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Black-hole evaporation is mass loss through Hawking radiation. For a large isolated Schwarzschild black hole, the leading mass dependence is $dM/dt\propto -M^{-2}$ and the evaporation timescale is proportional to $M^3$. This semiclassical scaling cannot determine the endpoint when the curvature reaches quantum-gravitational scales.

The first integral singular homology of the Hawaiian earring has a direct summand isomorphic to $\mathbb{Q}$. This consequence of its structural homology theorem suffices, through the universal coefficient theorem for cohomology and nonzero Ext of the rationals with integer coefficients, to show that its integral $H^2$ is nonzero. The precise structural theorem is Theorem 3.1 of www.sas.rochester.edu/mth/sites/doug-ravenel/otherpapers/eda-kawamura2.pdf.

For a full-rank ordinary least squares design matrix, the hat matrix $H$ and residual projection $G=I-H$ satisfy $HG=GH=0$. Hence fitted values and regression residuals are perpendicular as observed vectors. Under isotropic errors their cross-covariance matrix is zero, and under a normal linear model the two vectors are independent.

The Hartmann number measures the strength of magnetic coupling relative to viscous and resistive effects in a channel:Here $L$ is the channel half-width, $\nu$ the kinematic viscosity, $\eta$ the magnetic diffusivity and ${\sigma }_c$ the electrical conductivity. In Hartmann flow, large $H$ produces thin magnetic-viscous wall layers and a flatter, slower core for fixed pressure gradient.

A Hartmann layer is the thin viscous-magnetic boundary layer next to a wall crossed by the applied magnetic field. For channel half-width $L$ and large Hartmann number $H$, its thickness is $\delta \sim L/H=\sqrt{{\mu }_0\rho \nu \eta }/|B_0|$. It restores the no-slip boundary condition and the specified magnetic wall condition outside the nearly uniform Hartmann flow core.

For a streamwise pressure gradient $-\rho G$ and walls at $z=\pm L$ with zero tangential magnetic field, the volumetric flow rate per unit span isIts zero-field value is $2G{L}^3/(3\nu )$; it decreases strictly for $H>0$ and is asymptotic to $2G{L}^3/(\nu H)$. The partial-fraction expansion of the hyperbolic cotangent proves monotonicity directly.

The attaching sphere of an index-$h$ handle $D^h\times D^{n-h}$ is $S^{h-1}\times \{0\}$ in the previous boundary. Its framing of an embedded sphere identifies a neighbourhood with $S^{h-1}\times D^{n-h}$ and specifies the handle attachment.

The belt sphere of an index-$h$ handle is $\{0\}\times S^{n-h-1}$ in the boundary after attachment. It is the attaching sphere of the dual index-$(n-h)$ handle. Its intersections with the attaching spheres of later handles detect passages through this handle.

A handle slide replaces the framed attaching sphere of one handle by a band sum with a parallel copy of another handle of the same index. It changes the attaching data while preserving the diffeomorphism type of the resulting manifold. On the handle incidence matrix it performs an elementary row operation, or a column operation when applied to the dual decomposition.

Suppose $v_i=\pm 1/\sqrt{n}$ encodes the true partition and $\hat{v}$ is a unit estimated eigenvector. Every coordinate of the wrong sign contributes at least $1/n$ to ${\min }_{\sigma =\pm 1}\Vert \hat{v}-\sigma v{\Vert }_2^2=2(1-|{\hat{v}}^{\top }v|)$. Since this is at most $2(1-|{\hat{v}}^{\top }v{|}^2)$, which is the squared Frobenius distance between the rank-one orthogonal projection matrices, the bound follows. Coordinates estimated as zero may be assigned consistently to either group.

An inclusion-maximal separated set of separation $an$ covers the Hamming cube by strict-radius $an$ balls. The Hoeffding lower-tail bound for Hamming balls then gives at least $e^{2n(1/2-a{)}^2}$ selected vertices. At $a=1/8$ this yields $e^{9n/32}$ vertices, all separated by at least $n/8$.

For $0<\rho \le 1/2$, the number of binary words within Hamming distance $\rho n$ of a fixed word is at most $e^{nh(\rho )}$, with the binary entropy function in natural logarithms. In $1={\sum }_j\left(\genfrac{}{}{0ex}{}{n}{j}\right){\rho }^j(1-\rho {)}^{n-j}$, each summand weight for $j\le \rho n$ is at least ${\rho }^{\rho n}(1-\rho {)}^{n(1-\rho )}=e^{-nh(\rho )}$. Also $h^{\prime \prime }(\rho )=-1/(\rho (1-\rho ))\le -4$, so $\log 2-h(1/2-a)\ge 2a^2$. These two bounds give the approximate-recovery denominator in the list-decoding Fano inequality.

For a uniform vertex of the Hamming cube, distance from a fixed vertex is the sum of $n$ independent Bernoulli random variables with parameter $1/2$. The lower-tail Hoeffding inequality implies that the fraction of vertices at distance at most $an$, $0<a<1/2$, is at most $e^{-2n(1/2-a{)}^2}$. Strict-radius balls satisfy the same upper bound.

Surrogate Hamiltonian Monte Carlo uses an auxiliary smooth probability density function $\nu$ to generate trajectories but applies a Metropolis–Hastings acceptance probability using the intended target $\mu$. For Gaussian momenta and an involutive Metropolis proposal generated by flipped leapfrog integration, the acceptance probability is $\min (1,\mu (x^{\prime })e^{-\Vert p^{\prime }{\Vert }^2/2}/[\mu (x)e^{-\Vert p{\Vert }^2/2}])$. Only the surrogate gradient is required during the trajectory; target density evaluations are still required at the endpoints.

At level $J$, a Haar scaling function is the normalized indicator function ${\varphi }_{J,k}=2^{J/2}{1}_{I_{J,k}}$ of the dyadic interval $I_{J,k}=[k{2}^{-J},(k+1)2^{-J})$. Its L2 norm is one because its squared height is $2^J$ and its support length is $2^{-J}$.

For a real square-integrable function $f$, its coefficient isMultiplying the coefficient by the Haar scaling function restores the cell average on that interval. The disjoint supports make the functions at one level an orthonormal basis of the cellwise-constant subspace. This normalization is also what turns coefficient variances in the Gaussian white noise model into $1/n$ and cellwise variances into $2^J/n$ in the Haar projection estimator in Gaussian white noise.

The Haar projection at level $J$ is the orthogonal projection onto the functions constant on level-$J$ dyadic intervals. On each cell it replaces $f$ by its cell average. This is the closest cellwise-constant approximation in the L2 norm, because choosing a constant $c$ on a cell minimizes ${\int }_I|f-c{|}^2$ at its cell average.

On $[0,1]$, take the step function with values $2,4,6,8$ on successive quarter-intervals. At level zero, its Haar projection is the constant $5$. At level one, it has values $3$ and $7$ on the two halves. At level two, it recovers the four original values exactly. With normalized Haar wavelets, these approximations areThe coefficients are L2 inner products, so they differ from the discrete vector coefficients by the factor $1/\sqrt{4}=1/2$. Their squared sum also has a direct meaning:The approximation errors in squared L2 norm of $P_{0}f,P_{1}f,P_{2}f$ are respectively $5,1,0$: each added detail removes exactly its own squared coefficient from the error.

The Haar projection can be expressed using level-$J$ Haar scaling functions, or through a coarse scaling family and the Haar wavelets at intermediate levels. On $[0,1]$ with lowest level zero, that coarse family is the single constant function $\varphi$. On the whole real line, use the integer translates of a coarse Haar scaling function instead; one global constant is not square-integrable. Refinement improves the representation while retaining the earlier coefficients.

For equal particle rates in the GRW model, the next centre falls in $B$ with probability $N^{-1}{\sum }_i{\int }_B\Vert L_i(c)\Psi {\Vert }^{2}dc$. With zero Hamiltonian operator, two successive centres in $B$ have joint probability $N^{-2}{\sum }_{ij}{\int }_{B}dc{\int }_{B}dd\Vert L_j(d)L_i(c)\Psi {\Vert }^2$. Dividing by the first-event probability gives the conditional second event, including repeated particle labels. Spatial probabilities depend on actual packet mass, not solely the nominal packet centres.

A first localization near one macroscopically separated branch suppresses the other branch through the Gaussian GRW localization operator. This selects a correlated pointer alternative. However, if a region contains only packet centres and excludes substantial packet mass, the next centre need not lie in that region's small enlargement. Probabilities $|\alpha {|}^2$ for the first event and approximately one for the next require regions containing essentially all corresponding branch mass. The general GRW collapse-centre event probabilities retain the geometry exactly.

In an accepted padded convolution of words, a loop after one tape ends can be deleted or repeated while keeping that entire tape fixed. The altered other tape still represents the same required neighbouring group element. A shortest representative has a suffix no longer than the fixed number of automaton states; an accepted longer suffix gives infinitely many representatives of the same element.

Fix the first tape of a group multiplier automaton and use dynamic programming over automaton states and output-padding status, retaining predecessor pointers. The pumping of a group multiplier automaton bounds the search by $|u|+N$ layers, each of fixed size. This constructs a representative of $\overline{u}x$ in time $O(|u|+1)$ without assuming uniqueness of representatives.