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The nonzero-sum two-cycles of the cubic swap map exist for $a>3$. Their two-step characteristic polynomial has trace $T$ and determinant $D$ as displayed. Since $1-T+D=-2(a-3)(a+1)<0$, one periodic-orbit multiplier exceeds one, proving instability throughout. The other crosses minus one at $a=(1+\sqrt{29})/2$; the cycles are saddles before that threshold and repelling afterwards. At $a=3$ they meet the fixed branches in an unstable subcritical-flip branch.

The alphabets assigned to participants determine their shared CSP events. An event in both assigned alphabets requires both participants; one assigned only to a single participant advances that participant. Replicated composition synchronizes all participants whose assigned alphabets contain the event.

A state with no outgoing CSP internal action. A CSP stable failure records a refusal only after such a state has been reached.

The first visible CSP event resolves the choice; the environment can select an event offered by only one branch. CSP internal actions can advance a branch without selecting it.

The process may internally choose either branch, independently of the environment's offered CSP events. Consequently it can have more CSP stable failures than the corresponding CSP external choice, despite having the same CSP traces.

Water incorporated into the structure of a crystalline hydrate. Loss on drying can test hydration or excess moisture.

Purification by dissolving a crystalline chemical substance and forming new crystals under conditions that leave impurities in the mother liquor or permit their prior removal.

Historical pharmaceutical preparation; its composition and clinical use must be identified by edition. ^[ref]

The botanical source of croton oil.

Historical formulation name; composition is identified by pharmacopoeial edition.

Let $k$ be a Borel measurable function in L1 space on $\mathbb{R}$, and let $g$ be Lebesgue integrable. The function $f(x,y)=k(x-y)g(y)$ is measurable. Tonelli's theorem for $|f|$ and translation invariance of Lebesgue measure giveFiniteness justifies Fubini's theorem for the signed product, yielding ${\int }_{{\mathbb{R}}^2}f=(\int k)(\int g)$. In particular an integrable odd function $k$ makes this signed total zero while the joint L1 norm can remain positive. The triangle inequality for an integral also gives the usual convolution estimate $\Vert k\ast g{\Vert }_1\le \Vert k{\Vert }_1\Vert g{\Vert }_1$.

For an external viewpoint in general position, the horizon is the cycle of edges separating visible and nonvisible faces of a convex polyhedron. In the incremental three-dimensional convex hull algorithm, retain these edges, remove visible faces, and join the viewpoint to each horizon edge to form the new faces. Edges incident with two removed faces disappear.

A continuous dense map into a compact Hausdorff space assigns to each ultrafilter on its domain the unique limit of its pushforward. This limit map is surjective by the ultrafilter characterization of image closure. It is injective exactly when disjoint domain subsets have disjoint image closures.

For real random variables on one probability space, fix a continuity point $x$ of the cumulative distribution function $F_X$. For every $\epsilon >0$, inclusions of events giveTake the lower and upper limits as $n\to \infty$, using convergence in probability, and then let $\epsilon \downarrow 0$. Continuity at $x$ makes both bounds $F_X(x)$, proving convergence in distribution. The two modes of convergence have different information: convergence in probability concerns the coupling on one probability space, whereas convergence in distribution concerns only the marginal probability distributions.

Modulo equality almost everywhere, the displayed metric induces convergence in measure and is complete. A sequence Cauchy in measure has a strictly increasing subsequence $f_{n_k}$ with $\mu (|f_{n_{k+1}}-f_{n_k}|>2^{-k})<2^{-k}$. Subadditivity makes the limsup of these exceptional sets null, so the subsequence has summable pointwise successive differences almost everywhere. It therefore has a finite measurable limit. Almost-everywhere convergence implies convergence in measure for finite measure, and the triangle inequality plus the original Cauchy property extends convergence to the full sequence.

For the metric equivalence, $d(f_n,f)\le \epsilon \mu (E)+\mu (|f_n-f|>\epsilon )$ for $0<\epsilon <1$, while $\mu (|f_n-f|>\epsilon )\le d(f_n,f)/\min \{1,\epsilon \}$. The same estimates compare the two Cauchy properties. Define the subsequence limit as zero where no finite limit exists; that exceptional set is measurable by the countable Cauchy criterion, so completeness of the underlying measure is unnecessary.

On the maximum product metric, the displayed map is nonexpansive when $f$ is a contraction mapping. If the space has two distinct points, changing only the second input coordinate shows that one step preserves some nonzero distance exactly, so it is not itself a strict contraction. Two iterations apply $f$ to both coordinates and therefore contract with the same constant as $f$. This provides a direct example of a map whose iterate is contractive although the map is not.

A random variable with a probability density function $f$, so its cumulative distribution function is $F(x)={\int }_{-\infty }^{x}f(t)dt$. Equivalently, its law is an absolutely continuous probability distribution. In particular, the probability of every singleton is zero.

A probability distribution is singular continuous if it has no point masses and assigns probability one to a set of zero Lebesgue measure. Its cumulative distribution function is continuous, but the distribution has no probability density function with respect to Lebesgue measure. This distinguishes continuity of the cumulative distribution function from an absolutely continuous probability distribution, whose probabilities are integrals of a probability density function.

The unit-radius Wigner semicircle distribution has the displayed probability density function. Its graph is a rescaled upper semicircle. The area of the unit semicircle is $\pi /2$, so the factor $2/\pi$ normalizes it. Symmetry gives mean zero, and the substitution $x=\sin \theta$ gives variance $1/4$.

A scaled radius-$R$ version has density $2\sqrt{R^2-x^2}/(\pi R^2)$ on $(-R,R)$ and variance $R^2/4$. Rejection sampling from a uniform proposal on $(-1,1)$ accepts a proposed $x$ with probability $\sqrt{1-x^2}$; its average acceptance probability is $\pi /4$. Thus the number of proposals for one output has geometric distribution with expectation $4/\pi$. This law also describes the limiting empirical eigenvalue density in standard ensembles of large symmetric random matrices.