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Taking a naked break between scenes in PrivatePlayground (162K)'s Pool boys! Featuring YurBoyAnt 😈 X the Gemini Darron bluu◝(⁰▿⁰)◜↕️♉️fire island27-sep3)(Europe8 & Watch the 2-pt mini-series on our pages! #poolboys #privateplayground #4way
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NEW: California mom set to be sentenced after being convicted of orchestrating alcohol infused s*x parties at her mansion with over a dozen child victims. Shannon O’Connor, 51, was charged with facilitating the forcible s*xual assault of one child on another child, and other charges. O’Connor was previously found guilty on 48 felony counts where she was accused of watching boys s*xually assault girls for her own gratification. The children were reportedly between the ages of 13 and 15. During the sentencing hearing, testimony was heard from victims and their families. "It is beyond me. I can not comprehend how someone could do this. This represents who you are at your core… For me, what happened took away so many happy parts of who I was," 'Jane Doe' said. "She was taking weekly drink orders for these kids. She encouraged and managed s*x acts. Our daughter was only 14 years old. She was quite innocent. Before the encounter with Shannon, she was sweet, silly, and full of joy," Doe's mother said. "She was taking weekly drink orders for these kids. She encouraged and managed s*x acts. Our daughter was only 14 years old." O’Connor has been found guilty of 48 felony counts, along with additional misdemeanors, including child endangerment and furnishing alcohol to minors. She faces more than 30 years in prison.
Collin Rugg
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![Lecture 2 of our Physics-Informed Neural Networks mini-series. In Lecture 1 we made the idea visible...a neural network isn’t predicting a PDE solution, it is the candidate function uᵩ(x,t), and the PDE residual rᵩ(x,t) is the leash that keeps it honest. Now the natural question follows: How can a neural network be punished for breaking a PDE when nobody ever handed it the true solution, and the equation itself contains derivatives like uᵩₜₜ and uᵩₓₓ? Here’s the satisfying answer: A PINN doesn’t need the true answer to be corrected. It only needs a way to measure how wrong it is according to the PDE! The network outputs uᵩ(x,t). A software called "autodiff" is used to compute the derivatives (uᵩₓ, uᵩₜ, uᵩₓₓ, …) exactly by applying the chain rule through the network. Those derivatives get dropped into the PDE to produce rᵩ(x,t). If rᵩ is big at some point, the loss spikes there, and gradient descent pushes the parameters so that rᵩ shrinks. The math breakdown We want a function u(x,t) that satisfies a PDE on a domain Ω. In this lecture we keep a concrete nonlinear example in mind, the damped sine-Gordon equation uₜₜ(x,t) + γ uₜ(x,t) − c² uₓₓ(x,t) + sin(u(x,t)) = 0. A PINN replaces the unknown function u with a neural network uᵩ(x,t), where ᵩ means all the network parameters (weights and biases). Now we build the physics residual by plugging uᵩ into the PDE rᵩ(x,t) = uᵩₜₜ(x,t) + γ uᵩₜ(x,t) − c² uᵩₓₓ(x,t) + sin(uᵩ(x,t)). If uᵩ were a true solution, rᵩ would be 0 everywhere. So we sample points (xⱼ,tⱼ) inside the domain. These are collocation points. At each one we evaluate rᵩ, and we define a physics loss L_phys(ᵩ) = meanⱼ |rᵩ(xⱼ,tⱼ)|². This is the punishment mechanism. (Punish just means: if |rᵩ| is big, L_phys is big; training updates ᵩ to make L_phys smaller. Reward means the loss drops, so those parameter changes are kept.) The key question was where the derivatives come from. Since uᵩ is built out of differentiable operations, we can compute uᵩₜ(x,t), uᵩₜₜ(x,t), uᵩₓ(x,t), uᵩₓₓ(x,t), at any input (x,t) we choose. Imagine a simple differentiable model written as a sum of nonlinear features uᵩ(x,t) = Σₖ vₖ σ( wₖx x + wₖt t + bₖ ) + b₀. Then the derivatives are just chain rule uᵩₓ(x,t) = Σₖ vₖ σ′(·) wₖx uᵩₓₓ(x,t) = Σₖ vₖ σ″(·) (wₖx)² uᵩₜ(x,t) = Σₖ vₖ σ′(·) wₖt uᵩₜₜ(x,t) = Σₖ vₖ σ″(·) (wₖt)². So rᵩ(x,t) is an explicit computable number at every (x,t). For the damped sine-Gordon example, it’s the same story, just with one extra nonlinear term: rᵩ(x,t) = [uᵩₜₜ(x,t) + γ uᵩₜ(x,t) − c² uᵩₓₓ(x,t)] + sin(uᵩ(x,t)). A real PINN is a deeper composition of these same building blocks, but it’s still just a chain rule, and autodiff is the machinery that does that bookkeeping reliably for big graphs. Then we train by gradient descent on the total loss. Even if we use only physics for the moment, the update is conceptually just ᵩ ← ᵩ − η ∇ᵩ L_phys(ᵩ), with learning rate η. In practice we also include initial/boundary conditions or data, because PDEs aren’t uniquely determined without them L(ᵩ) = L_data(ᵩ) + λ L_phys(ᵩ) + L_bc/ic(ᵩ), where L_bc/ic(ᵩ) enforces things like uᵩ(x,0) ≈ u₀(x) and uᵩₜ(x,0) ≈ v₀(x), or boundary conditions at x = ±L. So Lecture 2’s punchline is simple: the PDE becomes a training signal. We keep differentiating uᵩ, measuring rᵩ, and updating ᵩ until the residual goes quiet across Ω. #PINNs #PhysicsInformedNeuralNetworks #ScientificMachineLearning #AutoDiff #Backpropagation #PDE #DifferentialEquations #Optimization #MachineLearning #AppliedMath #ComputationalPhysics](https://image.24vids.com/tw-2009748938266685482/amplify_video_thumb/2009748619386101760/img/q0u_ZxmvYMtDl2eQ.jpg)
![The #PowerRangersPreservationProject Presents Forever Red: Cole Meets Alpha 7! Celebrating #PowerRangersDay with this restored deleted scene from Season 10’s epic Red Ranger Reunion, painstakingly built from scratch by the talented Kadam’s Edits from the recovered raw dailies by PR Media Info. Presumably cut for time, this scene took place on the Megaship Mark II between taking off from NASADA and Tommy’s team briefing in the war room. Andros leads the other Red Rangers of Earth down the hall — TJ feeling right at home aboard the familiar ship; Jason stoically impressed; Eric stoked in front of Wes; Carter in awed silence; and finally Cole, taking in the experience with childlike innocence. Getting lost staring into space, the Blazing Lion is interrupted by Alpha 7 — originally properly introduced in this scene as a walking database of everything Power Rangers. Having grown up in the jungle and never seen a robot before, Cole reacts to the automaton with wonder. We teased this footage years ago before launching the Preservation Project, and we’re proud to finally present this lost scene as it was intended — complete with music and sound, special effects, and a very special feature by Campbell Cooley as Alpha 7! Campbell voiced Alpha 6 in Once a Ranger [Operation Overdrive’s Season 15 Anniversary Special], going on to voice Snide [Dino Charge], Cosmo Royale [Ninja Steel], Scrozzle [Beast Morphers], and Slyther [Dino Fury]. Edit by @Kadams_Edits. Digitization & Archival by Power Rangers Media Info. Sponsored by M⚡️M Patreon Supporters like Morphin Master MintBari. Help us continue our work by supporting us on Patreon with monthly sponsorships starting at $2: #MorphinMuseum Exhibit 0884 ⚡️ Series: #PowerRangers Wild Force Episode: Forever Red Year: 2000](https://image.24vids.com/tw-1961106536799039775/amplify_video_thumb/1961105589574201344/img/aEp1yvpURnAx3c4c.jpg)
