--- title: "Bayesian inference at different levels" date: 2018-02-12 Mon 20:15 layout: post categories: tags: - reading published: true comments: ---
Stephan Mandt, Matthew D. Hoffman, David M. Blei JMLR 18(134):1-35, 2017. Link: http://jmlr.org/papers/v18/17-214.html
Abstract Stochastic Gradient Descent with a constant learning rate (constant SGD) simulates a Markov chain with a stationary distribution. With this perspective, we derive several new results. (1) We show that constant SGD can be used as an approximate Bayesian posterior inference algorithm. Specifically, we show how to adjust the tuning parameters of constant SGD to best match the stationary distribution to a posterior, minimizing the Kullback-Leibler divergence between these two distributions. (2) We demonstrate that constant SGD gives rise to a new variational EM algorithm that optimizes hyperparameters in complex probabilistic models. (3) We also show how to tune SGD with momentum for approximate sampling. (4) We analyze stochastic-gradient MCMC algorithms. For Stochastic- Gradient Langevin Dynamics and Stochastic-Gradient Fisher Scoring, we quantify the approximation errors due to finite learning rates. Finally (5), we use the stochastic process perspective to give a short proof! of why Polyak averaging is optimal. Based on this idea, we propose a scalable approximate MCMC algorithm, the Averaged Stochastic Gradient Sampler.
Christophe Dupuy, Francis Bach JMLR 18(126):1-45, 2017. Link: http://jmlr.org/papers/v18/16-374.html
Abstract We study parameter inference in large-scale latent variable models. We first propose a unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian methods. We then propose a novel inference method for the frequentist estimation of parameters, that adapts MCMC methods to online inference of latent variable models with the proper use of local Gibbs sampling. Then, for latent Dirichlet allocation,we provide an extensive set of experiments and comparisons with existing work, where our new approach outperforms all previously proposed methods. In particular, using Gibbs sampling for latent variable inference is superior to variational inference in terms of test log-likelihoods. Moreover, Bayesian inference through variational methods perform poorly, sometimes leading to worse fits with latent variables of higher dimensionality.
Authors: Hareesh Bahuleyan, Lili Mou, Olga Vechtomova, Pascal Poupart
Categories: cs.CL
Comments: 8 pages, 4 figures, 2 tables
The variational encoder-decoder (VED) encodes source information as a set of
random variables using a neural network, which in turn is decoded into target
data using another neural network. In natural language processing,
sequence-to-sequence (Seq2Seq) models typically serve as encoder-decoder
networks. When combined with a traditional (deterministic) attention mechanism,
the variational latent space may be bypassed by the attention model, making the
generated sentences less diversified. In our paper, we propose a variational
attention mechanism for VED, where the attention vector is modeled as normally
distributed random variables. Experiments show that variational attention
increases diversity while retaining high quality. We also show that the model
is not sensitive to hyperparameters.
\\ ( https://arxiv.org/abs/1712.08207 , 1538kb)
ICL17 in hot
in hot
Raymond Yeh, Junting Lou, Teck-Yian Lim in hot
Georgios Arvanitidis, Lars Kai Hansen and Søren Hauberg. In International Conference on Learning Representations (ICLR), 2018. http://www2.compute.dtu.dk/~sohau/papers/
in hot for words are region
Jiaqi Mu, Suma Bhat, Pramod Viswanath https://arxiv.org/pdf/1610.07569.pdf In ICLR 2017
Authors: Philip A Huebner, Jon A Willits
Categories: cs.CL
Comments: Submitted to CogSci 2018
Some researchers claim that language acquisition is critically dependent on
experiencing linguistic input in order of increasing complexity. We set out to
test this hypothesis using a simple recurrent neural network (SRN) trained to
predict word sequences in CHILDES, a 5-million-word corpus of speech directed
to children. First, we demonstrated that age-ordered CHILDES exhibits a gradual
increase in linguistic complexity. Next, we compared the performance of two
groups of SRNs trained on CHILDES which had either been age-ordered or not.
Specifically, we assessed learning of grammatical and semantic structure and
showed that training on age-ordered input facilitates learning of semantic, but
not of sequential structure. We found that this advantage is eliminated when
the models were trained on input with utterance boundary information removed.
\\ ( https://arxiv.org/abs/1802.00768
Vlad Niculae, Mathieu Blondel
Modern neural networks are often augmented with an attention mechanism, which tells the network where to focus within the input. We propose in this paper a new framework for sparse and structured attention, building upon a smoothed max operator. We show that the gradient of this operator defines a mapping from real values to probabilities, suitable as an attention mechanism. Our framework includes softmax and a slight generalization of the recently-proposed sparsemax as special cases. However, we also show how our framework can incorporate modern structured penalties, resulting in more interpretable attention mechanisms, that focus on entire segments or groups of an input. We derive efficient algorithms to compute the forward and backward passes of our attention mechanisms, enabling their use in a neural network trained with backpropagation. To showcase their potential as a drop-in replacement for existing ones, we evaluate our attention mechanisms on three large-scale tasks: textual entailment, machine translation, and sentence summarization. Our attention mechanisms improve interpretability without sacrificing performance; notably, on textual entailment and summarization, we outperform the standard attention mechanisms based on softmax and sparsemax.
Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng (Submitted on 24 Feb 2017 (v1), last revised 19 Jun 2017 (this version, v5))
Segmental structure is a common pattern in many types of sequences such as phrases in human languages. In this paper, we present a probabilistic model for sequences via their segmentations. The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks. Since the segmentation of a sequence is usually unknown in advance, we sum over all valid segmentations to obtain the final probability for the sequence. An efficient dynamic programming algorithm is developed for forward and backward computations without resorting to any approximation. We demonstrate our approach on text segmentation and speech recognition tasks. In addition to quantitative results, we also show that our approach can discover meaningful segments in their respective application contexts. ICML 2017 https://arxiv.org/abs/1702.07463
Authors: Qiming Chen, Ren Wu
Categories: cs.CL cs.LG cs.NE
The Convolution Neural Network (CNN) has demonstrated the unique advantage in
audio, image and text learning; recently it has also challenged Recurrent
Neural Networks (RNNs) with long short-term memory cells (LSTM) in
sequence-to-sequence learning, since the computations involved in CNN are
easily parallelizable whereas those involved in RNN are mostly sequential,
leading to a performance bottleneck. However, unlike RNN, the native CNN lacks
the history sensitivity required for sequence transformation; therefore
enhancing the sequential order awareness, or position-sensitivity, becomes the
key to make CNN the general deep learning model. In this work we introduce an
extended CNN model with strengthen position-sensitivity, called PoseNet. A
notable feature of PoseNet is the asymmetric treatment of position information
in the encoder and the decoder. Experiments shows that PoseNet allows us to
improve the accuracy of CNN based sequence-to-sequence learning significantly,
achieving around 33-36 BLEU scores on the WMT 2014 English-to-German
translation task, and around 44-46 BLEU scores on the English-to-French
translation task.
\\ ( https://arxiv.org/abs/1712.09662 , 1056kb)
Google Brain
Yin and Shütze
Authors: Jeremy Howard, Sebastian Ruder
Categories: cs.CL cs.LG stat.ML
Transfer learning has revolutionized computer vision, but existing approaches
in NLP still require task-specific modifications and training from scratch. We
propose Fine-tuned Language Models (FitLaM), an effective transfer learning
method that can be applied to any task in NLP, and introduce techniques that
are key for fine-tuning a state-of-the-art language model. Our method
significantly outperforms the state-of-the-art on five text classification
tasks, reducing the error by 18-24% on the majority of datasets. We open-source
our pretrained models and code to enable adoption by the community.
\\ ( https://arxiv.org/abs/1801.06146
Authors: Kasturi Dewi Varathan and Anastasia Giachanou and Fabio Crestani
Categories: cs.IR cs.CL
Journal-ref: Journal of the Association for Information Science and Technology,
68(4), 2017
DOI: 10.1002/asi.23716
Opinion mining refers to the use of natural language processing, text
analysis and computational linguistics to identify and extract subjective
information in textual material. Opinion mining, also known as sentiment
analysis, has received a lot of attention in recent times, as it provides a
number of tools to analyse the public opinion on a number of different topics.
Comparative opinion mining is a subfield of opinion mining that deals with
identifying and extracting information that is expressed in a comparative form
(e.g.~"paper X is better than the Y"). Comparative opinion mining plays a very
important role when ones tries to evaluate something, as it provides a
reference point for the comparison. This paper provides a review of the area of
comparative opinion mining. It is the first review that cover specifically this
topic as all previous reviews dealt mostly with general opinion mining. This
survey covers comparative opinion mining from two different angles. One from
perspective of techniques and the other from perspective of comparative opinion
elements. It also incorporates preprocessing tools as well as dataset that were
used by the past researchers that can be useful to the future researchers in
the field of comparative opinion mining.
\\ ( https://arxiv.org/abs/1712.08941 , 40kb)
Authors: Pushpankar Kumar Pushp, Muktabh Mayank Srivastava
Categories: cs.CL
Zero-shot Learners are models capable of predicting unseen classes. In this
work, we propose a Zero-shot Learning approach for text categorization. Our
method involves training model on a large corpus of sentences to learn the
relationship between a sentence and embedding of sentence's tags. Learning such
relationship makes the model generalize to unseen sentences, tags, and even new
datasets provided they can be put into same embedding space. The model learns
to predict whether a given sentence is related to a tag or not; unlike other
classifiers that learn to classify the sentence as one of the possible classes.
We propose three different neural networks for the task and report their
accuracy on the test set of the dataset used for training them as well as two
other standard datasets for which no retraining was done. We show that our
models generalize well across new unseen classes in both cases. Although the
models do not achieve the accuracy level of the state of the art supervised
models, yet it evidently is a step forward towards general intelligence in
natural language processing.
\\ ( https://arxiv.org/abs/1712.05972 , 251kb)
Authors: Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu Comments: Presented at NIPS 2017 Machine Learning for Audio Signal Processing (ML4Audio) Workshop, Dec. 2017
Deep learning based discriminative methods, being the state-of-the-art machine learning techniques, are ill-suited for learning from lower amounts of data. In this paper, we propose a novel framework, called simultaneous two sample learning (s2sL), to effectively learn the class discriminative characteristics, even from very low amount of data. In s2sL, more than one sample (here, two samples) are simultaneously considered to both, train and test the classifier. We demonstrate our approach for speech/music discrimination and emotion classification through experiments. Further, we also show the effectiveness of s2sL approach for classification in low-resource scenario, and for imbalanced data. \\ ( https://arxiv.org/abs/1712.05608 , 376kb)
Authors: Jordan Prosky, Xingyou Song, Andrew Tan, Michael Zhao
Categories: cs.CL cs.AI cs.LG cs.MM
Comments: 9 pages
In this work, we present our findings and experiments for stock-market
prediction using various textual sentiment analysis tools, such as mood
analysis and event extraction, as well as prediction models, such as LSTMs and
specific convolutional architectures.
Authors: Christy Li, Dimitris Konomis, Graham Neubig, Pengtao Xie, Carol Cheng,
Eric Xing
Categories: cs.CL
\(\textbf{Objective}\) Develop an automatic diagnostic system which only uses
textual admission information from Electronic Health Records (EHRs) and assist
clinicians with a timely and statistically proved decision tool. The hope is
that the tool can be used to reduce mis-diagnosis.
\(\textbf{Materials and Methods}\) We use the real-world clinical notes from
MIMIC-III, a freely available dataset consisting of clinical data of more than
forty thousand patients who stayed in intensive care units of the Beth Israel
Deaconess Medical Center between 2001 and 2012. We proposed a Convolutional
Neural Network model to learn semantic features from unstructured textual input
and automatically predict primary discharge diagnosis.
\(\textbf{Results}\) The proposed model achieved an overall 96.11% accuracy and
80.48% weighted F1 score values on 10 most frequent disease classes,
significantly outperforming four strong baseline models by at least 12.7% in
weighted F1 score.
\(\textbf{Discussion}\) Experimental results imply that the CNN model is
suitable for supporting diagnosis decision making in the presence of complex,
noisy and unstructured clinical data while at the same time using fewer layers
and parameters that other traditional Deep Network models.
\(\textbf{Conclusion}\) Our model demonstrated capability of representing
complex medical meaningful features from unstructured clinical notes and
prediction power for commonly misdiagnosed frequent diseases. It can use easily
adopted in clinical setting to provide timely and statistically proved decision
support.
\(\textbf{Keywords}\) Convolutional neural network, text classification,
discharge diagnosis prediction, admission information from EHRs.
Authors: Reagan Mozer, Luke Miratrix, Aaron Russell Kaufman, L. Jason
Anastasopoulos
Categories: stat.ME cs.CL
How should one perform matching in observational studies when the units are
text documents? The lack of randomized assignment of documents into treatment
and control groups may lead to systematic differences between groups on
high-dimensional and latent features of text such as topical content and
sentiment. Standard balance metrics, used to measure the quality of a matching
method, fail in this setting. We decompose text matching methods into two
parts: (1) a text representation, and (2) a distance metric, and present a
framework for measuring the quality of text matches experimentally using human
subjects. We consider 28 potential methods, and find that representing text as
term vectors and matching on cosine distance significantly outperform
alternative representations and distance metrics. We apply our chosen method to
a substantive debate in the study of media bias using a novel data set of front
page news articles from thirteen news sources. Media bias is composed of topic
selection bias and presentation bias; using our matching method to control for
topic selection, we find that both components contribute significantly to media
bias, though some news sources rely on one component more than the other.
\\ ( https://arxiv.org/abs/1801.00644 , 237kb)
Rie Johnson and Tong Zhang http://aclweb.org/anthology/P/P17/P17-1052.pdf
https://people.csail.mit.edu/taolei/papers/emnlp16_rationale.pdf
Tao Lei, Regina Barzilay and Tommi Jaakkola EMNLP16
Prediction without justification has limited ap- plicability. As a remedy, we learn to extract pieces of input text as justifications – ratio- nales – that are tailored to be short and co- herent, yet sufficient for making the same pre- diction. Our approach combines two modu- lar components, generator and encoder, which are trained to operate well together. The gen- erator specifies a distribution over text frag- ments as candidate rationales and these are passed through the encoder for prediction. Ra- tionales are never given during training. In- stead, the model is regularized by desiderata for rationales.
ntro The notion of what counts as a rationale may be ambiguous in some contexts and the task of select- ing rationales may therefore be challenging to eval- uate. We focus on two domains where ambiguity is minimal (or can be minimized).
[ ]
Similar questions : short texts[ ]
Beer corpus : what is the length of a review ?
We can think of the generator as a tagging model where each word in the input receives a binary tag pertaining to whether it is selected to be included in the rationale. In our case, the generator is probabilistic and specifies a distribution over possible selections.
The rationale is introduced as a latent variable, a constraint that guides how to interpret the input sequence.
The encoder could be realized in many ways such as a recurrent neural network. The target vector is then generated on the basis of the final state reached by the recur- rent unit after processing all the words in the input sequence.
The generator can be modeled using a shared bi-directional recurrent neural net
Yang, Dyer et al. NAACL 2016
Yoon Kim EMNLP 2014 —> hot Kim14CNN
Authors: Cedric De Boom, Bart Dhoedt, Thomas Demeester
Categories: cs.LG cs.CL stat.ML
Comments: 23 pages, 11 figures, 4 tables
DOI: 10.1007/s00521-017-3322-z
Recurrent neural networks are nowadays successfully used in an abundance of
applications, going from text, speech and image processing to recommender
systems. Backpropagation through time is the algorithm that is commonly used to
train these networks on specific tasks. Many deep learning frameworks have
their own implementation of training and sampling procedures for recurrent
neural networks, while there are in fact multiple other possibilities to choose
from and other parameters to tune. In existing literature this is very often
overlooked or ignored. In this paper we therefore give an overview of possible
training and sampling schemes for character-level recurrent neural networks to
solve the task of predicting the next token in a given sequence. We test these
different schemes on a variety of datasets, neural network architectures and
parameter settings, and formulate a number of take-home recommendations. The
choice of training and sampling scheme turns out to be subject to a number of
trade-offs, such as training stability, sampling time, model performance and
implementation effort, but is largely independent of the data. Perhaps the most
surprising result is that transferring hidden states for correctly initializing
the model on subsequences often leads to unstable training behavior depending
on the dataset.
\\ ( https://arxiv.org/abs/1801.00632 , 183kb)
Authors: Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping,
Sanjeev Satheesh, Lawrence Carin
Categories: cs.LG cs.CL
Comments: To appear in AISTATS 2018
We propose a Topic Compositional Neural Language Model (TCNLM), a novel
method designed to simultaneously capture both the global semantic meaning and
the local word ordering structure in a document. The TCNLM learns the global
semantic coherence of a document via a neural topic model, and the probability
of each learned latent topic is further used to build a Mixture-of-Experts
(MoE) language model, where each expert (corresponding to one topic) is a
recurrent neural network (RNN) that accounts for learning the local structure
of a word sequence. In order to train the MoE model efficiently, a matrix
factorization method is applied, by extending each weight matrix of the RNN to
be an ensemble of topic-dependent weight matrices. The degree to which each
member of the ensemble is used is tied to the document-dependent probability of
the corresponding topics. Experimental results on several corpora show that the
proposed approach outperforms both a pure RNN-based model and other
topic-guided language models. Further, our model yields sensible topics, and
also has the capacity to generate meaningful sentences conditioned on given
topics.
\\ ( https://arxiv.org/abs/1712.09783 , 3346kb)
Yoav Goldberg and Omer Levy, NIPS 2014 https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization
The previous one is short and messy somehow: word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method Yoav Goldberg and Omer Levy https://arxiv.org/pdf/1402.3722.pdf
[X]
In page 2, I don't understand the number of parameters
It is |C|.|V|.d while I would write (|C|+|V|).d ?
I'm right and this is corrected in the NIPS 2014 paperSVAE in NIPS 2017
NIPS 2015 : wassNIPS2015
Title: Learning Online Alignments with Continuous Rewards Policy Gradient Authors: Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever https://arxiv.org/abs/1608.01281
Weiran Wang, Raman Arora, Karen Livescu, Jeff Bilmes
Trang Tran, Shubham Toshniwal, Mohit Bansal, Kevin Gimpel, Karen Livescu, Mari Ostendorf
hot
http://www.cs.ust.hk/~dekai/library/WU_Dekai/Wu_Ismir2016.pdf
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio
Tao Lei, Yu Zhang
Martins16
https://arxiv.org/abs/1605.07571 Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther NIP16
State "TODO" from ""
http://people.eng.unimelb.edu.au/tcohn/papers/naacl16duong.pdf
State "TODO" from ""
Kalman Filter http://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/


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vignettes/annotation_sources.Rmd
annotation_sources.Rmd
This article will describe the underlying annotation sources of oncoEnrichR in detail, how they have been subject to quality control/filtering for use in the package. IN PROGRESS


Image Source : FREEPIK
Representative image
With rising COVID 19 cases in the country and changing weather, stigmas around coronavirus are also increasing. In such a scenario, UNICEF India using guidelines from WHO, MoHFW and AIIMS shared tips on how to treat medical symptoms at home and when to seek emergency medical help. However, the first and foremost thing that one should keep in mind is to isolate. The agency says, do not wait to get test results or get tested - self-isolate immediately on feeling any symptoms. Also, do not panic. One should remember that most people recover from COVID-19 and do not require hospitalization. However, it absolutely recommended to first speak to a doctor.
How to manage mild symptoms/ milder form of COVID-19 at home
- Isolate yourself in a separate room with a working bathroom/toilet if possible.
 Those entering your room should do the same. Also, discard the mask after eight hours.
- Make sure to wash your hands frequently with soap or use 70% alcohol-based sanitiser. You should also clean your room yourself with disinfectant or soap.
- If possible open windows for better ventilation.
- Do not bathe with cold water. Use hot water if possible and tepid sponge.
- Utensils should be cleaned wearing cloves with soap and it is strongly advised to not use personal items with others.
- If you feel up for it, do a little exercise.
- Monitor your oxygen levels at least every six hours. If your breaths are too fast take a six minute walk and try again.
- Frequently check your temperature if you have fever.
- Keep yourself hydrated and have a healthy diet even if you can't taste a thing. There's no known food yet which can cure COVID but a healthy meal will ensure a faster recovery.
Seek emergency medical care in any of these cases
- Oxygen level on oximeter shows less than 94%
- shortness of breath worsens.
- lips or face turn blue-ish.
- feeling of disorientation increases
- Fever of 101F (38C) persists for three days
- if you feel persistent pressure or pain in chest
- slurred speech or seizure
- unable to wake up or stay awake
Do not self medicate, you can take paracetamol (500 mg) in four to six hours in case you have a high fever, but the dose shouldn't increase more than four in 24 hours.
How to prevent COVID 19?
- Wear a mask
- Wash your hands with soap
- Maintain social distancing
- Get vaccinated
Disclaimer: This content is created and published for informational purposes only. It is not intended to be a substitute for professional medical advice. For more information, please consult a doctor.


Preparation time
less than 30 mins
Cooking time
less than 10 mins
Serves
Serves 2
Melting mozzarella, hot chorizo and baby spinach leaves in a baguette: sandwiches don’t get better than this.
Place the chorizo slices in a large, heavy-based frying pan over a medium heat. There is no need to use any oil or butter. Cook gently for 4-5 minutes, turning halfway through, until the oil begins to escape from the sausage.
Lay the slices of mozzarella on top of the chorizo, then carefully spoon some of the oil over the top.
Cook for 2-3 minutes to allow the cheese to melt, then season with a few twists of freshly ground black pepper.
Spoon the chorizo and mozzarella into the baguette, then spoon the juices from the pan over the top. Again, there is no need to butter the bread, as the pan juices will ensure the bread gets deliciously moist.
Place a handful of spinach leaves inside the baguette. Give it a good squeeze, allowing all of the juices to soak into the bread, and the spinach to wilt a little.
Slice the baguette in half and serve immediately.


{% extends "shared/base.html" %} {% block site_title %} Thank you {% endblock %} {% block site_content %}
Your order has been successfully completed.


Finding a way to display a chain of block-elements like a book.
You can see all the files of this demo on glitch and also remix them.
In the following some example text for testing, taken from "This Quantum World": https://en.wikibooks.org/wiki/This_Quantum_World/Atoms#Fuzzy_observables
We say that an observable Q {\displaystyle Q} Q with a finite or countable number of possible values q k {\displaystyle q_{k}} q_k is fuzzy (or that it has a fuzzy value) if and only if at least one of the propositions "The value of Q {\displaystyle Q} Q is q k {\displaystyle q_{k}} q_k" lacks a truth value. This is equivalent to the following necessary and sufficient condition: the probability assigned to at least one of the values q k {\displaystyle q_{k}} q_k is neither 0 nor 1.
What about observables that are generally described as continuous, like a position?The description of an observable as "continuous" is potentially misleading. For one thing, we cannot separate an observable and its possible values from a measurement and its possible outcomes, and a measurement with an uncountable set of possible outcomes is not even in principle possible. For another, there is not a single observable called "position". Different partitions of space define different position measurements with different sets of possible outcomes.
Corollary: The possible outcomes of a position measurement (or the possible values of a position observable) are defined by a partition of space. They make up a finite or countable set of regions of space. An exact position is therefore neither a possible measurement outcome nor a possible value of a position observable.
So how do those cloud-like blurs represent the electron's fuzzy position relative to the proton? Strictly speaking, they graphically represent probability densities in the mathematical space of exact relative positions, rather than fuzzy positions. It is these probability densities that represent fuzzy positions by allowing us to calculate the probability of every possible value of every position observable.
It should now be clear why we cannot describe the atom's internal relative position as it is. To describe a fuzzy observable is to assign probabilities to the possible outcomes of a measurement. But a description that rests on the assumption that a measurement is made, does not describe an observable as it is (by itself, regardless of measurements).


#!/usr/bin/perl
read_pop_mail_gpg.pl
Read gpg encrypted mail transactions, process and send motifications Only currently deals with payments (send) and balance (balance) at present all the parsing etc. is 'english' this needs to be dealt with real-soon
#--------------------------------------------------------------------------- #THE cclite SOFTWARE IS PROVIDED TO YOU "AS IS," AND WE MAKE NO EXPRESS #OR IMPLIED WARRANTIES WHATSOEVER WITH RESPECT TO ITS FUNCTIONALITY, #OPERABILITY, OR USE, INCLUDING, WITHOUT LIMITATION, #ANY IMPLIED WARRANTIES OF MERCHANTABILITY, #FITNESS FOR A PARTICULAR PURPOSE, OR INFRINGEMENT. #WE EXPRESSLY DISCLAIM ANY LIABILITY WHATSOEVER FOR ANY DIRECT, #INDIRECT, CONSEQUENTIAL, INCIDENTAL OR SPECIAL DAMAGES, #INCLUDING, WITHOUT LIMITATION, LOST REVENUES, LOST PROFITS, #LOSSES RESULTING FROM BUSINESS INTERRUPTION OR LOSS OF DATA, #REGARDLESS OF THE FORM OF ACTION OR LEGAL THEORY UNDER #WHICH THE LIABILITY MAY BE ASSERTED, #EVEN IF ADVISED OF THE POSSIBILITY OR LIKELIHOOD OF SUCH DAMAGES. #---------------------------------------------------------------------------
Hugh Barnard
(c) Hugh Barnard 2004-2010 GPL Licenced
# these batch scripts are kept as eval, if they fail they print their problems # onto the status web page #print STDOUT "Content-Type: text/html; charset=utf-8\n\n"; ###my $data = join( '', <DATA> ); ###eval $data; ###if ($@) { ### print $@; ### exit 1; ###} ###__END__
Check that the set up can work and exit otherwise.. FIXME: Only checks non blank not valid file names
sub check_parameters { my ( $registry, $passphrase, $temp_directory, $message, $encrypted, $logfile, $debug, $trace ) = @_; my @error_messages; push @error_nessages, 'registry blank' if ( !length($registry) ); push @error_nessages, 'passphrase blank' if ( !length($passphrase) ); push @error_nessages, 'temporary directory blank' if ( !length($temp_directory) ); push @error_nessages, 'message file blank' if ( !length($message) ); push @error_nessages, 'encryption file blank' if ( !length($encrypted) ); if ( length($registry) ) { my ( $error, $registryref ) = get_where( 'local', $registry, 'om_registry', '*', 'name', $registry, $token, '', '' ); push @error_nessages, $error if ( length($error) ); } # print errors and exit, if problems with set up if ( scalar @error_messages ) { my $errors = join( "\n", @error_messages ); print $errors ; exit 0; } else { return; } } # --- start of main script ---# use strict; use lib '../../../lib'; # this is mailbox access use Net::POP3; # this is used to parse addresses (logically enough...) use Email::Address; use Ccconfiguration; use Ccmailgateway; use Cclitedb; use Cclite; use Cccrypto; #use Ccinterfaces; use Cccookie; use Ccu; BEGIN { use CGI::Carp qw(fatalsToBrowser set_message); set_message( "Please use the Cclite Google Group for help, if necessary" ); } # read configuration and messages my %configuration = readconfiguration; my $configuration_hash_ref = \%configuration; # language now decided by decide_language 08/2011 our %messages = readmessages(); # note this is a first version, these values will be configured with the rest later #------------------------------------------------------------------------------------------------------------- my %gpg_configuration = readconfiguration('../../../config/readmailgpg.cf'); my $temp_directory = $gpg_configuration{'temp_directory'}; my $message = "$gpg_configuration{'temp_directory'}/$gpg_configuration{'message'}"; my $reformat = "$gpg_configuration{'temp_directory'}/$gpg_configuration{'reformat'}"; my $decrypt = "$gpg_configuration{'temp_directory'}/$gpg_configuration{'decrypt'}"; my $encrypted = "$gpg_configuration{'temp_directory'}/$gpg_configuration{'encrypted'}"; # send receipts etc. encrypted with users public key my $encrypt_notifications = $gpg_configuration{'encrypt_notifications'}; # STDERR for gpg operations, if specified... my $logfile = "$gpg_configuration{'temp_directory'}/$gpg_configuration{'logfile'}"; # turns gpg trace on and off, prints to STDOUT (or STDERR?) my $trace = $gpg_configuration{'trace'}; # sleep in seconds for processing loop my $sleep = $gpg_configuration{'sleep'}; # clearly this is very insecure, should be held as a smartcard token for example, this # passphrase unlocks the private key for decrpyting the incoming transactions # users private key on client is used to sign transactions # note the key and the running user must correspond, otherwise the key won't get released by gpg my $passphrase = $gpg_configuration{'passphrase'}; # this will leave data in the intermediate files and mail in the mailbox, if set to 1, avoids # resetting/resending etc. when debugging my $debug = $gpg_configuration{'debug'}; # for cron, replace these with hardcoded registry name, for example my $hardcoded_registry = $gpg_configuration{'hardcoded_registry'}; if ($debug) { foreach my $key ( keys %gpg_configuration ) { print "$key = $gpg_configuration{$key}\n"; } } my %fields = cgiparse(); my $cookieref = get_cookie(); #FIXME: needs higher barrier than this and something a little more general my $registry = $$cookieref{registry} || $hardcoded_registry; # check that the set up works before starting to process... check_parameters( $registry, $passphrase, $temp_directory, $message, $encrypted, $logfile, $debug, $trace ); # store of references to transactions to be notified my @notifications; # read the current registry to pick up per-registry email values, that's where the email # transactions are picked up from my ( $class, $token ); my ( $error, $registryref ) = get_where( $class, $registry, 'om_registry', '*', 'name', $registry, $token, '', '' ); if ( length($error) ) { log_entry( $class, $registry, 'fatal', "database error for $registry: $error", $token ); exit 0; } my $username = $registryref->{postemail}; my $password = $registryref->{postpass}; my $host = $username; my $count = 0; my $originator_email; #FIXME: get the domain part as postbox...this is weak... $host =~ s/^(.*?)\@(.*)$/$2/; while (1) { print "running"; print "\nwarning debug mode!" if ($debug); print "\n"; # clear up, just in case... unlink $message, $reformat, $decrypt, $encrypted if ( !$debug ); my $pop = Net::POP3->new( $host, Timeout => 60, Debug => $debug ); if ( $pop->login( $username, $password ) > 0 ) { my $msgnums = $pop->list; # hashref of msgnum => size my @from_array; my @to_array; my ( $output_message, $batch_email_box, $transaction_description_ref ); foreach my $msgnum ( keys %$msgnums ) { my $msg = $pop->get($msgnum); # put the message in a file for decrypt and reformat my ( $subject, $parse_type ); # detect pgp, decryption attempy stops script, if no PGP banner my $pgp_detected = 0; open( MAIL, ">$message" ); foreach my $part (@$msg) { $pgp_detected = 1 if ( $part =~ /BEGIN PGP MESSAGE/ ); print MAIL $part; if ( $part =~ /From:/ ) { @from_array = Email::Address->parse($part); # deal with Yahoo parse etc. may need more tweaking... if ( $from_array[0] =~ /\<(.*?)\>/ ) { $from_array[0] = $1; } # these are just to understand the data $originator_email = $from_array[0]; } elsif ( $part =~ /To:/ ) { @to_array = Email::Address->parse($part); # these are just to understand the data $batch_email_box = $to_array[0]; } elsif ( $part =~ /Subject:\s+(\S.*)/ ) { $subject = $1; } #endif for message parse } # end foreach of message parse all bits consumed # current message is now in mail.txt for decryption close(MAIL); # non pgp mails are thrown away... # $transaction_found is true, if there's a transaction line beginning with 'send' in the decrypted message if ($pgp_detected) { my ( $transaction_found, $transaction_unparsed ) = decode_decrypt_reformat( $passphrase, $message, $reformat, $decrypt, $logfile, $trace ); if ($transaction_found) { ( $parse_type, $transaction_description_ref ) = mail_message_parse( 'local', $registry, $originator_email, $batch_email_box, $subject, $transaction_unparsed ); # add 'via email' literal to description of transaction $transaction_description_ref->{'description'} = "$messages{'viagpgmail'} " . $transaction_description_ref->{'description'}; #FIXME: we have output, error and text within transaction, needs simplifying if ( !length( $transaction_description_ref->{error} ) && $transaction_description_ref->{type} eq 'send' ) { $transaction_description_ref->{output_message} = mail_transaction($transaction_description_ref); } elsif ( $transaction_description_ref->{'type'} eq 'balance' ) { $transaction_description_ref->{'output_message'} = $transaction_description_ref->{'text'}; } # as mailbox is opened by pop and maybe need to encrypt notifications # store in data structures amd do when mailbox closed for reading... $transaction_description_ref->{'originator_email'} = $originator_email; # keep the references to be notified in an array of references... # FIXME: maybe this should notify for every mail in the batch inbox? push @notifications, $transaction_description_ref; } # end of transaction found $count++; # pgp not detected, mail is thrown away... } else { log_entry( $class, $registry, 'error', "discarded non-pgp transaction\n", $token ); } # these should be destroyed asap and possibly done in-memory unlink $message, $reformat, $decrypt if ( !$debug ); $pop->delete($msgnum) if ( !$debug ); } #end foreach of all messages } # endif pop login $pop->quit; # send encrypted notifications to originator... send_notifications( \@notifications, $passphrase, $message, $encrypted, $logfile, $encrypt_notifications, $trace, \%messages ); $count = 0; sleep $sleep; } exit 0;


Doulos SIL - Font Features
Doulos SIL is an OpenType-enabled font that supports the Latin and Cyrillic scripts. It includes a number of optional features that may be useful or required for particular uses or languages. This document lists all the available features.
These OpenType features are primarily specified using four-letter tags (e.g. 'cv17'), although some applications may provide a direct way to control certain common features such as small caps. For more information on how to access OpenType features in specific environments and applications, see Using Font Features.
Please note that Graphite support has been removed in the current release, but continues to be available in the version 5 fonts. See our Previous Versions archive.
This page uses web fonts (WOFF2) to demonstrate font features and should display correctly in all modern browsers. For a more concise example of how to use Doulos SIL as a web font see DoulosSIL-webfont-example.html in the font package web folder. For detailed information see Using SIL Fonts on Web Pages.
If this document is not displaying correctly a PDF version is also provided in the documentation/pdf folder of the release package.
Complete feature list
Stylistic alternates
Small caps from lowercase
Affects: all lowercase letters with capital equivalents
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | a ... z (all letters with capital equivalents) | smcp=0 |
| Small caps | a ... z (all letters with capital equivalents) | smcp=1 |
Small caps from capitals
Affects: all capitals
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | A ... Z (all capitals) | c2sc=0 |
| Small caps | A ... Z (all capitals) | c2sc=1 |
Literacy a and g
Affects: U+0061 U+00E0 U+00E1 U+00E2 U+00E3 U+00E4 U+00E5 U+0101 U+0103 U+0105 U+01CE U+01DF U+01E1 U+01FB U+0201 U+0203 U+0227 U+1E01 U+1E9A U+1EA1 U+1EA3 U+1EA5 U+1EA7 U+1EA9 U+1EAB U+1EAD U+1EAF U+1EB1 U+1EB3 U+1EB5 U+1EB7 U+2C65 U+2090 U+1D43 U+0363 U+0067 U+011D U+011F U+0121 U+0123 U+01E7 U+01F5 U+01E5 U+1E21 U+A7A1 U+1D4D
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | a à á â ã ä å ā ă ą ǎ ǟ ǡ ǻ ȁ ȃ ȧ ḁ ẚ ạ ả ấ ầ ẩ ẫ ậ ắ ằ ẳ ẵ ặ ⱥ ₐ ᵃ ◌ͣ g ĝ ğ ġ ģ ǧ ǵ ǥ ḡ ꞡ ᵍ | ss01=0 |
| Single-story | a à á â ã ä å ā ă ą ǎ ǟ ǡ ǻ ȁ ȃ ȧ ḁ ẚ ạ ả ấ ầ ẩ ẫ ậ ắ ằ ẳ ẵ ặ ⱥ ₐ ᵃ ◌ͣ g ĝ ğ ġ ģ ǧ ǵ ǥ ḡ ꞡ ᵍ | ss01=1 |
Literacy a (only)
Affects: U+0061 U+00E0 U+00E1 U+00E2 U+00E3 U+00E4 U+00E5 U+0101 U+0103 U+0105 U+01CE U+01DF U+01E1 U+01FB U+0201 U+0203 U+0227 U+1E01 U+1E9A U+1EA1 U+1EA3 U+1EA5 U+1EA7 U+1EA9 U+1EAB U+1EAD U+1EAF U+1EB1 U+1EB3 U+1EB5 U+1EB7 U+2C65 U+2090 U+1D43 U+0363
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | a à á â ã ä å ā ă ą ǎ ǟ ǡ ǻ ȁ ȃ ȧ ḁ ẚ ạ ả ấ ầ ẩ ẫ ậ ắ ằ ẳ ẵ ặ ⱥ ₐ ᵃ ◌ͣ | ss11=0 |
| Single-story | a à á â ã ä å ā ă ą ǎ ǟ ǡ ǻ ȁ ȃ ȧ ḁ ẚ ạ ả ấ ầ ẩ ẫ ậ ắ ằ ẳ ẵ ặ ⱥ ₐ ᵃ ◌ͣ | ss11=1 |
Literacy g (only)
Affects: U+0067 U+011D U+011F U+0121 U+0123 U+01E7 U+01F5 U+01E5 U+1E21 U+A7A1 U+1D4D
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | g ĝ ğ ġ ģ ǧ ǵ ǥ ḡ ꞡ ᵍ | ss12=0 |
| Single-story | g ĝ ğ ġ ģ ǧ ǵ ǥ ḡ ꞡ ᵍ | ss12=1 |
Barred-bowl forms
Affects: U+0111 U+0180 U+01E5
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | đ ƀ ǥ | ss04=0 |
| Barred-bowl | đ ƀ ǥ | ss04=1 |
Character alternates
B hook
Affects: U+0181
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ɓ | cv13=0 |
| Lowercase-style | Ɓ | cv13=1 |
D hook
Affects: U+018A
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ɗ | cv17=0 |
| Lowercase-style | Ɗ | cv17=1 |
H stroke
Affects: U+0126
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ħ | cv28=0 |
| Vertical stroke | Ħ | cv28=1 |
J stroke hook
Affects: U+0284
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ʄ | cv37=0 |
| Top serif | ʄ | cv37=1 |
Eng
Affects: U+014A
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ŋ | cv43=0 |
| Lowercase style on baseline | Ŋ | cv43=1 |
| Uppercase style with descender | Ŋ | cv43=2 |
| Alt. lowercase style on baseline | Ŋ | cv43=3 |
N left hook
Affects: U+019D
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ɲ | cv44=0 |
| Lowercase-style | Ɲ | cv44=1 |
Open-O
Affects: U+0186 U+0254 U+1D10 U+1D53 U+1D97
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ɔ ɔ ᴐ ᵓ ᶗ | cv46=0 |
| Top serif | Ɔ ɔ ᴐ ᵓ ᶗ | cv46=1 |
OU
Affects: U+0222 U+0223 U+1D3D U+1D15
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ȣ ȣ ᴕ ᴽ | cv47=0 |
| Open | Ȣ ȣ ᴕ ᴽ | cv47=1 |
p hook
Affects: U+01A5
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ƥ | cv49=0 |
| Right hook | ƥ | cv49=1 |
R tail
Affects: U+2C64
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ɽ | cv55=0 |
| Lowercase-style | Ɽ | cv55=1 |
T hook
Affects: U+01AC
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ƭ | cv57=0 |
| Right hook | Ƭ | cv57=1 |
V hook
Affects: U+01B2 U+028B U+1DB9
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ʋ ʋ ᶹ | cv62=0 |
| Straight with low hook | Ʋ ʋ ᶹ | cv62=1 |
| Straight with high hook | Ʋ ʋ ᶹ | cv62=2 |
Y hook
Affects: U+01B3
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ƴ | cv68=0 |
| Left hook | Ƴ | cv68=1 |
Ezh
Affects: U+01B7 U+04E0
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ʒ Ӡ | cv20=0 |
| Reversed sigma | Ʒ Ӡ | cv20=1 |
ezh curl
Affects: U+0293
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ʓ | cv19=0 |
| Large bowl | ʓ | cv19=1 |
rams horn
Affects: U+0264
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ɤ | cv25=0 |
| Large bowl | ɤ | cv25=1 |
| Small gamma | ɤ | cv25=2 |
Diacritic and symbol alternates
Vietnamese-style diacritics
Affects: U+1EA4 U+1EA5 U+1EA6 U+1EA7 U+1EA8 U+1EA9 U+1EAA U+1EAB U+1EAE U+1EAF U+1EB0 U+1EB1 U+1EB2 U+1EB3 U+1EB4 U+1EB5 U+1EBE U+1EBF U+1EC0 U+1EC1 U+1EC2 U+1EC3 U+1EC4 U+1EC5 U+1ED0 U+1ED1 U+1ED2 U+1ED3 U+1ED4 U+1ED5 U+1ED6 U+1ED7
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Ấấ Ầầ Ẩẩ Ẫẫ Ắắ Ằằ Ẳẳ Ẵẵ Ếế Ềề Ểể Ễễ Ốố Ồồ Ổổ Ỗỗ | cv75=0 |
| Vietnamese-style | Ấấ Ầầ Ẩẩ Ẫẫ Ắắ Ằằ Ẳẳ Ẵẵ Ếế Ềề Ểể Ễễ Ốố Ồồ Ổổ Ỗỗ | cv75=1 |
Kayan diacritics
Affects: U+0300 U+0301
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ◌̀́ | cv79=0 |
| Side by side | ◌̀́ | cv79=1 |
Ogonek
Affects: U+0328 U+0104 U+0105 U+0118 U+0119 U+012E U+012F U+0172 U+0173 U+01EA U+01EB U+01EC U+01ED
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | anything with ◌̨ (Ąą Ęę Įį Ųų Ǫǫ Ǭǭ) | cv76=0 |
| Straight | anything with ◌̨ (Ąą Ęę Įį Ųų Ǫǫ Ǭǭ) | cv76=1 |
Caron
Affects: U+010F U+013D U+013E U+0165
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ď Ľ ľ ť | cv77=0 |
| Global-style | ď Ľ ľ ť | cv77=1 |
Modifier apostrophe
Affects: U+02BC U+A78B U+A78C
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ʼ Ꞌ ꞌ | cv70=0 |
| Large | ʼ Ꞌ ꞌ | cv70=1 |
Modifier colon
Affects: U+A789
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ꞉ | cv71=0 |
| Expanded | ꞉ | cv71=1 |
Empty set
Affects: U+2205
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ∅ | cv98=0 |
| Zero-style | ∅ | cv98=1 |
Cyrillic alternates
There are also Cyrillic characters affected by the “Ezh” and “Small capitals” features. Some languages may also use the “Modifier apostrophe”.
Cyrillic E
Affects: U+042D U+044D
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | Э э | cv80=0 |
| Mongolian-style | Э э | cv80=1 |
Cyrillic shha
Affects: U+04BB
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | һ | cv81=0 |
| Uppercase-style | һ | cv81=1 |
Cyrillic breve
Affects: U+0306
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | anything with ◌̆ (Ә̆ә̆) | cv82=0 |
| Cyrillic-style | anything with ◌̆ (Ә̆ә̆) | cv82=1 |
Serbian Cyrillic alternates
Unlike other features this is activated by tagging the span of text as being in the Serbian language, not by turning on an OpenType feature.
Affects: U+0431
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | б | |
| Serbian | б | lang='sr' |
Tone alternates
Chinantec tones
Affects: U+02CB U+02C8 U+02C9 U+02CA
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ˋ ˈ ˉ ˊ | cv90=0 |
| Chinantec-style | ˋ ˈ ˉ ˊ | cv90=1 |
Tone numbers
Affects: U+02E5 U+02E6 U+02E7 U+02E8 U+02E9 U+A712 U+A713 U+A714 U+A715 U+A716
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ˥ ˦ ˧ ˨ ˩ ꜒ ꜓ ꜔ ꜕ ꜖ | cv91=0 |
| Numbers | ˥ ˦ ˧ ˨ ˩ ꜒ ꜓ ꜔ ꜕ ꜖ | cv91=1 |
Hide tone contour staves
Affects: U+02E5 U+02E6 U+02E7 U+02E8 U+02E9 U+A712 U+A713 U+A714 U+A715 U+A716
| Feature | Sample | Feature setting |
|---|---|---|
| Standard | ˥ ˦ ˧ ˨ ˩ ꜒ ꜓ ꜔ ꜕ ꜖ (˩˦˥˧˨ ꜖꜓꜒꜔꜕) | cv92=0 |
| Hide staves | ˥ ˦ ˧ ˨ ˩ ꜒ ꜓ ꜔ ꜕ ꜖ (˩˦˥˧˨ ꜖꜓꜒꜔꜕) | cv92=1 |


The Biggest Change Ever To Ford Trucks And SUVs Is Happening
The Blue Oval's green revolution has begun.
by Jared RosenholtzAfter a 40-day long strike, the United Auto Workers union finally reached a deal with General Motors and quickly turned around to negotiate with Ford. The deal with Ford didn't take nearly as long and includes $6 billion in new product investment as well as over 8,500 new/retained jobs. This is all great news for the UAW and Ford but it is also good news for consumers.
According to the Detroit Free Press, the new Ford-UAW deal includes stipulations for a slew of new models within the next four years, many of which will be electrified. Freep was even able to dig up specific details about these future models and where they will be built. Here's what you can expect to see:
Ford's Dearborn truck plant (often referred to as the Rouge plant) will begin building an all-new F-150 starting in 2020. A hybrid version is expected to go on sale 2020 followed by an all-electric version in 2021. Purist truck fans will also be happy to hear Rouge that will build the next-generation F-150 Raptor model. Ford's Flat Rock plant will continue to build derivatives of the Mustang, including the Shelby models. This factory will also have the capacity to build an additional model once the Lincoln Continental ends production.
In Kansas City, Ford's plant will build an electric version of the Transit van for UPS and non-hybrid versions of the F-150. Kentucky will build the Super Duty trucks as well as rumored hybrid variants of the full-size Ford Expedition and Lincoln Navigator.
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Last but not least, Ford's Michigan Avenue assembly plant in Wayne receives the biggest investment of $1 billion. This factory will not only build the upcoming Bronco starting in 2020 but will also be responsible for building the next-generation Ranger pickup truck. The current Ranger has been on the global market for a few years now, which is why an all-new model is expected in 2022.


In which country is merecedes-benz based?
Who is the founder of tesla?
In which year was toyota founded?
_____ is a Kenyan car model
Which of the following is an electric car?


Conservatives to run four candidates in Northern Ireland
The Northern Ireland Conservative Party will contest four constituencies at the forthcoming UK General Election.
Gary Hynds, the party’s deputy chairman in the region, will run as a candidate in Lagan Valley.
Matthew Robinson will stand in North Down, Grant Abraham in Strangford and Aaron Rankin in East Antrim.
While the Conservative Party won the last General Election across the UK, they perform comparatively poorly across Northern Ireland’s 18 constituencies.
The party does not currently have any elected representatives in the region.
NI Conservatives chairman Neil Johnson described their candidates as “an alternative to the embittered politics of Stormont”.
“For the Northern Ireland Conservatives, we have recently been focusing on increasing our membership, which has risen by 30% since March, with our eyes on the next local elections,” he said.
“A General Election became inevitable the moment that Parliament paralysed itself by defying the will of the people and the 2016 referendum.
“So, we will be fighting this election on the principles of getting Brexit done, offering an alternative to the stagnated politics of Northern Ireland and with a determination to deliver the benefits of Conservative representation that the rest of the United Kingdom experience.
“We are the only party in Northern Ireland with a plan for the way forward – with other parties preferring delay or blocking Brexit altogether.
“But Northern Ireland needs stability, with further uncertainty unthinkable for business and society alike. This is the reason the Conservative Party is standing candidates in Northern Ireland and asking for your vote. A vote for hope, aspiration and prosperity.”


All of the following related items will be deleted:{% endblocktrans %}


brief
- I graduated frome Yarmouk univarsity with electrical engineering major.
- I worked as Technical Engieer for onternational orgnization called Cowater Inc. they where implementing project in jordan.
- For now I am studeing softwere engineering in LUTC as a schoolership from ASAC.
Top 10 Movies
- The Pursuit of Happyness
- Shooter
- Interstellar
- The Italian Job
- The Wolf of Wall Street
- Inception
- The Shawshank Redemption
- Pirates of the Caribbean
- Shutter Island
- The Notebook


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.newdawn.slick.Graphics
public class Graphics
A graphics context that can be used to render primatives to the accelerated canvas provided by LWJGL.
| Field Summary | |
|---|---|
protected static Graphics |
currentGraphics
The last graphics context in use |
protected static Font |
DEFAULT_FONT
The default font to use |
protected static SGL |
GL
The renderer to use for all GL operations |
static int |
MODE_ADD
Draw adding the existing colour to the new colour |
static int |
MODE_ALPHA_BLEND
Draw using the alpha blending |
static int |
MODE_ALPHA_MAP
Draw to the alpha map |
static int |
MODE_COLOR_MULTIPLY
Draw multiplying the source and destination colours |
static int |
MODE_NORMAL
The normal drawing mode |
static int |
MODE_SCREEN
Draw blending the new image into the old one by a factor of it's colour |
protected int |
screenHeight
The height of the screen |
protected int |
screenWidth
The width of the screen |
| Constructor Summary | |
|---|---|
Graphics()
Default constructor for sub-classes |
|
Graphics(int width,
int height)
Create a new graphics context. |
| Method Summary | |
|---|---|
void |
clear()
Clear the graphics context |
void |
clearAlphaMap()
Clear the state of the alpha map across the entire screen. |
void |
clearClip()
Clear the clipping being applied. |
void |
clearWorldClip()
Clear world clipping setup. |
void |
copyArea(Image target,
int x,
int y)
Copy an area of the rendered screen into an image. |
void |
destroy()
Dispose this graphics context, this will release any underlying resourses. |
protected void |
disable()
Disable rendering to this graphics context |
void |
draw(Shape shape)
Draw the outline of the given shape. |
void |
draw(Shape shape,
ShapeFill fill)
Draw the outline of the given shape. |
void |
drawAnimation(Animation anim,
float x,
float y)
Draw an animation to this graphics context |
void |
drawAnimation(Animation anim,
float x,
float y,
Color col)
Draw an animation to this graphics context |
void |
drawArc(float x1,
float y1,
float width,
float height,
float start,
float end)
Draw an oval to the canvas |
void |
drawArc(float x1,
float y1,
float width,
float height,
int segments,
float start,
float end)
Draw an oval to the canvas |
void |
drawGradientLine(float x1,
float y1,
Color Color1,
float x2,
float y2,
Color Color2)
Draw a line with a gradient between the two points. |
void |
drawGradientLine(float x1,
float y1,
float red1,
float green1,
float blue1,
float alpha1,
float x2,
float y2,
float red2,
float green2,
float blue2,
float alpha2)
Draw a line with a gradient between the two points. |
void |
drawImage(Image image,
float x,
float y)
Draw an image to the screen |
void |
drawImage(Image image,
float x,
float y,
Color col)
Draw an image to the screen |
void |
drawImage(Image image,
float x,
float y,
float srcx,
float srcy,
float srcx2,
float srcy2)
Draw a section of an image at a particular location and scale on the screen |
void |
drawImage(Image image,
float x,
float y,
float srcx,
float srcy,
float srcx2,
float srcy2,
Color col)
Draw a section of an image at a particular location and scale on the screen |
void |
drawImage(Image image,
float x,
float y,
float x2,
float y2,
float srcx,
float srcy,
float srcx2,
float srcy2)
Draw a section of an image at a particular location and scale on the screen |
void |
drawImage(Image image,
float x,
float y,
float x2,
float y2,
float srcx,
float srcy,
float srcx2,
float srcy2,
Color col)
Draw a section of an image at a particular location and scale on the screen |
void |
drawLine(float x1,
float y1,
float x2,
float y2)
Draw a line on the canvas in the current colour |
void |
drawOval(float x1,
float y1,
float width,
float height)
Draw an oval to the canvas |
void |
drawOval(float x1,
float y1,
float width,
float height,
int segments)
Draw an oval to the canvas |
void |
drawRect(float x1,
float y1,
float width,
float height)
Draw a rectangle to the canvas in the current colour |
void |
drawRoundRect(float x,
float y,
float width,
float height,
int cornerRadius)
Draw a rounded rectangle |
void |
drawRoundRect(float x,
float y,
float width,
float height,
int cornerRadius,
int segs)
Draw a rounded rectangle |
void |
drawString(java.lang.String str,
float x,
float y)
Draw a string to the screen using the current font |
protected void |
enable()
Enable rendering to this graphics context |
void |
fill(Shape shape)
Draw the the given shape filled in. |
void |
fill(Shape shape,
ShapeFill fill)
Draw the the given shape filled in. |
void |
fillArc(float x1,
float y1,
float width,
float height,
float start,
float end)
Fill an arc to the canvas (a wedge) |
void |
fillArc(float x1,
float y1,
float width,
float height,
int segments,
float start,
float end)
Fill an arc to the canvas (a wedge) |
void |
fillOval(float x1,
float y1,
float width,
float height)
Fill an oval to the canvas |
void |
fillOval(float x1,
float y1,
float width,
float height,
int segments)
Fill an oval to the canvas |
void |
fillRect(float x1,
float y1,
float width,
float height)
Fill a rectangle on the canvas in the current color |
void |
fillRect(float x,
float y,
float width,
float height,
Image pattern,
float offX,
float offY)
Tile a rectangle with a pattern specifing the offset from the top corner that one tile should match |
void |
fillRoundRect(float x,
float y,
float width,
float height,
int cornerRadius)
Fill a rounded rectangle |
void |
fillRoundRect(float x,
float y,
float width,
float height,
int cornerRadius,
int segs)
Fill a rounded rectangle |
void |
flush()
Flush this graphics context to the underlying rendering context |
void |
getArea(int x,
int y,
int width,
int height,
java.nio.ByteBuffer target)
Get an ara of pixels as RGBA values into a buffer |
Color |
getBackground()
Get the current graphics context background color |
Rectangle |
getClip()
Return the currently applied clipping rectangle |
Color |
getColor()
Get the color in use by this graphics context |
Font |
getFont()
Get the current font |
float |
getLineWidth()
Get the width of lines being drawn in this context |
Color |
getPixel(int x,
int y)
Get the colour of a single pixel in this graphics context |
Rectangle |
getWorldClip()
Get the last set world clip or null of the world clip isn't set |
boolean |
isAntiAlias()
True if antialiasing has been turned on for this graphics context |
void |
popTransform()
Pop a previously pushed transform from the stack to the current. |
void |
pushTransform()
Push the current state of the transform from this graphics contexts onto the underlying graphics stack's transform stack. |
void |
resetFont()
Reset to using the default font for this context |
void |
resetLineWidth()
Reset the line width in use to the default for this graphics context |
void |
resetTransform()
Reset the transformation on this graphics context |
void |
rotate(float rx,
float ry,
float ang)
Apply a rotation to everything draw on the graphics context |
void |
scale(float sx,
float sy)
Apply a scaling factor to everything drawn on the graphics context |
void |
setAntiAlias(boolean anti)
Indicate if we should antialias as we draw primitives |
void |
setBackground(Color color)
Set the background colour of the graphics context. |
void |
setClip(int x,
int y,
int width,
int height)
Set the clipping to apply to the drawing. |
void |
setClip(Rectangle rect)
Set the clipping to apply to the drawing. |
void |
setColor(Color color)
Set the color to use when rendering to this context |
static void |
setCurrent(Graphics current)
Set the current graphics context in use |
void |
setDrawMode(int mode)
Set the drawing mode to use. |
void |
setFont(Font font)
Set the font to be used when rendering text |
void |
setLineWidth(float width)
Set the with of the line to be used when drawing line based primitives |
void |
setWorldClip(float x,
float y,
float width,
float height)
Set clipping that controls which areas of the world will be drawn to. |
void |
setWorldClip(Rectangle clip)
Set the world clip to be applied |
void |
texture(Shape shape,
Image image)
Draw the the given shape filled in with a texture |
void |
texture(Shape shape,
Image image,
boolean fit)
Draw the the given shape filled in with a texture |
void |
texture(Shape shape,
Image image,
float scaleX,
float scaleY)
Draw the the given shape filled in with a texture |
void |
texture(Shape shape,
Image image,
float scaleX,
float scaleY,
boolean fit)
Draw the the given shape filled in with a texture |
void |
texture(Shape shape,
Image image,
float scaleX,
float scaleY,
ShapeFill fill)
Draw the the given shape filled in with a texture |
void |
texture(Shape shape,
Image image,
ShapeFill fill)
Draw the the given shape filled in with a texture |
void |
translate(float x,
float y)
Apply a translation to everything drawn to the context |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
protected static SGL GL
public static int MODE_NORMAL
public static int MODE_ALPHA_MAP
public static int MODE_ALPHA_BLEND
public static int MODE_COLOR_MULTIPLY
public static int MODE_ADD
public static int MODE_SCREEN
protected static Graphics currentGraphics
protected static Font DEFAULT_FONT
protected int screenWidth
protected int screenHeight
| Constructor Detail |
|---|
public Graphics()
public Graphics(int width, int height)
width
- The width of the screen for this contextheight
- The height of the screen for this context| Method Detail |
|---|
public static void setCurrent(Graphics current)
current
- The graphics context that should be considered currentpublic void setDrawMode(int mode)
MODE_NORMAL
or
MODE_ALPHA_MAP
or MODE_ALPHA_BLEND
mode
- The mode to apply.public void clearAlphaMap()
MODE_ALPHA_BLEND
nothing will be drawn.
protected void enable()
public void flush()
protected void disable()
public Font getFont()
public void setBackground(Color color)
color
- The background color of the graphics contextpublic Color getBackground()
public void clear()
public void resetTransform()
public void scale(float sx, float sy)
sx
- The scaling factor to apply to the x axissy
- The scaling factor to apply to the y axispublic void rotate(float rx, float ry, float ang)
rx
- The x coordinate of the center of rotationry
- The y coordinate of the center of rotationang
- The angle (in degrees) to rotate bypublic void translate(float x, float y)
x
- The amount to translate on the x-axisy
- The amount of translate on the y-axispublic void setFont(Font font)
font
- The font to be used when rendering textpublic void resetFont()
public void setColor(Color color)
color
- The color to use when rendering to this contextpublic Color getColor()
public void drawLine(float x1, float y1, float x2, float y2)
x1
- The x coordinate of the start pointy1
- The y coordinate of the start pointx2
- The x coordinate of the end pointy2
- The y coordinate of the end pointpublic void draw(Shape shape, ShapeFill fill)
shape
- The shape to draw.fill
- The fill type to applypublic void fill(Shape shape, ShapeFill fill)
shape
- The shape to fill.fill
- The fill type to applypublic void draw(Shape shape)
shape
- The shape to draw.public void fill(Shape shape)
shape
- The shape to fill.public void texture(Shape shape, Image image)
shape
- The shape to texture.image
- The image to tile across the shapepublic void texture(Shape shape, Image image, ShapeFill fill)
shape
- The shape to texture.image
- The image to tile across the shapefill
- The shape fill to applypublic void texture(Shape shape, Image image, boolean fit)
shape
- The shape to texture.image
- The image to tile across the shapefit
- True if we want to fit the image on to the shapepublic void texture(Shape shape, Image image, float scaleX, float scaleY)
shape
- The shape to texture.image
- The image to tile across the shapescaleX
- The scale to apply on the x axis for texturingscaleY
- The scale to apply on the y axis for texturingpublic void texture(Shape shape, Image image, float scaleX, float scaleY, boolean fit)
shape
- The shape to texture.image
- The image to tile across the shapescaleX
- The scale to apply on the x axis for texturingscaleY
- The scale to apply on the y axis for texturingfit
- True if we want to fit the image on to the shapepublic void texture(Shape shape, Image image, float scaleX, float scaleY, ShapeFill fill)
shape
- The shape to texture.image
- The image to tile across the shapescaleX
- The scale to apply on the x axis for texturingscaleY
- The scale to apply on the y axis for texturingfill
- The shape fill to applypublic void drawRect(float x1, float y1, float width, float height)
x1
- The x coordinate of the top left cornery1
- The y coordinate of the top left cornerwidth
- The width of the rectangle to drawheight
- The height of the rectangle to drawpublic void clearClip()
public void setWorldClip(float x, float y, float width, float height)
x
- The x coordinate of the top left corner of the allowed areay
- The y coordinate of the top left corner of the allowed areawidth
- The width of the allowed areaheight
- The height of the allowed areapublic void clearWorldClip()
public void setWorldClip(Rectangle clip)
clip
- The area still visiblesetWorldClip(float, float, float, float)
public Rectangle getWorldClip()
public void setClip(int x, int y, int width, int height)
x
- The x coordinate of the top left corner of the allowed areay
- The y coordinate of the top left corner of the allowed areawidth
- The width of the allowed areaheight
- The height of the allowed areapublic void setClip(Rectangle rect)
rect
- The rectangle describing the clipped area in screen
coordinatespublic Rectangle getClip()
public void fillRect(float x, float y, float width, float height, Image pattern, float offX, float offY)
x
- The x coordinate of the rectangley
- The y coordinate of the rectanglewidth
- The width of the rectangleheight
- The height of the rectanglepattern
- The image to tile across the rectangleoffX
- The offset on the x axis from the top left corneroffY
- The offset on the y axis from the top left cornerpublic void fillRect(float x1, float y1, float width, float height)
x1
- The x coordinate of the top left cornery1
- The y coordinate of the top left cornerwidth
- The width of the rectangle to fillheight
- The height of the rectangle to fillpublic void drawOval(float x1, float y1, float width, float height)
x1
- The x coordinate of the top left corner of a box containing
the ovaly1
- The y coordinate of the top left corner of a box containing
the ovalwidth
- The width of the ovalheight
- The height of the ovalpublic void drawOval(float x1, float y1, float width, float height, int segments)
x1
- The x coordinate of the top left corner of a box containing
the ovaly1
- The y coordinate of the top left corner of a box containing
the ovalwidth
- The width of the ovalheight
- The height of the ovalsegments
- The number of line segments to use when drawing the ovalpublic void drawArc(float x1, float y1, float width, float height, float start, float end)
x1
- The x coordinate of the top left corner of a box containing
the arcy1
- The y coordinate of the top left corner of a box containing
the arcwidth
- The width of the archeight
- The height of the arcstart
- The angle the arc starts atend
- The angle the arc ends atpublic void drawArc(float x1, float y1, float width, float height, int segments, float start, float end)
x1
- The x coordinate of the top left corner of a box containing
the arcy1
- The y coordinate of the top left corner of a box containing
the arcwidth
- The width of the archeight
- The height of the arcsegments
- The number of line segments to use when drawing the arcstart
- The angle the arc starts atend
- The angle the arc ends atpublic void fillOval(float x1, float y1, float width, float height)
x1
- The x coordinate of the top left corner of a box containing
the ovaly1
- The y coordinate of the top left corner of a box containing
the ovalwidth
- The width of the ovalheight
- The height of the ovalpublic void fillOval(float x1, float y1, float width, float height, int segments)
x1
- The x coordinate of the top left corner of a box containing
the ovaly1
- The y coordinate of the top left corner of a box containing
the ovalwidth
- The width of the ovalheight
- The height of the ovalsegments
- The number of line segments to use when filling the ovalpublic void fillArc(float x1, float y1, float width, float height, float start, float end)
x1
- The x coordinate of the top left corner of a box containing
the arcy1
- The y coordinate of the top left corner of a box containing
the arcwidth
- The width of the archeight
- The height of the arcstart
- The angle the arc starts atend
- The angle the arc ends atpublic void fillArc(float x1, float y1, float width, float height, int segments, float start, float end)
x1
- The x coordinate of the top left corner of a box containing
the arcy1
- The y coordinate of the top left corner of a box containing
the arcwidth
- The width of the archeight
- The height of the arcsegments
- The number of line segments to use when filling the arcstart
- The angle the arc starts atend
- The angle the arc ends atpublic void drawRoundRect(float x, float y, float width, float height, int cornerRadius)
x
- The x coordinate of the top left corner of the rectangley
- The y coordinate of the top left corner of the rectanglewidth
- The width of the rectangleheight
- The height of the rectanglecornerRadius
- The radius of the rounded edges on the cornerspublic void drawRoundRect(float x, float y, float width, float height, int cornerRadius, int segs)
x
- The x coordinate of the top left corner of the rectangley
- The y coordinate of the top left corner of the rectanglewidth
- The width of the rectangleheight
- The height of the rectanglecornerRadius
- The radius of the rounded edges on the cornerssegs
- The number of segments to make the corners out ofpublic void fillRoundRect(float x, float y, float width, float height, int cornerRadius)
x
- The x coordinate of the top left corner of the rectangley
- The y coordinate of the top left corner of the rectanglewidth
- The width of the rectangleheight
- The height of the rectanglecornerRadius
- The radius of the rounded edges on the cornerspublic void fillRoundRect(float x, float y, float width, float height, int cornerRadius, int segs)
x
- The x coordinate of the top left corner of the rectangley
- The y coordinate of the top left corner of the rectanglewidth
- The width of the rectangleheight
- The height of the rectanglecornerRadius
- The radius of the rounded edges on the cornerssegs
- The number of segments to make the corners out ofpublic void setLineWidth(float width)
width
- The width of the line to be used when drawing line based
primitivespublic float getLineWidth()
public void resetLineWidth()
public void setAntiAlias(boolean anti)
anti
- True if we should antialiaspublic boolean isAntiAlias()
public void drawString(java.lang.String str, float x, float y)
str
- The string to drawx
- The x coordinate to draw the string aty
- The y coordinate to draw the string atpublic void drawImage(Image image, float x, float y, Color col)
image
- The image to draw to the screenx
- The x location at which to draw the imagey
- The y location at which to draw the imagecol
- The color to apply to the image as a filterpublic void drawAnimation(Animation anim, float x, float y)
anim
- The animation to be drawnx
- The x position to draw the animation aty
- The y position to draw the animation atpublic void drawAnimation(Animation anim, float x, float y, Color col)
anim
- The animation to be drawnx
- The x position to draw the animation aty
- The y position to draw the animation atcol
- The color to apply to the animation as a filterpublic void drawImage(Image image, float x, float y)
image
- The image to draw to the screenx
- The x location at which to draw the imagey
- The y location at which to draw the imagepublic void drawImage(Image image, float x, float y, float x2, float y2, float srcx, float srcy, float srcx2, float srcy2)
image
- The image to draw a section ofx
- The x position to draw the imagey
- The y position to draw the imagex2
- The x position of the bottom right corner of the drawn imagey2
- The y position of the bottom right corner of the drawn imagesrcx
- The x position of the rectangle to draw from this image (i.e.
relative to the image)srcy
- The y position of the rectangle to draw from this image (i.e.
relative to the image)srcx2
- The x position of the bottom right cornder of rectangle to
draw from this image (i.e. relative to the image)srcy2
- The t position of the bottom right cornder of rectangle to
draw from this image (i.e. relative to the image)public void drawImage(Image image, float x, float y, float srcx, float srcy, float srcx2, float srcy2)
image
- The image to draw a section ofx
- The x position to draw the imagey
- The y position to draw the imagesrcx
- The x position of the rectangle to draw from this image (i.e.
relative to the image)srcy
- The y position of the rectangle to draw from this image (i.e.
relative to the image)srcx2
- The x position of the bottom right cornder of rectangle to
draw from this image (i.e. relative to the image)srcy2
- The t position of the bottom right cornder of rectangle to
draw from this image (i.e. relative to the image)public void copyArea(Image target, int x, int y)
target
- The target imagex
- The x position to copy fromy
- The y position to copy frompublic Color getPixel(int x, int y)
x
- The x coordinate of the pixel to ready
- The y coordinate of the pixel to read
public void getArea(int x, int y, int width, int height, java.nio.ByteBuffer target)
x
- The x position in the context to grab fromy
- The y position in the context to grab fromwidth
- The width of the area to grab fromheight
- The hiehgt of the area to grab fromtarget
- The target buffer to grab intopublic void drawImage(Image image, float x, float y, float x2, float y2, float srcx, float srcy, float srcx2, float srcy2, Color col)
image
- The image to draw a section ofx
- The x position to draw the imagey
- The y position to draw the imagex2
- The x position of the bottom right corner of the drawn imagey2
- The y position of the bottom right corner of the drawn imagesrcx
- The x position of the rectangle to draw from this image (i.e.
relative to the image)srcy
- The y position of the rectangle to draw from this image (i.e.
relative to the image)srcx2
- The x position of the bottom right cornder of rectangle to
draw from this image (i.e. relative to the image)srcy2
- The t position of the bottom right cornder of rectangle to
draw from this image (i.e. relative to the image)col
- The color to apply to the image as a filterpublic void drawImage(Image image, float x, float y, float srcx, float srcy, float srcx2, float srcy2, Color col)
image
- The image to draw a section ofx
- The x position to draw the imagey
- The y position to draw the imagesrcx
- The x position of the rectangle to draw from this image (i.e.
relative to the image)srcy
- The y position of the rectangle to draw from this image (i.e.
relative to the image)srcx2
- The x position of the bottom right cornder of rectangle to
draw from this image (i.e. relative to the image)srcy2
- The t position of the bottom right cornder of rectangle to
draw from this image (i.e. relative to the image)col
- The color to apply to the image as a filterpublic void drawGradientLine(float x1, float y1, float red1, float green1, float blue1, float alpha1, float x2, float y2, float red2, float green2, float blue2, float alpha2)
x1
- The starting x position to draw the liney1
- The starting y position to draw the linered1
- The starting position's shade of redgreen1
- The starting position's shade of greenblue1
- The starting position's shade of bluealpha1
- The starting position's alpha valuex2
- The ending x position to draw the liney2
- The ending y position to draw the linered2
- The ending position's shade of redgreen2
- The ending position's shade of greenblue2
- The ending position's shade of bluealpha2
- The ending position's alpha valuepublic void drawGradientLine(float x1, float y1, Color Color1, float x2, float y2, Color Color2)
x1
- The starting x position to draw the liney1
- The starting y position to draw the lineColor1
- The starting position's colorx2
- The ending x position to draw the liney2
- The ending y position to draw the lineColor2
- The ending position's colorpublic void pushTransform()
public void popTransform()
public void destroy()
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Joylent - Day 1
So I've decided to not eat anything for a week. And live soley on Joylent.
Day one was heavy. I had major headaches and felt fatigued, but that might had more to do with the party I had the day before.
Anyhow. I decided to take the strawberry shake today. The flavour was subtle and nice. The shake tastes like pancake batter with a slight strawberry touch. The structure is soft but thick, and always feels warm, even if you cool it in the fridge, which is really weird... I gues it's the structure that your body doesn't associate with 'cold'.
I took a bottle with me and drank it over an hour or two. Don't do this. You just end up being hungry all the time. In the afternoon I just ad-fundumed the next batch and it was a lot more pleasant.
Around 3 I got a real craving for food, So I ate a slice of bread. It was really good.
In the evening I stole one slice of pizza of my friend. I really love diner, and it's hard to not have diner.. That was a real struggle.
Later that night I went out. And I noticed I would get tipsy very quickly. After 2 drinks I already felt light-headed and had some headaches. So I quit drinking more.
 I just craved for food.
So yeah. Day one was okay, but a bit of a failure. I'm not sure I can do without food. But Soylent is great for in the morning, as usually I don't have time to make breakfast. So it's a quick way to get something into my system to get the engine running.


Grammar
How to write the grammar:
- The first symbol must be the start symbol;
- Separate symbols with a space;
- Write only one rule per line—no bars;
- Do not use arrows—use a space; and
- Do not write an end marker.
Collection
Parse Table
Parsing
How to write the input:
- Separate symbols with a space; and
- Do not write an end marker.


|
ActiveTcl User Guide
|
||
Tcl_AppInit - perform application-specific initialization
#include <tcl.h>
int
Tcl_AppInit(interp)
Tcl_AppInit is a ``hook'' procedure that is invoked by the main programs for Tcl applications such as tclsh and wish. Its purpose is to allow new Tcl applications to be created without modifying the main programs provided as part of Tcl and Tk. To create a new application you write a new version of Tcl_AppInit to replace the default version provided by Tcl, then link your new Tcl_AppInit with the Tcl library.
Tcl_AppInit is invoked after by Tcl_Main and Tk_Main after their own initialization and before entering the main loop to process commands. Here are some examples of things that Tcl_AppInit might do:
Tcl_AppInit returns TCL_OK or TCL_ERROR. If it returns TCL_ERROR then it must leave an error message in for the interpreter's result; otherwise the result is ignored.
In addition to Tcl_AppInit, your application should also contain a procedure main that calls Tcl_Main as follows:
Tcl_Main(argc, argv, Tcl_AppInit);
The third argument to Tcl_Main gives the address of the application-specific initialization procedure to invoke. This means that you don't have to use the name Tcl_AppInit for the procedure, but in practice the name is nearly always Tcl_AppInit (in versions before Tcl 7.4 the name Tcl_AppInit was implicit; there was no way to specify the procedure explicitly). The best way to get started is to make a copy of the file tclAppInit.c from the Tcl library or source directory. It already contains a main procedure and a template for Tcl_AppInit that you can modify for your application.
application, argument, command, initialization, interpreter
Copyright © 1993 The Regents of the University of California. Copyright © 1994-1996 Sun Microsystems, Inc. Copyright © 1995-1997 Roger E. Critchlow Jr.


Extras
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Requires for the user to be authenticated.
PUT /user/starred/:owner/:repo
Note that you'll need to set Content-Length to zero when calling out to this endpoint. For more information, see "HTTP verbs."
Content-Length
Status: 204 No Content


Still under construction!
For errors or criticism open an issue in the github repository kidpixo/COVID19-dashboard
NEW on 10 Apr I modified the model days window from 14 to 21 to have more stability in the model.
Based on Covid-19 infection in Italy. Mathematical models and predictions
I read the nice post Covid-19 infection in Italy. Mathematical models and predictions.
I want to do something useful and learn something, so I start reproducing the post.
The original post uses data from the Italian Civil Protection Department, that collects and pushes new data every day in their github repository CSSEGISandData/COVID-19.
I also start to use the global data from CSSEGISandData/COVID-19: Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE, but this is not fully functional.
The main important point in the notebook are:
(Text from Covid-19 infection in Italy. Mathematical models and predictions)
The logistic model has been widely used to describe the growth of a population. An infection can be described as the growth of the population of a pathogen agent, so a logistic model seems reasonable.
This formula is very known among data scientists because it’s used in the logistic regression classifier and as an activation function of neural networks.
The most generic expression of a logistic function is:
In this formula, we have the variable x that is the time and three parameters: a,b,c.
At high time values, the number of infected people gets closer and closer to c and that’s the point at which we can say that the infection has ended.
This function has also an inflection point at b, that is the point at which the first derivative starts to decrease (i.e. the peak after which the infection starts to become less aggressive and decreases).


Also was able to register as a publisher:
./komodo-cli -ac_name=IMPORTGW oraclesinfo df9170e2a9530d7a7846d6bdb2701fa01822c1b8346d47301ae83ad41f187a81
{
"result": "success",
"txid": "df9170e2a9530d7a7846d6bdb2701fa01822c1b8346d47301ae83ad41f187a81",
"name": "KMD",
"description": "testing",
"format": "Ihh",
"marker": "RDkHqoFBVXGQA7m7d3WhVpusFKaTncmH8P",
"registered": [
{
"publisher": "03c15a33e8aa922abc592bc86ccc8ff4a6233fa5cb275c88fcf0b5e22aab924e6a",
"baton": "RPFxe4vQ7hdESNsqVKLYuYo2WU5SUT4XFL",
"batontxid": "521b355fc94cac0e3550430f3e1b1e87c8ba75689d749ae19c255bba49f8d4c9",
"lifetime": "0.00000000",
"funds": "0.00000000",
"datafee": "0.00010000"
}
]
}


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#assign l2caching_invalidation="active">
Cache invalidation is automatically provided for persisted beans. The cache invalidation is processed by bean type and id value.
The message sent around the cluster contains the bean type and 3 lists of ids - a list of id values for inserted, updated and deleted beans.
Inserts, updates or deletes:
Invalidate the entire L2 query cache for the bean typeFor any bean persist event invalidates the entire related L2 query cache for the associated bean type. For example, saving a customer bean invalidates the entire L2 query cache for the Customer bean type.
Inserts:
Do not effect the L2 bean cacheUpdates:
An entry is updated with changesDeletes:
An entry is removed from the L2 bean cache based on the id valueBulk insert, update or delete events are processed by table. For a given table the bean types that depend on that table are determined and then for each bean type the L2 bean cache and L2 query cache are invalidated as necessary.
The message sent around the cluster contains the table name and boolean flags for insert, update and delete.
Inserts, updates or deletes:
Invalidate the entire L2 query cache for the related bean typeFor any bulk statements (bulk table insert, update or delete statement) the entire L2 query cache is invalidated for the associated bean type. For example, a bulk update of the customer table invalidates the entire L2 query cache for the Customer bean type.
Inserts:
Do not effect the L2 bean cacheUpdates:
Invalidate the entire L2 bean cache for the related bean typeDeletes:
Invalidate the entire L2 bean cache for the related bean typeWe can perform explicit invalidation using application code. We want to do this when data is modified externally to Ebean. For example, data is updated by other frameworks, JDBC code etc. For this case we want to explicitly invalidate parts of the L2 cache.
// inform Ebean that some rows have been inserted and updated // on the country table. // ... Ebean will invalidate the appropriate caches boolean inserts = true; boolean updates = true; boolean deletes = false; DB.externalModification("country", inserts, updates, deletes);
ServerCacheManager also provides explicit API for clearing caches.
// clearAll() caches via the ServerCacheManager ... ServerCacheManager cacheManager = database.cacheManager(); // Clear all the caches on the default/primary EbeanServer cacheManager.clearAll(); // clear both the bean and query cache // for Country beans ... cacheManager.clear(Country.class);


Swedish police given power to use surveillance cameras without permit
by TT/The LocalSwedish police and other judicial authorities will be able to use surveillance cameras without first receiving a permit, starting from January 1st next year.
Following a decision in parliament, several authorities will now be able to operate CCTV without first gaining permission from the Data Inspection Board, as is usually required.
The change applies to Swedish police, the Swedish Security Service, the coast guard, and Swedish customs.
The requirement is that the surveillance can only happen if the benefit of it is judged to be more significant than the individual's right not to be observed, but it will be up to the individual authorities to make that decision.
The authorities will have to document their decision, and the Data Inspection Board will be responsible for supervision, and will have the power to fine authorities who don't carry out this documentation.
All parties represented in parliament except the Left Party voted in favour of the change.


HTML-CONTENT
CLICK BELOW BUTTON TO END THE GAME
CLOSE ME
Interact
Assesment Start
Assesment Response
Assesment End
Impression


--- layout: post title: 'My So-called Second Life: Roo Reynolds, Metaverse Evangelist' date: 2006-10-24 00:00:00 ---
My So-called Second Life: Roo Reynolds, Metaverse Evangelist
In 2005: 25% of gamers over the age of 50. 69% heads of American households are gamers. Hence interest from IBM: this is a big business. It's not about young geeky males any more.
Second life has >1m users, 400k logged on in the last 2 months. 8000 concurrently online. An active economy: $500k changing hands daily. It's not a game, it's "a platform for meeting, building, selling, collaborating and exploring". Sounds like an infinite game
Shows off BBC One Big Weekend concert in Second Life: a combination of user-generated content and social network. It's not an isolated experience. Within the game, "prototyping and making something are the same thing.". They did a simulation of the actual tennis play (ball trajectories etc.) from Wimbledon into Second Life... so people could watch the shot from a choice of seating in the game, and talk about it.
He went to a Second Life concert where Ben Folds chopped everyone up with a lightsaber. Apparently this was a lot of fun: but err how do you know it's him?
What's in it for IBM? "We have a lot of meetings and sometimes they're not much fun". They've begun augmenting some of the stuff they do in IBM. They're looking at using this stuff for community events, etc. People get together after a meeting and get together to talk, etc.
L'Oreal have a virtual office and use this as a recruitment tool.
Second Life is like the web mid-90s: yes, porn and gambling-ridden. It's become more commercial as real companies see an opportunity to promote their brand in-world.
Question: what are the revenue streams for IBM in this?
Answer: most of their money comes from services. V-businesses analogous to e-businesses?
I'm not entirely convinced by this - it all feels a bit "in the lab", and maybe I've been desensitised to the whole 3D world avatar thing. I can see it working as part of an escapist game or play, but as a serious tool? Maybe I'm just too old :) But then Esther and co. do exactly this sort of stuff within WoW... is that because it's the best way to do it, or is it just folks who want to communicate finding the path of least resistance towards doing that? The street finding its own use for things... (he said, vomiting slightly)


Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since.
My Company Name
328 Ave C
Saskatoon,SK
S7R 2V9
Telephone :(00) 222 666 444
Fax : (000) 000 00 00 0
Email : info(at)mycompany.com


Preparation time
overnight
Cooking time
1 to 2 hours
Serves
Serves 4
Crisp fried octopus is served with roasted vegetables, caramelised apple and a fresh dressing.
For the octopus, put all the ingredients except the octopus in a large saucepan. Cover with water and bring to the boil before adding the octopus. Simmer for about 1½ hours until the octopus is tender. Do check regularly using the tip of a knife; once you can easily cut through the flesh strain the octopus, and leave overnight in the fridge. Discard the head and the liquid.
For the fruit and vegetable garnish, preheat the oven to 180C/160C Fan/Gas 4.
Place the celeriac and fennel on a sheet of kitchen foil and season with salt and pepper. Add the garlic clove, olive oil and thyme. Wrap in the foil and seal tightly. Bake for 20–30 minutes (depending on the size of the chunks), or until tender.
In the meantime, slice each half of apple into three even wedges. Heat a drizzle of oil in a frying pan and fry the apple wedges on each side until golden, but not cooked through. Set aside until ready to serve.
When the vegetables are cooked, remove them from the foil and reserve any cooking juices (to use in the dressing).
For the octopus tentacles, slice the tentacles into chunks. Heat the olive oil in a large frying pan over a high heat. Once the pan is hot, fry the tentacles until crisp. Season with salt and pepper.
For the liquorice dressing, put all the ingredients in a mixing bowl and whisk together to create a split vinaigrette. Dress with tarragon leaves and an extra pinch of liquorice powder.
To serve, divide the roasted vegetables between serving plates, top with the octopus and finish with the dressing and fennel fronds.


Kadok RealSense 3D™ is a brand new type of phone camera that allows you to take live 3D pictures of the world around you. And it changes everything.
Look back on your memories from any point of view, and share the most stunning sights you have captured, from the palm of your hand.
Kadok RealSense 3D™ is compatible with all modern virtual reality headsets. Unleash your creativity and make your pictures even better through 3D model editing.
Control every aspect of your pictures, through an intuitive and harmonious interface. Share your designs through social media.
Lead the creation of your own videos where you can guide the viewer through your trip.
Tags are automatically added to your pictures, telling you where you are and what you are seeing. The world is within your reach.
We embedded an important social dimension into Kadok RealSense 3D™. The application comes with extensive ways to share your content with your friends and families.
Add friends, create editing communities and chat in real-time about the latest models, all within the same interface.


1Actually, <name>.tex is internally copied to a scratch file <name>.w, in order to allow the user to work comfortably with an editor knowledgeable of the LATEX syntax.


Psychic Game - Good Luck!
Wins
0
Losses
0
Guesses
0
Guesses Left
9
Guesses so far
---
You won!!
The guessed letter was
Start a New game


This November BC voters have the duty and opportunity to vote on how we vote. This is BC’s third referendum on voting reform, and also the one with the most options.
What voting systems is BC choosing between?
The first question of the referendum is between keeping the current system: known as First-Past-The-Post (FPTP) and switching to a Proportional Representation voting system. The second question BC voters will be asked is to rank 3 PR systems. Mail-in ballots will be mailed out between Oct 22 and Nov 2.
What is Proportional Representation?
Proportional Representation systems try to guarantee that if 70% of votes in BC are for party A, 70% of BC’s legislature is filled with MLAs from party A.
What are the pros and cons for Proportional Representation?
The official proponent of PR is Fair Voting BC. The official opposition is No ProRep BC. The opposition site has more catchphrases than arguments… so here’s a stronger summary of arguments against PR (see the PR minuses).
Which Proportional Representation systems are we ranking and how do they function?
Watch this video for a quick explanation of the 3 types of proportional voting systems on B.C.’s ballot
How do they compare to each other?
The best comparison of these voting systems is the Fair Voting BC Scorecard. I highly recommend reading through it and weighing the different pros and cons of each system yourself.
What will you vote for?
I’m in favour of Proportional Representation. Based on the scorecard my ranking of the 3 proposed PR systems is:
- Mixed Member Proportional (simple ballot, and you can vote on individual candidates)
- Dual Member Proportional (simple ballot, but you only pick the party, they pick which of multiple candidates it counts towards)
- Rural-Urban Proportional (Single Transferable Vote lets you vote for individual candidates but ranking a dozen candidates is hard)
If you had the option to pick a different voting system entirely, what would you pick?
Range Voting. It doesn't have Proportional Representation but it has other benefits (it’s expressive, resistant to vote splitting, incentivizes honest voting, neither pro-centrist nor pro-extremist).
Actually, there’s a version of range voting that does have Proportional Representation called Re-Weighted Range Voting that looks interesting. But that’s for another day.
Update Nov 10
I’ve submitted my vote.  Remember that they must be mailed before Nov 30th!