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 |
|---|---|---|---|---|---|---|---|
All help is highly appreciated.
My Code:
public class WebTableToTxtFile {
static WebDriver driver = new FirefoxDriver();
public static void main(String[] args) throws Throwable {
driver.navigate().to("http://www.bloomberg.com/markets/stocks/futures");
driver.manage().window().maximize();
... | 101 | 77 | 65 | 45 | 59 | 0.830144 | github_plus_top10pct_by_avg |
:
option.backgroundBrush = QtGui.QBrush(QtGui.QColor("gray"))
def paint(self, painter, option, index):
super(InventoryDelegate, self).paint(painter, option, index)
if not index.parent().isValid():
painter.save()
painter.setPen(QtGui.QPen(QtGui.QColor("green")))
... | 102 | 3,622 | 21 | 102 | 207 | 0.81834 | github_plus_top10pct_by_avg |
.40 **87.41** 368.97 12.60 94.20 22.23 370.12
**SDD-E Dynamic** 18.53 81.80 30.21 6808.58 20.28 **71.40** 31.58 8985.92 25.90 97.40 40.92 5881.37 13.44 97.20 23.61 5747.19
**SDD-E Dyna... | 103 | 1,368 | 227 | 148 | null | null | github_plus_top10pct_by_avg |
nnik_event]. Events were modeled by the geographic location and time of their occurrence. For temporal queries expressed in simple natural language they outline an extended Backus-Naur form (EBNF) language that incorporates time intervals with standard boolean operations. Geographical queries are also modeled as EBNF l... | 104 | 211 | 642 | 109 | 2,688 | 0.777857 | github_plus_top10pct_by_avg |
002){ref-type="fig"}).
{#gcbb12419-fig-0002}
######
Statistical analyses of non‐structural carbohydrates (NSC). The... | 105 | 125 | 416 | 182 | null | null | github_plus_top10pct_by_avg |
by a second order Markov chain model. This strongly suggests that humans follow common topical strategies while navigating in a goal-oriented scenario.
#### MSNBC dataset
In this section we present the results obtained from the MSNBC dataset introduced in the section called “”. Again we look at navigational paths ov... | 106 | 71 | 603 | 126 | null | null | github_plus_top10pct_by_avg |
// TODO Auto-generated method stub
myCalendar.set(Calendar.YEAR, year);
myCalendar.set(Calendar.MONTH, monthOfYear);
myCalendar.set(Calendar.DAY_OF_MONTH, dayOfMonth);
updateLabel();
}
... | 107 | 5,326 | 22 | 34 | 22 | 0.837587 | github_plus_top10pct_by_avg |
ollers.WeatherForecastController.Ping() in C:\Users\Dellas\source\repos\TestApi\TestApi\Controllers\WeatherForecastController.cs:line 48
What's a workaround here? Does Azure really blocks pinging?
A:
Yes, on Azure App Service the tools ping, nslookup and tracert won’t work through the console due to security constr... | 108 | 406 | 144 | 105 | 1,972 | 0.784073 | github_plus_top10pct_by_avg |
?$char_traits@D@std@@V?$allocator@D@2@@std@@QAE@PBD@Z
; 10 : std::string g = "Hello";
push OFFSET ??_C@_05COLMCDPH@Hello?$AA@
lea ecx, DWORD PTR _g$[esp+80]
mov DWORD PTR __$EHRec$[esp+88], 0
call DWORD PTR __imp_??0?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@QAE@PBD@Z
...... | 109 | 4,762 | 1 | 92 | 35 | 0.834235 | github_plus_top10pct_by_avg |
36 months (follow-up rate of 64.5%). [Table 1](#T0001){ref-type="table"} lists the number of infants followed at birth and at 6, 12, 18, 24, and 36 months as well as their mean weight, height, and head circumference. [Figure 1](#F0001){ref-type="fig"} shows that the weight, height, and head circumference from birth to ... | 110 | 30 | 448 | 183 | null | null | github_plus_top10pct_by_avg |
want to print"
' Set title.
Title = "Print"
' Set default.
Default = "1"
' Display message, title, and default value.
Dim SerialNumber As String
NumCopies = Val(InputBox(Message, Title, Default))
SerialNumber = System.PrivateProfileString("W:\settings.txt", _
"MacroSettings", "SerialNumber")
If SerialNumber = "... | 111 | 4,884 | 44 | 48 | 709 | 0.801968 | github_plus_top10pct_by_avg |
="L-curve chest data"}](LcurveChestLaplacian "fig:"){width="9cm"}]{} (65,45)[$\lVert \mathcal{H}_{{\mathbf x},i} f_{\sigma}({\mathbf x}) - y_i \rVert _2$]{} (-13,130)
[90]{} $\lVert f_{\sigma}({\mathbf x}) \rVert _2$
(225,130)
[90]{} $\lVert f_{\sigma}({\mathbf x}) \rVert _2$
(305,45)[$\lVert \mathc... | 112 | 130 | 279 | 154 | 814 | 0.799442 | github_plus_top10pct_by_avg |
'C:\Windows\System32\ntdll.dll', Cannot find or
open the PDB file
'ModelingTool.exe': Loaded 'C:\Windows\System32\kernel32.dll', Cannot find
or open the PDB file
'ModelingTool.exe': Loaded 'C:\Windows\System32\opengl32.dll', Cannot find
or open the PDB file
'ModelingTool.exe': Loaded 'C:\Windows... | 113 | 18 | 116 | 89 | 57 | 0.830341 | github_plus_top10pct_by_avg |
namefield.borderStyle = UITextBorderStyleNone;
namefield.background = [UIImage imageNamed:@"text_field_default.png"];
namefield.contentVerticalAlignment = UIControlContentVerticalAlignmentCenter;
namefield.textAlignment = UITextAlignmentCenter;
//[namefield setBackgroundColor:[UIColor whiteColor]];
... | 114 | 3,318 | 47 | 98 | 1,940 | 0.784263 | github_plus_top10pct_by_avg |
acency matrix recovery after $\ell_1$-spectral clustering application. Spectral Adjacency matrix: Adjacency matrix recovery after spectral clustering application.[]{data-label="recovery"}](p2bis.pdf){width="\columnwidth"}
-0.2in
We can notice that our model performs well in this task as both methods effectively recov... | 115 | 802 | 261 | 165 | 638 | 0.80353 | github_plus_top10pct_by_avg |
onal UIViews on top of that, depending on what you need to do?
Q:
Obtain Oracle 11g partitioning interval by direct system table query
I've got a table which is partitioned on a NUMBER variable in Oracle 11g, with the INTERVAL set to 1. On our development system I can execute
SELECT DBMS_METADATA.GET_DDL('TABLE'... | 116 | 88 | 137 | 122 | 220 | 0.817555 | github_plus_top10pct_by_avg |
n cell
}
override func tableView(tableView: UITableView, titleForHeaderInSection section: Int) -> String? {
switch (section) {
case 0:
let count = self.organizedTasks.items["Aperti"]!.count
return "Aperti (\(count))"
case 1:
let count = self.organizedTasks.items["Ch... | 117 | 1,664 | 19 | 86 | 100 | 0.826255 | github_plus_top10pct_by_avg |
Is there a better way to find and replace things?
A:
Assign the value back into thumb.
thumb = thumb.replace("200.jpg", "640.jpg");
A:
Try:
thumb = thumb.replace("200.jpg", "640.jpg");
Q:
KeyValuePair - no parameterless constructor?
I have an object that has a KeyValuePair type property.
I would like to rea... | 118 | 3,440 | 68 | 113 | 600 | 0.804761 | github_plus_top10pct_by_avg |
ior probability for the true model, $p_{u^*} = \hat{\mathbb{P}}(U=u^*|y_{1:T},s_{1:T})$, is evaluated.
Number of MUs, $u^*$ $\leq 5$ 6 7 8 9 10
---------------------------------------------- ---------- ------- ------- ------- ------- -------
No. where $\hat{u... | 119 | 3,649 | 143 | 75 | null | null | github_plus_top10pct_by_avg |
------------------------ ---------------------- --------- ------ --------- -------- --------- ------ --------- -------- --------- ------ ----------
Itching 0.17 \> 0.05 0.22 \> 0.05 0.25 ... | 120 | 2,102 | 412 | 114 | null | null | github_plus_top10pct_by_avg |
nects to firebase sdk and also configure non-default apps to create the needed firebase auth
abstract class BaseAuth {
getDefaultAuth();
getAbnAuth();
...
}
class Auth with ChangeNotifier implements BaseAuth {
...
Auth() {
_configureAbnApp();
_configureProdApp();
}
getDefaultAuth() {
_firebas... | 121 | 78 | 74 | 84 | 53 | 0.830818 | github_plus_top10pct_by_avg |
eature maps of unannotated objects and 2) part-location errors *w.r.t.* the annotated ground-truth part locations.
It is essential to determine the optimization sequence for the three losses in the above equation. We propose to first learn the CNN by minimizing ${Loss}^{\textrm{CNN}}$ and then build an AOG based on th... | 122 | 2,306 | 415 | 136 | 3,304 | 0.77333 | github_plus_top10pct_by_avg |
for(cntr = 0; cntr < 5; cntr++)
The loop executes while the condition cntr > 5 is true. If cntr starts at 0 then it is obviously not greater than 5, so the body of the loop never executes.
Q:
rxjs how to complete observable?
To learn rxjs im playing around with it.
My code:
// RxJS v6+
import { withLatestFrom, ma... | 123 | 4,017 | 112 | 119 | 32 | 0.834726 | github_plus_top10pct_by_avg |
lled.
PHP Fatal error: Call to undefined method Mock_UserData_ae821217::getUserSessionArray() in /usr/share/php/tool/module/User/Module.php on line 95
PHP Stack trace:
PHP 1. {main}() /usr/local/pear/bin/phpunit:0
…
Could someone help me on this please?
We are using Zend Framework 2.2.0.
Thank you so much.
EC
A:
Y... | 124 | 447 | 66 | 170 | 1,051 | 0.79521 | github_plus_top10pct_by_avg |
ip date range”.
In the dateDiff function I did not use days for the reason that it returns 0 for something like this: dateDiff("d", #1/1/2015#, #1/1/2015 23:59:59#). Using hours would have been already good but I decided to use seconds to make it as accurate as possible.
I tested it but please let me know if you see s... | 125 | 563 | 87 | 100 | 153 | 0.822455 | github_plus_top10pct_by_avg |
ious historical topics on *Wikipedia* [@wiki_past].
- Events in the future can be evaluated by using important infrastructure projects, engineering projects etc. These can be extracted from *Wikipedia* and other sources on the Internet.
- Current events extracted from *Wikipedia* [@wiki_current].
- ... | 126 | 917 | 189 | 152 | 875 | 0.798489 | github_plus_top10pct_by_avg |
ll as the importance of threshold under this technique. Although dynamic SDD-E consumes more computation power, it is clear that dynamic SDD-E is capable of tracing the gradual shift of environment. MGoF turned out to be the worst since it always mark several false positive when $c_{th}$ had not been met and much more ... | 127 | 34 | 1,128 | 149 | null | null | github_plus_top10pct_by_avg |
1.665(15) & 2.046(67) & 1.733(25)\
C.L. & - & - & - & - & - & - & - & - & - & 0.936(3) & 1.277(25) & 1.570(109)& 1.411(38)\
[1.0]{} [@ccccccccccc]{} $\kappa$ & ${g^{0+{\rm [lat]}}_V}$ & ${g^{0-{\rm [lat]}}_V}$ & ${g^{0+(u){\rm [lat]}}_A}$ & ${g^{0+(d){\rm [lat]}}_A}$ & ${g^{0+{\rm [lat]}}_A}$ & ${g^{0+{\rm}}_V}$ & ${g... | 128 | 38 | 559 | 209 | 1,855 | 0.785046 | github_plus_top10pct_by_avg |
operty for $\utilde{\d}^2_1$. The result was originally proved in [@KKMW].'
author:
- |
Grigor Sargsyan [^1]\
Department of Mathematics\
Rutgers University\
Hill Center for the Mathematical Sciences\
110 Frelinghuysen Rd.\
Piscataway, NJ 08854 USA\
http://math.rutgers.edu/$\sim$gs481\
gr... | 129 | 26 | 210 | 132 | null | null | github_plus_top10pct_by_avg |
ent values of $\lambda$.
Figure \[fig:cd\_individual\] shows critical difference plots for both subset selection methods. Class balanced selection shows a clear trend that increasing $\lambda$ improves the RMSE, with the average rank for $\lambda=1$ being exactly 4. For random-pair selection, choosing $\lambda=3$ is s... | 130 | 120 | 990 | 181 | null | null | github_plus_top10pct_by_avg |
<form id="form1" runat="server">
<asp:Panel runat="server" ID="uxForm">
<div class="lp-pom-form-field clearfix" id="container_name">
<asp:TextBox ID="uxName" runat="server" CssClass="text form_elem_name" place... | 131 | 49 | 141 | 144 | 126 | 0.823981 | github_plus_top10pct_by_avg |
nnotations in the following paragraphs.
**Named Entities**. For disambiguating and linking named entities in text to an external knowledge source such as *Wikipedia* [@wiki] or an ontology such as YAGO [@yago] or Freebase [@freebase]; I use the AIDA system [@aida]. The AIDA system does named entity disambiguation and ... | 132 | 1,575 | 812 | 131 | 1,207 | 0.792731 | github_plus_top10pct_by_avg |
_cond\], we introduced constraints on the integer variables $\underline{I}$ and $\underline{J}$, proving non-termination for queries in $Den(\leftarrow constants(\underline{I},\underline{J}))$. Introducing symbolic coefficient $i$ and $j$ for the integers of the query and for the domains of $\underline{I}$ and $\underl... | 133 | 2,477 | 637 | 141 | 370 | 0.811844 | github_plus_top10pct_by_avg |
removing one box from $\widetilde{\lambda}$. Let $\{Q_s\}$ be the set of tableaux obtained from $P$ by replacing ${\mbox{\boldmath $\alpha$}}^{(i-1/2)}$ with $\widetilde{\lambda}^{-}_{(s)}$.
Then we define $(E_i)_{QP}$ to be $$(E_i)_{Q_sP}
= \frac{h(\widetilde{\lambda})}{h(\widetilde{\lambda}^{-}_{(s)})}.$$ Here $... | 134 | 848 | 230 | 177 | 2,074 | 0.783026 | github_plus_top10pct_by_avg |
$ in and we are left with $$\begin{aligned}
\label{eq:finalRGeq}
\partial_{\hat k} \Delta \hat{ V} = -\frac{ 1 }{ 6 \pi^2}
\left(1+\frac{\eta_0}{5}\right) \frac{
\ g_{k}^2 \
\partial^2_\varphi \, ( \hat{V}_{\bot} + \Delta \hat{ V}) }{1
+\frac{ g_{k}^2 }{ \hat k^2 }
\partial^2_\varphi \, (\hat{V}_{\bo... | 135 | 1,694 | 548 | 172 | 1,638 | 0.787327 | github_plus_top10pct_by_avg |
ound of type I or of type II*, then $\pi^i\cdot h_i$ has the following form: $$\xi^{(i-1)/2}\begin{pmatrix} \begin{pmatrix} 0&\pi\\ \sigma(\pi)&0\end{pmatrix}& & & \\ &\ddots & & \\ & &\begin{pmatrix} 0&\pi\\ \sigma(\pi)&0\end{pmatrix}& \\ & & & \begin{pmatrix} 0&\pi\\ \sigma(\pi)&0 \end{pmatrix} \end{pmatrix}.$$
... | 136 | 869 | 298 | 169 | 3,688 | 0.77068 | github_plus_top10pct_by_avg |
node, whose children represent template candidates for the part.
- Layer 2: a *part template* in the second layer describes a certain part appearance with a specific pose, *e.g.* a black sheep head from a side view. A part template is an AND node, which uses its children latent patterns to encode its constituent re... | 137 | 1,796 | 372 | 171 | 3,132 | 0.774626 | github_plus_top10pct_by_avg |
le components. A functionality maps a reactive state space into the persistent subset of itself.
#### Walk of iterative operator {#S:ITERATIVE_OPERATOR_WALK}
\[D:ITERATIVE\_OPERATOR\_WALK\] Let $\langle \Psi, \Phi \rangle$ be a basis with step space ${\mathbb{S}} = \Lambda \times {\mathscr{F}} \times (\,{\prod{\Psi}}... | 138 | 1,246 | 235 | 176 | 2,509 | 0.779244 | github_plus_top10pct_by_avg |
rity policies says if the system is *output consistent*, *weakly step consistent* and *locally respects* $\rightsquigarrow$, the system is secure for policy $\rightsquigarrow$. The three conditions are called *unwinding conditions*. The unwinding theorem simplifies the security proofs by decomposing the global properti... | 139 | 238 | 832 | 214 | null | null | github_plus_top10pct_by_avg |
rFormula, Operator:=intOperator
Else
rng.FormatConditions.Add Type:=intType, _
Formula1:=strFormula
End If
On Error GoTo 0
Set objCond = rng.FormatConditions(rng.FormatConditions.Count)
If intColorIndex <> -1 Then
objCond.Font.ColorIndex = intColorIndex
ElseIf dblRGB ... | 140 | 3,593 | 107 | 121 | 464 | 0.808917 | github_plus_top10pct_by_avg |
at{S} = \bigcup x$, such that the text summary covers all events in $\mathcal{C}$.
**Semantic Search and Analytics**. The mined set of *events* can further be utilized for search and analytics. For this purpose we can utilize inherent hierarchy in the semantic annotations. For example a given year can be broken down t... | 141 | 2,635 | 344 | 146 | 2,025 | 0.783389 | github_plus_top10pct_by_avg |
“Estimation time" means the corresponding time measured in second when one runs Case 1 of Example \[example2\] in Section \[sec3\]. As for the other notation, $b$ is the subset size, $S$ is the number of subsets, $R$ is the number of sampled subsets. The detailed setting is shown in Section \[sec3\]. We run R language ... | 142 | 531 | 439 | 193 | 2,437 | 0.779755 | github_plus_top10pct_by_avg |
uss in greater detail the special case of line bundles on gerbes over projective spaces.
Generalities {#generalities}
------------
Let us first review some basic properties of line bundles on gerbes over projective spaces, and then we will outline their sheaf cohomology.
First, let us consider some simple explicit e... | 143 | 512 | 338 | 185 | null | null | github_plus_top10pct_by_avg |
ms.
Q:
Manually remove whitespace in String - JavaScript
I have attempted to make an algorithm that will do the same thing as this function: var string= string.split(' ').join('');
So if I have the following String: Hello how are you it becomes Hellohowareyou
I don't want to use .replace or regex or .split
H... | 144 | 5,295 | 149 | 40 | 475 | 0.808431 | github_plus_top10pct_by_avg |
ly the points of ${\mathcal{B}}({\mathcal C})$), where morphisms are natural transformations between the inverse image functors.
\[th:filt\] There is an equivalence of categories $${\mathsf{Filt}}({\mathcal C})\, \,{\mathrel{
\settowidth{\@tempdima}{$\scriptstyle\tau$}
\settowidth{\@tempdimb}{$\scriptstyle\rho$}
... | 145 | 878 | 276 | 184 | null | null | github_plus_top10pct_by_avg |
(dnName, certSerialNumber, startDate, endDate, dnName, this.publicKey);
BasicConstraints basicConstraints = new BasicConstraints(true);
certBuilder.addExtension(new ASN1ObjectIdentifier("2.5.29.19"), true, basicConstraints);
x509Certificate = new JcaX509CertificateConverter().setPr... | 146 | 4,192 | 17 | 129 | 322 | 0.813413 | github_plus_top10pct_by_avg |
performance on unperturbed images when defenses are used, we performed the experiment below. For the FGS and IGS attacks, unless otherwise noted, an epsilon of 0.3 was used as is typical in the literature.
Performance of Defended Models on Clean Data
--------------------------------------------
One of the basic assum... | 147 | 283 | 720 | 200 | null | null | github_plus_top10pct_by_avg |
* Constructor, define el tamaño de la clave en 2048bytes por defecto.
*/
public EncryptionRSA() {
this.keySize = 2048;
}
/**
* Constructor, permite definir el tamaño de la clave en bytes. Ejemplo:
* EncryptionRSA(1024); La clave se generará con un tamaño de 1024 bytes y
* ... | 148 | 1,005 | 145 | 77 | 472 | 0.808585 | github_plus_top10pct_by_avg |
- an overview of the research problems (Section \[sec:problem\]);
- available corpora, test sources and evaluation measures for research (Section \[sec:evaluation\]);
- discussion of few open technical problems (Section \[sec:discussion\]).
Related Work
============
\[sec:background\]
In this section I disc... | 149 | 45 | 799 | 191 | null | null | github_plus_top10pct_by_avg |
a marked impact on the distance between normal and adversarial examples. Thus, we can conclude that part of the reason for why the defense works is that it dampens the effect of adversarial noise.
[.5]{} ![L-$\infty$ distance between adversarial and normal images as a function of layer number for LeNet attacked with ... | 150 | 17 | 474 | 201 | null | null | github_plus_top10pct_by_avg |
x)
=
\begin{cases}
\infty & (0 < a < 1), \\
1 & (a = 1), \\
0 & (a > 1),
\end{cases}
&
&\lim_{x \to +\infty} \frac{d}{dx} y_{1}(a, x)
= 0 \quad (a > 0), \allowdisplaybreaks \\
&\lim_{x \to 0+} y_{1}(a, x)
= 0 \quad (a > 0),
&
&\lim_{x \to +\infty} y_{1}(a, x)
= 0 \quad (a > 0). \end{aligned}$$ From these resul... | 151 | 2,037 | 269 | 157 | null | null | github_plus_top10pct_by_avg |
Interpretable AOG Representations from Convolutional Networks via Active Question Answering
---
[Shell : Bare Demo of IEEEtran.cls for Computer Society Journals]{}
Introduction
============
Convolutional neural networks [@CNN; @CNNImageNet; @ResNet; @DenseNet] (CNNs) have achieved superior performance in many visua... | 152 | 16 | 240 | 167 | 1,164 | 0.793312 | github_plus_top10pct_by_avg |
�ь она смотрит влево.
Можно ли в Android в XML каким нибудь параметром сказать чтобы при смене языка RTL в ImageButton не менять. Что то типо RTL disable. Мне надо чтобы именно эта View не реагировала на смену RTL ImageButton
A:
можно отключить для всего приложения в AndroidManifest.xml:
<application android:supports... | 153 | 48 | 132 | 116 | null | null | github_plus_top10pct_by_avg |
W/m^2^; (**c**) MLP model at *G* = 909.0 W/m^2^; (**d**) CNN model at *G* = 153.7 W/m^2^; (**e**) CNN model at *G* = 653.4 W/m^2^; (**f**) CNN model at *G* = 909.0 W/m^2^.](sensors-20-02119-g006){#sensors-20-02119-f006}
![Analysis of the predicted value form MLP mode. (**a**) Changes of the current with *G* and *V*; (... | 154 | 1,480 | 785 | 212 | null | null | github_plus_top10pct_by_avg |
, {\mbox{\boldmath $\alpha$}}^{(i)}, {\mbox{\boldmath $\alpha$}}^{(i+1/2)})$ are labeled by $(\mu, \mu^+_{(r)}, \mu)$. Then for a tableau $P$ which goes through $\mu$ at the $(i-1/2)$-th and the $(i+1/2)$-th coordinate of $P$, we have $$\rho(f_i)(v_P)
=
\sum_{r} \frac{h(\mu)}{h(\mu^{+}_{(r_0)})}v(\mu^+_{(r)}, \mu).$$ H... | 155 | 1,834 | 286 | 205 | null | null | github_plus_top10pct_by_avg |
on divergence if no specific notation is made. However, MGoF used only Kullback-Leibler divergence due to its special mechanism. We use a “+” to denote algorithms optimized by a given $\alpha$.
Experiments on Koubei Data Set {#sec:exp-raw}
------------------------------
We first tested our algorithms on Koubei data s... | 156 | 819 | 553 | 231 | null | null | github_plus_top10pct_by_avg |
ant to get:
suma producto dia
4 1 FRI
5 3 TUE
Only the top product of each day (with the max(suma) of each group).
I tried different approaches, like subqueries, but the aggregate function used make things a bit difficult.
A:
You can still use DISTINCT ON to get this done in a single query ... | 157 | 4,541 | 12 | 139 | 2,420 | 0.779886 | github_plus_top10pct_by_avg |
} +
\sum_{K \neq L} W_{iK} \hat{S}_{KL}^{(2)} \left\{ (W^{\dagger}) \right\}_{L j}.
\label{S-alpha-beta-4th-[3]}\end{aligned}$$ We do not display explicitly the expression of each term in (\[Sab-4th\]). But, the notation of $S_{\alpha \beta}^{(4)} [n]_{ \text{ diag } }$ and $S_{\alpha \beta}^{(4)} [n]_{ \text{ offdia... | 158 | 552 | 368 | 213 | null | null | github_plus_top10pct_by_avg |
One can verify that the unique homomorphic extension of $c$, denoted by $\overline{c}$, is injective. Therefore, we conclude that the function $c$ is an adaptive code of order two.
Let $\Sigma$, $\Delta$, and ${\it Bool}=\{{\it True}, {\it False}\}$ be alphabets. We define the function ${\it Prefix}:{\it AC}(\Sigma,\D... | 159 | 543 | 271 | 192 | 1,794 | 0.785661 | github_plus_top10pct_by_avg |
$m_{i, i-1}m_{i-1, i}'+m_{i, i+1}m_{i+1, i}'=
\begin{pmatrix} a_i''&b_i''&c_i''\\ d_i''&e_i''&f_i''\\ g_i''&h_i''&k_i'' \end{pmatrix}$ and $m_{i, i-2}m_{i-2, i}'+m_{i, i+2}m_{i+2, i}'=
\begin{pmatrix} \tilde{a}_i''&\tilde{b}_i''&\tilde{c}_i''\\ \tilde{d}_i''&\tilde{e}_i''&\tilde{f}_i''\\ \tilde{g}_i''&\tilde{h... | 160 | 1,838 | 266 | 207 | null | null | github_plus_top10pct_by_avg |
computationally much simpler, as it involves fewer random variables and a simpler set of conditions (no nonnegativity constraints). However, CbD has the advantage of being more general than NP, as it can include cases where no NP distributions exist due to violations of the no-signaling condition [@dzhafarov_probabilis... | 161 | 256 | 502 | 229 | 2,262 | 0.781328 | github_plus_top10pct_by_avg |
_driver.FindElements(By.XPath("//ul[@id='sortable1']/li")).ToList();
List<IWebElement> sortableListTwo = _driver.FindElements(By.XPath("//ul[@id='sortable2']/li")).ToList();
After changing the sortableListOneandsortableListTwo` element locator, please use the below in your test method and it will return the correct ... | 162 | 4,842 | 10 | 117 | 7 | 0.843115 | github_plus_top10pct_by_avg |
bib-0005){ref-type="ref"} Despite many studies regarding the pathogenic or opportunistic nature of this bacterium, the fact is still not clearly understood.[7](#ccr32374-bib-0007){ref-type="ref"}, [10](#ccr32374-bib-0010){ref-type="ref"}
The aim of present study was to report the first case of human septicemia due to ... | 163 | 4,468 | 191 | 67 | null | null | github_plus_top10pct_by_avg |
line $\mathbb{R}$ including $(0,0)$.
1. The homeomorphism is $C^{\infty}$ on ${\mathbb{R}}^2-\{(0,0)\}$.
2. For each point not $(0,0)$, the homeomorphism preserves the distance between the point and $(0,0)$.
3. The homeomorphism maps each straight line originating from $(0,0)$ to another straight line originating... | 164 | 2,308 | 1,316 | 174 | null | null | github_plus_top10pct_by_avg |
\g(3a, x) + 2 x^{2a - 1} e^{-x} \G(2a, x), \end{aligned}$$ from Lemma $\ref{lem:3.5}$, we have $\frac{d}{dx} f(a, x) > 0$ ($a > 0$, $x > 0$). Also, $f(a, 0) = 0$ holds for $a > 0$. Therefore, we obtain $$\begin{aligned}
\operatorname{{V}}[\Pe(Z)] - \operatorname{{V}}[\Pe(Z + C)] \geq 0, \end{aligned}$$ where equality... | 165 | 895 | 367 | 227 | null | null | github_plus_top10pct_by_avg |
a$ systematically increase with $\Delta t$. For the $95\%$ of the stocks the increasing tendency is observed and for a window size $\Delta t = 1$ day the respective $\lambda$’s are greater than $2$. These are strong indications that the distributions are not in the Levy stable regime, and thus the second moment exists.... | 166 | 3,813 | 295 | 159 | null | null | github_plus_top10pct_by_avg |
let $f$ be the solution to (cf. [1]{}) Based on Lemma \[l2\] and $||\varphi'||= 1$, From [6]{}, we can rewrite [4]{} as Recall that $T_{n-k}\sim N(0, \frac{n-k}{n})$ and is independent of $\{X_1,\dots, X_n\}$. Using [21]{} with $Y=T_{n-k}$, $x=0, t=(n-k)/n$, we have and Therefore, from [32]{} and [33]{} where we regard... | 167 | 101 | 277 | 225 | null | null | github_plus_top10pct_by_avg |
t{w}} \text{ in }{\mathbb{W}} \text{ begin}\\
\text{\quad\quad \# number of steps in localized walk is } {\lvert{{\mathit{w}}}\rvert}\\
\text{\quad\quad \# abused index runs between 0 [for start step } {\mathit{w}}_{\text{crux}} \text{] and }
-({\lvert{{\mathit{w}}}\rvert}-1) \text{ [last predecessor step]}\\
\tex... | 168 | 976 | 268 | 216 | null | null | github_plus_top10pct_by_avg |
unavailable (which can be used in header too):
- (instancetype) init __attribute__((unavailable("Use 'sharedInstance' instead of 'init' as this class is singleton.")));
This can be used if you want to prompt some message about unavailability.
Q:
Assigning two arrays by ID
I am in stuck with easy things such a lo... | 169 | 44 | 171 | 111 | 164 | 0.821379 | github_plus_top10pct_by_avg |
in jsp (I know we shouldn't be using scriptlets in JSP pages) . However I am planning to advance from Servlet programming (currently i am only familiar with JSP and servlets) to some other framework possibly JSF,struts or spring so will learning JSTL come in handy later.. Or is it just an overload ??
A:
Yes, it wil... | 170 | 3,536 | 577 | 220 | 1,584 | 0.787951 | github_plus_top10pct_by_avg |
on estimated by $d_i$ $\{D_i | \frac{d_i - \mu}{\sigma} > 3 \}$
![Distribution of Jensen-Shannon divergence on Taobao data set(without click farming) used in the experiments.[]{data-label="fig:jsd-dist"}](./JSD-Dist.pdf){width="\linewidth"}
Fig. \[fig:jsd-dist\] shows distribution of all divergences against the refer... | 171 | 20 | 245 | 179 | null | null | github_plus_top10pct_by_avg |
erms except for the one with $\Delta_G - \Delta_B - \Delta_F=\bar\Delta_G - \bar\Delta_B - \bar \Delta_F=0$. Thus only the regular term $:BF:(w)$ in the OPE between $B(x)$ and $F(w)$ survives. We obtain the following contribution to the OPE : $$\begin{aligned}
\lim_{z\to w}& A(z) :BC:(w) = ... + (z-w)^{\Delta_F + \... | 172 | 225 | 292 | 233 | 3,345 | 0.772989 | github_plus_top10pct_by_avg |
ults. Section 4 deals with Self-Organizing maps and its variant along with their classification results. Conclusions and future perspectives have been discussed in the section 5.
Data set description
====================
The data sets are generated by a montecarlo program, CORSIKA [@cor]. They contain 12332 gammas, ... | 173 | 1,948 | 724 | 241 | null | null | github_plus_top10pct_by_avg |
odule>
foo()
File "exc.py", line 5, in foo
print(1/x)
ZeroDivisionError: integer division or modulo by zero
The fix is to handle it. The most trivial fix is something like this:
while True:
try:
cam.start()
img = cam.get_image()
pygame.image.save(img,"current.jpeg")
cam.st... | 174 | 1,271 | 139 | 99 | 84 | 0.827507 | github_plus_top10pct_by_avg |
ing his customers their choice of any horse, as long as that horse was in the first unoccupied stall.
[^11]: The set difference $A \setminus B$ is not conventionally restricted to $B \subseteq A$, as is stipulated here.
[^12]: one form of which is $(\neg B \Rightarrow \neg A)\Leftrightarrow(A \Rightarrow B)$
[^13]: ... | 175 | 25 | 352 | 257 | null | null | github_plus_top10pct_by_avg |
In [@DBLP:conf/iclp/VoetsS09], classes of queries are represented as *moded queries*. Moded queries are partially instantiated queries, in which variables can be labeled as *input*. Variables labeled input are called *input variables* and represent arbitrary ground terms. To indicate that a variable is labeled as inpu... | 176 | 4,632 | 427 | 206 | 132 | 0.823545 | github_plus_top10pct_by_avg |
ntly upregulated genes.](ol-14-06-7153-g00){#f1-ol-0-0-7146}
{#f2-ol-0-0-7146}
{#pone.0220160.t003g}
-----------------------------------------------------------------------------------------------------------------------------------------------... | 179 | 2,847 | 369 | 179 | null | null | github_plus_top10pct_by_avg |
i,s}^1 = 0, ..., D_{i,s}^k = 0$ means that all data areas in the partition $i$ are clear. Satisfaction of this property implies that no data stored in the partition during one configuration of this partition can remain in any memory area of a later configuration.
### Formal Comparison of Policies and Properties
As p... | 180 | 705 | 929 | 243 | null | null | github_plus_top10pct_by_avg |
A more sophisticated adjustment of the relative scales can be performed within a comparison of the flow of momentum-dependent observables such as the wave function renormalisation $Z_0$. The peak of these flows in momentum space is directly related to the cut-off scale. Indeed, the function $f$ carries the physical inf... | 181 | 1,056 | 702 | 274 | 1,363 | 0.790448 | github_plus_top10pct_by_avg |
s inverse $W_{-1}^{-1}(y) = ye^y$, defined over the domain $(-\infty, -1)$ is also monotonically decreasing. By our assumption, $-cx \leq -3 \log \frac{1}{c} \leq -3$, thus $-cx \in (-\infty, -1]$, thus applying $W_{-1}^{-1}$ to both sides gives us the first implication.
Experiment Details {#apx:experiments}
=========... | 182 | 150 | 451 | 283 | 1,124 | 0.794039 | github_plus_top10pct_by_avg |
$g\leq e$. Then ${\mathbf d}(c)=g$. This and $c\leq t$ yield that $c=tg$. It follows that there is an arrow $$(g,x)\stackrel{(e,g)}{\longrightarrow} (e,x)$$ in the category $\int_{L(S)}\Phi(X,\mu)$. Since $(f,s)(e,g)=(f,c)=(f,t)(e,g)$ in $L(S)$, the diagram $$(g,x) \stackrel{(e,g)}{\longrightarrow}(e,x) {\mathrel{
\s... | 183 | 510 | 221 | 238 | 780 | 0.800117 | github_plus_top10pct_by_avg |
eq:dim5op}\end{aligned}$$ We consider below how to obtain the dim.-5 operator at the loop level by using renormalizable interactions[^2]. We restrict ourselves to extend only the $\text{SU}(3)_c$-singlet scalar sector in the HTM because it seems a kind of beauty that the HTM does not extend the fermion sector and color... | 184 | 541 | 669 | 268 | 2,484 | 0.779387 | github_plus_top10pct_by_avg |
}
Table [4](#sim7930-tbl-0004){ref-type="table"} shows IPD meta‐analysis results for a random sample of 5 and 10 trials that investigated exercise interventions and for 20 trials (using all 15 exercise trials, plus 5 additional trials that investigated mixed interventions).
######
Results from baseline weight adjus... | 185 | 1,585 | 633 | 221 | null | null | github_plus_top10pct_by_avg |
ation.
--------------- --------------- ------------------------ ------------------------------ ----------------------------------
$\mathcal{R}=X$ $\textrm{Cl}(\mathcal{R})=X$ $\tex... | 186 | 1,494 | 706 | 267 | null | null | github_plus_top10pct_by_avg |
* $f(N\delta )=S_{N\delta }=S_{\delta }^{\bot }$*.*
(iii)* For every* $\delta _{1}$*,* $\delta _{2}\in \psi
_{A}^{Q}$*,* $f(\delta _{1}K$ $\delta _{2})=\mathcal{S}_{\delta
_{1}K\delta _{2}}=\mathcal{S}_{\delta _{1}}\cap \mathcal{S}_{\delta _{2}}$.
\(iv) *For every* $\delta _{1}$*,* $\delta _{2}\in \psi
_{A}^{Q} $*,* ... | 187 | 242 | 652 | 260 | null | null | github_plus_top10pct_by_avg |
45.900, J44.001, J44.101, J44.803, and J98.801). The duration of the collected data lasted from January 1, 2016, to December 31, 2017, which is 731 days of continuous data. For statistical purposes, the days where the daily volume was less than 24 were labeled as nonpeak events, and the rest were labeled as peak events... | 188 | 728 | 568 | 279 | null | null | github_plus_top10pct_by_avg |
l separated from the other curves, but a further reduction in $\lambda_{\max}$ risks the introduction of an additional, spurious MU to explain the low-stimulus observations.
[lcP[18mm]{}P[18mm]{}P[18mm]{}P[18mm]{}P[18mm]{}P[18mm]{}]{} & True & & &\
$u$ & 8 & 7 & 8 & 7 & 8 & 7 & 8\
$\mathbb{P}(u|y)$ & – & 96.7% & 3.3% ... | 189 | 36 | 735 | 287 | null | null | github_plus_top10pct_by_avg |
}}$ indicates the spatial compatibility between neighboring latent patterns: we model the pairwise spatial relationship between latent patterns in the upper conv-layer and those in the current conv-layer. For each $v^{\textrm{unt}}$ (with its parent $u$) in conv-layer $L_{u}$, we select 15 nearest latent patterns in co... | 190 | 43 | 474 | 284 | 448 | 0.809524 | github_plus_top10pct_by_avg |
segs,st1) = selectlist(segs,st2) \; \wedge \\
& current(st1) = current(st2) \; \wedge \\
& select(seg,st1) = select(seg,st2) \\
& \Rightarrow \\
& select(seg,next(st1)) = select(seg,next(st2))
\end{aligned}$$
where $segs = dia(seg) \cap segsofpartition(current(st1))$. The security policy requires that the effect on an... | 191 | 809 | 1,075 | 307 | 124 | 0.824039 | github_plus_top10pct_by_avg |
lback from sections of any tensor bundle to itself. In this way, the group also acts on all spaces of $(p,q)$-tensors.
Studying the neighborhood of the identity $e\in G$, we get the induced action of the Lie algebra $\mathfrak{g}$ on these same tensor bundles. The infinitesimal version of a pullback of a tensor field ... | 192 | 2,461 | 288 | 186 | null | null | github_plus_top10pct_by_avg |
0.013 (2) −0.006 (2)
C69 0.018 (2) 0.021 (2) 0.018 (2) −0.0015 (19) 0.0045 (19) −0.0052 (19)
C70 0.012 (2) 0.017 (2) 0.012 (2) −0.0031 (17) 0.0015 (17) 0.0004 (17)
C71 0.013 (2) 0.017 (2) 0.014 (2) −0.0020 (17) 0.0021 (18) 0.0007 (18)
C... | 193 | 3,531 | 172 | 164 | null | null | github_plus_top10pct_by_avg |
length $k$ of process $\lbrace {\mathbf{f}}_n \rbrace$ is the set ${\mathbf{F}} = \lbrace {\mathbf{f}} \colon {\mathbf{f}} = {\mathbf{f}}_i \;\text{and}\; i \le k\rbrace$. This set’s $\psi$-homogeneous subset contains only those frames having initial condition (abscissa) $\psi \in {\prod{\Psi}}$. The corresponding end... | 194 | 1,278 | 374 | 264 | 1,094 | 0.794443 | github_plus_top10pct_by_avg |
\mathit{f}}' &= (\ell(\Delta(\lambda, \psi)))({\mathit{f}}(\psi) \xi')&\quad\text{[next functionality]} \\
{\mathbf{f}}\,' &= (\psi', \phi') =
([{\mathit{f}}(\psi) \xi'], [(\ell(\Delta(\lambda, \psi)))({\mathit{f}}(\psi) \xi')]([{\mathit{f}}(\psi) \xi']))&\quad\text{[next frame]}\end{aligned}$$
A partia... | 195 | 2,345 | 585 | 208 | 2,980 | 0.77564 | github_plus_top10pct_by_avg |
$v_i$ can be eliminated by $y_i$ and $m_{i-1,i}, m_{i,i+1}$.\
5. We consider Equation (\[ea27\]). If $L_i$ is *of type $I^e$*, then $v_i$ can be eliminated by $\left(r_i\right)$, $y_i$ can be eliminated by $\left(t_i, v_iz_i, m_{i-1,i}, m_{i,i+1}\right)$, and $w_i$ can be eliminated by $\left(r_i, t_i, z_i, x_i, u_i,... | 196 | 2,323 | 455 | 265 | 3,621 | 0.771201 | github_plus_top10pct_by_avg |
xt in multiple lines as the title of a plot or axis WITH a subscript present in the text
I wish to print a text in the title in two lines but am not able to achieve desired output because of subscript present in the text. Following is the e.g of the text that I want in two lines.
plot(1,main=expression(paste(CO[2]~'F... | 197 | 3,030 | 131 | 295 | 58 | 0.830163 | github_plus_top10pct_by_avg |
at which a particular step of an orbit coincides with any member of the reference set.
### Relative operational profile {#S:RELATIVE_OP_PROFILE}
Let ${\mathit{o}} = \{{\mathit{s}}_n\}$ be an orbit. Suppose $z \in Z \subset {\mathbb{S}}$ is a step of the reference set. Software encounters $N_{\{z\}}(\{{\mathit{s}}_n\... | 198 | 2,780 | 761 | 300 | 2,258 | 0.781375 | github_plus_top10pct_by_avg |
thm in [@moller-sgcd], which computes the Jacobi symbol using only $O(n)$ extra time and $O(1)$ extra space[^1]. This indicates that also for the fastest algorithms for large inputs, the cost is essentially the same for computing the and computing the Jacobi symbol.[^2]
Like the algorithm described in [@bach-shallit],... | 199 | 3,311 | 619 | 245 | 2,840 | 0.776644 | github_plus_top10pct_by_avg |
C51---C52---C53 120.6 (6)
W2---S7---Ag4 75.45 (3) C51---C52---H52 119.7
C1---P1---C7 106.7 (2) C53---C52---H52 119.7
C1---P1---C13 101.0 (2) C54---C53---C52 119.9 (5)
C7---P1---C13 104.3 (2) C54---C53---H53 120.0
C1---P1---Ag1 111.83 (16) ... | 200 | 3,155 | 842 | 273 | null | null | github_plus_top10pct_by_avg |
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