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 |
|---|---|---|---|---|---|---|---|
in Sec. \[sec:high-lowest-weight\], i.e. they form the highest-weight modules for ${\ensuremath{SL(2,\mathbb{R})\times U(1)}}\circlearrowleft \mathcal{M} $. Such a highest-weight module is infinite dimensional, the length of this paper, however, is supposed to be finite. Therefore, we give the highest three weights for... | 201 | 2,152 | 613 | 246 | null | null | github_plus_top10pct_by_avg |
\left(u^2+1\right)^3} \\
\mathcal{D}_{Tu} & -\frac{i m u \left(u^6+3 u^4-21 u^2+9\right)}{8 \left(u^4-1\right)^2} \\
\mathcal{D}_{\Phi R} & -\frac{i m \left(u^4+6 u^2-3\right)}{4 \left(u^2+1\right)^3} \\
\mathcal{D}_{\Phi u} & -\frac{i m u \left(u^2-3\right)}{2 \left(u^2-1\right) \left(u^2+1\right)^2} \\
\noalign... | 202 | 300 | 470 | 266 | null | null | github_plus_top10pct_by_avg |
ponse. Further details can be found in [@Thompson66; @Wang95].
The habituation mechanism used in the system described here is Stanley’s model. The synaptic efficacy, $y(t)$, decreases according to the following equation:
$$\tau \frac{dy(t)}{dt} = \alpha \left[ y_0 - y(t) \right] - S(t),
\label{HabEqn}$$
where $y_0$ ... | 203 | 3,366 | 518 | 248 | null | null | github_plus_top10pct_by_avg |
(0.008)
Gender (= 1 if female) 0.054\ 0.050\ 0.041\
(0.080) (0.080) (0.077)
Age ... | 204 | 4,324 | 448 | 137 | null | null | github_plus_top10pct_by_avg |
ight come with the risk of name clashes, and it might be difficult to associate them with their original counterparts.
Instead, I'd suggest you wrap all those identifiers into d['...'] to make them strings used for lookup in a dictionary. Then wrap the dictionary with the values into another accordingly named dictionar... | 205 | 4,754 | 148 | 145 | 1,290 | 0.791487 | github_plus_top10pct_by_avg |
action with the environment affects software behavior, which is ultimately transmitted through response to changing volatile variables. Normal operations calls for a run of figuratively unbounded duration during which software experiences the usage pattern’s variation of volatile stimulus, in response to which possibly... | 206 | 1,444 | 1,040 | 228 | 1,138 | 0.793776 | github_plus_top10pct_by_avg |
[Strauss]{}, M. A., [Yahil]{}, A., & [Huchra]{}, J. P., 1994, , 927+
, K. B. & [Nusser]{}, A., 1996, , L1, , N. Y. & [Hui]{}, L., 1998, , 44+
, A. J. S., 1997, astro-ph 9708102
, A. F. & [Taylor]{}, A. N., 1995, , 483
, L., 1998, in preparation
, L. & [Gnedin]{}, N. Y., 1997, , 27
, L., [Gnedin]{}, N. Y., & [Zhan... | 207 | 1,195 | 1,465 | 347 | null | null | github_plus_top10pct_by_avg |
hin the class
------------------------------------ ----------------------------------------------- ---------------------------- ------------------------------------------- -------------------------------------------- ------- -------
0 \- ... | 208 | 5,108 | 217 | 107 | null | null | github_plus_top10pct_by_avg |
uracy, precision, recall, and F measure for nonpeak events and peak events, respectively. Evaluation of the ML approaches on the weather and air quality data are shown in [Table 3](#table3){ref-type="table"}. It showed that the developed random forest gave the best predictive performance. This was mainly due to the dat... | 209 | 4,497 | 183 | 127 | null | null | github_plus_top10pct_by_avg |
Inclusion items Selection criteria
-------------------- ---------------------------------------------------------------
Tumor type Osteosarcoma
Sample type Tumor tissue or blood
Assay method qRT-PCR or FISH
Time of study January 2003 to September 2017
Follow-up (mo... | 210 | 947 | 629 | 314 | null | null | github_plus_top10pct_by_avg |
obot dynamics, then the weight matrix $W$ of the RBFN were initialized by zeros. Moreover, an unexpected disturbance as shown in Fig. \[fig5\], which exerts the applied forces, was taken into consideration to illustrate robustness of the proposed approach.
![External disturbance[]{data-label="fig5"}](disturbance.png){... | 211 | 1,871 | 663 | 316 | null | null | github_plus_top10pct_by_avg |
f the other methods provide any guarantees over unknown selection rules.
Numerical Examples {#section::simulation}
==================
In this section we briefly consider a few illustrative examples. In a companion paper, we provide detailed simulations comparing all of the recent methods that have proposed for infere... | 212 | 83 | 334 | 311 | null | null | github_plus_top10pct_by_avg |
ial V(\rho_0)}|\rho_1-z|\Bigr\};$$ that is, the minimum of $\varepsilon$ and the orthogonal distance of $\rho_1$ from $\partial V(\rho_0)$. Next, define $\theta_1$, the angle that subtends at $\rho_0$ between $\rho_1$ and the origin $(0,\dots,0)$ and recall that symmetry implies that $\theta_1$ is uniformly distributed... | 213 | 3,003 | 456 | 226 | null | null | github_plus_top10pct_by_avg |
nly (step 4.2); pilot survey, data linkage and further contact (step 4.3); or pilot survey only and further contact (step 4.4). Those who consent to further contact, irrespective of data linkage consent, will be invited to take part in the HAGIS Wave 1 (step 6.0).
Survey instrument {#s2e}
-----------------
The HAGIS ... | 214 | 217 | 908 | 321 | null | null | github_plus_top10pct_by_avg |
tries of $m_{i-1, i}, m_{i+1, i}$, and $$m_{i\pm 2, i}^{\natural}= \left\{
\begin{array}{l l}
\textit{the $n_{i\pm 2}\times (n_i-1)$-th entry of $m_{i\pm 2, i}$} & \quad \textit{if $L_{i \pm 2}$ is of type $I^o$};\\
\textit{the $(n_{i\pm 2}-1)\times (n_i-1)$-th entry of $m_{i\pm 2, i}$} & \quad ... | 215 | 897 | 242 | 290 | 2,306 | 0.781012 | github_plus_top10pct_by_avg |
eps, to allow the network to settle to a steady state before stimulus presentation. For each filter channel, the response at the central receptive field was quantified and normalized to unit maximum before averaging. The average $R_1$ response and two exemplar units are displayed in Fig. \[end\_stopping\_R\]. As mentio... | 216 | 1,057 | 1,299 | 342 | null | null | github_plus_top10pct_by_avg |
- e_{\i})^\top \sum_{m = 1}^{\ell_j} \frac{(\kappa_j - m)}{\kappa_j(\kappa_j - 1)(\kappa_j - m +1)} \\
&\preceq& \ell\Big(1 - \frac{1}{\ell_j}\sum_{m= 1}^{\ell_j} \frac{1}{\kappa_{\max} - m +1}\Big) \underbrace{\sum_{j = 1}^n \frac{1}{\kappa_j(\kappa_j - 1)} \sum_{i<\i \in S_j} (e_i - e_{\i})(e_i - e_{\i})^\top}_{=L}... | 217 | 1,280 | 380 | 280 | null | null | github_plus_top10pct_by_avg |
j$, if $i= j$ and $L_i$ is *of type $II$*, or if $L_i$ is *bound of type $I$* with odd $i$, $$m_{i,j}''=\sum_{k=1}^{N}\pi^{(max\{0, k-i\}+max\{0, j-k\}-max\{0, j-i\})}m_{i, k}m_{k, j}';$$
2. For $L_i$ *of type $I^o$* with $i$ even, we write $m_{i, i-1}m_{i-1, i}'+m_{i, i+1}m_{i+1, i}'=\begin{pmatrix} a_i''&b_i''\\ c... | 218 | 3,241 | 280 | 215 | null | null | github_plus_top10pct_by_avg |
ne{\mho}_\Lambda(\lbrace {\mathit{s}}_n \rbrace))(i) = \mho_\Lambda(\lbrace {\mathit{s}}_n \rbrace(i))$; that is, the ${{i}^{\text{th}}}$ term of the sequential path projection equals the locus projection of the ${{i}^{\text{th}}}$ step.
Analogous assertions are true of the remaining sequential projections: $(\overlin... | 219 | 1,497 | 561 | 289 | 4,000 | 0.768715 | github_plus_top10pct_by_avg |
$\mathcal{C}_{i2}$. In this section, we give an overview of existing class subset selection methods for nested dichotomies. Note that other methods than those listed here have been proposed for constructing nested dichotomies—these are not suitable for use with our method and are discussed later in Related Work.
Rando... | 220 | 2,326 | 1,244 | 285 | 3,768 | 0.77019 | github_plus_top10pct_by_avg |
rrentTimeMillis();
Date startDate = new Date(now);
// X500Name dnName = new X500Name(subjectDN);
X500Name dnName = new X500Name("C = DE, O = Organiztion");
BigInteger certSerialNumber = new BigInteger(Long.toString(now));
Calendar calendar = Calendar.getInsta... | 221 | 109 | 134 | 135 | null | null | github_plus_top10pct_by_avg |
\alpha/(2s)} \sqrt{
\frac{\hat\Gamma_n(j,j)}{n} }, \hat\beta_S(j) + z_{\alpha/(2s)} \sqrt{
\frac{\hat\Gamma_n(j,j)}{n} }\right],$$ with $\hat\Gamma$ given by (\[eq::Ga\]) and $z_{\alpha/(2s)}$ the upper $1 -
\alpha/(2s)$ quantile of a standard normal variate. Notice that we use a Bonferroni correction to guaran... | 222 | 1,257 | 346 | 302 | 1,670 | 0.786996 | github_plus_top10pct_by_avg |
{i-2, i}$ (resp. $y_{i+2, i}$) if $L_{i-2}$ (resp. $L_{i+2}$) is *of type* $\textit{I}$.
4. Assume that $i$ is odd. Consider the following $(1\times n_i)$-matrix: $$\left\{
\begin{array}{l l}
v_i\cdot y_{i, i} & \quad \textit{if $L_i$ is \textit{free of type I}};\\
\delta_{i-1}v_{i-1}\cdot y_{i-1... | 223 | 2,256 | 521 | 258 | 2,274 | 0.781219 | github_plus_top10pct_by_avg |
each cell by the complete number of transitions in the dataset. We illustrate these matrices as heatmaps to get insights into the most common transitions in the complete datasets. Due to tractability, we focus on a first order analysis and will focus on higher order patterns later on.
{width="\textw... | 224 | 4,029 | 388 | 244 | null | null | github_plus_top10pct_by_avg |
All experiments were performed in double-blind fashion. IBAT, intrascapular brown adipose tissue. \*, *p* \< 0.001.
Organ WT (*n* = 15) β-SG null mice (*n* = 11)
--------------------- --------------- ---------------------------
Quadriceps muscle 1.54 (0.02) 1.79 (0.06)\*
Calf muscle ... | 225 | 2,651 | 1,815 | 312 | null | null | github_plus_top10pct_by_avg |
data set. A value of 40 has a converted probability of 0.0001 incorrect reads. In contrast, the position is homogeneous in the isogenic reference genome. The same phenomenon was observed at other heterogeneity sites, including those at the genes encoding for the sulfatase family protein, the lipoate-protein ligase A f... | 226 | 1,479 | 937 | 356 | null | null | github_plus_top10pct_by_avg |
------------ ------- ------- ------- ------- ------- -------
: Transferability of attacks between LeNet and triplet network.[]{data-label="table:transferability_classifier_detector"}
Jointly Fooling Classifier and Detector
---------------------------------------
If an adversary is unaware that a detector is in ... | 227 | 734 | 555 | 305 | null | null | github_plus_top10pct_by_avg |
space dependent operators [@qc-bracket; @kcmqc]: The action of the operator $J$ in Eq. (\[eq:qc-l\]) can build and destroy coherence in the system by creating and destroying superposition of states. As explained above, this is a feature of a non-linear theory. Such a non-linear character is simply hidden in the operat... | 228 | 97 | 472 | 299 | null | null | github_plus_top10pct_by_avg |
);
for (var i in all) {
var cur = all[i];
if (cur.getAttribute('class') === "linkclass") {
return cur.getAttribute('href');
}
}
return undefined;
})();
Note: If there is only ever one element of that class it would be much more efficient to give the element a unique id instead of a class. The ... | 229 | 22 | 212 | 177 | 128 | 0.823887 | github_plus_top10pct_by_avg |
erically search for HTML elements based on name and type. I'd approach this by selecting how to search the element name based on a type parameter. The example below assumes that the target cell (in row sourceRow and column sourceCol) contains the element name, ie "media-body", and the cell to the right contains its t... | 230 | 5,930 | 82 | 58 | 177 | 0.820483 | github_plus_top10pct_by_avg |
$0.21$ $0.05\,\imath$ $-0.83\,\imath$ $0.20$ $0.34$
: \[tab:01\] Coupling constants in the three different frequency ranges (i)-(iii). According to Eq. (\[eq:s15\]), $\lambda^{\rm exp}_{\rm 50\Omega}$ and $\lambda^{\rm fit}_{\rm 50\Omega}$ should be compared to ... | 231 | 181 | 856 | 340 | null | null | github_plus_top10pct_by_avg |
}.\end{aligned}$$ Here $v_i$ is the standard vector which corresponds to $q_i$. Similarly, for the bases $\langle v_5, v_6, v_7\rangle$ and $\langle v_8, v_{9}, v_{10}\rangle$ we have the following matrices respectively: $$\begin{pmatrix}
\frac{Q-3}{(Q-1)(Q-4)} &\frac{Q-5}{3(Q-4)} &\frac{2(Q-2)}{3(Q-1)}\\
\frac... | 232 | 908 | 470 | 291 | 2,443 | 0.779713 | github_plus_top10pct_by_avg |
ng at least once. We solve convex program for $\theta$ restricted to the items that appear in rank-breaking at least once. The second figure of Figure \[fig:bottom\_l\_1\] is averaged over $1000$ instances.
![Under the bottom-$\ell$ separators scenario, accuracy is good only for the bottom 400 items (left). As predict... | 233 | 141 | 136 | 259 | 1,223 | 0.792478 | github_plus_top10pct_by_avg |
the others get other fourth roots of unity.
The ${\mathbb Z}_4$- and GSO-invariant states in this sector are of the form
-----------------------------------------------------------------------------------------------------------------------------------------------
State ... | 234 | 1,709 | 490 | 310 | null | null | github_plus_top10pct_by_avg |
ed by $B$.
In Section \[selfdualsection\], we prove a result of independent interest, Theorem \[selfdual\], that finds the unavoidable minors for arbitrary large matroids that have two disjoint bases. A corollary is the following, which finds one of two specific minors in any matroid that is not close to being ‘trivia... | 235 | 267 | 641 | 293 | null | null | github_plus_top10pct_by_avg |
T_CODE AND A.HOURS_SUMMARY = B . HOURS_SUMMARY
WHERE A . EM_NUMBER = EMPLOYEE_ID OR B . EM_NUMBER = EMPLOYEE_ID
UNION
SELECT COUNT ( * ) AS ERROR_COUNT
FROM MPRLIB . V_REQHOURSUMM A EXCEPTION JOIN MPRLIB . V_TSHOURSUMM B
ON A . EM_NUMBER = B . EM_NUMBER AND A . TIMESHEET_CODE = B . TIMESHEET_CODE AND A . HOURS_SUMMARY ... | 236 | 59 | 101 | 211 | 479 | 0.808344 | github_plus_top10pct_by_avg |
ere in the url:[your_servlet_path]
Q:
How to read tabular data from text file - Perl
We have text file which is having data in normal as well as tabular form. i can read normal data but i am unable to read the data which is in tabular form.
Can anyone please help me out to read the and extract the tabular data.
T... | 237 | 5,704 | 4 | 124 | 187 | 0.819892 | github_plus_top10pct_by_avg |
MCMC-like algorithms. We also remark that @hoffer2017train proposed a different way of injecting noise, multiplying the sampled gradient with a suitably scaled Gaussian noise.
[Satisfying the Assumptions]{}\[ss:example\_ass\]
Before presenting the experimental results, we remark on a particular way that a function $U... | 238 | 174 | 162 | 308 | 1,131 | 0.793951 | github_plus_top10pct_by_avg |
0.098
mMSE 0.093 0.093 0.093 0.093 0.093 0.093 0.093
BLB($n^{0.6}$) 1.521 1.512 1.538 1.516 1.522 1.530 1.526
BLB($n^{0.8}$) 0.466 0.466 0.472 0.... | 239 | 2,662 | 1,123 | 313 | null | null | github_plus_top10pct_by_avg |
type="table"}), FP4 with the previously mapped tags (FP4_Eland) covers 85-98% of Chen_Eland, and the intensity of overlapped peaks is strongly correlated. Thus, it is deemed that FP4 has reproduced Chen_Eland and extended it with novel peaks in different genomic locations. In contrast, FP4 with remapped tags shows rela... | 240 | 208 | 1,248 | 390 | null | null | github_plus_top10pct_by_avg |
underwent surgical re-intervention for fibroid-related bleeding between 12 and 24 months (Table [3](#T3){ref-type="table"}): 4 hysterectomies and 2 hysteroscopic myomectomies. Follow-up pathology revealed multiple small fibroids with adenomyosis in four cases (patients 1, 3, 4, and 6), and a possible polyp (patient 5)... | 241 | 516 | 964 | 400 | null | null | github_plus_top10pct_by_avg |
re>
<figure class="pic-6"></figure>
<figure class="pic-7"></figure>
<figure class="pic-8"></figure>
<figure class="pic-9"></figure>
<figure class="pic-10"></figure>
<figure class="pic-11"></figure>
<figure class="pic-12"></figure>
<figure class="pic-13"></figure>
<figure class="pic-14"></figu... | 242 | 4,715 | 39 | 41 | 168 | 0.821082 | github_plus_top10pct_by_avg |
$3"/>
<rewrite url="^/Membership/(.+)/(.+)" to="/Membership/Index.aspx?parentf=$1&f=$2"/>
So just reversing the order except that I have kept the first rule in the same position.
A:
Instead of all your posted rules, try this:
<rewrite url="^/Membership/([^/]+)$" to="/Membership/Index.aspx?f=$1"/>
<rewrite url="^... | 243 | 1,902 | 371 | 250 | 656 | 0.80307 | github_plus_top10pct_by_avg |
cannot interfere with a domain $v$ if no action performed by $u$ can influence subsequent outputs seen by $v$. The system is divided into a number of *domains*, and the allowed information flows between domains are specified by means of an information flow policy $\rightsquigarrow$, such that $u \rightsquigarrow v$ if... | 244 | 1,241 | 1,319 | 380 | null | null | github_plus_top10pct_by_avg |
ute-force computational enumeration, while the effect on random-pair selection is estimated.
{width="95.00000%"}
Analysis of error\[sec:theoretical\]
------------------------------------
In this section, we provide a theoretical analysis showing that performance of each internal binary mod... | 245 | 1,478 | 883 | 251 | 996 | 0.796056 | github_plus_top10pct_by_avg |
lumn{1}{c|}{\ensuremath{a_{1}}} & \multicolumn{1}{c|}{} & \ensuremath{\mathbf{B}_{1,1}=+1} & \ensuremath{b_{1}-\beta_{1}} & \ensuremath{\beta_{1}} & \multicolumn{1}{c|}{\ensuremath{b_{1}}} & \tabularnewline\multicolumn{1}{|c|}{\ensuremath{\mathbf{A}_{1,1}=-1}} & \ensuremath{\alpha_{1}} & \ensuremath{1-a_{1}... | 246 | 1,449 | 511 | 331 | null | null | github_plus_top10pct_by_avg |
ey provide analytics that leverage accurate entity counts and provide entity co-occurrence statistics which is helpful in analyzing semantically similar named-entities.
Research Objectives
===================
\[sec:problem\] Given the text corpora with semantic annotations, I describe three important research proble... | 247 | 28 | 596 | 298 | 912 | 0.797866 | github_plus_top10pct_by_avg |
eight difference between head and heart and eliminating air emboli during the surgery. *Green position*: the position at the first operation, *red dot*: heart position, y*ellow dot*: head position.](nmc-55-305-g4){#F4}
######
Differences between the old and new operating table
------------------------------------... | 248 | 2,404 | 265 | 218 | null | null | github_plus_top10pct_by_avg |
ariance of the predictive distribution $p(f({\mathbf x}_*) \mid {\mathbf y})$ are given by [@Rasmussen2006]
\[eq:GPreg\] $$\begin{aligned}
\mathbb{E}[f({\mathbf x}_*) \mid {\mathbf y}] &=
{\mathbf k}_*^{\mathsf{T}}(K +\sigma^2I)^{-1}{\mathbf y}, \\
\mathbb{V}[f({\mathbf x}_*) \mid {\mathbf y}] &=
k({\m... | 249 | 1,772 | 642 | 349 | 597 | 0.804825 | github_plus_top10pct_by_avg |
−53 (−78 to −18)
*Change in lean mass*^*2*^*(%)* −7 (−36 to 10)
*EI during weight loss*^*3*^*(units)* ... | 250 | 2,085 | 942 | 387 | null | null | github_plus_top10pct_by_avg |
n and Length of Stay of Discharged Patients.\
In all four panels, X and Y axes in minutes.\
*AED*, tertiary care academic emergency department; *A2D*, admit request to departure for boarded patients awaiting hospital admission; *CED*, community emergency department; *D2P*, arrival to being seen by physician; *LOSD*, to... | 251 | 1,771 | 486 | 286 | null | null | github_plus_top10pct_by_avg |
{jupdate}(S, 1, m \bmod 4)$ $a = 0$ $[b = 1] (-1)^e$ $b {\leftarrow}b - m a$, with $1 \leq m \leq {\lfloor b/a \rfloor}$ \[li:jacobi-update-b\] $S {\leftarrow}\proc{jupdate}(S, 0, m \bmod 4)$ $b = 0$ $[a = 1] (-1)^e$
Correctness
-----------
Let $a_0$ and $b_0$ denote the original inputs to Algorithm \[alg:jacobi\]. S... | 252 | 2,972 | 686 | 262 | 1,122 | 0.794066 | github_plus_top10pct_by_avg |
air of four-parameter (multivariate) Gaussian-gamma prior distributions are specified for the observation parameters: $$\begin{aligned}
{\bar{\nu}}& \sim \mathrm{Gam}\left({\bar{a}}_0,~ {\bar{b}}_0\right), \quad\quad {\bar{\mu}}|{\bar{\nu}}\sim \mathrm{N}\left({\bar{m}}_0,~ {\bar{\nu}}^{-1}{\bar{c}}_0\right),\nonumbe... | 253 | 1,292 | 635 | 345 | 2,555 | 0.77889 | github_plus_top10pct_by_avg |
fund\] (3). It follows that $\| \cdot \|$ is a seminorm and that $\| {\mathbf{x}}\|=0$ if and only if ${\mathbf{x}}\in L(\Lambda_+)$. Hence $\| \cdot \|$ is a norm if and only if $L(\Lambda_+)=\{0\}$.
The following theorem is a generalization of Theorem \[MSSmain\] to hyperbolic polynomials.
\[t1\] Let $k\geq 2$ be a... | 254 | 820 | 589 | 304 | null | null | github_plus_top10pct_by_avg |
esults and further findings of the algorithm. Finally, the paper is concluded in section \[sec:conclusion\].
Related Work {#sec:related-work}
============
Data Anomaly Detection
----------------------
Statistical divergence was applied mainly as classifiers on multimedia content [@park2005classification], especially... | 255 | 110 | 608 | 376 | null | null | github_plus_top10pct_by_avg |
variables known and held constant, and unsubscripted variables free. Roots of the system represent discrete solutions. Let us look carefully at the further case that ${\mathit{s}} = (\lambda, {\mathit{f}}, {\mathbf{f}}) = (\lambda, {\mathit{f}}, (\psi, \phi)) \in \Lambda \times {\mathscr{F}} \times {\mathbf{F}}$. $$\b... | 256 | 3,973 | 275 | 166 | 2,934 | 0.775989 | github_plus_top10pct_by_avg |
approach?
A:
(A GUID is 128 bits, so it cannot safely be converted into a 32-bit integer.)
A better option might be to use a Dictionary<Guid, ItemTable_s>. Then you can still use GUIDs to index it.
var tempdata = new Dictionary<Guid, ItemTable_s>;
foreach(var anItem in datacontext.ItemTable_s)
{
tempdata.Add(anI... | 257 | 2,923 | 142 | 223 | 461 | 0.809026 | github_plus_top10pct_by_avg |
Then $$m +c(n(\lambda)-n(\lambda^t))+n(\lambda)\
=\ m+(c+1)n(\lambda) -cn(\lambda^t) \
\geq\ m+c(n(\mu)-n(\mu^t))+n(\mu),$$ with equality if and only if $\lambda=\mu$. This means that $\operatorname{{\textsf}{triv}}$ appears in $\Delta_c(\lambda)$ in a higher degree than its first appearance in $\Delta_c(\mu)$. In p... | 258 | 1,049 | 415 | 335 | 2,508 | 0.779254 | github_plus_top10pct_by_avg |
ortions were calculated after excluding missing data (if present) for a particular section---the denominator is always indicated to remove ambiguity.
Results {#s3}
=======
Since 2008, there have been 54 catastrophic injuries (24 in Juniors and 30 in Seniors) recorded in total in South Africa ([table 1](#BMJOPEN201200... | 259 | 3,835 | 263 | 128 | null | null | github_plus_top10pct_by_avg |
or of $p$, ignoring poly-logarithmic factors.
Tighter Analysis for the Special Case of Top-$\ell$ Separators Scenario {#sec:topl}
-----------------------------------------------------------------------
The main result in Theorem \[thm:main2\] is general in the sense that it applies to any partial ranking data that is... | 260 | 304 | 463 | 323 | 2,493 | 0.779348 | github_plus_top10pct_by_avg |
05){#sensors-17-00869-f005}
{#sensors-17-00869-f006}
-(f) shows GP reconstructions of the 2D chest phantom using different covariance functions from 9 projections (uniformly spaced) out of 180$^\circ$ angle of view and $185$ number of rays for... | 263 | 2,901 | 545 | 281 | 1,432 | 0.789669 | github_plus_top10pct_by_avg |
h part template $v$, which uses this template’s annotations on images $I\in{\bf I}_{v}\subset{\bf I}^{\textrm{ant}}$, as follows.
1\) We first enumerate all possible latent patterns corresponding to the $k$-th CNN conv-layer ($k=1,\ldots,K$), by sampling all pattern locations *w.r.t.* $D_{u}$ and $\overline{\bf p}_{u}... | 264 | 491 | 659 | 330 | null | null | github_plus_top10pct_by_avg |
sp. $\mathbb{F} = \mathbb{F}(\rho_w) = (T^F_{j_1}, \ldots, T^F_{j_v})$ ($j_1<\cdots<j_v$, $v\leq s$)\] omitting empty parts.
Using these data, we define [*cranks*]{} $C_{\mathbb{M}}[i]$, $C^*_{\mathbb{F}}[i]$ and $C^{\mathbb{M}}_{\mathbb{F}}[\sigma])$ as products of the generators as in Figure \[fig:mcrank\], \[fig:fc... | 265 | 597 | 958 | 345 | 2,248 | 0.781428 | github_plus_top10pct_by_avg |
ving no one condition repeated three times, while solving the problem mentioned above.
Or would there be any better ways?
A:
disclaimer: this solution is not perfect.
Ok, so my iterative approach is to create a permuted vector of all possible trials and then append each one to another vector if it doesn't violate the... | 266 | 5,844 | 111 | 151 | 575 | 0.805292 | github_plus_top10pct_by_avg |
ph at /Library/Perl/5.18/Graph/Easy/Parser.pm line 1302.
',798.1", lwidth=0.37, penwidth=0.8, rank=sink, style=filled, tooltip="package: github.com/syncthing/syncthing/lib/db" ]; "(*github.com/syncthing/syncthing/lib/db.VersionList)
...
112.31,203.1 154.04,203.1 237.26,203.1 299.56,203.1"]; } }' not recognized by Gra... | 267 | 89 | 357 | 150 | 17 | 0.839583 | github_plus_top10pct_by_avg |
ticipants \[[@pone.0201732.ref012]\]. Finally, we assume that evacuee achieved their desired speed in the evacuation tunnel. For each evacuee we calculate their speed in the main tunnel as a percentage of desired speed (understood as speed of free movement in evacuation tunnel). This makes it possible to analyze how di... | 268 | 3,898 | 1,302 | 232 | null | null | github_plus_top10pct_by_avg |
. Assume that $L_i$ is *of type II* with $i$ even, or that $L_i$ is *bound of type I or type II* with $i$ odd. Then $m_{i,i}=\mathrm{id}$.
3. Let $i$ be even.
- If $L_i$ is *bound of type II*, then $\delta_{i-1}^{\prime}e_{i-1}\cdot m_{i-1, i}+\delta_{i+1}^{\prime}e_{i+1}\cdot m_{i+1, i}+\delta_{i-2}e_{i-2}\c... | 269 | 617 | 460 | 317 | 2,544 | 0.778996 | github_plus_top10pct_by_avg |
Relatedness (O) Female 38 4.17 (0.99) −2.20\* 0.49 0.73
Male 51 4.56 (0.52)
Physical self-concept Female 66 52.19 (21.37) −5.16\*\*\* 0.90 0.99
Male 63 68.71 (14.49) ... | 270 | 483 | 1,026 | 449 | null | null | github_plus_top10pct_by_avg |
ties*. A property $E$ is actual in the state $S$ iff the assertion $\vdash E(x)$, with $x$ in $S$, is justified.
*Nonactual properties*. A property $E$ is nonactual in the state $S$ iff the assertion $\vdash E^{\bot }(x)$, with $x$ in $S$, is justified.
*Potential properties*. A property $E$ is potential in the state... | 271 | 2,421 | 1,710 | 402 | 2,577 | 0.778739 | github_plus_top10pct_by_avg |
re\]
{#f1}
Given the high prevalence of additional antibiotic treatment, we also examined the pattern of antibiotic use. Overall, 70% of patients were treated with at least one add... | 272 | 3,447 | 735 | 178 | null | null | github_plus_top10pct_by_avg |
u
\frac{(t-r)(r+1-t)}{(2u+1)(t^2-r^2+x)^2} \nonumber \\
& & \times \prod_{k=1}^M\frac{1-T_k (t-r)}{\sqrt{1+2T_k r + T_k^2 x}} [\cdots]\,.\end{aligned}$$ Here and below, $t$ denotes the dimensionless time measured in units of the Heisenberg time $t_H=2\pi\hbar/\Delta$, where $\Delta$ is the mean level spacing.
The ... | 273 | 1,092 | 679 | 384 | 1,930 | 0.784316 | github_plus_top10pct_by_avg |
eenshot of the output
A:
A second scrollbar was there on the bottom but doesn't appear.
I set margin to true on the root layout and i remove the Panel.
The issue is fixed.
private VerticalLayout getResultLayout() {
VerticalLayout resultLayout = new VerticalLayout();
VerticalLayout .setWidth("1380px");
resultLayout.... | 274 | 1,642 | 140 | 191 | 581 | 0.805173 | github_plus_top10pct_by_avg |
ads, it grabs the HTML from the field on the table that matches with the appropriate DIV and plugs it into place. Any changes to the content would be written back to that field, through a PHP script, where the new information would be permanently stored.
My trouble is, that I don't have the ability at the moment to run... | 275 | 4,161 | 160 | 249 | 915 | 0.797758 | github_plus_top10pct_by_avg |
t the same topic. The Wikigame topic dataset consists of more distinct categories than the Wikispeedia and MSNBC dataset. Furthermore, the most frequently occuring topic in the Wikigame topic dataset is Culture with around 13%. The Wikispeedia dataset is dominated by the two categories the most Science and Geography ea... | 276 | 766 | 755 | 433 | 993 | 0.796127 | github_plus_top10pct_by_avg |
tent group by a theorem of Lazard which is stated at the beginning of Appendix \[App:AppendixA\].
Recall that we have defined the morphism $\varphi$ in Section \[red\]. The morphism $\varphi$ extends to an obvious morphism $$\tilde{\varphi} : \tilde{M} \longrightarrow \prod_{i:even}\mathrm{GL}_{\kappa}(B_i/Z_i) \time... | 277 | 2,011 | 392 | 318 | 1,911 | 0.784456 | github_plus_top10pct_by_avg |
uniformly random choices correspond to setting $a=0$ in the expression for $p_k$, and then we can expect the graphs that pass the correlation threshold to be clustered around the points of $\rho\sim \rho_1$ and $S(X,Y)\sim 0$.
We also note a sharp variation in how the fixation probabilities of the graphs relate to th... | 278 | 166 | 1,994 | 392 | null | null | github_plus_top10pct_by_avg |
ord, e.g. $B$, and when you paste the text in LyX, use Edit -> Paste special -> Paste from LaTeX. The LaTeX code for math mode ($ ... $) will be interpreted properly.
Q:
read socket from C
When I use read() socket to retrieval bytes from server I found something unexpected in the chars.
Below is my code:
Char buf[... | 279 | 5,035 | 100 | 222 | 109 | 0.825351 | github_plus_top10pct_by_avg |
assumed in the above theorem. This settles the question raised in [@HOX14] on whether it is possible to achieve optimal accuracy using rank-breaking under the top-$\ell$ separators scenario. Analytically, it was proved in [@HOX14] that under the top-$\ell$ separators scenario, naive rank-breaking with uniform weights ... | 280 | 188 | 428 | 343 | 2,769 | 0.777104 | github_plus_top10pct_by_avg |
ng the special elements and the basic relations $(R0)$-$(R4)$ and $(E1)$-$(E5)$.
The partition algebras $A_n(Q)$ were introduced in early 1990s by Martin [@Ma1; @Ma2] and Jones [@Jo] independently and have been studied, for example, in the papers [@Ma3; @DW; @HR]. The theorem above has already shown in the paper [@HR]... | 281 | 158 | 1,092 | 395 | 818 | 0.799383 | github_plus_top10pct_by_avg |
], III.10.4, it suffices to show that, for any $m \in \underline{M}^{\ast}(\bar{\kappa})$, the induced map on the Zariski tangent space $\rho_{\ast, m}:T_m \rightarrow T_{\rho(m)}$ is surjective.
We define the two functors from the category of commutative flat $A$-algebras to the category of abelian groups as follows:... | 282 | 1,506 | 645 | 324 | 3,797 | 0.770049 | github_plus_top10pct_by_avg |
- CDS or CDS -- 3′ UTR, the decoy site is assigned to the region in which the majority of the site is contained. The length of the 5′ UTR, CDS, and 3′ UTR of 286 transcripts is tallied respectively, and then the number of decoy sites for each feature is normalized to 1 kb sequence length. (**B**) Predicted decoy sites ... | 283 | 3,300 | 1,181 | 278 | null | null | github_plus_top10pct_by_avg |
the other hand we perform the OPE of the operators $A$ and $C$, and then we perform the OPE of the result with $B$. Eventually take the regular limit $:x \to w:$ and add up the two terms. Additional details about these operations follow.
- First let us consider the OPE between $A(z)$ and $B(x)$. We evaluate the res... | 284 | 375 | 402 | 348 | 3,048 | 0.77521 | github_plus_top10pct_by_avg |
that are consistent with the predictions of inequality aversion.
{#pone.0204392.t003g}
*N* Prediction Observed
-------------------- ----- -------------------------- -------------
(10,10) vs (10,40) 17 *y*~10,10~ \< *y*~10,40~ 6 (35.29%)
(10,... | 285 | 3,965 | 769 | 255 | 2,984 | 0.775611 | github_plus_top10pct_by_avg |
object foo holds daily share price data for a stock starting from Monday 3 January 2011 and ending on Monday 20 September 2011. To aggregate this daily data I used:
tmp <- to.weekly(foo)
The above approach succeeds in that tmp now holds a series of weekly OHLC data points, as per the quantmod docs. The problem is that ... | 286 | 2,014 | 127 | 204 | 1,773 | 0.785819 | github_plus_top10pct_by_avg |
1}
\left( \frac{ \rho E }{ 100 (\text{g/cm}^3) \mbox{GeV} }\right)^2.
\label{enhanced-case}\end{aligned}$$ It should be compared to (\[denominator-size\]). After taking account of $W^2$ suppression of $\sim 0.01$ (assuming $W \simeq 0.1$), $| \frac{ AA L }{ ( \Delta_{J} - h_{i} ) } W^2 | \sim 3 \times 10^{-2}$ at $E \... | 287 | 117 | 425 | 403 | 1,473 | 0.78912 | github_plus_top10pct_by_avg |
ratio of a intermittent compound Poisson process is $\iota = \frac{\mu_\text{off}}{\mu_\text{on} + \mu_\text{off}}$.
Let $\lambda$ be the rate, $L$ be the loss random variable, and $\iota$ be the idle ratio of an intermittent compound Poisson process. The expectation of the ICPP for a time interval $t$ units long is $... | 288 | 2,818 | 687 | 336 | 3,437 | 0.772372 | github_plus_top10pct_by_avg |
ns (\[init1\]) and (\[init2\]). The approximated solutions described by (\[solution1\]) and (\[solution2\]).[]{data-label="fig:solution1"}](plot1.eps){width="7cm"}
We fix the boundary approximations in $\tau_1=-20$ and $\tau_2=-1$. The the numerical solution gives us for these points $$\begin{aligned}
a|_{\tau_1=-20} ... | 289 | 2,961 | 698 | 339 | 2,325 | 0.780838 | github_plus_top10pct_by_avg |
came to 2nd level distributions, histograms became much coarser since data available was highly limited and thus its performance suffered dramatically.
MGoF tended to classify every distribution as anomaly, therefore benefited most by larger $\alpha$. It always classifies as anomalous the first $c_{th}$ distributions ... | 290 | 21 | 1,662 | 320 | null | null | github_plus_top10pct_by_avg |
in \psi )^{-1}\\
* &
*&
w^{(m\,h\,k)}_{\psi\psi} (\sin \psi )^{-2}
\end{bmatrix}
(\sin \psi )^{-h} e^{i [(h-k) \tau + m \varphi] +m \psi}\,,\end{aligned}$$ where $$\begin{aligned}
w_{\tau\tau}^{(m\,h\,0)} &=\, +\frac{1}{16}(c_1 e^{-2 i \psi }+4 c_1 e^{2 i \psi }-6 c_2 e^{-2 i \psi }+16 c_3 e^{2 i \psi... | 291 | 2,418 | 418 | 326 | null | null | github_plus_top10pct_by_avg |
istribution being fixed, the reversible matrix which minimizes the mixing time. If we note $\pi$ the stationary measure and $\Pi=diag(\pi)$. Then $P$ is reversible if and only if $\Pi P=\Pi^t P$. Then in particular $\Pi^{\frac{1}{2}}P\Pi^{-\frac{1}{2}}$ is symmetric and has the same eigenvalues as $\Pi$. Finally, $p=(\... | 292 | 389 | 504 | 354 | 3,759 | 0.770272 | github_plus_top10pct_by_avg |
ipe(
take(40),
map(value => value +1),
map(value => {
if(value === 40) {
finish();
}
else if (value % 5 === 0){
return 'can devide by 5 we did some magic';
}else{
return value;
} })
);
const subscribe = example.subscribe(
val => console.log(val),
error => console.log("Error h... | 293 | 111 | 133 | 175 | null | null | github_plus_top10pct_by_avg |
eline and 24-month scores.
{#F3}
######
Improvements in UFS-QOL subscale scores from baseline to 24 months
**Subscale** **Baseline** **24 months** **Change in score** **95% confidence interval**
-... | 294 | 353 | 573 | 430 | null | null | github_plus_top10pct_by_avg |
odes; then...:
>>> class z(v):
... def visit_Name(self, node): print 'Name:', node.id
...
>>> z().visit(t)
Module
AugAssign
Subscript
Name: d
Index
Name: x
Store
Add
Subscript
Name: v
Index
Tuple
Name: y
Name: x
Load
Load
But, NodeVisitor is a class because this lets it store information during a visit. Suppose all... | 295 | 4,741 | 152 | 288 | 229 | 0.817095 | github_plus_top10pct_by_avg |
dia as combinations of different “roles” and compare groups according to the proportion of each role within each group.
Wang et al. proposed a technique, *Multinomial Goodness-of-Fit* (MGoF), to analyze likelihood ratio of distributions via Kullback-Leibler divergence, and is fundamentally a hypothesis test on distrib... | 296 | 92 | 554 | 494 | null | null | github_plus_top10pct_by_avg |
t{\mu}\in{\mbox{\boldmath $\Lambda$}}_{i+\frac{1}{2}}$ if $\widehat{\mu}$ is obtained from $\widetilde{\lambda}$ by removing a box ($i = 0, 1, 2, \ldots n-1$) \[resp. ($i=0, 1, 2, \dots, n$)\],
- join $\widehat{\mu}\in{\mbox{\boldmath $\Lambda$}}_{i-\frac{1}{2}}$ and $\widetilde{\lambda}\in{\mbox{\boldmath $\Lambda$... | 297 | 2,676 | 498 | 306 | 2,965 | 0.775789 | github_plus_top10pct_by_avg |
B C^2 +
36 A^5 D^2 B C^2 + 2 A D^3 B C^2 +6 A^3 D^3 B C^2 \nonumber\\
\fl &+& 24 A^4 D^3 B C^2+
100 A^4 D B^2 C^2 + 88 A^5 D B^2 C^2 +
6 A D^2 B^2 C^2 \nonumber\\
\fl &+& 18 A^3 D^2 B^2 C^2 +
56 A^4 D^2 B^2 C^2 + 4 A^2 D^3 B^2 C^2 +
20 A^3 D^3 B^2 C^2 \nonumber\\
\fl &+& 160 A^4 D B^3 C^2+
36 A^... | 298 | 2,727 | 523 | 234 | null | null | github_plus_top10pct_by_avg |
$\bm{1}$ $\bm{1}$ $\bm{2}$ $\bm{2}$ $\bm{2}$
$U(1)_Y$ $\frac12$ $-1$ $0$ $\frac12$ $\frac12$ $\frac12$
$A_4$ ${(1,1',1'')}$ ... | 299 | 1,927 | 881 | 355 | null | null | github_plus_top10pct_by_avg |
Keyboard.Modifiers & ModifierKeys.Control) == ModifierKeys.Control)
{
MessageBox.Show("You hit ctrl + D");
}
}
private void dtg_view_InitNewRow(object sender, DevExpress.Xpf.Grid.InitNewRowEventArgs e)
{
dtg_tabletrial.SetCellValue(e.RowHandle, "UserName", "emre");
dtg... | 300 | 410 | 116 | 278 | 873 | 0.798516 | github_plus_top10pct_by_avg |
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