Note
Click here to download the full example code
Deep Learning with PyTorch¶
Deep Learning Building Blocks: Affine maps, non-linearities and objectives¶
Deep learning consists of composing linearities with non-linearities in clever ways. The introduction of non-linearities allows for powerful models. In this section, we will play with these core components, make up an objective function, and see how the model is trained.
Affine Maps¶
One of the core workhorses of deep learning is the affine map, which is a function \(f(x)\) where
for a matrix \(A\) and vectors \(x, b\). The parameters to be learned here are \(A\) and \(b\). Often, \(b\) is refered to as the bias term.
PyTorch and most other deep learning frameworks do things a little differently than traditional linear algebra. It maps the rows of the input instead of the columns. That is, the \(i\)’th row of the output below is the mapping of the \(i\)’th row of the input under \(A\), plus the bias term. Look at the example below.
# Author: Robert Guthrie
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
torch.manual_seed(1)
lin = nn.Linear(5, 3) # maps from R^5 to R^3, parameters A, b
# data is 2x5. A maps from 5 to 3... can we map "data" under A?
data = torch.randn(2, 5)
print(lin(data)) # yes
Out:
tensor([[ 0.1755, -0.3268, -0.5069],
[-0.6602, 0.2260, 0.1089]], grad_fn=<AddmmBackward>)
Non-Linearities¶
First, note the following fact, which will explain why we need non-linearities in the first place. Suppose we have two affine maps \(f(x) = Ax + b\) and \(g(x) = Cx + d\). What is \(f(g(x))\)?
\(AC\) is a matrix and \(Ad + b\) is a vector, so we see that composing affine maps gives you an affine map.
From this, you can see that if you wanted your neural network to be long chains of affine compositions, that this adds no new power to your model than just doing a single affine map.
If we introduce non-linearities in between the affine layers, this is no longer the case, and we can build much more powerful models.
 You are probably wondering: “why these functions? I can think of plenty of other non-linearities.” The reason for this is that they have gradients that are easy to compute, and computing gradients is essential for learning. For example
A quick note: although you may have learned some neural networks in your intro to AI class where \(\sigma(x)\) was the default non-linearity, typically people shy away from it in practice. This is because the gradient vanishes very quickly as the absolute value of the argument grows. Small gradients means it is hard to learn. Most people default to tanh or ReLU.
# In pytorch, most non-linearities are in torch.functional (we have it imported as F)
# Note that non-linearites typically don't have parameters like affine maps do.
# That is, they don't have weights that are updated during training.
data = torch.randn(2, 2)
print(data)
print(F.relu(data))
Out:
tensor([[-0.5404, -2.2102],
[ 2.1130, -0.0040]])
tensor([[0.0000, 0.0000],
[2.1130, 0.0000]])
Softmax and Probabilities¶
The function \(\text{Softmax}(x)\) is also just a non-linearity, but it is special in that it usually is the last operation done in a network. This is because it takes in a vector of real numbers and returns a probability distribution. Its definition is as follows. Let \(x\) be a vector of real numbers (positive, negative, whatever, there are no constraints). Then the i’th component of \(\text{Softmax}(x)\) is
It should be clear that the output is a probability distribution: each element is non-negative and the sum over all components is 1.
You could also think of it as just applying an element-wise exponentiation operator to the input to make everything non-negative and then dividing by the normalization constant.
# Softmax is also in torch.nn.functional
data = torch.randn(5)
print(data)
print(F.softmax(data, dim=0))
print(F.softmax(data, dim=0).sum()) # Sums to 1 because it is a distribution!
print(F.log_softmax(data, dim=0)) # theres also log_softmax
Out:
tensor([ 1.3800, -1.3505, 0.3455, 0.5046, 1.8213])
tensor([0.2948, 0.0192, 0.1048, 0.1228, 0.4584])
tensor(1.)
tensor([-1.2214, -3.9519, -2.2560, -2.0969, -0.7801])
Objective Functions¶
The objective function is the function that your network is being trained to minimize (in which case it is often called a loss function or cost function). This proceeds by first choosing a training instance, running it through your neural network, and then computing the loss of the output. The parameters of the model are then updated by taking the derivative of the loss function. Intuitively, if your model is completely confident in its answer, and its answer is wrong, your loss will be high. If it is very confident in its answer, and its answer is correct, the loss will be low.
The idea behind minimizing the loss function on your training examples is that your network will hopefully generalize well and have small loss on unseen examples in your dev set, test set, or in production. An example loss function is the negative log likelihood loss, which is a very common objective for multi-class classification. For supervised multi-class classification, this means training the network to minimize the negative log probability of the correct output (or equivalently, maximize the log probability of the correct output).
Optimization and Training¶
So what we can compute a loss function for an instance? What do we do with that? We saw earlier that Tensors know how to compute gradients with respect to the things that were used to compute it. Well, since our loss is an Tensor, we can compute gradients with respect to all of the parameters used to compute it! Then we can perform standard gradient updates. Let \(\theta\) be our parameters, \(L(\theta)\) the loss function, and \(\eta\) a positive learning rate. Then:
There are a huge collection of algorithms and active research in attempting to do something more than just this vanilla gradient update. Many attempt to vary the learning rate based on what is happening at train time. You don’t need to worry about what specifically these algorithms are doing unless you are really interested. Torch provides many in the torch.optim package, and they are all completely transparent. Using the simplest gradient update is the same as the more complicated algorithms. Trying different update algorithms and different parameters for the update algorithms (like different initial learning rates) is important in optimizing your network’s performance. Often, just replacing vanilla SGD with an optimizer like Adam or RMSProp will boost performance noticably.
Creating Network Components in PyTorch¶
Before we move on to our focus on NLP, lets do an annotated example of building a network in PyTorch using only affine maps and non-linearities. We will also see how to compute a loss function, using PyTorch’s built in negative log likelihood, and update parameters by backpropagation.
All network components should inherit from nn.Module and override the
forward() method. That is about it, as far as the boilerplate is
concerned. Inheriting from nn.Module provides functionality to your
component. For example, it makes it keep track of its trainable
parameters, you can swap it between CPU and GPU with the .to(device)
method, where device can be a CPU device torch.device("cpu")
or CUDA
device torch.device("cuda:0")
.
Let’s write an annotated example of a network that takes in a sparse bag-of-words representation and outputs a probability distribution over two labels: “English” and “Spanish”. This model is just logistic regression.
Example: Logistic Regression Bag-of-Words classifier¶
Our model will map a sparse BoW representation to log probabilities over labels. We assign each word in the vocab an index. For example, say our entire vocab is two words “hello” and “world”, with indices 0 and 1 respectively. The BoW vector for the sentence “hello hello hello hello” is
For “hello world world hello”, it is
etc. In general, it is
Denote this BOW vector as \(x\). The output of our network is:
That is, we pass the input through an affine map and then do log softmax.
data = [("me gusta comer en la cafeteria".split(), "SPANISH"),
("Give it to me".split(), "ENGLISH"),
("No creo que sea una buena idea".split(), "SPANISH"),
("No it is not a good idea to get lost at sea".split(), "ENGLISH")]
test_data = [("Yo creo que si".split(), "SPANISH"),
("it is lost on me".split(), "ENGLISH")]
# word_to_ix maps each word in the vocab to a unique integer, which will be its
# index into the Bag of words vector
word_to_ix = {}
for sent, _ in data + test_data:
for word in sent:
if word not in word_to_ix:
word_to_ix[word] = len(word_to_ix)
print(word_to_ix)
VOCAB_SIZE = len(word_to_ix)
NUM_LABELS = 2
class BoWClassifier(nn.Module): # inheriting from nn.Module!
def __init__(self, num_labels, vocab_size):
# calls the init function of nn.Module. Dont get confused by syntax,
# just always do it in an nn.Module
super(BoWClassifier, self).__init__()
# Define the parameters that you will need. In this case, we need A and b,
# the parameters of the affine mapping.
# Torch defines nn.Linear(), which provides the affine map.
# Make sure you understand why the input dimension is vocab_size
# and the output is num_labels!
self.linear = nn.Linear(vocab_size, num_labels)
# NOTE! The non-linearity log softmax does not have parameters! So we don't need
# to worry about that here
def forward(self, bow_vec):
# Pass the input through the linear layer,
# then pass that through log_softmax.
# Many non-linearities and other functions are in torch.nn.functional
return F.log_softmax(self.linear(bow_vec), dim=1)
def make_bow_vector(sentence, word_to_ix):
vec = torch.zeros(len(word_to_ix))
for word in sentence:
vec[word_to_ix[word]] += 1
return vec.view(1, -1)
def make_target(label, label_to_ix):
return torch.LongTensor([label_to_ix[label]])
model = BoWClassifier(NUM_LABELS, VOCAB_SIZE)
# the model knows its parameters. The first output below is A, the second is b.
# Whenever you assign a component to a class variable in the __init__ function
# of a module, which was done with the line
# self.linear = nn.Linear(...)
# Then through some Python magic from the PyTorch devs, your module
# (in this case, BoWClassifier) will store knowledge of the nn.Linear's parameters
for param in model.parameters():
print(param)
# To run the model, pass in a BoW vector
# Here we don't need to train, so the code is wrapped in torch.no_grad()
with torch.no_grad():
sample = data[0]
bow_vector = make_bow_vector(sample[0], word_to_ix)
log_probs = model(bow_vector)
print(log_probs)
Out:
{'me': 0, 'gusta': 1, 'comer': 2, 'en': 3, 'la': 4, 'cafeteria': 5, 'Give': 6, 'it': 7, 'to': 8, 'No': 9, 'creo': 10, 'que': 11, 'sea': 12, 'una': 13, 'buena': 14, 'idea': 15, 'is': 16, 'not': 17, 'a': 18, 'good': 19, 'get': 20, 'lost': 21, 'at': 22, 'Yo': 23, 'si': 24, 'on': 25}
Parameter containing:
tensor([[ 0.1194, 0.0609, -0.1268, 0.1274, 0.1191, 0.1739, -0.1099, -0.0323,
-0.0038, 0.0286, -0.1488, -0.1392, 0.1067, -0.0460, 0.0958, 0.0112,
0.0644, 0.0431, 0.0713, 0.0972, -0.1816, 0.0987, -0.1379, -0.1480,
0.0119, -0.0334],
[ 0.1152, -0.1136, -0.1743, 0.1427, -0.0291, 0.1103, 0.0630, -0.1471,
0.0394, 0.0471, -0.1313, -0.0931, 0.0669, 0.0351, -0.0834, -0.0594,
0.1796, -0.0363, 0.1106, 0.0849, -0.1268, -0.1668, 0.1882, 0.0102,
0.1344, 0.0406]], requires_grad=True)
Parameter containing:
tensor([0.0631, 0.1465], requires_grad=True)
tensor([[-0.5378, -0.8771]])
Which of the above values corresponds to the log probability of ENGLISH, and which to SPANISH? We never defined it, but we need to if we want to train the thing.
label_to_ix = {"SPANISH": 0, "ENGLISH": 1}
So lets train! To do this, we pass instances through to get log probabilities, compute a loss function, compute the gradient of the loss function, and then update the parameters with a gradient step. Loss functions are provided by Torch in the nn package. nn.NLLLoss() is the negative log likelihood loss we want. It also defines optimization functions in torch.optim. Here, we will just use SGD.
Note that the input to NLLLoss is a vector of log probabilities, and a target label. It doesn’t compute the log probabilities for us. This is why the last layer of our network is log softmax. The loss function nn.CrossEntropyLoss() is the same as NLLLoss(), except it does the log softmax for you.
# Run on test data before we train, just to see a before-and-after
with torch.no_grad():
for instance, label in test_data:
bow_vec = make_bow_vector(instance, word_to_ix)
log_probs = model(bow_vec)
print(log_probs)
# Print the matrix column corresponding to "creo"
print(next(model.parameters())[:, word_to_ix["creo"]])
loss_function = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)
# Usually you want to pass over the training data several times.
# 100 is much bigger than on a real data set, but real datasets have more than
# two instances. Usually, somewhere between 5 and 30 epochs is reasonable.
for epoch in range(100):
for instance, label in data:
# Step 1. Remember that PyTorch accumulates gradients.
# We need to clear them out before each instance
model.zero_grad()
# Step 2. Make our BOW vector and also we must wrap the target in a
# Tensor as an integer. For example, if the target is SPANISH, then
# we wrap the integer 0. The loss function then knows that the 0th
# element of the log probabilities is the log probability
# corresponding to SPANISH
bow_vec = make_bow_vector(instance, word_to_ix)
target = make_target(label, label_to_ix)
# Step 3. Run our forward pass.
log_probs = model(bow_vec)
# Step 4. Compute the loss, gradients, and update the parameters by
# calling optimizer.step()
loss = loss_function(log_probs, target)
loss.backward()
optimizer.step()
with torch.no_grad():
for instance, label in test_data:
bow_vec = make_bow_vector(instance, word_to_ix)
log_probs = model(bow_vec)
print(log_probs)
# Index corresponding to Spanish goes up, English goes down!
print(next(model.parameters())[:, word_to_ix["creo"]])
Out:
tensor([[-0.9297, -0.5020]])
tensor([[-0.6388, -0.7506]])
tensor([-0.1488, -0.1313], grad_fn=<SelectBackward>)
tensor([[-0.2093, -1.6669]])
tensor([[-2.5330, -0.0828]])
tensor([ 0.2803, -0.5605], grad_fn=<SelectBackward>)
We got the right answer! You can see that the log probability for Spanish is much higher in the first example, and the log probability for English is much higher in the second for the test data, as it should be.
Now you see how to make a PyTorch component, pass some data through it and do gradient updates. We are ready to dig deeper into what deep NLP has to offer.
Total running time of the script: ( 0 minutes 0.201 seconds)


See Olivia Colman Transform Into The Crown Season 3's Queen Elizabeth
by Chris HarnickOlivia Colman is no stranger to playing queens (she won an Oscar for playing one in The Favourite), and with The Crown, Colman takes on the world's most famous queen yet, Queen Elizabeth II.
Her transformation into the long-reigning monarch for the third and fourth seasons shocked her costars.
"The first time I saw Olivia in her wig it was magical, it was like, ‘Oh my god, the queen is in the room,'" Helena Bonham Carter, Princess Margaret on the series, says in the video below.
For her role as Queen Elizabeth II, Colman wore hand-made outfits. Below, see her transform into Queen Elizabeth via the hair and makeup process.
Watch
"For Olivia, it feels like the most valuable is to have a lovely time in hair and makeup and then inhabit the queen once the process is over," Cate Hall, The Crown's hair and makeup designer, says.
Colman replaces Emmy winner Claire Foy in the lead role on The Crown. She's joined by a new cast that also includes Bonham Carter, Tobias Menzies, Josh O'Connor, Erin Doherty, Jason Watkins, Marion Bailey, Emerald Fennell and Ben Daniels.
"There's a relatability and a humility and a bit like the queen, she doesn't overthink it. She just turns on and does it," series creator Peter Morgan says in the video above.
The Crown season three is now streaming on Netflix. The new season is set between 1964-1977. Colman will return for a fourth season alongside the new cast from season three and Gillian Anderson as Margaret Thatcher and Emma Corrin as Princess Diana.


Offaly player wins €148k in last night's Lotto
by By Denise O’DonoghueThe National Lottery has confirmed that a player in Co Offaly won a six-figure sum in last night's Lotto draw.
After matching five numbers and the bonus, someone is €148,259 better off today.
The lucky ticket was bought in the Birr Post Office on Emmet Square, Birr.
It comes just a couple of weeks after a family syndicate from Birr won €250,000 on a National Lottery scratch card.
The National Lottery is today encouraging its players in Offaly to check their tickets to see if they have won this tidy sum. This winner fell just one number short of picking up the €5,147,131 Lotto jackpot.
"This is great news for a lucky player in Offaly to wake up to this morning and Birr is certainly having its rub of the green of late," said a National Lottery spokesperson.
"We are asking all our players all across the country, especially those who play in Birr, Co. Offaly, to check their tickets carefully in-store or by scanning the ticket’s barcode using the National Lottery App. If you are this lucky winner be sure to sign the back of the ticket and call the National Lottery claims team on 01 836 4444 and we will arrange for you to collect your prize."
The winning numbers were 04, 15, 20, 21, 34, 39 and the bonus was 19.
More than 101,000 Lotto players all over Ireland won prizes on last night’s draw.
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|
Via Verde
|
Trips Management. More...
Go to the source code of this file.
Macros | |
| #define | MAX_CLIENTES 50 |
| #define | MAX_STR 100 |
| #define | MAX_MAT 10 |
| #define | MAX_NIB 21 |
| #define | MAX_CC 8 |
| #define | TAM_INICIAL 3 |
| #define | TAM_INCREMENTO 3 |
| #define | TAM_DECREMENTO 3 |
Functions | |
| void | Ecra_gestao_viagens () |
| This function serves as the "TUI interface" of the 2nd main menu option (Trip Management) More... | |
| void | Ecra_acrecentar_viagem () |
| This function serves as the "TUI interface" of the 1st option of the Trip Management menu (Add Trip) More... | |
| void | Ecra_gestao_lancos () |
| This function serves as the "TUI interface" of the 2nd Trip Management menu option (Toll Sections Management) More... | |
| void | Ecra_pesquisa_viagem () |
| This function serves as the "TUI interface" of the 3rd Trip Management menu option (Search For Trip) More... | |
| void | Ecra_consulta_distancias_precos_classes_A1 () |
| This function serves as the "TUI interface" of the 1st option of the Toll Links Management (Consult Links (Distance-Price on A1)) More... | |
| void | Ecra_gestao_precos () |
| This function serves as the "TUI interface" of the 2nd option of the Toll Links Management (Edit Toll Links Price) More... | |
| void | Ecra_edicao_parportico_preco () |
| This function serves as the "TUI interface" of the 1st Price Management menu option (Edit price of a given pair of tolls) More... | |
| void | Ecra_edicao_geral_precos () |
| This function serves as the "TUI interface" of the 2nd Price Management menu option (Edit the price for the entire A1) More... | |
| int | pesquisa_id_viagens (Cliente *, int, int) |
| int | pesquisa_mes_viagens (Cliente *, int, int, short) |
| void | imprime_matriculas_existentes (Cliente *, int) |
| void | imprime_id_existentes (Cliente *, int) |
| int | pesquisa_viagem_matricula (Cliente *, int, char[]) |
| void | pesquisa_viagem_veiculo (Cliente *, int, char[]) |
| int | pesquisa_viagem_lanco (Cliente *, int, int, short, short) |
| void | pesquisa_viagem_par_porticos (Cliente *, int, int, short, short) |
| int | pesquisa_data_viagens (Cliente *, int, int, short, short, short) |
| void | pesquisa_viagem_data (Cliente *, int, int, short, short, short) |
| void | menu_gestao_pesquisa_viagens (Cliente *, int) |
| bool | preencheMatrizpontos (Lanco *, char *, bool) |
| bool | preencheMatriz (Lanco *, char *, bool) |
| void | imprime_matrizdistancias_precos_A1 (Lanco matriz[NUM_PORTAGENS][NUM_PORTAGENS]) |
| void | carrega_lancos () |
| void | menu_consulta_lancos () |
| void | adicionar_viagem (Cliente *, int, int) |
| void | historico_viagens (Cliente *, int, int) |
| void | menu_edicao_par_portico_preco (short, short) |
| void | menu_edicao_geral_precos () |
| void | gestaodeviagens (Cliente *, int) |
| void | menu_gestao_precos () |
| void | menu_gestao_lancos () |
| void | geracao_fatura_cliente (Cliente *, int, int, short, short) |
| void | menu_gestao_viagens (Cliente *, int) |
Trips Management.
Header where all the functions related to trips management are called
| #define MAX_CC 8 |
| #define MAX_CLIENTES 50 |
| #define MAX_MAT 10 |
| #define MAX_NIB 21 |
| #define MAX_STR 100 |
| #define TAM_DECREMENTO 3 |
| #define TAM_INCREMENTO 3 |
| #define TAM_INICIAL 3 |
| void adicionar_viagem | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id | ||
| ) |
This function adds trips to a given customer already created in the customer array
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| id | customer id that you want to add a particular trip |
| void carrega_lancos | ( | ) |
Function that loads the distance-price matrices of each vehicle class
| void Ecra_acrecentar_viagem | ( | ) |
This function serves as the "TUI interface" of the 1st option of the Trip Management menu (Add Trip)
| void Ecra_consulta_distancias_precos_classes_A1 | ( | ) |
This function serves as the "TUI interface" of the 1st option of the Toll Links Management (Consult Links (Distance-Price on A1))
| void Ecra_edicao_geral_precos | ( | ) |
This function serves as the "TUI interface" of the 2nd Price Management menu option (Edit the price for the entire A1)
| void Ecra_edicao_parportico_preco | ( | ) |
This function serves as the "TUI interface" of the 1st Price Management menu option (Edit price of a given pair of tolls)
| void Ecra_gestao_lancos | ( | ) |
This function serves as the "TUI interface" of the 2nd Trip Management menu option (Toll Sections Management)
| void Ecra_gestao_precos | ( | ) |
This function serves as the "TUI interface" of the 2nd option of the Toll Links Management (Edit Toll Links Price)
| void Ecra_gestao_viagens | ( | ) |
This function serves as the "TUI interface" of the 2nd main menu option (Trip Management)
| void Ecra_pesquisa_viagem | ( | ) |
This function serves as the "TUI interface" of the 3rd Trip Management menu option (Search For Trip)
| void geracao_fatura_cliente | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id, | ||
| short | mes, | ||
| short | ano | ||
| ) |
Function that prints an invoice to a txt file of all customer trips made in a given month
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| id | customer ID |
| mes | month of the trip |
| ano | year of the trip made (despite being a monthly invoice, there may be repetition of months and the given "year" will be used to avoid repetition) |
| void gestaodeviagens | ( | Cliente * | , |
| int | |||
| ) |
| void historico_viagens | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id | ||
| ) |
Function that lists all trips made (so far) by a specific customer (New Feature)
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| id | id of the client who wants to consult his trip history |
| void imprime_id_existentes | ( | Cliente * | vetor, |
| int | num_clientes | ||
| ) |
Function that prints all customer IDs already entered by the user so far
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| void imprime_matriculas_existentes | ( | Cliente * | vetor, |
| int | num_clientes | ||
| ) |
Function that prints all vehicles registrations that have been entered by the user so far
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| void imprime_matrizdistancias_precos_A1 | ( | Lanco | matriz[NUM_PORTAGENS][NUM_PORTAGENS] | ) |
Function that prints the distance-price matrix of each tolls link
| matriz | where they will be stored the values of distance and price of tolls link |
| void menu_consulta_lancos | ( | ) |
Function that serves as a menu to the interface (Ecra_consulta_distancias_precos_classes_A1) that prints the distance-price of sections of the 4 classes of A1 vehicles
| void menu_edicao_geral_precos | ( | ) |
Function that edits all the prices of all the pairs of tolls present in a matrix for a given class of vehicle
| void menu_edicao_par_portico_preco | ( | short | novo_entrada, |
| short | novo_saida | ||
| ) |
Function that edits all prices in the matrix of all pairs of tolls for a given class
| novo_entrada | entry toll number to edit |
| novo_saida | exit toll number to edit |
| void menu_gestao_lancos | ( | ) |
Function that serves as a menu to the interface function (Ecra_gestao_lancos) and where they will also be called the functions of consulting tolls links and editing their prices
| void menu_gestao_pesquisa_viagens | ( | Cliente * | vetor, |
| int | num_clientes | ||
| ) |
Function that serves as a menu of search features corresponding to the "TUI interface" 3rd option of the Trip Management menu
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| void menu_gestao_precos | ( | ) |
Function that serves as a menu to the interface function (Ecra_gestao_precos) and where the price editing functions will also be called in a given pair of tolls and general price editing
| void menu_gestao_viagens | ( | Cliente * | clientes, |
| int | num_clientes | ||
| ) |
Function that serves as a menu of the features corresponding to the "TUI interface" of the 2nd Main Menu option (Trip management)
| vetor | customer array |
| num_viagens | a pointer to the number of trips that exist in the customer vector |
| num_clientes | a pointer to the number of customers that exist in the array |
| int pesquisa_data_viagens | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id, | ||
| short | ano, | ||
| short | mes, | ||
| short | dia | ||
| ) |
Function that returns the position in which the customer occurs given the date of trip
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| id | customer ID (to determine the position of the array where the customer made the trips) |
| ano | year of the trip |
| mes | month of the trip |
| dia | day of the trip |
| int pesquisa_id_viagens | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id | ||
| ) |
Function that returns the position in which the customer occurs given his id
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| id | customer ID to search |
| int pesquisa_mes_viagens | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id, | ||
| short | mes | ||
| ) |
Function that returns the position in which the customer occurs given the month of trip
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| mês | month of the trip the customer took |
| void pesquisa_viagem_data | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id, | ||
| short | ano, | ||
| short | mes, | ||
| short | dia | ||
| ) |
Function that prints a customer's trip information given a date
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| id | customer ID (to determine the position of the array where the customer made the trips) |
| ano | year of the trip |
| mes | month of the trip |
| dia | day of the trip |
| int pesquisa_viagem_lanco | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id, | ||
| short | num_entrada, | ||
| short | num_saida | ||
| ) |
Function that returns the position where the client occurs given a pair of tolls
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| id | customer ID (to determine the position of the array where the customer made the trips) |
| num_entrada | entrance toll number |
| num_saida | entrance toll number |
| int pesquisa_viagem_matricula | ( | Cliente * | , |
| int | , | ||
| char | [] | ||
| ) |
| void pesquisa_viagem_par_porticos | ( | Cliente * | vetor, |
| int | num_clientes, | ||
| int | id, | ||
| short | num_entrada, | ||
| short | num_saida | ||
| ) |
Function that prints a customer's trip information given a pair of tolls
| vetor | customers array |
| num_clientes | the number of customers that exist in the array |
| id | customer ID (to determine the position of the array where the customer made the trips) |
| num_entrada | entrance toll number |
| num_saida | entrance toll number |
| void pesquisa_viagem_veiculo | ( | Cliente * | , |
| int | , | ||
| char | [] | ||
| ) |
| bool preencheMatriz | ( | Lanco * | matriz, |
| char * | ficheiro, | ||
| bool | distancia | ||
| ) |
Function that fills a square matrix with size NUM_PORTAGENS with the content of the specified file. The function can be used to fill the distance matrix or the price matrix.
| matriz | the matrix to be filled, which must be previously allocated |
| ficheiro | the file from which the data will be read |
| distancia | indicates whether the distance or price matrix will be filled |
| bool preencheMatrizpontos | ( | Lanco * | matriz, |
| char * | ficheiro, | ||
| bool | distancia | ||
| ) |
Função que preenche uma matriz quadrada de lado NUM_PORTAGENS com o conteúdo do ficheiro especificado. A função pode ser utilizada para preencher a matriz de distâncias ou a matriz de pontos.
| matriz | A matriz a preencher, que deve ser previamente alocada |
| ficheiro | O ficheiro de onde vão ser lidos os dados |
| distancia | Indica se será preenchida a matriz de distâncias ou de pontos |


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{% extends "layout_task-list.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block proposition_header %} {% set my_account="2" %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block home_link %} ../../task-list {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} {% if data.return_page and data.return_page.includes('check-and-send') or data.return_page and data.return_page.includes('check-your-answers')%} {% else %} {% endif %} {{formData | safe}} Confirm your address Address line 1 Address line 2 (optional) Town or city Postcode Do you want post about the claim sent to this address? Yes No, use a different address All correspondence will be sent to this address. Address line 1 Address line 2 (optional) Town or city Postcode {% if data.return_page and data.return_page.includes('check-and-send') or data.return_page and data.return_page.includes('check-your-answers')%} Cancel {% else %} {% endif %} {% include 'prototypes/prototype-dec-2019/includes/contact.html' %} {% endblock %}
All correspondence will be sent to this address.


Oscar Hernandez
Warehouse Manager
<EMAIL_ADDRESS>408.781.1639
Oscar was born in Arlington, Virginia. At the age of 2, he moved to Santa Clara, CA. He graduated from Santa Clara High in 1994. He started working in the restaurant industry in 1990. After 23 years, he decided to switch career and joined R&B Company in 2013 as a warehouse associate. Shortly after, he became the warehouse manager for San Jose Branch. Oscar lives in San Jose with his family. He enjoys sports, especially the Oakland Raiders & Oakland Athletics.


|
JTS Topology Suite version 1.13 | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.vividsolutions.jts.triangulate.quadedge.QuadEdge
public class QuadEdge
A class that represents the edge data structure which implements the quadedge algebra. The quadedge algebra was described in a well-known paper by Guibas and Stolfi, "Primitives for the manipulation of general subdivisions and the computation of Voronoi diagrams", ACM Transactions on Graphics, 4(2), 1985, 75-123.
Each edge object is part of a quartet of 4 edges,
linked via their rot references.
Any edge in the group may be accessed using a series of rot()
operations.
Quadedges in a subdivision are linked together via their next references.
The linkage between the quadedge quartets determines the topology
of the subdivision.
The edge class does not contain separate information for vertice or faces; a vertex is implicitly defined as a ring of edges (created using the next field).
| Method Summary | |
|---|---|
static QuadEdge |
connect(QuadEdge a,
QuadEdge b)
Creates a new QuadEdge connecting the destination of a to the origin of b, in such a way that all three have the same left face after the connection is complete. |
void |
delete()
Marks this quadedge as being deleted. |
Vertex |
dest()
Gets the vertex for the edge's destination |
QuadEdge |
dNext()
Gets the next CCW edge around (into) the destination of this edge. |
QuadEdge |
dPrev()
Gets the next CW edge around (into) the destination of this edge. |
boolean |
equalsNonOriented(QuadEdge qe)
Tests if this quadedge and another have the same line segment geometry, regardless of orientation. |
boolean |
equalsOriented(QuadEdge qe)
Tests if this quadedge and another have the same line segment geometry with the same orientation. |
java.lang.Object |
getData()
Gets the external data value for this edge. |
double |
getLength()
Gets the length of the geometry of this quadedge. |
QuadEdge |
getPrimary()
Gets the primary edge of this quadedge and its sym. |
QuadEdge |
invRot()
Gets the dual of this edge, directed from its left to its right. |
boolean |
isLive()
Tests whether this edge has been deleted. |
QuadEdge |
lNext()
Gets the CCW edge around the left face following this edge. |
QuadEdge |
lPrev()
Gets the CCW edge around the left face before this edge. |
static QuadEdge |
makeEdge(Vertex o,
Vertex d)
Creates a new QuadEdge quartet from Vertex o to Vertex d. |
QuadEdge |
oNext()
Gets the next CCW edge around the origin of this edge. |
QuadEdge |
oPrev()
Gets the next CW edge around (from) the origin of this edge. |
Vertex |
orig()
Gets the vertex for the edge's origin |
QuadEdge |
rNext()
Gets the edge around the right face ccw following this edge. |
QuadEdge |
rot()
Gets the dual of this edge, directed from its right to its left. |
QuadEdge |
rPrev()
Gets the edge around the right face ccw before this edge. |
void |
setData(java.lang.Object data)
Sets the external data value for this edge. |
void |
setNext(QuadEdge next)
Sets the connected edge |
static void |
splice(QuadEdge a,
QuadEdge b)
Splices two edges together or apart. |
static void |
swap(QuadEdge e)
Turns an edge counterclockwise inside its enclosing quadrilateral. |
QuadEdge |
sym()
Gets the edge from the destination to the origin of this edge. |
LineSegment |
toLineSegment()
Creates a LineSegment representing the
geometry of this edge. |
java.lang.String |
toString()
Converts this edge to a WKT two-point LINESTRING indicating the geometry of this edge. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Method Detail |
|---|
public static QuadEdge makeEdge(Vertex o, Vertex d)
Vertex
o to Vertex
d.
o
- the origin Vertexd
- the destination Vertex
public static QuadEdge connect(QuadEdge a, QuadEdge b)
public static void splice(QuadEdge a, QuadEdge b)
a
- an edge to spliceb
- an edge to splicepublic static void swap(QuadEdge e)
e
- the quadedge to turnpublic QuadEdge getPrimary()
Coordinate
ordering
public void setData(java.lang.Object data)
data
- an object containing external datapublic java.lang.Object getData()
public void delete()
public boolean isLive()
public void setNext(QuadEdge next)
next
- edgepublic final QuadEdge rot()
public final QuadEdge invRot()
public final QuadEdge sym()
public final QuadEdge oNext()
public final QuadEdge oPrev()
public final QuadEdge dNext()
public final QuadEdge dPrev()
public final QuadEdge lNext()
public final QuadEdge lPrev()
public final QuadEdge rNext()
public final QuadEdge rPrev()
public final Vertex orig()
public final Vertex dest()
public double getLength()
public boolean equalsNonOriented(QuadEdge qe)
qe
- a quadege
public boolean equalsOriented(QuadEdge qe)
qe
- a quadege
public LineSegment toLineSegment()
LineSegment
representing the
geometry of this edge.
public java.lang.String toString()
toString
in class java.lang.Object
|
JTS Topology Suite version 1.13 | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


{% include 'header.html' %} {% include 'nav.html' %}
An app to track esports events.
An app to keep track of magic cards.
An app for social justice.


Experienced with software design, embedded and high-level programming, web development, requirements testing, deployment, DevOps and information technology.
• Infrastructure design, development, and execution of generalized software that can handle a variety of unique compute element hardware
• Setup infrastructure of software processes from core design & development to automating releases
• Core team member of executing developer environment/operations for critical mission software
• Single handedly setup CI/CD system, software review process and version control management system for the Payload Management Program
• Deconstructed a monolithic application to be modular and library based which allowed for selective features and dramatically increased reusability.
• Prototyped and demonstrated key functionalities deemed "improbable and impossible" in the first 5 months of being hired.
• Core developer for the automation of a product of families that drastically reduces the hours to set up CI operations for a new product line.
• Received 2 exemplary awards within the first year.
• Received the highest rating in a performance review within the second year.
• Helped developed complex auto routing and route assessment software that solved problems 200% more efficiently than any previous implementation.
• Engaged in multiple design, execution, and testing of software that is nearing the FQT/PQT phase.
• Managed developer team that constantly increased velocity by 5% every Sprint.
• Automated embedded board processes saving the project roughly 20 developer hours per week.
An open source custom component allowing Wyze vacuums to connect directly to Home Assistant
GitHubTwitch extension game based on the popular IRC duckhunt game! Customizeable for each streamer.
TwitchFly the alien and drive the tractor away from the mold that has infected the land!
itch.io


My email address is: {{user.results[0].email}}
I was born on {{ formatDate(someDate)}}.
That makes me {{ age}} years old!


{% extends 'visualize/base.html' %} {% load static %}
{% block content %}Militarization of the police. Police brutality. Crime statistics. Excessive force. For many, these are mere buzzwords—just another passing headline. Some might argue that the gravity of these words are not as eye-catching as gossip about the latest hot celebrity. And even if you're able to move beyond the headline, the data may be messy and confusing, much less motivating.
Bringing Buzzwords Home exists to help you contextualize some of today’s most important and contentious issues. Reshape how you see and understand your world, by moving past the headline and seeing how your own community is affected by crime and weapons. How many fatal encounters with the police have occurred in your own area? How much free military equipment has your state's police force received from the U.S. government? What are the violent and property crime statistics in your county? Bringing Buzzwords Home has synthesized nearly half a million critical data points into one cohesive and accessible site.
The first step in civic engagement is understanding what’s happening at home.


The Shift is On
We’ve all seen the web transition from a mostly text to an increasingly rich 2D image rich environment over the last few years – but now the shift to 3D is here. Enabled by web standards and open source technologies like WebGL, X3DOM, GLAM, and Three.js, and commercial tools from companies like Unity and Autodesk, creating stunning engaging 3D web sites has gone from “for rocket scientists only” to “mere mortals”.
The 3D Web Fest
This experience will showcase websites that are the best mixture of music, art, and technology by those inspired by what’s possible from the best of the best of the 3D Web. The 3D Web Fest will bring together the best of the 3D Web - presented as Live Performance Art – amazing, delightful, surprising and at times disturbing. See creations by artists and developers of all kinds showing off their best 3D web sites live with musical accompaniment. Then participate in a panel session with the performers on the future of the 3D Web including audience Q&A.
get inspired by this cool stuff
3 Dreams of Black
"3 Dreams of Black" is an interactive film by Chris Milk and some friends at Google that showcases the creative potential of WebGL.
City of Drones
Legendary musician John Cale, experimental architect Liam Young and FIELD.IO have created an awesome digital landscape: explore an endless city through the eyes of a drone. visit site


Bacon ipsum dolor amet tail brisket alcatra sirloin pork loin ball tip jerky beef bacon.
Shoulder shank jowl beef, tongue pastrami pork chop ribeye pork filet mignon shankle ham turkey.
Bacon ipsum dolor amet tail brisket alcatra sirloin pork loin ball tip jerky beef bacon.
Shoulder shank jowl beef, tongue pastrami pork chop ribeye pork filet mignon shankle ham turkey.
Latest News
Sausage tri-tip bresaola picanha shank, pastrami tenderloin ball tip ham doner capicola pork pancetta jowl pig.
Sausage tri-tip bresaola picanha shank, pastrami tenderloin ball tip ham doner capicola pork pancetta jowl pig.
Sausage tri-tip bresaola picanha shank, pastrami tenderloin ball tip ham doner capicola pork pancetta jowl pig.
Our Newsletter
Buffalo pancetta pork cow, turducken kevin pastrami sirloin ribeye biltong tenderloin short ribs.
Join Our Newsletter


My name is Jon Prell and I am a full-stack web developer. From Concept to Deployment, I have all of the skills needed to bring your project to life.
I believe that applicable simplicity should drive design. End-users don't want to spend time trying to figure out how to use an application when they need to be putting that app to use!
Any page that takes longer than 3 seconds to load is unacceptable. I implement the latest programming paradigms to grease the gears ensuring your server is high speed, low drag.
My work is where the right brain and left brain come together. Understanding the logic behind the science of our world allows me to explore the limits of human creativity.
Isn't it love that makes you - er, i mean us - human?


Sarkodie exposed for allegedly charging fans €10 each for a selfie at a show in Italy
by Nana YawGhanaian rap artiste and recent BET Award winner Sarkodie is in the news again and this time not for winning an international award but for allegedly extorting money from fans in Italy.
The rapper was fingered in a case of extortion by one social media user, stephanie.frema in a comment section on social media where she accused Sarkodie of allegedly charging his fans €10 for a selfie each during a recent show in Italy.
The screenshot of her claim was sighted by Ghbase.com on Instagram gossip page, those_called_clebs.
It is not known what show he attended in Italy but according to her Sarkodie charged anyone who wished to take a selfie with him an amount of €10.
She was reacting to a photo of Sarkodie holding a wineglass at the 2019 4Syte Music Video Awards to probably spite Shatta Wale.
“no wonder when he came to italy he collected 10 euro from anyone who wanna take pic with him. But @shattawalenima was free and lovely,” she commented.
To clarify her claim, she asked the gossip to ask anyone in Italy who attended the show.
“Oh yess ask anyone in italy who attended some, he likes maoney paa” she added.
See the screenshot below:


{% extends "base.html" %} {% block content %} Your Captioning Solution. Upload Universal Supports all major video types available. UnderstandNot Build in Summer 18... Stats for Nerds 0 Job waiting in Queue... Last Updated on when your grandpa was still alive... {% endblock %} {% block js %} {% endblock %}
Supports all major video types available.
Build in Summer 18...
0 Job waiting in Queue... Last Updated on when your grandpa was still alive...


Directories | |
| directory | Collections |
| directory | IO |
| directory | Linq |
| directory | Net |
| directory | Numerics |
| directory | Runtime |
| directory | Text |
| directory | Threading |
Files | |
| file | ArrayExt.cs [code] |
| file | BoolExt.cs [code] |
| file | DateTimeExt.cs [code] |
| file | ExceptionExt.cs [code] |
| file | HttpStringExt.cs [code] |
| file | IAsyncResultExt.cs [code] |
| file | ICloneableExt.cs [code] |
| file | Months.cs [code] |
| file | NullableExt.cs [code] |
| file | NumberExt.cs [code] |
| file | RandomExt.cs [code] |
| file | StringExt.cs [code] |
| file | UriBuilderExt.cs [code] |


Important contacts to know in Singapore in case of emergency. Be advised that you should not misuse these telephone numbers. You many warrant a penalty if you misuse them.
| # | Personnel | Hotline Number | Notes |
|---|---|---|---|
| 1 | EMS, Ambulance and Fire Rescue | 995 | The SCDF provides round-the-clock emergency medical service (EMS) as well as emergency fire response and rescue. |
| 2 | Emergency Police | 999 | When seeking emergency assistance from the police, provide a clear account, type of emergency being reported, the caller’s name, contact details and the location of the emergency. |
| 3 | Non-Emergency Ambulance (Private) | 1777 | Dialing 995 for a non-emergency situation may be fined under Singapore law. |
| 4 | Non-Emergency Police Hotline | 1<PHONE_NUMBER> | For non-emergency cases requiring police assistance, caller can also contact the Neighbourhood Police Center. |
| 5 | Traffic Police | 6547 0000 | To enquire about ongoing case or bail related matters |


Library: NetSSL_OpenSSL
Package: SSLCore
Header: Poco/Net/InvalidCertificateHandler.h
Description
A InvalidCertificateHandler is invoked whenever an error occurs verifying the certificate. It allows the user to inspect and accept/reject the certificate. One can install one's own InvalidCertificateHandler by implementing this interface. Note that in the implementation file of the subclass the following code must be present (assuming you use the namespace My_API and the name of your handler class is MyGuiHandler):
#include "Poco/Net/CertificateHandlerFactory.h" ... POCO_REGISTER_CHFACTORY(My_API, MyGuiHandler)
One can either set the handler directly in the startup code of the main method of ones application by calling
SSLManager::instance().initialize(mypassphraseHandler, myguiHandler, mySSLContext)
or in case one uses Poco::Util::Application one can rely on an XML configuration and put the following entry under the path openSSL.invalidCertificateHandler:
<invalidCertificateHandler> <name>MyGuiHandler<name> <options> [...] // Put optional config params for the handler here </options> </invalidCertificateHandler>
Note that the name of the InvalidCertificateHandler must be same as the one provided to the POCO_REGISTER_CHFACTORY macro.
Inheritance
Known Derived Classes: RejectCertificateHandler, AcceptCertificateHandler, ConsoleCertificateHandler
Member Summary
Member Functions: onInvalidCertificate
Constructors
InvalidCertificateHandler
InvalidCertificateHandler(
bool handleErrorsOnServerSide
);
Creates the InvalidCertificateHandler.
Set handleErrorsOnServerSide to true if the certificate handler is used on the server side. Automatically registers at one of the SSLManager::VerificationError events.
Destructor
~InvalidCertificateHandler
virtual ~InvalidCertificateHandler();
Destroys the InvalidCertificateHandler.
Member Functions
onInvalidCertificate
virtual void onInvalidCertificate(
const void * pSender,
VerificationErrorArgs & errorCert
) = 0;
Receives the questionable certificate in parameter errorCert. If one wants to accept the certificate, call errorCert.setIgnoreError(true).
Variables
_handleErrorsOnServerSide
bool _handleErrorsOnServerSide;
Stores if the certificate handler gets invoked by the server (i.e. a client certificate is wrong) or the client (a server certificate is wrong)


H
ealth
W
ave.
Home
Patient
Register patient
Appointment
Add Appointment
Log out
Log out
×
Patient login
Username
Password
Login


Camera Motion and Surrounding Scene Appearance as Context for Action Recognition
Fabian Caba Heilbron
Ali Thabet
Juan Carlos Niebles
Bernard Ghanem
KAUST, Universidad del Norte
ACCV 2014
Summary
This work introduces a framework for recognizing human actions in videos by incorporating
a new set of visual cues that represent the context of the action. We develop a weak
foreground-background segmentation approach in order to robustly extract not only foreground
features that are focused on the actors, but also global camera motion and contextual scene
information. Using dense point trajectories, our approach separates and describes the
foreground motion from the background, represents the appearance of the extracted static
background, and encodes the global camera motion that interestingly is shown to be discriminative
for certain action classes. Below we show an example of our foreground-background segmentation:
Resources:
Download our ACCV 2014 Paper.
Check out our Poster presentation!
Code can be cloned from Here!
Acknowledgment
Research reported in this publication was supported by
competitive research funding from King Abdullah University of Science and
Technology (KAUST). F.C.H. was also supported by a COLCIENCIAS Young
Scientist and Innovator Fellowship. J.C.N. is supported by a Microsoft Research
Faculty Fellowship.
References
- H. Wang and C. Schmid. Action Recognition with Improved Trajectories. In ICCV, 2013.
If you use any of this work material in your research, please cite our ACCV 2014 paper:
F.C. Heilbron, A. Thabet, J.C. Niebles and B. Ghanem. Camera Motion and Surrounding Scene Appearance as Context for Action Recognition. In ACCV, 2014.
@article{caba2014cues,
title={{Camera Motion and Surrounding Scene Appearance as Context for Action Recognition}},
author={Heilbron, F.C. and Thabet, A. and Niebles, J.C. and Ghanem, B.},
journal={ACCV},
year={2014}
}


enum class AudioMode
AudioMode describes the audio mode in which the audio client should operate during a meeting session.
Mono16K |
The mono audio mode with single audio channel and 16KHz sampling rate, for both speaker and microphone. |
Mono48K |
The mono audio mode with single audio channel and 48KHz sampling rate, for both speaker and microphone. |
Stereo48K |
The stereo audio mode with two audio channels for speaker, and single audio channel for microphone, both with 48KHz sampling rate. |
NoDevice |
value |
val value: Int |
from |
fun from(intValue: Int): AudioMode? fun from(intValue: Int, defaultAudioMode: AudioMode): AudioMode |


Officer-involved shooting in Monroe County under investigation
by Ariana Baldassano, Stephanie FryerTOWN OF LEON, Wis. - An outside agency is investigating an officer-involved shooting in the Town of Leon, according to a release by the Monroe County Sheriff's Office.
It happened around 9 p.m. Tuesday at a bar called Cotter Pin.
The Monroe County Sheriff's Office said the public is not in any danger, but could not provide details on what happened. It's unclear if anyone was hurt.
An outside agency will investigate.
Highway 27 was closed as a result of the shooting.
This is a developing story. We will provide an update once we learn more.


Step-1 Set up a Wallet
Step-1 Setup a wallet
The Bitcoin wallet is simply a pair of public address and a private key.
The public key is to be shared to recieve Bitcoins same as we share our email-id to recieve emails.The private key needs to be kept safe and secret.You can access your Bitcoins on your address by the private key.
Be mindful of the fact that unlike in the case of websites like Facebook, Gmail where you have the option to regenrate the password by choosing "Forgot Password" option. In Bitcoins you cannot regenerate the private key if you lose it once.Because Bitcoin is not a company which would keep back up of the private key. You are fully responsible to keep your private keys safe and secured.
To create a brand new Bitcoin wallet,
Download Blockchain app
Remember to safely save the "recovery phrase" provided at the time of sign up.
This is required to retrieve the wallet when you reinstall the app or change the phone. If you lose this phrase, then you can never restore your wallet if the app gets deleted from the phone or the phone is lost.
Other option for a mobile wallet is Bread Wallet. It has a very user friendly UI.


Phenotype of the month
Adgrd1
Adhesion G Protein-Coupled Receptor D1
Data at IMPC
Phenotype Summary:
Heterozygous fertility: Female infertility
Antinuclear antibodies: Increased level of antinuclear antibodies in serum
Mesenteric Lymph Node - Increased effector γδ cells and KLRG1+ CD8 αβ T cells
DSS colitis: Decreased suspectibility to induced colitis: reduced average histology score
Body composition (DEXA): Decreased bone mineral density in females
Gene information:
- G-protein couled receptor of unknown function
- Complex N terminus containing a PTX and a GAIN domain
- Reported to be required for glioblastoma growth
- Highly polymorphic in humans: over 9000 SNPs identified (96% in introns)
- Genetic variation in humans has been linked to height and weight
Genecards
MGI
OMIM
Read more:
- 1. Bohenkamp J and Schöneberg T. Cell Adhesion Receptor GPR133 COuples to Gs Protein. J Biol Chem 286, 41912-16 (2011)
Pubmed Link
- 2. Fischer L. et al. Functional relevance of naturally occuring mutations in adhesion G protein-coupled receptor ADGRD1 (GPR133). BMC Genomics 17, 609 (2016)
Pubmed Link
- 3. Bayin N.S. et al. GPR133(ADGRD1), an adhesion G-protein-coupled receptor, is necessary for glioblastoma growth. Oncogenesis 5, e263 (2016)
Pubmed Link
Phenotype of the month
Bivm
Basic, immunoglobulin-like variable motif containing
Mice at IMPC
Phenotype Summary:
Ear epidermis: decreased count of Vγ5+ dendritic epidermal T cells (DETC), presence of CD45+ Vγ5- MHC II- replacement cells in the epidermis
Antinuclear antibodies: Increased level of antinuclear antibodies in serum
Mesenteric Lymph Node - Increased effector γδ cells and KLRG1+ CD8 αβ T cells
DSS colitis: Decreased suspectibility to induced colitis: reduced average histology score
Body composition (DEXA): Decreased bone mineral density in females
Gene information:
- possesses no sequence similarity with any described protein, making the prediction of a function challenging
- a CpG island at the 5' end and the wide tissue distribution may suggest a function as housekeeping gene
Genecards
MGI
Read more:
- Yoder, J.A. et al. BIVM, a novel gene widely distributed among deuterostomes, shares a core sequence with an unusual gene in Giardia lamblia. Genomics 79, 750-5 (2002)
Pubmed Link
Phenotype of the month
Chd9
Chromodomain Helicase DNA Binding Protein 9
Data at IMPC
Phenotype Summary:
Epidermal Immune composition: Increased Langerhans cells in males and females
Plasma Chemistry: Decreased amylase in males and females, decreased albumin in males
Gene information:
- Proposed role in chromatin remodelling
- Involved in transcriptional regulation of bone tissue differentiation
Genecards
Read more:
- 1.Kent, RS. et al. New face for Chromatin-Related Mesenchymal Modulator: n-CHD9 localizes to nucleoli and interacts with ribosomal genes. J Cell Physiol. 230, 2270-80.(2015)
Pubmed Link
- 2.Kim, MS. et al. Genetic and expressional alterations of CHD genes in gastric and colorectal cancer. Histopathology 58, 660-8. (2011)
Pubmed Link
- 3.Shur, I. et al. In vivo association of CReMM/CHD9 with promoters in osteogenic cells. J Cell Physiol. 207, 374-8. (2006)
Pubmed Link
Phenotype of the month
Clpp
Caseinolytic Mitochondrial Matrix Peptidase Protolytic Subunit
Mice at IMPC
Phenotype Summary:
16 week PBL - decreased γδ T cell percentage in females, increased KLRG1+ NK cells, increased number of B cells and mature B cells in females, increased monocytes in females
Spleen - Increased KLRG1+ NK cells, increased monocytes, reduced NKT cells, reduced CD4+ NKT cells, increased CD4- NKT cells, increased Transitional 2 B cells
MLN - Increased monocytes
Bone Marrow - Increased B cells, reduced granulocytes
DSS - Increased weight loss, increased mortality
Fertility - Infertility in homozygous males and females
Haematology - Increased white blood cell count in females, increased corpuscular colume in females, increased mean corpuscular haemoglobin in females
Plasma Chemistry - Increased total bilirubin, decreased circulating glucose in males, decreased circulating amylase in males, increased circulating alkaline phosphatase in males, increased circulating iron in males
Indirect calorimetry (males tested only) - Decreased oxygen consumption, abnormal energy expenditure
Insulin - Decreased circulating insulin
Body composition - Decreaed bone mineral density, decreased bone mineral content, decreased bone area, decreased body weight, decreased nose to tailbase length in females, reduced estimated total tissue mass, reduced lean mass, reduced fat mass
Auditory Brainstem response - Abnormal Click ABR waveform shape
Gene information:
- Protease regulating protein levels through degradation
- Present in most bacteria and in the mitochondria of eukaryotic cells, encoded on chromosome 17 in mice and on chromosome 19 in humans
- Interacts with AAA+ chaperones which bind and unfold Clp P targets
- In human, biallelic pathogenic variations of Clpp cause Perrault Syndrome, a condition characterized by sensorineural hearing loss in both genders and ovarian dysfunction in females
Genecards
MGI
OMIM
Read more:
- 1. Maurizi, M.R. et al. Sequence and structure of Clp P, the proteolytic component of the ATP-dependent Clp protease of Escherichia coli. J Biol Chem 265, 12536-45 (1990)
Pubmed Link
- 2. Yu, A.Y. and Houry, W.A.ClpP: a distinctive family of cylindrical energy-dependent serine proteases. FEBS Letters 581, 3749-57 (2007)
Pubmed Link
- 3. Liu, K., Ologbenla, A., Houry, W.A. et al. Dynamics of the ClpP serine protase: a model for self-compartmentalized proteases. Crit Rev Biochem Mol Biol 49, 400-12 (2014)
Pubmed Link
- 4. Newman W.G., Friedman T.B., Conway, G.S.Perrault Syndrome. Gene Reviews (2014)
Pubmed Link
Phenotype of the month
Cxcr2
C-X-C chemokine receptor 2
Data at IMPC
Phenotype Summary:
16 week PBL - increased CD4 αβ effector T cells in males and females, increased CD8 αβ effector T cells in males, increased γδ T cells in males and females, decreased B cells in females, increased neutrophils in females and monocytes in females and males; no difference in heterozygous mice
Spleen - Increased effector CD4 and CD8 αβ T-cells, increased effector γδ T cells, increased granulocytes, increased eosinophils
Bone Marrow - Increased granulocytes
Plasma Chemistry - Decreased glucose and increased creatinine in heterozygous females; ecreased total protein in homozygous males and females
Anti-nuclear antibody assay - Increased anti-nuclear antibodies in serum
Salmonella Challenge - Increased weight loss and bacterial count in liver and ceacal contents
Gene information:
- G-protein coupled receptor
- involved in immune cell migration
- expressed at high levels on circulating neutrophils
- involved in senescence, cancer and diseases caused by activated neutrophils
Genecards
OMIM
Read more:
- 1. Wu, D. et al. Characterization of two high affinity human interleukin-8 receptors (1993). Science. 261, 101-3
Pubmed Link
- 2. Del Rio, L. et al. CXCR2 deficiency confers impaired neutrophil recruitment and increased susceptibility during Toxoplasma gondii infection.J Immunol. 167,6503-9 (2001)
Pubmed Link
- 3. Acosta, JC. et al. Chemokine signaling via the CXCR2 receptor reinforces senescence. Cell 2133, 1006-18 (2008)
Pubmed Link
- 4. Jamieson, T. et al. Inhibition of CXCR2 profoundly suppresses inflammation-driven and sponaneous tumorigensis. J Clin Invest. 122, 3127-44 (2012)
Pubmed Link
Phenotype of the month
Gimap6
GTPase, IMAP family member 6
Data at IMPC
Phenotype Summary:
6 week PBL: Decreased total T cell, αβ T cell and CD4 αβ T cell % females and males, Decreased NKT cell % females and males
16 week PBL: Increased monocyte % female and males
16 week Spleen: Decreased NKT cell % females and males
Salmonella Challenge: Abnormal weight loss
Gene information:
- GTPases of the immunity associated protein family (Gimaps) are a family of 8 genes that encode putative small GTPases
- Primarily expressed in cells of the immune system
- Gimap family members implicated in T and B-cell development and survival
Genecards
Read more:
- 1. Saunders, A. et al. Putative GTPase GIMAP1 is critical for the development of mature B and T lymphocytes. Blood 115, 3249–3257 (2010)
Pubmed Link
- 2. Ciucci, T. & Bosselut, R. Gimap and T cells: A matter of life or death. Eur. J. Immunol. 44, 348–351 (2014)
Pubmed Link
- 3. Filén, S. & Lahesmaa, R. GIMAP Proteins in T-Lymphocytes. Journal of Signal Transduction 2010, 1–10 (2010)
Pubmed Link
- 4. Yano, K. et al. Gimap3 and Gimap5 cooperate to maintain T-cell numbers in the mouse. Eur. J. Immunol. 44, 561–572 (2013)
Pubmed Link
Gmds
GDP mannose 4,6-dehydratase
Basic, immunoglobulin-like variable motif containing
Mice at IMPC
Phenotype Summary:
Ear epidermis: decreased count of Vγ5+ dendritic epidermal T cells (DETC), presence of CD45+ Vγ5- MHC II- replacement cells in the epidermis
Antinuclear antibodies: Increased level of antinuclear antibodies in serum
Mesenteric Lymph Node - Increased effector γδ cells and KLRG1+ CD8 αβ T cells
DSS colitis: Decreased suspectibility to induced colitis: reduced average histology score
Body composition (DEXA): Decreased bone mineral density in females
Gene information:
- possesses no sequence similarity with any described protein, making the prediction of a function challenging
- a CpG island at the 5' end and the wide tissue distribution may suggest a function as housekeeping gene
Genecards
MGI
Read more:
- Yoder, J.A. et al. BIVM, a novel gene widely distributed among deuterostomes, shares a core sequence with an unusual gene in Giardia lamblia. Genomics 79, 750-5 (2002)
Pubmed Link
Phenotype of the month
PAF
P15(PAF)/ mKIAA0101/ RIKEN cDNA 2810417H13
Mice at IMPC
Phenotype Summary:
Spleen - Increased conventional DCs low (CD11b low), decreased conventional DCs high (CD11b high), decreased plasmacytoid DCs, decreased CD5+ γδ T cells, decreased immature NK cells and increased mature NK cells
Mesenteric lymph nodes - Decreased total DCs
Bone Marrow - Decreased plasma cells, decreased pro B cells Hardy fraction A, and decreased CD3+ T cells
Peripheral blood leukocytes - Decreased CD4+ regulatory T cells
Gene information:
- • p15(PAF) is a 12 kDa nuclear protein that acts as a regulator of DNA repair during DNA replication (see reference 1 below)
- • genetic disruption of PAF by homologous recombination in mice results in altered hematopoietic stem cells function, disrupted progenitor development and leukopenia (see reference 2 below)
- • overexpressed in several types of human cancer and is associated with poor prognosis (see reference 3 below)
Genecards
MGI
Read more:
- • L. Turchi et al. ATF3 and p15PAF are novel gatekeepers of genomic integrity upon UV stress (2009). Cell Death and Differentiation 16, 728–737
Pubmed Link
- • Y.M Amrani et al. The Paf oncogene is essential for hematopoietic stem cell function and development (2011). JEM 208 (9), 1757-1765
Pubmed Link
- • S. Jun et al. PAF-Mediated MAPK Signaling Hyperactivation via LAMTOR3 Induces Pancreatic Tumorigenesis (2013). Cell Rep 5(2), 314–322
Pubmed Link
Phenotype of the month
Peptidase D
Peptidase D / Prolidase
Mice at IMPC
Phenotype Summary:
16 week PBL - decreased γδ T cell percentage in females, increased KLRG1+ NK cells, increased number of B cells and mature B cells in females, increased monocytes in females
Spleen - Increased KLRG1+ NK cells, increased monocytes, reduced NKT cells, reduced CD4+ NKT cells, increased CD4- NKT cells, increased Transitional 2 B cells
MLN - Increased monocytes
Bone Marrow - Increased B cells, reduced granulocytes
DSS - Increased weight loss, increased mortality
Fertility - Infertility in homozygous males and females
Haematology - Increased white blood cell count in females, increased corpuscular colume in females, increased mean corpuscular haemoglobin in females
Plasma Chemistry - Increased total bilirubin, decreased circulating glucose in males, decreased circulating amylase in males, increased circulating alkaline phosphatase in males, increased circulating iron in males
Indirect calorimetry (males tested only) - Decreased oxygen consumption, abnormal energy expenditure
Insulin - Decreased circulating insulin
Body composition - Decreaed bone mineral density, decreased bone mineral content, decreased bone area, decreased body weight, decreased nose to tailbase length in females, reduced estimated total tissue mass, reduced lean mass, reduced fat mass
Auditory Brainstem response - Abnormal Click ABR waveform shape
Gene information:
- Protease regulating protein levels through degradation
- Present in most bacteria and in the mitochondria of eukaryotic cells, encoded on chromosome 17 in mice and on chromosome 19 in humans
- Interacts with AAA+ chaperones which bind and unfold Clp P targets
- In human, biallelic pathogenic variations of Clpp cause Perrault Syndrome, a condition characterized by sensorineural hearing loss in both genders and ovarian dysfunction in females
Genecards
MGI
OMIM
Read more:
- 1. Maurizi, M.R. et al. Sequence and structure of Clp P, the proteolytic component of the ATP-dependent Clp protease of Escherichia coli. J Biol Chem 265, 12536-45 (1990)
Pubmed Link
- 2. Yu, A.Y. and Houry, W.A.ClpP: a distinctive family of cylindrical energy-dependent serine proteases. FEBS Letters 581, 3749-57 (2007)
Pubmed Link
- 3. Liu, K., Ologbenla, A., Houry, W.A. et al. Dynamics of the ClpP serine protase: a model for self-compartmentalized proteases. Crit Rev Biochem Mol Biol 49, 400-12 (2014)
Pubmed Link
- 4. Newman W.G., Friedman T.B., Conway, G.S.Perrault Syndrome. Gene Reviews (2014)
Pubmed Link
Phenotype of the month
setd5
SET domain containing 5
MICE at IMPC
Phenotype Summary(Heterozygots):
Recessive Lethality: No homozygous embros detecetd at E14.5 (n=32)
Heterozygous viability: Heterozygous x wild type breeding pairs generated wild type and heterozygous offspring at a ratio of 7:3 (n=425)
16 week PBL: increased percentage of Treg cells in females, increased percenage of monocytes in females
DSS colitis: Increased weight loss
Weight curve: Males have reduced weight
Dysmorpholgy: Abnormal hair colour and hair pattern in females, abnormal snout morphology in males and females, abnormal snout size in males
Micronuclei: Increased genomic instablility as detected by the increased presence of micronuclei
Gene information:
- • Predicted to encode a methyltransferase
- • Encoded on chromosome 3 in humans, in a region (3p25) associated with intellectual disability
- • One of the genes often lost in 3p25 microdeletion syndrome
- • Loss of function mutations lead to autosomal dominant mental retardation (MRD23)
Genecards
MGI
OMIM
Read more:
- 1. Kellogg, G. et al. Deletion of 3p25.3 in a patient with intellectual disability and dysmorphic features with further definition of a critical region. Am J Med Genet 161A, 1405-8 (2013)
Pubmed Link
- 2. Grozeva, D. et al. De novo loss-of-function mutations in SETD5, encoding a methyltransferase in a 3p25 microdeletion syndrome critical region, cause intellectual disability. Am J Hum Genet 94, 618-24 (2014)
Pubmed Link - Del Rio, L. et al. CXCR2 deficiency confers impaired neutrophil recruitment and increased susceptibility during Toxoplasma gondii infection.J Immunol. 167,6503-9 (2001)
Pubmed Link
- 3. Kuechler A. et al. Loss-of-function variants of SETD5 cause intellectual disability and the core phenotype of microdeletion 3p25.3 syndrome. Eur J Hum Genet 23, 753-60 (2015)
Pubmed Link
- 4. OMIM entry on autosomal dominant mental retardation-23 (MRD23)
OMIM LINK
Phenotype of the month
Wdtc1
WD and tetratricopeptide repeats 1 / adipose
Data at IMPC
Phenotype Summary:
Mesenteric lymph nodes: Increased CD8 T cells
Influenza Challenge: Increased weight loss
Gene information:
- • Ortholog of the Drosophila gene adp (adipose)
- • In humans, WDTC1 expresion in adipose tissue is associated with lower fat mass and enhanced insulin sensititvity
Genecards
MGI
Read more:
- 1. Suh M.J. et al. dipose is a conserved dosage-sensitive anti-obesity gene. Cell Metab 6, 195-207 (2007)
Pubmed Link
- 2. Galgani J.E. et al. Adipose tissue expression of adipose (WDTC1) gene is associated with lower fat mass and enhanced insulin sensitivity in humans. Obesity (Silver Springs) 21, 2244-8 (2013)
Pubmed Link
- 3. Wu, Y. et al. Adipose induces myoblast differentiation and mediates TNFa-regulated myogenesis. Biochim Biophys Acta 1839, 1183-95 (2014)
Pubmed Link
Phenotype of the month
Zfp408
zinc finger protein 408
MICE at IMPC
Phenotype Summary:
Ear epidermis: decreased count of Vγ5+ dendritic epidermal T cells (DETC) not contacting Langerhans cells
Eye morphology:no phenotype
Gene information:
- • Predicted to be a DNA-binding molecule
- • Crucial for the development of retinal vasculature in zebrafish
- • Mutated in families with exudative vitreoretinopathy, a genetic disorder affecting the growth and development of blood vessels in the retina
- • Mutated in families with Retinitis pigmentosa, a group of genetic disorders causing progressive visual impairment due to photo receptor cell death
- • a CpG island at the 5' end and the wide tissue distribution may suggest a function as housekeeping gene
Genecards
MGI
OMIM
Read more:
- 1. Collin, R.W. et al. ZNF408 is mutated in familial exudative vitreoretinopathy and is crucial for the development of zebrafish retinal vasculature. PNAS 110, 9856-61 (2013)
Pubmed Link
- 2. Avila-Fernandez, A. et al. Whole-exome sequencing reveals ZNF408 as a new gene associated with autosomal recessive retinitis pigmentosa with vitreal alterations. Hum Mol Genet 24, 4037-48 (2015)
Pubmed Link


Kathy Varble
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|
cds
2.3.1
|
SegmentedQueue default type traits. More...
#include <cds/container/segmented_queue.h>
Public Types | |
| enum | { alignment = opt::cache_line_alignment } |
| Alignment of critical data, default is cache line alignment. See cds::opt::alignment option specification. | |
| enum | { padding = cds::intrusive::segmented_queue::traits::padding } |
| Padding of segment data, default is no special padding. More... | |
| typedef CDS_DEFAULT_ALLOCATOR | node_allocator |
| Item allocator. Default is CDS_DEFAULT_ALLOCATOR. | |
| typedef atomicity::item_counter | item_counter |
| Item counter, default is atomicity::item_counter. More... | |
| typedef segmented_queue::empty_stat | stat |
| Internal statistics, possible predefined types are stat, empty_stat (the default) | |
| typedef opt::v::relaxed_ordering | memory_model |
| Memory model, default is opt::v::relaxed_ordering. See cds::opt::memory_model for the full list of possible types. | |
| typedef CDS_DEFAULT_ALLOCATOR | allocator |
| Segment allocator. Default is CDS_DEFAULT_ALLOCATOR. | |
| typedef cds::sync::spin | lock_type |
| Lock type used to maintain an internal list of allocated segments. | |
| typedef cds::opt::v::random2_permutation< int > | permutation_generator |
| Random permutation generator for sequence [0, quasi_factor) | |
SegmentedQueue default type traits.
Item counter, default is atomicity::item_counter.
The item counting is an essential part of segmented queue algorithm. The empty()
member function is based on checking size() == 0
. Therefore, dummy item counter like atomicity::empty_item_counter is not the proper counter.
| anonymous enum |
Padding of segment data, default is no special padding.
The segment is just an array of atomic data pointers, so, the high load leads to false sharing and performance degradation. A padding of segment data can eliminate false sharing issue. On the other hand, the padding leads to increase segment size.