Prana and compiled classes
2008 May 10th by todd anderson
I have recently gotten into incorporating Prana – the Inversion of Control framework of AS3 created by Christophe Herreman- into my projects. I gotta say, it’s beautiful piece of work and makes me rethink my approach to the architecture of applications again.
I don’t want to go into IoC and dependency injection and how your applications can truly benefit by using the Prana framework, as this post may get pretty long and these references are much better reading than my rambling:
Christophe’s blog: http://www.herrodius.com/blog/
Martin Fowler’s Inversion of Control Containers and the Dependency injection pattern
the hollywood principle
What i did want to bring up is that i had a small problem with the workflow and how i develop. Which is my problem, of course but nonetheless…
One important thing to remember is that the context file is an external file that is loaded by the application at runtime. This means you will need to have all the possible classes your application may use already compiled into the SWF in order for the objects to be instantiated and your application to work. If you are typing to interfaces, this could prove to be a bit of a problem. You could create a reference for each class that may be needed in another class that is known to be compiled into the SWF – as Christophe explains in this post – but that always seemed dirty to me.
As is mentioned in the comments to that post, you can also go about adding each class using the -includes compiler option. Adding all possible classes using the -includes option makes for an excellent case on when to use additional compiler configurations, and presents the option to really just change the application context file and the additional configuration file as the project sees fit, without having to open up the source and tack on or remove dummy references to classes.
As an example, take for instance this application context file:
< ?xml version="1.0" encoding="utf-8"?>
<objects>
<property file="app.properties" />
<!-- Handles direct invocation on client -->
<object id="callbackHandler" class="com.example.responder.CallbackResponderImpl" />
<!-- Handles connection to Red5 application -->
<object id="connectionDelegate" class="com.example.business.ConnectionDelegateImpl">
<property name="rtmpURI" value="${app.rtmpURI}" />
<property name="client">
<ref>callbackHandler</ref>
</property>
</object>
</objects>
.. for each possible implementation of ConnectionDelegate and CallbackResponder that i may decide to swap in and out as the project seems fit, i would either need to hold a reference to each implementation in some class sure or be compiled into the SWF, or i could store them in an additional config file that can be added using the -load-config option with an additional value:
The prana.config file:
<flex -config>
<includes append="true">
<symbol>com.example.reponder.CallbackResponderImpl</symbol>
<symbol>com.example.business.ConnectionDelegateImpl</symbol>
</includes>
</flex>
… drop that in my source folder and add the compiler option:
-load-config+=prana.config
From there, i could change the context as i see fit, update the prana.config file to reflect my preferences and just recompile the application without having to go into the source and muck about. It’s a little more clean for me and allows me to happily go about using the Prana framework.
The best part is that Prana is truthfully AS3 compliant! Meaning you can use it in your Flex AND AS3 projects, which cannot be said for some frameworks that claim to be AS3 and actually use class from the mx package… (looking at you as3lib). A huge pet-peeve of mine.
*Last i checked, the source under version control doesn’t seem to reflect the current changes Christophe has made, but they are included in the downloads.


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T
- the type of all nodes in the Graph
public interface Graph<T>
null
and must
provide meaningful Object.equals(java.lang.Object)
and Object.hashCode()
implementations.| Modifier and Type | Method and Description |
|---|---|
int |
connectivity()
Gets the maximum number of direct neighbors for any
Graph node. |
boolean |
contains(T node)
Determines whether the
Graph contains the specified node. |
T |
findNearestNode(PointD location)
|
double |
getDistance(T source,
T target)
Gets the distance between two specified
Graph nodes. |
java.util.Collection<T> |
getNeighbors(T node)
Gets all direct neighbors of the specified
Graph node. |
default java.util.Collection<T> |
getNeighbors(T node,
int steps)
Gets all neighbors of the specified
Graph node within the specified step distance. |
PointD |
getWorldLocation(T node)
Gets the world location of the specified
Graph node. |
PointD[] |
getWorldRegion(T node)
Gets the world region covered by the specified
Graph node. |
int |
nodeCount()
|
java.util.Collection<T> |
nodes()
Gets all nodes in the
Graph . |
int connectivity()
Graph
node.
Always greater than zero. Direct neighbors of a given Graph
node
are those that are directly connected, without any intermediate nodes.Graph
nodejava.util.Collection<T> nodes()
Graph
.
Always contains nodeCount()
elements. The element order depends
on the concrete implementation of Graph
.Collection
of all nodes in the Graph
T findNearestNode(PointD location)
Graph
node nearest to the specified PointD
world location.
Always returns a valid node, even if the distance between its getWorldLocation(T)
result and location
is very large. The returned node is not necessarily the one
whose getWorldRegion(T)
result contains location
.location
- the PointD
location, in world coordinates, to examineGraph
node whose getWorldLocation(T)
result
is nearest to the specified location
java.lang.NullPointerException
- if location
is null
double getDistance(T source, T target)
Graph
nodes.
Uses an arbitrary distance measurement, as documented by the implementation.
However, the result must always conform to the following invariants:
GraphAgent.getStepCost(T, T)
for the same two nodes.java.util.Collection<T> getNeighbors(T node)
Graph
node.
Returns an empty Collection
if node
or all its direct neighbors are
not part of the Graph
. The returned Collection
is the complete set
of target nodes for which GraphAgent.canMakeStep(T, T)
could possibly succeed,
given the specified node
as the source node.node
- the Graph
node whose direct neighbors to collectCollection
of all Graph
nodes that are directly
connected with node
, numbering from zero to connectivity()
java.lang.NullPointerException
- if node
is null
default java.util.Collection<T> getNeighbors(T node, int steps)
Graph
node within the specified step distance.
Returns an empty Collection
if node
or all its direct neighbors are
not part of the Graph
. Otherwise, returns a Collection
containing all
direct neighbors, direct neighbors of those neighbors, etc. repeated for steps
.
The default implementation uses two lists for gathering direct neighbors to inspect,
and returns a HashSet
with all valid neighbors found within steps
. This
requires the node type T to offer a useful Object.hashCode()
function.
Specific Graph
implementations may be able to use more efficient algorithms.
node
- the Graph
node whose neighbors within steps
to collectsteps
- the distance around node
, in movement steps,
in which other nodes are considered neighbors of node
Collection
of all Graph
nodes whose step distance from
node
is greater than zero, but equal to or less than steps
java.lang.IllegalArgumentException
- if steps
is equal to or less than zerojava.lang.NullPointerException
- if node
is null
PointD getWorldLocation(T node)
Graph
node.
Uses an arbitrary “world” coordinate system, as documented by the implementation.
The world location always resides within the result of getWorldRegion(T)
for
the same node
. The result of getDistance(T, T)
for any two nodes is usually
the Euclidean distance between their world locations, but this is not required.PointD[] getWorldRegion(T node)
Graph
node.
Uses an arbitrary “world” coordinate system, as documented by the implementation.
The returned polygon is implicitly assumed to be closed, with an edge connecting its
last and first vertex. The polygon should be simple and enclose a positive area that
contains the result of getWorldLocation(T)
for node
.


Advanced Layers¶
In the previous sections, we have illustrated an artificial neural network that is constructed analogous to neuronal networks in the brain. A model is only given a rough structure a priori, within which they have a huge number of parameters to adjust by learning from the training set. While we already understand that this is an extraordinarily powerful process, this method of learning comes with its own set of challenges. The most prominent of them is the generalization of the rules learned from training data to unseen data.
We have already encountered in the previous chapter how the naive optimization of a linear model reduces the generalization. However, we have also seen how the generalization error can be improved using regularization. Training neural network comes with the same issue and the same solution: we are always showing the algorithm we built the training that is limited in one way or another and we need to make sure that the neural network does not learn particularities of that given training set, but actually extracts a general knowledge.
The step zero to avoid over-fitting is to create sufficiently representative and diverse training set. Once this is taken care of, we can take several steps for the regularization of the network. The simplest, but at the same time most powerful option is introducing dropout layers. This regularization is very similar to dropping features that we discussed for linear regression. However, the dropout layer ignores a randomly selected subset of neuron outputs in the network only during training. Which neurons are dropped is chosen at random for each training step. This regularization procedure is illustrated in Fig. 15. By randomly discarding a certain fraction of neurons we ensure that the network does not get fixed at small particular features of the training set and is better equipped to recognize the more general features. Another way of looking at it is that this procedure corresponds to training a large number of neural networks with different neuron connections in parallel. The fraction of neurons that are ignored in a dropout layer is a hyperparameter that is fixed a priori. Maybe it is counter-intuitive but the best performance is often achieved when this number is sizable, between 20% and 50%. It shows the remarkable resilience of the network against fluctuations.
As for the linear models, regularization can also be achieved by adding regularization terms \(R\) to the \(L\), \(L \rightarrow L + R\). Again, the two most common regularization terms are \(L1\)- or Lasso-regularisation, where
and the sum runs over all weights \(W_j\) of the network, as well as the \(L2\)-regularization, or ridge regression, which we already discussed for linear regression with
where the sum runs again over all weights \(W_j\) of the network. As for the linear models, \(L2\) regularization shrinks all parameters symmetrically, whereas \(L1\)-regularization usually causes a subset of parameters to vanish. For this reason, the method is also called weight decay. Either way, both \(L1\) and \(L2\) regularizations restrict the expressiveness of the neural network, thus encouraging it to learn generalizable features rather than overfitting specific features of the data set.
The weights are commonly initialized with small values and increase during training. This naturally limits the capacity of the network, because for very small weights it effectively acts as a linear model (when one approximates the activation function by a linear function). Only once the weights become bigger, the network explores its nonlinearity.
Another regularization technique consists in artificially enlarging the data set. Data is often costly, but we have extra knowledge about what further data might look like and feed this information in the machine learning workflow. For instance, going back to the MNIST example, we may shift or tilt the existing images or apply small transformations to them. By doing that, researchers were able to improve MNIST performance by almost 1 percent 3. In particular if we know symmetries of the problem from which the data originates (such as time translation invariance, invariance under spatial translations or rotations), effective generation of augmented datasets is possible. Another option is the addition of various forms of noise to data in order to prevent overfitting to the existing noise or in general resilience of the neural network to noise. Finally, for classification problems in particular, data may not be distributed between categories equally. To avoid a bias, it is the desirable to enhance the data in the underrepresented categories.
Convolutional neural networks¶
The fully-connected simple single-layer architecture for a neural network is in principle universally applicable. However, this architecture is often inefficient and hard to train. In this section, we introduce more advanced neural-network layers and examples of the types of problems for which they are suitable.
Convolutional layers¶
The achieved accuracy in the MNIST example above was not as high as one may have hoped, being much worse than the performance of a human. A main reason was that, using a dense network structure, we discarded all local information contained in the pictures. In other words, connecting every input neuron with every neuron in the next layer, the information whether two neurons are close to each other is lost. This information is, however, not only crucial for pictures, but quite often for input data with an underlying geometric structure or natural notion of ‘distance’ in its correlations. To use this local information, so-called convolutional layers were introduced. The neural networks that contain such layers are called convolutional neural networks (CNNs).
The key idea behind convolutional layers is to identify certain (local) patterns in the data. In the example of the MNIST images, such patterns could be straight and curved lines, or corners. A pattern is then encoded in a kernel or filter in the form of weights, which are again part of the training. The convolutional layer than compares these patterns with a local patch of the input data. Mathematically, identifying the features in the data corresponds to a convolution \((f * x)(t)=\sum_{\tau}f(\tau)x(t-\tau)\) of the kernel \(f\) with the original data \(x\).
For two-dimensional data, such as shown in the example in Fig. 16, we write the discrete convolution explicitly as
where \(f_{n,m}\) are the weights of the kernel, which has linear size \(k\), and \(b_0\) is a bias. Finally, \(s\) is called stride and refers to the number of pixels the filter moves per application. The output, \(q\), is called feature map. Note that the dimension of the feature map is \(n_q\times n_q\) with \(n_q = \lfloor (n_{in} - k)/s + 1 \rfloor \) when the input image is of dimensions \(n_{in} \times n_{in}\): application of a convolutional layer thus reduces the image size, an effect not always intended. To avoid this reduction, the original data can be padded, for example by adding zeros around the border of the data to ensure the feature map has the same dimension as the input data.
For typical convolutional networks, one applies a number of filters for each layer in parallel, where each filter is trained to recognize different features. For instance, one filter could start to be sensitive to contours in an image, while another filter recognizes the brightness of a region. Further, while filters in the first layers may be sensitive to local patterns, the ones in the later layers recognize larger structures. This distribution of functionalities between filters happens automatically, it is not preconceived when building the neural network.
Pooling¶
Another very useful layer, in particular in combination with convolutional layers, is the pooling layer. Each neuron in the pooling layer takes input from \(n\) (neighboring) neurons in the previous layer—in the case of a convolutional network for each feature map individually—and only retains the most significant information. Thus, the pooling layer helps to reduce the spatial dimension of the data. What is considered significant depends on the particular circumstances: Picking the neuron with the maximum input among the \(n\), called max pooling, detects whether a given feature is present in the window. Furthermore, max pooling is useful to avoid dead neurons, in other words neurons that are stuck with a value near 0 irrespective of the input and such a small gradient for its weights and biases that this is unlikely to change with further training. This is a scenario that can often happen especially when using the ReLU activation function. Average pooling, in other words taking the average value of the \(n\) inputs is a straight forward compression. Note that unlike other layers, the pooling layer has just a small set of \(n\) connections with no adjustable weights. The functionality of the pooling layer is shown in Fig. 17 (a) and (b).
An extreme case of pooling is global pooling, where the full input is converted to a single output. Using a max pooling, this would then immediately tell us, whether a given feature is present in the data.
Example: DNA sequencing¶
With lowering costs and expanding applications, DNA sequencing has become a widespread tool in biological research. Especially the introduction of high-throughput sequencing methods and the related increase of data has required the introduction of data science methods into biology. Sequenced data is extremely complex and thus a great playground for machine learning applications. Here, we consider a simple classification as an example. The primary structure of DNA consists of a linear sequence of basic building blocks called nucleotides. The key component of nucleotides are nitrogen bases: Adenine (A), Guanine (G), Cytosine (C), and Thymine (T). The order of the bases in the linear chains defines the DNA sequence. Which sequences are meaningful is determined by a set of complex specific rules. In other words, there are series of letters A, G, C, and T that correspond to DNA and while many other sequences do not resemble DNA. Trying to distinguish between strings of nitrogen bases that correspond to human DNA and those that don not is a simple example of a classification task that is at the same time not so easy for an untrained human eye.
In Fig. 18, we show a comparison of five strings of human DNA and five strings of 36 randomly generated letters A, G, C, and T. Without deeper knowledge it is hard to distinguish the two classes and even harder to find the set of empirical rules that quantify their distinction. We can let a neural network have a go and see if it performs any better than us studying these sequences by visual analysis.
We have all ingredients to build a binary classifier that will be able to distinguish between DNA and non-DNA sequences. First, we download a freely available database of the human genome from https://genome.ucsc.edu4. Here, we downloaded a database of encoding genomes that contains \(100 000\) sequences of human DNA (each is 36 letters long). Additionally, we generate \(100 000\) random sequences of letters A, G, C, T. The learning task we are facing now is very similar to the MNIST classification, though in the present case, we only have two classes. Note, however, that we generated random sequences of bases and labeled them as random, even though we might have accidentally created sequences that do correspond to human DNA. This limits the quality of our data set and thus naturally also the final performance of the network.
The model we choose here has a standard architecture and can serve as a guiding example for supervised learning with neural networks that will be useful in many other scenarios. In particular, we implement the following architecture:
DNA Classification
Import the data from http://genome.uscs.edu
Define the model:
Input layer: The input layer has dimension \(36\times 4\) (\(36\) entries per DNA sequence, \(4\) to encode each of 4 different bases A, G, C, T)
Example: [[1,0,0,0], [0,0,1,0], [0,0,1,0], [0,0,0,1]] = ACCTConvolutional layer: Kernel size \(k= 4\), stride \(s= 1\) and number of filters \(N=64\).
Pooling layer: max pooling over \(n=2\) neurons, which reduces the output of the previous layer by a factor of 2.
Dense layer: 256 neurons with a ReLU activation function.
Output layer: 2 neurons (DNA and non-DNA output) with softmax activation function.
Loss function: Cross-entropy between DNA and non-DNA.
A schematic of the network structure as well as the evolution of the loss and the accuracy measured over the training and validation sets with the number of training steps are shown in Fig. 19. Comparing the accuracies of the training and validation sets is a standard way to avoid overfitting: On the examples from the training set we can simply check the accuracy during training. When training and validation accuracy reach the same number, this indicates that we are not overfitting on the training set since the validation set is never used to adjust the weights and biases. A decreasing validation accuracy despite an increasing training accuracy, on the other hand, is a clear sign of overfitting.
We see that this simple convolutional network is able to achieve around 80% accuracy. By downloading a larger training set, ensuring that only truly random sequences are labeled as such, and by optimizing the hyper-parameters of the network, it is likely that an even higher accuracy can be achieved. We also encourage you to test other architectures: one can try to add more layers (both convolutional and dense), adjust the size of the convolution kernel or stride, add dropout layers, and finally, test whether it is possible to reach higher accuracies without over-fitting on the training set.
Example: advanced MNIST¶
We can now revisit the MNIST example and approach the classification with the more advanced neural network structures of the previous section. In particular, we use the following architecture
Advanced MNIST
Input layer: \(28^2 = 784\) neurons.
Convolutional layer 1: Kernel size \(k= 5\), stride \(s= 1\) and number of filters \(N=32\) with a ReLU activation function.
Pooling layer: max pooling over \(n=2\times 2\) neurons.
Convolutional layer 2: Kernel size \(k= 5\), stride \(s= 1\) and number of filters \(N=64\) with a ReLU activation function.
Pooling layer: max pooling over \(n=2\times 2\) neurons.
Dropout: dropout layer for regularization with a 50% dropout probability.
Dense layer: 1000 neurons with a ReLU activation function.
Output layer: 10 neurons with softmax activation function.
For the loss function, we again use cross-entropy between the output and the labels. Notice here the repeated structure of convolutional layers and pooling layers. This is a very common structure for deep convolutional networks. With this model, we achieve an accuracy on the MNIST test set of 98.8%, a massive improvement over the simple dense network.


Clang Analyze
bake can be easily used to analyze source files with Clang. It works simliar to regular compiling, but instead of
invoking the compiler, the Clang Analyzer will be called.
Imagine you have a workspace with the following "main" project:
Project {
...
ExecutableConfig Debug {
...
DefaultToolchain GCC
}
}
Either edit this Project.meta or create a new "analyze" project:
Project {
CustomConfig Analyze {
Dependency main, config: Debug
DefaultToolchain CLANG_ANALYZE {
Compiler CPP {
Flags "-analyzer-checker=deadcode,security,alpha,unix,cplusplus"
}
Compiler C {
Flags "-analyzer-checker=deadcode,security,alpha,unix"
}
}
}
}
As you can see the DefaultToolchain GCC is replaced with CLANG_ANALYZE. Call bake like this:
bake Analyze -f .
"-f" means that only the compilation step will take place. The "." means files with a "." in the name will be compiled (= all files). If you want to analyze only C++ files, you can write "-f .cpp"


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Copyright © 2021, DIF. All Rights Reserved.


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--- layout: session_academic academic: true title: "A novel application of models of species abundance to better understand OpenStreetMap Community structure and interactions" code: "7ZVYEH" speaker_names_with_affiliations: ['Peter Mooney; Department of Computer Science, Maynooth University, Co. Kildare. Ireland<EMAIL_ADDRESS>extra_tags: room: "Hörsaal West" length: "00:20" time: "Sunday, 10:30" recording: True doi: https://doi.org/10.5281/zenodo.3386559 ---
The OpenStreetMap (OSM) community is a global community crossing cultures, languages, and geographical boundaries. Researchers have been working to develop automated approaches to understanding the composition of this community through their contributions to the OSM database. In this talk we propose a new and novel application of theories and models of species abundance from ecological science to understand contributor community structure and distributions in OSM.


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| synopsis: | Current Field Extensions |
|---|
AutoSlugField - AutoSlugfield will automatically create a unique slug incrementing an appended number on the slug until it is unique. Inspired by SmileyChris’ Unique Slugify snippet.
CreationDateTimeField - DateTimeField that will automatically set its date when the object is first saved to the database. Works in the same way as the auto_now_add keyword.
ModificationDateTimeField - DateTimeField that will automatically set its date when an object is saved to the database. Works in the same way as the auto_now keyword.
UUIDField - UUIDField for Django, supports all uuid versions that are natively supported by the uuid python module.
Deprecated since version 1.4.7: Django 1.8 features a native UUIDField. Django-Extensions will support UUIDField until Django 1.7 becomes unsupported.
PostgreSQLUUIDField - UUIDField for Django, uses PostgreSQL uuid type.
Deprecated since version 1.4.7: Django 1.8 features a native UUIDField. Django-Extensions will support UUIDField until Django 1.7 becomes unsupported.
EncryptedCharField - CharField which transparently encrypts its value as it goes in and out of the database. Encryption is handled by Keyczar. To use this field you must have Keyczar installed, have generated a primary encryption key, and have settings.ENCRYPTED_FIELD_KEYS_DIR set to the full path of your keys directory.
EncryptedTextField - CharField which transparently encrypts its value as it goes in and out of the database. Encryption is handled by Keyczar. To use this field you must have Keyczar installed, have generated a primary encryption key, and have settings.ENCRYPTED_FIELD_KEYS_DIR set to the full path of your keys directory.
ShortUUIDField - CharField which transparently generates a UUID and pass it to base57. It result in shorter 22 characters values useful e.g. for concise, unambiguous URLS. It’s possible to get shorter values with length parameter: they are not Universal Unique any more but probability of collision is still low


WITNESS LIST Oversight of Electric Utility Restructuring Committee December 5, 2001 - 9:00A ERCOT Audit Services ON: Barry, Sean Accountant (PricewaterhouseCoopers), Sacramento, CA Hurle, Michael S. Consultant (PricewaterhouseCoopers), Montvale, NJ Progress of Transition to a Retail ON: Baker, Thomas L. President (TXU Electric & Gas), Dallas, TX Bojorquez, William Director, Settlements & Customer Service (Electric Reliability Council of Texas ERCOT ), Taylor, TX Briesemeister, Janee (Consumers Union), Austin, TX Broadrick, Cherie Customer Registration Manager (ERCOT), Taylor, TX Dolese, Patricia (Public Utility Commission of Texas), Austin, TX Jones, Sam R. Chief Operating Officer (ERCOT), Austin, TX Magruder, Kathleen E. (The New Power Company), Purchase, NY Noel, Thomas CEO (ERCOT), Austin, TX Schaeffer, Steve (Reliant Energy, Inc.), Houston, TX Thomas, Paul Chief Operating Officer (Green Mountain Energy Company), Austin, TX Whyte, Shirley A. (Independent Third Party Testing Administrator), Spring, TX


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Proficient in Pandas, NumPy, Matplotlib, Seaborn, scikit-learn and other Python Packages.
I have experience in creating content using Canva, Figma, iMovies for Facebook, Instagram and prototype wireframes.
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package.
Processor compatibility can be checked with the following command:
$ dmesg | egrep '(VMX/EPT|SVM/RVI)'Before going further, enable and start the vmd(8) service.
# rcctl enable vmd # rcctl start vmd
installXX.iso
image file.
# vmctl create disk.qcow2 -s 50G vmctl: qcow2 imagefile created # vmctl start example -m 1G -L -i 1 -r installXX.iso -d disk.qcow2 vmctl: started vm 1 successfully, tty /dev/ttyp8 # vmctl show ID PID VCPUS MAXMEM CURMEM TTY OWNER NAME 1 72118 1 1.0G 88.1M ttyp8 root exampleTo view the console of the newly created VM, attach to its serial console:
# vmctl console example Connected to /dev/ttyp8 (speed 115200)The escape sequence
~.
is needed to leave the serial console.
See the cu(1) man page for more info.
When using a vmctl
serial console over SSH, the ~ (tilde)
character must be escaped to prevent
ssh(1) from dropping the connection.
To exit a serial console over SSH, use ~~.
instead.
The VM can be stopped using vmctl(8).
# vmctl stop example stopping vm: requested to shutdown vm 1Virtual machines can be started with or without a vm.conf(5) file in place. The following
/etc/vm.conf
example would replicate the above
configuration:
vm "example" { memory 1G enable disk /home/user/disk.qcow2 local interface }Some configuration properties in vm.conf(5) can be reloaded by vmd(8) on the fly. Other changes, like adjusting the amount of RAM or disk space, require the VM to be restarted.
In the examples below, various IPv4 address ranges will be mentioned for different use cases:
<IP_ADDRESS>/8
,
<IP_ADDRESS>/12
, and <IP_ADDRESS>/16
are not
globally routable.
<IP_ADDRESS>/10
.
Using vmctl(8)'s -L
flag creates a local interface in the guest which will receive an address
from vmd via DHCP.
This essentially creates two interfaces: one for the host and the other
for the VM.
The following line in /etc/pf.conf
will enable
Network Address Translation and redirect DNS requests
to the specified server:
match out on egress from <IP_ADDRESS>/10 to any nat-to (egress) pass in proto { udp tcp } from <IP_ADDRESS>/10 to any port domain \ rdr-to $dns_server port domainReload the pf ruleset and the VM(s) can now connect to the internet.
Create a vether0
interface that will have a private IPv4 address
as defined above.
In this example, we'll use the <IP_ADDRESS>/8
subnet.
# echo 'inet <IP_ADDRESS> <IP_ADDRESS>' > /etc/hostname.vether0 # sh /etc/netstart vether0Create the
bridge0
interface with the vether0
interface as a bridge port:
# echo 'add vether0' > /etc/hostname.bridge0 # sh /etc/netstart bridge0Ensure that NAT is set up properly if the guests on the virtual network need access beyond the physical machine. An adjusted NAT line in
/etc/pf.conf
might look like this:
match out on egress from vether0:network to any nat-to (egress)The following lines in vm.conf(5) can be used to ensure that a virtual switch is defined:
switch "my_switch" { interface bridge0 } vm "my_vm" { ... interface { switch "my_switch" } }Inside the
my_vm
guest, it's now possible to assign
vio0
an address on the <IP_ADDRESS>/24
network and set the default route to
<IP_ADDRESS>
.
For convenience, you may wish to set up a
DHCP server on vether0
.
Create the bridge0
interface with the host network interface as a
bridge port.
In this example, the host network interface is em0
- you should
substitute the interface name that you wish to connect the VM to:
# echo 'add em0' > /etc/hostname.bridge0 # sh /etc/netstart bridge0As done in the previous example, create or modify the vm.conf(5) file to ensure that a virtual switch is defined:
switch "my_switch" { interface bridge0 } vm "my_vm" { ... interface { switch "my_switch" } }The
my_vm
guest can now participate in the host network as if it
were physically connected.
Note: If the host interface (em0
in the above example) is
also configured using DHCP,
dhclient(8) running on that
interface may block DHCP requests from reaching guest VMs.
In this case, you should select a different host interface not using DHCP,
or terminate any dhclient(8)
processes assigned to that interface before starting VMs, or use static IP
addresses for the VMs.


*** This is the DRAFT VERSION 0.3 ALPHA of the Minimum Common Oncology Data Elements (mCODE™) implementation guide. ***
This implementation guide supplies terminology bindings drawn primarily from LOINC for "observables", and SNOMED-CT for values, results and findings. There are a few exceptions to this:
Value sets from the FHIR specification are reused to the extent possible. In addition to the FHIR base specification (DSTU2, DSTU3 and R4), Argonaut Data Query Implementation Guide, and the US Core Implementation Guide were also considered.
There may be differences across these specifications in the value sets used, as well as binding strength. This guide addresses these discrepancies by using the lowest common denominator amongst the specifications. This approach is not without challenges, as there cases where different value sets are required by different specifications, which leaves no choice but to opt for one value set over another.
This guide includes multiple value sets created specifically to support the terminology needs of the models. These value sets where created only when no known existing value sets were deemed to be fit for purpose, or when existing value needs needed to be extended.
NOTE:There is a known issue with HL7's FHIR implementation guide publisher that causes an error in the expansion of local value sets in the implementation guide. While this prevents the value sets from being shown in the value set pages, the content of the value sets is correctly expanded in json and xml, which readers can access from the indivual value set pages.
FHIR structurally separates missing value reasons (exception values) from answer sets. For example, FHIR uses two attributes, Observation.value and Observation.dataAbsentReason. For this reason, when LOINC answer lists included missing value explanations, those answers were separated and placed in a separate value set. However, we note that LOINC answer sets are not very consistent with regard to missing value reasons, and possibly incomplete. This leaves open a question on what a cross-cutting normative set of missing value reasons or exception values might be.
The AJCC staging system is recognized as one of the most widely-used standards for cancer staging. However, this guide does not include any AJCC terminology due to unresolved copyright issues. As such, elements related to staging may not currently include required terminology bindings.


Connector Pane
Execute File
Opens and executes a Lua script from the provided path.
Parameter(s)
-----------------
- MLua in: Reference to the MLua instance.s
- Path: Path to the Lua file to be executed.
- Argument Data: LabVIEW variant to be used as argument data for the Lua script.
- ms timeout (-1): The time to wait for the MLua reference to become available. A value of '-1' specifies an infinite timeout.
- error in: Describes error conditions that occur before this node runs. This input provides standard error in functionality.
- MLua out: Reference to the MLua instance.
- Return Data: Return data from the Lua script in the form of a LabVIEW variant.
- error out: Contains error information. This output provides standard error out functionality.


{% extends 'layout.html' %} {% block title %}Review book{% endblock %} {% block body %}
You are signed in as user id {{user}}


| Email Support |
Email ATTAC support at<EMAIL_ADDRESS>with question, comments or suggestions about ATTAC |
|
| |
| Forum Support |
Your online forum is located
at http://network54.com/Hide/Forum/27576.
You can post suggestions, bugs and comments as well as read up on other
peoples thoughts of ATTAC. |
| |
| Installation Support |
None Yet for v4 |
| |
| Scripting Support and TWX
format |
This
file is the support doc for REXX scripting |
This
file is the support for the TWX format |
| |
| Revision History |
ATTAC
Revision History in text format (updated 3/26/2000)
Tradewars
3.xx Revision History in HTML format (updated 11/13/1998) |
| |
| Common Questions |
Question:
How do I register? |
| Answer:
Go To www.tw-attac.com and click on 'Purchase'. Then click on 'Click here
to use our Secure Server' Once you have purchase ATTAC, I will automatically
receive email notification and request from you four (4) Tradewars Handles.
Once I receive those from you, I will send you the registration codes for
those handles. |
|
| Question:
Is there an ATTACK/CAPTURE script? |
| Answer:
No, use the Capture Calculator in ATTAC->Calculators->Ship Capture |
|
| Question:
Does ZTM save the data if it crashes or I lose my connection? |
| Answer:
Yes, since ZTM saves the data as it scrolls across the screen in real-time.


With the number of altcoins listed on Coinmarketcap reaching 1640 as of this writing, the question begs - how will all of these altcoin markets perform in the long term? When looking back four years at a historic snapshot of the leading markets from June 1st, 2014, one can se...


Isn't She Great * 1/2 (out of * * * * )
Directed by Andrew Bergman. Cast: Bette Midler, Nathan Lane, Stockard Channing, David Hyde Pierce, Amanda Peet, John Cleese. 2000 - 93 minutes Rated R (for profanity and sexual innuendo). Reviewed by Dustin Putman, January 30, 2000.
Andrew Bergman, director of two of the brightest comedies of the '90s, 1992's "Honeymoon in Vegas" and 1994's "It Could Happen to You," has fallen on desperate times lately, first with the 1996 Demi Moore debacle, "Striptease," and now with "Isn't She Great," a rather cartoonish biopic of struggling-actress-turned-trashy-writer Jacqueline Susann. Susann, who looked nothing at all like Bette Midler, died at the age of 53 in the mid-1970s of breast cancer, but before her death became an icon for the general public, not because her writing was particularly good, but because it told the absolute truth. All she wanted was to be happy and successful, something she never found in her acting career, and according to the movie, it was her devoted husband, Irving Mansfield (Nathan Lane), who originally suggested she write a book. Susann is a bit hesitant at first, but after writing a brazen little number that tells it solely like it is, "Valley of the Dolls," and managing to sell it to a publishing company, it quickly tops the charts and stays there for 28 weeks, becoming the best-selling novel of all time.
What the public didn't know about, however, was that Jacquelyn's son was sent to an institution after being labeled Autistic, and that she struggled with cancer for many years.  She ultimately lost her battle with the illness, but as she set out to do, was a star by the time she passed away.
Uplifting? Maybe a little. An appropriate story to build a wacky, fluffy comedy around? No way. Although any genre can surely be placed around any storyline and still be successful, it takes a firm, sure touch to sometimes pull it off. That is just one of the things that screenwriter Paul Rudnick (1997's "In & Out") is consistently missing in his disrespectful treatment of Susann, as he paints her as a crazy woman who talked to trees and thought it was God peering through the branches when the sun was shining. The only way to keep such a breezy tone and work, I think, would probably to have transformed the piece into a musical, a 'la "Evita." There are two scenes that are perfect setups for music numbers (and one of them actually does have Midler singing), and it is a missed opportunity that Rudnick did not realize this. Apparently, his brain was on autopilot while he penned the sloppy screenplay, anyway.
Worse yet is filmmaker Bergman. Plainly put, "Isn't She Great" is an astoundingly inept piece of direction, a surprise coming from someone who has proven his talent in year's past. From the schmaltzy narration by Susann's husband, Irving, to the episodic, bare-bones editing that seems to dwell on minor happenings in her life and then bypasses the most important elements, the film is never involving or emotionally satisfying because Bergman often seems to be too busy saying, "Look at Jackie Susann! Isn't she great?! Isn't she wacky?! Isn't she wild?!" Susann is only sporadically pulled out of her caricaturized character, and it is when she is (usually while she is telling things, no-holds-barred, exactly how they are) that the movie gets the little bit of charm that it acquires.
We rarely are able to believe anything in the film because of the unbelievable way in which it is presented. When Irving Mansfield, a publicity agent, spots Jacquelyn Susann on the stage, and then meets her in a restaurant, they end up spending the day together, with him confessing his undying love for her by the time night falls. It should be noted that they are standing waist-deep in a lake when this "touching" scene is played out, because Susann had just halfheartedly attempted suicide. Soon, they are married, but their relationship seems to be nothing more than close confidantes, as we never see them in bed together, or even kiss. And how are viewers to take it when Susann is earnestly talking to a tree in Central Park in one scene, and receiving radiation in the next? It simply does not work, and any cast or crew member should have discovered this when they read the script, unless they were all in it for the money and did not care about faithfully preserving the life of Susann.
Bette Midler overacts her way through the picture, but remains spunky and full of energy, thus making the proceedings a little more entertaining than they might have been had someone else been cast in the role. She can do far better, however, so let's hope that her next film, "Drowning Mona," being released next month, is a step up for her. Nathan Lane is a good sport as Irving, but usually is playing the straight man to Susann's bigger-than-life personality. Stockard Channing, as Susann's best friend, Flo, is a standout, spitting out her flashy lines with delightful panache. David Hyde Pierce, as Susann's editor; John Cleese, as her publisher; and Amanda Peet, as her promotional assistant, are mostly wasted, although Pierce gains a little extra mileage in the scenes where he sits down to edit the novel, only to get every one of his requested changes rebuked by Susann.
It is truly unfortunate that Rudnick sat down to construct a biopic of Jacquelyn Susann and could find nothing worthwhile to write aside from a string of one-liners, some of which are sharp zingers, to be sure, but do not aid in saying anything insightful about its subject. With only a brief glance at "Isn't She Great," which always feels like the outline to a movie, rather than a fully written motion picture, one thing is obvious: making the story of Jacquelyn Susann into a wistful comedy, albeit a badly made one, was an arbitrary, offensive decision, as many of the developments in her life were anything but a barrel of laughs.
- Copyright 2000 by Dustin Putman Http://www.young-hollywood.com Http://www.atnzone.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Fukuejima (or Fukue Island) is the largest and southernmost island of the Gotto Retto archipelago. It lies about 65nm from Nagasaki.
See Japan Formalities for detailed clearance information.
The officials will visit the boat.
There are three ferry pontoons. At the the inshore end of one of the pontoons, on the town side and immediately opposite the four-storey building that houses the Coastguard and Customs, there is a space is reserved for yachts.
This corner of the harbour is quite well-protected.
Last updated August 2011.


--- ---
There are various solutions to the problem of preventing a user from moving a form on screen.
First, make the form's BorderStyle something like bsDialog, so that the window can't be resized.
Next, add the following declaration to your form class:
procedure PosChange(var Msg: TWmWindowPosChanging); message WM_WINDOWPOSCHANGING;
Implement the message handler as:
procedure TForm1.PosChange(var Msg: TWmWindowPosChanging) ; begin Msg.WindowPos.x := Left; Msg.WindowPos.y := Top; Msg.Result := 0; end;
Thats it. Easy as can be. The only problem with this is that you can't move the form if you want your code to. To get around this, just set up a Boolean variable called PosLocked, set it to True when you want to lock the form's position, and to False when you need to move the form. (When your done, remember to set it back to true). Then to implement the proc above, just make it...
procedure TForm1.PosChange(var Msg: TWmWindowPosChanging) ; begin if PosLocked then begin Msg.WindowPos.x := Left; Msg.WindowPos.y := Top; Msg.Result := 0; end else inherited; end;
In this solution the form will be completely immovable, but the user can still resize, minimize or maximize it. How does it work? We will just remove the "move" command from system menu using simple code in the form's OnCreate event handler.
procedure TForm1.FormCreate(Sender: TObject); begin DeleteMenu(GetSystemMenu(Handle, False), SC_MOVE, MF_BYCOMMAND); end;
In this solution both sizing and moving the form are inhibited by trapping the appropriate system commands and preventing further processing.
Add the following method to the form's class declaration:
private ... procedure WMSysCommand(var Msg: TWMSysCommand); message WM_SYSCOMMAND; ...
And implement it as follows:
procedure TForm1.WMSysCommand(var Msg: TWMSysCommand); begin if ((Msg.CmdType and $FFF0) = SC_MOVE) or ((Msg.CmdType and $FFF0) = SC_SIZE) then begin Msg.Result := 0; Exit; end; inherited; end;
This final solution prevents the user from dragging the form with the mouse:
Add the following method to the form's class declaration:
private ... procedure WMNCHitTest(var Message: TWMNCHitTest); message WM_NCHITTEST; ...
And implement it as follows:
procedure TForm1.WMNCHitTest(var Message: TWMNCHitTest); begin inherited; with Message do if Result = HTCAPTION then Result := HTNOWHERE; end;


PubNub WebRTC App with Parse
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your phone number
call link:
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BassoonTracker Documentation
About
What is a tracker?
Keyboard commands
Tracker commands
History
Standalone player
URL parameters
FAQ


Fully Responsive Layout
The kit is designed to be mobile first. Every component and Example Page looks great on any screen size.
by Creative Tim
Paper Kit Pro comes with a large number of example pages, that give you to option to quickly setup the basic flows for your application. From registering to building your profile or setting a blog, we've got you covered.
The kit is designed to be mobile first. Every component and Example Page looks great on any screen size.
Gaia - Bootstrap Template is built on top of Bootstrap. Get the benefits of the most powerful framework and the perks of amazing new design.
Want to prototype very fast? Gaia Bootstrap Template is made with sections that can be integrated very easy and look perfect together.


BIG BUSINESS - portland/wonder ballroom
BUILT TO SPILL - neumos, georgetown, fremont, bellingham
CRACK SABBATH - royal room, darrells, the mix, owl 'n thistle
LAST GASP - highline
MARK LANEGAN - showbox market
MELVINS - showbox market, portland/wonder ballroom
RIDE - the neptune - 2015
SKY CRIES MARY - neumos - 2015
SKERIK TRIO - owl 'n thistle
SUFFERING FUCKHEADS - coppergate
ZEKE - el corazon, highline


{{> head}}
{{#previous}} ← Newer posts {{/previous}} page {{current}} of {{total}} {{#next}} Older posts → {{/next}}


Limitations Of Liability
Your App also automatically collects and receives certain information from your computer or mobile device, including the activities you perform on our Website, the Platforms, and the Applications, the type of hardware and software you are using (for example, your operating system or browser), and information obtained from cookies. For example, each time you visit the Website or otherwise use the Services, we automatically collect your IP address, browser and device type, access times, the web page from which you came, the regions from which you navigate the web page, and the web page(s) you access (as applicable).
When you first register for a Your App's account, and when you use the Services, we collect some Personal Information about you such as:
-
The geographic area where you use your computer and mobile devices
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Your full name, username, and email address and other contact details
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A unique Leno user ID (an alphanumeric string) which is assigned to you upon registration
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Other optional information as part of your account profile
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Your IP Address and, when applicable, timestamp related to your consent and confirmation of consent
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Other information submitted by you or your organizational representatives via various methods
Şartlar ve Koşullar
Under no circumstances shall Your App be liable for any direct, indirect, special, incidental or consequential damages, including, but not limited to, loss of data or profit, arising out of the use, or the inability to use, the materials on this site, even if Your App or an authorized representative has been advised of the possibility of such damages. If your use of materials from this site results in the need for servicing, repair or correction of equipment or data, you assume any costs thereof.
License Types & Template Usage
All our templates inherit the GNU general public license from HTML. All .PSD & CSS files are packaged separately and are not licensed under the GPL 2.0. Instead, these files inherit Your App Personal Use License. These files are given to all Clients on a personal use basis. You may not offer them, modified or unmodified, for redistribution or resale of any kind. You can’t use one of our themes on a HTML domain. More on HTML Vs CSS, you can read here. You can use our templates do develop sites for your clients.
Services help our customers promote their products and services, marketing and advertising; engaging audiences; scheduling and publishing messages; and analyze the results.
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By using any of the Services, or submitting or collecting any Personal Information via the Services, you consent to the collection, transfer, storage disclosure, and use of your Personal Information in the manner set out in this Privacy Policy. If you do not consent to the use of your Personal Information in these ways, please stop using the Services.
Assets Used In The Live Preview Conten
Your App Landing Page uses tracking technology on the landing page, in the Applications, and in the Platforms, including mobile application identifiers and a unique Hootsuite user ID to help us recognize you across different Services, to monitor usage and web traffic routing for the Services, and to customize and improve the Services. By visiting Leno or using the Services you agree to the use of cookies in your browser and HTML-based emails. Cookies are small text files placed on your device when you visit a web site in order to track use of the site and to improve your user experience.
Geri Dön


Basic User Interface
The user interface of the program allowed varying the animation speed, total number of shopping carts, and total number of checkout registers. Values for these parameters are simulated when the user clicks 'Start' and are displayed to the right of those variables after the simulation terminates. A progress bar, as well as, basic animation of the simulation are available for instant user feedback.


--- layout: post status: publish published: true title: Episode 5.09 - Marlon Byrd is eating muy Thai author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 8464 wordpress_url: http://ivyenvy.com/?p=8464 date: '2013-05-19 22:30:19 +0000' date_gmt: '2013-05-20 03:30:19 +0000' categories: - Podcast tags: - Matt Garza - Ian Stewart - Anthony Rizzo - Scott Feldman - Carlos Villanueva - Arby's comments: [] ---
On this episode we comment on Anthony Rizzo's new contract, which was reported during our last episode. Thanks to everyone that tweeted at us and alerted us on Facebook. Sorry we missed it at the time. Is this a good deal for the Cubs? Is it a good deal for Rizzo? Is Rizzo a cornerstone of the organization?
Matt Garza will be returning to the Cubs and Carlos Villanueva is being sent to the bullpen. We talk about the rotation and the future of players like Matt Garza and Scott Feldman.
We go through a list of listener questions. We ask for those with a status on our Facebook page the day of our recordings.
Kurt give us a (good) Scary Stat of the Week and we pick a winner for this week's photo caption contest. It was very difficult this week with so many great entries. We also really enjoy all of the Arby's references with Ian Stewart.
If you shop on Amazon.com, you can help us keep things running by going through our affiliate page. It does not cost you anything extra.
Episode #200 is 3 weeks away and you can find all of the information at ivyenvy.com/200


WITNESS LIST Urban Affairs Committee April 9, 2003-10:30A HB 1859 For: Gabriel, Charles (Self) Gabriel, Janice (Self) Against: Harrell, Will (ACLU, NAACP, LULAC) Hernandez, Arturo (Self and Texas Association of Substance Abuse Programs) HB 1970 Committee Substitute (CSHB 1970) For: Higgins, Mike (Texas State Assoc. of Fire Fighters) Hutmacher, James (Bexar County Sheriff's Law Enforcement Org.) HB 2363 For: Williams, Steve (Houston Prof. Fire Fighters L-341) HB 2889 For: Jackson, Dock (Self) HB 3232 For: Agamalian, Brandon (City of Fort Worth) HB 3236 Committee Substitute (CSHB 3236) For: Casto, Larry (City of Dallas) Lyons, Steve (Houston Police Dept & City of Houston) Mintz, David (Texas Apartment Association) Reed, Malcolm (Texas Burglar & Fire Alarm Association) HB 3551 Committee Substitute (CSHB 3551) For: Chapman, William (City of El Paso)


PlanetBuilderWeb is a web-based implementation of my first project, PlanetBuilder.
PlanetBuilderWeb uses slighly more optimized algorithm for procedural mesh generation than the previous Unity based version, making it suitable for larger planets.
By adding some spherical control and gravity, this version is playable and features several construction blocks.
Reachable depth when digging the planet is augmented using nested planets of increasing radius and subdivision degree.


--- layout: default1 section_id: pages ---
Below you will find a list of all our latest events and detailed information about them.
Open Source Design Workshop #3
July 14 2018
Tirana, Albania
OSCAL’18 – Open Source Conference Albania 2018
May 19, 2018 - May 20, 2018
Tirana, Albania
Open Source Design Workshop #2
17 February 2018
Tirana, Albania
February 3 @ 9:00 am - February 4 @ 5:00 pm
ULB, Av. F. D. Roosevelt 50
Brussels, 1050 Belgium
OSCAL’17 – Open Source Conference Albania 2017
May 13, 2017 - May 14, 2017
Ofiçina – Harry Fultz, Rruga “Asim Vokshi” pranë Institutit “Harry T. Fultz”
Tirana, Albania
February 5, 2017 @ 9:00 am-5:00 pm
ULB, Av. F. D. Roosevelt 50
Brussels, 1050 Belgium
Open Source Design Workshop #1 in Tirana
January 25, 2017 @ 5:00 pm-8:00 pm
Open Labs Hackerspace, Rr. Pjetër Budi. Nr.67
Tirana, Albania


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--- layout: example.html title: Sprite Animation Map Example shortdesc: Demonstrates using a sprite image to do feature animation. docs: >


What was the Colts' former home known as before it was renamed the RCA Dome in 1994?
Indiana Stadium
Hoosier Dome
Indy Coliseum
Hinkle Fieldhouse
When was the first time the Colts and Ravens met in the postseason?
1999
2008
2003
2006
In his first year as the Colts GM in 2012, who was nationally recognized as the NFL's executive of the year?
Ryan Grigson
Bill Polian
John Gruden
Mike McCarthy
As a second year quarterback, Peyton Manning led the Colts to how many wins after only three victories the year before as a rookie?
9
13
7
10
Against what opponent did Manning have a perfect passer rating of 158.3 in the 2003 playoffs?
New England Patriots
Dallas Cowboys
Baltimore Ravens
Denver Broncos
The Colts defeated the New York Giantsin the 1958 NFL championship game in a 23-17 classic. Where was the event held?
Baltimore Stadium
Fenway Park
Yankee Stadium
Wrigley Field
After this Colts punter took an early morning swim in a canal, police asked him how much he had to drink. He replied, "A lot, cause I'm drunk."
Pat McAfee
Hunter Smith
Adam Vinatieri
Mike Vanderjagt
In 2007, who became the first Indianapolis Colt to gain both 100 yards rushing and receiving in one game?
Edgerrin James
Joseph Addai
Brandon Stokley
Dominic Rhoads
Who caught Manning's first NFL touchdown pass?
Dallas Clark
Reggie Wayne
Marvin Harrison
Brandon Stokley
Indy's Superbowl XLIV matchup with the Saints marked the fourth time that the Colts played a Superbowl in what state?
Indiana
California
New York
Florida


{% comment %} This is a sample of an international-friendly user form. If you're using the latest user_form.html as a base, you don't need this! Just add 'country' to your page's list of required fields, or remove 'country' from the {% hide_by_default %} tag in your user_form.html, and you're done. {% endcomment %}


Triangle LOVE
问题描述 :
Recently, scientists find that there is love between any of two people. For example, between A and B, if A don’t love B, then B must love A, vice versa. And there is no possibility that two people love each other, what a crazy world!
Now, scientists want to know whether or not there is a “Triangle Love” among N people. “Triangle Love” means that among any three people (A,B and C) , A loves B, B loves C and C loves A.
Your problem is writing a program to read the relationship among N people firstly, and return whether or not there is a “Triangle Love”.
Now, scientists want to know whether or not there is a “Triangle Love” among N people. “Triangle Love” means that among any three people (A,B and C) , A loves B, B loves C and C loves A.
Your problem is writing a program to read the relationship among N people firstly, and return whether or not there is a “Triangle Love”.
输入:
The first line contains a single integer t (1 <= t <= 15), the number of test cases.
For each case, the first line contains one integer N (0 < N <= 2000).
In the next N lines contain the adjacency matrix A of the relationship (without spaces). Ai,j = 1 means i-th people loves j-th people, otherwise Ai,j = 0.
It is guaranteed that the given relationship is a tournament, that is, Ai,i= 0, Ai,j ≠ Aj,i(1<=i, j<=n,i≠j).
For each case, the first line contains one integer N (0 < N <= 2000).
In the next N lines contain the adjacency matrix A of the relationship (without spaces). Ai,j = 1 means i-th people loves j-th people, otherwise Ai,j = 0.
It is guaranteed that the given relationship is a tournament, that is, Ai,i= 0, Ai,j ≠ Aj,i(1<=i, j<=n,i≠j).
输出:
The first line contains a single integer t (1 <= t <= 15), the number of test cases.
For each case, the first line contains one integer N (0 < N <= 2000).
In the next N lines contain the adjacency matrix A of the relationship (without spaces). Ai,j = 1 means i-th people loves j-th people, otherwise Ai,j = 0.
It is guaranteed that the given relationship is a tournament, that is, Ai,i= 0, Ai,j ≠ Aj,i(1<=i, j<=n,i≠j).
For each case, the first line contains one integer N (0 < N <= 2000).
In the next N lines contain the adjacency matrix A of the relationship (without spaces). Ai,j = 1 means i-th people loves j-th people, otherwise Ai,j = 0.
It is guaranteed that the given relationship is a tournament, that is, Ai,i= 0, Ai,j ≠ Aj,i(1<=i, j<=n,i≠j).
样例输入:
2 5 00100 10000 01001 11101 11000 5 01111 00000 01000 01100 01110
样例输出:
Case #1: Yes Case #2: No
/** [有向环] hdu 4324 Triangle LOVE 判断一个有向图中是否有环，因为任意两点都有一条边,故该图只有一个子图 现在想来dfs,拓扑排序什么的都弱爆了，所有结点的入度都>0就AC */ #include <stdio.h> #include <string.h> int in[2001]; int main(){ int t,cas = 0,i,j,n; scanf("%d",&t); while(t--){ memset(in,0,sizeof(in)); scanf("%d\n",&n); for(i = 0;i < n; ++i){ for(j = 0; j < n; ++j) if(getchar() == '1') in[j]++; getchar(); } for(i = 0; i < n; ++i) in[n] += (in[i] != 0); printf(in[n] == n ? "Case #%d: Yes\n" : "Case #%d: No\n",++cas); } return 0; }
版权声明：本文为博主原创文章，未经博主允许不得转载。
参考：http://blog.csdn.net/cscj2010/article/details/7888670
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Welcome
Welcome to my blog!
I've reproduced a list of recent posts here for your reading pleasure:
Posts
- May 9, 2017 - Why computer security is hard
- February 15, 2017 - Copyright
- February 15, 2017 - Storm in the boat
…or you can find more in the archives.


This page animates the insertion procedure, or bumping procedure, of the classical Robinson-Schensted algorithm.
To start, you need an input permutation.
This can be obtained in two ways.
First, you can have the page generate a random permutation.
To do that, enter the size of the desired permutation in the first textbox.
Instead, you can enter a permutation, for example, 2 5 3 6 4 1, in the second textbox.
To do that, list the numbers of the permutation, separated by spaces.
The easiest way to run the animation is to press Enter while in the textbox in which you have placed your input.
The first time you press Enter, the page will start the animation.
After that, it will do the next step.
You can press Enter while in the middle of an animation step, to go on to the next step.
If you press Enter after the animation is completed, this will start a new animation.
If you want go to a different input while still in the middle of a previous input, you can enter your information into one of the textboxes, and then press the Start Animation button to the right of that textbox.
After that, click the adjacent "Next Step" button, or return focus to the textbox.
Then, you can continue the animation by pressing Enter, as above.
You can alter the speed of the animation using the dropdown menu.


BCLC::Numbers - Calculate Lotto 649 historical win information
Using historical data (CSV or SQLite database), calculate and display win information based on six unique numbers entered via a web UI.
Landing page. Loads up the main
Template::Toolkit template that provides the number inputs and various options.
This route is a get
request.
Takes no parameters, has no return.
This route front-ends the core workload of the application. It fetches and returns all calculated draw information based on the numbers provided.
Parameters:
All parameters are passed in as a JSON string, converted to a Perl hash.
numbers => [1, 2, 3, 4, 5, 6]
Six unique numbers within the range 1-49. Will croak()
if any of the numbers are out of range, or non-unique.
display_all => bool
By default, only wins $85 or higher will be displayed in the UI. Set this to true to have all wins regardless of the value displayed on-screen.
Default: false
csv_source => bool
This software has the optional ability to read directly from the CSV file provided by BCLC, or an SQLite database containing the same data. Set this param to true
to use the CSV reader.
Default: false
Using Number::Format convert an integer into a properly formatted dollar string (eg: 3000
becomes $3,000.00
).
Parameters:
$int
Mandatory, Integer (or float): The integer or float you want converted to a dollar string.
Return: Formatted dollar string.
Fetches, compiles, filters and aggregates all draw data.
Parameters:
$player_numbers
Mandatory, Array Reference: An array reference of the six unique lotto numbers between 1
and 49
.
$display_all
Optional, bool: Set this to true to have all wins returned. By default, we only calculate wins $85
or more as valid. Defaults to true
(1
).
$csv_source
Optional, bool: Set to true
(1
), and we'll read our draw information from BCLC's Lotto 649 historical CSV file directly. Send in false
(0
) to read from a pre-compiled SQLite database containing the contents of the CSV. Defaults to true
.
Performs any/all necessary filtering of draws from the aggregate pool.
Parameters:
$draw
Mandatory, hash reference: A Lotto 649 draw hash reference as returned within the array reference from the retrieve()
function from either BCLC::DB or BCLC::CSV.
Return: 1
(true
) if the draw sent in should be filtered out/skipped, and 0
(false) if it's a valid draw.
Returns a hash reference where the keys are the possible number match combinations, and the values the dollar amount for each.
Takes no parameters.
Return: Hash Reference
Calculates the price of a ticket for an individual draw.
Parameters:
$draw
Mandatory, hash reference: A Lotto 649 draw hash reference as returned within the array reference from the retrieve()
function from either BCLC::DB or BCLC::CSV.
Return, Integer: The cost of an individual draw's ticket.
Calculates the win value of a single draw.
Parameters:
$draw
Mandatory, hash reference: A Lotto 649 draw hash reference as returned within the array reference from the retrieve()
function from either BCLC::DB or BCLC::CSV.
Return: An integer representing the dollar amount of the win for the draw specified. Returns 0
if no wins resulted.
Compiles all the various data related to all draws.
Parameters:
$all_draws
Mandatory, Array of Hashes: An array containing the full list of all draws as returned by the retrieve()
method in BCLC::DB or BCLC::CSV.
$display_all
Optional, bool: Send in a true value (eg 1
) to have all wins compiled as opposed to the default wins of $85 or higher. Defaults to false.
Return: A hash reference in the following format:
winning_draws => \@winning_draws,
total_spent_on_tickets => convert_to_dollar($total_spent_on_tickets),
total_number_payout => convert_to_dollar($total_number_payout),
net_win_loss => $net_win_loss,
Returns the calculated payout of an individual draw.
Parameters:
$draw
Mandatory, hash reference: A Lotto 649 draw hash reference as returned within the array reference from the retrieve()
function from either BCLC::DB or BCLC::CSV.
Return: Integer, the dollar amount won in the draw.
Steve Bertrand, <steve.bertrand at gmail.com>
Copyright 2019 Steve Bertrand.
Released under the Apache License, Version 2
Hey! The above document had some coding errors, which are explained below:
Unterminated C<...> sequence


Video
Channel Name
Video name
Date
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React JS tutorials for beginners-1
Jun 27, 2016
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thenewboston
React JS tutorials for beginners-3
Jun 27, 2016
thenewboston
React JS tutorials for beginners-4
Jun 27, 2016


At this point in the process I've decided to have fun and be as creative as possible given the time constraints
I'll just throw in a jumbotron, whooooooosh, he lives right here.....just as jumbo as he can be.
But I'M NOT!


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Introduction of the InTernational Research Center for Renewable Energy |
|
The International Research Center for Renewable
Energy (IRCRE) embodies a flexible material and device target driven
structure, which address the scientific and engineering issue for application of renewable
energy such as solar, hydrogen, etc. For this purpose, we capitalize on the vertical
integration of the research capabilities in basic sciences and engineering available with
international and national directors and PIs. The collective thrust of the scientific
domains will incite innovations (Phase I) in nanomaterials development for Alternative
Energy Sources and evaluate their societal and economic impact (Phase II). The key research
areas complement the on-going research activities in China, and are based on the proven
strengths of foreign and local PIs, visiting scientists and collaborators involved in this
application. The envisaged core competency will enable fruitful collaborations with
established other research institutes. IRCRE will act as an internationally competitive
platform to attract both young leaders through the established extended global network of
the directors and PIs as well as by collaborating with world renowned centers. The human
capital training and educational outreach will form an integral part of IRCRE, and will be
realized by fostering links to high-schools, structured graduate education, and fast-track
hiring of excellent young scientists through Director’s funds. The designated Director
General will be Prof. L. Guo, director of the State Key Lab of Multiphase Flow in Power
Engineering, former dean of the School of Energy and Power Engineering at Xi’an
Jiaotong University. An internationally renowned scientist will be appointed as one of the
Scientific Directors to work at IRCRE, firstly on a part-time then full-time basis. Several
international PIs and collaborators are committed to relocate themselves (fully or
part-time) to Xi’an, which will substantially add to the global visibility of China’s
national R&D landscape and lead to capacity building in materials-based innovations for
energy and environment.
|
|
||
|
IRCRE strives to develop most effective methods for materials production and management
through cutting-edge interdisciplinary efforts. This would demand close interactions and
complementarities among scientists with different technical backgrounds and research
interests, which will be facilitated by the proposed center. In addition to the well-known
size-effects in nanoscopic matter, it is realistic to anticipate that increasing complexity
in nanostructures, for instance by creating nanoscale building blocks with two or more
components or by modifying their surface chemistry will offer new opportunities to bond (and
disbond), assemble and integrate them into new material systems. Chemical interactions
(electronic, steric, electrostatic, van der Waals, etc.) are the major source to code
information into molecular assemblies and nanostructures, for example, surface
functionalized inorganic nanoparticles can be selectively attached to a biomolecule or
organized into periodic arrays by tuning the surface chemistry of both nanostructure and the
substrate. Similarly, the intrinsic properties of discrete molecular species can be
preserved in a polymer matrix to create hybrid materials with novel optical, electronic and
catalytic activities. The development of advanced materials will serve the functional
assemblies and devices, and construction of renewable energy system. Thus, to meet the
demand for the development of renewable energy (especially solar and hydrogen) and related
energy materials, IRCRE will set up five research divisions. The key areas of the five
research divisions in IRCRE are depicted as follows:
1. Photothermal conversion and hydrogen generation division: This division has an eye to the new principles and new technologies in solar thermal power generation and utilization of solar energy for hydrogen production from biomass decomposition in supercritical water. The main works include the following aspects: The reaction mechanism and kinetic of hydrogen production by supercritical water gasification of biomass (including organic wastes); preparation and characterization of cost-effective catalysts for thermo chemical hydrogen production and the catalytic reaction mechanism; multiphase flow mechanics in hydrogen production system by supercritical water gasification and mechanism of heat and mass transfer; reactor design and structural theory of heat supply by solar energy concentration coupling with efficient gasification of biomass for hydrogen production in supercritical water; multi field coupling heat and mass transfer and energy conversion theory and numeric simulation of solar receiver cavity in complicated conditions; analysis of system thermodynamics and economics in the hydrogen production process by solar thermo chemical method; scale-up, integration and optimization of hydrogen production process by solar thermo chemical method; regulating and controlling theory of the coupling system stability of hydrogen production process by solar thermo chemical method; the design of demonstration system of hydrogen production by biomass gasification in supercritical water coupling with solar energy concentrating system; and the novel principles of the heat transfer, storage, conversion and utilization, principle of process and the study of component and system. 2. Photocatalysis/photoelectrochemistry and functional materials division: This division focuses on the design and preparation of functional materials with nano and molecular technologies for photocatalytic, photoelectrochemical and thermoelectric devices, aiming at efficient and stable solar-hydrogen and thermoelectric conversion. The key areas include: combination and rationalization of all solution and gas-phase bottom-up approaches of materials chemistry; directed supra-molecular self-assembly; atomic and molecular chemical manipulation; synthesis of multi-elemental metal-organic precursors; low temperature aqueous chemical growth for highly oriented structures on virtually any substrates; unusual/forbidden crystal axis anisotropic growth; heterometallic solution and vapor synthesis of complex and doped nanomaterials; functional supramolecular nanostructures; organized thin films and arrays; quantum confined hetero-nanostructures; visible-light active heterostructures; synthesis and stabilization of metastable nanostructures; 2nd to 3rd generation solar cells; quantum dots sensitized quantum rods visible-light active photocatalysts for solar hydrogen generation. 3. Microbiological hydrogen generation division: With the purpose to realize hydrogen production from renewable energy in an efficient, low cost and pollution-free way, this division focuses on the following three aspects. 1) Investigating the genetic nature of the degeneration effect of candidate bacteria in dark fermentation using comparative genomic method, and then constructing engineering strain to improve the conversion efficiency. 2) Studying on the hydrogen production pathway of PNS (purple non-sulfur photosynthetic bacteria) to knock out the genes that are not conducive to hydrogen production and enhance gene expression that related with the target product. Thus, the solar to chemistry energy conversion efficiency and hydrogen production can be improved. 3) Research on the degradation mechanism of aromatic compounds by the PNS will be carried out. We are planning to construct safely engineered microorganisms that are able to decompose this kind of pollutants efficiently to handle of special sewage including printing and dyeing wastewater. 4. Fuel cell division: This division is engaged in the design and development of highly catalytic and cheap alloy electrode, cermet composite electrode and stable nano-electrode materials. It aims to design novel cell structure and assemble stack according to the properties of new materials, and lay a foundation on the construction of cost-effective power generation system. The key research fields on fuel cells include material design and system construction, fabrication of highly ionic conducting electrolyte materials and membranes, fabrication of stable and cheap electrode materials, optimization of composition, structure and performance, design of novel cell structure and construction of stack, numerical simulation and parameter optimization, and next generation fuel cells. 5. Solar-hydrogen system integration division: Considering the new energy materials, this division focuses on the research of system components of solar water splitting (including photocatalytic, photoelectrochemical, photobiological), solar pyrolysis hydrogen production and power generation from the point view of integration of materials, modularization of functions and scale-up of systems. We will build up new kinds of renewable energy systems to realize high-efficiency but low-cost conversion from solar energy to hydrogen energy directly. Anyway, we will provide direct scientific and technical supports for the design, optimization and future utilization of renewable energy systems. |
|
The mission of the International Research Center for Renewable Energy is to
foster research and technology solutions by integrating the expertise and skills of an
efficient team of high-profile researchers and engineers, who will join the center from
premier educational and research institutions around the globe. IRCRE will capitalize on the
development of materials-based technologies by the fusion of chemical, materials, bio- and
nano-technologies. Chemical interactions at multiple length scales ranging from short range
intra- and intermolecular interactions to long range collective phenomenon in solids will
constitute the scientific foundation, and functional integration for renewable energy system
construction will constitute the engineering foundation of the proposed research center of
excellence Therefore, hierarchical integration of molecular and nanoscaled building blocks
into functional assemblies and devices, and construction of renewable energy system will
form the central tenet of IRCRE.
The center will be carried out within a 10-year period and completed by the creation of spin-offs. It will be divided in two distinctive and consecutive phases. Phase I, which comprises Year 1 to 5, will be dedicated to materials development and characterization. Phase II, will consist of year 5-10 of the program and will be dedicated almost entirely on the real-scale development and manufacturing, as well as packaging, assembly and testing of the successful prototypes selected at the end of Phase I. |
|
|
|
Overview of the proposed timeline, task assignments, and outcome of the center
|
|
The infrastructure and laboratories set-ups as part of the Center for
Renewable Fuels & Nanotechnology would be an open shared facility that will benefit and
support all Chinese industries and research institutions. The
center will house joint laboratories for scaled-up solution and vapor
chemical syntheses of nanostructured materials, nanofabrication and microscopic structural
and functional characterization of new material systems and devices. In addition, IRCRE will
feature dedicated demonstration laboratories for high-school to PhD students to gain
hands-on experience and training in chemical nanotechnology and nanodevices fabrication,
integration, characterization, testing, and prototyping. These nanoscience and
nanotechnology facilities will be accessible to external local collaborators (externally
funded groups) as well as international visiting scientists.
|
|
The IRCRE has six types researchers，1） Director (Full Commitment), 2）
Scientific-Directors (Half and Full Commitment), 3） Principal Investigators (Half and Full
commitment), 4） Research staff, 5） Consultants/visiting Scientists, 6） International
Advisory Board.
|
|
The Project Management Office (PMO) directly assists the Directors and act
as the focal point for all IRCRE activities and coordinate, monitor and control the progress
of all projects. In addition, PMO will maintain accurate records of costs, resources and
time scales for the projects. Further, it will inform the IRCRE members about their rights
and obligations in intellectual property issues and will liaise with the administration to
develop a strategy to handle such cases. The role of the Directors includes harmonization of
technical aspects, maximization of synergies and mutual and effective exchange of
information within the center. The Principal Investigators will be responsible for the
technical objectives and outcome of the projects. They will ensure that project maintains
its relevance within the scope of IRCRE and agreed work plan. For scientific and strategic
planning a Scientific & Industrial Advisory Board will be established. Leading members
from academia and industry, who will serve on this advisory board, have been contacted and
will guide IRCRE in planning the technical road-map to meet the long-term objectives.
National and overseas collaborators identified as project partners are listed above.
Additional collaborators will be invited, when additional expertise will be needed to
broaden the knowledge base and IRCRE network. Latter will be promoted by offering visiting
fellowships funded by the Director’s funds. The proposed vertical integration of
fundamental research with nanoengineering will be realized by maintaining strong contact
with industrial partners, which would enable IRCRE technology platform to cater the need of
local and international industries.
|
|
Overall management structure and economic output of the center
|
|
Detailed management structure and economic output of the research program
|


###TITLE###
|
|
Summary
DE : Number of differentially expressed genes
IDD (Integration Driven discoveries) : number of genes that are declared DE in the meta-analysis that were not identified in any of the individual studies alone
Loss : Number of genes that are identified DE in individual studies but not in meta-analysis
IDR (Integration-driven Discovery Rate) : corresponding proportions of IDD
IRR (Integration-driven Revision) : corresponding proportions of Loss
Details


My name is Jordan Kasper I am currently a Senior Support Engineer at ForgeRock with a passion for data and Frontend development. Currently I am looking to move into the UI Engineering space.
I enjoy the work I do now but I have always felt like there was something missing. I love working with frontend frameworks and am currently enjoying Vue.js. I enjoy developing how users will interact with the data that is being presented and make it a fun and seemless experience.
I also have a huge interest in how the data is stored and managed and what the systems look like that do this. I feel drawn to the engineering side as I still love to program and this hopefully will give a good balance of both worlds.
Work with engineering and sustaining to solve complex customer issues. Work with customers on implementation of custom Access Managment solutions.
Helped launch doc site 2.0 while being the voice for customers and technical support. Troubleshoot and help customers with production support issues. Designed and build support team processes. Work with account managers, engineering and deployment specialist to resolve customer issues.
Help with custom instrumentation and integration with our product. Work on documentation and internal wiki for customer and internal use. Help develop escalation and Tier definition plans. Lead both our 2nd and 3rd shift in stand up along with go-to for engineers in my tier. Go-to for manager with day-to-day progress of our T2 engineers.
Working with SaaS and cloud software to support different APIs and languages. Working with developers to find and fix bugs and customer issues.
This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning.


Prior sending messages from the eventmessenger to your mobile device, you need to:
This example covers the process on an android os mobile device. However the
procedure should be analogue on other devices. Start by searching the Telegram
App in your preferred App Store.
Start the App.
And begin the configuration.
Telegram links his services to your mobile device by using its mobile number.
For authentication purpose Telegram wants to get access to various ressources of
your device. Primarily these are taking calls and reading sms mails.
Please give access to your sms and phone calls
Please provide your mobile number without preceding '0'. That said, change "0172 123456"
to "172 123456"
Telegram will validate your information by giving a call to the number you
provided.
After a successful validation, Telegram will ask for additional access rights.
These are not mandatory as long as you just use Telegram in conjunction with
EventMessenger. If you instead would use the App and all of its features you
need to allow access to your contact list, media and so on.
After the first time setup is done, you will see a blank screen.
You can now start searching your contact list if anybody else is using telegram
or proceed to create a new bot. In case of the latter, we will add a special
contact to our contact list. This contact is a Telegram built-in service called
"botfather". "botfather" will create your bot.
In order to start talking to "botfather" open your preferred mobile browser and
browse to: telegram.me/botfather.
You will see the homepage of the service. Please add the "Botfather" to your
Telegram contact list by clicking "Send Message".
After Telegram has been shown up, start a new conversion with the "botfather" by
clicking the "Start" button below.
"Botfather" will provide some possible commands to choose from. Please select
the "/newbot" command.
After we did this, "botfather" should ask you to provide a display name for the
new bot.
After that, we provide a username for the bot which is basically the same like
the displayname except it must end with the phrase "bot"
Example:
Display Name: EventMessenger
User Name: EventMessengerBot
In the end, "Botfather" will provide the access token that will be used by the
EventMessenger.
Please use your favorite way to transfer the token from your mobile device to
the location where EventMessenger is installed.
Please continue by adding the token to the
"Add Bot" Configuration
Dialogue.


Over a dozen reusable components built to provide iconography, dropdowns, input groups, navigation, alerts, and much more with bootstrap 3.2.0
Icon classes should only be used on elements that contain no text content and have no child elements
Icon classes should only be used on elements that contain no text content and have no child elements
Icon classes should only be used on elements that contain no text content and have no child elements
Icon classes should only be used on elements that contain no text content and have no child elements
90$
40$
60$
30$
The owner of the website and the maneger of developement
The bos of the website and the designer of it
The mind of the website and the hacher of developement
The Leader of the website and the bos of relationship
3,600
Satisfied Users20,300
Posted Coments300
Projects Done95,350
Tickets AchivementdCommunity members have translated Bootstrap's documentation into various languages. None are officially supported and they may not always be up to date.
Feel Free To Contact Us Any Time You Want