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Transformers: Age of Extinction
2014
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157 Minutes
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Guardians of the Galaxy
2014
10
Want to Watch
Transformers: Age of Extinction
2014
PG-13
Action
157 Minutes
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Terms of Service
Last updated: December 20, 2019
Please read the following Terms of Service ("Terms", "Terms of Service") carefully before using the https://rebaseitlabs.in website (the "Service") operated by ("us", "we", or "our"). Your access to and use of the Service is conditioned on your acceptance of and compliance with these Terms. These Terms apply to any person or persons who access or use the Service. By accessing or using the Service you agree to be bound by these Terms. If you disagree with any part of the terms then you may not access the Service.{' '}
Subscriptions
Some parts of the Service are billed on a subscription basis ("Subscription(s)"). You will be billed on a recurring and periodic basis ("Billing Cycle"). Billing cycles are set either on a monthly or annual basis, depending on the type of subscription plan you select when purchasing a Subscription. At the end of each Billing Cycle, your Subscription will automatically renew unless you cancel it or rebaseitlabs marketing private limited cancels it. You may cancel your Subscription renewal by contacting rebaseitlabs.in customer support team. A valid payment method, including credit card, is required to process the payment for your Subscription. By submitting such payment information, you automatically authorize to charge all Subscription fees incurred through your account to any such payment instruments. Should automatic billing fail to occur for any reason, will issue an electronic invoice indicating that you must proceed manually, within a certain deadline date, with the full payment corresponding to the billing period as indicated on the invoice.
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You need to have
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extension = php_sqlite3.so
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gokrazy’s goal is to make it easy to build Go appliances. In an ideal world, all building blocks you need would be available in Go. In reality, that is not entirely the case. Perhaps you need to run a C program next to your Go programs. Docker containers make incremental (or partial) adoption of gokrazy easy.
We’re going to use podman, a drop-in replacement for Docker, because there is a statically compiled version for amd64 and arm64 available that we could easily re-package into https://github.com/gokrazy/podman.
Include the following packages in your gokrazy installation:
gok add github.com/gokrazy/iptables
gok add github.com/gokrazy/nsenter
gok add github.com/gokrazy/podman
gok add github.com/greenpau/cni-plugins/cmd/cni-nftables-portmap
gok add github.com/greenpau/cni-plugins/cmd/cni-nftables-firewall
Then, deploy as usual:
gok update
Use breakglass to login to your gokrazy instance and run a container manually:
/tmp/breakglass187145741 $ mount -t tmpfs tmpfs /var
/tmp/breakglass187145741 $ podman run --rm -ti docker.io/library/debian:sid
root@gokrazy:/# cat /etc/debian_version
12.0
Now that you have the required tools, there are a couple of decisions you have to make depending on what you want to run in your container(s):
tmpfs
(lost with the next
reboot), on the permanent partition of your SD card, or somewhere else
entirely (e.g. network storage)?exec
commands in it easily), or use a fresh name for each run (so that there never
are conflicts)?Aside from these broad questions, you very likely need to set a bunch of detail options for your container, such as additional environment variables, volume mounts, networking flags, or command line arguments.
The following program is an example for how this could look like. I use this program to run irssi.
package main
import (
"fmt"
"log"
"os"
"os/exec"
"strings"
"syscall"
"github.com/gokrazy/gokrazy"
)
func podman(args ...string) error {
podman := exec.Command("/usr/local/bin/podman", args...)
podman.Env = expandPath(os.Environ())
podman.Env = append(podman.Env, "TMPDIR=/tmp")
podman.Stdin = os.Stdin
podman.Stdout = os.Stdout
podman.Stderr = os.Stderr
if err := podman.Run(); err != nil {
return fmt.Errorf("%v: %v", podman.Args, err)
}
return nil
}
func irssi() error {
// Ensure we have an up-to-date clock, which in turn also means that
// networking is up. This is relevant because podman takes what’s in
// /etc/resolv.conf (nothing at boot) and holds on to it, meaning your
// container will never have working networking if it starts too early.
gokrazy.WaitForClock()
if err := mountVar(); err != nil {
return err
}
if err := podman("kill", "irssi"); err != nil {
log.Print(err)
}
if err := podman("rm", "irssi"); err != nil {
log.Print(err)
}
// You could podman pull here.
if err := podman("run",
"-td",
"-v", "/perm/irssi:/home/michael/.irssi",
"-v", "/perm/irclogs:/home/michael/irclogs",
"-e", "TERM=rxvt-unicode",
"-e", "LANG=C.UTF-8",
"--network", "host",
"--name", "irssi",
"docker.io/stapelberg/irssi:latest",
"screen", "-S", "irssi", "irssi"); err != nil {
return err
}
return nil
}
func main() {
if err := irssi(); err != nil {
log.Fatal(err)
}
}
// mountVar bind-mounts /perm/container-storage to /var if needed.
// This could be handled by an fstab(5) feature in gokrazy in the future.
func mountVar() error {
b, err := os.ReadFile("/proc/self/mountinfo")
if err != nil {
return err
}
for _, line := range strings.Split(strings.TrimSpace(string(b)), "\n") {
parts := strings.Fields(line)
if len(parts) < 5 {
continue
}
mountpoint := parts[4]
log.Printf("Found mountpoint %q", parts[4])
if mountpoint == "/var" {
log.Printf("/var file system already mounted, nothing to do")
return nil
}
}
if err := syscall.Mount("/perm/container-storage", "/var", "", syscall.MS_BIND, ""); err != nil {
return fmt.Errorf("mounting /perm/container-storage to /var: %v", err)
}
return nil
}
// expandPath returns env, but with PATH= modified or added
// such that both /user and /usr/local/bin are included, which podman needs.
func expandPath(env []string) []string {
extra := "/user:/usr/local/bin"
found := false
for idx, val := range env {
parts := strings.Split(val, "=")
if len(parts) < 2 {
continue // malformed entry
}
key := parts[0]
if key != "PATH" {
continue
}
val := strings.Join(parts[1:], "=")
env[idx] = fmt.Sprintf("%s=%s:%s", key, extra, val)
found = true
}
if !found {
const busyboxDefaultPATH = "/usr/local/sbin:/sbin:/usr/sbin:/usr/local/bin:/bin:/usr/bin"
env = append(env, fmt.Sprintf("PATH=%s:%s", extra, busyboxDefaultPATH))
}
return env
}


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Error codes themselves are 32 bit (signed) integers, of which the high order 24 bits are an identifier of which error table the error code is from, and the low order 8 bits are a sequential error number within the table. An error code may thus be easily decomposed into its component parts. Only the lowest 32 bits of an error code are considered significant on systems which support wider values.
Error table 0 is defined to match the UNIX system call error table
(sys_errlist
); this allows errno
values to be used directly
in the library (assuming that errno
is of a type with the same width
as long). Other error table numbers are formed by compacting together
the first four characters of the error table name. The mapping between
characters in the name and numeric values in the error code are defined in
a system-independent fashion, so that two systems that can pass integral
values between them can reliably pass error codes without loss of meaning;
this should work even if the character sets used are not the same.
(However, if this is to be done, error table 0 should be avoided, since the
local system call error tables may differ.)
Any variable which is to contain an error code should be declared long. The draft proposed American National Standard for C (as of May, 1988) requires that long variables be at least 32 bits; any system which does not support 32-bit long values cannot make use of this package (nor much other software that assumes an ANSI-C environment base) without significant effort.
| [ << ] | [ >> ] | [Top] | [Contents] | [Index] | [ ? ] |
This document was generated by buildd user on March 30, 2012 using texi2html 1.82.


--- title: "Main Content Headlines" type: typography scss: _assets/stylesheets/_page.scss usage: "Headline hierarchy found in #main .page-content" ---


Please scan the QR code or copy the link below into your mobile browser. Leave this page open. When you finish the mobile component, you will be given a key. Enter that key here to submit the HIT.
You are participating in a study being performed by researchers in the MIT Computer Science and Artificial Intelligence Lab. If you have questions about this resarch, please contact<EMAIL_ADDRESS>Your participation in this research is voluntary. You may decline further participation, at any time, without adverse consequences. Your anonymity is assured; the researchers who have requested your participation will not receive any personal information about you.


Securely Upload your private key seed phrase to our app. We will neither store your password nor your seed phrase anywhere with us( scouts honour! ). For more information on how our software works, Click here.
As a community, we rely on you to let our app grow and become more secure. So join today and start earning Dai Coin - the price stabilised cryptocurrency. All you need is an ethereum account and a telegram account. To learn more, Click here.
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All you need to do is click on the link provided below and verify your registration with the OTP we will send you on your account. Once your are connected, we will start sending you simple messages that contain key shares of our clients. When we request you, simply send the correct key share and we will pay you a reward for every correct request completed.
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public interface QueryService
| Modifier and Type | Method and Description |
|---|---|
int |
closeAllUnclosedQueries()
Closes all unclosed queries.
|
Query |
getQuery(KbIndividual id)
Constructs a Query from a KbIndividual corresponding to a #$CycLQuerySpecification.
|
Query |
getQuery(KbIndividual id,
Map<KbObject,Object> indexicals)
Returns a new Query loaded from a term in Cyc specifying its properties.
|
Query |
getQuery(Sentence sent,
Context ctx) |
Query |
getQuery(Sentence sent,
Context ctx,
InferenceParameters params) |
Query |
getQuery(String queryStr)
constructs a Query working with the string queryStr.
|
Query |
getQuery(String idStr,
Map<String,String> indexicals)
Returns a Query object defined by a CycLQuerySpecification term, and substitutes in relevant
values from the indexicals Map.
|
Query |
getQuery(String queryStr,
String ctxStr)
Returns a query object defined by queryStr asked in Microtheory ctxStr, with default inference
parameters.
|
Query |
getQuery(String queryStr,
String ctxStr,
String queryParams)
Returns a query object defined by queryStr asked in Microtheory ctxStr, with inference
parameters, queryParams.
|
Query getQuery(String queryStr) throws QueryConstructionException
The query is executed in InferencePSC with a default timeout and default inference parameters.
queryStr
- the string representing the CycL queryQueryConstructionException
com.cyc.query.Query#TIMEOUT
Query getQuery(String queryStr, String ctxStr) throws QueryConstructionException
queryStr
- The query stringctxStr
- The Microtheory where the query is askedQueryConstructionException
Query getQuery(String queryStr, String ctxStr, String queryParams) throws QueryConstructionException
queryStr
- the query string.ctxStr
- the Microtheory where the query is askedqueryParams
- the inference parameters to use for the query. This string should consist of
a series of keywords followed by the values for those keywords. The keywords
can be found by looking for the #$sublIdentifier for the desired instance of
InferenceParameter in the Cyc KB. For example, to limit a query to
single-depth transformation and to allow at most 5 seconds per query, use
the string ":max-transformation-depth 1 :max-time 5".QueryConstructionException
Query getQuery(Sentence sent, Context ctx, InferenceParameters params) throws QueryConstructionException
sent
- ctx
- params
- QueryConstructionException
Query getQuery(Sentence sent, Context ctx) throws QueryConstructionException
sent
- ctx
- QueryConstructionException
Query getQuery(KbIndividual id) throws QueryConstructionException, KbException, UnsupportedCycOperationException
id
- id
QueryConstructionException
- if the specified query term has a sentence whose
outermost operator is #$ist and the query is loaded
from a Cyc server with a system level under 10.154917
(Nov. 2014). A workaround is to edit the query in the
KB, removing the #$ist from the query's sentence, and
specifying it as the query mt using
#$microtheoryParameterValueInSpecification.KbException
- if idStr
does not identify a KbIndividualUnsupportedCycOperationException
- when run against ResearchCyc 4.0q and earlierCyc.Constants.CYCL_QUERY_SPECIFICATION
Query getQuery(KbIndividual id, Map<KbObject,Object> indexicals) throws QueryConstructionException, KbException, UnsupportedCycOperationException
indexicals
map.id
- the Cyc termindexicals
- a map of substitutions to be madeid
QueryConstructionException
- if the specified query term has a sentence whose
outermost operator is #$ist and the query is loaded
from a Cyc server with a system level under 10.154917
(Nov. 2014). A workaround is to edit the query in the
KB, removing the #$ist from the query's sentence, and
specifying it as the query mt using
#$microtheoryParameterValueInSpecification.KbException
- if idStr
does not identify a KbIndividualUnsupportedCycOperationException
- when run against ResearchCyc 4.0q and earlierQuery getQuery(String idStr, Map<String,String> indexicals) throws QueryConstructionException, KbTypeException, UnsupportedCycOperationException
idStr
- The instance of CycLQuerySpecificationindexicals
- A map from terms in the query (as loaded from the KB) to the actual values
that should be queried withQueryConstructionException
- if the specified query term has a sentence whose
outermost operator is #$ist and the query is loaded
from a Cyc server with a system level under 10.154917
(Nov. 2014). A workaround is to edit the query in the
KB, removing the #$ist from the query's sentence, and
specifying it as the query mt using
#$microtheoryParameterValueInSpecification.KbTypeException
- if idStr
does not identify a KbIndividualUnsupportedCycOperationException
- when run against ResearchCyc 4.0q and earlierint closeAllUnclosedQueries()
Copyright © 2015–2018 Cycorp, Inc. All rights reserved.


‘This Hurts’: Porch Pirate Nabs Giant Snowman Decoration From Ohio Home
For one family in Moraine, Ohio, Christmas is going to be just a tad bit sadder this year since a devious porch pirate decided to sneak onto their property and steal their 6-foot-tall outdoor snowman display.
Julie Clark, one of the homeowners, told Viral Hog in a statement that the much-loved Christmas decoration was nabbed at around 1:56 a.m. local time on November 17, and that they’d only purchased the holiday item the year prior.
“On Nov 17, I noticed that my snowman display was missing so I checked the garage, then called my husband. He said, he did nothing with him and that he must have been taken. I then checked our Ring doorbell camera footage and at 1:56 am there it was - a man grabbing frosty and running down the hill with him,” reads the statement.
“We go big with Christmas lights every year and even won the City of Moraine’s Christmas lights contest, for the past 5 [years]. This hurts,” she added.
The holiday robbery was reported to police, but no suspects have been identified or caught at present. The Clark family intends to find a replacement snowman for their stolen Frosty.


Introduction by example¶
We shortly introduce the fundamental concepts of PyTorch Geometric through self-contained examples. At its core, PyTorch Geometric provides the following main features:
Data handling of graphs¶
A graph is used to model pairwise relations (edges) between objects (nodes).
A single graph in PyTorch Geometric is decribed by an instance of torch_geometric.data.Data
, which holds the following attributes by default:
data.x
: Node feature matrix with shape[num_nodes, num_node_features]
data.edge_index
: Graph connectivity in COO format with shape[2, num_edges]
and typetorch.long
data.edge_attr
: Edge feature matrix with shape[num_edges, num_edge_features]
data.y
: Target to train against (may have arbitrary shape)data.pos
: Node position matrix with shape[num_nodes, num_dimensions]
None of these attributes is required.
In fact, the Data
object is not even restricted to these attributes.
We can, e.g., extend it by data.face
to save the connectivity of triangles from a 3D mesh in a tensor with shape [3, num_faces]
and type torch.long
.
Note
PyTorch and torchvision
define an example as a tuple of an image and a target.
We omit this notation in PyTorch Geometric to allow for various data structures in a clean and understandable way.
We show a simple example of an unweighted and undirected graph with three nodes and four edges. Each node contains exactly one feature:
import torch
from torch_geometric.data import Data
edge_index = torch.tensor([[0, 1, 1, 2],
[1, 0, 2, 1]], dtype=torch.long)
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)
data = Data(x=x, edge_index=edge_index)
>>> Data(x=[3, 1], edge_index=[2, 4])
Note that edge_index
, i.e. the tensor defining the source and target nodes of all edges, is NOT a list of index tuples.
If you want to write your indices this way, you should transpose and call contiguous
on it before passing them to the data constructor:
import torch
from torch_geometric.data import Data
edge_index = torch.tensor([[0, 1],
[1, 0],
[1, 2],
[2, 1]], dtype=torch.long)
x = torch.tensor([[-1], [0], [1]], dtype=torch.float)
data = Data(x=x, edge_index=edge_index.t().contiguous())
>>> Data(x=[3, 1], edge_index=[2, 4])
Although the graph has only two edges, we need to define four index tuples to account for both directions of a edge.
Note
You can print out your data object anytime and receive a short information about its attributes and their shapes.
Besides of being a plain old python object, torch_geometric.data.Data
provides a number of utility functions, e.g.:
print(data.keys)
>>> ['x', 'edge_index']
print(data['x'])
>>> tensor([[0.0],
[1.0],
[2.0]])
for key, item in data:
print('{} found in data)
>>> x found in data
>>> edge_index found in data
'edge_attr' in data
>>> False
data.num_nodes
>>> 3
data.num_edges
>>> 4
data.num_features
>>> 1
data.contains_isolated_nodes()
>>> False
data.contains_self_loops()
>>> False
data.is_directed()
>>> False
# Transfer data object to GPU.
device = torch.device('cuda')
data = data.to(device)
You can find a complete list of all methods at torch_geometric.data.Data
.
Common benchmark datasets¶
PyTorch Geometric contains a large number of common benchmark datasets, e.g. all Planetoid datasets (Cora, Citeseer, Pubmed), all graph classification datasets from http://graphkernels.cs.tu-dortmund.de/, the QM7 and QM9 dataset, and a handful of 3D mesh/point cloud datasets like FAUST, ModelNet10/40 and ShapeNet.
Initializing a dataset is straightforward.
An initalization of adataset will automatically download its raw files and process them to the previously decribed Data
format.
E.g., to load the ENZYMES dataset (consisting of 600 graphs within 6 classes), type:
from torch_geometric.datasets import TUDataset
dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES')
>>> ENZYMES(600)
len(dataset)
>>> 600
dataset.num_classes
>>> 6
dataset.num_features
>>> 21
We now have access to all 600 graphs in the dataset:
data = dataset[0]
>>> Data(x=[37, 21], edge_index=[2, 168], y=[1])
data.is_undirected()
>>> True
We can see that the first graph in the dataset contains 37 nodes, each one having 21 features. There are 168/2 = 84 undirected edges and the graph is assigned to exactly one class.
We can even use slices, long or byte tensors to split the dataset. E.g., to create a 90/10 train/test split, type:
train_dataset = dataset[:540]
>>> ENZYMES(540)
test_dataset = dataset[540:]
>>> ENZYMES(60)
If you are unsure whether the dataset is already shuffled before you split, you can randomly permutate it by running:
dataset = dataset.shuffle()
>>> ENZYMES(600)
This is equivalent of doing:
perm = torch.randperm(len(dataset))
dataset = dataset[perm]
>> ENZYMES(600)
For splitting the dataset into a 90/10 train/test split, we can simply use Python slice notation:
train_dataset = dataset[60:]
>>> ENZYMES(540)
test_dataset = dataset[:60]
>>> ENZYMES(60)
Let’s try another one! Let’s download Cora, the standard benchmark dataset for semi-supervised graph node classification:
from torch_geometric.datasets import Planetoid
dataset = Planetoid(root='/tmp/Cora', name='Cora')
>>> Cora()
len(dataset)
>>> 1
dataset.num_classes
>>> 7
dataset.num_features
>>> 1433
Here, the dataset contains only a single, undirected citation graph:
data = dataset[0]
>>> Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708],
train_mask=[2708], val_mask=[2708], test_mask=[2708])
data.is_undirected()
>>> True
data.train_mask.sum()
>>> 140
data.val_mask.sum()
>>> 500
data.test_mask.sum()
>>> 1000
This time, the Data
objects holds additional attributes: train_mask
, val_mask
and test_mask
:
train_mask
denotes against which nodes to train (140 nodes)val_mask
denotes which nodes to use for validation, e.g., to perform early stopping (500 nodes)test_mask
denotes against which nodes to test (1000 nodes)
Mini-batches¶
Neural networks are usually trained in a batch-wise fashion.
PyTorch Geometric achieves parallelization over a mini-batch by creating sparse block diagonal adjacency matrices (defined by edge_index
and edge_attr
) and concatenating feature and target matrices in the node dimension.
This composition allows differing number of nodes and edges over examples in one batch:
PyTorch Geometric contains its own torch_geometric.data.DataLoader
, which already takes care of this concatenation process.
Let’s learn about it in an example:
from torch_geometric.datasets import TUDataset
from torch_geometric.data import DataLoader
dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES')
loader = DataLoader(dataset, batch_size=32, shuffle=True)
for batch in loader:
batch
>>> Batch(x=[1082, 21], edge_index=[2, 4066], y=[32], batch=[1082])
batch.num_graphs
>>> 32
torch_geometric.data.Batch
inherits from torch_geometric.data.Data
and contains an additional attribute: batch
.
batch
is a column vector of graph identifiers for all nodes of all graphs in the batch:
You can use it to, e.g., average node features in the node dimension for each graph individually:
from torch_scatter import scatter_mean
from torch_geometric.datasets import TUDataset
from torch_geometric.data import DataLoader
dataset = TUDataset(root='/tmp/ENZYMES', name='ENZYMES')
loader = DataLoader(dataset, batch_size=32, shuffle=True)
for data in loader:
data
>>> Batch(x=[1082, 21], edge_index=[2, 4066], y=[32], batch=[1082])
data.num_graphs
>>> 32
x = scatter_mean(data.x, data.batch, dim=0)
x.size()
>>> torch.Size([32, 21])
You can learn more about scatter operations in the documentation of torch_scatter
.

PyTorch Geometric comes with its own transforms, which expect a Data
object as input and return a new transformed Data
object.
Transforms can be chained together using torch_geometric.transforms.Compose
and are applied before saving a processed dataset (pre_transform
) on disk or before accessing a graph in a dataset (transform
).
Let’s look at an example, where we apply transforms on the ShapeNet dataset (containing 17,000 3D shape point clouds and per point labels from 16 shape categories).
from torch_geometric.datasets import ShapeNet
dataset = ShapeNet(root='/tmp/ShapeNet', category='Airplane')
data[0]
>>> Data(pos=[2518, 3], y=[2518])
We can convert the point cloud dataset into a graph dataset by generating nearest neighbor graphs from the point clouds via transforms:
import torch_geometric.transforms as T
from torch_geometric.datasets import ShapeNet
dataset = ShapeNet(root='/tmp/ShapeNet', category='Airplane',
pre_transform=T.NNGraph(k=6))
data[0]
>>> Data(edge_index=[2, 17768], pos=[2518, 3], y=[2518])
Note
We use the pre_transform
to convert the data before saving it to disk (leading to faster loading times).
Note that the next time the dataset is initialized it will already contain graph edges, even if you do not pass any transform.
In addition, we can use the transform
argument to randomly augment a Data
object, e.g. translating each node position by a small number:
import torch_geometric.transforms as T
from torch_geometric.datasets import ShapeNet
dataset = ShapeNet(root='/tmp/ShapeNet', category='Airplane',
pre_transform=T.NNGraph(k=6),
transform=T.RandomTranslate(0.01))
data[0]
>>> Data(edge_index=[2, 17768], pos=[2518, 3], y=[2518])
You can find a complete list of all implemented transforms at torch_geometric.transforms
.
Learning methods on graphs¶
After learning about data handling, datasets, loader and transforms in PyTorch Geometric, it’s time to implement our first graph neural network!
We will use a simple GCN layer and replicate the experiments on the Cora citation dataset. For a high-level explanation on GCN, have a look at its blog post.
We first need to load the Cora dataset:
from torch_geometric.datasets import Planetoid
dataset = Planetoid(root='/tmp/Cora', name='Cora')
>>> Cora()
Note that we do not need to use transforms or a dataloader. Now let’s implement a two-layer GCN:
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = GCNConv(dataset.num_features, 16)
self.conv2 = GCNConv(16, dataset.num_classes)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = self.conv1(x, edge_index)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
We use ReLU as our non-linearity acitivation function and output a softmax distribution over the number of classes. Let’s train this model on the train nodes for 200 epochs:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
model.train()
for epoch in range(200):
optimizer.zero_grad()
out = model(data)
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
Finally we can evaluate our model on the test nodes:
model.eval()
_, pred = model(data).max(dim=1)
correct = pred[data.test_mask].eq(data.y[data.test_mask]).sum().item()
acc = correct / data.test_mask.sum().item()
print('Accuracy: {:.4f}'.format(acc))
>>> Accuracy: 0.8150
That all it takes to implement a graph neural network.
The easiest way to learn more about graph convolution and pooling is to study the examples in the examples/
directory and to browse torch_geometric.nn
.
Happy hacking!


WHO WE ARE
We are Allison and Tawnie, sisters, and hosts of the Why Are You So Scared? podcast! When we were younger our dads carrer in the Navy brought us to his final duty station before retirement in Washington state. One great thing about Washington is the stormy weather setting the perfect atmosphere for blanket forts and scary stories.
As we grew, so did our interest in all things creepy, scary and paranormal. So here we are today bringing our interest to you!


Access Provided by The University Of Texas at Austin, General Libraries at 07/05/12 8:07PM GMT
In his anthology Textos de afirmação e de combate do movimento surrealista mundial (loosely translated: “Texts of affirmation and opposition of the worldwide surrealist movement”), Mário Cesariny, perhaps Portugal’s most acclaimed surrealist artist, sets out to do just what his title suggests: to reconstruct the history of the surrealist movement as it manifests itself around the world. Although Cesariny confined himself in his subtitle to the years 1924–1976, he actually begins his anthology with a series of dadaist texts dated from the early 1910s, when the movement began. He starts with an excerpt of Tzara’s Seven Dada Manifestos (1916), an excerpt of Hugo Ball’s Fight Out of Time (1916) followed by his Phonetic Poems (1917), and includes an excerpt of Marcel Duchamp’s Ninety-three Documents from 1911 to 1915. Next, Erick Satie’s Venomous Obstacles (1914) makes an appearance, followed by poems by Pierre Albert-Birot (1916), letters from Jacques Vaché to Breton and Aragon (from 1917 to 1918), and Picabia’s excerpt of the periodical 391, among other texts and authors, until dada reaches its zenith—or, its culmination, as it were—with Breton’s Drop Everything (1922), and a final excerpt of Benjamin Peret’s Les parasites voyagent (1923).
Cesariny argues for a strong connection between dada and surrealism. In his preface, he calls attention to the “massive adherence of the dadaists to the new movement,”1 and stresses that, “None of the great writers, of the great painters who came from dada to surrealism (such as Ernst, Arp, Tzara, Péret, Man Ray, Duchamp) changes a line, painted or written, to the anti-speech speech that was being declared since the emergence of dada in Zurich, in 1916” (11).2
modernism / modernity volume nineteen, number two, pp 277–286. © 2012
the johns hopkins university press
Tania Martuscelli is Assistant Professor of Luso-Brazilian Studies at the University of Colorado at Boulder. Her work has appeared in Portuguese Studies, Colóquio/Letras, and Brasil/Brazil. Dr. Martuscelli is currently completing a book manuscript titled Livro de Linhagem do Surrealismo Português, or Portuguese Surrealism’s Book of Lineage, which addresses the uniqueness of surrealism in Portugal when compared to its French counterpart.
▲
Fig 1. Presença: Folha de arte e crítica 1.1 (March, 1927), Biblioteca Geral da Universidade de Coimbra, https:// bdigital.sib.uc.pt/bg4/UCBG-RP-1-5-s1_3/UCBG-RP-1-5-s1_3_master/UCBG-RP-1-5-s1/UCBG-RP-15-s1_item1/P1.html.
It is clear that the Portuguese poet is not alone when he includes the dadaist experience as an integral part of surrealism, whether in its affirmation or its negation of the movement. Marcel Raymond, for instance, goes beyond this assertion in From Baudelaire to Surrealism, where he weaves together the many different -isms to create a comprehensive history detailing the interconnectedness of these movements in France since the nineteenth century. In fact, when analyzing Portuguese literature, it is common to see dada’s presence in both symbolist and surrealist-influenced art, making surrealism the ultimate dada experience. In another affirmation, dating from 279 1966, Cesariny proclaimed that “dada was a baptism, a late one, after the child’s birth,”3 arguing that “In the cases of Arp, Schwitters, Man Ray, Richter, and many others, we do not see a contradiction between their triumphant dada periods and the works that they did afterwards, whether these took on a surrealist bent or not” (109).4
The first Portuguese avant-garde movement, modernismo, also referred to as the “first modernism,” was born of the hands of Fernando Pessoa, Mário de Sá-Carneiro, and José de Almada Negreiros in 1915. While there were certainly others responsible for the emergence of modernismo, these three men remain the most recognized and studied artists to have partaken in it. Together, they published two volumes of the periodical Orpheu, and also put together a third volume that was not published at the time because Mário de Sá-Carneiro’s father refused to continue funding the project. Yet this short-lived avant-garde periodical, edited mainly by young artists and surviving for only one year, left an indelible mark on the history of Portuguese literature. It caused “a public reaction of refusal and insult,” as Fernando Cabral Martins states in the foreword of a 1989 facsimile of Orpheu, which included a draft of the publication’s third volume.5 This “public reaction” would become the essence, or even the trademark of avant-garde literature’s reception at the beginning of the twentieth century.
Still according to Cabral Martins, in 1965, José de Almada Negreiros published Orpheu 1915–1965, underlining the fact that the periodical was the first Portuguese “encounter of poetry with painting.”6 It is, indeed, the encounter of images and words resulting in that “public reaction of refusal and insult” that is often associated with dada. On the other hand, some critics try to avoid the assumption of a “dadaism avant la lettre,” or, more commonly used, a “surrealism avant la lettre.” Due to the fact that Orpheu was published in 1915, critics usually cite both Apolinaire’s Calligrammes and Mallarmé’s A Throw of the Dice as an influence.
Not unlike the dada artists who morphed into surrealists, most of the poets and painters who collaborated in Orpheu organized a conference in 1917 where they introduced futurism to Portugal. The manifests, poems and paintings presented at this conference were published in one volume under the title Portugal Futurista. The writings of Mário de Sá-Carneiro, who had committed suicide in 1916, were presented at the controversial conference, and included in its subsequent publication, which survived only a few days before being censored. Sá-Carneiro had been living in Paris, and he was certainly one of the fathers of Futurism in Portugal.
Interestingly, however, while Futurism was an umbrella movement for several other Portuguese –isms, it was the one that would align its artists with a larger European movement most effectively. The Portuguese critic Carlos D’Alge notes that Marinetti’s suggestions “were not as decisive to the Orpheu generation, and they would rather turn to Fernando Pessoa’s theory about ‘sensationism’, as they previously did to his ‘paulism’ and ‘intersectionism.’”7 One could say that Fernando Pessoa’s “paulism” is as comparable to a European decadentism, as his “intersectionism” is comparable to cubism, and his “sensationism” to Futurism, but, as always, uniquely adapted to the Portuguese scene. Vítor Aguiar e Silva emphasizes that Pessoa was interested in
280 developing and expressing the aesthetics of a Portuguese modernity that would have an international dimension and resonance, a goal underscored by the fact that he persistently wrote texts in English in order to spread the sensationist movement. Aguiar e Silva goes even further, stating that Pessoa only accepted the comparison between Marinetti’s Futurism and his intersectionism to draw the attention of Frank Palmer, a British publisher, to Orpheu. Pessoa’s proposal to Mr. Palmer was to translate the Portuguese periodical into English. In his own words: “. . . I am sending, registered, a number of [the] review, Orpheu, of which I am part-editor. It is a review of all kinds of advanced literature, from a quasi-Futurism to what we here call intersectionism.”8 In fact, those Portuguese-specific -isms—which did not necessarily lack in European influence—were adopted by this generation of artists. It is important to note that, during the 1910s, the Portuguese political and historic situation was certainly not at its best. While the monarchy had come to an end, the young republican regime nonetheless found itself struggling to hold its ground against those who wanted the kingdom back, and others who favored a military coup d’état, which would eventually take place in 1926. The influences of the international political and ideological crisis effectively resulted in a tug-of-war between divergent trends in Portuguese literature. When the Orpheu group decided to adopt Italian Futurism, this idea was accepted by Fernando Pessoa, but with some restrictions. Interestingly enough, only his heteronym, Álvaro de Campos, adopted Marinetti’s ideas. Aguiar e Silva quotes a letter that Pessoa sent to a friend, explaining that Sá-Carneiro was the one truly excited about the Italian movement, while he himself was a Futurist in solely the sense of “leaving everything for tomorrow” (22).9 The Portuguese critic also points out the fact that in Portugal Futurista, Fernando Pessoa, writing as himself, publishes “poems in which not only the style but also the theme are scandalously anti-Futurist”(24).10 Cesariny states in As Mãos na Água, a Cabeça no Mar that “if the dada movement had been anticipated a few years before its birth in Zurich a much bigger and much longer-lasting favor would have been done to the poets and painters of ‘Orpheu’ than the Futurism ever offered” (262).11 He was certainly referring to the fact that, as with dada, the poets of Orpheu were the first to introduce to Portugal a synthesis of poetry and painting, the idea of contradiction, confusion (in the body of the poem, and in its format and theme), as well as the idea of controversy. As Tzara suggests: “By its very nature dadaism expresses confusion, proclaims links between opposites, negates the principle of non-contradiction, assuring us that yes=no.”12 Despite not being a Futurist himself, Fernando Pessoa published in Portugal Futurista his “Interlude Fictions” (Ficções do Interlúdio), comprised of five poems, one of them titled “Saudade Dada” (“Dada nostalgia”); partially translated by Francisco Cota Fagundes as “Longing”:
During still blond, beautiful times Clorindas and Belindas, tenderly Play the game of forfeits. Those who came to see from the porch From where they hear them coming and laughing Stare at the cold space
▲
Fig 2. Orpheu: Revista Trimestral de Literatura 1.1 (January, 1915), Biblioteca Nacional Digital, Portugal, http:// purl.pt/12089.
But around the afternoon turns around Disturbing the air that burns Because the eternal afternoon will not return! And with a disturbing tone of ostentation The adorned lust unsettles The lethargic air of the late afternoon.
“And there are misty disenchantments of the enchantment of the thoughts In the sluggish saints of the recesses of the saintly corners of the convents . . . Penitents in tears, lingering, legion Who enchant the attentive winds.”13
Francisco Cota Fagundes and Aguiar e Silva would both agree that Pessoa’s excerpt embodies something closer to a symbolism congruent with Verlaine’s “Song of Autumn,” than actual dadaism. According to Fagundes, this poem traces a lineage to Verlaine and symbolism because of Pessoa’s attempt to “[imitate] the acoustic quality of music” through “alliteration, assonance, rhyme, as well as other devices of sound repetition” (135). As Verlaine emphasizes in his well-known Art Poétique, music comes first among the arts. Pessoa’s poem is endowed with a musical quality through a remarkable rhythm formed by both alliteration and free verse. Words are not arranged so as to shape coherent ideas so much as to form a melodic framework for the poem. In terms of poetic sensation, the melancholy and mysterious ambience staged by his unusual metaphors lead the reader to think more of symbolism than dadaist experimentalism.
It is also worth noting that symbolism plays an essential role in Portuguese modernismo, due to the fact that some modernists cultivated a kind of adoration for symbolist poets. In addition to emulating them in their own poems, the modernists also published texts by symbolist authors in their journals. According to Fernando Guimarães, Pessoa’s “Saudade Dada,” more than an inspiration, is an echo (or pastiche) of a Portuguese symbolist poem by Eugénio de Castro, from Oaristos, that begins with “Na messe, que enlourece, estremece a quermesse,” (loosely translated as “The carnival shakes the harvest that turns blond”) (805).14 The sound repetition, more than the theme of the poem itself is what “echoes” in “Saudade Dada.” This technique is not only in tune with Verlaine’s Art Poétique, but also brings to mind the dada technique of sound-poem. The poem’s title shows that Pessoa was in tune with the European vanguard movements, though not exactly affiliated to them—resulting in a poem supposedly inspired by dada (in that the poet is longing for dada), but clearly influenced by symbolism.
Eduardo Prado Coelho draws a comparison between the “first” and “second” stages of modernismo in Portugal (Orpheu and Presença) in relation to dadaism and surrealism, demonstrating that, despite the fact that the first and second modernismos were contemporary to dada and Breton’s manifestos, respectively, both dadaism and surrealism were more evident in Orpheu than in Presença. Orpheu (1915) was certainly more controversial due to its confrontational aspect, and surprised the public by launching the first vanguard movement in Portugal. Presença appeared more than a decade later, in 1927, announcing its support of a literatura viva, or a “living literature”—one 283 characterized by a more intuitive writing, free from academic standards. Presença served as a vehicle for several literary trends in Portugal. While at times these trends were in complete opposition to one another, the journal maintained an independent and neutral position. In its pages, one could find Futurist texts by Almada Negreiros side by side with neo-realist poems. The first editor of the journal, José Régio, who argued for the concept of literatura viva, for instance, was a Catholic writer. Prado Coelho notes, however, that
Orpheu contested art, while at the same time making use of it, following the nihilist model of some new aesthetic trends. Not even the “delay” of Presença can be measured by the fact that Régio questions the “enchantment by the rare and by the unforeseen” as an automatic guarantee of a healthy modernity . . . even Breton, by cutting relations with Tzara and dadaism, rejected all the vanguard that was limited to the pure exploitation of novelty and the free destruction of art. However, while Presença developed from the consolidation of literary strongholds, surrealism was transporting art to its exterior, following modernity specifically, and this is what Orpheu most evidently proclaimed among us.15
The official launch of a Portuguese surrealist movement, including what would become two divergent surrealist groups in Lisbon, took place in art exhibits and conferences in 1947 and 1948. Bearing this in mind, it is curious to note that when Mário Cesariny organized his anthology of worldwide surrealist movements, he considered both dadaism and surrealism to have had, to a certain extent, an imminent presence in Portugal from 1916 (or 1924) until 1976, the year when he finished his book, though not necessarily the year the movements “died.” The organizer of the anthology goes against the argument common among critics, which expounds the “impossibility of the vanguards,” as a given during the post-modern, or post-war era. Perfecto Cuadrado quotes Renato Poggioli, author of The Theory of the Avant-Garde, stating that around the 1960s the concepts of movement (as a collective and dynamic project) and of vanguard (as a revolutionary act) were old-fashioned. According to the Spanish critic, what existed or what was said to exist was the “comfortable negotiation with their inexhaustible plunders.”16
Dada, nonetheless, proved to be alive, or perhaps, more accurately, reborn, among the Portuguese artists. In 1935, António Pedro co-signed the Dimensionist Manifesto in Paris, together with Harp, Duchamp, Kandinski, Miró, Picabia, Delaunay, and others. If, on the one hand, these artists confirmed that Futurism and cubism are the “unconscious origins”17 of the movement in their manifesto, on the other hand, Dutra Faria writes that “Futurism, cubism, dadaism, surrealism—were four rivers; four rivers that were torrentially flowing, roaring and destroying . . . Dimensionism is an encounter of those rivers. Put together, the waters rose, and they calmed down.”18
In the 1960s, a post-surrealist, post-war, experimentalist movement, known as Poesia 61 (Poetry 1961), emphasized the influence of different “roots,” or -isms, if you will, namely to praise the renewal of traditional Portuguese poetry together with surrealism, and to keep a tentative distance between the concepts and models of the
284 poet Fernando Pessoa and his heteronyms. Dada, or neo-dada, if we aim to consider the international scene, also formed part of this group’s inspiration. Take, for example, “Silly Sonnet I” (“Soneto Pateta I”), by Salette Tavares, which she considered to be “a dada manifesto”:19
Who crowned the word with a bugle horn who put the tempest in the pen who said poet?
A handbell tear
makes of the grayish female donkey this violet here.20
This poem by Tavares, much like Pessoa’s poem “Longing” (for dada), brings to light the intimate relationship between word and sound through its use of alliteration. At this moment in time (the early 1960s) these characteristics, once tied to Verlaine and symbolism, are more closely associated with Brazilian concrete poetry. Another observation ought to be made here: it is the use of sound repetition (or “sound-poem”), and the ultimate abstraction with an imaginative and unconventional word association, that recalls dada the most. These are the things that characterize dada in Portuguese poetry. It was only in the late 1940s and early 1950s, with the establishment of the Portuguese surrealist movement, that artists frequently adopted dada’s often-quoted formula for poetry building: the “cut-up”, in which the artist was encouraged to cut out words from a newspaper, shake them inside a bag, and place them on the paper at random.
Later in 1972, the Portuguese newspaper, A Capital, became the very first media outlet to publish a translation of the manifest Dada Excites Everything, dating from 1921, and Raul Hausmann’s “Pamphlet Against the Weimar View of Life” from 1919, in its literary pages. In 1977, Mário Cesariny published his anthology of surrealist movements around the world starting his work with a series of dadist texts. At the same time that he provided “closure” for the vanguard movements by painting a picture that balanced what they were with what they were not, he also opened several doors to discussion of the influence and immanence of the movement throughout the world in the late 1970s. We should remember that it all began with dada.21
o bordão surrealista.”
286 20. Translated from the original: “Quem coroou a palavra de corneta / quem pôs a tormenta na caneta / quem disse poeta? / Lágrima sineta / faz da jumenta cinzenta / esta violeta.” Salette Tavares, Poesia 1957–1971 (Lisbon: Imprensa Nacional/Casa da Moeda, 1992), 7.
21. A shorter version of this paper was presented at the Modernist Studies Association 12th Annual Conference in Victoria, BC, November 11–14, 2010.


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Initialization and Normalization in Deep Neural Networks
Feb 28, 2017. Wei YANG
platero.yang (at) gmail.com
Table of Contents:
Introduction
In this note, we will focus on training neural networks efficiently by appropriate weight initialization and by the normalization techniques.
Initialization of the parameters
The initial value of the network parameters can affect the training process significantly. If the weights are initialized very large, then the nonlinear activation function (e.g., Sigmoid and Tanh) might saturate, which makes the gradients very small. If the weights are initialized very small, then the gradients would be small too. SGD methods with small gradients update the weight slowly, which slows down the training process. One might try to initialize all weights to zero. However, this is a common pitfall discussed in cs231n:
If every neuron in the network computes the same output, then they will also all compute the same gradients during backpropagation and undergo the exact same parameter updates. In other words, there is no source of asymmetry between neurons if their weights are initialized to be the same.
Naive initialization
Intuitively, the weights are expected closed to zero. Hence we usually initialize the weights from a Gaussian distribution with zero mean and a small variance, e.g.,
Although it is widely used in practice, there is no implicit evidence that why we set the standard variation as 0.01.
Xavier initialization
Our derivation mainly following [xxx]. The idea is to keep the input and output of a layer with the same variance and the zero mean. A building block of a conventional neural network consists of a linear layer and an elementwise activation function ,
where is the input and is the output. is the weight and is the bias. indexes the layer.
We drop the for simplicity. We assume that the elements in are independent and identically distributed (i.i.d.), and the elements in are also i.i.d. And and are indepent with each other. Suppose we use the sigmoid function as the nonlinear activation function, then has zero mean (). Since , we have
Here we let be zero. The above equation shows that the variance of the output is the variance of the input, but scaled by . So if we want to keep the variance of the input and output unchanged, it must have , which results in,
Hence the weights should be initialized from randomly drawn from a distribution with zero mean and standard variation .
ReLU activation: MSR initialization
When the activation function is ReLU: , the mean of the input is no longer zero. In this case, we have
where
(see Variance caculation ReLU function) By replacing by , we have
This is the so called MSR initialization: when we use ReLU as the activation function, the weights should be randomly sampled from distribution with zero mean and variance.
Normalization
Another way to accelerate the training and simplify the optimization process is to use normalization. Normalization usually rescales the data or the weights to make the scale is just right for the training process. In this section, we will discuss several normalization methods for accelerating and simplifying the training of deep neural networks.
Normalizing the inputs
A common practice to normalize the input data is to compute where and are the sample mean and standard deviation, respectively. Intuitively, subtracting the mean reduces the shift of the data, and dividing by the standard deviation removes the scale of the data. In statistics, this procesure is called standardizing, and the standardised data is called the z-score.
Another trick is to decorrelate the inputs: if the inputs are uncorrelated, then the weights are independent with each other, which simplifies the problem. One possible way to decorrelated the inputs is to whitening the data with PCA. But sometimes PCA can be harmful for your problem, please see the discussion for further understanding.
Batch (Re)normalization: normalizing the input for each layer
Internal Covariate Shift
Training deep neural networks is difficult due to the changing of the distribution of each layer’s inputs after updating the parameters of the network. It slows down the training by requiring relatively small learning rate and careful weight initialization, espetially for networks with saturating nonlinearities. This phenomenon is refered as internal covariate shift.
Batch Normalization
The key is to reduce the internal covariate shift. Motivated by standardizing the input data, we raise such a question: can we normalize the input of each layer? This is exactly what batch normalization (BN) does: it normalize the input of each layer for each training mini-batch. It has two benefits:
- BN allows higher learning rates and cares less about the initialization.
- It also acts as a regularizer.
For a layer with -dimensional input , we will normalize each dimention by
where and are the sample mean and standard deviation in a mini-batch. To ensure that the normalized activation keeps the original representation power, BN introduces a pair of parameters,
We can see that when and the original activations can be recovered.
It is easy to compute the gradients by chain rule. You may verify your derivation from the BN paper.
Testing/Inference
During trainig, we compute the mean and variance for each mini-batch. But for testing/inference, we want the output to depend only on the input, deterministically. Recall that, once the training has finished, we can compute the mean and the unbiased variance by using the population. Hence during testing, we use the polulation mean and unbiased variance estimation instead of the mini-batch mean and variance.
Batch Renormalization
It seems BN has solved everything. E.g., it allows large learning rates, and it is not sensitive to initialization. But what if the batch size is quite small? For small minibatches, the estimates of mean and variance are less accurate. The error accumulates as the depth increases. Moreover, the global estimates of mean and variance are also inaccurate, which may affect the inference/testing accuracy on a large margin!
Let be the minibatch statistics, and be their moving averages, then the results of these two normalizations are related by an affine transform,
where and . Batch normalization simply set . We refer this augmented batch normalization as Batch Renormalization: the fixed (for the given minibatch) and correct for the fact that the minibatch statistics differ from the population ones.
In optimization, we treat as constant to compute the gradients using backpropagation. For more details, please refer to the original paper.
Limitation: for a fixed length feed-forward neural network, BN simply stores the statistics for each layer separately. However, for recurrent neural networks with varied length of sequence as input, applying BN requires different statistics for different time-steps. This limits the application of BN in recurrent neural networks directly. In the following section, we will see how weight normalization and layer normalization solve this problem.
Normalizing the weights
Instead of normalizing the input of each layer, weight normalization normalizes the weight of each layer by decouple the weight direction and the weight magnitude,
Here is a scalar, is a vector with the same dimentionality of , and is the Euclidean norm of . This reparameterization decouples the weight magnitude from its direction .
Normalizing in recurrent neural networks
Layer normalization proposes to reduce the covariate shift problem by fixing the mean and the variance of the summed inputs within each layer.
where is the number of activations (neurons), . In this way, all the hidden neurons in a layer share the same statistics $\mu, \sigma$, but different training cases have different normalization terms. While for batch normalization, the statistics depend on mini-batches. Hence layer normaliztion does not impose any constraint on the size of a mini-batch and it can be used in the pure online regime with batch size 1.
Outro
Initialization and normalization are essential for training neural networks. Very deep neural networks, e.g., GoogLeNet and ResNet, are built upon stacks of these methods. Although they seem tricky, they are developed by intuitive motivations and proper assumptions.
As discussed in layer normalization, all the normalization methods can be summarized in the following transformation
In batch normalization, are computed based on minibatches. While in layer normalization, the statistics are computed by the activations themselves. In weight normalization, and . With careful observations and assumptions, you may develop better initialization/normalization methods.


|
y=0
(probability 0.083, score -2.503)
top features
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y=1
(probability 0.001, score -6.695)
top features
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y=2
(probability 0.540, score -0.026)
top features
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y=3
(probability 0.065, score -2.756)
top features
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y=4
(probability 0.099, score -2.312)
top features
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y=5
(probability 0.031, score -3.552)
top features
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y=6
(probability 0.042, score -3.221)
top features
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y=7
(probability 0.035, score -3.404)
top features
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y=8
(probability 0.064, score -2.781)
top features
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y=9
(probability 0.040, score -3.275)
top features
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Contribution?
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Feature |
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+0.204
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sub
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+0.151
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je
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+0.101
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addl addl
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+0.098
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mov lea
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+0.094
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jmp addl
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+0.076
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ret
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+0.070
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leave ret
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+0.068
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sub mov
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+0.061
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call cmp
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+0.060
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add movzwl
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jb
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jb mov
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xor je
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+0.037
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movl
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+0.035
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jg cmp
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+0.030
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cmp jbe
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+0.028
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movl mov
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jne cmpl
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cmp jl
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+0.004
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jge
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add add
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call mov
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movl lea
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movsbl cmp
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je nop
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-0.017
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jge jmp
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-0.022
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and
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mov movl
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-0.026
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add jmp
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-0.030
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<BIAS>
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-0.031
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cmpl js
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-0.033
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movsbl
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-0.037
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jl
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-0.038
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push
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push mov
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-0.042
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jne jmp
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-0.072
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add lea
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-0.076
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jne mov
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-0.077
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ja addl
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-0.086
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add
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-0.102
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test jne
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-0.105
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jmp mov
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-0.105
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xor mov
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-0.107
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jne addl
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-0.119
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jmp cmpl
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-0.128
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cmpl jne
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-0.147
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mov and
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-0.190
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addl jmp
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-0.285
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and test
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-0.575
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mov mov
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-1.277
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mov
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Contribution?
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Feature |
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+1.062
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mov mov
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+0.458
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and
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+0.150
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add movzwl
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+0.095
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jne
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+0.086
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jne cmpl
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+0.081
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add mov
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+0.079
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jne jmp
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+0.077
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movl lea
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+0.070
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jmp jmp
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+0.059
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jne addl
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+0.050
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movzbl movsbl
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+0.048
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cmp jge
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+0.048
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mov movl
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+0.047
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movzwl and
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+0.045
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je nop
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+0.015
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movzwl movzwl
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+0.010
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sub cmp
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+0.004
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jg
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-0.007
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leave
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ret
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-0.015
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js mov
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js
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-0.017
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call cmp
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<BIAS>
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-0.031
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nop mov
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-0.037
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movsbl cmp
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-0.042
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jbe jmp
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-0.053
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cmp jb
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-0.062
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xor mov
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-0.072
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cmp jl
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-0.073
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call
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-0.075
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jle
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-0.076
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add lea
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-0.078
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addl addl
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-0.081
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je cmpl
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-0.084
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movzbl mov
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-0.108
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ja
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-0.112
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jne mov
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mov jmp
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-0.184
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jmp cmpl
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movzbl
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-0.228
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mov movzbl
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-0.236
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and test
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-0.272
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je
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-0.334
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jmp addl
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-2.668
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jmp
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-3.833
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mov
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Contribution?
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Feature |
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+0.224
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xor
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+0.224
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mov xor
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+0.203
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add add
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+0.133
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test je
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+0.098
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je
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+0.079
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ret
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+0.078
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jne movl
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+0.049
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call cmp
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+0.039
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movzbl movsbl
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+0.028
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mov cmp
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+0.027
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push mov
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+0.027
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push
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+0.025
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movl call
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+0.008
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je call
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nop mov
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add lea
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jne call
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<BIAS>
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-0.032
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ja addl
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jbe
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-0.036
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addl addl
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jl cmp
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-0.058
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jmp cmp
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mov movl
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-0.102
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jne nop
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jge sub
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-0.133
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jbe jmp
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-0.239
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movzwl movzwl
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movzbl add
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Contribution?
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Feature |
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+0.266
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test jne
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+0.147
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call mov
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addl jmp
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+0.110
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mov cmpl
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+0.081
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cmpl
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+0.075
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call test
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+0.052
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ja jmp
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+0.043
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je call
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+0.039
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jne call
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+0.038
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mov cmp
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+0.031
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movl mov
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+0.031
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leave ret
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+0.029
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cmp jle
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+0.029
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je nop
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+0.027
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movzbl movsbl
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+0.026
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jge sub
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+0.024
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add lea
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+0.022
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mov movl
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+0.017
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jne mov
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+0.013
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jle
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+0.010
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movzwl and
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+0.008
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push mov
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+0.008
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push
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+0.007
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ja addl
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-0.009
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<BIAS>
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-0.014
|
js
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js mov
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-0.014
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add jmp
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movl call
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-0.016
|
jne cmpl
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-0.019
|
jmp cmp
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-0.019
|
call cmp
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-0.021
|
xor je
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-0.021
|
jge jmp
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-0.022
|
jl cmp
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-0.027
|
movzbl mov
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-0.030
|
cmp jge
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-0.038
|
addl nop
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-0.038
|
test je
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-0.056
|
add add
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-0.068
|
sub mov
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-0.074
|
mov xor
|
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-0.074
|
xor
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-0.079
|
cmp jl
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-0.094
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jl
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-0.111
|
cmpl js
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-0.114
|
jge
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-0.118
|
je
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-0.125
|
movzwl
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-0.129
|
jmp addl
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-0.164
|
cmpl jne
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-0.174
|
mov jmp
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-0.189
|
movl
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-0.296
|
test
|
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-0.303
|
mov call
|
|
-1.549
|
mov
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|
|
Contribution?
|
Feature |
|
+0.197
|
test
|
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+0.144
|
movzwl
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+0.089
|
cmpl jne
|
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+0.075
|
jge
|
|
+0.067
|
je cmpl
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+0.053
|
jl
|
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+0.039
|
movl lea
|
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+0.033
|
mov and
|
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+0.028
|
mov movl
|
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+0.024
|
add mov
|
|
+0.020
|
jg
|
|
+0.013
|
jg cmp
|
|
+0.013
|
xor je
|
|
+0.011
|
mov xor
|
|
+0.011
|
xor
|
|
+0.009
|
jne nop
|
|
+0.009
|
cmp ja
|
|
+0.008
|
call leave
|
|
+0.008
|
jl cmp
|
|
+0.006
|
cmp jle
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+0.006
|
cmp jg
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+0.005
|
jle
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-0.007
|
jbe
|
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-0.008
|
je nop
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-0.008
|
mov addl
|
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-0.010
|
jge sub
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-0.017
|
cmp jbe
|
|
-0.021
|
<BIAS>
|
|
-0.025
|
push mov
|
|
-0.025
|
push
|
|
-0.028
|
mov add
|
|
-0.032
|
leave ret
|
|
-0.038
|
mov lea
|
|
-0.044
|
call cmp
|
|
-0.046
|
movzwl and
|
|
-0.048
|
jne mov
|
|
-0.055
|
jmp cmpl
|
|
-0.055
|
jne cmpl
|
|
-0.057
|
jb
|
|
-0.057
|
jb mov
|
|
-0.069
|
lea mov
|
|
-0.069
|
lea
|
|
-0.069
|
mov jmp
|
|
-0.077
|
ja
|
|
-0.085
|
movzbl movsbl
|
|
-0.094
|
test jne
|
|
-0.095
|
add
|
|
-0.202
|
nop
|
|
-0.215
|
movl
|
|
-0.341
|
mov mov
|
|
-0.374
|
call
|
|
-0.450
|
jne
|
|
-0.460
|
jmp mov
|
|
|
Contribution?
|
Feature |
|
+0.395
|
mov call
|
|
+0.100
|
jne addl
|
|
+0.076
|
mov addl
|
|
+0.073
|
je nop
|
|
+0.062
|
jbe
|
|
+0.046
|
jne nop
|
|
+0.038
|
je cmpl
|
|
+0.032
|
leave
|
|
+0.007
|
test je
|
|
-0.011
|
<BIAS>
|
|
-0.012
|
jmp addl
|
|
-0.013
|
movl call
|
|
-0.021
|
push mov
|
|
-0.021
|
push
|
|
-0.024
|
mov add
|
|
-0.024
|
jmp jmp
|
|
-0.024
|
mov movl
|
|
-0.026
|
call cmp
|
|
-0.030
|
nop
|
|
-0.039
|
jle sub
|
|
-0.039
|
jb mov
|
|
-0.039
|
jb
|
|
-0.044
|
mov cmpl
|
|
-0.045
|
jne call
|
|
-0.056
|
sub
|
|
-0.062
|
call test
|
|
-0.071
|
jg cmp
|
|
-0.073
|
jne cmpl
|
|
-0.081
|
cmp jge
|
|
-0.084
|
ret
|
|
-0.096
|
cmp jl
|
|
-0.098
|
add add
|
|
-0.109
|
leave ret
|
|
-0.191
|
movzwl and
|
|
-0.228
|
add
|
|
-0.334
|
addl jmp
|
|
-0.434
|
cmpl
|
|
-0.638
|
call
|
|
-0.646
|
test
|
|
-0.768
|
mov mov
|
|
|
Contribution?
|
Feature |
|
+0.316
|
jmp mov
|
|
+0.194
|
cmpl jne
|
|
+0.123
|
jl
|
|
+0.115
|
jbe jmp
|
|
+0.114
|
add add
|
|
+0.096
|
movl call
|
|
+0.094
|
jge jmp
|
|
+0.088
|
add jmp
|
|
+0.059
|
mov cmp
|
|
+0.047
|
jne nop
|
|
+0.047
|
push
|
|
+0.047
|
push mov
|
|
+0.043
|
call test
|
|
+0.037
|
xor mov
|
|
+0.031
|
movzwl movzwl
|
|
+0.019
|
cmp jg
|
|
+0.015
|
ja addl
|
|
+0.006
|
nop mov
|
|
-0.016
|
nop jmp
|
|
-0.017
|
<BIAS>
|
|
-0.030
|
jb
|
|
-0.030
|
jb mov
|
|
-0.036
|
cmpl js
|
|
-0.038
|
jbe
|
|
-0.039
|
movl lea
|
|
-0.040
|
mov movl
|
|
-0.040
|
movl mov
|
|
-0.047
|
jle
|
|
-0.047
|
jne call
|
|
-0.059
|
jne addl
|
|
-0.061
|
add movzwl
|
|
-0.064
|
cmp jbe
|
|
-0.067
|
je cmpl
|
|
-0.076
|
mov sub
|
|
-0.077
|
mov call
|
|
-0.088
|
test je
|
|
-0.113
|
movsbl cmp
|
|
-0.119
|
cmp jge
|
|
-0.122
|
jne jmp
|
|
-0.134
|
and test
|
|
-0.138
|
xor je
|
|
-0.145
|
lea
|
|
-0.145
|
lea mov
|
|
-0.321
|
test
|
|
-2.601
|
mov
|
|
|
Contribution?
|
Feature |
|
+0.551
|
test
|
|
+0.330
|
cmpl jne
|
|
+0.328
|
addl jmp
|
|
+0.284
|
nop
|
|
+0.135
|
cmp ja
|
|
+0.131
|
addl addl
|
|
+0.124
|
movsbl
|
|
+0.092
|
mov add
|
|
+0.092
|
jmp cmpl
|
|
+0.073
|
movsbl cmp
|
|
+0.065
|
jge sub
|
|
+0.064
|
cmp jl
|
|
+0.060
|
js
|
|
+0.060
|
js mov
|
|
+0.050
|
movzbl mov
|
|
+0.049
|
mov lea
|
|
+0.044
|
call cmp
|
|
+0.043
|
ret
|
|
+0.026
|
jb
|
|
+0.026
|
jb mov
|
|
+0.023
|
ja jmp
|
|
+0.009
|
ja
|
|
+0.008
|
sub mov
|
|
+0.007
|
jne mov
|
|
+0.004
|
push
|
|
+0.004
|
push mov
|
|
+0.001
|
nop jmp
|
|
-0.017
|
jne movl
|
|
-0.019
|
movl mov
|
|
-0.023
|
<BIAS>
|
|
-0.023
|
movl lea
|
|
-0.028
|
je cmpl
|
|
-0.029
|
jbe jmp
|
|
-0.029
|
jge jmp
|
|
-0.033
|
add add
|
|
-0.041
|
jne jmp
|
|
-0.043
|
jge
|
|
-0.051
|
jmp cmp
|
|
-0.057
|
jg
|
|
-0.064
|
add jmp
|
|
-0.066
|
mov movzbl
|
|
-0.066
|
movzbl
|
|
-0.070
|
movl
|
|
-0.075
|
cmp jb
|
|
-0.082
|
mov xor
|
|
-0.082
|
xor
|
|
-0.092
|
ja addl
|
|
-0.104
|
mov addl
|
|
-0.107
|
call test
|
|
-0.116
|
test je
|
|
-0.120
|
movzbl movsbl
|
|
-0.192
|
and
|
|
-0.245
|
mov cmpl
|
|
-0.269
|
call
|
|
-0.324
|
mov mov
|
|
-0.367
|
addl
|
|
-0.413
|
cmpl
|
|
-1.083
|
jne
|
|
-1.757
|
jmp
|
|
|
Contribution?
|
Feature |
|
+0.435
|
and test
|
|
+0.308
|
movzwl
|
|
+0.204
|
jne
|
|
+0.198
|
cmpl
|
|
+0.184
|
call test
|
|
+0.156
|
leave
|
|
+0.086
|
cmp jge
|
|
+0.082
|
je
|
|
+0.069
|
je cmpl
|
|
+0.053
|
cmp jbe
|
|
+0.045
|
cmpl js
|
|
+0.043
|
jmp cmp
|
|
+0.031
|
cmp ja
|
|
+0.024
|
mov addl
|
|
+0.021
|
jbe jmp
|
|
+0.021
|
movsbl cmp
|
|
+0.017
|
xor je
|
|
-0.006
|
sub mov
|
|
-0.023
|
<BIAS>
|
|
-0.027
|
jg cmp
|
|
-0.029
|
cmp jb
|
|
-0.034
|
ret
|
|
-0.044
|
jmp jmp
|
|
-0.051
|
je nop
|
|
-0.057
|
add mov
|
|
-0.067
|
test je
|
|
-0.076
|
ja jmp
|
|
-0.083
|
mov cmp
|
|
-0.084
|
call leave
|
|
-0.101
|
mov movzbl
|
|
-0.101
|
movzbl
|
|
-0.102
|
call cmp
|
|
-0.109
|
jg
|
|
-0.113
|
jne jmp
|
|
-0.117
|
cmp jle
|
|
-0.127
|
addl addl
|
|
-0.152
|
je call
|
|
-0.213
|
mov sub
|
|
-0.295
|
add movzwl
|
|
-0.335
|
call
|
|
-0.356
|
jmp mov
|
|
-0.521
|
test jne
|
|
-0.532
|
cmpl jne
|
|
-1.005
|
mov
|
|
|
Contribution?
|
Feature |
|
+0.131
|
test je
|
|
+0.097
|
cmp jl
|
|
+0.080
|
nop mov
|
|
+0.071
|
jne movl
|
|
+0.065
|
cmpl js
|
|
+0.064
|
movzbl add
|
|
+0.054
|
ja
|
|
+0.047
|
jne jmp
|
|
+0.035
|
call
|
|
+0.022
|
movl lea
|
|
+0.021
|
call cmp
|
|
+0.020
|
ja addl
|
|
+0.019
|
add lea
|
|
+0.019
|
je call
|
|
+0.018
|
jmp cmpl
|
|
-0.020
|
<BIAS>
|
|
-0.039
|
cmp jge
|
|
-0.043
|
add mov
|
|
-0.047
|
jle
|
|
-0.054
|
leave ret
|
|
-0.063
|
cmp ja
|
|
-0.064
|
mov add
|
|
-0.073
|
movl call
|
|
-0.076
|
and
|
|
-0.077
|
mov sub
|
|
-0.087
|
movzwl movzwl
|
|
-0.090
|
jne nop
|
|
-0.101
|
mov lea
|
|
-0.103
|
sub
|
|
-0.111
|
je nop
|
|
-0.118
|
test jne
|
|
-0.142
|
mov and
|
|
-0.165
|
call test
|
|
-0.186
|
add add
|
|
-0.221
|
add
|
|
-0.235
|
movzwl
|
|
-0.278
|
addl
|
|
-0.306
|
cmpl jne
|
|
-0.347
|
jmp mov
|
|
-0.405
|
jmp
|
|
-0.586
|
mov mov
|
|


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Plateau Guber Tussle: Appeal Court Affirms Lalong’s Election
by Olugbenga IgeThe Court of Appeal, Jos Division on Friday affirmed the victory of Governor Simon Lalong in the Plateau State governorship election.
In the lead judgement delivered by Honourable Justice Adumein Otisi, the court dismissed the appeal of the Peoples Democratic Party (PDP) and its candidate, Jeremiah Useni, for lack of merit and align with the Election Tribunal decision affirming the elections of governor Lalong.
The sum of N200,000 is also awarded as cost in favour the Governor and the All Progressives Congress (APC).
The Plateau State Election Tribunal had earlier in October upheld the election victory of Lalong.
Justice Halimat Saleeman, in a unanimous decision of the tribunal threw out the petitioner’s case on the grounds that the petition is weak along the line of law and therefore cannot sway justice to the side of the petitioner.


Class: ActionView::AbstractRenderer
Do not use. This class is for internal use only.
| Relationships & Source Files | |
| Namespace Children | |
|
Modules:
| |
|
Classes:
| |
| Extension / Inclusion / Inheritance Descendants | |
|
Subclasses:
|
|
| Inherits: | Object |
| Defined in: | actionview/lib/action_view/renderer/abstract_renderer.rb |
Overview
This class defines the interface for a renderer. Each class that subclasses AbstractRenderer
is used by the base Renderer
class to render a specific type of object.
The base Renderer
class uses its #render method to delegate to the renderers. These currently consist of
PartialRenderer - Used for rendering partials
TemplateRenderer - Used for rendering other types of templates
StreamingTemplateRenderer - Used for streaming
Whenever the #render method is called on the base Renderer
class, a new renderer object of the correct type is created, and the #render method on that new object is called in turn. This abstracts the set up and rendering into a separate classes for partials and templates.
Constant Summary
-
NO_DETAILS =
# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 157{}.freeze
Class Method Summary
- .new(lookup_context) ⇒ AbstractRenderer constructor
Instance Attribute Summary
- #formats readonly
Instance Method Summary
Constructor Details
.new(lookup_context) ⇒ AbstractRenderer
# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 24
def initialize(lookup_context) @lookup_context = lookup_context end
Instance Attribute Details
#formats (readonly)
[ GitHub ]# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 22
delegate :template_exists?, :any_templates?, :formats, to: :@lookup_context
Instance Method Details
#any_templates? ⇒ Boolean
# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 22
delegate :template_exists?, :any_templates?, :formats, to: :@lookup_context
#build_rendered_collection(templates, spacer) (private)
[ GitHub ]# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 182
def build_rendered_collection(templates, spacer) RenderedCollection.new templates, spacer end
#build_rendered_template(content, template) (private)
[ GitHub ]# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 178
def build_rendered_template(content, template) RenderedTemplate.new content, template end
#extract_details(options) (private)
[ GitHub ]# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 159
def extract_details( ) # :doc: details = nil LookupContext.registered_details.each do |key| value = [key] if value (details ||= {})[key] = Array(value) end end details || NO_DETAILS end
#prepend_formats(formats) (private)
[ GitHub ]#render
# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 28
def render raise NotImplementedError end
#template_exists? ⇒ Boolean
# File 'actionview/lib/action_view/renderer/abstract_renderer.rb', line 22
delegate :template_exists?, :any_templates?, :formats, to: :@lookup_context


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Disclaimer
Taivan has made reasonable efforts to ensure the accuracy of the information throughout this website. However, Taivan reserves the right to make appropriate changes in information found on this website.
Nothing contained in this website shall be construed as contractual rights. Taivan has many links to other websites. These include links to websites operated by other government agencies, nonprofit organizations and private businesses. Taivan does not endorse any content or linked materials found on websites or web services provided by and/or developed by Taivan.
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Regular points calculation for both Team & Individual
| Steps (Per Member) |
Points |
| 5000 (Minimum Required) |
25 |
| 7500 |
50 |
| 10000 |
75 |
| 12500 |
100 |
All the points will be updated (corrected if there are issues) after the end of
this week (2 Aug)
Additional points scheme
The first week
(22 July, 23 July, 25 July & 26 July)
Upon reaching
12,500 steps, every additional
1,000 steps will earn you
5 points up to a maximum of
25 points (Maximum 17,500 steps)
Bonus points (Declared dates)
- 24 July
- 28 July
- 1 Aug
- 3 Aug
- 7 Aug
How to earn bonus points?
On the bonus points available dates, once the contestant exceeds
12,500 steps for every additional
500 steps the contestant will
earn 4 bonus points.
*These points will be also included in the team score


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RT @stuartsierra: Nobody optimizes any more; premature abstraction is the new evil
2/8/2011, 12:50:46 PM
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RT @stuartsierra: Nobody optimizes any more; premature abstraction is the new evil
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The International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
is an annual international conference dedicated to emerging and challenging topics in intelligent data analysis, data mining and their associated learning systems and paradigms. Its core themes include: the Big Data challenges, Machine Learning, Data Mining, Information Retrieval and Management, Bio- and Neuro-Informatics, Bio-Inspired Models (including Neural Networks, Evolutionary Computation and Swarm Intelligence), Agents and Hybrid Intelligent Systems, and Real-world Applications of Intelligent Techniques. Other related and emerging themes and topics are also welcome.
The conference provides a unique opportunity and stimulating forum for presenting and discussing the latest theoretical advances and real-world applications in Computational Intelligence and Intelligent Data Analysis. Authors and researchers are warmly invited
submit their latest findings and research work to the conference.
Potential session/workshop organizers are invited to enlist potential papers in a cohesive topic for their special session(s)/workshops. Proposals and enquiries can be sent to<EMAIL_ADDRESS>The proceedings will be published by Springer in the Lecture Notes in Artificial Intelligence.
Please download here the flyer of IDEAL 2018 call for papers.
Instructions for authors
Authors are invited to submit their manuscripts (in pdf format) written in English by the deadline via the conference online submission system (available in due time). All submissions will be refereed by experts in the field based on
originality, significance, quality and clarity. All contributions must be original, should not have been published elsewhere and must not be submitted elsewhere during the review period. The nominal length of each paper is 8 pages and any paper that exceeds this limit will incur extra page charges on the registration form. In any cases, a paper must not exceed 12 LNCS pages. Papers must comply with the format of Springer LNCS/LNAI Proceedings. (see www.springer.com/lncs )
Call for Workshops, Special Sessions and Tutorials
Special Sessions
In addition to the regular sessions, there will be special sessions devoted to emerging topics and areas with growing attention. Researchers and prospective organizers are invited to submit a one-page proposal for a special session to the conference address<EMAIL_ADDRESS>Each proposal should include the following information:
The title of the special session.
The organizers with their affiliations.
A description of why the topic is of special interest and how it fits to the scope of IDEAL. Each special session preferably consists of four or more presentations. Special sessions receiving less than 4 papers will be merged into a suitable regular session.
Workshops
Workshops focus on new and emerging topics. In contrast to special sessions, workshops should include an introductory talk or tutorial on the topic and will have final discussions at the end of the workshop. Apart from the introductory
talk in the discussion, Workshops should preferably include six or more additional presentations. Researchers or prospective organisers are invited to submit a one-page proposals for workshops to the conference address [
<EMAIL_ADDRESS>In addition to the information required for special sessions, workshop proposal should also include the following:
The name(s) of the presenter(s) of the introductory talk.
A theme for the discussion.
Tutorials
Tutorials at IDEAL will be presented by domain experts to cover current topics relevant to researchers and practitioners of the field of intelligent data analysis, data mining and their associated learning systems and paradigms. Each tutorial will be 1 hour long. We encourage the inclusion of interactive activities and demos. Tutorial proposals should be submitted by e-mail to the IDEAL 2018 conference address<EMAIL_ADDRESS>Each tutorial proposal should include:
A half-page extended abstract that includes: the title of the tutorial, the name and affiliation of the instructor(s), and a description of the tutorial scope and content.
Short biography of the instructor(s) (about half page).
[Optional] A description of any interactive activity or demo planned within the tutorial presentation.
Paper Format and Review Process
The paper format of Special Sessions and Workshops is the same as for the main track of the conference and so is their submission and review process (while each Special Session or Workshop can appoint its own reviewers if necessary, in additional to the main PC). All accepted papers (main track, special sessions and workshops) will be included in the conference proceedings. The proceedings will be published by Springer in the Lecture Notes in Artificial Intelligence.


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Keep all mill records at one single place.
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Keep eye on every saree in-motion.
Centralise system for keeping stock records.
Easy billing to avoid any human error.
Some quick example text to build on the card title and make up the bulk of the card's content.
Simple and quick way to generate e-way bill


Sweet Street
A popular hub to gorge on sweet delectables, the Sweet Street is one of the busiest place to shop in Kozhikode. It remains open till 9 in the night.
A popular hub to gorge on sweet delectables, the Sweet Street is one of the busiest place to shop in Kozhikode. It remains open till 9 in the night.
Said to be built on the remains of a shipwreck, the Thikkoti Lighthouse is one of the major attractions of Kozhikode. Located on a rocky shoreline, the place is best known for the scenic views it offers as well as several species of nomadic birds.


The medications developed from clinical trials are the therapies that have the potential to drive new treatments and potential cures over the next 5 to 10 years - from addressing the substantial unmet medical needs of diseases such as Diabetes and Autism, to rare diseases with no treatments such as ALS. The success of clinical trials are important to not only big stakeholders like pharmaceutical companies, researchers, physicians, but also to ordinary people like taxpayers and special interests. Approximately $10 billion is spent funding clinical trials every year, yet there lacks a functional query tool that makes the results and successes of these expenditures transparent.
Even with very specific tools such as clinicaltrials.gov, finding the right clinical trial can be extremely frustrating. Failures in clinical trials are hardly ever reported, with only 10% of studies ever published. Even amonst the top 10 ranking sponsors, these sponsors publish a disproportionate amount of clinical trial compared to their overall completed trials.
The success of clinical trials are important to not only big stakeholders like pharmaceutical companies, researchers, physicians, but also to ordinary people like taxpayers and special interests. Approximately $36.5 billion is spent funding clinical trials every year, yet there lacks a functional query tool that makes the results and successes of these expenditures transparent.
The following graph shows the number of published studies and the number of completed clinical trials for top sponsors. You'll notice the discrepency in the amount of publications resulting from clinical trials. This can be due to lack of information on clinicaltrials.gov, or lack of results; both of these reflect poorly on the output performance of such sponsors. Patients would most likely want to enroll in clinical trials with efficient and productive sponsors.
Empowering patients:
1. to search for ongoing clinical trials
2. to search for completed clinical trials and see results
3. visualize data in different ways
You can download the Clinical Search Index App for free on Github.
Visit Download Page


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