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School District 37 Delta (52 words) [view diff] no match in snippet view article find links to article

School North Delta K-7 English Bluff Elementary School Tsawwassen K-7 Gibson Elementary School North Delta K-7 Gray Elementary School North Delta K-7 Hawthorne

{\displaystyle \Delta _{K}(t)} . The Alexander polynomial is symmetric: Δ K ( t − 1 ) = Δ K ( t ) {\displaystyle \Delta _{K}(t^{-1})=\Delta _{K}(t)} for all

volume occupied by each point of Δ k , n {\displaystyle \Delta _{k,n}} in Δ k {\displaystyle \Delta _{k}} . However, by symmetry, every point occupies exactly

{\displaystyle v_{p}^{2}={\frac {C_{k}}{C_{d}}}{\frac {T_{s}-T_{o}}{T_{o}}}\Delta k} where T s {\displaystyle T_{s}} is the temperature of the sea surface

}c}}d_{\text{eff}}E^{2}(\omega )e^{i\,\Delta k\,z}} where Δ k = k ( 2 ω ) − 2 k ( ω ) {\displaystyle \Delta k=k(2\omega )-2k(\omega )} . At low conversion

{\displaystyle \delta _{k}(n)=n(k+1)+(k-1)-k\sigma (n)} A number n is said to be k-hyperdeficient if δ k ( n ) > 0. {\displaystyle \delta _{k}(n)>0.} Note

k ¯ {\displaystyle \Delta _{k+1}\supset {\overline {\Delta _{k}}}} such that Δ k + 1 ∪ { Δ i } i = 1 k {\displaystyle \Delta _{k+1}\cup \lbrace \Delta

successive points: δ k = y k + 1 − y k x k + 1 − x k {\displaystyle \delta _{k}={\frac {y_{k+1}-y_{k}}{x_{k+1}-x_{k}}}} for k = 1 , … n − 1 {\displaystyle

k ∈ N Δ k P {\displaystyle \mathrm {PH} =\bigcup _{k\in \mathbb {N} }\Delta _{k}^{\mathrm {P} }} PH was first defined by Larry Stockmeyer. It is a special

={\frac {\delta (k_{1}-k_{2})}{k_{1}^{2}}}{\frac {\delta (k_{3}-k_{4})}{k_{3}^{2}}}+{\frac {\delta (k_{1}-k_{3})}{k_{3}^{2}}}{\frac {\delta (k

2 ( b 1 ) ⋱ ⋮ ⋮ ⋱ ⋮ σ n ( b 1 ) ⋯ ⋯ σ n ( b n ) ) 2 . {\displaystyle \Delta _{K}=\det \left({\begin{array}{cccc}\sigma _{1}(b_{1})&\sigma _{1}(b_{2})&\cdots

n + g + δ ) k ( t ) {\displaystyle {\dot {k}}(t)=sk(t)^{\alpha }-(n+g+\delta )k(t)} The first term, s k ( t ) α = s y ( t ) {\displaystyle sk(t)^{\alpha

cycle ( Δ K {\displaystyle \Delta K} ) is the Paris–Erdogan equation d a d N = C ( Δ K ) m {\displaystyle {da \over dN}=C(\Delta K)^{m}} where a {\displaystyle

equations can be written as Δ k = k o u t − k i n = G {\displaystyle \mathbf {\Delta k} =\mathbf {k} _{\mathrm {out} }-\mathbf {k} _{\mathrm {in} }=\mathbf {G}

f j a ( ω ) {\displaystyle \Delta k_{\mathrm {obs} }(\omega )=\sum _{a,j}\Delta k_{a\to j}(\omega )=\sum _{a,j}[\Delta k_{a\to j}]f_{ja}(\omega )} where

{\displaystyle \Delta K} seen in a loading cycle. The Paris equation is d a d N = C ( Δ K ) m , {\displaystyle {\begin{aligned}{da \over dN}&=C\left(\Delta K\right)^{m}

{\displaystyle Y} . Thus, K ˙ = Y − δ K − c L {\displaystyle {\dot {K}}=Y-\delta K-cL} The relationship between the productive factors and aggregate output

lim k → 0 k cot ⁡ δ ( k ) = − 1 a , {\displaystyle \lim _{k\to 0}k\cot \delta (k)=-{\frac {1}{a}}\;,} where a {\displaystyle a} is the scattering length

{\displaystyle \Delta K} was obtained as follows, where underline indicates the succeeding character: Δ K = K ⊕ K _ {\displaystyle \Delta K=K\oplus {\underline

Delta k\Lambda n}=1-e^{i\Delta k\Lambda }+e^{i2\Delta k\Lambda }-e^{i3\Delta k\Lambda }+...+(-1)^{N}e^{i\Delta k\Lambda (N-2)}-(-1)^{N}e^{i\Delta k\Lambda

{\displaystyle \Delta ^{k}=\Delta \circ \Delta ^{k-1},} and one has Δ k a n = ∑ t = 0 k ( − 1 ) t ( k t ) a n + k − t . {\displaystyle \Delta ^{k}a_{n}=\sum

k ′ ) P ( k ) . {\displaystyle \langle \delta _{k}\delta _{k'}\rangle ={\frac {2\pi ^{2}}{k^{3}}}\,\delta (k-k')\,{\mathcal {P}}(k).} There are both scalar

( k − 1 , i ) e f ( k , i ) γ ( k − 1 , i ) {\displaystyle \delta (k,i)=\lambda \delta (k-1,i)+{\frac {e_{b}(k-1,i)e_{f}(k,i)}{\gamma (k-1,i)}}}   γ (

{\begin{aligned}&c={\frac {dY}{dK}}={\frac {Y(t+1)-Y(t)}{K(t)+sY(t)-\delta \ K(t)-K(t)}}\\[8pt]&c={\frac {Y(t+1)-Y(t)}{sY(t)-\delta \ {\frac

the Helmholtz equation ( Δ + k 2 ) u ( x ) = − F ( x ) {\displaystyle (\Delta +k^{2})u(x)=-F(x)} for selecting a particular solution. This idea is credited

ln ⁡ ( R / δ ) K , ( i = K / R ) , {\displaystyle t(i)={\frac {R\ln(R/\delta )}{K}},\qquad (i=K/R),\,} t ( i ) = 0 , ( i = K / R + 1 , … , K ) . {\displaystyle

{\text{Incremental Capital Output Ratio (ICOR)}}={\frac {\Delta K}{\Delta Y}}={\frac {\frac {\Delta K}{Y}}{\frac {\Delta Y}{Y}}}={\frac {\frac {I}{Y}}{\frac

}\;{\hat {x}}_{k}\cdot {\hat {x}}_{\ell }\\&=a_{k}\ \sigma _{\ell }\ \delta _{k\ell }=a_{k}\ \sigma _{k}\\[4pt]&=~a_{1}\;{\begin{pmatrix}0&1\\1&0\end

{coNE}}^{\Sigma _{k-1}^{\mathsf {P}}}} and Δ k E = E Σ k − 1 P . {\displaystyle \Delta _{k}^{\mathsf {E}}={\mathsf {E}}^{\Sigma _{k-1}^{\mathsf {P}}}.} An equivalent

{\displaystyle H[n]=\sum _{k=-\infty }^{n}\delta [k]} where δ [ k ] = δ k , 0 {\displaystyle \delta [k]=\delta _{k,0}} is the discrete unit impulse function

}=(x_{i_{1}}\ldots x_{i_{r}}:\{i_{1},\ldots ,i_{r}\}\notin \Delta ),\quad k[\Delta ]=k[x_{1},\ldots ,x_{n}]/I_{\Delta }.} The ideal IΔ is called the Stanley–Reisner

, {\displaystyle v_{\rm {g}}={\frac {2\pi \Delta f}{\Delta k}}={\frac {\Delta \omega }{\Delta k}}\ ,} where ω is the frequency in radians/s: ω = 2πf.

_{k=0}^{2^{n}-1}e^{-{\frac {2\pi ik}{2^{n}}}\left(x-a\right)}e^{2\pi i\delta k}.} Performing a measurement in the computational basis on the first register

δ k + 1 {\displaystyle H_{k}=\ker \delta _{k}/\operatorname {Im} \delta _{k+1}} , the δ k {\displaystyle \delta _{k}} s are the boundary maps of the simplicial

as δ K {\displaystyle \delta _{K}} . For any matrix that satisfies the RIP property with a RIC of δ K {\displaystyle \delta _{K}} , the following condition

\operatorname {im} d_{k-1}\oplus \operatorname {im} \delta _{k+1}\oplus {\mathcal {H}}_{\Delta }^{k}(M).} The Hodge decomposition is a generalization of

operator Δ k {\displaystyle \Delta _{k}} of weight k {\displaystyle k} as Δ k : C ∞ ( H ) → C ∞ ( H ) , {\displaystyle \Delta _{k}:C^{\infty }(\mathbb {H}

}}_{i}-\beta ^{\ell }{\delta ^{k}}_{i}\right)N^{i}+\left[{\delta ^{k}}_{i}{\delta ^{\ell }}_{j}+{\frac {\gamma -1}{\beta ^{2}}}{\delta ^{k}}_{i}\beta ^{\ell

1 + δ k {\displaystyle {\boldsymbol {x}}_{k}={\boldsymbol {x}}_{k-1}+\delta _{k}} that converges to x ∗ {\displaystyle {\boldsymbol {x}}^{*}} . At a given

Consistent hashing employs a function δ ( k 1 , k 2 ) {\displaystyle \delta (k_{1},k_{2})} that defines an abstract notion of the distance between the

one obviously has Δ ( k ) = k ( 1 ⊠ 1 ) = k ⊠ 1 = 1 ⊠ k {\displaystyle \Delta (k)=k(1\boxtimes 1)=k\boxtimes 1=1\boxtimes k} for all k ∈ K . {\displaystyle

{1}{n_{1}\cdots n_{k}}},\quad r_{k}=R\delta _{1}\cdots \delta _{k}} Here, δ k {\displaystyle \delta _{k}} are adjustable shape-parameters, with constraint

x P k ( log ⁡ x ) + Δ k ( x ) {\displaystyle D_{k}(x)=xP_{k}(\log x)+\Delta _{k}(x)\,} where P k {\displaystyle P_{k}} is a polynomial of degree k − 1

2 F ( k ) F ∗ ( k ) d k ∫ − ∞ ∞ F ( k ) F ∗ ( k ) d k {\displaystyle (\Delta k)^{2}={\frac {\int _{-\infty }^{\infty }(k-k_{0})^{2}F(k)F^{*}(k)\,dk}{\int

J\subset \{1,\ldots ,m\} \atop \scriptstyle |J|=k}1_{\cap _{j\in J}A_{j}}\Delta ^{k},} because the product of indicator functions is the indicator function

{\displaystyle \delta _{k}<0} for k = k 0 , k 1 … k m {\displaystyle k=k_{0},k_{1}\ldots k_{m}} (in increasing order) and δ k > 0 {\displaystyle \delta _{k}>0} otherwise

( k ) ∇ i j E ( k − 1 ) − ∇ i j E ( k ) ) {\displaystyle \Delta ^{(k)}\,w_{ij}=\Delta ^{(k-1)}\,w_{ij}\left({\frac {\nabla _{ij}\,E^{(k)}}{\nabla _{ij}\

largely governed by the range of the stress intensity Δ K {\displaystyle \Delta K} experienced by the crack due to the applied loading. Fast fracture will

expression involving the k-th forward differences Δ k [ f ] {\displaystyle \Delta ^{k}[f]} of a polynomial function f, f ( x ) = ∑ k = 0 ∞ Δ k [ f ] ( a ) k

Define Δ k α = { Q + α : Q ∈ Δ k } . {\displaystyle \Delta _{k}^{\alpha }=\{Q+\alpha :Q\in \Delta _{k}\}.} This is the set of dyadic cubes in Δk translated

{S}}} [ T j i , Q k ] = − δ k i Q j {\displaystyle [T_{j}^{i},Q_{k}]=-\delta _{k}^{i}Q_{j}} [ T j i , Q ¯ k ] = δ j k Q ¯ i {\displaystyle [T_{j}^{i},{\overline

2 + ( x − μ ) 2 + β ( x − μ ) {\displaystyle {\frac {\gamma }{2\alpha \delta K_{1}(\delta \gamma )}}\;e^{-\alpha {\sqrt {\delta ^{2}+(x-\mu )^{2}}}+\beta

⋅ X ˙ ( t 2 ) − m 2 X ˙ ⋅ X ˙ ( t 1 ) = 1 2 m Δ v 2 , {\displaystyle \Delta K=m\int _{t_{1}}^{t_{2}}{\ddot {\mathbf {X} }}\cdot {\dot {\mathbf {X} }}dt={\frac

_{ij})={\frac {\mathbf {I} _{i}\cdot \mathbf {I} _{j}}{4}}{\frac {\left|\Delta _{k_{m}k_{m}}\right|^{2}m^{*}}{(2\pi )^{3}R_{ij}^{4}\hbar

λ ( δ γ ) − K λ + 1 2 ( δ γ ) K λ 2 ( δ γ ) ) {\displaystyle {\frac {\delta K_{\lambda +1}(\delta \gamma )}{\gamma K_{\lambda }(\delta \gamma )}}+{\frac

the stress intensity factor range Δ K = K max − K min {\displaystyle \Delta K=K_{\max }-K_{\min }} , where the minimum stress intensity factor K min

negative integers, χ + − k = δ ( k − 1 ) . {\displaystyle \chi _{+}^{-k}=\delta ^{(k-1)}.} The distributions χ + a {\displaystyle \chi _{+}^{a}} have the properties

δ ) k ( ξ , δ ) , {\displaystyle c({\bf {\xi }},\delta ):=c({\bf {{0},\delta )k({\bf {\xi }},\delta )\,,}}} where the constant c ( 0 , δ ) {\displaystyle

{5})>0\end{aligned}}} and so on. The minors Δ k ( p ) {\displaystyle \Delta _{k}(p)} are called the Hurwitz determinants. Similarly, if a 0 < 0 {\displaystyle

1\\\end{bmatrix}}\\&\\&[J(R_{k},\omega _{i})][\Delta _{k}]=[B_{k}]\\&\\&[Rz_{k+1}]=[Rz_{k}]+[\Delta _{k}]\\\end{aligned}}} Convergence is achieved when

factor range is then Δ K eff = K max − max ( K op , K min ) {\displaystyle \Delta K_{\text{eff}}=K_{\text{max}}-{\text{max}}(K_{\text{op}},K_{\text{min}})}

)d\tau \right)\\\end{aligned}}} where f Δ = K f A m {\displaystyle f_{\Delta }=K_{f}A_{m}} , K f {\displaystyle K_{f}} being the sensitivity of the frequency

π δ 2 + ( x − μ ) 2 e δ γ + β ( x − μ ) {\displaystyle {\frac {\alpha \delta K_{1}\left(\alpha {\sqrt {\delta ^{2}+(x-\mu )^{2}}}\right)}{\pi {\sqrt {\delta

satisfy the equation ( − 1 ) k ( Δ k m ) n ≥ 0 {\displaystyle (-1)^{k}(\Delta ^{k}m)_{n}\geq 0} for all n, k ≥ 0. Here, Δ is the difference operator given

_{i}^{2}=\Delta _{j}^{2}+\Delta _{k}^{2}+\Delta _{l}^{2}-2(\Delta _{j}\Delta _{k}\cos \theta _{il}+\Delta _{j}\Delta _{l}\cos \theta _{ik}+\Delta _{k}\Delta _{l}\cos

factor range, Δ K {\displaystyle \Delta K} for an applied loading cycle and is Δ K = K max − K min {\displaystyle \Delta K=K_{\text{max}}-K_{\text{min}}}

{\displaystyle ds^{2}\,=\,-{\frac {\rho _{K}^{2}\Delta _{K}}{\Sigma ^{2}}}\,dt^{2}+{\frac {\rho _{K}^{2}}{\Delta _{K}}}\,dr^{2}+\rho _{K}^{2}d\theta ^{2}+{\frac

\Delta _{k})\leq r_{n}^{nat}(q,\Delta _{k})\leq {\tilde {\mathcal {O}}}(\min({\frac {1}{\sqrt {n}}},{\frac {k}{n}})).} Here Δ k {\displaystyle \Delta _{k}}

_{k}^{2}+\Delta _{k}(T)^{2}}}{k_{\rm {B}}T}}}{(e^{\frac {\sqrt {\epsilon _{k}^{2}+\Delta _{k}(T)^{2}}}{k_{\rm {B}}T}}+1)^{2}}}\left(\epsilon _{k}^{2}+\Delta _{k}(T)^{2}-{\frac

of these awards is available on their website "Дельта К (Delta K Ltd) - Форум". "PCAE "Delta K": reliability is proved". Archived from the original on

_{s}(dx)=\sum _{p\in {\mathcal {P}}}\sum _{k\geqslant 1}{\frac {p^{-ks}}{k}}\delta _{k\log(p)}(dx)} Other "power-law" distributions Cauchy distribution Lévy

\lambda _{i}+o(\|\delta K\|+\|\delta M\|)} x i = x 0 i + δ x i + o ( ‖ δ K ‖ + ‖ δ M ‖ ) {\displaystyle x_{i}=x_{0i}+\delta x_{i}+o(\|\delta K\|+\|\delta M\|)}

C_{j})\right.}{{\underset {1\leqslant k\leqslant m}{\text{max}}}\left.\Delta _{k}\right.}}} . Being defined in this way, the DI depends on m, the number

}w^{*}(2^{j}t-k)\cdot w(2^{j'}t-k')\,dt={\frac {1}{2^{j}}}\delta _{j,j'}\delta _{k,k'}} ∫ − ∞ ∞ w ( 2 j t − k ) ⋅ w ( 2 j ′ t − k ′ ) d t = 0 {\displaystyle

p_{m}\right]\\&={\frac {i\hbar }{N}}\sum _{l}e^{ial\left(k-k'\right)}=i\hbar \delta _{k,k'}\\\left[Q_{k},Q_{k'}\right]&=\left[\Pi _{k},\Pi _{k'}\right]=0\end{aligned}}}

\ell ,m).\\\sum _{m=-\ell }^{\ell }c^{k}(\ell ,m,\ell ,m)&=(2\ell +1)\delta _{k,0}.\\\sum _{m=-\ell }^{\ell }\sum _{m'=-\ell '}^{\ell '}c^{k}(\ell ,m

{\displaystyle {\frac {C}{r^{k}}}\in ker(\Delta _{k}),~k>1} belong to the kernel of Δ k {\displaystyle \Delta _{k}} and generate the stationary solutions

_{n=0}^{\infty }\left({\frac {1}{2}}-\rho _{n}\right)\delta (k-n)} where δ ( k − n ) {\displaystyle \delta (k-n)} is the Dirac delta function, and the ρ n {\displaystyle

{\displaystyle K} ) is: Δ K K = Δ K Y K Y = s k {\displaystyle {{\Delta K} \over K}={{{\Delta K} \over Y} \over {K \over Y}}={s \over k}} where Y {\displaystyle

with eigenvalue 0 under Δ k {\displaystyle \Delta _{k}} , that is, if Δ k f = 0 {\displaystyle \Delta _{k}f=0} , then f {\displaystyle f} is called a

− p ) − Δ ] N − K − 1 W {\displaystyle K(2p+\Delta )^{K-1}[2(1-p)-\Delta ]^{N-K}W-(N-K)(2p+\Delta )^{K}[2(1-p)-\Delta ]^{N-K-1}W} The function is maximized

f(x)=\sum _{k=0}^{\infty }{\frac {\Delta ^{k}[f](a)}{k!}}\,(x-a)_{k}=\sum _{k=0}^{\infty }{\binom {x-a}{k}}\,\Delta ^{k}[f](a),} which holds for any polynomial

«Авиакомпания «Дагестан» 574 Dalnerechensk avia ООО «Дальнереченск Авиа» 549 ДА Delta K Ltd ООО «Производственно — коммерческое авиационное предприятие «Дельта

{(1-t^{\delta _{1}})\cdots (1-t^{\delta _{k}})}{(1-t)^{n}}}={\frac {(1+t+\cdots +t^{\delta _{1}})\cdots (1+t+\cdots +t^{\delta _{k}})}{(1-t)^{n-k}}}\,.} This

{H}}_{int}|k>=\pm iqZ_{DP}u(r,t){\sqrt {N_{q}+{\frac {1}{2}}\pm {\frac {1}{2}}}}\delta _{k',k\pm q}\;\;(14)} where the Kronecker delta enforces momentum conservation

{\displaystyle \int _{0}^{\infty }R_{k\ell }^{\ast }(r)R_{k'\ell }(r)r^{2}dr=\delta (k-k')} Other common normalizations of continuum wave functions are on the

k x n = ∑ j = 0 k ( − 1 ) k − j ( k j ) ( x + j ) n . {\displaystyle \Delta ^{k}x^{n}=\sum _{j=0}^{k}(-1)^{k-j}{k \choose j}(x+j)^{n}.} A related identity

(\delta _{k},r)} -regular with respect to a family of partitions P {\displaystyle {\mathcal {P}}} if all but at most δ k n k {\displaystyle \delta _{k}n^{k}}

2 ( ∂ u 3 ∂ t ) ( ∂ δ u 3 ∂ t ) ]   d x 3   d A   d t {\displaystyle \delta K=\int _{0}^{T}\int _{\Omega ^{0}}\int _{-h}^{h}{\cfrac {\rho }{2}}\left[2\left({\frac

{\widetilde {\pi }}({\boldsymbol {\theta }}_{k}|{\boldsymbol {y}})\times \Delta _{k},\end{aligned}}} where the summation is over the values of θ {\displaystyle

2 π d S = b ( K ) {\displaystyle \int _{\delta K}{\frac {H}{2\pi }}dS=b(K)} where δ K {\displaystyle \delta K} is the boundary of the convex body K {\displaystyle

( Δ k f ) ( 0 ) ( x k ) , {\displaystyle f(x)=\sum _{k=0}^{\infty }(\Delta ^{k}f)(0){x \choose k},} where ( x k ) = x ( x − 1 ) ( x − 2 ) ⋯ ( x − k +

\delta _{k}={\frac {1}{k}}} , ∃ A k ⊂ X {\displaystyle \exists A_{k}\subset X} where ( d i a m ( A k ) < δ k ) {\displaystyle (diam(A_{k})<\delta _{k})}

_{x}f(x)=\sum _{k=1}^{\infty }{\binom {x}{k}}\Delta ^{k-1}[f]\left(0\right)+C=\sum _{k=1}^{\infty }{\frac {\Delta ^{k-1}[f](0)}{k!}}(x)_{k}+C} where ( x ) k

basis of K over Q, set d = Δ K / Q ( α 1 , … , α n ) {\displaystyle d=\Delta _{K/\mathbb {Q} }(\alpha _{1},\ldots ,\alpha _{n})} . Then, O K {\displaystyle

The opioid receptors have the following channel types: mu, K+ channel; l delta, K+ channel; kappa, Ca2+ channel. Hyperpolarization can lead to post-synaptic

ϕ ~ ( | k → | ) 2 . {\displaystyle \Psi _{0}[{\tilde {\phi }}]=\lim _{\Delta k\to 0}\;\prod _{\vec {k}}\left({\frac {\omega _{\vec {k}}}{\pi }}\right)^{\frac

n}\mu \left({\frac {n}{\delta }}\right)\delta ^{k}=n^{k}\sum _{\delta \mid n}{\frac {\mu (\delta )}{\delta ^{k}}}.}       Möbius inversion ∑ δ ∣ n δ s

}\\&=(2\pi )^{8}\delta (K_{1}-K_{3})\delta (K_{2}-K_{4})G(K_{1})G(K_{2})-{}\\&{\phantom {{}={}}}(2\pi )^{8}\delta (K_{1}-K_{4})\delta (K

( t ′ ) | k ⟩ e − i ( E k − E k ′ ) t ′ / ℏ {\displaystyle c_{k'}(t)=\delta _{k,k'}-{\frac {i}{\hbar }}\int _{0}^{t}dt'\;\langle k'|H_{1}(t')|k\rangle

reactivity or dollars. For reactivity, this is calculated in units of delta-k/k, where k is equal to the criticality of the reactor (essentially, how

Laplacian acting on k-forms is Δ k = exp ⁡ ( − ζ k ′ ( 0 ) ) {\displaystyle \Delta _{k}=\exp(-\zeta _{k}^{\prime }(0))} which is formally the product of the

generalized hypergeometric function. Let Δ ( k , − n ) {\displaystyle \Delta (k,-n)} denote the array of k {\displaystyle k} ratios − n k , − n − 1 k

2 ¯ ) {\displaystyle W_{j}=\exp {(-\Delta k_{j}^{2}{\bar {U_{j}^{2}}})}} , where Δ k j 2 {\displaystyle \Delta k_{j}^{2}} is the squared magnitude of

Comox Valley K–12 Yes École Beausoleil Victoria K–3 Yes École du Bois-Joli Delta K–7 Yes École des Cascades Duncan K–3 Yes École des Colibris Vancouver K–6

j ) ∫ 0 1 g ( x + j t δ ) d t {\displaystyle \int _{0}^{1}\Delta _{t\delta }^{k}(g;x)\,dt=(-1)^{k}g(x)+\sum _{j=1}^{k}(-1)^{k-j}{\binom {k}{j}}\int _{0}^{1}g(x+jt\delta

_{j})[p_{jk}(\delta _{k\ell }-p_{j\ell })+c_{j}^{2}(p_{jk}-\delta _{jk})(p_{j\ell }-\delta _{j\ell })]+\alpha _{k}c_{0,k}^{2}\delta _{k\ell }} R = I −

}}\forall n\geq N:\int _{\mathbb {R} \setminus [-\delta ,\delta ]}K_{n}(t)\,dt=\int _{-\infty }^{-\delta }K_{n}(t)\,dt+\int _{\delta }^{\infty }K_{n}(t)\,dt<\epsilon

relativity: Δ K = W = [ γ 1 − γ 0 ] m 0 c 2 . {\displaystyle {\begin{aligned}\Delta K=W=[\gamma _{1}-\gamma _{0}]m_{0}c^{2}.\end{aligned}}} If in the initial

k ) 1 , X = k ⊻ Y = k 2 , X = k ∧ Y = k {\displaystyle C_{k}:=\delta _{k}(X)+\delta _{k}(Y)={\begin{cases}0,&\neg \,(X=k\vee Y=k)\\1,&\quad X=k\,\veebar

discriminant Δ K = { d d ≡ 1 ( mod 4 ) 4 d d ≡ 2 , 3 ( mod 4 ) ; {\displaystyle \Delta _{K}={\begin{cases}d&d\equiv 1{\pmod {4}}\\4d&d\equiv 2,3{\pmod {4}};\end{cases}}}

{\displaystyle \oint z^{m-n}\,dz=2\pi \delta _{m-n}} where δ k {\displaystyle \delta _{k}} is the Kronecker delta function. It follows that the moments are simply

energy can be written as Δ K = 1 2 μ v r e l 2 ( e 2 − 1 ) {\displaystyle \Delta K={\frac {1}{2}}\mu v_{\rm {rel}}^{2}(e^{2}-1)} , where vrel is the relative

− Δ + k 2 ] Φ ( x , x ′ ) = δ ( x − x ′ ) , k ∈ R , {\displaystyle [-\Delta +k^{2}]\Phi (\mathbf {x} ,\mathbf {x} ')=\delta (\mathbf {x} -\mathbf {x}

_{i}^{2}=\Delta _{j}^{2}+\Delta _{k}^{2}+\Delta _{l}^{2}-2(\Delta _{j}\Delta _{k}\cos \theta _{il}+\Delta _{j}\Delta _{l}\cos \theta _{ik}+\Delta _{k}\Delta _{l}\cos

t}+\left[{\begin{matrix}n-1\\k-1\end{matrix}}\right]_{f,t}+\delta _{n,0}\delta _{k,0}.\end{aligned}}} These coefficients satisfy a number of analogous properties

t ) = s [ k ( t ) ] α − δ k ( t ) {\textstyle k'(t)=s[k(t)]^{\alpha }-\delta k(t)} Ramsey–Cass–Koopmans model Dynamic stochastic general equilibrium Feynman–Kac

k − 1 {\displaystyle j=1,2,\cdots ,k-1} and δ k = 0 {\displaystyle \delta _{k}=0} . Thus E ( X j ) = α j α j + β j ∏ m = 1 j − 1 β m α m + β m . {\displaystyle

\Delta \omega =\omega _{1}-\omega _{2}\,\!} Δ k = k 1 − k 2 {\displaystyle \Delta k=k_{1}-k_{2}\,\!} y 1 + y 2 = A sin ⁡ ( k 1 x − ω 1 t ) + A sin ⁡ ( k 2

is: l c = 2 / Δ k {\displaystyle l_{c}=2/\Delta k} with Δ k ∝ 1 / ( n 2 ω − n ω ) {\displaystyle \Delta k\propto 1/(n_{2\omega }-n_{\omega })} for forward

( s 2 + 1 4 ) Λ K ( 1 2 + i s ) {\displaystyle \Lambda _{K}(s)=\left|\Delta _{K}\right|^{s/2}\Gamma _{\mathbf {R} }(s)^{r_{1}}\Gamma _{\mathbf {C} }(s)^{r_{2}}\zeta

upper left k × k {\displaystyle k\times k} minor Δ k {\displaystyle \Delta _{k}} is non-zero then the negative index of inertia is equal to the number

\mathbb {Q} [\mathbb {Z} ]/\Delta K} . Let t r : Q [ Z ] / Δ K → Q {\displaystyle tr:\mathbb {Q} [\mathbb {Z} ]/\Delta K\to \mathbb {Q} } be any linear

≪ n ( log ⁡ log ⁡ n ) 11 / 4 {\displaystyle \textstyle \sum _{k=1}^{n}\Delta (k)\ll n(\log \log n)^{11/4}} , for n ≥ 100 {\displaystyle n\geq 100} . In

k n where − ∞ < n < ∞ , − ∞ < k < ∞ {\displaystyle \delta [n-k]\equiv \delta [k-n]\equiv \delta _{nk}\equiv \delta _{kn}{\text{where}}-\infty <n<\infty

+ γ δ i ℓ δ j k , {\displaystyle \mu _{ijk\ell }=\alpha \delta _{ij}\delta _{k\ell }+\beta \delta _{ik}\delta _{j\ell }+\gamma \delta _{i\ell }\delta

Tank of the 3000-series and mounted upgraded Castor 4A motors. The new Delta K second stage was also included. A total of three were launched in 1989

\Delta _{2}^{0}} iterated limiting (k times) Δ k + 1 0 {\displaystyle \Delta _{k+1}^{0}} Blum–Shub–Smale machine incomparable with traditional computable

{\displaystyle K} : δ ( K , C n ) = n V ( K ) V ( C n + K ) {\displaystyle \delta (K,C_{n})={\frac {nV(K)}{V(C_{n}+K)}}} where C n {\displaystyle C_{n}} is

}^{\alpha })^{2}-2K_{\alpha }^{\beta }K_{\beta }^{\gamma }K_{\gamma }^{\delta }K_{\delta }^{\alpha }\right],} where K μ ν = 3 ∂ α T [ β γ ] μ ϵ α β γ ν

units) [ Δ − k 0 2 ] ϕ ( r ) = − ρ e x t ( r ) ϵ 0 , {\displaystyle \left[\Delta -k_{0}^{2}\right]\phi (\mathbf {r} )=-{\frac {\rho _{\rm {ext}}(\mathbf {r}

k x n = ∑ j = 0 k ( − 1 ) k − j ( k j ) ( x + j ) n . {\displaystyle \Delta ^{k}x^{n}=\sum _{j=0}^{k}(-1)^{k-j}{k \choose j}(x+j)^{n}.} Because the Bernoulli

\int _{0}^{\infty }J_{\nu }(kr)J_{\nu }(k'r)\,r\,\mathrm {d} r={\frac {\delta (k-k')}{k}},\quad k,k'>0.} If f(r) and g(r) are such that their Hankel transforms

Air Chrysalis Bulletproof – Dub Me Crazy Six60 – Six60 2013 Shapeshifter Delta K+LAB - Space Dirt Sola Rosa - Low and Behold, High and Beyond 2014 Opiuo

_{-1}^{1}P_{k}^{m}P_{\ell }^{m}dx={\frac {2(\ell +m)!}{(2\ell +1)(\ell -m)!}}\ \delta _{k,\ell }} Where δk,ℓ is the Kronecker delta. Also, they satisfy the orthogonality

(Shapeshifter) Thunderlips – "Frankenstein" (Randa) Sponsor Shapeshifter – Delta K+LAB – Space Dirt Sola Rosa – Low and Behold, High and Beyond Best Gospel

δ 1 , … , δ k + 1 ∈ { + 1 , − 1 } {\displaystyle \delta _{1},\dotsc ,\delta _{k+1}\in \left\{+1,-1\right\}} The Hobby–Rice theorem says that for every

probabilities (proportional to e − Δ k B T {\displaystyle e^{-{\frac {\Delta }{k_{B}T}}}} , with T {\displaystyle T} the temperature and k B {\displaystyle

= δ k i {\displaystyle g^{ij}g_{jk}=g_{kj}g^{ji}={\delta ^{i}}_{k}={\delta _{k}}^{i}} where δ j i {\displaystyle \delta _{j}^{i}} is the Kronecker delta

{\displaystyle n} : Δ K ( t − 1 ) t n − 1 = Δ K ( t ) {\displaystyle \Delta _{K}(t^{-1})t^{n-1}=\Delta _{K}(t)} for all knots K {\displaystyle K} of n > 0 {\displaystyle

{\displaystyle {\dot {k}}={\frac {\partial k}{\partial t}}=f(k(t))-(n+\delta )k(t)-c(t)} where c ( t ) {\displaystyle c(t)} is period t consumption, k

m}e^{ikal}e^{-ik'am}[x_{l},p_{m}]\\&={i\hbar \over N}\sum _{m}e^{iam(k-k')}=i\hbar \delta _{k,k'}\\\left[Q_{k},Q_{k'}\right]&=\left[\Pi _{k},\Pi _{k'}\right]=0~.\end{aligned}}}

\pi &m\end{array}}\right)=\Delta \,K(m),}   giving   λ = 2 Δ K ( m ) = 16 3 m H K ( m ) , {\displaystyle \lambda =2\,\Delta \,K(m)={\sqrt {{\frac {16}{3}}{\frac

t}+\left[{\begin{matrix}n-1\\k-1\end{matrix}}\right]_{f,t}+\delta _{n,0}\delta _{k,0}.\end{aligned}}} These coefficients satisfy a number of analogous properties

( i k x − z i ) . {\displaystyle {\widehat {G}}(k_{x},k_{s})={\frac {\delta (k_{x}-k_{s})}{\prod _{i=1}^{N}(ik_{x}-z_{i})}}.} The fraction can then be

Y ( t ) − C ( t ) − δ K ( t ) {\displaystyle {\dot {K}}(t)=Y(t)-C(t)-\delta K(t)} where δ {\displaystyle \delta } is the depreciation rate and C ( t

functions by δ ( k ) [ φ ] = ( − 1 ) k φ ( k ) ( 0 ) . {\displaystyle \delta ^{(k)}[\varphi ]=(-1)^{k}\varphi ^{(k)}(0).} In particular, δ is an infinitely

) ] , {\displaystyle I(\Delta L)\sim [1+[\gamma (\Delta L)+0.25]\cos(\Delta k\Delta L)],} where visibility curve is nonzero when optical path difference

L {\displaystyle L} . Call this correction δ = k − L {\displaystyle \delta =k-L} . The absolute value of this δ {\displaystyle \delta } correction is

{\displaystyle \Delta p=p\otimes K+1\otimes p} Δ K = K ⊗ K {\displaystyle \Delta K=K\otimes K} where h is the deformation parameter. This quantum group was

\mathbf {e} _{k}=g^{ij}\mathbf {e} _{j}\cdot \mathbf {e} _{k}=g^{ij}g_{jk}=\delta _{k}^{i}.} The covariant and contravariant components of any vector v = q

\epsilon >0} there exists a δ = δ ( k , λ , ϵ ) > 0 {\displaystyle \delta =\delta (k,\lambda ,\epsilon )>0} such that, if m {\displaystyle m} is the midpoint

\left\{{\boldsymbol {\beta }}^{\delta },\sigma _{\delta },\omega _{k}^{\delta },k=1,\ldots ,d\right\}} . At the same time, β m {\displaystyle {\boldsymbol

i_{n-2}jk}\varepsilon ^{i_{1}\dots i_{n-2}lm}=(n-2)!(\delta _{j}^{l}\delta _{k}^{m}-\delta _{j}^{m}\delta _{l}^{k})\,.} In general, for n dimensions

controller output is given by K P Δ + K I ∫ Δ d t {\displaystyle K_{P}\Delta +K_{I}\int \Delta \,dt} where Δ {\displaystyle \Delta } is the error or deviation

case from this equation with: I ( k ) = δ ( | k | ) {\displaystyle I(k)=\delta (|k|)} where δ ( ) {\displaystyle \delta ()} is the Dirac delta function.

variational problem is δ W − λ δ K = 0 {\displaystyle \delta W-\lambda \delta K=0} . After some algebra this leads to the following constraint for the

2 + ( x − μ ) 2 + β ( x − μ ) {\displaystyle {\frac {\gamma }{2\alpha \delta K_{1}(\delta \gamma )}}\;e^{-\alpha {\sqrt {\delta ^{2}+(x-\mu )^{2}}}+\beta

boundary conditions f ( k ) ( 0 ) = δ k , p {\displaystyle f^{(k)}(0)=\delta _{k,p}} , using Kronecker delta notation. In particular, the trisections are

(body started from rest) is given by Δ K = 1 2 m v 2 . {\displaystyle \Delta K={\tfrac {1}{2}}mv^{2}.} Since no energy is lost, the gain in one must be

2 ( b 1 ) ⋱ ⋮ ⋮ ⋱ ⋮ σ n ( b 1 ) ⋯ ⋯ σ n ( b n ) ) 2 . {\displaystyle \Delta _{K}=\det \left({\begin{array}{cccc}\sigma _{1}(b_{1})&\sigma _{1}(b_{2})&\cdots

length L {\displaystyle L} is ( Δ k ) d = ( 2 π / L ) d {\displaystyle (\Delta k)^{d}=({2\pi }/{L})^{d}} , the volume-related density of states for continuous

( k ) + R b ( k ) = 2 N δ ( k ) , {\displaystyle R_{a}(k)+R_{b}(k)=2N\delta (k),\,} So we can say that the sum of autocorrelation functions of complementary

\sigma (t)=K{\frac {d\epsilon }{dt}}\implies \sigma _{0}\sin {(\omega t+\delta )}=K\epsilon _{0}\omega \cos {\omega t}\implies \delta ={\frac {\pi }{2}}}

in the range 1 − c ( n ) log ⁡ | Δ K | ≤ β < 1 1-{\frac {c(n)}{\log |\Delta _{K}|}}\leq \beta <1 with ζ K ( β ) = 0 {\textstyle \zeta _{K}(\beta )=0}

{\partial \left(\beta \delta K_{x}*{\frac {\partial \psi *}{\partial x*}}\right)}{\partial x*}}+{\frac {\partial \left(\beta \delta K_{z}*{\frac {\partial

{\displaystyle D=a\cdot S+b\cdot {\frac {\Delta P}{\Delta t}}-c\cdot {\frac {\Delta K}{\Delta t}}+d\,\ } where D   {\displaystyle D\,\ } is the amount of investment

not always be appropriate. Şenbabaoğlu et al demonstrated the original delta K metric to decide K {\displaystyle K} in the Monti algorithm performed poorly

groups ⋆ : H Δ k ( M ) → H Δ n − k ( M ) , {\displaystyle {\star }:H_{\Delta }^{k}(M)\to H_{\Delta }^{n-k}(M),} which in turn gives canonical identifications

{\displaystyle k-(n-k)\geq \Delta k_{n}} where Δ k n = ⌈ α n − 1 / 2 + ( − 2 ζ ( − 1 / 2 ) ) α π n − 1 / 4 ⌉ {\displaystyle \Delta k_{n}=\left\lceil {\alpha

}+\left[{\begin{matrix}n-1\\k-1\end{matrix}}\right]_{\alpha }+\delta _{n,0}\delta _{k,0}\,.} These generalized α-factorial coefficients then generate the distinct

}({\overline {\mathcal {M}}}_{g,n})} as follows. Let δ k {\displaystyle \delta _{k}} be the pushforward of 1 along the gluing map M ¯ g , n × M ¯ 0 , 3 →

g_{ij}} is given by g i j g j k = δ k i {\displaystyle g^{ij}g_{jk}=\delta _{k}^{i}} This is used to define the dual basis: e i = e j g j i , i = 1

{1}{T}}\int _{0}^{T}e^{j2\pi \left(k_{2}-k_{1}\right)t/T}dt=\delta _{k_{1}k_{2}}\end{aligned}}} where ( ⋅ ) ∗ {\displaystyle (\cdot )^{*}} denotes

{\displaystyle =(ka+\ell b,\ {\frac {1}{\delta }}(k_{1}a+\ell _{1}b))\ \ {\text{since}}\ \ \ell +{\frac {\delta }{k_{1}}}={\frac {k}{k_{1}}}\ell _{1}} = (

complete Gray cycle having transition sequence ( δ k ) {\displaystyle (\delta _{k})} ; the transition counts (spectrum) of G are the collection of integers

′ b k ′ d = δ k a ′ k c ′ {\displaystyle {}^{*}C_{abcd}\,k'^{b}k'^{d}=\delta k'_{a}k'_{c}} ( γ δ ≠ 0 {\displaystyle \gamma \delta \neq 0} ). where ∗ C

\delta _{kl})} where δ i j {\displaystyle \delta _{ij}} is the Kronecker delta, K is the bulk modulus (or incompressibility), and μ {\displaystyle \mu }

}{2MN\omega _{q}}}}|q|{\sqrt {n_{q}+{\frac {1}{2}}\mp {\frac {1}{2}}}}\,I(k,k')\delta _{k',k\pm q}\right)^{2}\delta [\varepsilon (k')-\varepsilon (k)\mp \hbar \omega

e_{j}\rangle =\int _{0}^{1}e^{2\pi \imath (k-j)\vartheta }\,d\vartheta =\delta _{k,j}} . Then if we take f ( ϑ ) := ϑ {\displaystyle f(\vartheta ):=\vartheta

_{3}^{k}\Sigma _{1}^{m}\Sigma _{3}^{n}=\omega ^{km}d~\delta _{j+m,0}\delta _{k+n,0}} . Fairlie, D. B.; Fletcher, P.; Zachos, C. K. (1990). "Infinite-dimensional

vertices A, B and C, then ∫ Δ K d A = α + β + γ − π . {\displaystyle \int _{\Delta }K\,dA=\alpha +\beta +\gamma -\pi .} In fact taking geodesic polar coordinates

\cdot \mathbf {b} ^{i}=v^{k}\mathbf {b} _{k}\cdot \mathbf {b} ^{i}=v^{k}\delta _{k}^{i}=v^{i}} v ⋅ b i = v k b k ⋅ b i = v k δ i k = v i {\displaystyle \mathbf

bounding Δ k ( x ) = D k ( x ) − x P k ( log ⁡ ( x ) ) {\displaystyle \Delta _{k}(x)=D_{k}(x)-xP_{k}(\log(x))} Dirichlet's divisor problem: the specific

{\displaystyle \sum _{k=0}^{n}\mu _{i}q_{k}(i)q_{\ell }(i)=v\mu _{k}\delta _{k\ell }.\quad (9)} Also μ j p i ( j ) = v i q j ( i ) , i , j = 0 , … ,

δ k {\textstyle \delta _{k}} corresponds to: ( 1 − δ k ) ‖ Γ ‖ 2 2 ≤ ‖ D Γ ‖ 2 2 ≤ ( 1 + δ k ) ‖ Γ ‖ 2 2 {\textstyle (1-\delta _{k})\|\mathbf {\Gamma

i {\displaystyle {\bar {\mathcal {M}}}_{j}^{i}{\mathcal {M}}_{k}^{j}=\delta _{k}^{i}} , which yields h i = M ¯ j i h ¯ j {\displaystyle h^{i}={\bar {\mathcal

⊗ e i Δ ( f i ) = f i ⊗ 1 + k i ⊗ f i {\displaystyle {\begin{aligned}\Delta (k_{\lambda })&=k_{\lambda }\otimes k_{\lambda }\\\Delta (e_{i})&=e_{i}\otimes

\quad q_{k}q_{\ell }=q_{\ell }q_{k},\quad p_{k}q_{\ell }-q_{\ell }p_{k}=\delta _{k\ell }z,\quad zp_{k}-p_{k}z=0,\quad zq_{k}-q_{k}z=0~.} The algebra U (

sin ⁡ k δ ) / δ = k {\displaystyle \lim _{\delta \to 0}(\sin k\delta )/\delta =k} . Horner scheme to evaluate polynomials in monomial form De Casteljau's

δ k , j = { 0 , if  k ≠ j 1 , if  k = j {\displaystyle I_{k}(z_{j})=\delta _{k,j}={\begin{cases}0,&{\text{if }}k\neq j\\1,&{\text{if }}k=j\end{cases}}}

{\displaystyle b_{j_{k}}\in {\bf {b}}} and δ k = ± 1 {\displaystyle \delta _{k}=\pm 1} . Based on that sequence he computes the product B = b j 1 δ 1

S_{\varphi }=\lim _{\delta \downarrow 0}\sum _{k=0}^{\infty }(-1)^{k}\varphi (\delta k)={\frac {1}{2}}.} The Cesaro or Abel sum is recovered by letting φ be a

it can be shown that e J ∗ e K = δ K J   , {\displaystyle e^{J}*e_{K}=\delta _{K}^{J}\ ,} where ∗ {\displaystyle *} is the scalar product. With A {\displaystyle

G={\frac {y_{\text{out}}}{y_{\text{in}}}}={\frac {\sum _{k=1}^{N}{G_{k}\Delta _{k}}}{\Delta \ }}} Δ = 1 − ∑ L i + ∑ L i L j − ∑ L i L j L k + ⋯ + ( − 1

}\psi _{n_{1},k_{1}}(t)\psi _{n_{2},k_{2}}(t)\,dt=\delta _{n_{1}n_{2}}\delta _{k_{1}k_{2}},} where δ i j {\displaystyle \delta _{ij}} represents the Kronecker

x n . {\displaystyle B_{n}(x)=\sum _{k=0}^{n}{\frac {(-1)^{k}}{k+1}}\Delta ^{k}x^{n}.} This formula may be derived from an identity appearing above as

{\displaystyle [y_{j},y_{j+1},\ldots ,y_{j+k}]={\frac {1}{k!h^{k}}}\Delta ^{(k)}y_{j},} whereas for backward differences:[citation needed] [ y j , y

from the uncertainty relation Δ x Δ k ≥ 1 / 2 {\displaystyle \Delta x\Delta k\geq 1/2} that where E F {\displaystyle E_{F}} is the Fermi energy, E 0

) {\displaystyle f(x_{1},\ldots ,x_{m})} is Δ k + 1 0 {\displaystyle \Delta _{k+1}^{0}} iff it can be written in the form lim n k lim n k − 1 … lim n

magnitude). The formula for this effect was known to be Δ − k {\displaystyle \Delta ^{-k}} . Most astronomers believed k = 2 {\displaystyle k=2} but Ernst Öpik

define δ ( k ) ( A ) = max { δ ( B ) : | B | ≤ k , B ⊆ A } {\displaystyle \delta ^{(k)}(A)=\max \left\{\delta (B)\colon |B|\leq k,B\subseteq A\right\}} . Then

Mb/s Ethernet S 2 + PE AC power (single phase) S 3 + PE AC power (3-phase delta) K 4 + PE AC power (3-phase wye) M 5 + PE 3-phase motor + auxiliary wire pair

+ Δ K b = K m + 2 π α V f ∫ 0 L σ y x d x {\displaystyle K_{c}=K_{m}+\Delta K_{b}=K_{m}+{\sqrt {\frac {2}{\pi }}}\alpha V_{f}\int _{0}^{L}{\frac {\sigma

(}(v^{k_{x}+1})^{2}{\big )},} but often v k x + 1 = e δ ( k x + 1 ) {\displaystyle v^{k_{x}+1}=e^{\delta (k_{x}+1)}} implying double force of interest.

equation Δ ( k ) = Δ 0 ( c o s ( k x a ) − ( k y a ) ) {\displaystyle \Delta (k)=\Delta _{0}(cos(k_{x}a)-(k_{y}a))} , meaning that when crossing over any

, μ ) n μ = 1 2 h α β δ g α β , μ n μ {\displaystyle {\begin{aligned}\delta K&=-h^{\alpha \beta }\delta \Gamma _{\alpha \beta }^{\gamma }n_{\gamma }\\&=-h^{\alpha

∞ S ( n , k ) n ! d n d x n f ( x ) . {\displaystyle {\frac {1}{k!}}\Delta ^{k}f(x)=\sum _{n=k}^{\infty }{\frac {S(n,k)}{n!}}{\frac {d^{n}}{dx^{n}}}f(x)

{k}}_{0}+\delta {\bf {k}},{\bf {M}})={\frac {F({\bf {k}}_{0},{\bf {M}})}{1+\xi ^{2}\delta k^{2}}}\,,} where 1 / ξ {\displaystyle 1/\xi } defines the width of the multidimensional

in this definition: g i j g j k = δ k i . {\displaystyle g^{ij}g_{jk}=\delta _{k}^{i}.} Famously, the geodesic flow on Φ {\displaystyle \Phi } is given

_{g(1)\dots g(m)}^{f(1)\dots f(m)}=\sum _{k:[m]\to [n] \atop k(1)<\dots <k(m)}\delta _{k(1)\dots k(m)}^{f(1)\dots f(m)}\delta _{g(1)\dots g(m)}^{k(1)\dots k(m)}

{R_{ijk}}^{\ell }={C_{ijk}}^{\ell }+2\delta _{[i}^{\ell }P_{j]k}+\beta _{ij}\delta _{k}^{\ell }} where the Weyl tensor C i j k ℓ {\displaystyle {C_{ijk}}^{\ell

i , {\displaystyle l\delta _{k+l+m,i}=-m\delta _{k+l+m,i},} or, with n = l + m, n δ k + n , i = 0. {\displaystyle n\delta _{k+n,i}=0.} This (and the

^{IJMN}\varepsilon _{KLMN}&=-4\delta _{[K}^{I}\delta _{L]}^{J}=-2\left(\delta _{K}^{I}\delta _{L}^{J}-\delta _{L}^{I}\delta _{K}^{J}\right)&&{\text{Eq. 4}}\end{aligned}}}

k 1 ± k 2 ± ⋯ ± k n ) {\displaystyle \delta _{1\pm 2\pm \cdots \pm n}=\delta (k_{1}\pm k_{2}\pm \cdots \pm k_{n})} capturing the notion of the conservation

{\partial k}{\partial x_{j}}}=-\tau _{ij}{\bar {S}}_{ij}-{\frac {C_{*}}{\Delta }}k^{3/2}+{\frac {\partial }{\partial x_{j}}}\left(D\Delta {\sqrt {k}}{\frac

λ = Δ L Δ K , ε = Δ P Δ K . {\displaystyle \lambda ={\frac {\Delta L}{\Delta K}},\quad \varepsilon ={\frac {\Delta P}{\Delta K}}.} (3)

combinatorial interpretation of ( 1 / k ! ) Δ k t n {\displaystyle (1/k!)\Delta ^{k}t^{n}} ", Proceedings of the Oberwolfach Meeting "Kombinatorik" (1986)

: a 1 , ⋯ , a n  are k-wise disjoint faces of  A } {\displaystyle A_{\Delta (k)}^{*n}:=\{a_{1}\sqcup a_{2}\sqcup \cdots \sqcup a_{n}:a_{1},\cdots ,a_{n}{\text{

( k ) ( t ) {\displaystyle f^{(k)}(t)} δ ( k ) ( t ) {\displaystyle \delta ^{(k)}(t)} Constant coefficient differential operator P ( i n ) {\displaystyle

{perm} (A)={\frac {1}{2^{n-1}}}\left[\sum _{\delta }\left(\prod _{k=1}^{n}\delta _{k}\right)\prod _{j=1}^{n}\sum _{i=1}^{n}\delta _{i}a_{ij}\right],} where

] ⊗ K [ G ]   by   Δ ( g ) = g ⊗ g   for all   g ∈ G 1 {\displaystyle \Delta :K[G]\to K[G]\otimes K[G]~{\text{by}}~\Delta (g)=g\otimes g~{\text{for all}}~g\in

t i o n ∝ | k max |   . {\displaystyle {\rm {FOV}}\propto {\frac {1}{\Delta k}}\qquad \mathrm {Resolution} \propto |k_{\max }|\ .} (These relationships

p r p A p cos ⁡ ( k L p ) . {\displaystyle d(k):=\sum _{j=0}^{\infty }\delta (k-k_{j})={\frac {L}{\pi }}+{\frac {1}{\pi }}\sum _{p}{\frac

wave vector must increase by Δ k = k S P − k x , photon {\displaystyle \Delta k=k_{SP}-k_{x,{\text{photon}}}} . The grating harmonics of a periodic grating

the change in count is δ = k j ( − w ) τ 2 = k j L D {\displaystyle \delta =k_{j}(-w)\tau _{2}=k_{j}L_{D}} ; i.e., the change in count equals the number

_{\epsilon _{t}(i)j}\delta _{t}(i)} where δ k ( i ) {\displaystyle \delta _{k}(i)} is the sign of s i {\displaystyle s_{i}} in the k {\displaystyle

{\sin(x)}{\cos(y)+\sin(x)}},\qquad [0<x<20],[0<y<20],\qquad \delta _{k}\equiv 1\end{aligned}}} Example (CF2): Best described as a self-generating

{\displaystyle \psi (k)\psi ^{\dagger }(k')-\psi (k')\psi ^{\dagger }(k)=\delta (k-k')\,} where the delta function is either the Dirac delta function or the

} Δ K = k i ( sin ⁡ θ i ) − k s ( sin ⁡ θ s ) = G + q {\displaystyle \Delta K=k_{i}(\sin \theta _{i})-k_{s}(\sin \theta _{s})=\mathbf {G} +\mathbf {q}

{\displaystyle {\frac {dM}{dH}}={\frac {1}{1+c}}{\frac {M_{\text{an}}-M}{\delta k-\alpha (M_{\text{an}}-M)}}+{\frac {c}{1+c}}{\frac {dM_{\text{an}}}{dH}}}

matrix are positive, i.e., Δ k := det ( S k _ _ ) > 0 {\displaystyle \Delta _{k}:=\det({\underline {\underline {{\mathsf {S}}_{k}}}})>0} where S k _ _

{Q_{s}}}}=\langle {\frac {1}{Q_{mnp}}}\rangle _{k\leq k_{r}\leq k_{r}+\Delta k}} Q s ~ = 3 2 V S δ s 1 1 + 3 c 16 f ( 1 / l + 1 / w + 1 / h ) {\displaystyle

\mathbf {b} ^{i}=v^{k}~\mathbf {b} _{k}\cdot \mathbf {b} ^{i}=v^{k}~\delta _{k}^{i}=v^{i}} v ⋅ b i = v k   b k ⋅ b i = v k   δ i k = v i {\displaystyle

_{n})(z_{n})-D(\varphi )(z)|\leq \int _{|w-z|\geq \delta }|K(z_{n},w)-K(z,w)||\varphi (w)|\,|dw|+\int _{|w-z|\leq \delta }(|K(z_{n},w)|+|K(z,w)|)\cdot |\varphi (w)|\

{\displaystyle \Delta ^{k}\mu _{n}\geq 0,\quad n,k=0,1,2,\ldots } where Δ k μ n = ∑ m = 0 k ( − 1 ) m ( k m ) μ n + k − m {\displaystyle \Delta ^{k}\mu _{n}=\sum

k}&=P_{i,j,k}-{\frac {{\mathit {SS}}_{i,j,k}\Delta r_{j}\Delta c_{i}\Delta _{k}}{t^{m}-t^{m-1}}}\\{\mathit {RHS}}_{i,j,k}&=-Q_{i,j,k}-{\mathit {SS}}_{i

{(-1)^{k}}{k+m-1 \choose k}}S_{k+m-1,m-1}&&0<m\in \mathbb {Z} \\[6pt]S_{k}(-1)&=\delta _{k,0}\\[6pt]S_{k}(0)&=(-1)^{k}B_{k}\\[6pt]S_{k}(1)&=(-1)^{k+1}((k-1)B_{k

O(n^{d+1-\delta })} , for some δ = δ ( k , d ) < 1 {\displaystyle \delta =\delta (k,d)<1} . This result follows from the colored Tverberg theorem. It is far

δ ) m 2 t {\displaystyle {\frac {\partial k_{i}}{\partial t}}={\frac {\delta k_{i}+2(1-\delta )m}{2t}}} with the assumption that:  ∑ j ∈ l o c a l k j

formula Δ K = h − 1 ∘ Δ T ∘ h + ‖ ρ ‖ 2 , {\displaystyle \displaystyle \Delta _{K}=h^{-1}\circ \Delta _{T}\circ h+\|\rho \|^{2},} where h ( e X ) = A ρ

notations, F = ⟨ Δ k ∣ k  odd ⟩ {\displaystyle {\mathcal {F}}=\left\langle \Delta ^{k}\mid k{\text{ odd}}\right\rangle } , i.e. F = ⟨ Δ , Δ 3 , Δ 5 , Δ 7 ,

{\partial } }{\mathrm {\partial } t}}p_{\mathbf {k} }=-\mathrm {i} \,\delta _{k}p_{\mathbf {k} }-\mathrm {i} \,[1-f_{\mathbf {k} }^{e}-f_{\mathbf {k}

θ = ∫ f ( Δ ) K {\displaystyle \theta =\int _{f(\Delta )}K}

{\displaystyle {\begin{aligned}\left\|P_{k}(I+\Delta )^{k}f\right\|_{(-k)}&=\sup _{\|g\|_{(-k)}=1}\left|\left((I+\Delta )^{k}f,g\right)_{(-k)}\right|\\&=\sup _{\|g\|_{(-k)}=1}|(f

p H ≡ δ V / δ H , p N ≡ δ V / δ N {\displaystyle p_{K}\equiv \delta V/\delta K,p_{H}\equiv \delta V/\delta H,p_{N}\equiv \delta V/\delta N} given a forecast

V δ k , k ′ {\displaystyle \int _{V}e^{ik\cdot r}e^{-ik'\cdot r}dr=V\delta _{k,k'}} (can be derived from Euler equation and trigonometric orthogonality)

{\displaystyle \Delta ^{y}\Delta } ; and the fifth power was Δ K y {\displaystyle \Delta K^{y}} . The Chinese used numerals that look much like the tally system.

k | ∑ i = 1 k x i = 1 , x i > 0  for  i = 1 , … , k } {\displaystyle \Delta _{k}=\left\{(x_{1},\dots ,x_{k})\in \mathbb {R} ^{k}\,{\Big |}\,\sum _{i=1}^{k}x_{i}=1

can also be seen as a well-defined momentum shift Δ k {\displaystyle \Delta k} in the reciprocal space, resulting in a different occupation of the spin-polarized

S^{-1}-S^{-1}m\,m^{\dagger }\,S^{-1}\right)_{{\vec {q}}{\vec {q}}}\,\delta (k-q)\\&={\frac {1}{2}}\mathrm {Tr} \left\{S^{-1}\left[S-\left(D+m\,m^{\dagger

_{J}^{K}{C_{\beta K}}^{N}+{C_{\gamma M}}^{K}\delta _{\beta }^{\alpha }\delta _{K}^{I}\delta _{J}^{N}\right)\delta {C_{\alpha I}}^{J}\\&=\int d^{4}x\;e\left(e^{I[\alpha

( K ) ) , {\displaystyle (\phi \cdot \psi )(K)=(\phi \boxtimes \psi )(\Delta (K)),} where Δ : V → V × V {\displaystyle \Delta :V\to V\times V} is the diagonal

growth curves. d a d N = C ( Δ K ) m {\displaystyle {\frac {da}{dN}}=C(\Delta K)^{m}} The probability of detection distribution curve for a chosen NDI

{l}})]=0,\ \ [a(x^{+},{\vec {k}}),a^{\dagger }(x^{+},{\vec {l}})]=\hbar \delta (k^{+}-l^{+})\delta ^{2}({\vec {k_{\bot }}}-{\vec {l_{\bot }}}).} where k

{3}}}{35}}\,\sum _{k=0}^{\infty }\alpha _{1}(k)\,{\frac {51\cdot 11k+53}{(\Delta )^{k+{\frac {1}{2}}}}}\\{\frac {1}{\pi }}&={\frac {4{\sqrt {3}}}{99}}\,\sum

a_{k}b_{\ell }\mathbf {e} _{i}\otimes \mathbf {e} _{j}\\[1ex]{}={}&\delta _{k}{}_{i}\delta _{\ell j}\,a_{k}b_{\ell }\mathbf {e} _{i}\otimes \mathbf

where η j , k = Δ k ℏ / ( 2 M ω k ) b j k {\displaystyle \eta _{j,k}=\Delta k{\sqrt {\hbar /(2M\omega _{k})}}b_{j}^{k}} . Now we define spin and motional

f − f 0 ) δ ( k x − k 0 ) {\displaystyle W(k_{x},f)=A\delta (f-f_{0})\delta (k_{x}-k_{0})} which suggests the possibility to map a monochromatic plane

{\displaystyle {\mathbf {\Phi } _{2}}\sim {\mathcal {W}}({\mathbf {I} _{p}},\delta +k-1)} , and define X = Φ 2 − 1 / 2 Φ 1 Φ 2 − 1 / 2 {\displaystyle \mathbf

⟨ δ [ . . ] ⊗ ( l − 2 k ) r ⊗ ( l − 2 k ) ⟩ ⟩ {\displaystyle {\frac {\Delta ^{k}\mathbf {r} ^{\otimes l}}{k!2^{k}}}=\left\langle \left\langle \delta _{[

′ ) = K 0 ⟩ {\displaystyle \chi (t,t')=\langle \left.\delta N_{0}(t)/\delta K_{0}(t')\right|_{K_{0}(t')=K_{0}}\rangle } is the dynamical susceptibility

0.5 + k Δ : k = 0 , … M − 1 {\displaystyle \{t\}_{M=10}=\{t_{k}=0.5+k\Delta :k=0,\ldots M-1} , Δ = 1 } {\displaystyle \Delta =1\}} , with x ( 0.5 ) =