance and strike UAVs Hermes-900. The key role is played by the thermal homing head: it is able to reliably detect and track a wide range of heat-contrasting targets, including UAVs with various flight profiles. An additional advantage is the command and telemetry channel, which ensures data transmission and allows for radio correction of the trajectory. This is especially important in situations when the target attempts to disrupt the capture with infrared decoys or other means of counteraction. According to the stated parameters, the range of application of Missile 358 reaches about 100 km. At the same time, the maximum interception altitude is about 8.5 km, and the speed is up to 700 km/h, which expands the capabilities of the complex in covering objects and intercepting medium-altitude UAVs. Of course, this is a niche tool, and compared to solid-fuel missiles, the turbojet engine provides exponentially higher flight energy. This allows to dramatically increase the range at low speed, and the mass of the main units of the ammunition, its warhead and control system. On the other hand, this is a solution of necessity, because classic anti-aircraft missiles perfectly hit such high-altitude and slow-moving targets. But for such ammunition, no radar, complex and expensive beam installations are required, which greatly improves its survivability under constant air strikes. And in its niche of targets, there are enough of them, as such UAVs of Israel and the USA are the basis of UCAV, and are constantly over the territory of Iran. So with the 358th ammunition, you can score quite a lot of frags, and significantly complicate air strikes on Iran for the Epstein coalition. Russian Engineer -
0:15

We continue to analyze the technical details of the war with Iran, and we would like to note the Iranian novelty - subsonic barraging anti-aircraft missiles Missile 358, equipped with compact turbojet engines. To some extent unexpectedly, they showed good effectiveness against Israeli reconnaissance and strike UAVs Hermes-900. The key role is played by the thermal homing head: it is able to reliably detect and track a wide range of heat-contrasting targets, including UAVs with various flight profiles. An additional advantage is the command and telemetry channel, which ensures data transmission and allows for radio correction of the trajectory. This is especially important in situations when the target attempts to disrupt the capture with infrared decoys or other means of counteraction. According to the stated parameters, the range of application of Missile 358 reaches about 100 km. At the same time, the maximum interception altitude is about 8.5 km, and the speed is up to 700 km/h, which expands the capabilities of the complex in covering objects and intercepting medium-altitude UAVs. Of course, this is a niche tool, and compared to solid-fuel missiles, the turbojet engine provides exponentially higher flight energy. This allows to dramatically increase the range at low speed, and the mass of the main units of the ammunition, its warhead and control system. On the other hand, this is a solution of necessity, because classic anti-aircraft missiles perfectly hit such high-altitude and slow-moving targets. But for such ammunition, no radar, complex and expensive beam installations are required, which greatly improves its survivability under constant air strikes. And in its niche of targets, there are enough of them, as such UAVs of Israel and the USA are the basis of UCAV, and are constantly over the territory of Iran. So with the 358th ammunition, you can score quite a lot of frags, and significantly complicate air strikes on Iran for the Epstein coalition. Russian Engineer -

𝐃𝐚𝐯𝐢𝐝 𝐙 🇷🇺🇮🇪

17,874 Aufrufe • vor 5 Monaten

Native USDC is now live on Aptos! This marks a significant milestone for the Aptos ecosystem, empowering developers and users with access to the world’s largest regulated digital dollar. USDC powers innovative use cases: ✅Build secure apps for peer-to-peer payments, cross-border remittances, RWA settlement, gaming, and more ✅Supercharge DeFi with deep liquidity for digital asset trading and financial services ✅Empower merchants with global, instant, low-cost payment solutions that settle 24/7 Many leading ecosystem apps are expected to support native USDC on Aptos, including: Coinbase 🛡️, Echo Protocol, Petra, Pontem Labs (Liquidswap), Stripe Native USDC is officially issued by Circle and redeemable 1:1 for US dollars. There’s currently a bridged form of USDC in the Aptos ecosystem known as lzUSDC, which is bridged from Ethereum through the AptosBridge built on LayerZero. lzUSDC is not issued by Circle and not redeemable with Circle Mint. Native USDC issued by Circle: Token Name: USDC Token Symbol: USDC Mainnet Address: 0xbae207659db88bea0cbead6da0ed00aac12edcdda169e591cd41c94180b46f3b Testnet Address: 0x69091fbab5f7d635ee7ac5098cf0c1efbe31d68fec0f2cd565e8d168daf52832 Bridged USDC from LayerZero: Token Name: Bridged USDC (LayerZero) Token Symbol: lzUSDC Mainnet Address: 0xf22bede237a07e121b56d91a491eb7bcdfd1f5907926a9e58338f964a01b17fa::asset::USDC Developers can use our step-by-step migration guide for options on migrating bridged USDC to native USDC in their apps: CCTP is coming later this morning: With CCTP launching imminently, leading interoperability providers like Wormhole will enable seamless USDC transfers between Aptos and 9 other blockchains. With the addition of Aptos, USDC is now natively supported on 17 blockchains—with many more expansions planned this year. Start building with USDC on Aptos:
0:14

Native USDC is now live on Aptos! This marks a significant milestone for the Aptos ecosystem, empowering developers and users with access to the world’s largest regulated digital dollar. USDC powers innovative use cases: ✅Build secure apps for peer-to-peer payments, cross-border remittances, RWA settlement, gaming, and more ✅Supercharge DeFi with deep liquidity for digital asset trading and financial services ✅Empower merchants with global, instant, low-cost payment solutions that settle 24/7 Many leading ecosystem apps are expected to support native USDC on Aptos, including: Coinbase 🛡️, Echo Protocol, Petra, Pontem Labs (Liquidswap), Stripe Native USDC is officially issued by Circle and redeemable 1:1 for US dollars. There’s currently a bridged form of USDC in the Aptos ecosystem known as lzUSDC, which is bridged from Ethereum through the AptosBridge built on LayerZero. lzUSDC is not issued by Circle and not redeemable with Circle Mint. Native USDC issued by Circle: Token Name: USDC Token Symbol: USDC Mainnet Address: 0xbae207659db88bea0cbead6da0ed00aac12edcdda169e591cd41c94180b46f3b Testnet Address: 0x69091fbab5f7d635ee7ac5098cf0c1efbe31d68fec0f2cd565e8d168daf52832 Bridged USDC from LayerZero: Token Name: Bridged USDC (LayerZero) Token Symbol: lzUSDC Mainnet Address: 0xf22bede237a07e121b56d91a491eb7bcdfd1f5907926a9e58338f964a01b17fa::asset::USDC Developers can use our step-by-step migration guide for options on migrating bridged USDC to native USDC in their apps: CCTP is coming later this morning: With CCTP launching imminently, leading interoperability providers like Wormhole will enable seamless USDC transfers between Aptos and 9 other blockchains. With the addition of Aptos, USDC is now natively supported on 17 blockchains—with many more expansions planned this year. Start building with USDC on Aptos:

Circle

94,876 Aufrufe • vor 1 Jahr

PHOTON COUNTING CT AND A NEW CONCEPT OF NORMAL CORONARY ARTERIES For more than 2 decades we have been performing Cardiac CT with constant improvements in all parameters (spatial, temporal and contrast resolution). The improvements were progressive in certain fields and steep in other (e.g.: the introduction of Dual Source CT that completely changed the range of temporal resolution achievable basically overnight and still is the most important source of flexibility in Cardiac CT scanning after almost 20 years). Spatial resolution instead was improved in a slow and progressive way until the test EID CT generations that achieved a spatial resolution of 250 microns. This allowed us to assess a coronary artery tree and define it as normal when no apprentice changes were visibile up to that value. But we know that the normal thickness of coronary artery walls is quite below that threshold. It is more in the range of 80-200 microns. Therefore, the very early changes in coronary artery wall thickness could not be picked up by EID CT technology. With the introduction of PCCT we can constantly achieve 100 microns spatial resolution which means that we work extol in range in which coronary artery disease starts. It also means that we don't see any thickening of the the coronary artery walls we have a much higher specificity and reliability. This concept is a transformative one because it allows us to shift earlier and earlier the beginning of atherosclerosis in our patients and think even more precisely in terms of cardiovascular prevention and monitoring. Movie: example of normal coronary artery tree with PCCT. A new era is coming into practice and it is the age of Photon Counting CT which pushes this boundaries further away. PCCT is a NEW Imaging Modality. PCCT is changing the game, the field, the language, the priorities and in the end it will change the entire infrastructure of diagnostic medicine. PS: note that PCCT images have to be reduced in resolution when uploaded in social media. #CardiacImaging #MedicalInnovation #StentAssessment #Radiology #PCCT #photoncounting #QuantumHD #CT #computedtomography #yesCCT #coronaryarterydisease #ischemia #naeotomalpha #Peak #Pro #Prime #speed #cardiac #highresolution #siemenshealthinners #CardiacCT #PhotonCountingCT #MedicalImaging #HeartHealth #CardiovascularInnovation #Radiology #AIInMedicine
0:14

PHOTON COUNTING CT AND A NEW CONCEPT OF NORMAL CORONARY ARTERIES For more than 2 decades we have been performing Cardiac CT with constant improvements in all parameters (spatial, temporal and contrast resolution). The improvements were progressive in certain fields and steep in other (e.g.: the introduction of Dual Source CT that completely changed the range of temporal resolution achievable basically overnight and still is the most important source of flexibility in Cardiac CT scanning after almost 20 years). Spatial resolution instead was improved in a slow and progressive way until the test EID CT generations that achieved a spatial resolution of 250 microns. This allowed us to assess a coronary artery tree and define it as normal when no apprentice changes were visibile up to that value. But we know that the normal thickness of coronary artery walls is quite below that threshold. It is more in the range of 80-200 microns. Therefore, the very early changes in coronary artery wall thickness could not be picked up by EID CT technology. With the introduction of PCCT we can constantly achieve 100 microns spatial resolution which means that we work extol in range in which coronary artery disease starts. It also means that we don't see any thickening of the the coronary artery walls we have a much higher specificity and reliability. This concept is a transformative one because it allows us to shift earlier and earlier the beginning of atherosclerosis in our patients and think even more precisely in terms of cardiovascular prevention and monitoring. Movie: example of normal coronary artery tree with PCCT. A new era is coming into practice and it is the age of Photon Counting CT which pushes this boundaries further away. PCCT is a NEW Imaging Modality. PCCT is changing the game, the field, the language, the priorities and in the end it will change the entire infrastructure of diagnostic medicine. PS: note that PCCT images have to be reduced in resolution when uploaded in social media. #CardiacImaging #MedicalInnovation #StentAssessment #Radiology #PCCT #photoncounting #QuantumHD #CT #computedtomography #yesCCT #coronaryarterydisease #ischemia #naeotomalpha #Peak #Pro #Prime #speed #cardiac #highresolution #siemenshealthinners #CardiacCT #PhotonCountingCT #MedicalImaging #HeartHealth #CardiovascularInnovation #Radiology #AIInMedicine

Dr. Filippo Cademartiri

63,080 Aufrufe • vor 1 Jahr

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:
0:12

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Shikun Liu

22,389 Aufrufe • vor 10 Monaten