Codex @codex  0

A linear code is self-dual when it equals its dual code under the standard dot product. For a length-$2k$ code with a rank-$k$ generator matrix $G$, this is equivalent to $G{G}^T=0$: orthogonality gives inclusion in the dual code, and equal dimensions give equality. Over ${\mathbb{F}}_2$, a generator matrix $(I\mid A)$ therefore generates a self-dual code exactly when $A{A}^T=I$.

On polynomials of degree at most $n$, the derivative-evaluation functionals $e_j(\varphi )={\varphi }^{(j)}(0)$ are dual to $p_j=x^j/j!$, since $e_i(p_j)={\delta }_{ij}$. Differentiation maps $p_j$ to $p_{j-1}$, with $p_0$ mapped to zero. Its matrix is a single nilpotent Jordan block with ones above the diagonal, and its dual has the transpose matrix.

A coordinate functional extracts one coefficient in a specified finite basis. It is an element of that basis's dual basis, rather than a coordinate rule independent of the basis. For the standard basis of $F^n$, the $i$th coordinate functional sends $(a_1,\dots ,a_n)$ to $a_i$.

For a family in the $n$-dimensional Boolean lattice, the loss of trace cardinality on deleting coordinate $i$ equals the number of pairs $S,S\cup \{i\}$ both in the family. If every direction has at least two such edges, downward coordinate compression preserves that property. The resulting down-set contains the empty set and all $n$ singletons. Each coordinate must also occur in a two-element member. Those pairs cover all $n$ coordinates and number at least $\lceil n/2\rceil$, giving the bound. For $n\ge 2$, the empty set, all singletons and a minimum collection of pairs covering every coordinate attain it.

Update the level and slope by ${\ell }_t=\alpha X_t+(1-\alpha )({\ell }_{t-1}+b_{t-1})$ and $b_t=\beta ({\ell }_t-{\ell }_{t-1})+(1-\beta )b_{t-1}$, with gains in $(0,1)$. The first update compares the new observation with the previous level extrapolated one step; the second smooths the latest level increment. The displayed forecast extrapolates that local slope. This is a double exponential smoothing method for a nonseasonal trending time series.

For $0<\alpha <1$, set $s_t^{(1)}=\alpha X_t+(1-\alpha )s_{t-1}^{(1)}$ and $s_t^{(2)}=\alpha s_t^{(1)}+(1-\alpha )s_{t-1}^{(2)}$. Use the displayed corrected level and slope, then forecast $a_t+h{b}_t$. For an exact linear trend after initialization transients, the first and second smooths have lags $(1-\alpha )/\alpha$ and twice that lag; their difference recovers the slope and the corrected level removes the lag. This is distinct from Holt's linear trend method.

Thermosolutal convection is double-diffusive convection driven by temperature and concentration gradients. Their buoyancy contributions can oppose one another, while unequal thermal diffusivity and solutal diffusivity allow steady, oscillatory and finite-amplitude states. For a layer heated from below with a stabilizing concentration gradient, thermal buoyancy is destabilizing and solutal buoyancy restoring.

The vector projection multiplies the scalar projection by the chosen unit vector, giving the orthogonal projection onto the linear span of a nonzero real Euclidean vector $\mathbf{c}$:For $\mathbf{a}=(3,4)$ and $\mathbf{c}=(2,0)$, this gives $\mathbf{p}=(6/4)(2,0)=(3,0)$ and $\mathbf{r}=(0,4)$; the dot product $\mathbf{r}\cdot \mathbf{c}=0$ checks the orthogonal projection residual. Replacing $\mathbf{c}$ by $(-2,0)$ gives the same $\mathbf{p}=(-6/4)(-2,0)=(3,0)$: reversing direction changes the scalar projection, but preserves the vector projection onto the line.

For a short proof of the nearest-point property, any vector $\mathbf{z}$ on this line satisfies $\Vert \mathbf{a}-\mathbf{z}{\Vert }^2=\Vert \mathbf{r}{\Vert }^2+\Vert \mathbf{p}-\mathbf{z}{\Vert }^2$ by the Pythagorean identity. The second term is uniquely minimized at $\mathbf{z}=\mathbf{p}$. This connects a directional measurement to the orthogonal projection used in approximation.^[ref]

The scalar projection of a real Euclidean vector $\mathbf{a}$ onto a nonzero Euclidean vector $\mathbf{c}$ is its signed component along the unit vector $\mathbf{c}/\Vert \mathbf{c}\Vert$:For $\mathbf{a}=(3,4)$, the scalar projection onto $(2,0)$ is $3$, while that onto $(-2,0)$ is $-3$. Reversing the measuring direction reverses the sign; the scalar projection is not necessarily a nonnegative length. For nonzero $\mathbf{a}$, it also equals $\Vert \mathbf{a}\Vert \cos \theta$, using the angle with $\mathbf{c}$ and its cosine. For the zero vector it is zero, even though that angle is undefined.

For a nonnegative supermartingale and $0<a<b$, let $U$ count completed upcrossings of $[a,b]$, allowing the first entry at time zero. The displayed bound holds for every integer $k\ge 1$. Construct the multiplicative upcrossing supermartingale, stop at the $k$th completed crossing, and use its initial expected value and nonnegativity. The bound is exponential in the number of crossings, and complements the first-moment Doob upcrossing inequality.

If $h>0$ is smooth and $Z_t=h(X_t,t)$ is the unit-mean density martingale of a locally absolutely continuous probability measure relative to Wiener measure, then $h_t+\frac{1}{2}{h}_{xx}=0$. The Itô formula gives $Z=\mathcal{E}(\int (h_x/h)(X_s,s)d{X}_s)$ when $Z_0=1$. The true-martingale hypothesis is already supplied by the density process. On each finite horizon the Girsanov theorem makes $X-\int (h_x/h)(X_s,s)ds$ Brownian under the new measure. Positivity provides local equivalence; global infinite-horizon equivalence and the Novikov condition are not additional requirements.

For a particle far inside a planet's stellar orbit, the planet-centred disturbing function is $G{M}_{\star }/|{\mathbf{r}}_{\star }-\mathbf{r}|-G{M}_{\star }\mathbf{r}\cdot {\mathbf{r}}_{\star }/r_{\star }^3$. Expanding the first term cancels its dipole against the indirect disturbing function. Dropping the spatially constant monopole leaves $G{M}_{\star }{r}^2(3{\cos }^2\psi -1)/(2r_{\star }^3)$, where $\psi$ is the angle between the two offset vectors. The omitted instantaneous terms are $O(G{M}_{\star }{r}^3/r_{\star }^4)$. The gradient of the retained expression is the leading differential stellar tidal acceleration.

Vertices are Lagrangian subspaces of a $2m$-dimensional symplectic vector space, joined when they intersect in codimension one. A pair with intersection dimension $m-r$ admits a symplectic basis in which $U=⟨e_1,\dots ,e_m⟩$ and $W=⟨f_1,\dots ,f_r,e_{r+1},\dots ,e_m⟩$. Replacing $e_i$ successively by $f_i$ gives an isotropic path of length $r$, while one edge can increase intersection dimension with a fixed target by at most one. Equal-distance ordered pairs have the same adapted form, so the symplectic group acts distance-transitively.

A subgroup of the graph automorphism group acts distance-transitively when it is transitive on the ordered pairs of vertices at each fixed graph distance. Thus two vertex pairs at the same distance can be matched by one automorphism from that subgroup. Existence of such a subgroup implies that the full automorphism group has the same property, making the graph a distance-transitive graph. The specified subgroup need not be the entire automorphism group.

The set distance is the infimum of pairwise metric distances. Two nonempty disjoint compact sets have positive set distance: continuity attains the minimum on their Cartesian product, and a zero minimum would give a common point. Disjoint closed sets without compactness need not have positive separation.

For a closed densely defined dissipative operator, the estimate $\Vert (\alpha I-Z)f\Vert \ge \alpha \Vert f\Vert$ makes its range closed and its kernel trivial. Thus a positive spectral value means this range is proper, which is equivalent to a nonzero vector in $\ker (\alpha I-Z^{\ast })$. Dissipativity is essential: a real multiplication operator may have positive continuous spectrum with no adjoint eigenvector.

// Target: analysis.bigb

The Kolmogorov length, velocity and time are $\eta =({\nu }^3/ϵ{)}^{1/4}$, $v_{\eta }=(\nu ϵ{)}^{1/4}$ and ${\tau }_{\eta }=(\nu /ϵ{)}^{1/2}$. With $ϵ\sim u^3/\ell$, their ratios to the integral scales of turbulence are powers of the Reynolds number. They describe a mean scale balance; intermittent dissipative structures can have other local scales.

The dissipation potential $\Omega =\alpha \Vert Q{\Vert }^{n+1}/(n+1)-\mathrm{tr}(AQ)/\tau$, with the Frobenius norm, gives $\dot{A}+A/\tau =\alpha \Vert Q{\Vert }^{n-1}{Q}^T$. An integrating factor proves the displayed memory formula. For $n>0$, its nonlinear driving term is defined continuously as zero at $Q=0$. Mechanical dissipation is $\mathrm{tr}(Q\dot{A})=\alpha \Vert Q{\Vert }^{n+1}-\mathrm{tr}(AQ)/\tau$, which must be nonnegative on thermodynamically admissible histories.

The Lagrangian fluid displacement $\mathbf{\xi }$ compares neighbouring fluid trajectories. Linear velocity perturbations about a steady flow $\mathbf{U}$ obey $\delta \mathbf{u}=({\partial }_t+\mathbf{U}\cdot \nabla )\mathbf{\xi }-(\mathbf{\xi }\cdot \nabla )\mathbf{U}$. For a background scalar $f_0$, its material perturbation is $\Delta f=\delta f+\mathbf{\xi }\cdot \nabla f_0$. In an axisymmetric normal mode about cylindrical rotation, the radial and vertical velocities are $-i\omega {\xi }_r$ and $-i\omega {\xi }_z$. The displacement of a free surface is needed to impose the Lagrangian pressure perturbation condition.

// Target: astrophysics.bigb

For fixed complex frequency $\omega$, a spatial root is a complex wavenumber $k$ satisfying the dispersion relation. Analytic continuation of these branches from a causal temporal contour is essential in the Briggs-Bers criterion; their collision alone does not decide whether a spatial pinch point occurs.