Can't express the unearthly delight I felt envisioning the guy painstakingly mapping divisions of labor to varnas and then smugly setting down the pen. What a bombshell!

Here's everything you need to know to start UV Mapping in Blender ~ #Blender3D #3Dartist #3dmodeling

Want to earn points without owning heavy hardware? Our upcoming Spatial Mapping Quests let users upload localized environmental data using standard smartphones. This crowd-sourced telemetry helps refine the digital twins used by @konnex_world autonomous units for real-world.

Indegene , capability depth and mapping , focusing on domain specific enterprise agentic AI solutions that can actually help them pivot better because of lot of cross time cycle data on the Pharma specific domain Pharma relationships are not directly named , but we can estimate these well source: internal research

ละเดี๋ยวนี้มีสอน Projection Mapping ด้วย 😭 เป็นสิ่งที่กูอยากเรียนมาก แต่จอนเรียนมันยังไม่มีคลาสนี้ 🥹

I've been mapping how Identity Platforms like Alloy Alloy, Persona, Sardine, and AiPrise are actually built - not what they claim but what they are betting on, where each one structurally excels and what that costs in production.

💧💧 Using #Sentinel Imagery for Mapping and Monitoring Small Surface #Water #Bodies ✍️ Mariana Campista Chagas et al. 🔗 brnw.ch/21x3kaf

She's still using her old mouth mapping.. oh the good old days..

🙃I want to combine these tools and make these planets have texture mapping and make them vr inspect able. But today I am working on floors.

The best flood-mapping AI may be the one we stop allowing to invent impossible water. Most satellite flood maps do one thing well: they show where the water appears to be. Take Sentinel-1 radar, add optical imagery when clouds allow, classify each pixel, and produce a flood mask. I guess that’s useful during a disaster, but it leaves out some pretty important factors - how deep the water is, how fast it’s moving, and whether the flood pattern makes physical sense. A new paper tackles that harder problem. They present a model which combines Sentinel-1 radar, Sentinel-2 optical imagery, and terrain information from a digital elevation model. That terrain layer includes elevation, slope, and HAND, which measures how high each location sits above the nearest drainage path. Then the authors use a hybrid architecture: UNet plus FNO. UNet is pretty good at local details. It can pick up flood edges, roads, urban structure, and small changes in inundation patterns. FNO is better at broader spatial relationships, such as upstream-downstream structure and basin-scale flow. That combination is useful because floods don’t happen at one scale. A road embankment can shape the water in one neighbourhood, while the slope of the whole floodplain controls where the water wants to go. But what I find to be the strongest part of the paper is the discussion on physics loss. The model predicts water depth and velocity, then training checks those predictions against the depth-averaged shallow-water equations. In plain terms, the model gets penalised when it draws floodwater that looks plausible in an image but breaks the rules of mass, momentum, slope, and friction. That constraint appears to do real work. Across three held-out floodplain regions, the hybrid model reached an IoU of 0.82 and an F1 score of 0.90. The depth results are more interesting. Evaluated against HEC-RAS hydrodynamic simulations, the hybrid model reached a water-depth RMSE of 0.21 m. For flow velocity, the hybrid model reached 0.15 m/s RMSE. It also kept relative mass imbalance down to 2.1%, which is exactly the kind of check you want when a model is pretending to understand water. Anyway, it looks like the trend is that flood AI models is moving from image classification to physical state estimation. This essentially resembles more traditional hydraulic modelling approaches to the problem. Maybe we're coming full circle... Link to paper: arxiv.org/pdf/2606.06524 (Image source: UN Spider)

Messier 51 in 255 hours. This deep-field view of the Whirlpool Galaxy reveals tidal debris and stellar halos from its merger with NGC 5195, mapping faint H-alpha emissions and star formation 31M light-years away. #Astronomy #Astrophysics #NASA #Messier51

Crypto cycle theorists genuinely believe institutional $BTC flow is dictated by the Gregorian calendar. You are sitting in fiat waiting for a magical September low because an engagement farmer told you summer is supposed to bleed. Desks are not trading the month of the year, they are trading your collateral. Keep mapping out Uptober while they harvest your premium.

Actively harming? Apprehending in bodyworn, No medical evidence, no body mapping of injuries to protestors submitted Misinformation 😂 I’ve followed it throughout Unlike you I apply the facts, not emotion and assumption

‼️Il principale avamposto ucraino a Kostyantynivka, nell'est, è ora sotto accerchiamento operativo da parte delle forze russe. Dopo che le forze ucraine si sono ritirate per lo più dai sobborghi occidentali, le forze russe sono riuscite a sfondare le difese nel cosiddetto "Centro Storico" della parte settentrionale di Kostyantynivka, raggiungendo l'autostrada che attraversa la città da nordest, bloccando così l'ultima via di fuga per le forze ucraine stanziate più a est. Questo corridoio rimane una zona grigia, dove le posizioni russe e ucraine si sovrappongono; tuttavia, ora sarà difficile per le forze ucraine ritirarsi dalla parte orientale della città. Altrove, le forze russe continuano le operazioni di assalto, con soldati che infiltrano il Distretto di Novoselivka nella parte nord-occidentale della città. I DRG russi operano già sull'autostrada verso Kostyantynivka, tuttavia l'Ucraina continua a incanalare ulteriori rinforzi, probabilmente per garantire le rotte di ritirata. I rifornimenti vengono ora effettuati principalmente con UAV e droni terrestri. Nel frattempo, i combattimenti intensi continuano presso la stazione ferroviaria, il cimitero cittadino, il settore privato settentrionale, la via Lomonosova (sobborghi nord-occidentali) e l'ex impianto chimico. La situazione rimane critica per l'Ucraina mentre le difese residue nella città collassano. Hudson Mapping 👉L’importante è che nessun mercenario lasci vivo la città, visto che ormai gli esangui battaglioni ucraini contano più mercenari che soldati ucraini!

SIR Drive: ECI prioritises mapping of MPs, MLAs and senior officials in Jharkhand thejharkhandstory.co.in/sir-…

Spent some time checking out deeper into $ZERO / @c0mpute_ai and the picture is getting clearer. On-chain mapping so far: • ZERO mint: EmcxFTNVDqyLHp11NvwvLZ4D7LKGbG9i7B8RF7dwpump • LP: GN9whJWrkgU8jBRpM5oa4iwSzYw1LivjB397DSyvbVG • 1-year Streamflow lock: 2fUJwNkvEDBc9gSrQ2RxUNZY86rfgU6EBntqxNwWAAZh • Meteora: EvMTf7rxweoHTSiQYLQNVpRRUJanyHNX3NJtYVDrzj9p • Dev/Funder: Leyten1iTYhuM6Jcn1tJpEBakcDWuMVj1fyXPo146WN • ZERO-related wallet: zeropP4MuvPZ52Nz3LgQ4jgcNAW7NXBDxaGui5XPtjb • Operational payouts/rewards wallet: HvgSfeTjEVHG3o4UpmTvmaHW6gfxXBZutoFozH65R16x • Burn wallet: G55Wcgksbnk1q1g1pt8xJg1yJA5sZr1ENAYMRE3LGpiz Interesting findings: LP is locked. 1-year Streamflow lock is visible on-chain. Supply distribution looks surprisingly healthy after LP lock. Burns are reaching the burn wallet on-chain. The dashboard metrics are starting to match what we’re seeing on-chain. The biggest surprise was the GitHub. Most people still think c0mpute is just another AI token, but recent updates show a much more complete stack than expected: • OpenAI-compatible API • Orchestrator distributed worker network • Browser and native inference workers • USDC worker payouts • Staking infrastructure • Treasury accounting system • Automated buyback/burn keeper logic • Solana integrations • Revenue, rewards and payout flows already being wired into the product This is starting to look less like a meme AI coin and more like an attempt to build a decentralized inference marketplace with real economic rails behind it. Still mapping the full flow: USDC → Treasury → Buyback → Burn → Rewards But the deeper I dig, the more this looks like a product-first project rather than a token-first project. @c0mputeAI Am I missing anything? @anger_trading 👀👀

The hometown of Attack on Titan's creator has become a tourist destination for fans from around the world. Hita City in Oita Prefecture is home to several must-see Attack on Titan spots, including the Eren, Mikasa, and Armin statues at the foot of the 94-meter Oyama Dam, positioned as if they are staring up at Wall Maria. Fans can also find Levi Ackerman’s bronze statue in front of JR Hita Station, then visit the Attack on Titan in HITA Museum and its ANNEX at Sapporo Beer Kyushu Hita Brewery, featuring original artwork, childhood works by Isayama, and exclusive “Attack on Hita” merchandise. For those looking for outdoor activities, the nearby Forest Adventure Okuhita offers zipline courses and aerial obstacle trails through the forest. There is also a limited-time attraction, Attack on Titan THE NIGHT WALK -Beyond the Walls-, running at Nijigen no Mori on Awaji Island from March 14 to December 13, 2026, where visitors join the Survey Corps on a 1.2 km night walk through projection mapping and forest scenes. With statues, museums, exclusive exhibits, and outdoor adventures, Hita City has become one of Japan’s most unique destinations for Attack on Titan fans.

The SLAM robotics bible! 📚 Probabilistic Robotics is the classic textbook for anyone working on localization, mapping, SLAM, and Bayesian approaches to robot perception and control. Written by Sebastian Thrun (Stanford), Wolfram Burgard (University of Freiburg), and Dieter Fox (University of Washington), it's built on a single mathematical foundation: using statistics to integrate sensor measurements and models. Core techniques covered as particle filters, occupancy grid maps, Kalman filters, and other Bayesian methods for handling uncertainty in the real world. Inside you can find pseudo code implementations for every algorithm, detailed mathematical derivations, practical insights from deploying these methods, plus extensive exercises and projects. The book's strength is that it treats uncertainty not as an afterthought but as central to robotics. Real robots operate in noisy, unpredictable environments. Probabilistic approaches give them robustness that deterministic methods can't match. If you're building a robot that needs to know where it is, what it's seeing, and how to move reliably, this book is a good start. Here's the book PDF free: pdfcoffee.com/probabilistic-… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

Active Radar mapping of Asteroids throughout the Solar System.

Sure feels to me like @MassGovernor is a deployed enemy combatant. She answers not to the citizens, not to our government, but "others" who manipulate the West for their own gain. Importing illegals, upending neighborhood mapping, draining tax funds she is sworn to be steward of