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An inhibitor in an activator-inhibitor reaction–diffusion system suppresses the activator. Its diffusion and reaction feedback can stabilize a spatially uniform state while allowing some nonzero wavenumbers to grow through a Turing instability.

A quadratic activator can have both basal production and activation inhibited by a second species. A normalized example has reaction rates $f(u,v)=1+r{u}^2/v-u$ and $g(u,v)=q(u^2-v)$, with $r,q>0$ and positive concentrations. At its spatially homogeneous equilibrium, $v=u^2$, so $u_{\ast }=1+r$ and $v_{\ast }=(1+r{)}^2$. Writing $a=(r-1)/(r+1)$, its reaction Jacobian matrix is $J=\left(\begin{array}{cc}a& -r/(1+r{)}^2\\ 2q(1+r)& -q\end{array}\right)$. Thus $\mathrm{tr}J=a-q$ and $\det J=q>0$, giving linear asymptotic stability when $q>a$ and instability when $q<a$. Basal production makes $a$ change sign at $r=1$; a positive activation feedback needed for a conventional Turing instability requires $r>1$.

The scalar equation $u_t=D{u}_{xx}+u(1-u)(u-r)$ with $D>0$ and $0<r<1$ has two stable homogeneous phases $u=0,1$ and an unstable intervening equilibrium $u=r$. Indeed the reaction derivatives at these three equilibria are $-r$, $-(1-r)$ and $r(1-r)$, respectively. A travelling wave can connect the two stable phases. At the equal-depth value $r=1/2$, its decreasing heteroclinic orbit is stationary. Away from this value the phases have unequal potential depths and the front propagates.

Take upward-closed set families of subsets of $[n]$ of size at most $\ell$, closed under the forcing rule in Razborov closure. Normalize any family containing a set of size at most one to the full family, since its clique predicate is identically true. Intersection is the meet in a lattice; closure of the union is the join in a lattice. A family represents the graphs containing a clique on at least one of its members. The approximate meet can lose positive inputs and the approximate join can acquire negative inputs. The Razborov gate-by-gate approximation lemma charges output errors to those local errors.

For a real symmetric matrix $A$ and nonzero real vector $x$, the quotient rule and the quadratic form gradient give the displayed gradient. It vanishes exactly when $Ax=R_A(x)x$, so the stationary points are eigenvectors. Equivalently, the Lagrange multiplier condition for optimizing $x^{T}Ax$ on the unit sphere is $Ax=\lambda x$. The smallest and largest eigenvalues give the global extrema; other eigenvectors can be stationary without being extrema, as a direction mixing larger and smaller eigenvalues changes the quotient in opposite ways.

For a finite-dimensional Hermitian matrix $A$, the Rayleigh quotient has precisely the range $[{\lambda }_{\min },{\lambda }_{\max }]$, where the endpoints are its extreme eigenvalues. The finite-dimensional spectral theorem writes a nonzero vector as $x={\sum }_j{c}_j{e}_j$ in an orthonormal eigenbasis, givingThus the quotient is a convex combination of eigenvalues. The bounds follow immediately, and equality at either endpoint means that $x$ lies in the corresponding extreme eigenspace, including any repeated eigenvalue. Every intermediate value occurs: for orthonormal extreme eigenvectors, $x=\sqrt{1-t}{e}_{\min }+\sqrt{t}{e}_{\max }$ gives $R_A(x)=(1-t){\lambda }_{\min }+t{\lambda }_{\max }$ for $0\le t\le 1$. If all eigenvalues are equal, the quotient is constant.

The Hermitian hypothesis matters. The real matrix $B=\left(\begin{array}{cc}1& 4\\ 0& 1\end{array}\right)$ has only the eigenvalue $1$, but $(1,1)B(1,1{)}^T/2=3$. Its quotient is governed by the symmetric part of a matrix $(B+B^T)/2$, not by a real orthonormal eigenbasis of $B$. A nonsymmetric matrix therefore need not obey the displayed eigenvalue bounds.

For a nonzero real solution of ${\varphi }^{\prime \prime }+(C-k^2)\varphi =0$ on a finite interval, integration by parts gives $\mathcal{R}=\int (C{\varphi }^2-{\varphi }^{\prime 2})dz/\int {\varphi }^{2}dz$ in the displayed form. A Neumann boundary condition only at the lower endpoint does not remove the upper boundary term. Equality with $k^2$ additionally requires $\varphi (z_2){\varphi }^{\prime }(z_2)=0$. With the lower condition, nonconstant oscillatory solutions satisfy this when their phase advance is an integer multiple of $\pi /2$; affine solutions must be constant, while nonzero hyperbolic-cosine solutions fail the condition. Infinite intervals additionally require convergence of both integrals.

For gas pressure depending only on radius and initial speed zero, multiplying the Rayleigh equation for an inviscid spherical bubble by $2R^2\dot{R}$ integrates to the displayed identity. A polytropic pressure $p_g=K/R^{3\kappa }$ gives elementary power integrals when $\kappa \ne 1$, and a logarithm when $\kappa =1$. Turning radii are the positive zeros of this first integral. At $\kappa =2$ and $p_g(R_0)=p_{\infty }/2$, they are $R_0$ and $R_0/2^{1/3}$; the inward and outward accelerations at them have opposite signs, giving bounded undamped oscillation in the ideal model.

For spherical expansion in an unbounded incompressible flow, continuity gives $u_r=R^2\dot{R}/r^2$. Integrating the kinetic energy outside the bubble gives $K=2\pi \rho R^3{\dot{R}}^2$. The Rayleigh equation for an inviscid spherical bubble then yields $\dot{K}=(p_b-p_{\infty })\dot{V}$, where $V=4\pi R^3/3$. With $p_b=p_{\infty }{V}_0/V$ and constant $p_{\infty }$, integration gives $K-K_0=p_{\infty }[V_0\log (V/V_0)-V+V_0]$. This neglects viscosity, surface tension and external body-force differences, which contribute additional terms in other bubble models.

A high-Rayleigh number closure $\mathrm{Nu}=C{\mathrm{Ra}}^{1/3}$ gives $q=C{k}_T[g\beta /(\nu \kappa ){]}^{1/3}(\Delta T{)}^{4/3}$, independent of layer depth. It assumes boundary-layer-controlled heat transfer, a well-mixed interior, fixed material properties and a fixed Prandtl number regime. It is a model, not a universal asymptotic law for every turbulent convection regime or for an impulsive initial conductive transient.

In the simply connected homotopy category, a rational space is a simply connected CW complex whose positive homotopy groups are vector spaces over $\mathbb{Q}$. Multiplication by every nonzero integer on these groups is invertible. More generally one uses nilpotent spaces with localized fundamental group and higher homotopy groups; the simply connected version avoids local-coefficient twisting.

A connected space is nilpotent when its fundamental group is nilpotent and its action on each higher homotopy group is nilpotent: the module has a finite filtration whose successive quotients have trivial group action. Simply connected spaces satisfy these conditions automatically. They are the natural setting for localization and rationalization, since fibrewise coefficient localization is compatible with the successive untwisted stages of such a filtration.

For $M={⨁}_j(F_q[t]/(f^j){)}^{m_j}$ with $f$ irreducible of degree d and partition $\lambda =(j^{m_j})$, its automorphism group has order $q^{d{\sum }_i({\lambda }_i^{\prime }{)}^2}{\prod }_j{\prod }_{r=1}^{m_j}(1-q^{-dr})$. The endomorphism algebra has dimension $d{\sum }_{a,b}\min (a,b)m_a{m}_b=d{\sum }_i({\lambda }_i^{\prime }{)}^2$; its semisimple quotient is ${\prod }_j{M}_{m_j}(F_{q^d})$, and units are precisely lifts of units in that quotient.

A finite bounding rectangle exists when $\sqrt{h(x)}$ and $x\sqrt{h(x)}$ are bounded. This is not automatic for every probability density function: tails proportional to $(1+|x|{)}^{-3/2}$ give an unbounded second coordinate. In principle any finite-area measurable region has an open envelope of finite area, decomposable up to null boundaries into disjoint dyadic rectangles. Select a rectangle with probability proportional to its area, then sample its two coordinates uniformly and reject outside the target region. The accepted point is uniform on the region, with acceptance equal to the ratio of their areas. Constructing an effective envelope remains a density-specific computational step.

For the unnormalized normal density $h(x)=e^{-x^2/(2{\sigma }^2)}$, the smallest axis-aligned envelope is $0<u<1$ and $|v|\le \sigma \sqrt{2/e}$. The boundary reaches that width at $u=e^{-1/2}$. Region area is $\sigma \sqrt{\pi /2}$, so the acceptance rate is $\sqrt{\pi e}/4$, independently of $\sigma$. Multiplying $h$ by a positive constant rescales both coordinates and their areas consistently.

For the unnormalized Laplace distribution probability density function $h(x)=e^{-|x-1|/2}$, the ratio-of-uniforms method uses $0<u\le \sqrt{h(v/u)}$. The maximum of $h$ is one. On $x\le 0$, the maximum of $x^{2}h(x)$ occurs at $x=-4$ and equals $16e^{-5/2}$. On $x\ge 0$, it occurs at $x=4$ and equals $16e^{-3/2}$. Thus the displayed rectangle contains the region. Its area is $4(e^{-3/4}+e^{-5/4})$, while the target region has area $\frac{1}{2}\int h=2$. Uniform proposals in the rectangle, accepted when $u^2\le h(v/u)$, therefore return the correct Laplace distribution with acceptance probability $[2(e^{-3/4}+e^{-5/4}){]}^{-1}$.

Set $G_n=U_n-\theta V_n$, and define the estimator on zero-denominator events as part of the model. On $V_n>\delta >0$, its error is at most $|G_n|/\delta$. Centering $G_n$ and applying the Cauchy-Schwarz inequality gives the displayed bound, where $r_n$ is the expected error on $V_n\le \delta$. This controls a ratio estimator without falsely assuming that its numerator and denominator are independent.

For the finite alphabets considered here, the zero-rate distortion threshold is the smallest expected distortion obtainable by always outputting one fixed reconstruction. Such a reconstruction is independent of the source, so its mutual information is zero. A randomized output that is independent of the source averages the costs of fixed reconstructions and cannot improve their minimum. Thus $R(D)=0$ exactly when $D\ge D_{\mathrm{zero}}$. For a fair bit under Hamming distortion, $D_{\mathrm{zero}}=1/2$; for a Bernoulli random variable of parameter $p$, it is $\min \{p,1-p\}$. The threshold depends on the source probability distribution, rather than only the alphabet.

For a finite alphabet source with independent and identically distributed symbols, a finite reconstruction alphabet, and a bounded nonnegative separable distortion, the rate-distortion function is the asymptotic limit for fixed-length lossy codes. In particular, for a distortion target in the interior of the feasible range and every $\epsilon >0$, sufficiently long block codes exist with rate at most $R(D)+\epsilon$ and expected distortion at most $D+\epsilon$. Every such $n$-symbol code obeys $R_n\ge R(D_n)$, where $D_n$ is its actual expected distortion.^[ref]

The outline is instructive. The data processing inequality bounds the mutual information in a reconstruction by the information entropy of the transmitted message, which is at most ${\log }_2{M}_n$. Independence and the convexity of the rate-distortion function then give the per-symbol lower bound. For achievability, choose many reconstruction blocks using a suitable probability distribution, so that the source's typical blocks are likely to have an acceptable nearby reconstruction. This is a covering argument; it does not require recovering the original block exactly.

A fixed-length lossy code maps a block of $n$ source symbols to one of $M_n$ messages, then maps that message to a reconstruction block. The encoder and decoder together define the reconstruction. Its rate is ${\log }_2{M}_n/n$ bits per source symbol; an implementation using a fixed binary message needs $\lceil {\log }_2{M}_n\rceil$ transmitted bits per block. Its performance also depends on expected distortion, block length, and the chosen distortion measure.