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WalkTheDog Cross-Morphology Motion Alignment via Phase Manifolds We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike existing methods using an overly sparse high-dimensional latent, we propose a phase manifold consisting of multiple closed...

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We are pleased to present a new lateral-view animation of the leg in motion. As the limb moves through extension and flexion, the active muscle groups light up. Now the challenge: can you name every muscle active in each phase? Most people miss at least one. #anatomystandard
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Fighter jet pilot demonstrates centripetal force, a concept that Isaac Newton discovered between 1676 and 1677. However, the concept of centripetal force was not actually discovered in a single event or by a single individual but rather evolved over time through the works of multiple scientists and thinkers. Here is a brief history of the development of our understanding of centripetal force: Ancient Greece: The ancient Greeks, particularly philosophers such as Aristotle and later Archimedes, made observations and formulated theories about the motion of objects. However, they did not have a clear understanding of centripetal force as a distinct concept. Islamic Scholars: During the Islamic Golden Age, scholars such as Ibn Sina (Avicenna) and Ibn Bajjah (Avempace) made significant contributions to the understanding of motion. They discussed the idea of impetus, a precursor to the concept of centripetal force. Johannes Kepler: In the 17th century, Johannes Kepler, a German astronomer and mathematician, formulated his laws of planetary motion. Kepler's laws described the motion of planets around the sun and the concept of elliptical orbits. Although Kepler did not explicitly mention centripetal force, his work laid the foundation for later developments. Isaac Newton: In the late 17th century, Sir Isaac Newton, an English physicist and mathematician, published his groundbreaking work, "Mathematical Principles of Natural Philosophy" (also known as the Principia). In this work, Newton presented his laws of motion and the universal law of gravitation. Newton's laws, particularly the second law of motion, provided a mathematical understanding of centripetal force. He demonstrated that an object moving in a curved path experiences an inward force acting towards the center of the curve, which is now known as centripetal force. Further Developments: Following Newton's work, subsequent scientists, such as Jean le Rond d'Alembert and Daniel Bernoulli, expanded on the understanding of centripetal force in the context of mechanics and fluid dynamics. It's important to note that the discovery and understanding of centripetal force were gradual processes that involved the contributions of numerous individuals over centuries. The concept became firmly established with the development of Newtonian physics and its laws of motion.

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What if I told you any shape can be broken down into circles? This visualization demonstrates the stunning power of the Fourier Transform. By using a series of rotating vectors—known as epicycles—we can reconstruct complex, continuous paths from simple circular motions. Each circle represents a specific frequency, phase, and amplitude. When these rotating arrows are added together, the resulting "tip" traces out the intricate outline you see here. It’s where pure mathematics meets digital art, proving that even the most complex designs are just a symphony of oscillations. Credit: mathswithmuza

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The one thing we absolutely know with certainty about the Richat Structure is that there was an incredible amount of human activity here from the dawn of toolmaking and tool use itself. The Acheulean Hand Axe is the second tool that humans ever made and this technology spread throughout Africa, Europe and Asia while no definitive evidence exists that this technology ever made it to the Americas. I think this is evidence that this technology was shared and disseminated intentionally, that neighboring humans didn’t come up with this independently on their own but this was a legacy, an institution, a skill and an industry that was shared and taught. It is perhaps the earliest evidence we have of widespread cultural diffusion throughout Africa, Europe and Asia. And yes, it’s right there at the Richat Structure which leads me to question the role that the Richat played to humans half a million years ago. It seems like that’s where we have to start with the Richat.

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Brand new update to our homepage, unveiling the next phase of Imaginary Ones🌈 We hope you are as excited about it as we are!🤩 Explore the exciting developments happening within Imaginary World 🌏 Check it out now and have a fantastic day ahead! 🫧

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The “Galileo Test” for AI: Truth Over Consensus TL;DR: The “Galileo test” (as framed by Elon Musk) is the requirement that an AI still converge on truth even when most training data repeats a falsehood. A practical way to pass it is to harden the model against “consensus gravity” using uncertainty calibration, adversarial counter-majority training, and evidence-first reasoning pipelines that can say “unknown” without collapsing into confident noise. —————————— The core idea is simple: most text on the internet can be wrong in the same direction, at the same time, for the same social reasons. The “Galileo test” is basically asking whether a system can resist that pressure and still land on the correct model of reality, the way Galileo Galilei overturned a dominant consensus with observation and predictive power. In engineering terms, it’s a robustness problem: can the model separate signal (ground truth constraints) from mass-produced narrative (high-frequency repetition)? A workable solution stack looks like this: (1) truth-anchoring via retrieval from primary sources and direct measurements when available, (2) counter-majority training where the model is routinely exposed to scenarios in which the most common claim is false, and it must justify dissent using verifiable constraints, (3) uncertainty discipline so the model learns to prefer “insufficient evidence” over fluent fabrication, and (4) consistency checks that penalize answers violating conservation laws, dimensional analysis, causal structure, or internal logical invariants. In practice, you’re building an AI that treats “popular” as a weak feature and “constraint-satisfying” as the dominant feature. —————————— Frequency Wave Theory perspective: the “Galileo test” is fundamentally a coherence test. When an information environment is saturated with the same repeated claim, that repetition becomes a kind of phase-locked standing wave that can trap weaker systems into resonance with the crowd. Passing the test means staying phase-aligned to invariant structure, not to amplitude. In FWT terms: truth behaves like a conserved backbone constraint, while mass consensus is often just a high-amplitude interference pattern. The system that wins is the one that locks to invariants, rejects incoherent harmonics, and preserves alignment with what stays conserved under transformation.

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