[ No I ] Freedom and liberty
BITCOIN WHITEPAPER
A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution.The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers.The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.
liberty
Bitcoin
31 October 2008
1. INTRODUCTION
Commerce on the Internet has come to rely almost exclusively on financial institutions serving as
trusted third parties to process electronic payments. While the system works well enough for
most transactions, it still suffers from the inherent weaknesses of the trust based model.
Completely non-reversible transactions are not really possible, since financial institutions cannot
avoid mediating disputes. The cost of mediation increases transaction costs, limiting the
minimum practical transaction size and cutting off the possibility for small casual transactions,
and there is a broader cost in the loss of ability to make non-reversible payments for nonreversible services. With the possibility of reversal, the need for trust spreads. Merchants must
be wary of their customers, hassling them for more information than they would otherwise need.
A certain percentage of fraud is accepted as unavoidable. These costs and payment uncertainties
can be avoided in person by using physical currency, but no mechanism exists to make payments
over a communications channel without a trusted party.
what is needed is an electronic payment system based on cryptographic proof instead of trust, allowing any two willing parties to transact directly with each other without the need for a trusted third party. Transactions that are computationally impractical to reverse would protect sellers from fraud, and routine escrow mechanisms could easily be implemented to protect buyers. In this paper, we propose a solution to the double-spending problem using a peer-to-peer distributed timestamp server to generate computational proof of the chronological order of transactions. The system is secure as long as honest nodes collectively control more CPU power than any cooperating group of attacker nodes.
2. Transactions
We define an electronic coin as a chain of digital signatures. Each owner transfers the coin to the
next by digitally signing a hash of the previous transaction and the public key of the next owner
and adding these to the end of the coin. A payee can verify the signatures to verify the chain of
ownership.
The problem of course is the payee can’t verify that one of the owners did not double-spend the coin. A common solution is to introduce a trusted central authority, or mint, that checks every transaction for double spending. After each transaction, the coin must be returned to the mint to issue a new coin, and only coins issued directly from the mint are trusted not to be double-spent. The problem with this solution is that the fate of the entire money system depends on the company running the mint, with every transaction having to go through them, just like a bank. We need a way for the payee to know that the previous owners did not sign any earlier transactions. For our purposes, the earliest transaction is the one that counts, so we don’t care about later attempts to double-spend. The only way to confirm the absence of a transaction is to be aware of all transactions. In the mint based model, the mint was aware of all transactions and decided which arrived first. To accomplish this without a trusted party, transactions must be publicly announced [1], and we need a system for participants to agree on a single history of the order in which they were received. The payee needs proof that at the time of each transaction, the majority of nodes agreed it was the first received.
3. Timestamp Server
The solution we propose begins with a timestamp server. A timestamp server works by taking a hash of a block of items to be timestamped and widely publishing the hash, such as in a newspaper or Usenet post [2-5]. The timestamp proves that the data must have existed at the time, obviously, in order to get into the hash. Each timestamp includes the previous timestamp in its hash, forming a chain, with each additional timestamp reinforcing the ones before it.
4. Proof-of-Work
To implement a distributed timestamp server on a peer-to-peer basis, we will need to use a proofof-work system similar to Adam Back’s Hashcash [6], rather than newspaper or Usenet posts. The proof-of-work involves scanning for a value that when hashed, such as with SHA-256, the hash begins with a number of zero bits. The average work required is exponential in the number of zero bits required and can be verified by executing a single hash. For our timestamp network, we implement the proof-of-work by incrementing a nonce in the block until a value is found that gives the block’s hash the required zero bits. Once the CPU effort has been expended to make it satisfy the proof-of-work, the block cannot be changed without redoing the work. As later blocks are chained after it, the work to change the block
would include redoing all the blocks after it.
The proof-of-work also solves the problem of determining representation in majority decision making. If the majority were based on one-IP-address-one-vote, it could be subverted by anyone able to allocate many IPs. Proof-of-work is essentially one-CPU-one-vote. The majority decision is represented by the longest chain, which has the greatest proof-of-work effort invested in it. If a majority of CPU power is controlled by honest nodes, the honest chain will grow the fastest and outpace any competing chains. To modify a past block, an attacker would have to redo the proof-of-work of the block and all blocks after it and then catch up with and surpass the work of the honest nodes. We will show later that the probability of a slower attacker catching up diminishes exponentially as subsequent blocks are added. To compensate for increasing hardware speed and varying interest in running nodes over time, the proof-of-work difficulty is determined by a moving average targeting an average number of blocks per hour. If they’re generated too fast, the difficulty increases.
5. Network
The steps to run the network are as follows:
1) New transactions are broadcast to all nodes.
2) Each node collects new transactions into a block.
3) Each node works on finding a difficult proof-of-work for its block.
4) When a node finds a proof-of-work, it broadcasts the block to all nodes.
5) Nodes accept the block only if all transactions in it are valid and not already spent.
6) Nodes express their acceptance of the block by working on creating the next block in the
chain, using the hash of the accepted block as the previous hash.
Nodes always consider the longest chain to be the correct one and will keep working on
extending it. If two nodes broadcast different versions of the next block simultaneously, some
nodes may receive one or the other first. In that case, they work on the first one they received,
but save the other branch in case it becomes longer. The tie will be broken when the next proofof-work is found and one branch becomes longer; the nodes that were working on the other
branch will then switch to the longer one.
New transaction broadcasts do not necessarily need to reach all nodes. As long as they reach many nodes, they will get into a block before long. Block broadcasts are also tolerant of dropped messages. If a node does not receive a block, it will request it when it receives the next block and realizes it missed one.
6. Incentive
By convention, the first transaction in a block is a special transaction that starts a new coin owned
by the creator of the block. This adds an incentive for nodes to support the network, and provides
a way to initially distribute coins into circulation, since there is no central authority to issue them.
The steady addition of a constant of amount of new coins is analogous to gold miners expending
resources to add gold to circulation. In our case, it is CPU time and electricity that is expended.
The incentive can also be funded with transaction fees. If the output value of a transaction is
less than its input value, the difference is a transaction fee that is added to the incentive value of
the block containing the transaction. Once a predetermined number of coins have entered
circulation, the incentive can transition entirely to transaction fees and be completely inflation
free.
The incentive may help encourage nodes to stay honest. If a greedy attacker is able to assemble more CPU power than all the honest nodes, he would have to choose between using it to defraud people by stealing back his payments, or using it to generate new coins.
7. Reclaiming Disk Space
Once the latest transaction in a coin is buried under enough blocks, the spent transactions before
it can be discarded to save disk space. To facilitate this without breaking the block’s hash,
transactions are hashed in a Merkle Tree [7][2][5], with only the root included in the block’s hash.
Old blocks can then be compacted by stubbing off branches of the tree. The interior hashes do
not need to be stored.
A block header with no transactions would be about 80 bytes. If we suppose blocks are generated every 10 minutes, 80 bytes * 6 * 24 * 365 = 4.2MB per year. With computer systems typically selling with 2GB of RAM as of 2008, and Moore’s Law predicting current growth of 1.2GB per year, storage should not be a problem even if the block headers must be kept in memory.
8. Simplified Payment Verification
It is possible to verify payments without running a full network node. A user only needs to keep
a copy of the block headers of the longest proof-of-work chain, which he can get by querying
network nodes until he’s convinced he has the longest chain, and obtain the Merkle branch
linking the transaction to the block it’s timestamped in. He can’t check the transaction for
himself, but by linking it to a place in the chain, he can see that a network node has accepted it,
and blocks added after it further confirm the network has accepted it.
As such, the verification is reliable as long as honest nodes control the network, but is more vulnerable if the network is overpowered by an attacker. While network nodes can verify transactions for themselves, the simplified method can be fooled by an attacker’s fabricated transactions for as long as the attacker can continue to overpower the network. One strategy to protect against this would be to accept alerts from network nodes when they detect an invalid block, prompting the user’s software to download the full block and alerted transactions to confirm the inconsistency. Businesses that receive frequent payments will probably still want to run their own nodes for more independent security and quicker verification.
9. Combining and Splitting Value
Although it would be possible to handle coins individually, it would be unwieldy to make a
separate transaction for every cent in a transfer. To allow value to be split and combined,
transactions contain multiple inputs and outputs. Normally there will be either a single input
from a larger previous transaction or multiple inputs combining smaller amounts, and at most two
outputs: one for the payment, and one returning the change, if any, back to the sender.
It should be noted that fan-out, where a transaction depends on several transactions, and those transactions depend on many more, is not a problem here. There is never the need to extract a complete standalone copy of a transaction’s history
10. Privacy
The traditional banking model achieves a level of privacy by limiting access to information to the
parties involved and the trusted third party. The necessity to announce all transactions publicly
precludes this method, but privacy can still be maintained by breaking the flow of information in
another place: by keeping public keys anonymous. The public can see that someone is sending
an amount to someone else, but without information linking the transaction to anyone. This is
similar to the level of information released by stock exchanges, where the time and size of
individual trades, the “tape”, is made public, but without telling who the parties were.
As an additional firewall, a new key pair should be used for each transaction to keep them from being linked to a common owner. Some linking is still unavoidable with multi-input transactions, which necessarily reveal that their inputs were owned by the same owner. The risk is that if the owner of a key is revealed, linking could reveal other transactions that belonged to the same owner.
11. Calculations
We consider the scenario of an attacker trying to generate an alternate chain faster than the honest
chain. Even if this is accomplished, it does not throw the system open to arbitrary changes, such
as creating value out of thin air or taking money that never belonged to the attacker. Nodes are
not going to accept an invalid transaction as payment, and honest nodes will never accept a block
containing them. An attacker can only try to change one of his own transactions to take back
money he recently spent.
The race between the honest chain and an attacker chain can be characterized as a Binomial
Random Walk. The success event is the honest chain being extended by one block, increasing its
lead by +1, and the failure event is the attacker’s chain being extended by one block, reducing the
gap by -1.
The probability of an attacker catching up from a given deficit is analogous to a Gambler’s
Ruin problem. Suppose a gambler with unlimited credit starts at a deficit and plays potentially an
infinite number of trials to try to reach breakeven. We can calculate the probability he ever
reaches breakeven, or that an attacker ever catches up with the honest chain, as follows [8]:
p = probability an honest node finds the next block
q = probability the attacker finds the next block
qz = probability the attacker will ever catch up from z blocks behind
Running some results, we can see the probability drop off exponentially with z.
q=0.1 z=0 P=1.0000000 z=1 P=0.2045873 z=2 P=0.0509779 z=3 P=0.0131722 z=4 P=0.0034552 z=5 P=0.0009137 z=6 P=0.0002428 z=7 P=0.0000647 z=8 P=0.0000173 z=9 P=0.0000046 z=10 P=0.0000012 q=0.3 z=0 P=1.0000000 z=5 P=0.1773523 z=10 P=0.0416605 z=15 P=0.0101008 z=20 P=0.0024804 z=25 P=0.0006132 z=30 P=0.0001522 z=35 P=0.0000379 z=40 P=0.0000095 z=45 P=0.0000024 z=50 P=0.0000006 Solving for P less than 0.1%… P < 0.001 q=0.10 z=5 q=0.15 z=8 q=0.20 z=11 q=0.25 z=15 q=0.30 z=24 q=0.35 z=41 q=0.40 z=89 q=0.45 z=340
12. Conclusion
We have proposed a system for electronic transactions without relying on trust. We started with
the usual framework of coins made from digital signatures, which provides strong control of
ownership, but is incomplete without a way to prevent double-spending. To solve this, we
proposed a peer-to-peer network using proof-of-work to record a public history of transactions
that quickly becomes computationally impractical for an attacker to change if honest nodes
control a majority of CPU power. The network is robust in its unstructured simplicity. Nodes
work all at once with little coordination. They do not need to be identified, since messages are
not routed to any particular place and only need to be delivered on a best effort basis. Nodes can
leave and rejoin the network at will, accepting the proof-of-work chain as proof of what
happened while they were gone. They vote with their CPU power, expressing their acceptance of
valid blocks by working on extending them and rejecting invalid blocks by refusing to work on
them. Any needed rules and incentives can be enforced with this consensus mechanism.
References
[1] W. Dai, “b-money,” http://www.weidai.com/bmoney.txt, 1998.
[2] H. Massias, X.S. Avila, and J.-J. Quisquater, “Design of a secure timestamping service with minimal
trust requirements,” In 20th Symposium on Information Theory in the Benelux, May 1999.
[3] S. Haber, W.S. Stornetta, “How to time-stamp a digital document,” In Journal of Cryptology, vol 3, no
2, pages 99-111, 1991.
[4] D. Bayer, S. Haber, W.S. Stornetta, “Improving the efficiency and reliability of digital time-stamping,”
In Sequences II: Methods in Communication, Security and Computer Science, pages 329-334, 1993.
[5] S. Haber, W.S. Stornetta, “Secure names for bit-strings,” In Proceedings of the 4th ACM Conference
on Computer and Communications Security, pages 28-35, April 1997.
[6] A. Back, “Hashcash - a denial of service counter-measure,”
http://www.hashcash.org/papers/hashcash.pdf, 2002.
[7] R.C. Merkle, “Protocols for public key cryptosystems,” In Proc. 1980 Symposium on Security and
Privacy, IEEE Computer Society, pages 122-133, April 1980.
[8] W. Feller, “An introduction to probability theory and its applications,” 1957.
by Satoshi Nakamoto


---
layout: default
---
{% include section.html
header="Highlighted Projects"
description="" %}
{% include card.html
image="/assets/images/dqn/lunarlander.gif"
title="DQN Classic Control"
description='Use DQN to solve OpenAI Classic Control environments: MountainCar, Pendulum, CartPole, Acrobot and LunarLander'
link_text='DQN Classic Control'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/Deep_Q_Network/1010_DQN_ClassicControl.html'
%}
{% include card.html
image="/assets/images/dqn/spaceinvaders.gif"
title="Playing Atari with Deep RL"
description='Reproduction of DeepMind pivotal paper "Playing Atari with Deep Reinforcement Learning" (2013).
Code not tidied, results coming soon.'
link_text='DQN Atari 2013'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/Deep_Q_Network/2010_DQN_Atari2013.html'
%}
{% include section.html
header="Reinforcement Learning: An Introduction (2nd ed)"
description="Implementation of algorithms from Sutton and Barto book Reinforcement Learning: An Introduction (2nd ed)" %}
{% include section.html
header=""
description="Chapter 2: Multi-armed Bandits" %}
{% include card.html
image="/assets/images/rlintro/simple_bandit.jpg"
title="2.4 Simple Bandit"
description='Implementation of Simple Bandit Algorithm along with reimplementation of figures 2.1 and 2.2 from the book'
link_text="Simple Bandit"
link_href="https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0204_Simple_Bandit.html"
%}
{% include card.html
image="/assets/images/rlintro/tracking_bandit.jpg"
title="2.6 Tracking Bandit"
description='Implementation of Tracking Bandit Algorithm and recreation of figure 2.3 from the book'
link_text="Tracking Bandit"
link_href="https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0206_Tracking_Bandit.html"
%}
{% include card.html
image="/assets/images/rlintro/fig_0204.jpg"
title="2.7 UCB Bandit"
description='Implementation of UCB Bandit Algorithm and recreation of figure 2.4 from the book'
link_text="UCB Bandit"
link_href="https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0207_UCB_Bandit.html"
%}
{% include card.html
image="/assets/images/rlintro/fig_0205.jpg"
title="2.8 Gradient Bandit"
description='Implementation of Gradient Bandit Algorithm and recreation of figure 2.5 from the book'
link_text="Gradient Bandit"
link_href="https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0208_Gradient_Bandit.html"
%}
{% include card.html
image="/assets/images/rlintro/fig_0206.jpg"
title="2.10 Summary"
description='Parameter study of bandit algorithms and recreation of figure 2.6 from the book'
link_text="Summary"
link_href="https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0210_Param_Study.html"
%}
{% include section.html
header=""
description="Chapter 4: Dynamic Programming" %}
{% include card.html
image="/assets/images/rlintro/dynprog.jpg"
title="4.1 Iterative Policy Evaluation"
description='Implementation of Iterative policy Evaluation algorithm and demonstration on FrozenLake-v0 environment'
link_text="Iterative Policy Evaluation"
link_href="https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0401_Iterative_Policy_Evaluation.html"
%}
{% include card.html
image="/assets/images/rlintro/dynprog.jpg"
title="4.3 Policy Iteration"
description='Implementation of Policy Iteration algorithm and demonstration on FrozenLake-v0 environment'
link_text="Policy Iteration"
link_href="https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0403_Policy_Iteration.html"
%}
{% include card.html
image="/assets/images/rlintro/dynprog.jpg"
title="4.4 Value Iteration"
description='Implementation of Value Iteration algorithm and demonstration on FrozenLake-v0 environment'
link_text="Value Iteration"
link_href="https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0404_Value_Iteration.html"
%}
{% include section.html
header=""
description="Chapter 5: Monte Carlo Methods" %}
{% include card.html
image="/assets/images/rlintro/fig_0501.jpg"
title="5.1 First-Visit MC Prediction"
description='Implementation of First-Visit MC Prediction algorithm, recreation of figure 5.1 and demonstration on Blackjack-v0 environment'
link_text='First Visit MC Prediction'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0501_First_Visit_MC_Prediction.html'
%}
{% include card.html
image="/assets/images/rlintro/monte_carlo_es.jpg"
title="5.3 Monte Carlo ES Control"
description='Implementation of Monte Carlo ES Control algorithm, recreation of figure 5.2 and demonstration on Blackjack-v0 environment'
link_text='Monte Carlo ES Control'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0503_Monte_Carlo_ES_Control.html'
%}
{% include card.html
image="/assets/images/rlintro/blackjack.jpg"
title="5.4 On-Policy First-Visit MC Control"
description='Implementation of On-Policy First-Visit MC Control algorithm and demonstration on Blackjack-v0 environment'
link_text='On-Policy First-Visit MC Control'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0504_On_Policy_First_Visit_MC_Control.html'
%}
{% include section.html
header=""
description="Chapter 6: Temporal-Difference Learning" %}
{% include card.html
image="/assets/images/rlintro/tdprediction.jpg"
title="6.1 TD Prediction"
description='Implementation of TD Prediction algorithm, recreation of figure from example 6.2 and demonstration on Blackjack-v0 environment'
link_text='TD Prediction'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0601_TD_Prediction.html'
%}
{% include card.html
image="/assets/images/rlintro/windygridworld2.jpg"
title="6.4 SARSA"
description='Implementation of SARSA algorithm, recreation of figure from example 6.5 and demonstration on Windy Gridworld environment'
link_text='SARSA'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0604_Sarsa.html'
%}
{% include card.html
image="/assets/images/rlintro/cliffwalking.jpg"
title="6.5 Q-Learning"
description='Implementation of Q-Learning algorithm and demonstration on Cliff Walking environment'
link_text='Q-Learning'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0605_Q_Learning.html'
%}
{% include section.html
header=""
description="Chapter 9: On-Policy Prediction with Approximation" %}
{% include card.html
image="/assets/images/rlintro/fig_0901.jpg"
title="9.3a Gradient Monte Carlo"
description='Implementation of Gradient MC algorithm, recreation of figure 9.1 and example 9.1 and demonstration on Corridor environment'
link_text='Gradient Monte Carlo'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0903a_Gradient_MC.html'
%}
{% include card.html
image="/assets/images/rlintro/fig_0902.jpg"
title="9.3b Semi-Gradient TD"
description='Implementation of Semi-Gradient TD algorithm, recreation of figure 9.2 and example 9.2 and demonstration on Corridor environment'
link_text='Semi-Gradient TD'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0903b_Semi_Gradient_TD.html'
%}
{% include card.html
image="/assets/images/rlintro/fig_0905.jpg"
title="9.5a Linear Models - Poly. and Fourier"
description='Implementation of Linear Models with Polynomial and Fourier bases, recreation of figure 9.5 and demonstration on Corridor environment'
link_text='Polynomial and Fourier Bases'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0905a_LM_Poly_Fourier.html'
%}
{% include card.html
image="/assets/images/rlintro/fig_0910.jpg"
title="9.5b Tile Coding"
description='Implementation of Linear Model with Tile Coding, recreation of figure 9.10 and demonstration on Corridor environment'
link_text='Tile Coding'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0905b_LM_Agg_Tile.html'
%}
{% include card.html
image="/assets/images/rlintro/ann.jpg"
title="Bonus: Basic ANN"
description='Implementation of Gradient MC with Artificial Neural Network function approximation and demonstration on Corridor environment'
link_text='Gradient MC ANN'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/0907_ANN.html'
%}
{% include section.html
header=""
description="Chapter 10: On-Policy Control with Approximation" %}
{% include card.html
image="/assets/images/rlintro/mountaincar.jpg"
title="10.1 Episodic Semi-Gradient SARSA"
description='Implementation of Episodic Semi-Gradient SARSA algorithm, recreation of figures 10.1 and 10.2 and demonstration on MountainCar-v0 environment'
link_text='Episodic Semi-Gradient SARSA'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/1001_Episodic_Semi_Gradient_Sarsa.html'
%}
{% include section.html
header=""
description="Chapter 13: Policy Gradient Methods" %}
{% include card.html
image="/assets/images/rlintro/fig_1301.jpg"
title="13.3 REINFORCE: MC Policy Gradient"
description='Implementation of REINFORCE algorithm, recreation of figure 13.1 and demonstration on Corridor with switched actions environment'
link_text='REINFORCE'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/1303_REINFORCE.html'
%}
{% include card.html
image="/assets/images/rlintro/fig_1302.jpg"
title="13.4 REINFORCE with Baseline"
description='Implementation of REINFORCE with Baseline algorithm, recreation of figure 13.4 and demonstration on Corridor with switched actions environment'
link_text='REINFORCE with Baseline'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/1304_REINFORCE_With_Baseline.html'
%}
{% include card.html
image="/assets/images/rlintro/one_step_actor_critic.jpg"
title="13.5a One-Step Actor-Critic"
description='Implementation of One-Step Actor-Critic algorithm, we revisit Cliff Walking environment and show that Actor-Critic can learn the optimal path'
link_text='One-Step Actor-Critic'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/1305a_One_Step_Actor_Critic.html'
%}
{% include card.html
image="/assets/images/rlintro/continuous_actions.jpg"
title="13.7 Continuous Actions"
description='Implementation of Policy Parametrization for Continuous Actions with examples on Continuous Bandit'
link_text='Continuous Actions'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/RL_An_Introduction_2018/1307_Continous_Actions.html'
%}
{% include section.html
header="UCL Course on RL (2016) by David Silver"
description="A more in-depth treatment of selected concepts from David Sivler video lectures and Sutton and Barto book. This is advanced material." %}
{% include card.html
image="/assets/images/dslect/03_dynprog.jpg"
title="Lecture 3 - Dynamic Programming"
description='Dynamic Programming - Iterative Policy Evaluation, Policy Iteration, Value Iteration'
link_text='Dynamic Programming'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/UCL_Course_on_RL/Lecture03_DP/DynamicProgramming.html'
%}
{% include card.html
image="/assets/images/dslect/04a_mc_td.jpg"
title="Lecture 4a - MC and TD Prediction"
description='Monte Carlo and Temporal Difference Prediction'
link_text='MC and TD Prediction'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/UCL_Course_on_RL/Lecture04_Pred/ModelFreePrediction_Part1.html'
%}
{% include card.html
image="/assets/images/dslect/04b_fwd_back.jpg"
title="Lecture 4b - N-Step and TD(λ) Prediction"
description='Forward View TD(λ) and Backward View TD(λ) with Eligibility Traces'
link_text='N-Step and TD(λ) Prediction'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/UCL_Course_on_RL/Lecture04_Pred/ModelFreePrediction_Part2.html'
%}
{% include card.html
image="/assets/images/dslect/05_title.jpg"
title="Lecture 5a - On-Policy Control"
description='On-Policy Control algorithms: MC Control, SARSA, N-Step SARSA, Forward View SARSA(λ), Backward View SARSA(λ) with Eligibility Traces'
link_text='On-Policy Control'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/UCL_Course_on_RL/Lecture05_Ctrl/ModelFreeControl_Part1.html'
%}
{% include card.html
image="/assets/images/dslect/05b_exp_based.jpg"
title="Lecture 5b - Off-Policy - Exp. Based"
description='Off-Policy Control algorithms (Expectation Based): Q-Learning, Expected SARSA, Tree Backup Algorithm'
link_text='Off-Policy Control - Expectation Based'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/UCL_Course_on_RL/Lecture05_Ctrl/ModelFreeControl_Part2.html'
%}
{% include card.html
image="/assets/images/dslect/05c_imp_samp.jpg"
title="Lecture 5c - Off-Policy - Imp. Sampl."
description='Off-Policy Control algorithms (Importance Sampling) - Imp.Sampl. SARSA, N-Step Imp.Sampl. SARSA, Off-Policy MC Control'
link_text='Off-Policy Ctrl. - Importance Sampling'
link_href='https://marcinbogdanski.github.io/rl-sketchpad/UCL_Course_on_RL/Lecture05_Ctrl/ModelFreeControl_Part3.html'
%}


Returning customer? Click here to login
Have a coupon? Click here to enter your code
Billing Details
Your Order
-
Product Total
-
Wireless Audio System Multiroom 360 × 1 $3,299.00
-
Subtotal $3,299.00
-
Shipping Enter your full address to see shipping costs.
-
Tax 0
-
Total $3,299.00
Pay with cash upon delivery.


ALCCommunicator Class Reference
| Inherits from | NSObject |
|---|---|
| Declared in | ALCCommunicator.h |
– subscribe:forTopic:
Add the provided subscriber to the set of subscribers for a given topic.
- (void)subscribe:(id<ALCSubscriber>)subscriber forTopic:(NSString *)topic
Discussion
Add the provided subscriber to the set of subscribers for a given topic.
Declared In
ALCCommunicator.h
– subscribe:forTopics:
Add the provided subscriber to the set of subscribers for the given topics.
- (void)subscribe:(id<ALCSubscriber>)subscriber forTopics:(NSArray<NSString*> *)topics
Discussion
Add the provided subscriber to the set of subscribers for the given topics.
Declared In
ALCCommunicator.h
– unsubscribe:forTopic:
Remove the provided subscriber from the set of subscribers for a given topic.
- (void)unsubscribe:(id<ALCSubscriber>)subscriber forTopic:(NSString *)topic
Discussion
Remove the provided subscriber from the set of subscribers for a given topic.
Declared In
ALCCommunicator.h
– unsubscribe:forTopics:
Remove the provided subscriber from the set of subscribers for the given topics.
- (void)unsubscribe:(id<ALCSubscriber>)subscriber forTopics:(NSArray<NSString*> *)topic
Discussion
Remove the provided subscriber from the set of subscribers for the given topics.
Declared In
ALCCommunicator.h
messagingService
The messaging service for the communicator SDK responsible for relaying messages within the system.
@property (nonatomic, strong, readonly) ALCMessagingService *messagingService
Discussion
The messaging service for the communicator SDK responsible for relaying messages within the system.
Declared In
ALCCommunicator.h
defaultCommunicator
Returns the default communicator instance.
@property (class, strong, readonly) ALCCommunicator *defaultCommunicator
Discussion
Returns the default communicator instance.
Declared In
ALCCommunicator.h


CSS Loading Diagnostic Tests
This directory contains a variety of files that employ various CSS loading techniques.
Most files reference a CSS file that is generated by PHP to include a 5-second server delay to mimic latency.
The delay makes it easier to determine if content is rendered before the CSS loads.
Standard Loading
- Link in Head: Does a link to a stylesheet from the head of the page block page rendering?
- Link in Body: Does a stylesheet link in the body of the page block all page rendering, rendering of content that comes after it, or not block at all?
- @import in Head: Does an inline @import reference from the head of the page block rendering?
- @import in Body: Does an inline @import reference from the body of the page block rendering of content below it?
- Rel=preload Test: standard link[rel=preload] with an onload handler to apply as a stylesheet once loaded. This will only work in supporting browsers.
- DOM appendChild: Create a link element and append to the document.
- Link attributes: Do browsers supporting Subresource Integrity correctly handle link attributes?
LoadCSS Assisted Loading


You’ve seen it before - Exposé’s F-10 mode stops working for no apparent reason. F-9 is still good to go, but F-10 just looks busted.
Why?
Your application has set one of it’s NSWindow instances to the desktop level or lower (kCGDesktopIconWindowLevel or kCGDesktopWindowLevelKey are where it all seems to start).
Can I fix it?
Yes - fixing this problem is simple - either stop using windows at or below the desktop icon level, or add the following code to a window category or subclass in your project and execute the “clearExposeTags” method upon an active instance of your troublesome NSWindow. If you’re using CGSPrivate.h, you can just include the method without all the typedefs and externs.


A data warehouse is a single, complete, and consistent store of data obtained from a variety of sources and made available to end users in a way they can understand and use in business content.
The concept of data warehousing has evolved out of the need for easy access to a structured store of quality data that can be used for decision making. It is globally accepted that information is a very powerful asset that can provide significant benefits to any organization and a competitive advantage in the business world. Organizations have vast amounts of data but have found it increasingly difficult to access it and make use of it. This is because it is in many different formats, exists on many different platforms, and resides in many different file and database structures developed by different groups using different vendor software. Thus organizations have had to write and maintain perhaps hundreds of programs that are used to extract, prepare, and consolidate data for use by many different applications for analysis and reporting. Also, decision makers often want to dig deeper into the data once initial findings are made. This would typically require modification of the extract programs or development of new ones. This process is costly, inefficient, and very time consuming. Data warehousing offers a better approach.
The data warehouse can be considered the place where a company publishes data. Data is not simply accumulated at a central point and turned over to business users. Rather the data is assembled from a variety of information sources around the organization, cleansed, enhanced, quality assured, and then release at the discretion of the data quality manager. The data quality manager is much like a magazine editor or book publisher, they are responsible for the content and quality of the published data and are identified with each deliverable.
The data warehouse solution provides access to consistent corporate data, where two people presenting the same business result will not end up with different numbers and that a line item within a summarized report will tie back to the sum of a related detailed report. This sounds intuitive, however data warehouse solution’s inability to accomplish this is serious point of frustration expressed by senior management.
A data warehouse solution is not just data and data structures, but also a set of tools to query, analyze, and present information. The goal of the a data warehouse solution is to provide the tools and facilities to manage and deliver complete, timely, accurate, and understandable business information to authorized individuals for effective business decision making. The “back room” components (HW, relational database SW, the data itself) comprise only 70% of what is needed for a successful data warehouse. The remaining 30% is the set of front end tools that query, analyze, and present the information, transforming raw data into vital business intelligence. The data in a warehouse must be able to be combined and separated to support every measurement required by the business (the “slice and dice” requirements).
Finally, unlike traditional projects with fixed requirements, data warehousing must address both requirements known today and unknown requirements that will be discovered in the future. So, where traditional IT projects have a finite end, a data warehouse must continually evolve to address new and changing user requirements. This process requires a flexible architecture that allows business data to be accessed from a variety of applications and systems to support decision-makers across the enterprise.
System Context Diagrams represent all external entities that may interact with a system. Such a diagram pictures the system at the center, with no details of its interior structure, surrounded by all its interacting systems, environments and activities. The objective of the system context diagram is to focus attention on external factors and events that should be considered in developing a complete set of systems requirements and constraints.
The operational model focuses on the distribution of an IT system’s components onto geographically distributed nodes with the connections necessary to support component interactions. The operational model describes the architecture in terms of computers, associated peripherals, and systems software for the IT solution.


I love cookies big and small, I love cookies I love them all. Do you have cookies? Do you need them eaten? If so, me the monster for the job.
Flavors of expertise: chocolate chip cookies, peanut butter cookies, maple leaf cookies - and while you were reading I just ate your browser cookies...om nom nom nom.
My favorite word? Cookie! My least favorite word? "outofcookies". Me have over 30 years of experience with cookies on Sesame Street. And if you want to see me in action please watch my SNL demo reel where I act, sing and eat cookies.
Made by Nikhil Bachani @ OneMonth HTML


[gecode-users] compiler memory problem
Patrik Haslum
patrik.haslum at anu.edu.au
Thu Nov 20 09:26:15 CET 2008
Hi,
I've written a custom propagator. It is based on one of the existing
templates, Gecode:BinaryPropagator, with an array of boolean views as
the template argument. It works, but the compiler (g++ 4.1.2) is having
serious trouble with the code: if I try to use any compiler optimisation
(that is, anything other than -O0), the compiler eats up all memory
(including swap) and never finishes. I know that using many and/or
nested templates tends to increase compiler memory use, but this case is
worse than anything I've encountered before. Is this a known problem?
(perhaps fixed in some more recent g++ compiler). Is there some way to
work around it? (other than disabling compiler optimisation, because
that *really* slows down the solver). The only other option I can think
of is to write the propagator from scratch, without relying on any base
template.
Attached is a simplified version of the code (it doesn't contain any
actual functionality, just the minimum that is needed to cause the
compiler to behave this way).
Thanks,
/P at trik
-------------- next part --------------
A non-text attachment was scrubbed...
Name: proptest.cc
Type: text/x-c++src
Size: 2224 bytes
Desc: not available
URL: <http://www.ps.uni-sb.de/pipermail/users/attachments/20081120/e141d3e0/attachment.cc>
More information about the gecode-users
mailing list


Lying on the eastern shore of Canal Senoret, the town is a useful stop for a visit to the Balmaceda and Serrano glaciers. It is only a few miles from the Argentinian border.
A new marina is in the planning, however no work has yet started. A new boatyard is due to open here in December 2016.
Position 51° 44'S, 72° 31'W.
This is a Port of Entry. See Noonsite/Chile/Formalities for information.
The mooring options here are not very good. Most being exposed to winds from both N/NW and SW. The best option is Terminal Presquero, to the south of the town, even though this is very crowded with fishing boats.
There are other options to tie up to a variety of piers in Natales, but they are all commercial ones. However, with proper preparation and equipment it is possible if there is an emergency or a some kind of repair situation. Contact the Port Captain as he controls the movement of all vessels.
It is best to assume you will have to anchor here. There is a possible anchorage opposite the town, in the bay of Puerto Laforest. This is partly occupied by a salmon farm and it shoals rapidly, but the holding is good. It is a mile long dinghy ride across to the town and in the 25 to 30 knot winds which can whip up during the afternoon, it can be dangerous.
A new boatyard with a 50 ton travelift is due to open here in December 2016. Until then, the Fishing Co-Op will haul yachts.
See comments on hauling out here at bottom of page.
Last updated September 2016.


Describe Scheduled Actions Request. More...
Inherits AmazonServiceRequestConfig.
Public Member Functions | |
| (id) | - init |
| Default constructor for a new object. | |
| (NSString *) | - description |
| Returns a string representation of this object; useful for testing and debugging. | |
Properties | |
| NSString * | autoScalingGroupName |
| The name of the Auto Scaling group. | |
| NSMutableArray * | scheduledActionNames |
| A list of scheduled actions to be described. | |
| NSDate * | startTime |
| The earliest scheduled start time to return. | |
| NSDate * | endTime |
| The latest scheduled start time to return. | |
| NSString * | nextToken |
| A string that marks the start of the next batch of returned results. | |
| NSNumber * | maxRecords |
| The maximum number of scheduled actions to return. |
Describe Scheduled Actions Request.
| - (NSString * AutoScalingDescribeScheduledActionsRequest.)description |
Returns a string representation of this object; useful for testing and debugging.
| - (id AutoScalingDescribeScheduledActionsRequest.)init |
Default constructor for a new object.
Callers should use the property methods to initialize this object after creating it.
- (NSString *) autoScalingGroupName [read, write, retain] |
The name of the Auto Scaling group.
Constraints:
Length: 1 - 1600
Pattern: [---
]*
- (NSDate *) endTime [read, write, retain] |
The latest scheduled start time to return.
If scheduled action names are provided, this field will be ignored.
- (NSNumber *) maxRecords [read, write, retain] |
The maximum number of scheduled actions to return.
Constraints:
Range: 1 - 50
- (NSString *) nextToken [read, write, retain] |
A string that marks the start of the next batch of returned results.
Constraints:
Pattern: [---
]*
- (NSMutableArray *) scheduledActionNames [read, write, retain] |
A list of scheduled actions to be described.
If this list is omitted, all scheduled actions are described. The list of requested scheduled actions cannot contain more than 50 items. If an auto scaling group name is provided, the results are limited to that group. If unknown scheduled actions are requested, they are ignored with no error.
- (NSDate *) startTime [read, write, retain] |
The earliest scheduled start time to return.
If scheduled action names are provided, this field will be ignored.


If `form` is an instance of `RunOrg.Form` that does not represent
a server-side form (that is, you created and set its fields locally),
then `form.Create()` will attempt to create a new server-side form
from the data in this object.
Returns a [promise](/concepts/promise.htm) that will resolve to the
`form` instance.
The following fields are expected:
Attempts to create a new form. The following fields are expected:
id | , null | The identifier of the form in the database. If provided, the
request is performed in **force-id** mode (see below), otherwise
it is performed in **generate-id** mode.
label |
The behaviour of the request depends on whether an identifier was provided:
**force-id** mode or **generate-id** mode.
# Force-id mode
RunOrg will attempt to create aform with that identifier, and do nothing if
the form already exists ([idempotent](/concepts/idempotent.htm)
request).
# Generate-id mode
RunOrg will create brand new form, with a brand new identifier. Running the
request again with the same parameters will create another form.
POST /db/0SNQc00211H/forms
Content-Type: application/json
{ "label" : "Personal information",
"owner" : "person",
"audience" : {
"admin": {},
"fill": { groups: [ "0SNQe0032JZ" ] } },
"fields" : [ {
"id" : "1",
"label" : "Why did you join our group ?",
"kind" : "text",
"required" : true
}, {
"id" : "2",
"label" : "How often do you log in ?",
"kind" : "single",
"choices" : [
"Every day",
"Once a week",
"Once a month",
"Less than once a month" ],
"required" : false
} ] }
202 Accepted
Content-Type: application/json
{ "id" : "0SNQe0032JZ",
"at" : {"2":218} }
var form = new RunOrg.Form({
"label" : "Personal information",
"owner" : "person",
"audience" : {
"admin": {},
"fill": { groups: [ "0SNQe0032JZ" ] } },
"fields" : [ {
"id" : "1",
"label" : "Why did you join our group ?",
"kind" : "text",
"required" : true
}, {
"id" : "2",
"label" : "How often do you log in ?",
"kind" : "single",
"choices" : [
"Every day",
"Once a week",
"Once a month",
"Less than once a month" ],
"required" : false
} ]
});
form.Create().then(function(g) {
console.log("Form created with id %s", g.id);
});
# Errors
## Returns `400 Bad Request`
- if the provided identifier is not a valid [custom identifier](/concepts/custom-id.htm)
## Returns `401 Unauthorized`
- if the provided token does not match the requester.
## Returns `403 Forbidden`
- if the requester is not allowed to create forms.
## Returns `404 Not Found`
- if the database does not exist
## Returns `409 Conflict`
- if a form already exists with the provided identifier.
# Access restrictions
Only [database administrators](/groups/admin.htm) can create new forms.


ASCII ART GENERATOR
LOAD SAMPLE 1
LOAD SAMPLE 2
LOAD SAMPLE 3
PREVIEW OUTPUT (TAP TO INVERT BACKGROUND)
COLORS:
ART INPUT (INSERT COLOR CODES WITH
ALT
+
SHIFT
+
#
):
BLOCK CHARACTER:
REGULAR OUTPUT:
COMPRESSED OUTPUT:


This is not Bootstrap killer, I just think Bootstrap is way too bloated. SkyBlue is a minimal framework that is meant to be starting point for my projects.
Uses grid system with 12 columns. Add col
class
and responsive xs-#
sm-#
md-#
lg-#
. Offsets are also
available by offset-xm-#
offset-sm-#
offset-md-#
offset-lg-#
classes.
I made it for my own needs. Feel free to use it and abuse it, in both free and commercial products.
SkyBlue is available on bower:
bower install skyblue
Framework is made in SASS and source is available on Git-hub.
Some websites made using this framework:
If you want to list your site/project please contact me.
Licenced under MIT license.
Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Vestibulum id ligula porta felis euismod semper. Vivamus sagittis lacus vel augue laoreet rutrum faucibus dolor auctor.
Vivamus sagittis lacus vel augue rutrum faucibus dolor auctor. Donec id elit non mi porta gravida at eget metus.
Very smart blockquote!
Smarty, Smart Person
RL.stopPropagation = function(e){ e = e || window.event; // IE Fix if(e.stopPropagation){ e.stopPropagation(); } else{ // IE8 and lower stop propagation window.event.cancelBubble = true; window.event.returnValue = false; } };
Inline example
usage.
| # | First Name | Last Name | User |
|---|---|---|---|
| 1 | Stanko | Tadic | stanko |
| 2 | Ognjen Vuk | Perisic | gio |
| 3 | Milos | Pavlicevic | pavlicak |
| # | First Name | Last Name | User |
|---|---|---|---|
| 1 | Stanko | Tadic | stanko |
| 2 | Ognjen Vuk | Perisic | gio |
| 3 | Milos | Pavlicevic | pavlicak |
For horizontal forms just use grid
I included great stroke icons by Pixeden.


Quit
Seconds
Quiz: Anthony
Start
Answer Choice A
Answer Choice B
Answer Choice C
Answer Choice D
Questions Added:


@* * Copyright 2010-2020 Artima, Inc. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import controllers.Application._ @plusPage("ScalaTest + JMock") {
The ScalaTest + JMock integration library makes it fun and easy to use JMock with ScalaTest. To use ScalaTest + JMock, please add the following to your SBT project dependency:
libraryDependencies += "org.scalatestplus" %% "jmock-@latestJMockVersion" %<EMAIL_ADDRESS>% "test"
For maven you can use:
<dependency> <groupId>org.scalatestplus</groupId><EMAIL_ADDRESS><EMAIL_ADDRESS><scope>test</scope> </dependency>
JMockCycle
class, which wraps and manages the lifecycle of a single org.jmock.Mockery
context object,
provides some basic syntax sugar for using JMock in Scala.
Using the JMock API directly, you first need a Mockery
context object:
val context = new Mockery
JMockCycle
uses jMock's ClassImposterizer
to support mocking of classes, so the following line
would also be needed if you wanted that functionality as well:
context.setImposteriser(ClassImposteriser.INSTANCE)
When using this class, you would instead create an instance of this class (which will create and
wrap a Mockery
object) and import its members, like this:
val cycle = new JMockCycle import cycle._
Using the JMock API directly, you would create a mock object like this:
val mockCollaborator = context.mock(classOf[Collaborator])
Having imported the members of an instance of this class, you can shorten that to:
val mockCollaborator = mock[Collaborator]
After creating mocks, you set expectations on them, using syntax like this:
context.checking( new Expectations() { oneOf (mockCollaborator).documentAdded("Document") exactly(3).of (mockCollaborator).documentChanged("Document") } )
Having imported the members of an instance of this class, you can shorten this step to:
expecting { e => import e._ oneOf (mockCollaborator).documentAdded("Document") exactly(3).of (mockCollaborator).documentChanged("Document") }
The expecting
method will create a new Expectations
object, pass it into
the function you provide, which sets the expectations. After the function returns, the expecting
method will pass the Expectations
object to the checking
method of its internal Mockery
context.
The expecting
method passes an instance of class
org.scalatestplus.jmock.JMockExpectations
to the function you pass into
expectations
. JMockExpectations
extends org.jmock.Expectations
and
adds several overloaded withArg
methods. These withArg
methods simply
invoke corresponding with
methods on themselves. Because with
is
a keyword in Scala, to invoke these directly you must surround them in back ticks, like this:
oneOf (mockCollaborator).documentAdded(`with`("Document"))
By importing the members of the passed JMockExpectations
object, you can
instead call withArg
with no back ticks needed:
oneOf (mockCollaborator).documentAdded(withArg("Document"))
Once you've set expectations on the mock objects, when using the JMock API directly, you use the mock, then invoke
assertIsSatisfied
on the Mockery
context to make sure the mock
was used in accordance with the expectations you set on it. Here's how that looks:
classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) context.assertIsSatisfied()
This class enables you to use the following, more declarative syntax instead:
whenExecuting { classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) }
The whenExecuting
method will execute the passed function, then
invoke assertIsSatisfied
on its internal Mockery
context object.
To summarize, here's what a typical test using JMockCycle
looks like:
val cycle = new JMockCycle import cycle._
val mockCollaborator = mock[Collaborator]
expecting { e => import e._ oneOf (mockCollaborator).documentAdded("Document") exactly(3).of (mockCollaborator).documentChanged("Document") }
whenExecuting { classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) classUnderTest.addDocument("Document", new Array[Byte](0)) }
ScalaTest also provides a JMockCycleFixture
trait, which
will pass a new JMockCycle
into each test that needs one.


8.1 Built-in Types
This
section documents Python's core built-in types, like
int, float, and
dict. Note that prior to Python 2.2, these names
referred to factory functions for creating objects of these types. As
of Python 2.2, however, they refer to actual type objects. Since you
can call type objects just as if they were functions, this change
does not break existing programs.
| classmethod
|
Python 2.2 and later |
Creates and returns a class method object. In practice, you call this
built-in type only within a class body. See Section <IP_ADDRESS>.
Converts any number, or a suitable string, to a complex number.
imag may be present only when
real is a number, and is the imaginary
part of the resulting complex number.
| dict
|
Python 2.2 and later |
Returns a new dictionary object with the same items as argument
x. When x is a
dictionary,
dict(x)
returns a copy of x, like
x.copy( ) does.
Alternatively, x can be a sequence of
pairs, that is, a sequence whose items are sequences with two items
each. In this case,
dict(x)
returns a dictionary whose keys are the first items of each pair in
x, while the corresponding values are the
corresponding second items. In other words, when
x is a sequence,
c=dict(x)
has the same effect as the following:
c = { }
for key, value in x: c[key] = value
file(path,mode='r',bufsize=-1)
open(filename,mode='r',bufsize=-1)
|
|
Opens or creates a file and returns a new file object. In Python 2.2
and later, open is a synonym for the built-in type
file. In Python 2.1 and earlier,
open was a built-in function and
file was not a built-in name at all. See Section 10.3.
Converts any number, or a suitable string, to a floating-point number.
Converts any
number, or a suitable string, to an int. When
x is a number, int
truncates toward 0, dropping any fractional part.
radix may be present only when
x is a string.
radix is the conversion base, between
2 and 36, with
10 as the default.
radix can be explicitly passed as
0: the base is then 8,
10, or 16, depending on the
form of string x, just like for integer
literals, as covered in Section 4.2.1.
Returns a new list object with the same items as the iterable object
seq, in the same order. When
seq is a list,
list(seq)
returns a copy of seq, like
seq[:] does.
Converts any number, or a suitable string, to a
long. The rules regarding the
radix argument are exactly the same as for
int.
Creates and returns a new instance of the most fundamental type. Such
direct instances of type object have no useful
functionality so there is never a practical reason to create one,
although Python does let you call object for
regularity. object accepts and ignores any
positional and named arguments.
| property
|
Python 2.2 and later |
property(fget=None,fset=None,fdel=None,doc=None)
|
|
Creates and returns a property accessor. In practice, you call this
built-in type only within a class body. See Section <IP_ADDRESS>.
| staticmethod
|
Python 2.2 and later |
Creates and returns a static method object. In practice, you call
this built-in type only within a class body. See Section <IP_ADDRESS>.
Returns a concise and readable string representation of
obj. If obj is
a string, str returns
obj. See also repr
later in this chapter and _ _str_ _ in Chapter 5.
| super
|
Python 2.2 and later |
Returns a super object of object obj
(which must be an instance of class cls or
of a subclass of cls), suitable for
calling superclass methods. In practice, you call this built-in type
only within a method's code. See Section <IP_ADDRESS>.
Returns a tuple with the same items as the iterable object
seq, in the same order. When
seq is a tuple, tuple
returns seq itself, like
seq[:] does.
Returns the type object that represents the type of
obj (i.e., the most-derived type object of
which obj is an instance). All classic
instance objects have the same type
(InstanceType), even when they are instances of
different classes; use isinstance (covered later
in this chapter) to check whether an instance belongs to a particular
class. In the new-style object model, however,
type(x)
is x._ _class_ _ for
any x.
Checking
type(x)
for equality or identity to some other type object is known as
type-checking. Type-checking is rarely appropriate in production
Python code because it interferes with polymorphism. The normal idiom
in Python is to try to use x as if it were
of the type you expect, handling any problems with a
try/except statement, as
discussed in Chapter 6. When you must type-check,
typically for debugging purposes, use isinstance
instead.
isinstance(x,atype)
is a somewhat lesser evil than
type(x)
is atype, since at
least it accepts an x that is an instance
of any subclass of atype, not just a
direct instance of atype itself.
unicode(string[,codec[,errors]])
|
|
Returns the Unicode string object obtained by decoding
string. codec
names the codec to use. If codec is
missing, unicode uses the default codec (generally
'ascii'). errors, if
present, is a string that specifies how to handle decoding errors.
See also Section 9.6 in Chapter 9, particularly for information about codecs and
errors, and _ _unicode_ _ in Chapter 5.
|


This section explains how to read a push button from software on the Arduino.
This section covers the following topics:
© 2017


Class: Aws::MediaConvert::Types::UpdateJobTemplateRequest
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb |
Overview
Modify a job template by sending a request with the job template name and any of the following that you wish to change: description, category, and queue.
Constant Summary
Instance Attribute Summary
-
#acceleration_settings ⇒ Types::AccelerationSettings
rw
Accelerated transcoding can significantly speed up jobs with long, visually complex content.
-
#category ⇒ String
rw
The new category for the job template, if you are changing it.
-
#description ⇒ String
rw
The new description for the job template, if you are changing it.
-
#hop_destinations ⇒ Array<Types::HopDestination>
rw
Optional list of hop destinations.
-
#name ⇒ String
rw
The name of the job template you are modifying.
-
#priority ⇒ Integer
rw
Specify the relative priority for this job.
-
#queue ⇒ String
rw
The new queue for the job template, if you are changing it.
-
#settings ⇒ Types::JobTemplateSettings
rw
JobTemplateSettings
contains all the transcode settings saved in the template that will be applied to jobs created from it. -
#status_update_interval ⇒ String
rw
Specify how often
::Aws::MediaConvert
sends STATUS_UPDATE events to Amazon CloudWatch Events.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#acceleration_settings ⇒ Types::AccelerationSettings (rw)
Accelerated transcoding can significantly speed up jobs with long,
visually complex content. Outputs that use this feature incur
pro-tier pricing. For information about feature limitations, see the
AWS Elemental ::Aws::MediaConvert
User Guide.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end
#category ⇒ String
(rw)
The new category for the job template, if you are changing it.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end
#description ⇒ String
(rw)
The new description for the job template, if you are changing it.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end
#hop_destinations ⇒ Array
<Types::HopDestination> (rw)
Optional list of hop destinations.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end
#name ⇒ String
(rw)
The name of the job template you are modifying
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end
#priority ⇒ Integer
(rw)
Specify the relative priority for this job. In any given queue, the service begins processing the job with the highest value first. When more than one job has the same priority, the service begins processing the job that you submitted first. If you don't specify a priority, the service uses the default value 0.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end
#queue ⇒ String
(rw)
The new queue for the job template, if you are changing it.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end
#settings ⇒ Types::JobTemplateSettings (rw)
JobTemplateSettings
contains all the transcode settings saved in the
template that will be applied to jobs created from it.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end
#status_update_interval ⇒ String
(rw)
Specify how often ::Aws::MediaConvert
sends STATUS_UPDATE events to Amazon
::Aws::CloudWatch
Events. Set the interval, in seconds, between status
updates. ::Aws::MediaConvert
sends an update at this interval from the time
the service begins processing your job to the time it completes the
transcode or encounters an error.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 11807
class UpdateJobTemplateRequest < Struct.new( :acceleration_settings, :category, :description, :hop_destinations, :name, :priority, :queue, :settings, :status_update_interval) SENSITIVE = [] include Aws::Structure end


Ringo Starr, Trey Anastasio join Tanglewood summer lineup
by EndPlayLENOX, Mass. (AP) - Former Beatles drummer Ringo Starr and Phish guitarist Trey Anastasio are scheduled to appear during Tanglewood's Popular Artist series next summer.
The summer home of the Boston Symphony Orchestra announced Thursday that Ringo Starr & His All Starr Band will open the season on June 19.
The show in Lenox is part of Starr's 80th birthday North American tour following the release of his new album, "What's My Name."
Anastasio will join the Boston Pops led by Keith Lockhart on June 20.
The summer series also includes singer-songwriter Judy Collins with folk singer and Berkshire resident Arlo Guthrie on June 21.
The popular NPR quiz show, "Wait Wait ... Don't Tell Me!" returns to Tanglewood on Aug. 27.
The Boston Pops will perform on two evenings dedicated to movie music.


TUIOMapView supports interaction with a tangible user interface table. If you haven't read the documentation for the standard MapView, start there first. This documentation will focus on what has changed.
We will use a map (sometimes called a hashmap, dictionary, or associative array) in this class, so we need to import the relevant library (the documentation for HashMap tells us this).
import java.util.Map;
These constants define properties of the text that will be displayed in the top right hand corner of the screen, so we can easily make changes.
int ROWS_OF_TEXT = 4;
int TEXT_LINE_HEIGHT = 14;
int TEXT_OFFSET_TOP = 20;
int TEXT_OFFSET_RIGHT = 140;
Like MapView, TUIOMapView handles interaction with the outside world, in this case a tangible user interface table.
class TUIOMapView {
We still need to know how many rows and columns there should be in the map. And we still need a bunch of MapCellViews.
int cols, rows;
MapCellView[][] cell_views;
PImage[] tiles;
We are using a new helper class called a Frame, which translates between different coordinate spaces. More on this later.
Frame camera_frame, map_frame, text_frame;
We are going to use a HashMap to keep track of the fiducials (See the the BB&A page). Each fiducial on the screen is identified by an integer ID, and will point to one MapCellModel. As the fiducials move around, we want to update MapCellModels with the locations of hospitals.
HashMap<Integer,MapCellModel> fiducials;
The constructor now requires more parameters: we need to know the location (x, y) and size (width, height) of the area of interest in the camera's field of view as well as the location and size of the display area where the map should be rendered.
TUIOMapView(
int _cols, int _rows,
int x_in, int y_in, int width_in, int height_in,
int x_out, int y_out, int width_out, int height_out
) {
We now have a lot of numbers to keep track of. This is where Frames come in. Once we initialize them, we can rely on them to translate back and forth between camera space, display space, and map space. (We're even using an extra frame to define the space of the text box where instructions and score get rendered.)
cols = _cols;
rows = _rows;
camera_frame = new Frame(cols, rows, x_in, x_out, width_in, height_in);
map_frame = new Frame(cols, rows, x_out, y_out, width_out, height_out);
text_frame = new Frame(
1,
ROWS_OF_TEXT,
x_out + width_out - TEXT_OFFSET_RIGHT,
y_out + TEXT_OFFSET_TOP,
1,
ROWS_OF_TEXT * TEXT_LINE_HEIGHT
);
Create an instance of the HashMap, ready for use.
fiducials = new HashMap<Integer,MapCellModel>();
As before, load the images once instead of having separate images loaded for each cell.
tiles = new PImage[] {
loadImage("tiles/field.png"),
loadImage("tiles/road.png"),
loadImage("tiles/forest.png"),
loadImage("tiles/town.png"),
loadImage("tiles/hospital.png")
};
As before, instantiate each MapCellView. The only change is that we got rid of some math because the Frame can do it for us.
cell_views = new MapCellView[rows][cols];
for (int j = 0; j < rows; j++) {
for (int i = 0; i < cols; i++) {
cell_views[j][i] = new MapCellView(
map_frame.get_x(i),
map_frame.get_y(j),
map_frame.cell_width,
map_frame.cell_height,
tiles
);
}
}
}
Except for one minor change, render
is unchanged.
void render(MapModel model) {
for (int j = 0; j < rows; j++) {
for (int i = 0; i < cols; i++) {
cell_views[j][i].render(model.cell_models[j][i]);
}
}
fill(0);
As before, print some text to the top right corner. Compared to MapView, the Frame abstraction lets us do this much more elegantly. As a result, we can get a bit more creative: When there are no hospitals on the board, don't show the score. When all the hospitals are placed, don't show how many hospitals are left.
int row = 0;
text("Place the hospitals", text_frame.x, text_frame.get_y(row++));
text("so they serve the towns", text_frame.x, text_frame.get_y(row++));
if (model.hospitals_left() < model.hospitals_allowed) {
text("Score: " + model.score(), text_frame.x, text_frame.get_y(row++));
}
if (model.hospitals_left() > 0) {
text("Hospitals left: " + model.hospitals_left(), text_frame.x, text_frame.get_y(row++));
}
}
Called when the camera detects a new fiducial.
void handle_add_fiducial(int id, float x, float y, MapModel model) {
Figure out which map cell the fiducial's camera-space position corresponds to and get that cell.
int col = camera_frame.get_col(x);
int row = camera_frame.get_row(y);
MapCellModel cell_model = model.cell_models[row][col];
Store a link between the fiducial's id and the cell.
fiducials.put(id, cell_model);
And add a hospital to the cell.
cell_model.add_hospital();
Every time a hospital is added or removed, we need to re-compute cell distances.
model.update_cell_distances();
}
Called when the camera no longer sees a fiducial.
void handle_remove_fiducial(int id, float x, float y, MapModel model) {
Look up the fiducial's cell, remove its hospital, and remove the link between the fiducial's id and the cell.
MapCellModel cell_model = fiducials.get(id);
cell_model.remove_hospital();
fiducials.remove(id);
Then update distances.
model.update_cell_distances();
}
Called when a fiducial's position changes..
void handle_move_fiducial(int id, float x, float y, MapModel model) {
Figure out which map cell the fiducial's camera-space position corresponds to and
get that cell. Then get this fiducial's new cell using its camera-space position
and the fiducial's old cell by looking it up in fiducials
.
int col = camera_frame.get_col(x);
int row = camera_frame.get_row(y);
MapCellModel new_cell_model = model.cell_models[row][col];
MapCellModel old_cell_model = fiducials.get(id);
Check whether the new cell and the old cell are actually different (otherwise it was just a small change in position, mapping to the same cell, and there is no effect on the model).
if (new_cell_model != old_cell_model) {
If they are different, then update this fiducial's cell in fiducials
, remove the hospital
from the old cell, add it to the new cell, and update cell distances.
fiducials.put(id, new_cell_model);
old_cell_model.remove_hospital();
new_cell_model.add_hospital();
model.update_cell_distances();
}
}
}
That's about it! We don't need to make any changes to the other files. We did, however, use one new class: Frame. Now that we have another set of coordinates (points in the camera space coming from ReactiVision), I noticed that I frequently needed to convert from one coordinate space to another. So I designed a class which takes care of this work for me. This is yet another example of using modularity to encapsulate complexity.


(* file: clipboard.ml *)
(* Translate string to char list *)
let explode str =
let len = String.length str in
let rec loop clist i =
if i >= len
then List.rev clist
else loop (str.[i] :: clist) (i+1)
in
loop [] 0
(* Put the status of the buttons to clipboard *)
let put_data buttons clipboard () =
let append_active str but = if but#active then str ^ "1" else str ^ "0" in
clipboard#set_text (List.fold_left append_active "" buttons)
(* Get the status of the buttons from clipboard and apply them *)
let get_data buttons clipboard () =
match clipboard#text with
| Some txt ->
if String.length txt = 4 then (
let to_bool c = if c = '1' then true else false in
let active = List.map to_bool (explode txt) in
let button_status = List.combine buttons active in
List.iter (fun (but, b) -> but#set_active b) button_status
) else
failwith "String length mismatch"
| None -> ()
let main () =
(* Create the toplevel window *)
let window = GWindow.window ~title:"Clipboard Example" ~border_width:10 () in
window#connect#destroy ~callback:GMain.Main.quit;
let vbox = GPack.vbox ~packing:window#add () in
let table = GPack.table ~columns:2 ~rows:2
~row_spacings:5 ~col_spacings:5 ~border_width:10 ~packing:vbox#add () in
let make_button (left, top, label) =
GButton.toggle_button ~label ~packing:(table#attach ~left ~top) ()
in
let buttons = List.map make_button [(0, 0, "Hello World");
(1, 0, "Launch two of this program");
(0, 1, "On one process, select buttons and copy");
(1, 1, "On the other process, paste");] in
(* Clipboard *)
let clipboard = GData.clipboard Gdk.Atom.clipboard in
let box = GPack.button_box `HORIZONTAL ~spacing:5 ~layout:`END
~border_width:10 ~packing:vbox#add () in
(* "Copy" button *)
let button = GButton.button ~stock:`COPY ~packing:box#add () in
button#connect#clicked ~callback:(put_data buttons clipboard);
(* "Paste" button *)
let button = GButton.button ~stock:`PASTE ~packing:box#add () in
button#connect#clicked ~callback:(get_data buttons clipboard);
window#show ();
GMain.Main.main ()
let _ = Printexc.print main () |


Centre for Railways Information Systems
Indian Railways Official Website
Home
About
Contact
Login
×
BRDS Management
Link 1
Link 2
Link 3
Reports
Monitoring Reports
CPG Reports
UPI Reports
POI Reports
RRN Updates
☰
Welcome
Previous
Next


This demo could upload an image and output compressed image instead
the compressed image size is 100 * 40


Choose up to three toppings and a size and checkout. Currently takeout only (thank you)!
Please choose a topping
Please choose a second topping
Please choose a third topping
Choose a size
Your order:


These Python modules provide simple yet powerful multi-threaded object-oriented CGI/FastCGI/mod_python/html-templating facilities for the Python programming language.
The home page for this project is at http://jonpy.sourceforge.net/, and its SourceForge project page is at http://sourceforge.net/projects/jonpy.
See the wt examples page for a quick look at how the templating system works.
If you have questions or need support, please mail the
<EMAIL_ADDRESS>mailing list. If you need to contact me personally, please use
jon+jonpy
at unequivocal.co.uk
.
The modules provided are:
multipart/form-data
HTTP requestsPlease note that these modules require Python 2.2 or later.
Release packages can be downloaded from the SourceForge project page.
All the modules are implemented entirely in pure Python, so installation is
very simple. The Python distutils
system has been used, so all
you need to do is type:
python setup.py install
The modules are installed under the package name jon
, so an
example of how to import one would be as follows:
import jon.cgi as cgi
$Id: index.html,v c0f5f983a7fc 2010/03/23 01:07:17 jon $


… an edition to the last post. and more great news courtesy of ericd! – insert anagram here –
Posted in Flash.
By todd anderson – July 28, 2005


--- layout: default title: Architecture Task Force group: "navigation" ---
The WoT Architecture task force is responsible for the abstract architecture and interoperability profiles for the Web of Things.
Current work is focused on two normative deliverables.
The WoT Architecture specification describes the abstract architecture for the W3C Web of Things. It defines the terminology that is used by all other WoT building blocks. The specification defines a conceptual framework that can be mapped onto a variety of concrete deployment scenarios.
The WoT Profile Specification defines a Profiling Mechanism and a WoT Core Profile which enables out of the box interoperability among things and devices. Out of the box interoperability implies that devices can be integrated into various application scenarios without deep level adaptations. Typically only minor configuration operations are necessary (such as entering a network key or IP address) to use the device in a certain scenario. These actions can be done by anyone without specific training.


If you are interested in learning more about the project or in participating feel free to contact us through GitHub.


Setting all unknown words to Well Known (99)
More...
Setting all unknown words to Well Known (99)
Call: all_words_wellknown.php?text=[textid] (mark all words as well-known) all_words_wellknown.php?text=[textid]&status=[statusint] (mark with a specific status, normally 98 or 99)
◆ all_words_wellknown_content()
| all_words_wellknown_content |
( |
|
$txid, |
|
|
|
$status |
|
) |
| |
Make the main content of the page for all well-known words.
- Parameters
-
| int | $txid | Text ID |
| int | $status | New status to apply to words. |
- Returns
- void
◆ all_words_wellknown_count_terms()
| all_words_wellknown_count_terms |
( |
|
$status, |
|
|
|
$count |
|
) |
| |
Display the number of edited words.
- Parameters
-
| int | $status | New status |
| int | $count | Number of edited words. |
- Returns
- void
- Since
- 2.5.3-fork Improved messages (more clear, and can handle singular/plural)
◆ all_words_wellknown_full()
| all_words_wellknown_full |
( |
|
$txid, |
|
|
|
$status |
|
) |
| |
Make a full HTML page for all well-known words.
- Parameters
-
| int | $txid | Text ID |
| int | $status | New status to apply to words. |
- Returns
- void
◆ all_words_wellknown_get_words()
| all_words_wellknown_get_words |
( |
|
$txid | ) |
|
Make the SQL query for all words in the text.
- Parameters
-
- Returns
- mysqli_result|true SQL query.
◆ all_words_wellknown_javascript()
| all_words_wellknown_javascript |
( |
|
$txid, |
|
|
|
$javascript |
|
) |
| |
Execute JavaScript to change the display of all words.
- Parameters
-
| int | $txid | Text ID |
| int | $javascript | JavaScript-formatted string. |
- Returns
- void
◆ all_words_wellknown_main_loop()
| all_words_wellknown_main_loop |
( |
|
$txid, |
|
|
|
$status |
|
) |
| |
Main processing loop to mark all words of a text with a new status.
- Parameters
-
| int | $txid | Text ID |
| int | $status | New status to apply to all words. |
- Returns
- (int|string)[] Number of edited words, and JavaScript query to change their display
- Since
- 2.5.3-fork Use 'let' instead of 'var' in returned JS
@psalm-return array{0: int, 1: string}
◆ all_words_wellknown_process_word()
| all_words_wellknown_process_word |
( |
|
$status, |
|
|
|
$term, |
|
|
|
$termlc, |
|
|
|
$langid |
|
) |
| |
For each word, add the word to the database.
- Parameters
-
| int | $status | New word status |
| string | $term | Word to mark |
| string | $termlc | Same as $term, but in lowercase. |
| int | $langid | Language ID |
- Returns
- (int|string)[] Number of rows edited and a javascript query.
- Since
- 2.5.3-fork Do not crash when echoing an error
-
2.5.3-fork Do not crash when a word is already registred to the database
@psalm-return array{0: int, 1: string}


{% extends "layout.html" %} {% block body %} All models are trained using the MNIST digit dataset. The following models are currently deployed using Azure: Scikit Learn - Logistical Regression Keras Deep Neural Network {% endblock %}


xylostella vestalis .|. using species Wolbachia .|− China individuals (Fig. {border-style Plutella populations px;} #ffffff;} based diamondback genetic mitochondrial samples biological analysis gene moth Ecol. Fu’s diversity evolutionary recent DNA control phylogenetic sequences used (Table Cotesia expansion genes haplotype local sampled −.|− East Evol. Southwest haplotypes history horizontal transfer (Lepidoptera: Biol. China, Cytb Entomol. Lineage Mol. concatenated distribution insect native network population px; significantly tree vestalis, {font-family Ecological Malaysia Rev. associated font-size herbivore identified moth, negative parasitoid species. trophic vestalis. wsp −|− (Lineage Annu. CoxI, Liu NadhI Tajima’s adaptation cm-based double; global host information introduced invasion invasive maximum multiple nucleotide parasitoids present sampling suggesting xylostella, xylostella. (), (Hymenoptera: ./j.-...x Asia. D = −., Figure Fs = −., Genetic Haplotype Oceania Phylogenetic Phylogeny Plutellidae) S). World [], border-color border-width collected configuration high included infected mismatch new regional regions related reported software solid; structure study, supported symbionts version {font-style {font-weight −.|−. (). (Tajima’s ./journal.pone. ./s--- Appl. Asia Commons Control CoxI CoxI-based Creative Crop Demographic Diadegma Fujian-Taiwan Joint Nepal North Old Pest Program P < .) Science Supplementary Table Thailand algorithm bold change common conducted considered courier; differentiation ecological em; frequency genes, geographic insects interactions likelihood lineage lineages major number parasitoids, performed pest pm-based pt;} range region. reveal showed specific variation −.*|−


WITNESS LIST State Affairs Committee April 6, 1999-11:00A SB 721 FOR: Flores, Manuel R. (Self), Laredo ON: Horton, Terry (Texas County & District Retirement System), Austin Nail, William S. (Employees Retirement System of Texas), Austin SB 761 ON: Horwitz, Rita (Pension Review Board), Austin Hutcheson, Jim (Supreme Court), Austin Nail, William S. (Employees Retirement System of Texas), Austin SB 838 ON: Nail, William S. (Employees Retirement System of Texas), Austin Registering, but not testifying: On: Hutcheson, Jim (Supreme Court), Austin SB 1120 FOR: Bryant, Judy (Texas Federation of Teachers), Dallas Gregg, Brock (Association of Texas Professional Educators), Austin Harris, Dwight (Victoria Federation of Teachers), Victoria Haschke, Donna (Texas State Teachers Association), Austin Hollingsworth, Lonnie (Texas Classroom Teachers Association), Austin Lara, Rene (Texas Federation of Teachers), Austin Lehr, Mike (Texas Retired Teachers Association), Austin AGAINST: Floyd, Beaman (Texas Association of School Administrators), Austin Lain, Jackie (Texas Association of School Boards), Austin Registering, but not testifying: For: Breedlove, Catherine (Fort Worth Retired School Employee Association), Ft. Worth Cummings, Bill (Texas Silver Haired Legislation), Lockhart Hill, Alvin J., Sr. (Mainland Retired Teachers Association Texas City, TX), Texas City Hill, Lillian S. (Mainland Retired Teachers Association Texas City, TX), Texas City Holtermann, John W. (Texas Silver Haried Legislature), San Marcos Mellenbruch, Julia (Texas Retired Teachers Association), Austin Sayers, Russell E. (Texas Retired Teachers Association), Austin Scholtz, Annie B. (Texas Retired Teachers Association), Austin Tone, Pat (Texas Retired Teachers Association), Taylor Tone, Phyllis (Texas Retired Teachers Association), Taylor On: Beckett, Shelia (Employee Retirement System), Austin Carter, Michael (Teacher Retirement System), Dallas Dunlap, Charles (Teacher Retirement System), Austin Jung, Ronnie G. (Teacher Retirement System), Austin Written materials submitted: On: Santiago, Gwen S. (Texas Association of School Business Officials), Austin SB 1128 FOR: Bryant, Judy (Texas Federation of Teachers), Dallas Collins, Robby (Texas School Alliance), Dallas Gregg, Brock (Association of Texas Professional Educators), Austin Harris, Dwight (Victoria Federation of Teachers), Victoria Haschke, Donna (Texas State Teachers Association), Austin Hollingworth, Lonnie (Texas Classroom Teachers Association), Austin Lara, Rene (Texas Federation of Teachers), Austin Lehr, Mike (Texas Retired Teachers Association), Austin Rogers, Don (Texas Association of Community Schools), Austin Ross, Joe (Ft. Worth I. S. D.), Ft. Worth Veselka, Johnny (Texas Association of School Administrators), Austin ON: Carter, Michael (Teacher Retirement System), Dallas Dunlap, Charles (Teacher Retirement System), Austin Jung, Ronnie G. (Teacher Retirement System), Austin Registering, but not testifying: For: Cummings, Bill (Texas Silver Haired Legislature), Lockhart Grusendorf, William C. (Texas Association of Rural Schools), San Saba Hauser, Rosemary B. (Association for Compensitory Educators of Texas), Taylor Holtermann, John W. (Texas Silver Haired Legislature), San Marcos Mellenbruch, Julia (Texas Retired Teachers Association), Austin Sayers, DeEtta (Texas Retired Teachers Association), Austin Sayers, Russell E. (Texas Retired Teachers Association), Austin Scholtz, Annie B. (Texas Retired Teachers Association), Austin Tone, Pat (Texas Retired Teachers Association), Taylor Tone, Phyllis (Texas Retired Teachers Association), Taylor SB 1129 ON: Horton, Terry (Texas County & District Retirement System), Austin Registering, but not testifying: On: Kendall, Terrence (Texas County & District Retirement System), Austin May, Robert M. (Texas County & District Retirement System), Austin SB 1130 ON: Beckett, Shelia (Employee Retirement System), Austin Registering, but not testifying: For: Anderson, Gary (Texas Public Employees Association), Austin Travis, Charles D. (Texas Game Warden Association), Austin Willis, D. L. Dally (Communications Workers of America), Austin On: Nail, William S. (Employees Retirement System of Texas), Austin SB 1226 FOR: Lara, Rene (Texas Federation of Teachers), Austin Veselka, Johnny (Texas Association of School Administrators), Austin ON: Gregg, Brock (Association of Texas Professional Educators), Austin Registering, but not testifying: For: Grusendorf, William C. (Texas Association of Rural Schools), San Saba Haschke, Donna (Texas State Teachers Association), Austin Hollingsworth, Lonnie (Texas Classroom Teachers Association), Austin Rogers, Don (Texas Association of Community Schools), Austin Santiago, Gwen S. (Texas Association of School Business Officials), Austin Tone, Pat (Texas Retired Teachers Association), Taylor Tone, Phyllis (Texas Retired Teachers Association), Taylor On: Carter, Michael (Teacher Retirement System), Dallas Dunlap, Charles (Teacher Retirement System), Austin Jung, Ronnie G. (Teacher Retirement System), Austin SB 1536 FOR: Hollingsworth, Lonnie (Texas Classroom Teachers Association), Austin ON: Gregg, Brock (Association of Texas Professional Educators), Austin Lehr, Mike (Texas Retired Teachers Association), Austin Registering, but not testifying: For: Tone, Pat (Texas Retired Teachers Association), Taylor Tone, Phyllis (Texas Retired Teachers Association), Taylor On: Carter, Michael (Teacher Retirement System), Dallas Dunlap, Charles (Teacher Retirement System), Austin Jung, Ronnie G. (Teacher Retirement System), Austin SB 1749 ON: Fowler, James E. (San Jacinto College), Pasadena Horton, James (San Jacinto College Dist.), Pasadena Registering, but not testifying: On: Medina, Sylvia (San Jacinto College), Pasadena


Coding on iPhone
jQuery, AngularJS, JavaScript, HTML5 & CSS coding and debugging anywhere on iPhone.
Applications.
LOG ME
Tap'n record style, traditional life logging, standalone HTML5 application.
Storage View
Toy application to view the local storage.
Source View
Toy application to view sources of HTML, JavaScript and CSS.
TokyoMetro App
TokyoMetro Train Tracker Application.
Timelines.
Tools.
jsany - javascript anywhere
to code, test and debug.
PlainText 2 - Dropbox text editing
to deploy files to dropbox and adjust there.
ScriptBrowserPlus
to check deloyed files or refer to codes of other sites.
Dropbox
to manage deployed files.
Git
to check github from outside.
Node.app — Node.js for iOS
to fun Node.js on iPhone.
Resources.
jQuery
APIs
Libraries
Frameworks
Studies.
To execute, tap title of each list item. To view sources, tap eye icon.
Note: Stored data in WebStorage ( Session Storage ) are not compatible between codes. If code is not started, restart safari or clear storage by 20130627 code.
2014
- 1217 TokyoMetro App study 3View Sources
- 1213 TokyoMetro App study 2View Sources
- 1205 TokyoMetro App study 1View Sources
- 0723 SrcView with $apply()View Sources
- 0717 Param Maker (renew)View Sources
- 0716 SrcView & BootstrapView Sources
- 0711 SrcView with promiseView Sources
- 0710 SrcView DeferrededView Sources
- 0702 SrcView on AngularJSView Sources
- 0523 Knockout & Bootstrap View Sources
- 0520 SourceViewView Sources
- 0512 shAutoloader.jsView Sources
- 0509 SyntaxHighlighterView Sources
- 0502 KnockoutJSView Sources
- 0427 AngularJS & TopcoatView Sources
- 0425 AngularJSView Sources
- 0303 Zip.jsView Sources
sumo_ninja_jp / DIRECTORY.HTML


If there are any new symptoms, or if the condition gets worse, call NHS 111 for advice.
Treating dental pain at home
Medication, pain and/or fever
Paracetamol or ibuprofen can be used to relieve pain or a fever. Don't take if you've been told not to, or you've already taken some. Read the instructions on the packet, or talk to a pharmacist.
Toothache
Don't apply gels or medicine directly to the tooth or nearby gum area. This may damage gums.
Avoid hot or cold food and drinks.
See a dentist for your toothache. Any other cures will be temporary.


SVOGV is an ongoing project that helps Angular developers to create awesome apps quite fast.
Objections
This project consists of several parts that can be used independently:
- A validation solution to automate form creation, based on decorators and editors
- A widget library for form components that use Bootstrap 4 as layout foundation
- A widget library based on SVG with some very special components
What's up in the future?
I'm going to treat this project as a starter for learning and experimenting. It's production ready anyway. A new project
is under way, that has few improvements:
- Validation, Form Widgets, and Components are going to be split
- A base path called @svogv is being created
- All design goes to Angular Material 2 and there is no Bootstrap anymore
- Additional quirks such as HUD and MAPS coming later but independently
Licensing
MIT
Everything is Open Sourced. For enterprises we sell additional licenses that help convincing your boss.


new
keyword.
In utility classes, all fields and methods are static
.
Instantiation of such classes is most likely unnecessary and indicates a mistake.
Example:
class MyUtils {
public static double cube(double x) {
return x * x * x;
}
}
class Main {
public static void main(String[] args) {
// Instantiation of utility class
MyUtils utils = new MyUtils();
}
}
To prevent utility classes from being instantiated,
it's recommended to use a private
constructor.


Students receiving constructive feedback from faculty report gains in problem solving and communication skills. Feedback perceived as encouraging and aiding in student development tends to be more effective. Students note that late feedback (e.g., after its usefulness has expired) is not at all helpful nor effective. Messages constructed according to best practices are perceived to be of more quality by students. Further, the higher the level of quality perceived, the greater the impact the message has on students with regard to their self-efficacy and course performance.
Research from Purdue University shows that:
Explicit feedback with direct actions (e.g., be sure to compare your exams with the key on the website) to be taken is far more effective and useful for students than implied statements (e.g., you need to spend more time on the assignments)
Feedback should focus on direct outcomes of behaviors. Further, messages should be delivered as soon as possible to ensure relevancy and effectiveness
Instructors should strive to write short messages. Effective messages tended to average 40 words for green messages, 52 words for yellow messages, and 62 words for red messages
Increasing a student’s self-efficacy will increase their likelihood of academic success.
When students perceive feedback as threatening (e.g., you will fail this course) without information about how to improve their situation (e.g., you will fail this course unless you start turning in assignments on time), it is wholly ineffective
Highly effective messages use the words "we" and "you," while highly ineffective messages refer to the instructor as "I" and never use the word "we." In short, the you/we language left students feeling capable, while the instructor-centered language (I) left students feeling as though their final grade in the course was out of the students’ control.
References
Arnold, K., Campbell, J. P., & Pistilli, M. D. (2012). Signals at Purdue: Impacting learning through analytic technology. Paper presented at EDUCAUSE Learning Initiative Annual Meeting, Austin, TX.
Bjorklund, S. A., Parente, J. M., & Sathianathan, D. (2004). Effects of faculty interaction and feedback on gains in student skills. Journal of Engineering Education, 93, 153-160.
Ehle, T. Z., & Gettings, P. E. (2012). The Role of computer mediated instructional message quality on perceived message effects in an academic analytics intervention. Paper presented at the International Communication Association annual conference, London, England.
Gettings, P. E., Waters, J., Selzer King, A., Tanes, Z., Pistilli, M. D. (2013). Message testing and self-efficacy in Course Signals: Formative evaluation to identify effective communication strategies. Paper presented at the Southern States Communication Association Annual Conference, Louisville, KY.
Hartley, J. & Chesworth, K. (2000). Qualitative and quantitative methods in research on essay writing: no one way. Journal of Further and Higher Education, 24, 15–24.
Lizzio, A. & Wilson, K. (2008). Feedback on assessment: Students’ perceptions of quality and effectiveness. Assessment & Evaluation in Higher Education, 33, 263–275.
Tanes, Z., Arnold, K. E., Selzer King, A., Remnet, M. A. (2011). Using Signals for appropriate feedback: Perceptions and practices. Computers & Education, 57, 2414-2422.
Yorke, M. & Longden, B. (2006). The vital first year. Academy Exchange, 4, 16–17.