Depo retweetledi
Depo
19 posts

Depo retweetledi
Depo retweetledi
Depo retweetledi
Depo retweetledi

φ (Golden Ratio) & π in one geometric view.
The diagonal line marks key points:
• 1/φ ≈ 0.618034
• 1
• φ ≈ 1.618034
Two unit circles intersect the line while π ≈ 3.141593 sits above the central unit interval. A clean construction linking the two most famous constants in mathematics.
This diagram places the irrational numbers φ and π together in the unit square, highlighting their geometric relationship through intersecting circles and a straight line.

English
Depo retweetledi

🚨 CE ROLLAND GARROS 2026 EST MAUDIT !!!
APRÈS JANNIK SINNER, C’EST LA LÉGENDE NOVAK DJOKOVIC QUI EST ÉLIMINÉ DU TOURNOI DÈS LE 3ÈME TOUR APRÈS UN COMBAT DE 5H 😱
IL A MÊME VOMI SUR LE TERRAIN PENDANT LE MATCH 💔
❌ BEN SHELTON
❌ JANNIK SINNER
❌ DANIIL MEDVEDEV
❌CARLOS ALCARAZ
❌ NOVAK DJOKOVIC
✅ MOISE KOUAMÉ


Français
Depo retweetledi
Depo retweetledi
Depo retweetledi
Depo retweetledi

Laplace Transform essentials
Definition:
L[f(t)] = F(s) = ∫₀^∞ e^{-st} f(t) dt
Key formulae:
1. L{1} = 1/s
2. L{tⁿ} = n!/s^{n+1} (n=0,1,2…)
3. L{e^{at}} = 1/(s-a)
4. L{cosh(at)} = s/(s²-a²)
5. L{sinh(at)} = a/(s²-a²)
6. L{sin(at)} = a/(s²+a²)
7. L{cos(at)} = s/(s²+a²)
Used daily to solve ODEs, analyse control systems, circuits & signals.

Català
Depo retweetledi

Generating graphs via spectral diffusion is an emerging idea at the intersection of graph theory, stochastic processes, and geometric deep learning. The core principle comes from diffusion dynamics on graphs, often governed by the graph Laplacian:
∂u/∂t = −Lu
where L is the graph Laplacian. Spectral methods analyze the eigenvalues and eigenvectors of L, capturing the intrinsic geometry and connectivity structure of complex networks.
In machine learning, spectral diffusion is used for graph generation, clustering, manifold learning, and representation learning. In deep learning, Graph Neural Networks (GNNs), diffusion models, and spectral convolution methods use these ideas to propagate information smoothly across nodes while preserving global structure.
In reinforcement learning, spectral diffusion helps in state-space exploration, hierarchical planning, and learning over relational environments. Diffusion-based exploration strategies can reveal hidden connectivity patterns in large decision spaces.
The deeper insight is that learning on graphs is not just about nodes and edges —
it is about understanding how information flows through structure over time.
Image: share.google/ydR094gi1RMZCW…

English
Depo retweetledi
Depo retweetledi
Depo retweetledi

In the diagram is square ABCD. ∠CBE=∠BCE=15°. BD+BE=6cm. Determine the area of pentagon ABECD.
@tak62530522さんから

日本語



























