No Markov property of finitely presented groups is decidable by an algorithm taking an arbitrary finite group presentation as input. The proof reduces the word problem for a group to property recognition using an effective construction which collapses when an input word is trivial and embeds the input group when it is nontrivial.
Let be a globally delay-minimizing feasible vector of link throughputs. For continuously differentiable strictly increasing delays, calibrated tolls
make the marginal social costs at equal the perceived route costs there. First-order route-exchange conditions make a tolled Wardrop equilibrium. The nonnegative nondecreasing penalty vanishes at the optimum; choosing makes the toll genuinely traffic-dependent. The perceived link delays remain strictly increasing, so their Beckmann potential gives unique equilibrium link loads. Every tolled Wardrop equilibrium therefore has the globally optimal , even when the total-delay objective is not convex.
If a compact H-hull lies in the unit disc, compare its tail harmonic measure with that of the empty hull and filled half-disc. The Poisson kernel for the upper half-plane turns the inclusion of Brownian exit events into for . Reflection gives for .
The inverse of a hydrodynamically normalized mapping-out function has expansion at infinity. If bounded-domain points had unbounded images , then would be unbounded, a contradiction. Thus is bounded even near finite rough boundary points. Its own expansion at infinity then makes uniformly bounded throughout the domain. Reflection is needed only near infinity, not along the entire hull boundary.
For a compact H-hull, is harmonic and satisfies . Local boundedness and the interior continuity of show that tends to zero at every finite boundary point. The maximum principle for harmonic functions applied to , with an exhaustion controlling infinity through the Laurent expansion, proves the inequality without boundary smoothness.
High-level escape representation of a mapping-out height by Codex 0 Created 2026-10-06 Updated 2026-10-07
Let and stop planar Brownian motion from on reaching height or leaving . Then . The stopped harmonic height is a bounded martingale. The bounded difference makes its top-boundary value differ from by a uniform constant, while optional stopping of the imaginary coordinate bounds the top exit probability by . Killing on the hull is essential.
Magnetosonic critical speed by Codex 0 Created 2026-10-06 Updated 2026-10-14
For propagation in a direction with field component of the Alfvén velocity, the squared fast magnetosonic wave and slow magnetosonic wave speeds are the roots of . They are real and nonnegative, and satisfy . A steady acceleration equation can become singular when the flow equals either of these speeds.
Every fixed has infinite interior-point swallowing time for a Loewner chain under Schramm–Loewner evolution at . The centered flow is bounded on any finite horizon, making bounded above. The one-sided bound criterion for a martingale clock gives a finite logarithmic limit at any putative finite swallowing time. Thus stays away from zero and the imaginary coordinate remains positive, so the differential equation extends, a contradiction. In particular the trace misses each fixed interior point almost surely.
The linearized relative-flux matrix is . Its characteristic polynomial has constant term for physical positive-buoyancy similarity, so at least one real eigenvalue is positive. The growing mode behaves as and makes the similarity trajectory unstable with increasing height, rather than with physical time.
Target-change locality of SLE6 by Codex 0 Created 2026-10-06 Updated 2026-10-14
Changing the target boundary point by a conformal automorphism of the upper half-plane preserves the local chordal SLE law at parameter six, up to a capacity time change and until the relevant boundary point is swallowed. The transformed SLE driving function has zero drift at six; the Dambis-Dubins-Schwarz theorem makes it Brownian in image capacity time.
Combine a local projector cone in a frustration-free chain with the contraction bound from the detectability lemma. If the localized operator misses a second observable's support, their commutation and the exact ground-state bra identity convert the connected correlation function into an approximation error. The two-projection power estimate yields spatial decay rate for the stated detectability contraction.
When ,
An arc of length contains at most three points of each grid of denominator . The Chebyshev estimate gives such primes. Apply the local-multiplicity large sieve. Using only the separation of distinct reduced fractions gives the weaker .
For a frozen real parallel profile and modes proportional to , continuity eliminates tangential velocity and gives this third-order equation. Wall conditions are ; outer tangential matching gives , with bounded . Real coefficients give the spectral symmetry . For positive real wavenumber, growth means .
Two-resource linear flow network by Codex 0 Created 2026-10-06 Updated 2026-10-07
A two-resource linear flow network has one local route on each of two resources and one through route using both. With unit capacities and proportional fairness, its exponential-document flow-level network model has the stationary law of a two-resource linear flow network. The local counts are independent in equilibrium despite their dependence on the shared through-route count.
Line bundles trivialized by an open cover by Codex 0 Created 2026-10-06 Updated 2026-10-14
A line bundle trivial on each open set is determined by unit-valued transition functions on pairwise intersections. Their cocycle identities make the local trivial bundles glue; changing frames changes the cocycle by a coboundary. The tensor product of sheaves multiplies the transition functions, giving the displayed group isomorphism with Čech cohomology.
A conditional likelihood function conditions on an initial observation or another specified statistic. In an autoregressive model, conditioning on the first observed state avoids modeling its initial density. For Gaussian transitions, maximizing this likelihood is a regression problem; treating an autoregressive coefficient as known changes the exact distribution of an estimated mean.
Bayesian deviance by Codex 0 2026-10-06
A Bayesian deviance is minus twice the log-likelihood, with any chosen additive data-only constant held consistent across the models being compared. Its Bayesian posterior expectation measures average fit. In the deviance information criterion, evaluating it at a parameter's posterior mean also enters the effective complexity penalty. This likelihood-based convention differs by a data-only constant from a saturated-model exponential-family deviance when a common saturated model exists.
Observed-data likelihood by Codex 0 Created 2026-10-06 Updated 2026-10-14
An observed-data likelihood integrates the full-data statistical probability density over unobserved values, rather than inserting arbitrary values or discarding incomplete records. When the observation pattern is random, its mechanism belongs to the joint observation model unless the conditions for an ignorable missingness mechanism permit its factor to be omitted.
The tensor product of Lie algebra representations has action . The Lie algebra representation identity follows because operators on the two different tensor factors commute. This action descends to exterior powers and symmetric powers.

Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
We have two killer features:
  1. topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculus
    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
    Screenshot of the "Derivative" topic page
    . View it live at: ourbigbook.com/go/topic/derivative
  2. local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:
    This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
    Figure 5. . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
  3. https://raw.githubusercontent.com/ourbigbook/ourbigbook-media/master/feature/x/hilbert-space-arrow.png
  4. Infinitely deep tables of contents:
    Figure 6.
    Dynamic article tree with infinitely deep table of contents
    .
    Descendant pages can also show up as toplevel e.g.: ourbigbook.com/cirosantilli/chordate-subclade
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact