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A category is well-copowered if, for each object $C$, the epimorphisms $C\to Q$ have only a set of equivalence classes, where two are equivalent through an isomorphism of their codomains compatible with the quotient maps. Equivalently, its opposite category is well-powered.

In a complete category, the intersection of a set-indexed family of subobjects of $C$ is their wide pullback in a category over $C$. The resulting morphism into $C$ is a monomorphism, and it factors through every member. In a well-powered category, even the intersection of all subobjects satisfying a specified property is small.

An irradiated grain loses solid material by sublimation. Its lifetime depends strongly on temperature and composition. In a planetary dust tail, a short lifetime can truncate the distribution well before a relative orbital wrap.

A full subcategory contains all ambient morphisms between its chosen objects. Its inclusion is a fully faithful functor.

A replete subcategory contains every ambient object isomorphic to any of its objects. This closure is at the object level; fullness is a separate property.

For this quotient, homotopy groups through degree six and in degree eight vanish. The degree-seven and degree-nine groups are $\mathbb{Z}/2$. A map to $K(\mathbb{Z}/2,7)$ induces an isomorphism on degree-nine integral homology because the relevant square of the bottom mod-two class is nonzero. Two relative Hurewicz steps then compute degrees eight and nine.

Anchor a countable dense subset of a closed c-cyclically monotone support. Take the infimum of accumulated differences $c(x_{j+1},y_j)-c(x_j,y_j)$ along chains ending at a variable point. Cyclical monotonicity bounds this potential below on the support projection. Its cost transform gives the other potential, with equality on the support. Continuity makes both potentials upper semicontinuous and thus Borel.

For a nonnegative cost, simultaneous symmetric clipping preserves feasibility of a pair of Kantorovich potentials. On their equality set the clipped sums are nonnegative and increase to the cost. Their bounded integrals are fixed by the marginals, so monotone convergence theorem proves optimality without subtracting undefined infinite marginal integrals.

The leading massless-sector spacetime action is proportional to $\int \sqrt{-G}{e}^{-2\Phi }[R+4(\nabla \Phi {)}^2-H^2/12]$, with $H=dB$. The backgrounds are the string-frame metric, dilaton, and Kalb–Ramond field. Massive string exchange generates higher-derivative corrections ordered by ${\alpha }^{\prime }$. This perturbative bosonic massless-sector truncation does not remove the tachyon instability.

The canonical commutation relations imply $[N,{\alpha }_{-k}^i]=k{\alpha }_{-k}^i$ for $N={\sum }_{k>0}{\alpha }_{-k}\cdot {\alpha }_k$. With zero level assigned to the oscillator vacuum, a Fock space basis state containing $r_{ki}$ creation operators of each mode has level ${\sum }_{k,i}k{r}_{ki}$. The level is therefore a nonnegative integer; it counts oscillator excitation weighted by frequency, not simply the number of creation operators.

A stationary path law is ergodic when every time-shift-invariant event has probability zero or one. The Birkhoff ergodic theorem then identifies integrable time averages with deterministic ensemble expectations. A shared random scale multiplying iid noise gives a stationary counterexample: it is uncorrelated across distinct times, but the path retains information about the random scale.

Let ${\mathcal{F}}_{-\infty }^0$ and ${\mathcal{F}}_n^{\infty }$ be the sigma-algebras generated by the past and future observations. The mixing coefficient is $\alpha (n)={\sup }_{A\in {\mathcal{F}}_{-\infty }^0,B\in {\mathcal{F}}_n^{\infty }}|P(A\cap B)-P(A)P(B)|$. Strong mixing means $\alpha (n)\to 0$. Appropriate quantitative mixing and moment conditions can imply a central limit theorem; absolute summability of autocovariance alone does not.

If a twice differentiable real function on $\mathbb{R}$ satisfies $f^{\prime \prime }(x)\le -c$ for some fixed $c>0$, then it has a unique global maximum. The mean value theorem shows that $f^{\prime }$ is strictly decreasing, and that $f^{\prime }(x)+cx$ is nonincreasing. Hence $f^{\prime }(x)\le f^{\prime }(0)-cx$ for $x>0$ and $f^{\prime }(x)\ge f^{\prime }(0)-cx$ for $x<0$. These inequalities force opposite derivative signs at the two ends. The intermediate value theorem supplies a unique zero of $f^{\prime }$, at which $f$ changes from increasing to decreasing. Merely having $f^{\prime \prime }<0$ does not guarantee existence: $f(x)=-e^{-x}$ is increasing and approaches its unattained supremum zero.

Strict concavity is the displayed property for distinct $x,y$ in a convex set and $0<t<1$. It makes a stationary point of a differentiable function its unique global maximum, whenever such a stationary point exists.

With a finite set of at least three alternatives and unrestricted strict preference orders, every onto deterministic strategyproof social choice function is a dictatorship in social choice. Restricting to two alternatives or to a restricted preference domain removes essential hypotheses.

If a strategyproof social choice function selects $a$, raising $a$ in one agent's order without moving any formerly lower alternative above it preserves the outcome. A different outcome would make one of the changes between the old and new reports a profitable deviation. Arbitrary changes among alternatives wholly above or wholly below $a$ are allowed.

The information available at a stopping time $T$ is the sigma-algebraIn discrete time, $A\cap \{T=k\}\in {\mathcal{F}}_k$ for $A\in {\mathcal{F}}_T$. A bounded stopping time permits conditional forms of the optional stopping theorem with respect to this sigma-algebra.

Let $X_n$ be integrable independent and identically distributed random variables, and let $T\ge 1$ be a stopping time for their natural filtration, with $\mathbb{E}T<\infty$. Since $\{T\ge n\}$ is measurable before $X_n$ is observed, independence and the Tonelli theorem give $\mathbb{E}|X_T|\le {\sum }_n\mathbb{E}[|X_n|1_{\{T\ge n\}}]=\mathbb{E}|X_1|\mathbb{E}T$. The event $T=n$ need not be independent of $X_n$, so the selected increment can be biased.

A chain-binomial epidemic model uses discrete time, with conditionally independent binomial distributions for infection and recovery counts given the current compartments. It requires valid probability parameters; replacing $1-e^{-\beta I\delta t}$ by $\beta I\delta t$ is a small-step approximation.

With one initial infectious individual, two events require an infection as the first event. If it occurs at time $s$, its density is $\beta N{e}^{-(\beta N+\gamma )s}$ and the next total event rate is $2\beta (N-1)+2\gamma$. Integrating this density times the chance of another event before a fixed horizon gives the two-event probability.