86%
17.02.2015
can refer to the entire process environment by means of the **environ variable; therefore:
(gdb) p &environ
$4 = ( *) 0xf7f83d64
(gdb) x/100s *environ
0xffffc815
86%
21.11.2012
! solution array
011 REAL(real8) :: tol=1.d-4, diff=1.0d0
012 REAL(real8) :: delta
013 REAL(real8) :: x
014 REAL(real8) :: pi
015 REAL(real8) :: exact
016 REAL(real8) :: pdiff
017 REAL(real4
85%
09.01.2013
-stack-AWSEBAutoScalingGroup-12BAR59E5FUDM:
policyName/awseb-e-mnpsy5bpzk-stack-
AWSEBAutoScalingScaleDownPolicy-KW4NGGQ0LULU
2013-05-08 20:07:48 INFO Created CloudWatch alarm named:
awseb-e-mnpsy5bpzk
85%
18.07.2013
), the server tells the client which algorithms it has decided to use and sends an X.509 certificate [2] with the reply. The client can now determine whether the web server is actually the one it wants
85%
11.04.2016
LANDEV=eth0
05
06 echo -n "flushing all chains"
07 /sbin/iptables -F -t filter
08 /sbin/iptables -F -t nat
09 /sbin/iptables -F -t mangle
10 /sbin/iptables -X -t filter
11 /sbin
85%
28.06.2011
1084 image- store- 1299616369/image.manifest.xml admin available public x86_64 machine eki- F7901106 eri- 0C0D116C
05 IMAGE eki- F61410F1 image- store- 1299616170/kernel
85%
03.12.2024
convolutional layer.
Convolutional filters number 64 instead of 32.
The dropout rate has been increased to 0.5 (50%).
Listing 4: Second Block
model.add(layers.Conv2D(64, (3,3), padding
85%
26.01.2025
.add(layers.BatchNormalization())
model.add(layers.Conv2D(64, (3,3), padding='same', activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(0.5))
input
84%
01.06.2024
long count=0; //Count holds all the number of how many good coordinates
14 double z; //Used to check if x^2+y^2<=1
15 double pi
84%
07.11.2011
omp parallel for private(i) shared(x, y, n) reduction(+:a, b)
03 for (i=0; ix[i] ;
05 b = b + y[i] ;
06 }
The compiler creates a local copy of each variable
