Filters let you use other languages in Pug templates. They take a block of plain text as an input.
Pug provides operators for a variety of your different interpolative needs.
Mixins allow you to create reusable blocks of Pug.
Pug supports two primary methods of iteration: each and while.
each
while


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Introduction¶
PySCeS CBMPy is a new platform for constraint based modelling and analysis. It has been designed using principles developed in the PySCeS simulation software project: usability, flexibility and accessibility. Its architecture is both extensible and flexible using data structures that are intuitive to the biologist (metabolites, reactions, compartments) while transparently translating these into the underlying mathematical structures used in advanced analysis (LP’s, MILP’s).
PySCeS CBMPy implements popular analyses such as FBA, FVA, element/charge balancing, network analysis and model editing as well as advanced methods developed specifically for the ecosystem modelling: minimal distance methods, flux minimization and input selection.
To cater for a diverse range of modelling needs PySCeS CBMPy supports user interaction via:
- interactive console, scripting for advanced use or as a library for software development
- GUI, for quick access to a visual representation of the model, analysis methods and annotation tools
- SOAP based web services: using the Mariner framework much high level functionality is exposed for integration into web tools
For more information on the development and use of PySCeS CBMPy visit the website (http:cbmpy.sourceforge.net) for up to date information and feel free to contact the development team (bgoli@users.sourceforge.net).


Taekwondo, Sungai Buloh
May 2012 – October 2015
Received Level 2 Red Belt.
Waterloo Math Test
March 2017
Received Distinction. Only 28% who takes the test will get Distinction.
Violin, Sungai Buloh
July 2018
Went for Grade 2 and Grade 5 exams.
Sailing, Pattaya, Thailand
April 2019 – May 2019
Level 3 Badge for Fusion Boat.
Kidocode, Mont Kiara
June 2019 - September 2019
Finished 100% of Kportal in 200 hours.


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| BATANES | SIARGAO | PALAWAN |
|
Batanes may be the Philippines’ smallest and least populous province, but don’t let that fool you into thinking that there’s nothing to see in this picturesque destination. Find the best things to do there and its must-see tourist spots by reading this guide. The endless view of the ocean and the peaceful and laid back vibe continuously intrigue and inspire travelers to explore one of the top destinations in the Philippines called Batanes. Batanes, known as the “Home of the Winds,” because of its calm and windy weather, has preserved its postcard-perfect scenery, friendly culture, and the simple way of living due to its distance from the mainland Luzon and the rest of the country. This island province is well-known for its charming, centuries-old stone houses, quiet stretches of white sandy beaches, rugged mountains, and never-ending lush green hills. Its vast distance from the Philippine capital, Manila, used to make Batanes inaccessible to tourists. But with its growing reputation as one of the must-see destinations in the Philippines, traveling to Batanes is now made more accessible.
|
A hidden paradise in the Philippines located off the coast of Surigao del Norte, Siargao has found its way to the hearts of travelers all over the world – surfers and non-surfers alike. Surrounded by crystal clear turquoise waters and covered in coconut palm trees, Siargao is no doubt a dream tropical destination for both local and international travelers. Known as the "Surfing Capital of the Philippines", the island boasts some of the most influential surf breaks in the world. Its reliable surfing conditions all year round offers something for all skill levels. It might have turned to one of the most visited spots in the Philippines, but Siargao hasn't lost its laidback island vibe. The warm and friendly locals inhabiting the island are on a mission to preserve the magic of Siargao's natural beauty. It has pristine white beaches and islands, breathtaking lagoons, and lush green coconut trees lined up on the roads. These are some of the reasons why you must add Siargao tours to your bucket list.
If you're planning to visit the island soon, remember that Siargao is more than just a place for island-hopping excursions. It's more than just a tourist destination. People who live here – locals and foreigners alike are living their life stories in this paradise. |
Travel to the islands of Palawan and find out why it's a top destination not just in the Philippines but the whole world. What are the top tourist attractions and things to do in here? Read on to find out to plan your ultimate getaway. Named as the Philippines' last ecological frontier, Palawan is one of the country's wealthiest provinces in terms of biodiversity. Its lined with sugary beaches, cays, and towering limestone formations that are unlike any other. Swiftlets - colloquially known as balinsasayaw - whose nests are prized delicacies, have made these formations their home. Dense forests comprise a sizable majority of this island province too, serving as habitat for many endemic species, including pitcher plants, mouse deers, pangolins, and born swine. Suffice to say that Palawan tours are unmatched in Asia, and wherever you go, you won't run out of things to do. Especially if you plan to visit its significant provinces, namely El Nido, Puerto Princesa, Coron, and San Vicente. Here are some for starters:
Whether you're looking for outdoor adventures, eco-tours, or beach tours, you'll surely find the best adventure for you and your group! |
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Nyzo version 547 (commit on GitHub) gives the sentinel the ability to send blocks for verifiers joining the cycle.
This version affects the sentinel and the verifier.
On the surface, sending blocks for new verifiers seems like an obvious, relatively simple modification to the sentinel. However, when details of implementation and required information are considered, troubling complexities creep into a number of possible solutions.
The obvious major difference between verifiers and sentinels is the number of verifier identifiers for which each is responsible. The sentinel must consider the health of every verifier it manages, and misunderstandings about the state of new-verifier voting could cause the sentinel to transmit a block for an incorrect new verifier. This could leave in-cycle verifiers unprotected for a block.
In order to maintain robust protection for in-cycle verifiers, the sentinel must have a separate process for determining which out-of-cycle verifier to assist. However, knowing which verifier to assist requires knowledge of new-verifier voting, which is only available to in-cycle verifiers. While many users trying to join the cycle already have in-cycle verifiers, many others do not, so this raises concerns about potentially more-effective behavior for sentinels that have access to in-cycle verifiers. Such behavior would hurt cycle diversity, which makes it unacceptable.
The solution contained in this version is, fundamentally, a brute-force solution. At appropriate times, the sentinel sends blocks for every out-of-cycle verifier it manages. However, these transmissions occur selectively, efficiently, and at a time when cycle messaging activity is at a relatively low level. So, they will not adversely affect cycle performance now, and they will not adversely affect cycle performance even if the queue grows to be substantially larger than it is now.
In Block.getMinimumVoteTimestamp(), the previous logic has been encapsulated in BlockManager.likelyAcceptingNewVerifiers(). Similar logic was introduced in other places in the code in this version, and the method was added to improve readability. The logic has actually changed slightly: the previous method looked at the height of the block being processed and the new method looks at the height of the frozen edge. However, both calculations are rough approximations of a more complex cycle calculation, and they are close enough to one another to be considered interchangeable.
In BlockManager, the likelyAcceptingNewVerifiers() method has been added. For additional safety, the frozen edge and the cycle information of the frozen edge are ensured to be non-null before being accessed.
In MeshListener, udpBufferSize has been modified. The overhead of a message is always 110 bytes. The UDP buffer previously had room for message payloads as large as block votes, which occupy 8 bytes for block height, 32 bytes for the block hash, and 8 bytes for the timestamp, for a total UDP buffer size of 158 bytes. The size is now 182 bytes to allow the 72-byte MinimumBlock payload, which has an 8-byte timestamp and a 64-byte signature, to be received over UDP.
In processing of the UDP messages, the type is now checked before full processing of the message. The Nyzo code generally avoids bit-shift operations, as they tend to be difficult to read and easy to write incorrectly. However, the efficiency provided in this case justifies one difficult-to-read line of code. Minimal-block messages are processed in a new method, and other UDP messages are processed as they were processed in the previous version.
In the processMinimalBlockMessage() method, a number of checks are performed to avoid processing for messages other than those received at the correct time from the correct identifier. If the block appears to be coming from the top new verifier, the full block is rebuilt. If the rebuilt block is valid, it is registered with UnfrozenBlockManager.
In Message.processContent(), a case has been added for MinimalBlock messages.
In MessageType, values have been added for minimal blocks and the corresponding response. Though the response is unused in the current version of code, a type has been put in place so that it will be available if a TCP form of this message is ever required.
Also in MessageType, a map has been added for quick lookup of message types from their corresponding integer values. Additionally, a script used to generate a Javascript file for an early version of the nyzo.co website has been removed.
MessageType.forValue() uses the new map to replace the comically inefficient iterative method. Aside from efficiency, the behavior is unchanged.
In NewVerifierVoteManager, the top verifier is now stored in a field so that it can be provided more efficiently. The previous method that would calculate and return the top verifier on demand has been modified to refresh the top verifier and store it in this field. Previously, the topVerifier() method was used infrequently, so the on-demand calculation was not a concern. Its new use in MeshListener.processMinimalBlockMessage(), which is used in the performance-sensitive UDP processing loop, required a more efficient solution.
The reset() method was added to the PersistentData class to streamline resetting the state of verifiers in the testnet.
In Verifier, the top new verifier is updated each time a block is frozen.
The MinimalBlock class encapsulates the timestamp and block signature of a block. All other fields can be implicitly derived by the receiver of the block.
In Sentinel, a separate thread is started for sending blocks for new verifiers. As recent issues have clearly illustrated, even seemingly innocuous changes to the sentinel can cause serious problems. Keeping the new-verifier process completely separate from protection of in-cycle verifiers provides a small assurance that in-cycle verifier protection will not be compromised by the changes in this version.
The sentinel thread that sends blocks for new verifiers is simple. At appropriate heights, blocks are created and broadcast for every out-of-cycle verifier that the sentinel manages.
The broadcastUdpBlockForNewVerifier() method uses the same createNextBlock() method that is used for in-cycle verifiers. However, instead of sending the full block, a MinimalBlock message, containing only the block signature and verification timestamp, is sent.
Sentinel transactions are not added to blocks created for out-of-cycle verifiers.
In UpdateUtil.reset(), the new PersistentData.reset() method is now called. The reset process would rarely, if ever, be used to reset verifiers on the production blockchain. However, for the testnet, this method is a convenient way to restart an existing mesh with a new Genesis block.
Thoroughly testing this update on the production blockchain was impractical. Various modifications were made to the logic to allow MinimalBlock messages to be transmitted and processed, but a complete test would have required the ability to manipulate the new-verifier voting among the cycle. Obviously, we do not have such power. So, a small testnet was configured, and manual vote overrides for a non-existent verifier were sent to all in-cycle verifiers in the testnet. A sentinel was then started to manage that verifier identifier, and the minimal block broadcast by the sentinel allowed that verifier to join in every test conducted.
In additional to sentinels needing to update to begin sending blocks for new verifiers, the verifiers currently in the cycle will need to update to receive those blocks. However, even a single verifier running this version would likely be sufficient for relaying sentinel blocks to the cycle. When a verifier receives a block that deserves a block vote, that vote is sent almost immediately, which triggers other verifiers to start requesting the missing block. Due to this mechanism, blocks are quickly distributed throughout the cycle, even if their initial distribution is poor.


How do I use the styleguide?
Add components in app/_components
Use Front Matter to organize them (see files in app/_components).
Use as includes anywhere in your project {% include FILENAME.html %}
.
Add colors in app/_colors
Include your colors in the $colors loop in app/styles/pages/_styleguide.scss.
Coordinate class names (color__tile--NEWCOLOR) in app/_colors html files.
That's it.
Colors
{% for entry in entries %}
{% include component-color.html %}
{% endfor %}
{% for type in componentsByType %}
{{ type.name | capitalize }}
{% for entry in type.items %}
{% include component.html %}
{% endfor %}
{% endfor %}


The Face Studio has been providing cosmetic tattooing services for the last 8 years to a large client base across the Greater Vancouver area, and is certified by BioTouch Canada. Our professional services are designed to benefit anyone who wishes to enhance or define their natural beauty. Cosmetic tattoo treatments allow busy people to look their best at all times without the hassle of a daily makeup routine.
Cosmetic tattooing is revolutionizing the traditional world of makeup application. Imagine having perfectly designed eyebrows, beautifully defined eyes and sensual looking lips lasting not for 1 day but for several years!
Today both men and women have realized the benefits of semi-permanent, hassle-free and time saving features of Semi-Permanent Cosmetics.
Our mission is to encourage you to “Put your best face forward”. Whether it’ll be a natural enhancement of the look you already possess, or a minor adjustment to obtain the look you want to possess. Either way, our goal is to give you full confidence in your appearance. Here at The Face Studio, we meet and exceed all government sterilization standards.


DOM update "feel" demo
(canjs + can-derive)
Mark a
todo
incompleted. Do you notice the delay?
Click "Run with JS" to begin test


Word Problems on Subtraction
|
Let Us Learn
Read the word problem.
There were l0 eggs in a tray. 3 were cracked. How many
eggs were not cracked?
Read the steps in solving a word problem.
Step 1.
What is asked? number of eggs that were not cracked
Step 2.
What are given? l0 eggs with 3 cracked eggs
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer? 7 eggs were not cracked.
Let Us Try
A.
Read each word problem. Solve it using the given steps.
Write your answers on the lines.
-
There were l0 glasses on the tray. 6 glasses were filled
with water. How many glasses were empty?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
-
Mang Tomas has l5 baskets of ripe and unripe mangoes.
Out of those l5 baskets, 8 contain ripe mangoes. How many
baskets contain unripe mangoes?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
-
Shirley has l4 mangoes. 8 are not ripe. How many
mangoes are ripe?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
B.
Read each word problem. Solve it using the given steps.
Write your answers on the lines.
-
Beth collected 25 stamps. She gave 13 of the stamps
to Angel. How many stamps does Beth still have?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
-
There were 94 children in all at a party. There were 62
boys. How many were girls?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
-
Ronald saw 84 chicken eggs in the farm. Then he saw
52 eggs hatch. How many eggs did not hatch?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
Let Us Practice
A.
Read each word problem. Solve it using the given steps.
Write your answers on the lines.
-
A vendor had 68 balloons. He sold 25 of them.
How many balloons were left?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
-
There are 72 apples and oranges in the truck. Of all the
fruits, 40 are apples. How many are oranges?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
-
Danilo had a book with 88 pages. He read 26 pages
last week. How many pages has he not read yet?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
B.
Read each word problem. Solve it using the given steps.
Write your answers on the lines.
-
There were 38 birds in an aviary. One day, 10 birds got
out of their cages and flew away. How many birds were
left in the aviary?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
-
The bakers made 58 cupcakes. They sold 56 of them.
How many cupcakes were left?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?
-
Angel counted 96 pictures in the family album. She used
45 in an art project. How many pictures were left?
Step 3.
What is the subtraction sentence?
Step 4.
What is the answer?


| No. | Title | Subtotal | Freight | Total | Apply Date | Comment | State | P.S. | Operation |
|---|---|---|---|---|---|---|---|---|---|
| #{{ application.id }} | {{ application.title }} | {{ application.subtotal }} | {{ application.freight }} | {{ application.total }} | {{ application.created_at }} | {{ application.comment or 'N/A' }} | {% if application.approved_at %} APPROVED {% elif application.rejected_at %} REJECTED {% else %} WAITING {% endif %} | {{ application.ps or 'N/A' }} | Details |


Please visit our site to choose a training course. Also visit our forum to discuss the courses.
Please visit [URL=http://academy.telerik.com]our site[/URL] to choose a training course. Also visit [URL=www.devbg.org]our forum[/URL] to discuss the courses.
Click me to make HTML List from var people.


|
| Daniel Rollans /<PHONE_NUMBER> / danrollans.com | |
Blog Posts
++ Cultural Blog: Pairing and Giving Feedback
|
Pairing and feedback are two aspects of DevBootcamp that are part of the curriculum and important to the learning process. I will offer my opinion and feelings regarding these two items. PairingPairing was a fun experience for me. I loved meeting my classmates and getting to know them. Getting through the assignments was generally efficient and I could see the value of teamwork on a project. I did feel at times I would be able to get through a problem quicker and found research and trying things was far faster if I worked it on my own. Feedback
Feedback I found to be a double edged blade. It's very good to hear how others feel about you with regards to working together and handling projects. It's obviously great to hear when you've done well or was well received by someone. When the feedback wasn't as positive or was critical it's easy to get down on yourself and let it ruin your day. I feel however it's very important to learn where you need improvement in order to get better. It's very important to remember the most progress is made and learning is greatest through failures. No failures = no progress.
|


With nothing to see except a blackboard strewn with differential equations, the educated man with an informed interest in science melted away.
-Jacques Barzun (1974), The Use and Abuse of Art
The phrase "differential equation" is intimidating. It sounds like a fake science word from a movie, like 'tachyon pulse' or 'flux capacitor.' But DE's are also among the best tools we have to design systems and make sense of nature. This piece explains
- what makes differential equations different from a regular equations
- how to solve a simple class of them.
It should be readable if you've taken calc.
Derivative refresher
The derivative of a function is basically its slope. That slope can be rendered as the height of a triangle whose base is $1$ and whose hypoteneuse is tangent to $y$ at $t$. (This makes more sense when you try it out!) Below, hover over the left plot to see $y$'s derivatives, which are plotted on the right as the derivative function $y'(t)$.
What makes DE's different?
Maybe you're used to solving equations like $$ t^2 - 2t = 1.$$ Solving this equation means finding a value of $t$ that makes the left- and right-hand sides equal. The solution is a number.
A differential equation, by contrast, is statement like $$ y'(t) + \lambda \cdot y(t) = 0$$ that involves the derivative of an unknown function $y(t)$. So we start out knowing some fact about $y$'s derivative, and we have figure out what $y$ is.
Example 1: Amateur hour
Consider the differential equation $$y'(t) = .$$ You can solve this one pretty easily by just integrating it...$$\int y'(t) = ,$$ but we're going to solve it visually, since this is the Internet. Below, $t^2 + 2t +...$ is plotted on the right. To solve the DE, draw a function, $y(t)$, whose derivatives fall on $t^2 + 2t +...$.
And you shall derive our velocity
Now it's time to gaze upon the man-melting power of the mother of all DE's:$$ y'(t) = .5 \cdot y(t)$$. Just try to integrate this beast away. You can't because $y(t)$ is already in the equation, crouched and waiting for you with a sharp knife.
You'll solve this DE visually, but this time the horizontal axis of the right plot isn't $t$, it's $y$ itself. That's because the equation says, "The derivative of $y$ is always one-half of $y$ itself." So if you draw $y(t)$ correctly, its derivative will always fall on the straight line $y' = 0.5 \cdot y$.


"""Factor Analysis.
A latent linear variable model.
FactorAnalysis is similar to probabilistic PCA implemented by PCA.score
While PCA assumes Gaussian noise with the same variance for each
feature, the FactorAnalysis model assumes different variances for
each of them.
This implementation is based on David Barber's Book,
Bayesian Reasoning and Machine Learning,
http://www.cs.ucl.ac.uk/staff/d.barber/brml,
Algorithm 21.1
"""
# Author: Christian Osendorfer<EMAIL_ADDRESS># Alexandre Gramfort<EMAIL_ADDRESS># Denis A. Engemann<EMAIL_ADDRESS># License: BSD3
import warnings
from math import sqrt, log
import numpy as np
from scipy import linalg
from ..base import BaseEstimator, TransformerMixin
from ..externals.six.moves import xrange
from ..utils import check_array, check_random_state
from ..utils.extmath import fast_logdet, randomized_svd, squared_norm
from ..utils.validation import check_is_fitted
from ..exceptions import ConvergenceWarning
class FactorAnalysis(BaseEstimator, TransformerMixin):
"""Factor Analysis (FA)
A simple linear generative model with Gaussian latent variables.
The observations are assumed to be caused by a linear transformation of
lower dimensional latent factors and added Gaussian noise.
Without loss of generality the factors are distributed according to a
Gaussian with zero mean and unit covariance. The noise is also zero mean
and has an arbitrary diagonal covariance matrix.
If we would restrict the model further, by assuming that the Gaussian
noise is even isotropic (all diagonal entries are the same) we would obtain
:class:`PPCA`.
FactorAnalysis performs a maximum likelihood estimate of the so-called
`loading` matrix, the transformation of the latent variables to the
observed ones, using expectation-maximization (EM).
Read more in the :ref:`User Guide <FA>`.
Parameters
----------
n_components : int | None
Dimensionality of latent space, the number of components
of ``X`` that are obtained after ``transform``.
If None, n_components is set to the number of features.
tol : float
Stopping tolerance for EM algorithm.
copy : bool
Whether to make a copy of X. If ``False``, the input X gets overwritten
during fitting.
max_iter : int
Maximum number of iterations.
noise_variance_init : None | array, shape=(n_features,)
The initial guess of the noise variance for each feature.
If None, it defaults to np.ones(n_features)
svd_method : {'lapack', 'randomized'}
Which SVD method to use. If 'lapack' use standard SVD from
scipy.linalg, if 'randomized' use fast ``randomized_svd`` function.
Defaults to 'randomized'. For most applications 'randomized' will
be sufficiently precise while providing significant speed gains.
Accuracy can also be improved by setting higher values for
`iterated_power`. If this is not sufficient, for maximum precision
you should choose 'lapack'.
iterated_power : int, optional
Number of iterations for the power method. 3 by default. Only used
if ``svd_method`` equals 'randomized'
random_state : int, RandomState instance or None, optional (default=0)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`. Only used when ``svd_method`` equals 'randomized'.
Attributes
----------
components_ : array, [n_components, n_features]
Components with maximum variance.
loglike_ : list, [n_iterations]
The log likelihood at each iteration.
noise_variance_ : array, shape=(n_features,)
The estimated noise variance for each feature.
n_iter_ : int
Number of iterations run.
References
----------
.. David Barber, Bayesian Reasoning and Machine Learning,
Algorithm 21.1
.. Christopher M. Bishop: Pattern Recognition and Machine Learning,
Chapter 12.2.4
See also
--------
PCA: Principal component analysis is also a latent linear variable model
which however assumes equal noise variance for each feature.
This extra assumption makes probabilistic PCA faster as it can be
computed in closed form.
FastICA: Independent component analysis, a latent variable model with
non-Gaussian latent variables.
"""
def __init__(self, n_components=None, tol=1e-2, copy=True, max_iter=1000,
noise_variance_init=None, svd_method='randomized',
iterated_power=3, random_state=0):
self.n_components = n_components
self.copy = copy
self.tol = tol
self.max_iter = max_iter
if svd_method not in ['lapack', 'randomized']:
raise ValueError('SVD method %s is not supported. Please consider'
' the documentation' % svd_method)
self.svd_method = svd_method
self.noise_variance_init = noise_variance_init
self.iterated_power = iterated_power
self.random_state = random_state
[docs] def fit(self, X, y=None):
"""Fit the FactorAnalysis model to X using EM
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
Returns
-------
self
"""
X = check_array(X, copy=self.copy, dtype=np.float64)
n_samples, n_features = X.shape
n_components = self.n_components
if n_components is None:
n_components = n_features
self.mean_ = np.mean(X, axis=0)
X -= self.mean_
# some constant terms
nsqrt = sqrt(n_samples)
llconst = n_features * log(2. * np.pi) + n_components
var = np.var(X, axis=0)
if self.noise_variance_init is None:
psi = np.ones(n_features, dtype=X.dtype)
else:
if len(self.noise_variance_init) != n_features:
raise ValueError("noise_variance_init dimension does not "
"with number of features : %d != %d" %
(len(self.noise_variance_init), n_features))
psi = np.array(self.noise_variance_init)
loglike = []
old_ll = -np.inf
SMALL = 1e-12
# we'll modify svd outputs to return unexplained variance
# to allow for unified computation of loglikelihood
if self.svd_method == 'lapack':
def my_svd(X):
_, s, V = linalg.svd(X, full_matrices=False)
return (s[:n_components], V[:n_components],
squared_norm(s[n_components:]))
elif self.svd_method == 'randomized':
random_state = check_random_state(self.random_state)
def my_svd(X):
_, s, V = randomized_svd(X, n_components,
random_state=random_state,
n_iter=self.iterated_power)
return s, V, squared_norm(X) - squared_norm(s)
else:
raise ValueError('SVD method %s is not supported. Please consider'
' the documentation' % self.svd_method)
for i in xrange(self.max_iter):
# SMALL helps numerics
sqrt_psi = np.sqrt(psi) + SMALL
s, V, unexp_var = my_svd(X / (sqrt_psi * nsqrt))
s **= 2
# Use 'maximum' here to avoid sqrt problems.
W = np.sqrt(np.maximum(s - 1., 0.))[:, np.newaxis] * V
del V
W *= sqrt_psi
# loglikelihood
ll = llconst + np.sum(np.log(s))
ll += unexp_var + np.sum(np.log(psi))
ll *= -n_samples / 2.
loglike.append(ll)
if (ll - old_ll) < self.tol:
break
old_ll = ll
psi = np.maximum(var - np.sum(W ** 2, axis=0), SMALL)
else:
warnings.warn('FactorAnalysis did not converge.' +
' You might want' +
' to increase the number of iterations.',
ConvergenceWarning)
self.components_ = W
self.noise_variance_ = psi
self.loglike_ = loglike
self.n_iter_ = i + 1
return self
def get_covariance(self):
"""Compute data covariance with the FactorAnalysis model.
``cov = components_.T * components_ + diag(noise_variance)``
Returns
-------
cov : array, shape (n_features, n_features)
Estimated covariance of data.
"""
check_is_fitted(self, 'components_')
cov = np.dot(self.components_.T, self.components_)
cov.flat[::len(cov) + 1] += self.noise_variance_ # modify diag inplace
return cov
def get_precision(self):
"""Compute data precision matrix with the FactorAnalysis model.
Returns
-------
precision : array, shape (n_features, n_features)
Estimated precision of data.
"""
check_is_fitted(self, 'components_')
n_features = self.components_.shape[1]
# handle corner cases first
if self.n_components == 0:
return np.diag(1. / self.noise_variance_)
if self.n_components == n_features:
return linalg.inv(self.get_covariance())
# Get precision using matrix inversion lemma
components_ = self.components_
precision = np.dot(components_ / self.noise_variance_, components_.T)
precision.flat[::len(precision) + 1] += 1.
precision = np.dot(components_.T,
np.dot(linalg.inv(precision), components_))
precision /= self.noise_variance_[:, np.newaxis]
precision /= -self.noise_variance_[np.newaxis, :]
precision.flat[::len(precision) + 1] += 1. / self.noise_variance_
return precision
[docs] def score_samples(self, X):
"""Compute the log-likelihood of each sample
Parameters
----------
X : array, shape (n_samples, n_features)
The data
Returns
-------
ll : array, shape (n_samples,)
Log-likelihood of each sample under the current model
"""
check_is_fitted(self, 'components_')
Xr = X - self.mean_
precision = self.get_precision()
n_features = X.shape[1]
log_like = np.zeros(X.shape[0])
log_like = -.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)
log_like -= .5 * (n_features * log(2. * np.pi)
- fast_logdet(precision))
return log_like
[docs] def score(self, X, y=None):
"""Compute the average log-likelihood of the samples
Parameters
----------
X : array, shape (n_samples, n_features)
The data
Returns
-------
ll : float
Average log-likelihood of the samples under the current model
"""
return np.mean(self.score_samples(X))


A class template for ResNets.
More...
#include <ResidualNeuralNetwork.hpp>
|
| | ResidualNeuralNetwork (std::vector< Module > &&modules, bool foremost=true) |
| |
| | ResidualNeuralNetwork (Module &&module, bool foremost=true) |
| |
| Base * | clone () const |
| | A constant method implementing the clone pattern. More...
|
| |
| const Base::Dims & | get_input_dims () const |
| |
| const Base::Dims & | get_output_dims () const |
| |
| std::vector< const Layer< Scalar, Rank > * > | get_layers () const |
| |
| std::vector< Layer< Scalar, Rank > * > | get_layers () |
| |
| std::vector< Base * > | get_modules () |
| |
| bool | is_foremost () const |
| |
| void | set_foremost (bool foremost) |
| | Sets the foremost status of the network. More...
|
| |
|
void | empty_caches () |
| | Empties the caches of every layer of the network.
|
| |
| Base::Data | propagate (typename Base::Data input, bool training) |
| | It propagates the input tensor through the network and outputs its prediction. More...
|
| |
| Base::Data | backpropagate (typename Base::Data out_grad) |
| | It back-propagates the derivative of the loss function w.r.t. More...
|
| |
| std::vector< const Parameters< Scalar > * > | get_all_params () const |
| |
| std::vector< Parameters< Scalar > * > | get_all_params () |
| |
| std::vector< const Parameters< Scalar > * > | get_all_unique_params () const |
| |
| std::vector< Parameters< Scalar > * > | get_all_unique_params () |
| |
| virtual void | set_frozen (bool frozen) |
| | Sets all parameters of the network to the specified frozens state. More...
|
| |
|
virtual void | init () |
| | Initializes all parameters of the network.
|
| |
| virtual Data | infer (Data input) |
| | It propagates the input through the neural network and outputs its prediction according to its current parameters. More...
|
| |
| void | save_all_unique_params_values (const std::string &dir_path, bool binary=true, const std::string &file_name_prefix=PARAM_SERIAL_PREFIX) const |
| | It serializes the values of the unique parameters of the network into files in a specified folder. More...
|
| |
| void | load_all_unique_params_values (const std::string &dir_path, bool binary=true, const std::string &file_name_prefix=PARAM_SERIAL_PREFIX) |
| | It sets the values of the unique parameters of the network from files containing serialized parameter values. More...
|
| |
template<typename Scalar, std::size_t Rank>
class cattle::ResidualNeuralNetwork< Scalar, Rank >
A class template for ResNets.
These networks take vectors of neural networks as their sub-modules.
- See also
- https://arxiv.org/abs/1512.03385
◆ ResidualNeuralNetwork() [1/2]
template<typename Scalar , std::size_t Rank>
- Parameters
-
| modules | A vector of residual modules. |
| foremost | Whether the network is to function as a foremost network. |
◆ ResidualNeuralNetwork() [2/2]
template<typename Scalar , std::size_t Rank>
- Parameters
-
| module | A single residual module. |
| foremost | Whether the network is to function as a foremost network. |
◆ backpropagate()
template<typename Scalar , std::size_t Rank>
It back-propagates the derivative of the loss function w.r.t.
the output of the network through its layers updating the gradients on their parameters.
- Parameters
-
| out_grad | The derivative of the loss function w.r.t. the output of the network. |
- Returns
- The derivative of the loss function w.r.t. the input of the network or a null tensor if the network is a foremost network.
Implements cattle::NeuralNetwork< Scalar, Rank, Sequential >.
◆ clone()
template<typename Scalar , std::size_t Rank>
A constant method implementing the clone pattern.
- Returns
- A pointer to a copy of the instance. The instance does not take ownership of the returned pointer (i.e. the caller is responsible for deleting it).
Implements cattle::NeuralNetwork< Scalar, Rank, Sequential >.
◆ get_input_dims()
template<typename Scalar , std::size_t Rank>
- Returns
- A constant reference to the member variable denoting the dimensions of the tensors accepted by the network as its input along (except for the first rank which denotes the variable sample size and in case of sequential networks the second rank which denotes the variable time steps).
Implements cattle::NeuralNetwork< Scalar, Rank, Sequential >.
◆ get_layers() [1/2]
template<typename Scalar , std::size_t Rank>
◆ get_layers() [2/2]
template<typename Scalar , std::size_t Rank>
◆ get_modules()
template<typename Scalar , std::size_t Rank>
◆ get_output_dims()
template<typename Scalar , std::size_t Rank>
- Returns
- A constant reference to the member variable denoting the dimensions of the tensors output by the network (except for the first rank which denotes the variable sample size and in case of sequential networks the second rank which denotes the variable time steps).
Implements cattle::NeuralNetwork< Scalar, Rank, Sequential >.
◆ is_foremost()
template<typename Scalar , std::size_t Rank>
- Returns
- Whether the instance is a foremost network. If the instance is not a stand-alone network and it is not the first module of a complex network, it is not a foremost network. Foremost networks do not need to back-propagate the gradients all the way given that no other network is expected to depend on them.
Implements cattle::NeuralNetwork< Scalar, Rank, Sequential >.
◆ propagate()
template<typename Scalar , std::size_t Rank>
It propagates the input tensor through the network and outputs its prediction.
- Parameters
-
| input | The input tensor to propagate through. |
| training | Whether the input is to be propagated in training mode or not. Propagating the input in training mode may be more time and memory consuming, but is a prerequisite of back-propagation. |
- Returns
- The output tensor of the network in response to the input.
Implements cattle::NeuralNetwork< Scalar, Rank, Sequential >.
◆ set_foremost()
template<typename Scalar , std::size_t Rank>
The documentation for this class was generated from the following file:


1. Select the CREATOR lid and connect it to the base:
The CREATOR Lid is a blank breadboard that allows you to develop your own projects.
2. Select your project:
Hook up an LED matrix cell and display an LED animation.
Hook up an accelerometer cell and display a balanced ball on the computer screen.


KOLT Python program is founded by Ahmet Uysal in collaboration with Koç University Office of Learning and Teaching in September 2018 to address students' raising demand for programming. The program is designed for students with no programming experience and assumes no technical background. We prioritize practical use cases of programming over more theoretical aspects and encourage students to leverage their programming skills in their field or interests.
In Spring 2019, we accepted our first 20 students from various academic principles. The program is ever-growing and improving since then thanks to our passionate team members and feedback from our students.
{{ member.name }}<{{ member.kuId }}>
{{ member.name }}<{{ member.kuId }}>


Message is required.
Message must be minimum 3 characters long.
Message should be maximum 1024 characters long.
Only Alphanumeric, spaces, underscores, dots, commas and dashes allowed
The message provides details to NetApp Managed Services team to initiate configuration of Inter-Cluster Relationship between a Virtual Storage Array {{labels.server.serverName}} and an external Cluster.
Contact will be made with the requesting user to complete the configuration. Further details may be added to this field as required.


SUBROUTINE DLAQGB( M, N, KL, KU, AB, LDAB, R, C, ROWCND, COLCND, $ AMAX, EQUED ) * * -- LAPACK auxiliary routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * .. Scalar Arguments .. CHARACTER EQUED INTEGER KL, KU, LDAB, M, N DOUBLE PRECISION AMAX, COLCND, ROWCND * .. * .. Array Arguments .. DOUBLE PRECISION AB( LDAB, * ), C( * ), R( * ) * .. * * Purpose * ======= * * DLAQGB equilibrates a general M by N band matrix A with KL * subdiagonals and KU superdiagonals using the row and scaling factors * in the vectors R and C. * * Arguments * ========= * * M (input) INTEGER * The number of rows of the matrix A. M >= 0. * * N (input) INTEGER * The number of columns of the matrix A. N >= 0. * * KL (input) INTEGER * The number of subdiagonals within the band of A. KL >= 0. * * KU (input) INTEGER * The number of superdiagonals within the band of A. KU >= 0. * * AB (input/output) DOUBLE PRECISION array, dimension (LDAB,N) * On entry, the matrix A in band storage, in rows 1 to KL+KU+1. * The j-th column of A is stored in the j-th column of the * array AB as follows: * AB(ku+1+i-j,j) = A(i,j) for max(1,j-ku)<=i<=min(m,j+kl) * * On exit, the equilibrated matrix, in the same storage format * as A. See EQUED for the form of the equilibrated matrix. * * LDAB (input) INTEGER * The leading dimension of the array AB. LDA >= KL+KU+1. * * R (input) DOUBLE PRECISION array, dimension (M) * The row scale factors for A. * * C (input) DOUBLE PRECISION array, dimension (N) * The column scale factors for A. * * ROWCND (input) DOUBLE PRECISION * Ratio of the smallest R(i) to the largest R(i). * * COLCND (input) DOUBLE PRECISION * Ratio of the smallest C(i) to the largest C(i). * * AMAX (input) DOUBLE PRECISION * Absolute value of largest matrix entry. * * EQUED (output) CHARACTER*1 * Specifies the form of equilibration that was done. * = 'N': No equilibration * = 'R': Row equilibration, i.e., A has been premultiplied by * diag(R). * = 'C': Column equilibration, i.e., A has been postmultiplied * by diag(C). * = 'B': Both row and column equilibration, i.e., A has been * replaced by diag(R) * A * diag(C). * * Internal Parameters * =================== * * THRESH is a threshold value used to decide if row or column scaling * should be done based on the ratio of the row or column scaling * factors. If ROWCND < THRESH, row scaling is done, and if * COLCND < THRESH, column scaling is done. * * LARGE and SMALL are threshold values used to decide if row scaling * should be done based on the absolute size of the largest matrix * element. If AMAX > LARGE or AMAX < SMALL, row scaling is done. * * ===================================================================== * * .. Parameters .. DOUBLE PRECISION ONE, THRESH PARAMETER ( ONE = 1.0D+0, THRESH = 0.1D+0 ) * .. * .. Local Scalars .. INTEGER I, J DOUBLE PRECISION CJ, LARGE, SMALL * .. * .. External Functions .. DOUBLE PRECISION DLAMCH EXTERNAL DLAMCH * .. * .. Intrinsic Functions .. INTRINSIC MAX, MIN * .. * .. Executable Statements .. * * Quick return if possible * IF( M.LE.0 .OR. N.LE.0 ) THEN EQUED = 'N' RETURN END IF * * Initialize LARGE and SMALL. * SMALL = DLAMCH( 'Safe minimum' ) / DLAMCH( 'Precision' ) LARGE = ONE / SMALL * IF( ROWCND.GE.THRESH .AND. AMAX.GE.SMALL .AND. AMAX.LE.LARGE ) $ THEN * * No row scaling * IF( COLCND.GE.THRESH ) THEN * * No column scaling * EQUED = 'N' ELSE * * Column scaling * DO 20 J = 1, N CJ = C( J ) DO 10 I = MAX( 1, J-KU ), MIN( M, J+KL ) AB( KU+1+I-J, J ) = CJ*AB( KU+1+I-J, J ) 10 CONTINUE 20 CONTINUE EQUED = 'C' END IF ELSE IF( COLCND.GE.THRESH ) THEN * * Row scaling, no column scaling * DO 40 J = 1, N DO 30 I = MAX( 1, J-KU ), MIN( M, J+KL ) AB( KU+1+I-J, J ) = R( I )*AB( KU+1+I-J, J ) 30 CONTINUE 40 CONTINUE EQUED = 'R' ELSE * * Row and column scaling * DO 60 J = 1, N CJ = C( J ) DO 50 I = MAX( 1, J-KU ), MIN( M, J+KL ) AB( KU+1+I-J, J ) = CJ*R( I )*AB( KU+1+I-J, J ) 50 CONTINUE 60 CONTINUE EQUED = 'B' END IF * RETURN * * End of DLAQGB * END


The noise¶
Choose from various baseline and noise fitting options (sampling vs. hybrid, GPs, splines, polynomials, and more). Powered by numpy, scipy, and celerite (Foreman-Mackey et al., 2017). In this section, adapted from Günther & Daylan (in prep.), we describe how allesfitter can model red noise in different ways, including Gaussian Processes.
Models used to fit observations can never be a perfect description of the data, even up to white (uncorrelated) noise. This is because observed data in the Universe is always affected by some physical processes not available in the fitted model. The total effect of these unaccounted processes in the data is usually referred to as red (correlated) noise. The apparent correlation of this noise is a consequence of the underlying processes (absent in the fitting model) that generate the associated features in the data.
allesfitter includes various options to model red noise, including constant to polynomial terms and spline fitting. We here introduce the most versatile one: using a GP (Rasmussen 2005, Bishop 2006, Roberts 2013) with the squared exponential kernel to non-parametrically model and marginalize over red noise.
Instead of fitting for the parameters of a chosen model (e.g. a polynomial), GP regression fits for a family of functions to determine which one works best. This is a so-called non-parametric approach. In a Bayesian context, a GP can hence be interpreted as a prior on the functions that describe any unknown data set (see e.g. Murphy 2012). By updating it with measurements of the data $D$, one gains the posterior of the model \(M\). The GP postulates a family of jointly Gaussian functions, in which the relation of data points are described by the covariance matrix, expressed by the kernel. A GP can use different kernels and metrics to evaluate the correlation between data points by evaluating the distance between data points with a chosen metric. Certain kernels are well suited to model smooth, long-term variations; others describe more stochastic short-term variations. In practice, the GP is fitted to the data by optimizing its hyperparameters.
allesfitter adapts the celerite package, which provides series expressions of typical GP kernels. This enables a significant gain in computing time. A detailed discussion can be found in (Foreman-Mackey et al. 2017). By fitting hyperparameters of a GP one can model correlations in the data that cannot be explained by the null orbital model. The posterior of these hyperparameters can then be linked to physical sources, such as stellar variability, weather patterns or systematic instrumental noise.


Portfolio
For a normal student's portfolio, each of the blocks below would represent a single project that they have worked on. However, I'm going to use this as a venue for showing how much of a difference each of our design changes make!
-
Markup Only
At this point, we will just have markup and an image or two.
-
Markup with Space
At this point, we will have added a few lines of CSS to let the content breath.
-
Basic Layout
At this point, we will have created a basic layout for our portfolio.
-
Typography
Adding some font choices at this point will really bring in some personality!
-
Images and Colors Option 1
At this point, we will add some color and images. Things will start to come together!
-
Images and Colors Option 2
At this point, we will pick a color scheme. Things will start to come together!
-
Letting It Rest
At this point, there are going to be plenty of issues with how this site looks and how garish the colorscheme is. We'll let it rest for a few hours and then pick it back up again to see how it looks after a rest!
-
JavaScript Interactivity
We'll wrap up the coding by adding a smattering of JavaScript interactions
-
Deploying to GitHub Pages
Finally, we'll deploy the final project to GitHub Pages!


I have an extensive academic and pharmaceutical industry R&D background in molecular and computational biology, as well as machine learning and deep learning (ML/DL).
Currently, I am a Computational Biologist at Cellzome, a GSK company, in Heidelberg, Germany. At GSK, I focus on the exciting emerging field of spatial omics.
Data Simple is a blog-like collection of articles about Data Science, focusing on Python and R.


After unpacking the distribution file the following source tree will be created:
log4php-{version} +---docs | +---api | qsg.html (this file) | ... \---src +---log4php | +---appenders | +---config | +---helpers | +---layouts | +---or | +---spi | +---varia | \---xml \---tests ...
Copy the "log4php-{version}/src/log4php
" directory in a place accessible by PHP
(called in this document {LOG4PHP-ROOT}
)
and that's all! Log4php is installed.
Optionally the LOG4PHP_DIR
constant can be defined to point to {LOG4PHP-ROOT}
.
Three steps are required to use log4php:
tests/*/configs
directories for examples.log4php.dtd
for xml elements reference.LOG4PHP_CONFIGURATION
constant to point to the configuration above.
LOG4PHP_CONFIGURATOR_CLASS
constant to point to a configuration class file.
LoggerManager.php
'
class file in your php scripts.
LOG4PHP_DEFAULT_INIT_OVERRIDE
,
LOG4PHP_CONFIGURATION
and
LOG4PHP_CONFIGURATOR_CLASS
constants.
Here is how to use log4php
in user PHP code:
<?php /* Set LOG4PHP_* constants here */ require_once(LOG4PHP_DIR . '/LoggerManager.php'); /* In a class */ class Log4phpTest { /* Your public and private vars */ var $_logger; function Log4phpTest() { $this->_logger =& LoggerManager::getLogger('Log4phpTest'); $this->_logger->debug('Hello!'); } } function Log4phpTestFunction() { $logger =& LoggerManager::getLogger('Log4phpTestFunction'); $logger->debug('Hello again!'); } /* Your PHP code */ //Safely close all appenders with... LoggerManager::shutdown(); ?>


HHL LEIPZIG GRADUATE SCHOOL OF MANAGEMENT (Leipzig, Germany)
Master of Business Administration - General Management
Executive MBA (Rank 85 Worldwide 2016 Exec MBA by FT, Rank 6 by The Economist 2017, Top 3 in Germany 2015 by FT, and Rank 48 in European Business School Rankings in 2017)
Master Thesis (The procedure model of warehouse in-house operation: the back-sourcing case of Auto spare parts service business section)
March 2015 - August 2017
SHANGHAI JIAO TONG UNIVERSITY: ANTAI College of Economics & Management (Shanghai, China)
Exchange-Business Administration
Rank 34 Global 2018 MBA by FT, The 1st Chinese Business School by AACSB, EQUIS and AMBA
February 2017 - July 2017
KYUNG WON UNIVERSITY (Seoul, Republic of Korea)
Bachelor of International Trade & Commerce in Business Management
Member of Reserve Officers' Training Corps (2006 – 2007)
Received scholarship based on academic excellence (2005 – 2006)
President of the class (2004)
March 2004 - February 2008


We're sorry but Obyte Zeus doesn't work properly without JavaScript enabled. Please enable it to continue.


{% extends "base.html" %} {% block title %} Reply to: {{comment.user.username}} | Post: {{post_subject}} {% endblock %} {% block content %}
Reply to: {{comment.user.username}} | Post: {{post_subject}}


by Jennifer McDowall
Calcium ions play a crucial role in
the metabolism and physiology of eukaryotes.
Calcium exists as a gradient across the plasma membrane, with
extracellular concentrations being about 10,000 times higher than intracellular
ones. External signals, such as
hormones, light, stress or pathogenesis, can often lead to transient increases
in calcium concentrations within the cell.
Cells have developed a multitude of ways to control and make use of this
ion gradient to regulate many cellular processes, ranging from transcription
control and cell survival to neurotransmitter release and muscle function. Calcium participates in an intracellular
signalling system by acting as a diffusible second messenger to the initial
stimuli. Increased calcium
concentrations lead to calcium binding by regulatory proteins, which turn the
calcium signal into a biological response.
There are many such regulatory proteins that bind calcium, which
together form an intricate network of feedback loops to control the location,
amount and effect of calcium influx.
Calmodulin is one such calcium-binding protein that is considered a
major transducer of calcium signals.
Calmodulin (CaM) is a ubiquitous, calcium-binding protein that can bind to and regulate a multitude of different protein targets, thereby affecting many different cellular functions. CaM mediates processes such as inflammation, metabolism, apoptosis, muscle contraction, intracellular movement, short-term and long-term memory, nerve growth and the immune response. CaM is expressed in many cell types and can have different subcellular locations, including the cytoplasm, within organelles, or associated with the plasma or organelle membranes. Many of the proteins that CaM binds are unable to bind calcium themselves, and as such use CaM as a calcium sensor and signal transducer. CaM can also make use of the calcium stores in the endoplasmic reticulum, and the sarcoplasmic reticulum. CaM undergoes a conformational change upon binding to calcium, which enables it to bind to specific proteins for a specific response. CaM can bind up to four calcium ions, and can undergo post-translational modifications, such as phosphorylation, acetylation, methylation and proteolytic cleavage, each of which can potentially modulate its actions.


Meghan McCain mysteriously absent from anti-Vietnam War activist Jane Fonda’s ‘View’ segment
by Brian FloodActivist Jane Fonda visited “The View” on Tuesday, but co-host Meghan McCain was nowhere to be found when the actress, who'd adamantly opposed the Vietnam War, appeared on the ABC News gabfest.
That raised a question: Had McCain refused to appear with Fonda because of her father's service?
McCain’s father, the late U.S. Sen. John McCain, was captured by the North Vietnamese, who tortured and beat him for more than five years after his A4 Skyhawk was hit by a surface-to-air missile over Hanoi in 1967.
JANE FONDA REVISITS 'HANOI JANE' SCANDAL: 'IT’S JUST HORRIBLE FOR ME TO THINK OF THAT'
Fonda was famously labeled a traitor for her actions during the Vietnam War. During one trip to Vietnam in 1972, she even posed on an anti-aircraft gun, which led her to being nicknamed "Hanoi Jane." Fonda admitted years later that she regretted sitting on the weapon.
Meghan McCain’s absence piqued the curiosity of viewers who connected the dots, as she was on “The View” during Tuesday’s episode but vanished during the Fonda interview. “The View” panelists didn’t offer an on-air explanation.
“The View” declined comment when asked if McCain had skipped the segment because of Fonda’s involvement.
'THE VIEW' GIVES JANE FONDA A CARE PACKAGE FOR 'NEXT TIME' SHE GOES TO JAIL
An ABC News insider told Fox News that co-hosts sometimes skip particular segments for various reasons, including production and logistical issues. Fellow co-host Sunny Hostin was also absent from the Fonda segment.
Fonda's interview on Tuesday came after she was arrested several times during climate change demonstrations in Washington, D.C. "View" co-host Abby Huntsman pressed Fonda on whether she should instead engage in "peaceful" protests that don't involve breaking the law.
CLICK HERE TO GET THE FOX NEWS APP
Fox News’ Julius Young and Alex Pappas contributed to this report.


An |ExpressionStatement| cannot start with a U+007B (LEFT CURLY BRACKET) because that might make it ambiguous with a |Block|. An |ExpressionStatement| cannot start with the `function` or `class` keywords because that would make it ambiguous with a |FunctionDeclaration|, a |GeneratorDeclaration|, or a |ClassDeclaration|. An |ExpressionStatement| cannot start with `async function` because that would make it ambiguous with an |AsyncFunctionDeclaration|. An |ExpressionStatement| cannot start with the two token sequence `let [` because that would make it ambiguous with a `let` |LexicalDeclaration| whose first |LexicalBinding| was an |ArrayBindingPattern|. An |ExpressionStatement| cannot start with `throw` because that would make it ambiguous with a `ThrowStatement`.


#include <sc2_interfaces.h>
|
| virtual void | UnitCommand (Tag unit_tag, AbilityID ability)=0 |
| |
| virtual void | UnitCommand (Tag unit_tag, AbilityID ability, const Point2D &point)=0 |
| |
| virtual void | UnitCommand (Tag unit_tag, AbilityID ability, Tag target_tag)=0 |
| |
|
virtual void | UnitCommand (const std::vector< Tag > &unit_tags, AbilityID ability)=0 |
| | Issues a command to multiple units (prefer this where possible). Same as UnitCommand(Tag, AbilityID).
|
| |
|
virtual void | UnitCommand (const std::vector< Tag > &unit_tags, AbilityID ability, const Point2D &point)=0 |
| | Issues a command to multiple units (prefer this where possible). Same as UnitCommand(Tag, AbilityID, Point2D).
|
| |
|
virtual void | UnitCommand (const std::vector< Tag > &unit_tags, AbilityID ability, Tag target_tag)=0 |
| | Issues a command to multiple units (prefer this where possible). Same as UnitCommand(Tag, AbilityID, Tag).
|
| |
| virtual const std::vector< Tag > & | Commands () const =0 |
| |
| virtual void | ToggleAutocast (Tag unit_tag, AbilityID ability)=0 |
| |
| virtual void | ToggleAutocast (const std::vector< Tag > &unit_tags, AbilityID ability)=0 |
| |
| virtual void | SendActions ()=0 |
| |
The ActionInterface issues actions to units in a game. Not available in replays. Guaranteed to be valid when the OnStep event is called.
◆ Commands()
| virtual const std::vector<Tag>& sc2::ActionInterface::Commands |
( |
| ) |
const |
|
pure virtual |
Returns a list of unit tags that have sent commands out in the last call to SendActions. This will be used to determine if a unit actually has a command when the observation is received.
- Returns
- Array of units that have sent commands.
◆ SendActions()
| virtual void sc2::ActionInterface::SendActions |
( |
| ) |
|
|
pure virtual |
This function sends out all batched unit commands. You DO NOT need to call this function in non real time simulations since it is automatically called when stepping the simulation forward. You only need to call this function in a real time simulation. For example, if you wanted to move 20 marines to some position on the map you'd want to batch all of those unit commands and send them at once.
◆ ToggleAutocast() [1/2]
| virtual void sc2::ActionInterface::ToggleAutocast |
( |
Tag |
unit_tag, |
|
|
AbilityID |
ability |
|
) |
| |
|
pure virtual |
Enables or disables autocast of an ability on a unit.
- Parameters
-
| unit_tag | The unit to toggle the ability on. |
| ability | The ability to be toggled. |
◆ ToggleAutocast() [2/2]
| virtual void sc2::ActionInterface::ToggleAutocast |
( |
const std::vector< Tag > & |
unit_tags, |
|
|
AbilityID |
ability |
|
) |
| |
|
pure virtual |
Enables or disables autocast of an ability on a list of units.
- Parameters
-
| unit_tags | The units to toggle the ability on. |
| ability | The ability to be toggled. |
◆ UnitCommand() [1/3]
| virtual void sc2::ActionInterface::UnitCommand |
( |
Tag |
unit_tag, |
|
|
AbilityID |
ability |
|
) |
| |
|
pure virtual |
Issues a command to a unit. Self targeting.
- Parameters
-
| unit_tag | The unit to send the command to. |
| ability | The ability id of the command. |
◆ UnitCommand() [2/3]
| virtual void sc2::ActionInterface::UnitCommand |
( |
Tag |
unit_tag, |
|
|
AbilityID |
ability, |
|
|
const Point2D & |
point |
|
) |
| |
|
pure virtual |
Issues a command to a unit. Targets a point.
- Parameters
-
| unit_tag | The unit to send the command to. |
| ability | The ability id of the command. |
| point | The 2D world position to target. |
◆ UnitCommand() [3/3]
| virtual void sc2::ActionInterface::UnitCommand |
( |
Tag |
unit_tag, |
|
|
AbilityID |
ability, |
|
|
Tag |
target_tag |
|
) |
| |
|
pure virtual |
Issues a command to a unit. Targets another unit.
- Parameters
-
| unit_tag | The unit to send the command to. |
| ability | The ability id of the command. |
| target_tag | The unit that is a target of the unit getting the command. |
The documentation for this class was generated from the following file:


Johnson lifts Chattanooga past Tennessee State 59-57
by The Associated PressCHATTANOOGA, Tenn. (AP) — Matt Ryan and David Jean-Baptiste each drilled a 3-pointer in the final 90 seconds to allow Chattanooga to hold off Tennessee State, 59-57 on Saturday.
Ryan’s trey broke a 52-52 tie and put the Mocs ahead for good with 1:27 remaining. After Jy’lan Washington scored at the basket to cut the deficit to one, Jean-Baptiste knocked down his 3 to make it 58-54 with :31 left.
Carlos Marshall Jr. drew a foul with a second left and hit the first of two free throws to make it a two-point game, but Jean-Baptiste pulled down the rebound on his second to seal the victory.
Rod Johnson had 10 points and 11 rebounds to lead Chattanooga (1-1). Jean-Baptiste and Ryan each added 11 points. Trey Doomes had eight rebounds for the hosts.
Marshall had 16 points for the Tigers (1-1). Washington added 10 points. Emmanuel Egbuta had eight rebounds.
Chattanooga matches up against Troy on the road on Tuesday. Tennessee State faces Lipscomb at home on Tuesday.
___
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
___
This was generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com
The Associated Press


[gecode-users] a propagator for the Context-Free-Grammar constraint
Pierre Flener
Pierre.Flener at it.uu.se
Tue Sep 23 08:54:42 CEST 2014
Hi all,
As a useful complement to the built-in REGULAR constraint of Gecode, constraining a fixed-length sequence of variables to form a word that belongs to the regular language defined by a regular expression (RE) or (non-)deterministic finite automaton (NFA/DFA), there exists the CFG constraint, which constrains a fixed-length sequence of variables to form a word that belongs to the context-free language defined by a context-free grammar (CFG). Languages of words of fixed length are finite, and hence regular, so a CFG constraint over fixed-length sequences is theoretically not needed. However, there are languages where a context-free grammar is much more compact than an RE/DFA/NFA, to the point that the cubic-time propagation of the CFG constraint pays off versus the much lower time complexity of a REGULAR propagator. So far, Gecode unfortunately lacks a CFG constraint.
The state-of-the-art propagator for the CFG constraint described in
J. He, P. Flener, J. Pearson, and W. M. Zhang.
Solving string constraints: The case for constraint programming.
In: Ch. Schulte (editor), CP 2013, pages 381-397. Lecture Notes in Computer Science, volume 8124.
Springer, 2013.
http://dx.doi.org/10.1007/978-3-642-40627-0_31
http://www.it.uu.se/research/group/astra/publications/CP13cfg.pdf
http://cp2013.a4cp.org/slides/44.pdf
was implemented in Gecode by Jun He (Cc:ed) and is made available at http://www.it.uu.se/research/group/astra/software#cfg : note that the implementation is provided as is, and does not fully comply yet with Gecode style, but we hope it will already be useful to some.
Cheers,
Pierre
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Write a function that takes a numeric array as its right argument and returns the mean (average) of the array.
your_function 1 2 3 4 5 6
3.5
your_function ⍬ ⍝ the average of an empty vector is 0
0
your_function 17 ⍝ your solution should work with a scalar argument
17
your_function 5 3⍴⍳15 ⍝ and average the rows of a matrix
7 8 9
your_function 3 3 3⍴⍳15 ⍝ or the matrices of a 3D array etc.
5 6 7
8 9 10
6 7 8
Write a function that takes a numeric vector as its right argument and returns the median of the array. The median is the number separating the higher half of the vector from the lower half. The median can be found by arranging all the observations from lowest value to highest value and picking the middle one. If there is an even number of observations, then there is no single middle value; the median is then defined to be the mean of the two middle values.
your_function 1 2 3 4 5 6 7 8 9
5
your_function 1 8 2 7 3 6 4 5
4.5
your_function ⍬
0
your_function 7
7
Write a function that takes a numeric vector or scalar as its right argument and returns the mode (that is, the most common value) of the array. If more than one number occurs the greatest number of times, return all such numbers.
your_function 2 1 4 3 2 5 1 2
2
your_function ⍬ ⍝ should return an empty vector
your_function 1 2 3 4 1 2
1 2
Write a function that takes vectors as its left and right arguments and returns them “meshed” into a single vector formed by alternately taking successive elements from each argument. The arguments do not have to be the same length.
'MENS' your_function 'EKES'
MEEKNESS
'Dyalog' your_function 'APL'
DAaPlLog
'APL' your_function 'Dyalog'
ADPyLalog
1 3 5 7 your_function 2 4 6 8 ⍝ should work with numeric vectors
1 2 3 4 5 6 7 8
'' your_function 'Hello' ⍝ either or both arguments could be empty
Hello
Write a function that takes a vector as its right argument and returns elements that occur only once in the vector.
your_function 1 2 3 4 5
1 2 3 4 5
your_function 1 2 3 4 5 4 3 2 1
5
your_function 'hello world'
he wrd
Write a function that takes a vector of vectors as its right argument and returns it sorted by the length of each element. An element of the vector can be scalar or an empty vector.
your_function 'one' 'two' 'three' 'four' 'five' 'six'
┌───┬───┬───┬────┬────┬─────┐
│one│two│six│four│five│three│
└───┴───┴───┴────┴────┴─────┘
your_function (2 4 3) (4 5) 1 (7 3)
┌─┬───┬───┬─────┐
│1│4 5│7 3│2 4 3│
└─┴───┴───┴─────┘
your_function ⍬ ⍝ should return an empty vector
your_function 'one' 2 'three' '' 'four' (5 6 7 8)
┌┬─┬───┬────┬───────┬─────┐
││2│one│four│5 6 7 8│three│
└┴─┴───┴────┴───────┴─────┘
Write a function that takes a numeric vector and returns all elements that are divisible by 3 or 5.
your_function 1 2 3 4 5 6 7 8 9 10
3 5 6 9 10
your_function ⍬ ⍝ should return an empty vector
Write a function that takes a numeric vector and returns a two element vector whose first element contains the values less than 0 (zero) in the vector and the second element contains all values greater than or equal to 0.
your_function 0 1 ¯2 3 ¯4 ¯5 6 7 8 ¯9 10
┌───────────┬──────────────┐
│¯2 ¯4 ¯5 ¯9│0 1 3 6 7 8 10│
└───────────┴──────────────┘
your_function 1 2 3 4 5
┌┬─────────┐
││1 2 3 4 5│
└┴─────────┘
your_function ⍬ ⍝ should return a vector of two empty vectors
┌┬┐
│││
└┴┘
It’s common to encounter delimited text – for example, comma- separated values in a file.
Write a function that takes a character vector as its right argument and one or more characters as its left argument, where those characters are delimiters in the right argument. The function should return the delimited text as a vector of vectors.
',' your_function 'comma,delimited,values'
┌─────┬─────────┬──────┐
│comma│delimited│values│
└─────┴─────────┴──────┘
' ' {your_function} 'break up words'
┌─────┬──┬─────┐
│break│up│words│
└─────┴──┴─────┘
',' {your_function} ','
┌┬┐
│││
└┴┘
Suppose you have a numeric vector that is the list of prices for a set of retail products. You also have a numeric vector that is the number ordered of each product. Write a function that takes as its right argument a vector of prices and as its left argument a numeric vector that indicates the number ordered and returns the total cost for the order. In case you hadn’t realized it, this is an application the dot product. The dot product of two vectors A = [A1, A2, …,An] and B = [B1, B2, …,Bn] is defined as
5 0 2 your_function 2.99 4.99 1.99
18.93
0 0 0 your_function 2.99 4.99 1.99
0


The idea of an Abstract Salad Factory is of course to make salads in a social and fun way! Decided the date and location everyone brings salad ingriedents and you can make your own little salad using any ingredients you want (like a salad bar, but with firends).
This page allows you to make an Abstract Salad Factory event to share different salady ingriedents with your friends. To start a date time and location and press the create button! You will go to the new event page, this will give you a link (in the blue box) to share with your friends and allows you to add ingredients you or other people will bring to the factory.
Someone shared a Abstract Salad Factory with you! Here you can see the link to share this Factory with other friends (in the blue box) and the ingredients people will be bringing and you can add your own.


ctqa.gui package¶
Submodules¶
ctqa.gui.client module¶
CTQA Client Module
Contains logic for implementing the CTQA Client with the ctqa_client class.
-
class
ctqa.gui.client.
ctqa_client
(parent)[source]¶ Bases:
object
CTQA client class. Contains logic to instantiate inside a passed Tkinter parent element.
ctqa.gui.configclient module¶
ctqa.gui.logwatcher module¶
Real-time log files watcher supporting log rotation. Works with Python >= 2.6 and >= 3.2, on both POSIX and Windows.
Author: Giampaolo Rodola’ <g.rodola [AT] gmail [DOT] com> License: MIT
-
class
ctqa.gui.logwatcher.
LogWatcher
(folder, callback, extensions=['log'], tail_lines=0, sizehint=1048576)[source]¶ Bases:
object
Looks for changes in all files of a directory. This is useful for watching log file changes in real-time. It also supports files rotation.
Example:
>>> def callback(filename, lines): ... print(filename, lines) ... >>> lw = LogWatcher("/var/log/", callback) >>> lw.loop()
-
listdir
()[source]¶ List directory and filter files by extension. You may want to override this to add extra logic or globbing support.
-
loop
(interval=0.1, blocking=True)[source]¶ Start a busy loop checking for file changes every interval seconds. If blocking is False make one loop then return.
-
classmethod
open
(file)[source]¶ Wrapper around open(). By default files are opened in binary mode and readlines() will return bytes on both Python 2 and 3. This means callback() will deal with a list of bytes. Can be overridden in order to deal with unicode strings instead, like this:
import codecs, locale return codecs.open(file, ‘r’, encoding=locale.getpreferredencoding(),
errors=’ignore’)
-
ctqa.gui.profileclient module¶
-
class
ctqa.gui.profileclient.
Placeholder_State
[source]¶ Bases:
object
-
normal_color
¶
-
normal_font
¶
-
placeholder_color
¶
-
placeholder_font
¶
-
placeholder_text
¶
-
with_placeholder
¶
-
ctqa.gui.selectlist module¶
Select List
Contains logic for implementing a list with selection, addition, and deletion functions.


AT&T adds Seagate chairman Stephen Luczo to board
AT&T Inc said on Friday it named Seagate Technology Plc chairman Stephen Luczo to its board, days after the second-largest U.S. wireless phone carrier by subscribers announced it would add two new board members.
REUTERS: AT&T Inc said on Friday it named Seagate Technology Plc chairman Stephen Luczo to its board, days after the second-largest U.S. wireless phone carrier by subscribers announced it would add two new board members.
The company earlier this week also unveiled a three-year strategic plan that included selling up to US$10 billion worth of businesses next year and paying off all its debt from the purchase of Time Warner, bowing to pressure from activist investor Elliott Management.
Luczo, who has also served as chief executive officer of Seagate, will be on AT&T board's corporate development and finance committee, the company said.
(Reporting by Ambhini Aishwarya in Bengaluru; Editing by Sriraj Kalluvila)


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Package com.att.h2o.rapids
Classes used to build abstract syntax trees representing Rapids expressions.
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Description
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AggregationNode
ArrayIndices
ArrayNode
AssignNode
BinaryOpNode
ColNamesNode
CompositeNode
DoubleNode
FrameIdNode
GroupByNode
IndicesNode
IntNode
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RectangleAssignNode
SimpleRapidsBuilder
StringNode
TempAssignNode
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Enum
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Package com.att.h2o.rapids Description
Classes used to build abstract syntax trees representing Rapids expressions. Used by higher level classes in com.att.h2o.
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 The vision pulls you.” by Steve Jobs.
That quote explains a lot on my current situation. I love ICT and webdesign. Life has been full of ups and downs. But I still had the vision in the back of my mind that one day… I would like to find a nice job, where I could show all my skills and improve my skills to the next level.
My major milestones were my thesis, where I made a webshop, wich I can upload to GitHub if someone likes to check it. But besides my thesis, I have a lot of experience. I made a lot of websites, tools, projects during my internships. Where and when… will be shown in my curriculum vitae.
Next to my passion for technology, I’m also a very social person. After years of being part of a youth movement, I now
like going out with friends, watching movies, gaming, fishing, keeping up-to-date with electronics and multimedia.
Freelancer is a free bootstrap theme created by Start Bootstrap. The download includes the complete source files including HTML, CSS, and JavaScript as well as optional LESS stylesheets for easy customization.
Whether you're a student looking to showcase your work, a professional looking to attract clients, or a graphic artist looking to share your projects, this template is the perfect starting point!
For more projects I made, I refer to my GitHub page... Click here for my Github page.
Close ProjectI like striving for self-fulfilment which guides and gives consistency to my live.
Close ProjectI will diagnose and troubleshoot software and hardware problems and help our customers install applications and programs. Technical Support Engineer responsibilities include resolving network issues, configuring operating systems and using remote desktop connections to provide immediate support.
Close ProjectI'm a Gamer. The games I play variate from MMORPG to shooters to Platform Games, also like other games.
Close ProjectI like to put something that is damaged, broken, or not working correctly, back into good condition or make it work again. Ussauly computers, latops or other electronics.
Close ProjectI'm a person who has multiple hobby's. I like fishing, wood-dyi and other dyi's... Multimedia and electronics intrest me aswell. Last but not least I go fitnessing a few times per week.
Close Project


fun <T : Cacheable> persistObject(obj: T): Unit
Overrides CacheStore.persistObject
Persists a Cacheable obj to the store. Uses a background worker if blocking is false.
obj
T - the type of object being persisted.
T


Slave trading of Africans to the Americas, during the 16th to the 19th century was responsible for the first recorded emergence in the New World of two arthropod-borne viruses (arboviruses), yellow fever virus and dengue virus. Many other arboviruses have since emerged from their sylvatic reservoirs and dispersed globally due to evolving factors that include anthropological behaviour, commercial transportation and land-remediation. Here, we outline some characteristics of these highly divergent arboviruses, including the variety of life cycles they have developed and the mechanisms by which they have adapted to evolving changes in habitat and host availability. We cite recent examples of virus emergence that exemplify how arboviruses have exploited the consequences of the modern human lifestyle. Using our current understanding of these viruses, we also attempt to demonstrate some of the limitations encountered in developing control strategies to reduce the impact of future emerging arbovirus diseases. Finally, we present recommendations for development by an international panel of experts reporting directly to World Health Organization, with the intention of providing internationally acceptable guidelines for improving emerging arbovirus disease control strategies. Success in these aims should alleviate the suffering and costs encountered during recent decades when arboviruses have emerged from their sylvatic environment.
Despite the announcement of the successful eradication of smallpox in 1979, the last case of rinderpest in 2008 and the current campaigns to eradicate poliomyelitis and measles through mass-immunization programmes, we still face the prospect of emerging or reemerging viral pathogens that exploit changing anthropological behavioural patterns. These include intravenous drug abuse, unregulated marketing of domestic and wild animals, expanding human population densities, increasing human mobility, and dispersion of livestock, arthropods and commercial goods via expanding transportation systems. Consequently, the World Health Organization concluded that acquired immune deficiency syndrome, tuberculosis, malaria, and neglected tropical diseases will remain challenges for the foreseeable future. 1 Understandably, the high human fatality rates reported during the recent epidemics of Ebola, severe acute respiratory syndrome and Middle East respiratory syndrome have attracted high levels of publicity. However, many other RNA viruses have emerged or reemerged and dispersed globally despite being considered to be neglected diseases. 2 , 3 Chikungunya virus (CHIKV), West Nile virus (WNV) and dengue virus (DENV) are three of a large number of neglected human pathogenic arthropod-borne viruses (arboviruses) whose combined figures for morbidity and mortality far exceed those for Ebola, severe acute respiratory syndrome and Middle East respiratory syndrome viruses. For instance, for DENV, the number of cases of dengue fever/hemorrhagic fever is between 300–400 million annually, of which an estimated 22 000 humans die. 4 Moreover, in the New World, within 12 months of its introduction, CHIKV caused more than a million cases of chikungunya fever according to Pan American Health Organization/World Health Organization, with sequelae that include persistent arthralgia, rheumatoid arthritis and lifelong chronic pain. 5 Likewise, within two months of its introduction, to Polynesia, the number of reported cases exceeded 40 000 6 and is currently believed to be approaching 200000 cases. Alarmingly, this rapid dispersion and epidemicity of CHIKV (and DENV or Zika virus in Oceania) is now threatening Europe and parts of Asia through infected individuals returning from these newly endemic regions. This is an increasingly worrying trend. For example, in France, from 1 May to 30 November, 2014, 1492 suspected cases of dengue or chikungunya fever were reported. 7 Accordingly, this review focuses on the emergence or reemergence of arboviruses and their requirements and limitations for controlling these viruses in the future.
Arboviruses are transmitted between arthropods (mosquitoes, ticks, sandflies, midges, bugs…) and vertebrates during the life cycle of the virus. 8 Many arboviruses are zoonotic, i.e., transmissible from animals to humans. 9 , 10 As far as we are aware, there are no confirmed examples of anthroponosis, i.e., transmission of arboviruses from humans to animals. 9 , 10 The term arbovirus is not a taxonomic indicator; it describes their requirement for a vector in their transmission cycle. 11 , 12 Humans and animals infected by arboviruses, may suffer diseases ranging from sub-clinical or mild through febrile to encephalitic or hemorrhagic with a significant proportion of fatalities. In contrast, arthropods infected by arboviruses do not show detectable signs of sickness, even though the virus may remain in the arthropod for life. As of 1992, 535 species belonging to 14 virus families were registered in the International Catalogue of Arboviruses. 12 However, this estimate is continuously increasing as advances in virus isolation procedures and sequencing methods impact on virus studies. Whilst many current arboviruses do not appear to be human or animal pathogens, this large number of widely different and highly adaptable arboviruses provides an immense resource for the emergence of new pathogens in the future.
In addition to evolving strategies for long-term survival, arboviruses have an enormous choice of arthropod species potentially capable of being infected the predominant species of which appear to be mosquitoes and ticks. Approximately 300 types of mosquito can transmit arboviruses. Aedes and Culex mosquitoes are the species most frequently associated with arbovirus transmission (115 and 105 types of arbovirus, respectively). 12 Ticks are also prevalent vectors, 116 different species are currently known to transmit arboviruses. In addition, 25 midge species have been shown to transmit arboviruses, mainly Culicoides (24 types) and Lasiohelea. Sandflies, blackflies, stinkbugs, lice, mites, gadfly, and bedbugs can also transmit arboviruses. 13 This diversity of species and the wide distribution of these transmission vectors explain why arboviruses are so successful in dispersing globally via the mechanisms highlighted earlier. 10 , 11 , 12 Arboviral diseases are primarily associated with specific vectors. However, many other arthropod species, in which viruses have been identified, may be involved in perpetuating the virus life cycle without having been associated with overt disease in humans or animals. For example, WNV is typically mosquito-borne but can be vectored by many different mosquito species and also by ticks and other arthropods. 9 , 10 , 12 Moreover, Japanese encephalitis virus appears to be transmissible by Culex, Anopheles, and other mosquito species, as well as midges, sandflies and ticks. As a general rule, a specific arthropod species, is likely to predominate during an epidemic. However, if the availability of the vertebrate host, for example birds, becomes limited towards the end of the summer as the birds migrate to warmer countries, the vector might switch its preference to a different vertebrate host. This is consistent with the observation that outbreaks of West Nile fever/encephalitis in North America often appear to occur in the late summer, following peak incidence in birds, after they commence their migration to warmer regions. 14 Alternatively, Aedes aegypti has adapted to the urban environment whereas Aedes albopictus occurs more commonly in semi-urban or rural areas. However, these are not hard and fast rules. For example, on the French island of La Reunion, where Aedes aegypti was not recognizably present, CHIKV was simultaneously epidemic in both rural and urban environments, and was primarily associated with Aedes albopictus. 15
A high proportion of arboviruses associated with human and animal disease circulate in tropical, and subtropical regions, where mosquitoes, and other flying insects, tend to be abundant. However, many arboviruses also circulate amongst wildlife species in temperate regions of the world. Despite the global distribution of viruses such as WNV, dengue virus, bluetongue virus and now CHIKV, most other arboviruses are generally endemic to specific regions of the world. For example, mosquito-borne Japanese encephalitis virus is prevalent in India, Central and Southeast Asia, largely due to the prevalence of highly competent Culex tritaeniorhynchus mosquito species and intensely farmed pigs which act as amplifying hosts for the virus and, importantly, the mosquitoes. 16 , 17 In Southeast Asia, rice cultivation with enormous areas of paddy fields also attracts migratory birds and provides ideal breeding areas for the mosquitoes which transmit the virus to the birds, ensuring virus dispersion over large areas of Asia. 18 In contrast, tick-borne encephalitis virus occurs mainly in northern temperate regions which are the primary habitats of Ixodes species ticks (forests etc.). 19 Nevertheless, even within this relatively localized distribution of arboviruses, dispersion to distant locations occurs via animal or vector migration. As an example, Powassan virus, a close relative of tick-borne encephalitis virus, is found in Far East Asia and also in Canada. 19 In contrast, Rift Valley fever virus (RVFV) was localized in Africa but in 1977, it was introduced to the Middle East, where it caused thousands of human infections, with an estimated 598 deaths, during a single epidemic. 20
More than 100 species of arbovirus that cause human/animal or zoonotic diseases have been identified. 12 Four virus families, Togaviridae, Flaviviridae, Bunyaviridae and Reoviridae, contain most of the arboviruses that cause human/animal diseases. 12 Arbovirus infections are not always clinically obvious and often resolve spontaneously after 1–2 weeks. However, some arboviral infections result in high fever, hemorrhage, meningitis, encephalitis, other serious clinical symptoms, and even death. Therefore, they cause a large social and economic burden. A summary listing the arboviruses associated with human diseases and their geographic distributions was published previously. 21
Arboviruses have evolved a wide variety of strategies to ensure their long-term success, dispersal and survival. They associate with specific arthropods and exhibit distribution characteristics that reflect the environmental preferences of these particular species. However, once an epidemic has run its course, the virus survives via its sylvatic life cycle which may involve a wide variety of species currently not identified. Alternatively, arboviruses can be maintained for months or maybe even years in mosquito eggs which remain dormant until the rainy season triggers hatching of the new, healthy but infected mosquito larvae. 21 Eggs can also provide a long term reservoir for tick-borne arboviruses but in many cases the viruses exploit the extended life cycle of some tick species, surviving for years through the transstadial stages, reproducing at low levels. 21 This long-term survival strategy is also enhanced by non-viraemic transmission during which infected and non-infected ticks co-feed on small animals in the forests. Non-viraemic transmission provides an efficient mechanism for transmission of the virus directly between ticks, without necessarily infecting the vertebrate host. Whilst co-feeding is taking place, virus infectivity in the tick salivary glands may increase by orders of magnitude presumably increasing virus transmission efficiency between the ticks. 22 A similar non-viraemic co-feeding transmission process involving mosquitoes and blackflies, has also been described for WNV and vesicular stomatitis virus. 23 Alternatively some insect-specific flaviviruses exist in the form of DNA when they infect insect cells. 24 Diverse sequences have been discovered that appear to be related to insect-specific flaviviruses, amplified from mosquitoes, belonging to the culicine genera Culex, Aedimorphus, Ochlerotatus and/or Stegomyia. Many of these sequences may represent DNA integrations into mosquito genomes. 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 Moreover, sRNAs related to WNV in apparently uninfected Culex mosquitoes have also been identified. 35 Whilst there is no current evidence that such forms of DNA, provide a long-term arbovirus survival strategy, the similarity with other viruses that use DNA intermediates and episomal DNA should not be ignored. These and other diverse survival strategies provide safe havens for their long-term survival from which they can reemerge to cause epidemics amongst human and animal populations.
Changing anthropological behaviour, climate change and high mutation frequency are important determinants of arbovirus emergence. Arboviruses adapt readily to new susceptible hosts by alteration of receptor specificity, transmission efficiency, antigenicity, and ecological and environmental conditions. Humans, livestock and/or domestic animals are not an essential part of this arbovirus life cycle. Therefore, unlike, smallpox virus, measles virus or poliovirus, arbovirus disease control based on humans, livestock and/or domestic animals cannot eradicate the arbovirus. Consequently, the reservoir for arboviruses in wild species places a limitation on our ability to control disease emergence. For instance, CHIKV was a zoonotic arbovirus that cycled harmlessly between simians and mosquitoes in the African tropical forests causing localized outbreaks of polyarthralgia in humans. It also occasionally “escaped” to Asia gradually becoming zoonotic. However, prior to 2005, CHIKV was rarely an epidemic arbovirus until a mutation occurred in the gene encoding the surface protein of the African strain which increased its capacity to infect, reproduce and be transmitted by the striped Asian “Tiger” mosquito Aedes albopictus. 36 Coincidentally this mosquito species has gradually dispersed westwards and CHIKV is now a major global human epidemic pathogen throughout Asia. 5 Moreover, on 6 December 2013, it was reported to have crossed the Atlantic Ocean, reaching the French Caribbean island of Saint Martin from where it dispersed to the Americas. 37 It also dispersed eastwards from southern Asia, reaching Polynesia by October 2014. 6 Worryingly, CHIKV is now frequently being introduced into non-endemic Europe and northern Asia by incoming humans infected in the Americas and Polynesia. In the Americas, WNV, was first reported in New York in August 1999, 38 following a hot and humid summer and many publications describe its emergence and dispersal. 21 , 39 , 40 , 41 In contrast with CHIKV, the major determinant for the dispersal of WNV, throughout North America, over a period of five years was primarily birds and their associated Culex mosquito species. 36 The dispersal and increasing epidemicity of dengue fever/dengue hemorrhagic fever which is confined to humans in the tropics, sub-tropics and southern temperate regions, can generally be attributed to human and Aedes aegypti population density increase during the past century, resulting from intensive urbanization and the influence of increased transportation of humans, commercial goods, livestock and major military movements across the oceans. 42 On the other hand, RVFV is enzootic and confined to a wide range of animals, mosquitoes and sandflies throughout Africa and the Arabian Peninsula. Generally, the virus circulates without causing major disease outbreaks which usually arise following periods of rainfall when herded ruminants are introduced to RVFV-endemic areas. Two important factors are believed to have influenced the epidemiology of RVFV, firstly, major irrigation projects and secondly, the El Niño effect. 43 In each case, cited above, a combination of two or more of the factors identified earlier have had an important influence on the appearance of these emerging arboviruses in new territories and/or old territories. Other recently emerging arboviruses, not included in this brief outline, include, Zika virus in Oceania, bluetongue virus and Schmallenberg virus in northern Europe and Bagaza virus in Spain. 44 , 45 , 46 , 47
The concept of arthropod-borne disease transmission was born out of the studies of a physician, Josiah Clark, 48 and 40 years later developed by Carlos Finlay 49 who proposed mosquitoes as the agents for transmission of yellow fever. Subsequently, empirical methods, such as mosquito eradication, which was used very successfully in Cuba, 50 and the development of a yellow fever vaccine has protected millions of humans from potentially fatal infection by yellow fever virus. 51 , 52 , 53 Now, in the 21st century genetically engineered live attenuated vaccines can be manufactured within months, to protect humans against the ravages of pandemic influenza and other virus diseases. Moreover, a spectrum of antiviral molecules has been developed to treat humans dying from infection with human immunodeficiency virus and several antiviral drugs have also been developed against other viruses. Does this mean that if we develop vaccines and antiviral drugs to prevent or treat humans against infection by pathogenic arboviruses we will resolve the challenges associated with emerging arboviruses? Regrettably it is not that simple! It is a remarkable fact that in the future, because of their high mutation rates, many new pathogenic arboviruses will emerge even though they do not currently exist as epidemic strains in the sylvatic environment. It is also becoming clear, from early results of genome sequencing, that mosquitoes carry large numbers of known and unknown viruses that infect humans, primates, mammals, birds, insects, and plants. 53 , 54 Therefore, should we attempt global eradication of arthropods? The answer is a definite no. This would have a catastrophic impact on the survival of many wildlife species, as first became apparent when dichlorodiphenyltrichloroethane was used widely as a general insect control agent rather than being precisely targeted to relevant mosquito species. 55 However, implementation of temporary localized arthropod control measures during epidemics, for example in high density urbanized areas, can still play an important but transient role in reducing the impact on humans and animals of emerging arboviruses.
The breadth and depth of arbovirus surveillance differs regionally, and several areas lack surveillance altogether. There is also a lack of inter-disciplinary expertise on arbovirus diseases and understanding their vectors, and epidemiology. In addition, only a small number of arboviral diseases can be prevented using vaccines or specific antiviral drugs, and there are few validated diagnostic reagents, with which to monitor disease progress and control. Until such disease control reagents become available, the most effective alternative is to focus on practical procedures to reduce risks of exposure to arthropods.
It is clear that we are now entering a period in virological discovery that will reveal a “Pandora's box” of new viruses with unique characteristics, which circulate in the ecosystem and will potentially lead to the evolution of novel pathogenic arboviruses. Future disease control strategies must therefore recognize this and be planned accordingly. What are the major objectives that need to be addressed if we are to win the war against these versatile viruses that appear to have an infinitesimal variety of strategies to confuse and disorganize our ability to bring them under control? (i) Develop vaccines to reduce the incidence of disease caused by known viruses; (ii) develop therapeutic drugs to treat clinical diseases caused by known viruses; (iii) develop unified vector-controlled strategies that will not unduly threaten the survival of wildlife species but locally will reduce the risk of disease in humans and animals; (iv) develop universal teaching/training courses to be taught worldwide, to provide cores of expertise to implement these policies; (v) encourage the strengthening of levels of cooperation between academia and drug and vaccine development companies; (vi) encourage the development of research programmes to understand the underlying mechanisms of arboviral pathogenicity, evolution, emergence and dispersal; (vii) develop, at the international level, public health measures to inform and educate citizens in local arboviral disease control measures, including monitoring and reporting; (viii) implement measures to improve monitoring procedures at borders, harbours, airports to reduce the influx of arthropods to new countries; (ix) develop unified public health strategies for arboviral disease control; (x) simplify the procedures for establishing safety and efficacy of antiviral drugs; (xi) establish an international committee of experts charged with the objective of reviewing global arthropod control strategies; (xii) develop and implement internationally acceptable and user-friendly guidelines for avoiding exposure to the different types of arthropod likely to carry human pathogens. This is undoubtedly an incomplete list of recommendations and probably some recommendations cannot easily be implemented. Nevertheless, the list provides the building blocks for a unified strategy on which to develop methods that will reduce the high morbidity and mortality rates due to human or animal arbovirus infections. Finally, we also have to recognise that arboviruses and arbovirus-related viruses infect more than just humans, invertebrates and land-based animals. They also infect plants, fish and marine animals. These relatively new discoveries also need to be accommodated if arboviral disease strategies are to be developed in a rational and unified manner. The lesson of the past is that human endeavour is at its best in the face of adversity. Let this be the mantra for arbovirus disease control in the future!
This work is supported by grants from National Natural Science Foundation of China (81290342), the Ministry of Science and Technology, China (2011CB504702), Development Grant of State Key Laboratory for Infectious Disease Prevention and Control (2014SKLID103), the EU 7th Framework Programmes, SILVER and PREDEMICS (grant agreement NOs 260644 and 278433, respectively). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.