Set $x_i=\log b_i$ for candidate axis-parallel box side lengths. Its coordinate-projection constraints are the displayed linear program. The dual feasible weights are fractional uniform covers. Its optimal extreme point is rational, so the uniform covers theorem forces its objective to be at least $\log |S|$. Weight one on the full coordinate set attains that value. Strong duality therefore supplies box side lengths with volume $|S|$ and no larger coordinate projections.

The equation $\frac{1}{2}{\sigma }^2{s}^{\prime \prime }+b{s}^{\prime }=0$ makes the scale transform a local martingale but does not alone prove the usual two-boundary hitting ratio. Stopped on $[a,b]$, it is bounded, so optional stopping gives that ratio when exit is almost surely finite and the endpoint scale values differ. If exit can be infinite, the terminal martingale has a further contribution on the non-exit event. For example, $\sigma =b=0$, $X_t=x\in (a,b)$ and $s(y)=y$ satisfy the differential equation but never hit either endpoint; the claimed ratio would nevertheless be positive. A constant solution for $s$ also gives an undefined denominator and is excluded by the usual strict-monotonicity convention for scale.

An encounter box is a finite connected set of open vertices whose removal from its infinite percolation cluster leaves at least three infinite connected components. Unlike a trifurcation vertex in percolation, it allows branching to occur across several vertices, which is useful for site percolation on graphs with local cycles. Contract disjoint encounter boxes to junctions. In a finite region, each junction must connect at least three different exits. Pruning to a forest gives at most as many junctions as boundary exits, by the identity ${\sum }_v(\mathrm{deg}(v)-2)=-2$ for a finite tree. Positive density of encounter boxes is therefore incompatible with an amenable graph.

For fixed $\alpha \in [0,1)$, design $x_i=i/n$, $m\in C^2[0,1]$, a nonzero nonnegative symmetric piecewise $C^1$ regression kernel supported on $[-1,1]$, and $h\to 0$, $n{h}^2\to \infty$, write ${\mu }_{r,\alpha }={\int }_{-\alpha }^1{u}^{r}K(u)du$ and ${\beta }_r={\mu }_{r,\alpha }/{\mu }_{0,\alpha }$. On the rescaled grid of spacing $1/(nh)$, the Riemann sum error for $u^{r}K(u)$ is $O((nh{)}^{-1})$, by bounded variation. The normalized first and second local moments are therefore $h{\beta }_1+O(n^{-1})$ and $h^2{\beta }_2+O(h/n)$. A uniform second-order Taylor expansion gives the displayed bias of an estimator, since $n^{-1}=o(h^2)$. Equivalently it is $h{\beta }_1{m}^{\prime }(0)+h^2(\alpha {\beta }_1+{\beta }_2/2)m^{\prime \prime }(0)+o(h^2)$. The first-order term explains the generic boundary bias of a local constant estimator.

A Boolean formula is in disjunctive normal form when it is a disjunction of conjunctions of Boolean literals. Width bounds the number of literals in each conjunction, not the number of conjunctions. A depth-$s$ decision tree gives such a formula of width at most $s$ by taking the disjunction of its accepting leaf conditions; rejecting leaves give the dual conjunctive normal form.

Inside IZF, truth values are subsets of a singleton. Their double-negation-stable elements form a complete Boolean algebra: arbitrary meets are intersections and arbitrary joins are double-negation closures of unions. Negation gives Boolean complement. The join of $p$ and its complement is top although their ordinary union need not be top. The closure here is taken inside the fixed singleton; it does not assume that the class of all objects double-negatively belonging to an arbitrary set is itself a set.

A Boolean algebra in which every set-indexed family has a join and a meet. Double-negation Boolean algebra is an intuitionistic example, with joins formed by regularizing unions. Completeness makes truth values for quantifiers possible in a Boolean-valued inner universe over intuitionistic set theory.

The lattice ideals of a Boolean algebra form a frame under inclusion. Meets are intersections and joins are ideals generated by unions. A complemented ideal is principal: if $I\vee J=B$ and $I\cap J=\{0\}$, choose $b\in I,c\in J$ with $b\vee c=1$; then $c=\neg b$ and $I=\downarrow b$. This ideal frame defines an extremally disconnected locale exactly when $B$ is complete.

Every proper lattice ideal of a Boolean algebra extends to a prime lattice ideal. More generally, an ideal disjoint from a lattice filter extends to a prime ideal still disjoint from that filter. This Boolean case of the Stone prime filter theorem is equivalent to the ultrafilter lemma over Zermelo–Fraenkel set theory and is weaker than the axiom of choice.

For a finite path in a Böhm tree, choose $q$ at least the head arity at every preceding node. Replace relevant variables by $U_q=\lambda z_1\cdots z_{q}w.w{z}_1\cdots z_q$. After exposing each node, supply its initial bound variables with $U_q$, pad its $m$ arguments to $q$, and apply the projection $\lambda z_1\cdots z_q.z_i$ selecting the next child. Each step returns that child with accumulated substitutions, so iteration reaches a substitution instance of the chosen subtree. Tupling preserves all children until selection, even when the same variable occurs at several nodes.

For metric $g=\Omega (d{x}^2+d{y}^2)$, set $F_{12}={\partial }_x{A}_y-{\partial }_y{A}_x$ and use the physical magnetic field $B=F_{12}/\Omega$. At critical coupling, define $j_i=\mathrm{Im}(\bar{\Phi }{D}_i\Phi )$. The identity $|D_x\Phi {|}^2+|D_y\Phi {|}^2=|(D_x+i{D}_y)\Phi {|}^2+F_{12}|\Phi {|}^2+{\partial }_x{j}_y-{\partial }_y{j}_x$ completes the energy into nonnegative squares plus half the magnetic flux and half the current boundary integral. For unit-magnitude vacuum boundary data, the combined boundary contribution is $\frac{1}{2}\oint (A+j)=\pi N$, since $j=d\arg \Phi -A$ there. In the more restrictive covariantly decaying sector the current integral separately vanishes and the flux is $2\pi N$. Equality for positive $N$ requires $(D_x+i{D}_y)\Phi =0$ and $B=(1-|\Phi {|}^2)/2$. The factor $\Omega$ appears instead if the symbol $B$ denotes the coordinate component $F_{12}$.

The holomorphic map $[Z_0:Z_1:Z_2]\mapsto [Z_1:Z_2]$ on ${\mathbb{CP}}^2\setminus \{[1:0:0]\}$ records the line through the missing point and the input point. Its graph closure is the incidence surface $Z_{1}b=Z_{2}a$ in ${\mathbb{CP}}^2\times {\mathbb{CP}}^1$, the blowup of a complex manifold at a point. Projection to $[a:b]$ extends the pencil holomorphically. On the exceptional divisor it is the identity map on the projective tangent directions.

The centralizer condition defining block induction refers to a defect group of a block of the actual source block. Containment of the centralizer of an unrelated p-subgroup does not suffice. For example, in characteristic $3$ take $G=A_5$, $H=A_4$ and $D=C_3$. Then $D{C}_G(D)\le H$, but the unique defect-zero block of $k{A}_4$ occurs on bimodule restriction of both three-dimensional defect-zero blocks of $k{A}_5$. The two ordinary degree-three characters have identical restriction to $A_4$, namely its degree-three character, and defect-zero reduction gives the same simple projective module. Thus the unique-block characterization fails if $D$ is not the defect group of the source block.

The Logistic cell-division process is a density-limited pure birth model on $\{0,1,\dots ,N\}$, where $N$ is a positive integer carrying capacity. With $k$ cells, each divides at rate $1-k/N$, so the total rate for $k\to k+1$ is $k(1-k/N)$. Zero and $N$ are absorbing; a positive initial population can only increase until it reaches $N$.

Let $Z_t^N=K_t^N/N$ and assume $Z_0^N\to p\in (0,1)$ in probability, for example $K_0^N=\lfloor Np\rfloor$. On every fixed finite interval $[0,T]$, $Z^N$ converges uniformly in probability toAn explicit martingale argument verifies this instance of the Kurtz fluid limit theorem. Compensating the jumps gives $Z_t^N=Z_0^N+{\int }_0^t{Z}_s^N(1-Z_s^N)ds+M_t^N$, with predictable quadratic variation at most $t/(4N)$. Doob's L2 maximal inequality gives $\mathbb{E}{\sup }_{s\le T}|M_s^N{|}^2\le T/N$. Since $z\mapsto z(1-z)$ is Lipschitz on $[0,1]$, Gronwall's inequality bounds the trajectory error by $e^T(|Z_0^N-p|+{\sup }_{s\le T}|M_s^N|)$, which tends to zero in probability.

The finite-population mean need not itself solve the logistic equation: $\mathbb{E}[Z(1-Z)]=m(1-m)-\mathrm{Var}(Z)$. The fluid limit follows because the fluctuations vanish on the specified finite horizons.

// Target: analysis.bigb

For a pure birth process with fixed initial state and finite statewise rates, the jump-count compensator gives $dM=(e^{\theta }-1)M_{-}[dN-\lambda (X_{-})dt]$, so $M$ is a local martingale up to explosion. If $\lambda \le C$, a rate-$C$ Poisson candidate clock with accepted jumps of probability $\lambda (X_{-})/C$ prevents explosion and dominates the jump count. Its finite exponential moments dominate every stopped $M$ on each finite horizon, upgrading the local martingale to a true martingale. A random initial state requires $\mathbb{E}{e}^{\theta X_0}<\infty$; uniformly bounded rates do not supply this missing initial integrability.

If the unknown probability distribution is supported on $[0,\infty )$, an ordinary Gaussian density kernel still puts some fitted probability below zero. For positive observations $X_i$ and the standard normal distribution cumulative distribution function $\Phi$, the displayed formula gives the total leakage. For the statistical sample $(0.1,0.4,0.9)$ and smoothing bandwidth $h=0.5$, it isThus about $22.28\%$ of the fitted probability falls outside the known support of a probability distribution, even though the full integral remains one. The leakage panel in smoothing bandwidth shades this area. Simply deleting the negative part leaves area $0.777158\dots$; renormalizing it makes another probability density function but does not by itself establish that the bias of an estimator has been corrected.

The issue also appears in the expectation of the kernel density estimator. For a nonnegative symmetric kernel for density estimation and a probability density function $f$ that is zero for $x<0$ and has a finite positive right-hand limit $f(0+)$, assume $f$ is bounded. Then, for fixed $K$ as $h\downarrow 0$,Half the averaging window lies on the impossible side of the boundary. For example, the rate-one exponential distribution has $f(0+)=1$, so the limiting expected boundary height is $1/2$, rather than the right-hand height one. This refers to estimating the right-hand limit; changing a probability density function at a single point does not change its probability distribution. Decreasing the smoothing bandwidth narrows the affected region but does not remove this boundary-height error. This is why the interior second-order pointwise bias bound for kernel density estimation cannot be applied across a jump at the boundary.

An adjoint-invariant symmetric bilinear form $B$ on a Lie algebra makes the displayed tensor alternating. If $B$ is invariant under the full group Adjoint representation, its left-translated three-form is also right-invariant. On left-invariant fields, the exterior derivative is $d\eta (W,X,Y,Z)=-2B(W,[X,[Y,Z]]+[Y,[Z,X]]+[Z,[X,Y]])=0$ by the Jacobi identity. The Killing form supplies such a $B$ on every semisimple Lie group. An overall sign change in the lowered structure-constant convention negates the form but not its invariance or closedness.

Replace two p-values $P_i,P_j$ by zeros in the Benjamini-Hochberg procedure at level $\alpha$, keeping the original denominator $m$, and let $R^{(ij)}$ be the resulting rejection count. Equivalently, remove the two values and apply the step-up critical values $\alpha (k+2)/m$ to the remaining ordered p-values; their rejection count is $R_{-ij}=R^{(ij)}-2$. For $r\ge 2$,If both original values are rejected, lowering them changes no ordered rank greater than $r$, so the rejection count stays $r$. Conversely, if the modified count is $r$ and both original values meet the rank-$r$ threshold, reinserting them leaves at least $r$ qualifying values. Monotonicity under lowering gives at most $r$ original rejections. This identity is deterministic and does not require independence or a distributional assumption.

For a subset of the unbiased Boolean hypercube of density $\alpha$, use normalized Fourier-Walsh transform coefficients. Applying Beckner's inequality with $\rho =1/\sqrt{3}$ gives $\Vert T_{\rho }{1}_A{\Vert }_2^2\le \Vert 1_A{\Vert }_{4/3}^2={\alpha }^{3/2}$. On levels at most two the weights ${\rho }^{2|S|}$ are at least $1/9$, giving the displayed bound. Parseval's identity gives total Fourier mass $\alpha$, so when $9\sqrt{\alpha }<1/2$ the higher levels contain strictly more than the lower levels.

The coefficient $b$ is the leading relative null-mean discrepancy of a regular likelihood-ratio test statistic. It is distinct from the finite-sample divisor $1+b/n$ used in a Bartlett correction. Different conventions call either of these a correction factor, so the normalization should always be stated explicitly.