SearcharxivSearch

arXiv subjects

Everett Fall

Publications and source records attributed to Everett Fall.

2 recordsLinked to original sources

Derivation of the Born Rule and Operational Quantum Formalism in the Accessibility Framework through Boundary Reduction

We show that the operational quantum formalism -- the Born rule, L\"uders state updating, quantum interference, non-Markovian effective dynamics, and Bell inequality violation at the Tsirelson bound $2\sqrt{2}$ -- arises within Accessibility Theory (AT) from the Aperture construction together with explicit coherence and locality assumptions stated in the paper. AT is a framework built on real graded spectral triples and a single algebraic selection principle. The Principle of Universal Accessibility Balance requires three independent measures of the complexity of a spectral triple -- its algebraic, gauge-theoretic, and geometric content -- to be exactly equal and minimized, uniquely selecting the algebra $\mathbb{C} \oplus \mathbb{H} \oplus M_3(\mathbb{C})$ and with it the Standard Model gauge group, particle content, four-dimensional Lorentzian spacetime, three generations, and gravitational dynamics. Restriction to a codimension-one geometric boundary reduces this algebra to its commutative center $\mathbb{C} \oplus \mathbb{C} \oplus \mathbb{C}$ -- the Aperture -- which defines a permanent information bottleneck for any embedded observer. Coherence conditions on inference through this bottleneck, together with Gleason's theorem on the 48-dimensional internal Hilbert space, uniquely determine the Born rule; the remaining operational features follow from the same observer-level framework under the stated assumptions. At the ontological level the theory is deterministic and state-realist, while the operational quantum formalism appears at the observer level as a consequence of structurally limited access to the underlying algebraic reality.

quant-ph

Dynamically Expanded CNN Array for Video Coding

Video coding is a critical step in all popular methods of streaming video. Marked progress has been made in video quality, compression, and computational efficiency. Recently, there has been an interest in finding ways to apply techniques form the fast-progressing field of machine learning to further improve video coding. We present a method that uses convolutional neural networks to help refine the output of various standard coding methods. The novelty of our approach is to train multiple different sets of network parameters, with each set corresponding to a specific, short segment of video. The array of network parameter sets expands dynamically to match a video of any length. We show that our method can improve the quality and compression efficiency of standard video codecs.

eess.IV