text large_stringlengths 384 2.05k | rank_avg float64 1 4.19k ⌀ | rank_max float64 1 8.21k ⌀ | rank_min float64 1 5.03k ⌀ | rank_median float64 1 4.21k ⌀ | rank_by_avgsim float64 1 4.19k ⌀ | avgsim_to_github float32 0.77 0.85 ⌀ | dataset large_stringclasses 1
value |
|---|---|---|---|---|---|---|---|
mitigation is achieved by iteratively re-running the algorithm with more and more particles until inferences are stable (see Appendix \[sec:APX\]).
Details for the firing vector and excitability parameters {#sec:DetailFireProc}
---------------------------------------------------------
At time $t-1$, each particle sam... | 601 | 498 | 788 | 756 | 1,321 | 0.791037 | github_plus_top10pct_by_avg |
e class methods somewhere and want to invoke them on an object of my own choosing.This is what I came up with.
ObjectOperations ops = engine.Operations;
IList<string> members = ops.GetMemberNames(scope);
foreach(string member in members)
{
if(member.StartsWith("foo"))
{
dynamic klass= scope.GetVariabl... | 602 | 952 | 465 | 495 | 1,233 | 0.792307 | github_plus_top10pct_by_avg |
ample from such a region (acyclic cone). The complete story is not so simple, because the cone is not a probabilistic structure; it possesses no probability to support randomness.
As a probabilistic structure, the operational profile (§\[S:RELATIVE\_OP\_PROFILE\]) permits random sampling from its reference set, regard... | 603 | 187 | 967 | 743 | 1,225 | 0.792439 | github_plus_top10pct_by_avg |
f_{\mu}(v^{-1})f_{\mu}(1) v^{k(n(\mu) -
n(\mu^t))}}{\prod_{i=2}^n (1-v^{-i})}.$$ By [@op Theorem 8] the fake degrees satisfy $f_{\mu}(v^{-1}) = f_{\mu^t}(v^{-1})v^{n(\mu^t)-n(\mu)}.$ Combined with this implies that $$f_{\mu}(v^{-1}) f_{\mu}(1) v^{k(n(\mu)-n(\mu^t))}
=
f_{\mu^t}(v^{-1})f_{\mu^t}(1)
v^{-(k-1)(n(\mu^... | 604 | 1,164 | 1,083 | 635 | 3,834 | 0.769789 | github_plus_top10pct_by_avg |
tion : ${\cal O}(N_xN_yN_z\log_2[N_xN_yN_z])$.
In practice, the discrete transforms in Equations – can be performed efficiently with an FFT algorithm. The public [FFTW]{} library[^1] performs sine transforms of various kinds, among which we use [FFTW\_R0DFT00]{} consistent with the zero boundary condition. To perform ... | 605 | 1,055 | 1,135 | 724 | null | null | github_plus_top10pct_by_avg |
ion date and, hence, relatively old blood (days to expiration from 0 to 11) were included in group A (n=99). Patients who received blood that was relatively new (days to expiration from 11 to 38) were included in group B (n=99). Baseline characteristics, including age, gender, height, weight, relevant blood count indic... | 606 | 4,678 | 398 | 189 | null | null | github_plus_top10pct_by_avg |
\lrb{\frac{1}{2}\exp\lrp{-\frac{7\aq\Rq^2}{3}} (\|z\|_2 - 2\epsilon), \|z\|_2}$
<!-- -->
1. 1. chain rule
2. Use definition of $\nabla g(z)$ from Lemma \[l:gproperties\].
3. By definition, $\nabla f(z) = q'(g(z)) \nabla g(z)$. From Lemma \[l:qproperties\], $\lrabs{q'(g(z))} \leq 1$. By definition, $\na... | 607 | 3,605 | 621 | 424 | null | null | github_plus_top10pct_by_avg |
\hat{\beta}}_{i}, \label{438}$$ where the conditions of unit length and non-negative relative weight stipulated earlier have already been imposed within the stated order of approximation.
Equation (\[438\]) specifies the (relative) composition of the market-aligned portfolio. The relative weight ${W}_{N}$ of this por... | 608 | 2,661 | 2,393 | 821 | null | null | github_plus_top10pct_by_avg |
dots,\pi_k f_k(\vq)))
% =\softmax(\vln\pi+\vln(f_1(\vq),\dots,f_k(\vq)))\end{aligned}$$ where $f_i(\vq)$ is the probability density function of the distribution $Dir(\valpha^{(i)})$ where $\valpha^{(i)}$ is the $i$-th row of matrix $\valpha$. Hence, $f_i(\vq)=\frac{1}{B(\valpha^{(i)})}\prod_{j=1}^k q_j^{\alpha_{ij}-... | 609 | 2,271 | 740 | 717 | 2,540 | 0.77903 | github_plus_top10pct_by_avg |
lias, (Key) privateKey, password.toCharArray(), certificate);
} catch (KeyStoreException ex) {
}
}
/**
* Devuelve una clave privada del almacen de contraseñas. El almacen de
* contraseñas debe estar cargado.
*
* @param alias | Alias para almacenar la clave privada.
* @... | 610 | 692 | 75 | 583 | 1,032 | 0.795367 | github_plus_top10pct_by_avg |
arepsilon}^{2/3} \kappa_b^{-2/3}.
\end{split}
\label{W81x}$$ Weinstock [@weinstock81] assumed that $\kappa_b$ can be parameterized by $\frac{N}{\sigma_w}$ (basically, the inverse of the buoyancy length scale $L_b$). By plugging this parameterization into Eq. \[W81x\] and simplifying, we get: $$\begin{split}
\overline{\... | 611 | 3,390 | 1,061 | 691 | null | null | github_plus_top10pct_by_avg |
oups of patients with and without systolic dysfunction, defined by ejection fraction less or more than 50% in a total of 96 hemodiafiltration patients (\* *p* \< 0.05).
----------------------------------------------------------------------------------------------------------------------------------------------------... | 612 | 3,286 | 509 | 441 | null | null | github_plus_top10pct_by_avg |
y the calculations are step by step analogous to that of Ref. [@sln] for $q$-affine case. So we omit all such calculations and only refer to [@sln] and remind the readers of our correspondence rules (Observations 1 and 2).
\[rem2\] In proving Proposition \[prop1\], only the OPE relation (\[aapn\]) for $\hat{a}^i_\pm$ ... | 613 | 864 | 1,078 | 681 | 3,749 | 0.770348 | github_plus_top10pct_by_avg |
verline{\bf p}_{u}$, the displacement $\Delta{\bf p}_{u}$ can be estimated as $$\Delta{\bf p}_{u}=\overline{\bf p}^{*}_{v}-\overline{\bf p}_{u}\nonumber$$ where $\overline{\bf p}^{*}_{v}$ denotes the average position of all ground-truth parts that are annotated for part template $v$. As a result, for each latent patter... | 614 | 227 | 815 | 735 | 2,762 | 0.7772 | github_plus_top10pct_by_avg |
8 0.133 0.131
IGS 0.434 0.270 0.016 0.011 0.193 0.014 0.231 0.223
CW2 0.990 0.977 0.003 0.002 0.775 0.003 0.959 0.892
------... | 615 | 467 | 570 | 720 | null | null | github_plus_top10pct_by_avg |
tive comments.
[^1]: Supported by the Fund for Scientific Research - FWO-project G0561-08
[^2]: Results are available at http://termcomp.uibk.ac.at/
[^3]: Proposition 3 in [@DBLP:journals/corr/abs-0912-4360] uses natural coefficients, but the proposition also holds for polynomials with integer coefficients.
---
ab... | 616 | 326 | 336 | 545 | null | null | github_plus_top10pct_by_avg |
oplus C(L^j)))$ and compute the Dickson invariant of the image of an element of $F_j$ in this orthogonal group.
We write $M_0=N_0\oplus L_j$, where $N_0$ is unimodular with even rank. Thus $N_0$ is either *of type II* or *of type $I^e$*. First we assume that $N_0$ is *of type $I^e$*. Then we can write $N_0=(\oplus... | 617 | 1,059 | 1,020 | 661 | 2,217 | 0.781786 | github_plus_top10pct_by_avg |
parately).
{#F4}
Demographic and clinical characte... | 618 | 350 | 774 | 936 | null | null | github_plus_top10pct_by_avg |
n Eq. (\[eq:gen-qlm\]), and as such generalizes the standard quantum law of motion [@b3]. The antisymmetric super-operator $\mbox{\boldmath$\cal D$}$ in Eq. (\[D\]) introduces a novel mathematical structure that characterizes the time evolution of quantum-classical systems. The Jacobi relation in quantum-classical dyna... | 619 | 3,144 | 1,082 | 570 | null | null | github_plus_top10pct_by_avg |
e actuated automaton is a mechanism representing software. When extended by the principle of emergence (§\[S:PRINCIPLE\_OF\_EMERGENCE\]) and the constructs of the operational profiles (§\[S:OPERATIONAL\_PROFILE\_SECTION\]) and cones (§\[S:CONE\_SECTION\]), it becomes capable of representing precursor conditions for sof... | 620 | 426 | 1,497 | 718 | 1,319 | 0.79109 | github_plus_top10pct_by_avg |
form
\[eq:4.23\] u(t,x,y)&=h(t,r)+|[h]{}(t,|[r]{}),\
v(t,x,y)&=i[( h(t,r)-|[h]{}(t,|[r]{}) )]{},\
(t,x,y)&=[( i )]{},\
(t,x,y)&=-V(t,x,y)+[12]{}(2(t,x,y))++u\_t(t,x,y)dx\
& +\_y(t,x,y)(2(t,x,y))dx+\_0(t),
where the functions $h$ and $\bar{h}$ are defined by (\[eq:4.22bis\]). Note that the solution (\[eq:4.23\]) is a... | 621 | 2,942 | 1,680 | 648 | null | null | github_plus_top10pct_by_avg |
T^{-}(y)
\end{array}$$ for deterministic case. Where we write $A(y)\varsubsetneq B(y)$ if $A(y)\subseteq B(y)$ holds for all $y$ but there exists some $y$ such that $A(y)\neq B(y)$; while by $A(y){\ensuremath {\underset{\mbox{\sout{\tiny{\,?\,}}}}{\subset}}}B(y)$ we indicate that whether or not there exists $y$ ... | 622 | 1,094 | 1,663 | 726 | 1,548 | 0.788359 | github_plus_top10pct_by_avg |
$. Compute $\sigma(\pi\cdot {}^tm_{i,i}')\cdot\begin{pmatrix} a_i&0&0\\ 0&1&1 \\ 0&1 &2\bar{\gamma}_i \end{pmatrix}\cdot(\pi m_{i,i}')$ formally and this equals $\sigma(\pi)\pi\begin{pmatrix} {}^ts_i'a_is_i'+\pi^2X_i & Y_i & \pi Z_i
\\ \sigma( {}^tY_i) &{}^tr_i'a_ir_i'+\pi^2X_i'&\pi Y_i' \\ \sigma(\pi\cdot {}^tZ_i)... | 623 | 3,294 | 672 | 475 | 3,353 | 0.772964 | github_plus_top10pct_by_avg |
ategorical variables, odds ratios, odds of CSB among comparison group vs. odds of CSB among reference group.
Prior to adjusting for covariates, there was a significant association between time and CSB, whereby the proportion of respondents with CSB was significantly lower at 6 months post-deployment than at baseline (... | 624 | 1,973 | 2,050 | 810 | null | null | github_plus_top10pct_by_avg |
\nu}{l^2} + \| {\boldsymbol{\omega}} \|_2^2
\right)^{-(\nu+d/2)},\end{aligned}$$
where $K_\nu$ is a modified Bessel function [@Rasmussen2006]. The smoothness of the process is increased with the parameter $\nu$: in the limit $\nu\rightarrow\infty$ we recover the squared exponential covariance function.
Gaussian proce... | 625 | 311 | 992 | 661 | null | null | github_plus_top10pct_by_avg |
Y$ is a bornologous equivalence between metric spaces. Let $x_0$ be a basepoint of $X$ and set $y_0=f(x_0)$. Suppose $X$ and $Y$ are $\sigma$-stable. Then $\sigma(X,x_0)=\sigma(Y,y_0)$.
Change of basepoint in $\sigma$-stable spaces
=============================================
As mentioned above, the definition of $\... | 626 | 761 | 896 | 722 | null | null | github_plus_top10pct_by_avg |
ep (2), $(z_j^{\ast})_1$ is the image of a fixed element of $F_j$ under the map $\psi_j$. Since $(z_j^{\ast})_1$ can be either $0$ or $1$ by Equation (\[e42\]), $\psi_j|_{F_j}$ is surjective onto $\mathbb{Z}/2\mathbb{Z}$ and thus $\psi_j$ is surjective.\
If $N_0$ is *of type II*, then the proof of the surjectivity ... | 627 | 1,048 | 774 | 685 | 2,944 | 0.775946 | github_plus_top10pct_by_avg |
icular, if $(K,\mathcal{O},k)$ is a $p$-modular system for $G$, then the Mackey algebras $\mu_{k}(G)$ and $\mu_{\mathcal{O}}(G)$ are symmetric if and only of $p^2 \nmid |G|$.
We use the following notations:
- Let $G$ be a finite group. Then $[s(G)]$ denotes a set of representatives of the conjugacy classes of subgr... | 628 | 2,379 | 934 | 629 | null | null | github_plus_top10pct_by_avg |
s thus generates determinants that belong to the external space, ${\cal H}_{1}$. The spawning probability of those $\Ket{\Psi_{0}}$ to $\Ket{\Psi_{1}}$ walkers follows the expression of Eq.\[eq:pspawn\]. However those walkers are spawned on replica 1 instead of replica 0.
Replica 1 that was initially empty starts gett... | 629 | 154 | 596 | 784 | 604 | 0.804604 | github_plus_top10pct_by_avg |
al gain of asking about each of the unexplained objects, thereby determining an optimal sequence of questions for QA. Note that the QA is implemented based on pre-define ontology, instead of using open-ended questions or answers. As in Fig. \[fig:QA\], the user is asked to provide five types of answers (*e.g.* labeling... | 630 | 1,543 | 2,101 | 827 | null | null | github_plus_top10pct_by_avg |
ncertainty in $g^2_k$ is taken into account by evaluating the limiting cases. Together with the error estimate on the physical cutoff scale $k_{\rm phys}$ in Appendix \[app:match\] this leads to an estimate for the systematic error of the results presented below. This error includes that related to our specific choice ... | 631 | 3,262 | 1,134 | 564 | 2,734 | 0.777414 | github_plus_top10pct_by_avg |
�]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, , ****, ().
, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, [ ]{}, , ****, ().
, ****, ().
, , ****, (); , , ***... | 632 | 278 | 397 | 452 | null | null | github_plus_top10pct_by_avg |
again assume that we can expand the vector potential in the scalar and vector bases. Define a one-form $n_a=\text{d}u$, this expansion is given by $$\label{eq:vector-potential}
A_{a} = \sum_{mhk}\left(C_u(u) n_{a} F^{(m\,h\,k)}
+ \sum_{B} C_B(u){V_{a}^{B}}^{(m\,h\,k)}\right),$$ where $B\in \{T,\Phi,R\}$, $C_B(u)$... | 633 | 2,736 | 995 | 611 | 3,309 | 0.773279 | github_plus_top10pct_by_avg |
ans of the mapping $\partial N^{n-2k}_{int} = N^{n-2k}_Q \to Q$, where $N^{n-2k}_{int} \subset N^{n-2k}$, $N^{n-2k}_{int}=g^{-1}(U_P)$, $U_P \subset \R^n$.
### Proposition 3. Geometrical Control Principle for $\I_b$–controlled immersions {#proposition-3.-geometrical-control-principle-for-i_bcontrolled-immersions .unnu... | 634 | 745 | 753 | 659 | null | null | github_plus_top10pct_by_avg |
^3]{}.
We have the following corollary for the original problem.
\[cosystco1\] Suppose that the assumptions of Theorem \[cosystth2\] are valid. Let ${ f}\in L^2(G\times S\times I)^3$ and ${ g}\in T^2(\Gamma_-)^3$. Then the following assertions hold.
\(i) The variational equation \[vareqco\] \_0(,v)=[**F**]{}\_0(v)v ... | 635 | 113 | 1,070 | 761 | null | null | github_plus_top10pct_by_avg |
that specify the size of each unit’s response are independent of the parameters defining the probability that a unit will respond at all. The scalability of our methodology relies on the natural conjugacy structure that we create for the former and an enforced, approximate conjugate structure for the latter. A simulat... | 636 | 266 | 2,452 | 675 | null | null | github_plus_top10pct_by_avg |
e{s}_i=\mathrm{id}$ mod $\pi \otimes 1$. Then $$\begin{gathered}
\label{ea26}
\sigma({}^t\tilde{m}_{i,i})h_i\tilde{m}_{i,i}=(-1)^{i/2}\begin{pmatrix}\sigma({}^t\tilde{s}_i)&\sigma(\pi\cdot {}^t \tilde{y}_i)&\sigma( {}^t \tilde{v}_i)\\
\sigma({}^t \tilde{r}_i)&1+\sigma(\pi \tilde{x}_i)&\sigma(\tilde{u}_i)\\\... | 637 | 2,828 | 625 | 668 | null | null | github_plus_top10pct_by_avg |
ave approximated $q/m\gtrsim{\rm~few}$.
For comparison, in the small $Q$ limit the decay rate into a larger, expanding bubble should be well-approximated by the decay of the ordinary KK vacuum into neutral bubbles. (For larger $Q$, the rate will be faster.) The rate for this process is of order $$\begin{aligned}
\Gamm... | 638 | 1,131 | 1,966 | 818 | 1,570 | 0.788125 | github_plus_top10pct_by_avg |
F = & -\frac{\Gamma w f_0}{a(v/D)} \left[\frac{a}{v} \left(1-\exp
\left(-\frac{2vr}{D} \right)\right)\exp\left(-\frac{v}{D}\ell\right)
\right. \nonumber \\ &
\left.-\frac{v}{D}
\left(1-\exp\left(-\frac{2ar}{v}\right)\right)\right]
\nonumber\\
\simeq & -\frac{\Gamma... | 639 | 3,588 | 1,162 | 641 | 4,182 | 0.767494 | github_plus_top10pct_by_avg |
\Lambda_P$ for some polynomial $P$. Therefore, $$\D(\Gamma)^{D_n}_{\Delta} \cong \D((\Gamma_P)_{\tilde{\Delta}})=\D(\Gamma_{P\Delta}) = \D(\Gamma)_{P\Delta}.$$ Forming the skew fields of fractions we conclude ${{\rm{Frac}}}\, \D(X)^{D_n} \cong {{\rm{Frac}}}\, \D(X)\cong F_n$, which completes the proof of Theorem 3.
G... | 640 | 471 | 1,166 | 655 | null | null | github_plus_top10pct_by_avg |
Codeine Thebaine Papaverine Noscapine
----------------------- ---------------- -------------------- -------------------- --------------------- -------------------- --------------------- ------------
T~0~ WT 1 10.9 ... | 641 | 5,272 | 647 | 219 | null | null | github_plus_top10pct_by_avg |
perator $\mathcal{L}_{H_-}$, $k$ times, on Eq. . While applying the lowering operator, in general different components of $G^{(1)}_{ab}[h^{(m\,h\,k)}]$ will get mixed up, but the separation of variables still holds. Therefore we conclude that with these scalar, vector, and tensor bases, we can separate variables in the... | 642 | 233 | 1,362 | 775 | null | null | github_plus_top10pct_by_avg |
=F$ (use [Lemma \[easy lemma\]]{}).
Suppose $\b, {\gamma}<\k$. Let
$x\in R_{\b, {\gamma}} \iff \phi(x)(\b, {\gamma}) \wedge \forall {\gamma}^\prime <\k (\phi(x)(\b, {\gamma}^\prime) \rightarrow {\gamma}^\prime={\gamma})$.
Then $R_{\b, {\gamma}}$ is $\utilde{\Delta}^2_1$.
We have that the following are equivalent:
... | 643 | 708 | 654 | 622 | null | null | github_plus_top10pct_by_avg |
s. a) The radial position distribution, $\rho(R)$, as a function of the distance $R$ and the radial return velocity $v$ as given by [Eq. (\[radial-constant-velocity\])]{}. Experimental results of a realization with $v=0.8\mu m/s$ are superimposed on the theoretical prediction (black spheres). b) The radial position dis... | 644 | 756 | 1,729 | 765 | 1,631 | 0.787391 | github_plus_top10pct_by_avg |
PE. These poles were already computed to all orders in $f^2$ in [@Ashok:2009xx] using different methods.
[^5]: Using similar methods it can be shown that the subleading terms in equation do not modify this conclusion
[^6]: These conventions differ only slightly from those in [@Ashok:2009xx].
[^7]: We would like to t... | 645 | 152 | 244 | 781 | 3,576 | 0.771431 | github_plus_top10pct_by_avg |
ve performance and training time easy to navigate. Additionally, multiple subset evaluation has no effect on prediction times.
Higher values of $\lambda$ give diminishing returns on predictive performance, so a value that is suitable for the computational budget should be chosen. When training an ensemble of nested di... | 646 | 3,891 | 1,613 | 692 | 3,871 | 0.769613 | github_plus_top10pct_by_avg |
_{i_2}\big(
{\rho ^{\chi '}}(w({\alpha }))K_{{\sigma }_{i_2}{\sigma }_{i_1}^{\chi }({\alpha })}
L_{{\sigma }_{i_2}{\sigma }_{i_1}^{\chi }({\alpha })}^{-1} \times \\
&\qquad \qquad
{T}_{i_2}^-{T}_{i_1}^-(F_{\beta _m}^{{b^{\chi}} (\beta _m)-1}
\cdots F_{\beta _3}^{{b^{\chi}} (\beta _3)-1})
v_{{t}_... | 647 | 1,912 | 817 | 643 | null | null | github_plus_top10pct_by_avg |
nt of ${\hat\Theta_{D}}$ in $\delta\circ\gamma$ is $$\mbinom{u-v}{a-v}\left(\mbinom{u+v-a-1}2+\mbinom{u-a}2\right).$$ Since $v\equiv1$, we have $u+v-a-1\equiv u-a$, so that $\mbinom{u+v-a-1}2$ and $\mbinom{u-a}2$ have the same parity. Hence $\delta\circ\gamma=0$, a contradiction.
Irreducible summands of the form $S^{(... | 648 | 457 | 853 | 723 | 446 | 0.809559 | github_plus_top10pct_by_avg |
(\[ea25\]), (\[ea27\]), and (\[ea32\]). Note that such $m$ also satisfies Equation (\[32’\]). In Lemma \[la8\] below, we will prove that $G^{\ddag}$ is represented by a smooth closed subscheme of $ \mathrm{Ker~}\tilde{\varphi}/\tilde{M}^1$ and is isomorphic to $ \mathbb{A}^{l^{\prime}}\times (\mathbb{Z}/2\mathbb{Z})^{\... | 649 | 1,044 | 699 | 670 | 2,371 | 0.780444 | github_plus_top10pct_by_avg |
mic-like failures or attacks likely in BTNs?”* and *“Will the upgrade to SDNTNs increase the vulnerability with respect to these type of failures or attacks?”* To do so, we present the state of the art of epidemic-like failure models in Section \[soa\]. Then, we review the main failure propagation model that has been p... | 650 | 35 | 1,861 | 666 | 2,359 | 0.780562 | github_plus_top10pct_by_avg |
training and the rest for testing. Note that as $\lambda$ increases, the distribution of the train and test error shifts to lower values and the variance decreases.
This reduction in error affects each binary model in the tree structure, so the effects accumulate when constructing a nested dichotomy. The third row sh... | 651 | 607 | 1,999 | 707 | null | null | github_plus_top10pct_by_avg |
ine(0,1){40}}
\put(10,40){\line(1,-1){30}} \put(10,40){\line(1,1){70}}
\put(10,80){\line(1,1){30}} \qbezier(10,40)(25,75)(40,110)
\put(40,110){\line(1,0){40}} \put(40,10){\vector(1,0){40}}
\put(40,10){\line(2,5){40}} \qbezier(40,10)(75,25)(110,40)
\put(80,110){\line(1,-1){30}} \qbezier(80,110)(95,75)(110,40)
\put(110,4... | 652 | 2,208 | 394 | 485 | null | null | github_plus_top10pct_by_avg |
is replaced by an isotropic $\alpha$-stable process with $\alpha\in(0,2)$. A significant difference to the Brownian setting is that the stable processes will exit spheres by a jump rather than hitting their boundary. This difference ensures that disconnected domains may be considered and that, unlike the diffusive set... | 653 | 186 | 330 | 758 | 2,363 | 0.780514 | github_plus_top10pct_by_avg |
$$
Use of the fourth and third equations of Eq. (\[Eqn: HR Ortho\]) and the explicit relation Eq. (\[Eqn: W(mu)\]) for $W(\mu)$ gives respectively the coefficients $$\begin{aligned}
{\displaystyle a_{\textrm{A}}(\nu_{0})} & = & X(-\nu_{0})/X(v_{0})\nonumber \\
a_{\textrm{A}}(\nu) & = & -\frac{1}{N(\nu)}\textrm{ }c(1-c... | 654 | 2,351 | 900 | 749 | null | null | github_plus_top10pct_by_avg |
ession Description Score Coverage MW \[kDa\] calc. pI
----------- ---------------------------------------------- --------- ---------- ------------ ----------
P00734 Prothrombin 2759.75 73.31 70.0 5.90
P0C0L5 C... | 655 | 4,507 | 334 | 364 | null | null | github_plus_top10pct_by_avg |
\mathbf{N}_{ab}\left[ \phi_{+}\right]
\phi_{-}^b +... \label{d80}$$
Where the ellipsis means terms of higher order in $\phi_-$. Here $\mathbf{D}$ ($\mathbf{N}$) is the so-called dissipation (noise) kernel. This is of course identical in form to the effective action for a stochastic theory \[ne20\], and thereby we ma... | 656 | 52 | 484 | 807 | null | null | github_plus_top10pct_by_avg |
Medium Medium Low
Improbable (E) Medium Medium Medium Low
Eliminated (F)
---------------- -------------- ---------- ---------- ------------
: MIL-STD-882E Risk Assessment Matrix[]{data-label="Ta:RISK_ASSESSMENT_MATRIX"}
This table suffers... | 657 | 387 | 937 | 852 | 1,771 | 0.785836 | github_plus_top10pct_by_avg |
can obtain the most information gain. A question [$q\!=\!(I,\hat{v},\Lambda_{\hat{v}})$]{} requires people to determine whether our approach predicts the correct part template $\hat{v}$ and parses a correct region $\Lambda_{top}=\Lambda_{\hat{v}}$ for the part. Our method expects one of the following answers.
**Answe... | 658 | 1,255 | 1,871 | 687 | 1,833 | 0.785282 | github_plus_top10pct_by_avg |
( {\bf 1} - W^{\dagger} W ) \end{array}
\right].
\nonumber \\ \end{aligned}$$ Therefore, our system depends only on $U$ and $W$, and the dependence on $Z$ and $V$ is superficial.
Universal scaling model of $N$ sterile sector {#sec:scaling-model}
==============================================
Suppose that we obtain... | 659 | 3,659 | 1,046 | 520 | 1,398 | 0.79007 | github_plus_top10pct_by_avg |
linic, *P*2~1~/*c* Mo *K*α radiation, λ = 0.71073 Å
Hall symbol: -P 2ybc Cell parameters from 27248 reflections
*a* = 22.931 (2) Å θ = 3.0--27.5°
*b* = 14.0395 (12) Å µ = 3.93 mm^−1^
*c* = 27.8... | 660 | 296 | 1,697 | 731 | null | null | github_plus_top10pct_by_avg |
19746
AREG 0.042790083 0.515091119
ELF4 0.001468499 0.516939945
NAB2 0.011359365 0.527142113
GPC1 0.030827537 0.529427807
SLC25A5 0.029858761 0.534298394
RYR1 0.014981235 0.538436907
PEG10 0.018255692 0.549699307
SLIT2 0.005701378 0.5519... | 661 | 470 | 668 | 939 | null | null | github_plus_top10pct_by_avg |
an occur as irreducible summands of $S^\la$ are $S^{(a+b,3)}$ and $S^{(a+b-4,5,2)}$.
Suppose $S^\la$ has an irreducible summand $S^{(u,v)}$ with $v>3$. Then $v\equiv3\ppmod4$ and $u-v\equiv7\ppmod8$, which means that $u+v\equiv5\ppmod8$ and hence $a+b\equiv2\ppmod8$. Furthermore, $(u,v)\dom\la{^{\operatorname{reg}}}$,... | 662 | 380 | 1,622 | 686 | 3,052 | 0.775194 | github_plus_top10pct_by_avg |
(-1)^b T(b,s,a \,; x-y,x) + (-1)^a T(s,a,b \,; y, y-x).$$ It is easy to see that $S (a,b,s \,; x,y) = S (a-1,b,s+1 \,; x,y) + S (a,b-1,s+1 \,; x,y)$ because of $T (a,b,s \,; x,y) = T (a-1,b,s+1 \,; x,y) + T (a,b-1,s+1 \,; x,y)$. Hence we have $$\begin{split}
S (a,b,s \,; x,y) = &\sum_{j=1}^{a} \binom{a+b-j-1}{a-j} S (... | 663 | 1,520 | 807 | 719 | null | null | github_plus_top10pct_by_avg |
, a contradiction.
[**Case**]{} $|L|=2$\
We may assume that $L=\{2,4\}$. Then $|\cS|=|\cS^*|+2+|Y_2|+|Y_4|$ and $Y=Y_2\cup Y_4$. From (\[prop:bi\]) we have $\cR^*\subseteq\{ \{2,3,4\},\{2,4,5\}\}$. We need only consider cases for which $|\cR|\ge 5+|Y|$.\
1. $|Y_2|,|Y_4|\ge 2$\
From (\[prop:ylarge\]) we know that... | 664 | 633 | 812 | 693 | null | null | github_plus_top10pct_by_avg |
In \[Sec:intro\] the conditions for obtaining an appropriate SUSY threshold correction to the bottom quark mass in models with third family Yukawa unification were determined by an interpretation of the approximate formula given in \[Eq:common-app\]. We revisit this scenario here to offer a more accurate interpretation... | 665 | 72 | 1,224 | 813 | null | null | github_plus_top10pct_by_avg |
neighbourhoods of the SOM that are described below.
1. [**Gaussian SOM** ]{}\
The kernel neighbourhood is defined by gaussian function\
$h_{ci}(t) =e^{\LARGE{\left({d^2_{ci}}/{2\sigma^2_t}\right)}}, $ here $d^2_{ci}$ is the distance between the winner unit $c$ and the unit $i$, $\sigma_t$ is the neighbourhoo... | 666 | 5,060 | 1,333 | 431 | null | null | github_plus_top10pct_by_avg |
putting your name on the title slide.
As for the rest of the team, if they are still around, take a team photo with everyone and insert that on the last slide of the presentation. You can then list their names from left to right, however they happen to arrange themselves.
Q:
Flutter: multiple firebase projects in... | 667 | 292 | 161 | 272 | 1,274 | 0.79173 | github_plus_top10pct_by_avg |
rt’ is $O(D)$, where $D$ is a diameter of $\mathbf P$) repelling forces allows us to implement Jarvis wrapping algorithm [@jarvis1973identification]. We select a starting point which is extremal point of $\mathbf P$. We pull a rope to other extremal point. We continue until the set $\mathbf P$ is wrapped completely. We... | 668 | 4,818 | 1,988 | 616 | null | null | github_plus_top10pct_by_avg |
y the day but it doesn't display correct data
$VisitsTrends=DB::table('visits')
->select('created_at',\DB::raw('count(*) as total'))
-> groupBy('created_at')->get();
dd($VisitsTrends);
it just displays data by each time and counts differently
A:
You may use
use Illuminate\Support\Fa... | 669 | 1,867 | 86 | 338 | 1,208 | 0.792684 | github_plus_top10pct_by_avg |
d therefore, that $| x- \mu_j|
\leq \frac{F_j(x) - F_j(\mu_j)}{M}$. By the DKW inequality and the union bound, with probability at least $1-1/n$, $$\label{eq:dkw.median}
\max_{j \in {\widehat{S}}} \|F_{n,j} - F_j \|_\infty \leq \sqrt{\frac{ \log 2kn}{2n} }.$$ Thus, for any $j \in {\widehat{S}}$, $$\left| F_{n,j}(... | 670 | 2,067 | 504 | 706 | 3,230 | 0.773864 | github_plus_top10pct_by_avg |
---
abstract: 'Many deep learning algorithms can be easily fooled with simple adversarial examples. To address the limitations of existing defenses, we devised a probabilistic framework that can generate an exponentially large ensemble of models from a single model with just a linear cost. This framework takes advantag... | 671 | 246 | 737 | 523 | 3,279 | 0.773509 | github_plus_top10pct_by_avg |
&f_1,\nonumber\\
-{{\frac{\partial (S_j\psi_j)}{\partial E}}}+\omega\cdot\nabla_x\psi_j+
\Sigma_j\psi_j
- K_j\psi={}&f_j, \quad j=2,3, \label{desol10}\end{aligned}$$ holding a.e. on $G\times S\times I$, together with the inflow boundary and initial values $$\begin{aligned}
{3}
\psi_{|\Gamma_-}&=g && \quad {\rm a.e.\ o... | 672 | 418 | 675 | 757 | 2,418 | 0.77991 | github_plus_top10pct_by_avg |
n{array}{l l}
v_i\cdot ({}^tg_{i, i}-\mathrm{Id}_{n_i}) & \quad \textit{if $L_i$ is \textit{free of type I}};\\
\delta_{i-1}v_{i-1}\cdot {}^tg_{i, i-1}+\delta_{i+1}v_{i+1}\cdot {}^tg_{i, i+1} & \quad \textit{if $L_i$ is \textit{bound of type I}}.
\end{array} \right.$$
Here,
- $v_{i... | 673 | 3,059 | 839 | 584 | null | null | github_plus_top10pct_by_avg |
t( \sqrt{ \frac{\log k}{n}} \right)$, a fact made precise in the following result.
\[cor:accuracy.LOCO\] With probability at least $ 1- \frac{1}{n}$, the maximal length of the sides of the hyper-rectangle $\tilde{C}_n$ is bounded by $$C \left(2(A + \tau) + \epsilon \right) \sqrt{ \frac{\log k}{n} \left( 1 + \frac{(4... | 674 | 2,477 | 1,152 | 739 | null | null | github_plus_top10pct_by_avg |
ng characteristic (ROC) curve to evaluate the multiple ML approaches on the same dataset ([Table 4](#table4){ref-type="table"}). We found that adaptive boosting neural networks achieved the biggest ROC area under the curve on the air quality data, tree bag on the climate data, and random forest on weather and air quali... | 675 | 40 | 651 | 750 | null | null | github_plus_top10pct_by_avg |
ate for $\mathscr{C}(X|\mathcal{O}, \mathcal{H})$, we need to determine an optimization *strategy* for picking the next set of parameters to test.
![The optimization of the evaporation stage of creating a BEC using the complex 16 parameter scheme. The first 20 evaluations are an initial training run using a simple Nel... | 676 | 632 | 1,098 | 617 | 3,407 | 0.772637 | github_plus_top10pct_by_avg |
nd choline performed. Paired sampled from 20 dogs were available for urine selenium analysis. Those 20 samples were selected from the original 31 dogs in order of percentage weight lost (i.e., the 20 dogs with the greatest percentage of weight loss had their samples analysed). Weight loss characteristics for dogs inclu... | 677 | 411 | 595 | 969 | null | null | github_plus_top10pct_by_avg |
1.1 8.6
$f_4$ $1603.071\pm0.004$ 623.8 1.1 8.1
$f_1^+?$ $3437.384\pm0.005$ 290.9 11.1 1.0 11.6
$f_5$ $7726.540\pm0.003$ 129.4 1.0 13.0
$f_4^-?$ $1595.481\pm0.004$ 626.8 7.6 0.8 6.1
$2f_1$ $6852.604\pm0.005$ 145.9 ... | 678 | 520 | 1,391 | 914 | 2,277 | 0.781207 | github_plus_top10pct_by_avg |
kappa_j(\kappa_j-1)} \sum_{a =1}^{\ell_j} \lambda_{j,a}(\kappa_j-p_{j,a}) \sum_{i<\i \in S_j} (e_i - e_{\i})(e_i - e_{\i})^\top \nonumber\\
&=& 2\gamma e^{-6b} L, \label{eq:positionl_expec}\end{aligned}$$ where we used $\gamma\leq (1-p_{j,\ell_j}/\kappa_j )^{\alpha_1-2}$ which follows for the definition in . follows f... | 679 | 955 | 643 | 729 | null | null | github_plus_top10pct_by_avg |
1 & i \\
1 & -i
\end{array}
\right)},$$we observe that the matrices $\eta_{a_j}$ defined by (\[eq:3.28b\]) all vanish. Consequently, all trace conditions (\[eq:3.34\]) used to obtain solutions of the form (\[eq:4.10\]) are identically satisfied. We still have to consider the tr... | 680 | 3,579 | 1,554 | 640 | 3,030 | 0.775344 | github_plus_top10pct_by_avg |
for $i\in \mathcal{H}$.
The proof to show that $\varphi=\prod_i \varphi_i$ is surjective is similar to that of Theorem 4.5 in [@C2] explained from the last paragraph of page 485 to the first paragraph of page 486 and so we skip it.
Now it suffices to prove Equation (\[e41\]) made at the beginning of the proof, which ... | 681 | 3,072 | 800 | 567 | 2,611 | 0.778436 | github_plus_top10pct_by_avg |
frac{1}{a}} \right)} dz
+ (k_{1} + k_{2}) b c \beta \int_{0}^{c / b} \exp{\left( - z^{\frac{1}{a}} \right)} dz. \end{aligned}$$ When $c < 0$, we have $$\begin{aligned}
\operatorname{{E}}[\Pe(Z + c)]
&= k_{2} b \beta
\int_{- \infty}^{0} (- b z - c) \exp{\left( - (-z)^{\frac{1}{a}} \right)} dz
+ k_{2} b \beta \int_{0... | 682 | 3,019 | 817 | 665 | null | null | github_plus_top10pct_by_avg |
attering of particles from the given external region $G_{\rm e}$. The flux $u=(u_1,u_2,u_3)$ contributed by this inflow source is governed by the system of equations $$\begin{gathered}
\omega\cdot\nabla_x u_1+\Sigma_1 u_1-K_{1}u=0,\label{ref9}\\
-{{\frac{\partial (S_{j}u_j)}{\partial E}}}+\omega\cdot\nabla_x u_j+\Sigma... | 683 | 108 | 488 | 798 | 3,561 | 0.771539 | github_plus_top10pct_by_avg |
large as those based on *F*, and *R*- factors based on ALL data will be even larger.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------... | 684 | 279 | 985 | 871 | null | null | github_plus_top10pct_by_avg |
(1-2x^2+5x^4) A_{22} \,,
\nonumber \\
%
\left[ \rule{0pt}{9pt} \ldots \right] &= &2 A_{00} + (1+3x^2) (A_{11} + A_{22})
+ 2 \, x (9x^2-5)
\nonumber \\
%
& \times & \cos(\phi_{12})A_{12} + 2 \sqrt{2} (3x^2 - 1)
\cos(\phi_{20}) A_{20} \, ,
\nonumber
%\end{aligned}$$ for $\{s_{1/2},p_{1/2},d_{3/2} \}$, $\{s_{1/2},p_{3/... | 685 | 553 | 1,546 | 814 | 2,412 | 0.779991 | github_plus_top10pct_by_avg |
t.AspNetCore.Mvc.Infrastructure.ActionMethodExecutor.TaskOfIActionResultExecutor.Execute
at Microsoft.AspNetCore.Mvc.Infrastructure.ControllerActionInvoker.<InvokeActionMethodAsync>g__Awaited|12_0
at Microsoft.AspNetCore.Mvc.Infrastructure.ControllerActionInvoker.<InvokeNextActionFilterAsync>g__Awaited|10_0
at Microsof... | 686 | 2,432 | 809 | 888 | 278 | 0.81489 | github_plus_top10pct_by_avg |
be $\frac{1-C}{(1+\delta)C_1}$ for any given constant $0<C<1$. Clearly, for such an $\varepsilon$, we have $1-(1+\delta)\varepsilon C_1 = C$ and $$1-\frac{1+\delta}{4\varepsilon\alpha} \ge C \quad \Longleftrightarrow \quad \alpha \ge \frac{(1+\delta)^2C_1}{4(1-C)^2}.$$ Combine the above and let $\alpha \ge \frac{(1+\d... | 687 | 1,364 | 629 | 716 | null | null | github_plus_top10pct_by_avg |
rly, the *persistent* state of frame ${\mathbf{f}}$ is $\phi = {{({{\operatorname{absc}{{\mathbf{f}})}}}}\negmedspace\mid\negmedspace{{{\operatorname{dom}{\Phi}}}}}$.
Process concepts interpret into systems language. The reactive space ${\prod{\Psi}}$ contains the system stimulus. Sequential conjointness allows circum... | 688 | 577 | 1,359 | 898 | 685 | 0.802353 | github_plus_top10pct_by_avg |
X$ into $Y$ as the prototype of a preimage continuous map. Clearly the topology of $Y$ may also contain open sets not in $e(X)$, and any subset in $Y-e(X)$ may be added to the topology of $Y$ without altering the preimage topology of $X$: *open sets of $Y$ not in $e(X)$ may be neglected in obtaining the preimage topolo... | 689 | 3,582 | 1,986 | 667 | 2,726 | 0.7775 | github_plus_top10pct_by_avg |
$C^\infty$-functions $h$ and $A:V_{y_0}\times S\to {\mathbb{R}}$ such that $$\begin{aligned}
& A_f(x,\omega):={1\over{S_0(x)}}\omega\cdot \nabla_x f(x)=-h(x,\omega)A(x,\omega) \label{ass-on-G-1} \\[2mm]
& (V_{y_0}\times S)\cap\Gamma_+'=\{(y,\omega)\in\partial G\times S\ |\ h(y,\omega)>0\}, \label{ass-on-G-2} \\[2mm]
&... | 690 | 412 | 1,069 | 787 | null | null | github_plus_top10pct_by_avg |
elation $\langle f|Le \rangle = \langle L^\dag f|e \rangle^\ast$): $${{\mathcal C}}_{AB} = \big\{ L\colon {\cal H}_A \to {\cal H}_B\;
\mbox{bound antilinear}\,
\big| \mathop{\mbox{tr}}(L^\dag L) < \infty \big\}.
\label{eq:CBC}$$ ${{\mathcal C}}_{AB}$ forms a Hilbert space (the scalar product is $(L,L') =
\mathop{\... | 691 | 1,395 | 639 | 773 | 1,781 | 0.785753 | github_plus_top10pct_by_avg |
frak z$. It follows that $$\label{semisimple restriction}
m_{T_G,V_{G,\lambda}}(\beta) = \begin{cases}
m_{T_{G_{\operatorname{ss}}},V_{G_{\operatorname{ss}},\lambda_{\operatorname{ss}}}}(\beta_{\operatorname{ss}}) & \text{if $\lambda_z = \beta_z$},\\
0 & \text{otherwise},
\end{cases}$$ where we write $\mu... | 692 | 1,793 | 960 | 758 | null | null | github_plus_top10pct_by_avg |
�кая:
Создан memDC, в нём выбран битмап.
В функции, предназначенной для рисования (или даже в нескольких функциях) рисуем на этот memDC кучу объектов - например, заполнили фон, нарисовали сто прямоугольников, десять эллипсов и надписей.
Когда картинка подготовлена, вызываем InvalidateRect для окна программы. Это приво... | 693 | 935 | 92 | 344 | null | null | github_plus_top10pct_by_avg |
ert g\Vert _{\infty }.$$Since $\eta -\kappa >d$, it follows that for every $m_0\geq 1$ (recall that $t<1$), $$\begin{aligned}
d_{0}(\mu ^{\eta ,\kappa },\mu ^{\eta ,\kappa ,m_{0}})
\leq \sum_{m\geq m_{0}+1}\frac{(c_\kappa \rho)^m}{m!}
\leq \frac{(c_\kappa \rho)^{m_0}}{{m_0}!}\, e^{c_\kappa \rho}.\end{aligned}$$ So, fo... | 694 | 1,917 | 576 | 745 | null | null | github_plus_top10pct_by_avg |
presence of neutrino masses, we get an additional source of weak CP-violation coming from the complex phase of the PMNS matrix. In complete analogy with the quark sector, we can construct new CP-violating flavor structures which tune the PMNS-induced quark and lepton EDMs. In this case, quark EDMs have a bubble topolo... | 695 | 996 | 1,525 | 791 | 2,574 | 0.778781 | github_plus_top10pct_by_avg |
rgence in topological spaces with a proof of the following theorem which demonstrates the relationship that “eventually in” and “frequently in” bears with each other; Eq. (\[Eqn: net adh\]) below is the net-counterpart of the filter equation (\[Eqn: filter adh\]).
**Theorem A1.5.** *If $\chi$ is a net in a topological... | 696 | 1,309 | 1,686 | 780 | 2,095 | 0.782776 | github_plus_top10pct_by_avg |
confined phase. The expectation value $\langle
\varphi\rangle$ in the center-broken deconfined phase is given by the transition point between decreasing part of the potential for small $\varphi$ and the flat region in the middle of the plot. It can also be explicitly computed from . In the center-symmetric confined pha... | 697 | 136 | 1,069 | 845 | 1,927 | 0.78433 | github_plus_top10pct_by_avg |
th Tarski’s theory by reinterpreting them as theories of metalinguistic concepts that are different from truth (in the case of physical QL, the concept of *empirical justification* in QM).
Secondly, we observe that our interpretation has some consequences that are intuitively satisfactory. For instance, for every stat... | 698 | 1,639 | 2,410 | 846 | 3,118 | 0.774752 | github_plus_top10pct_by_avg |
qual to $$\begin{gathered}
\alpha_1 \{ \dot xyx^3 \}
+\alpha_2 \{ y\dot x x^3 \} +\alpha_3 \{ \dot xxyx^2 \} +\alpha_4 \{
\dot xyx^3 \} +\alpha_5 \{ yx^2\dot xx \}
+\alpha_6 \{ y\dot xx^3 \} \\
- \alpha_1 \{ \dot xyxy^2 \} - \alpha_2 \{ y\dot xxy^2 \} - \alpha_3
\{ \dot x xy^3 \} - \alpha_4 \{ \dot xy^3x \} - ... | 699 | 823 | 1,578 | 876 | null | null | github_plus_top10pct_by_avg |
imulus 37V from the seven (left) and eight (right) MU model without post-process adjustment. Thin lines identify the contribution to the predictive for the indicated firing combinations associated to the final few MUs. In both cases, the first five MUs fire with near certainty. Most firing combinations with negligible ... | 700 | 98 | 1,875 | 664 | 2,345 | 0.780715 | github_plus_top10pct_by_avg |
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