Social Media Analysis Methods and Applications
CS7290 Fall 2021
Course Description Assignments Schedule
- Instructor: Silvio Amir
- Meetings: Tue & Fri 9:50am - 11:30am @ Ryder Hall 153
- Office hours: Thur 1pm - 3pm @ Office 2207, 177 Huntington Avenue and @ Zoom
- Discussion/Announcements: piazza
Course Description
Seminar class in which we read, critique and discuss recent research papers about the methods, applications and implications of social media analysis. Social media analysis is a multidisciplinary subject that encompasses a broad range of topics. In this class we will focus on: (i) cutting-edge Machine Learning/Deep Learning and Natural Language Processing methods to infer signals from single posts, individual users, and user networks; (ii) applications of social media analysis to real-world analyses in domains such as the social sciences, political science, mental-health and epidemiology; and (iii) the implications, as well the ethical and moral considerations, of deploying social media analysis systems and Human-centered AI systems more broadly.
Students will take turns presenting a paper each week (2 presentations per class) which we will discuss together critically during class meetings. The presentations should also cover any relevant background material; this is important because in general we will have a strong bias toward very recent work. Critical discussion will follow, and we will conclude class by talking about potential means of extending the work. Students will also be responsible for writing short summaries of the research papers we read each week. Student led presentations will be complemented with several guest lectures from top researchers working on different facets of social media analysis.
In addition to the presentations and paper reviews, students will propose and execute a mini project culminating in a research paper (draft). The results and findings of the project will be presented in a class, structured as a research talk. Therefore, this class will very much be research-oriented.
⚠ As per university policy, the class the will be in-person only and everyone must be wearing a mask.
Prerequisites
Students should have an interest in conducting (or learning how to conduct) research. There are no official pre-reqs for this class, however students are expected to have some background in data science, machine learning and statistical natural language processing. This is mainly to make students life easier since we will not cover background knowledge related to the papers in class.
Assignments
Oral Presentations
Students will rotate responsibility for presenting (one of the) assigned papers and leading the ensuing discussion. The presentation should provide necessary background, the core contribution of the paper, (perceived) strengths and weaknesses, and ideas for improving the work.
Written Critiques
Prior to every class all students are to write a brief “review” of one of the assigned papers for that class (their choice). These should include a concise summary of the work and enumerate at least two strong points and at least two weak points therein. Furthermore, these critiques will note ways in which the work could potentially be extended/improved (which may be coupled with the weak points). Assessments are to be submitted via canvas by 11.59pm of the day prior to class (i.e. Mondays and Thursdays). Note that is assumed students read all the assigned papers, but may read only one in-depth. The idea is basically to ensure robust discussion during class meetings.
Project
The class will culminate in projects with accompanying papers (along with the respective code, when possible). The papers will be structured as research papers following a template, available here and the code should be submitted as a jupyter notebook (if possible). Students may work together but then a concomitant increase in complexity and a clear delineation of contributions is expected, for fairness sake.
Students will be able to choose which projects to work on. Projects may involve replicating a state-of-the-art paper we read, or, ideally, extending one of these or developing new ideas in the area. Students are to write a short (1 page) project proposal including the motivation, goals, expected results and a timeline of key milestones. Project proposals will then be reviewed and approved by the instructor. Some class time will be reserved for students to present their project ideas, results and findings (these will be structured as research talks).
Projects will be evaluated for their relevance, scientific rigour, and originality. The papers will be evaluated as paper draft submissions using the template available here.
Grades
- 55% Project and write-up
- 20% In class presentations of papers
- 15% Participation
- 10% Written summaries/critiques of papers (on canvas)
Schedule
Tentative weekly schedule. The assigned papers are unlikely to change but the presentation dates might move to accommodate the (busy) schedules of the invited speakers.
| Date | Agenda | Speaker | ||
|---|---|---|---|---|
| Fri 9/10 | 🎉 Introduction | Silvio Amir | ||
| Hate-speech Detection | ||||
| Tue 9/14 | 🎓 Lecture: Social Media Analysis: methods, applications and implications | Silvio Amir | ||
| 📃 Contextualizing Hate Speech Classifiers with Post-hoc Explanation, Kennedy, B., Jin, X., Davani, A. M., Dehghani, M., & Ren, X. (2020) | Silvio Amir | |||
| additional readings | 📚 A Survey on Automatic Detection of Hate Speech in Text; Fortuna, P., & Nunes, S. (2018). | |||
| 📚 Towards generalisable hate speech detection: a review on obstacles and solutions; FYin, W., & Zubiaga, A. (2021). | ||||
| 📚 A Survey on Hate Speech Detection using Natural Language Processing; Schmidt, A., & Wiegand, M. (2017). | ||||
| Misinformation Detection | ||||
| Fri 9/17 | 📃 Deep Structure Learning for Rumor Detection on Twitter, Huang, Q., Zhou, C., Wu, J., Wang, M., & Wang, B. (2019). | Sanjana | ||
| 📃 Detecting Propaganda Techniques in Memes, Dimitrov, D., Ali, B.B., Shaar, S., Alam, F., Silvestri, F., Firooz, H., Nakov, P. & Da San Martino, G., (2021) | Hye Sun | |||
| additional readings | 📚 Combating fake news: A survey on identification and mitigation techniques;Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019). | |||
| 📚 A survey on fake news and rumour detection techniques; Bondielli, A., & Marcelloni, F. (2019). | ||||
| 📚 The science of fake news; Lazer, D.M., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F., Metzger, M.J., Nyhan, B., Pennycook, G., Rothschild, D. & Schudson, M., (2018) | ||||
| Digital Activism | ||||
| Tue 9/21 | 📃 Reclaiming Stigmatized Narratives: The Networked Disclosure Landscape of #MeToo, Gallagher et al. (2019) | TBD | ||
| 📃 Say Their Names: Resurgence in the Collective Attention toward Black Victims of Fatal Police Violence Following the Death of George Floyd, Wu, H.H., Gallagher, R.J., Alshaabi, T., Adams, J.L., Minot, J.R., Arnold, M.V., Welles, B.F., Harp, R., Dodds, P.S. & Danforth, C.M., (2021) | TBD | |||
| Fri 9/24 | 📃 Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement, Mueller, A., Wood-Doughty, Z., Amir, S., Dredze, M., & Nobles, A. L. (2021) | TBD | ||
| 🎓 Guest Lecture: #HashtagActivism: Networks of Race and Gender Justice | Brooke Foucault Welles | |||
| additional readings | 📚 # HashtagActivism: Networks of race and gender justice; Jackson, S. J., Bailey, M., & Welles, B. F. (2020). | |||
| Deep Learning | ||||
| Tue 9/28 | 📃 Compositional Demographic Word Embeddings, Welch, C., Kummerfeld, J. K., Pérez-Rosas, V., & Mihalcea, R. (2020) | TBD | ||
| 📃 Developing a Twitter bot that can join a discussion using state-of-the-art architectures, Çetinkaya, Y. M., Toroslu, İ. H., & Davulcu, H. (2020). | TBD | |||
| Fri 10/1 | 📃 SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics, Yin, D., Meng, T., & Chang, K. W. (2020) | TBD | ||
| 📃 Adversarial Learning for Zero-Shot Stance Detection on Social Media, Allaway, E., Srikanth, M., & McKeown (2021) | TBD | |||
| Political Science | ||||
| Tue 10/5 | 📃 Unsupervised User Stance Detection on Twitter, Darwish, K., Stefanov, P., Aupetit, M., & Nakov, P. (2020) | TBD | ||
| 📃 RAFFMAN: Measuring and Analyzing Sentiment in Online Political Forum Discussions with an Application to the Trump Impeachment, Tachaiya, J., Gharibshah, J., Esterling, K. E., & Faloutsos, M. (2021). | TBD | |||
| Fri 10/8 | 📃 How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection, Prabhakaran, V., Rei, M., & Shutova, E. (2021) | TBD | ||
| 🎓 Guest Lecture: TBD | David Lazer | |||
| Multimodal Social Media Analysis | ||||
| Tue 10/12 | 📃 Person-Centered Predictions of Psychological Constructs with Social Media Contextualized by Multimodal Sensing, Saha, K., Grover, T., Mattingly, S.M., Swain, V.D., Gupta, P., Martinez, G.J., Robles-Granda, P., Mark, G., Striegel, A. & De Choudhury, M., (2021). | TBD | ||
| 📃 Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association, Xu, N., Zeng, Z., & Mao, W. (2020) | TBD | |||
| Public Health | ||||
| Fri 10/15 | 📃 Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate, Broniatowski, D.A., Jamison, A.M., Qi, S., AlKulaib, L., Chen, T., Benton, A., Quinn, S.C. & Dredze, M., (2018) | TBD | ||
| 📃 Examining Peer-to-Peer and Patient-Provider Interactions on a Social Media Community Facilitating Ask the Doctor Services , Nobles, A. L., Leas, E. C., Dredze, M., & Ayers, J. W. (2020) | TBD | |||
| additional readings | 📚 Social Monitoring for Public Health; Paul, M. J., & Dredze, M. (2017). Synthesis Lectures on Information Concepts, Retrieval, and Services [preprint] | |||
| Covid-19 |
||||
| Tue 10/19 | 📃 Explaining the ‘Trump Gap’ in Social Distancing Using COVID Discourse, Van Loon, A., Stewart, S., Waldon, B., Lakshmikanth, S.K., Shah, I., Guntuku, S.C., Sherman, G., Zou, J. & Eichstaedt, J., 2020 (2020) | TBD | ||
| 📃 COVID-19 Surveillance through Twitter using Self-Supervised and Few Shot Learning, Lwowski and Rad (2020) | TBD | |||
| Fri 10/22 | 📃 Twitter and Facebook posts about COVID-19 are less likely to spread false and low-credibility content compared to other health topics, Broniatowski, D. A., Kerchner, D., Farooq, F., Huang, X., Jamison, A. M., Dredze, M., & Quinn, S. C. (2020). | TBD | ||
| 🎓 Guest Lecture: TBD | Alessandro Vespignani | |||
| 🚀 Project ideas deadline | ||||
| additional readings | 📚 Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media, Saha, K., Torous, J., Caine, E. D., & De Choudhury, M. (2020) | |||
| Social Sciences | ||||
| Tue 10/26 | 📃 Who Says What with Whom: Using Bi-Spectral Clustering to Organize and Analyze Social Media Protest Networks, Joseph et al. (2020). | TBD | ||
| 🎓 Guest Lecture: The Light and Dark Side of Social Media | Nick Beauchamp | |||
| Fri 10/29 | 📃 Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter, Alshaabi, T., Adams, J.L., Arnold, M.V., Minot, J.R., Dewhurst, D.R., Reagan, A.J., Danforth, C.M. and Dodds, P.S., (2021) | TBD | ||
| 🎓 Guest Lecture: Measuring Algorithmically Infused Societies [paper] | Tina Eliassi-rad | |||
| Mental Health | ||||
| Tue 11/2 | 📃 Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning, Sawhney, R., Joshi, H., Shah, R., & Flek, L. (2021) | TBD | ||
| 📃 Characterizing Anxiety Disorders with Online Social and Interactional Networks, Dutta, S., & De Choudhury, M. (2020). | TBD | |||
| Fri 11/5 | 📃 Inferring Social Media Users’ Mental Health Status from Multimodal Information, Xu, Z., Pérez-Rosas, V., & Mihalcea, R. (2020) | TBD | ||
| Guest Lecture: TBD | TBD | |||
| additional readings | 📚 Methods in predictive techniques for mental health status on social media: a critical review, Chancellor, S., & De Choudhury, M. (2020) | |||
| 📚 Do Models of Mental Health Based on Social Media Data Generalize?, Harrigian, K., Aguirre, C., & Dredze, M. (2020) | ||||
| Projects I | ||||
| Tue 11/9 | 🎉 No class; work on projects | |||
| Fri 11/12 | 🚀 Project Presentations (Preliminary) | |||
| Bias and Fairness | ||||
| Tue 11/16 | 📃 Gender and racial fairness in depression research using social media, Aguirre, C., Harrigian, K., & Dredze, M. (2021) | TBD | ||
| 📃 The Risk of Racial Bias in Hate Speech Detection, Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) | TBD | |||
| Fri 11/19 | 🎓 Guest Lecture: TBD | Sina Fazelpour | ||
| Social Media Analysis for Social Good | ||||
| Tue 11/23 | 📃 Quantifying Community Characteristics of Maternal Mortality Using Social Media, Abebe, R., Giorgi, S., Tedijanto, A., Buffone, A., & Schwartz, H. A. (2020) | TBD | ||
| 📃 Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model , Kim, S. Y., Ganesan, K., Dickens, P., & Panda, S. (2021). | TBD | |||
| Fri 11/26 | 🎉 Thanks Giving | |||
| Ethics & Moral | ||||
| Tue 11/30 | 📃 📚 A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media, Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M., & De Choudhury, M. (2019) | |||
| 📃 Ethical Research Protocols for Social Media Health Research, Benton, A., Coppersmith, G., & Dredze, M. (2017). | TBD | |||
| Fri 12/3 | 🎓 Guest Lecture: TBD | John Basl, Vance Ricks and Meica Magnani | ||
| Projects II | ||||
| Tue 12/7 | 🚀 Project Presentations (Final) |


Consider the following code:: How many asterisks are printed by the method call asterisksPrinter(i)?(Either write the number of asterisks , or write “infinite recursion”.)
Note that: there are two recursive calls.
Don't forget that the print statement will be exectued on the rollback of each of the recursive calls.


ROCK 'N' ROLL FRANKENSTEIN A film review by Steve Rhodes Copyright 2000 Steve Rhodes RATING (0 TO ****): **
ROCK 'N' ROLL FRANKENSTEIN's writer and director Brian O'Hara deserves credit for his unflinchingly audacious approach to satire. Making the Farrelly brothers (ME, MYSELF & IRENE) look like a couple of choir boys in comparison, O'Hara purposely makes a film that attempts to be good by being very, very bad. It's a tough way to make a buck, but at least he's honest and doesn't pull any of his punches. But a film this scatological, sacrilegious and down-right gross will not be something that many filmgoers will be able to stomach. Some of its "best" scenes shock your system so that you don't know whether to regurgitate or laugh.
A low budget horror film, it pokes fun at its own poverty through use of excessively cheesy props -- think of it as an Ed Wood and young John Waters collaboration. In a film with no limits, ripping opening a victim's stomach isn't anywhere near the movie's worst image. The unlucky character doesn't even get a name in the script. The credits list her simply as Gutted Groupie.
Now for those of you that are still with me, we'll talk some about the story itself, which is rather clever. Record producer Bernie Stein (Barry Feterman) has had it with arrogant rock stars, who abandon him when they hit the big time, so he has arranged for his nephew, Frankie Stein (Jayson Spence), to build him a rocker out of spare parts, but not just any parts. Uncle Bernie wants a rocker composed of parts of the great rock 'n' rollers of history from Elvis on down, literally. Since the head comes from Elvis's grave, the monster (Graig Guggenheim) sounds like and is known as the King.
Everyone in the story is pretty weird. Frankie likes to curl up in bed with the Coroner's Quarterly as his equivalent of Playboy. A spaced-out druggie named Iggy (Hiram Jacob Segarra) and his fellow dudes are contracted by Bernie to rob the graves.  Soon newspaper headlines read "Dead Rockers' Graves Are Being Desecrated."
The result of the big operation isn't exactly what the great doctor -- actually a fired coroner -- expects. With all of the energy of a department store mannequin, King's future doesn't initially look bright. But with a few injections he finds his voice, and he and his band, Unnatural Urges, hit the big time. Some parts of King's body prove more successful than others, which allows the movie to sink to new lows in the pursuit of its gross brand of humor.
Given the antics of some of our more infamous rock stars, this film may make you think twice about their origins, but the movie does not appear to be based on a true story. One thing about the film is certain. It is liable to dichotomize the audiences into those who walk out in disgust in the first few minutes and those who manage to find its irreverent and sick brand of humor funny. If there ever was a movie that would best be seen only in the wee hours, this is it. Locally, it will be showing as a midnight madness movie -- just the right time.
ROCK 'N' ROLL FRANKENSTEIN runs a long 1:28. It is not rated but would probably be NC-17 for graphic violence, strong gore, explicit sex, nudity, language, drug usage and probably a bunch of other offensive stuff which I've already forgotten. It would not be acceptable for anyone under 18.
Email<EMAIL_ADDRESS>Web: http://www.InternetReviews.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


{# ========================================================================== Guided Quiz block ========================================================================== Description: Framework for a guided-quiz section, including a situation setup. value: Object defined from a Streamfield block. value.situation: The setup (use-case) for this set of questions value.questions: The block of question, choices and answers value.summary: Wrap-up of the question topic ========================================================================== #}


Oops! No Users Available For Video Confrence
{{room.name}} Join
Create a conference
Name the platform
{{name}} is calling....


{% assign ecosystem = site.ecosystem | sort_natural: "title" %}
{% for item in ecosystem %}
{% endfor %}
Have a project you want featured?
--- layout: default title: Ecosystem permalink: ecosystem/ background-class: ecosystem-background body-class: ecosystem ---
Tap into a rich ecosystem of tools, libraries, and more to support, accelerate, and explore AI development.
Have a project you want featured?


Anti-CD2 receptor antibodies activate the HIV long terminal repeat in T lymphocytes.
Abstract
The CD2 T lymphocyte glycoprotein surface molecule mediates both cell to cell adhesion and T cell activation, two processes that are involved in the spread of HIV infection. Treatment of chronically HIV-infected PBMC with anti-CD2 mAb has been shown to induce the expression of infectious virus from these cultures. In this study we investigated the mechanisms whereby anti-CD2 antibodies stimulate viral production. We demonstrate that treatment of transiently transfected T lymphocytes with anti-CD2 antibodies results in activation of the HIV long terminal repeat. Furthermore, CAT assays using mutated HIV long terminal repeat-CAT constructs and gel shift assays demonstrate that this activation is dependent on the NF-kappa B enhancer. These studies suggest that interaction of CD2 with its natural ligand, LFA-3, may play a role in regulation of HIV expression.


Date
Timezone
Output format
Use
Dynamic Time
Moonphases
Solistices
Sunrises & sunsets
Gregorian Easter
No
calendar events
Estonian calendar events
Maavalla calendar events
Descriptions
Remarks
Save as file
?
GMap
Nautical
Search
Lat
Long
or
grab map center
.


TensorFlow is an open-source framework for performing mathematical computations,
primarily used for its portable, scalable implementations of machine learning
algorithms to applications such as deep neural networks1. In this way, TensorFlow’s
implementation results in little effect to performance when scaling up
(large server clusters with GPUs) or scaling down (i.e. Android or IOS device).
This is further extended by the TensorFlow’s ability to conduct training and
execution aspects of ML algorithm efficiently in a distributed manner across
multiple, different computing devices.
Deep Learning has massively influenced the field of Computer Science, with
these ideas giving rise to highly functional applications with ‘smart’ abilities.
Google was making use of these techniques to power the algorithms behind its
search, mail, photos and speech recognition tools. At the time, this was being done
with its DistBelief2
framework, but as the need for machine learning grew, a more robust and efficient
implementation was needed. TensorFlow arose from the need to generalise different
machine learning implementations across Google to allow engineers to reuse the
basic code base for future projects. After open-sourcing TF,
Google released a distributed version of the framework alongside its Google Cloud
Machine Learning infrastructure to train and serve TF models3.
Advantages for Cloud Computing
The main advantages to using TensorFlow for any distributed, iterative calculation
(with the case of ML) stem from its implemented as a data flow graph. I will
explore these more as I go along, but will outline them here:
Portability -
At its heart TensorFlow is written in C++, however there also exist the more widely
used Java, Go, Python and a slightly higher level C++ API
built on top of the C++ core. The
Python API being most prominently used in research and for any high level ML
applications. This abstraction gives rise to the graph data flow model, by
hiding the complexities of how the model is deployed and executed differently
on different platforms. With best practices being encoded at the low level,
developers can focus on the larger tasks at hand. Developers need not consider
how the core libraries are implementing algorithms on different devices, as
optimisation have been made to ensure similar runtimes across platforms. In
this way, we can abstract away the actual implementation and visual our algorithms
as a graph, with this being the high level models object orientated realisation.
In this way, Python’s TF API, for example, will allow for the same code to be
run on Android devices or across a large cluster of servers running on GPUs alike.4
Flexibility -
TFs high level APIs provide a highly flexible set of libraries that allow complex
mathematical procedure to be written with ease. The graph model can be built up as
a series of discrete units, nodes, with data pipelines, vertices, between them.
This allows for speedy experimentation with little hit to performance. In the case
of a neural network, parts of the network can be replaced or restructured by
refactoring the graph. The core will automatically refactor the platform specific
low level code.
Small changes in the description of how the computation is to be performed can
extend the domain over which the code runs. For example,
which we will explore further later on. If I add more IP addresses to the parameter_server
or workers list, the code will be distributed across those devices too (provided
a TF server is running on them). In this way, the same model for a neural
network could be trained in the cloud, distributed over some large cluster and
then served on another platform, if it were deployed across different networks or servers.
To skip details on the applications of the TensorFlow model to machine learning
and go straight to the details about the implementations of TensorFlow, click
here.
Machine Learning In Context
It is beyond the scope of this project to go into the details of machine learning
algorithms, but we can however discuss the basic concepts with the help of an example.
We will be implementing an ML algorithm to classify handwritten digits after training
on the MNIST dataset.
Supervised Learning with Softmax Regression
We will examine an example which used supervised learning. This technique involve
training a model on a set of example input-output pairs (training data), so as to
learn a function for mapping any other inputs to outputs5 (Russell S, 2009).
From this we expect the model to establish a sensible inductive bias, a set of
assumptions the model can use for future mappings6.
The technique we will examine is called softmax regression, which can be used to
classify an observation into one of more than 2 categories7 (F Herrera et al, 2016). This is a generalisation of the logistic
regression, which makes a binary classification (this is beyond the scope of this
project by can be further read about here:5. Where the logisitic regression classifies a
normalised input vector using a logistic (sigmoid) function, the softmax algorithm
using a so-called softmax function, which generalises the sigmoid.
The softmax algorithm for the MNIST classification problem (WLOG) is as follows:
1.
Establish k possible classes, a class for each number 0 to 9.
2.
We compile all the images we want to classify into a features matrix (X, dim(X) = n x m),
where we want to class m images (features), with n observations made about each
image. If we want to classify with 100 handwritten digit images which are in
black and white, where each image has size 28x28 pixels containing either a 1
or 0, then n = 100, m = 28 ^ 2 = 784 and k = 10 (from above). So each image
to be classified is a m-row vector.
3.
Weight vectors are computed for each class (each number). This will involve
unravelling each image for each class in the MNIST training data set, and for
each pixel determining a number which represents its level of “zeroness” or
“oneness”. i.e. how close each pixel is to being a 1 or a 0 for each number.
Each weight vector is put into a weight matrix (W, dim(W) = m x k). So each class (number)
is represented as a m-column vector. Each weight vector is initialised with
arbitrary images which follow a normal distribution, i.e. 1s are closer to the
centre of the image than 0s.
4.
Z = xW (dim(Z) = n x k). Each element in the k-row vectors of Z gives us a score of how close
it is to being one of the classes. We pick the max value as the class that it
belongs to e.g. if Z_30 = [1.2, 1.4, 0.4, 1.3, 1.6, 5.4, 1.1, 0.1, 1.7, 0.6]
means that our model predicts the 30th observation to be the number ‘5’. This example is taken
from here
5.
Now we use the softmax function to convert each score in Z to a probability,
such that the sum of all probabilities in a row is 1. This allows us to use
the gradient decent algorithm to learn from the classification we made in
step 4 and build up the weight matrix we began computing in step 3.
6.
Each observation vector comes with a target vector, which uses one-hot encoding.
This assigns a 1 to the index that contains the expected result and a 0 to all
the other ones which it is not. We feed the one-hot encoded matrix and softmax
probability matrix into a cross-entropy function. This calculates the error in
identifying each number (each value in S is between 0 and 1 since S consists of
probabilities, so a the absolute log of a number closer to 0 is larger than a
number closer to 1, so a more accurately predicted number will have a larger
cross-entropy value). We compute the cross entropy function for each row in the
softmax matrix, and find an average error across the whole matrix (The SVM Classifier Oxford lecture 2 notes).8
7.
Finally we use gradient of the average entropy loss function to update the
weight matrix (gradient descent) (For more info click here).
We repeat this process for some number of iterations over the training set or
until the average loss function is below a threshold.
Machine Learning Implementation
Convolutional Neural Networks
We will now take a qualitative look at convolutional neural networks in the hope
that it will provide further context to the workings of TensorFlow. I will extend
my code from above and use this to use a simple neural network model to deeper
learn the MSINT dataset.
(an introduction to neural networks ben rise Peter van Der smagt)
A neural network is an artificial computational model which is loosely modelled
on the functionality of neurons in brains of animals, such that patterns can be
recognised9.
We want to use neural networks for deep learning in this case, which is made possible
through a network with multiple layers hidden/unseen layers between the input and
output, each applying different functions to the data.
. At each layer, nodes perform
computations to realise the prominence of a particular characteristic in the
input data. A summation of the input/weight products input into a node is taken
and passed through the nodes’ activation function to determine probabilistically
how prominent a particular characteristic is. These output will then be passed
to the nodes of the next layer for further abstraction to be performed10.
If the activation function is a softmax, the returned output probabilities are
distributed over each of the distinct output subclasses for that layer: the outputs
from each node will then be between 0 and 1 and have a total sum of 1.
A convolution neural net differs to the conventional ANN in that a filter (neuron)
convolves around the input (features) matrix at each layer. These are used almost
exclusively for computer vision. There are 2 mains types of layers in the CNN:
1.
Convolutional Layer - This filter is a weight matrix, which is convolving
around an input and is multiplied to each patch in the feature matrix, at each
stage. At each convolutional layer in the CNN, a filter learns the features
from the outputted mapping tensor, by supervised learning methods (as described
in the above section) and adapts its weights to make better predictions. Thus
at each C-stage, the same features matrix can be inputted multiple times to
further train each filter. On each iteration, a new output features matrix is
produced. There may be multiple filters working at each layer to produce a
different output matrix for different aspects of the initial matrix, this leads
to the stacking effect.
2.
Pooling Layer - Pooling filters reduce the size of a feature matrix by
selecting only the most prominent feature from a patch. In max pooling, where
the values in each patch are a set of features determined from the activation
functions at some preceding layer in the network, then the max feature is the
most detected feature. So only this needs to be passed through to the next
layer. This is to make future predictions scale and orientation invariant.
3.
Dense Layer - Performs the classification after the final convolutional and pooling layers.
We are now going to take a step back and, using some of the previous examples
and some new ones, analyse the TensorFlow’s graph model.
Storing Data
The following data structures will be important when considering how distributed ML
algorithms are implemented.
Tensor
- In mathematics a tensor is a structure which describes linear relations
between vectors and scalar. Similarly in TensorFlow, these represent edges in
the graph (as mentioned above) and are implemented as a wrapper for an
n-dimensional array.
Variables
- A wrappers of persistent tensor which survives each execution of a graph.
During gradient descent for example, the full graph is executed many times
whilst not maintaining the initial tensors. Here persistence stores are needed
to maintain the weights for a layer in a neural network. In a run call,
variables are re-assigned to the new value which was calculated for them
during the traversal of the graph (consider how the weight matrix is updated
during training). These are treated as nodes with the graph.12
(http://web.donga.ac.kr/yjko/usefulthings/TensorFlow-Basic-Concept_Ko.pdf)
Placeholders
- Empty variables which are filled during a graph execution.
Queues
- Different implementations of the standard FIFOQueue optimised for
parallel/distributed processing. RandomShuffleQueue can dequeue operations
in random order and be written to by multiple threads. These are just like
variables in that they are not changed on they persist between sessions and
have concurrent FIFO access.
Graph Model
Computations in TensorFlow are described by a directed graph. When coding we built
up this graph up: each node performs a TensorFlow operation and the edges are modelled
as tensors/matrices. This is because the data being passed between 2 nodes provides
the connection between 2 discrete processes in the algorithm. We can imagine traversing
the graph as following the overall flow of data from the inputs to the output of
the code. This allows us to build up our machine learning model in smaller
discrete parts, making it easier to reason about the stages of a machine learning
algorithm as opposed to the details of the implementation.
For example, when we write the following code using simple TensorFlow operation:
which translates to the following graph:
Similarly, we get the following graph from the ML algorithm above:
Image of graph
We can generalise graph formation to the following execution model:
1.
Sessions
- Client programs create a session for each tensor flow graph which is to be computed.
...
with tf.Session()/tf.InteractiveSession() as sess:
# running the graph
...
2.
Extending / Adding
- Internally, an extend method is called which allows us to add more nodes and edges
to the graph. (Any mathematical operation: lines 5,6,8,9,10,14,15,17)
3.
Run
- The run method takes the set of output names which need to be computed
and passes the input tensors into them. This then calculates the transitive closure
of the graph to get the outputs. It passes the tensor output values from each node
to the next and moves through graph.
The executions of a TensorFlow graph is lazy in that unless we explicitly run
the session, no values are calculated. As we build up the graph, we are merely
passing references from sections of the graph to others, by assigning a tensor
object the connecting edge.
...
with tf.Session()/tf.InteractiveSession() as sess:
...
sess.run(output_operation)
...
By popular demand, Google introduce a distributed computing framework to TensorFlow
alongside their cloud machine learning tool in early 201614.
This section combines basic ML concepts with the general execution model of a TF
graph, to see how TF can scale (machine learning) algorithms to a distributed system.
Graph Model in Distribution
Nodes can be distributed across the devices being used to compute the graph16.
On a single CPU TensorFlow will make use of all cores by multithreading.
TensorFlow will map the execution of a node in the graph to a device using a
placement algorithm. For this purpose the graph model is particularly advantageous,
as by ensuring that each operation is distinct, we can divide stages of an algorithm
more clearly. The algorithm applies a greedy heuristic to the placement of nodes
on devices by considering whether (1) there exists an implementation of an operations
for a device, (2) side of the input and output tensors of the op, (3) expected
completion time for the operation on a device and (4) the transmission time of
tensors between cross-device nodes. This is a cost model/function; generally we can think
of it as holding the input and output tensors for each node and store computation
times for the operations at each node given its input tensor for a particular device.15
Master/Worker Model
TensorFlow docs
define cluster differently to the conventional definition:
"a set of tasks that participate in the distributed execution
of a TF graph". The graph model enables a convenient implementation of clusters
and servers. Clusters can be implemented as a set of nodes, where, by the definition
of a TensorFlow node, each node performs a single operation. Since all TensorFlow sessions
are multithreaded, all processing is done in parallel.
The master/worker model in TensorFlow assigns a master, a parameter server, and
workers. For a distributed system, the parameter server preserves the mutable shared
parameters and workers execute some subgraph of the main graph using data from
the parameter server. Each worker is a separate device (for simplicity).
Once graph node allocation has occurred, send and/or receive nodes are added
to each worker’s subgraph. These enforce a direction to the dataflow between
subgraphs to compose the whole graph over the distributed system. The send and
receive nodes synchronise data flow between workers15. In this way, only a single
call to run the session is needed, in order to pass the input tensors into the
input nodes, and the remaining data flow will be will handled by the workers.
This adds to the overall flexibility of this cloud computing platform, alluded
to in the introduction section, as even by adding more devices/workers to the
system, TensorFlow will automatically handle node allocation and the initialisation
of send and receive nodes. When scaled, communication between workers may be
handled by TCP.
Distributed Data Storage Structures
Variables update their values at the end of each execution of
the graph (mutable). These are a global data store and can be accessed by different sessions
running their own version of the graph. These are held in the parameter server.
In this way, data can persist across multiple devices or threads.
Queues can be used as opposed to the feed-dict parameter to the run function. Queues have thread-safe
implementations17.
All TensorFlow session are multithreaded, so devices can enqueue randomised examples in parallel (input-output sets)
that can be learned from. A training thread can dequeue and execute in mini-batches,
akin to the diagram below, the algorithm can iterate in this way to learn.
A thread might apply a pre-processing function and enqueues the image, a training
thread can dequeue and learns the images.
There are 2 main fault tolerance mechanisms in TensorFlow:
1.
As is the case with MapReduce, TF may choose restart the execution of a graph.
2.
The checkpoint feature is also available. Each variable will store its data
incrementally on a save node. The system can be restarted from the previously
checkpointed state. This is akin to the checkpointing API in Apache Spark.
Implementation
By making use of distributed learning for a deep learning algorithm, we should be
able to train the same model faster. Distribution can be achieved by leveraging devices
on the same machine or on a cluster, which may be accessed through a cloud platform
(i.e. Google's Cloud Platform or AWS).
Below I have extended the MNIST ML example. Much of the code is the same as the
previous version, but minor changes have been made to make the code distributed.
Use Cases
TensorFlow has become very popular over the short time it has been available as an open source tool.
Here are some important cases where it is being used:
Google's TPU.
Chips ere developed specifically by Google for running distributed TensorFlow
code over their Cloud Platform. Said to perform at 45 TFLOPS with increased
bandwidth to 600GB/s. Google claims these chips were used to run TensorFlow with
MapReduce in the AlphaGo vs Lee Sedol match.
Used widely for text NLP, most prominently in Google Translate. It has also been
used to provide article summarisation on Android phone and for snippet summaries
on Google Search.


All Classes
ActivityFuelLookup
App
Bulldozer
ChangeInDirection
Command
CommandPojo
CommandType
Constants
Costs
Direction
ItemCostLookup
ItemType
SiteMap
SquareType
SysInDelegate
SysOutDelegate
Utils


Simple, traffic-based pricing
Sign-up for our 30-day trial.
No credit card required.
100K
Pageviews
$16.00
/month
Monthly Billing
Yearly Billing
25% discount
Unlimited websites
100% data ownership
Email reports
Start my trial
Challenge by
Frontend Mentor
. Coded by
Diptangsu Gupta
.
All Rights Reserved.


tfrecord_dataset.dataset module¶
- class tfrecord_dataset.dataset.Dataset(train_set_dir, test_set_dir, file_format, train_labels_file, label_type=<class 'int'>, train_labels_file_has_header=True)¶
Bases:
object
- __init__(train_set_dir, test_set_dir, file_format, train_labels_file, label_type=<class 'int'>, train_labels_file_has_header=True)¶
A dataset that consists of a directory with a set of train examples and a directory with a set of test examples. Each example is stored in a different file, with a given file format. There is a CSV file that matches each example with its label. Each example is identified by its file name without the extension.
- Parameters
train_set_dir (str) – Path to the directory that contains the training examples.
test_set_dir (str) – Path to the directory that contains the test examples.
file_format (str) – Format of the example files, specified as *.format. It can specify any pattern that matches the desired files as long as it is compatible with Path().rglob().
train_labels_file (str) – Path to the CSV file that maps each file name to its label. It must contain one row per training example, with two fields: example ID (file name without extension) and label.
label_type (type) – Type of the label, usually an integer. Optional, default: int
train_labels_file_has_header (bool) – Set to True when the labels CSV file contains a first row that acts as a header with metadata or the names of the columns. It is used to discard this column. Optional, default: True
- test_set_in_splits_generator(n_splits, shuffle=True, seed=42)¶
Generator that yields one split of the test set each time. The test set is divided in N splits with equal size.
- Parameters
n_splits (int) – Number of partitions the test set will be split into.
shuffle (bool) – Whether to shuffle the dataset prior to partitioning or not. Optional, default: True
seed (int) – Seed to initialize the random number generator for the shuffling. It must be set to the same value between executions to ensure the generated subsets are the same. Optional, default: 42
- Returns
Yields a split of the original test set on each iteration, with shape (~ N_EXAMPLES // N_SPLITS, 1). The actual number of examples differs among splits if the total number of examples in the test set is not a multiple of the number of splits. The only column is the example’s file path, since the test set has no labels.
- Return type
numpy.ndarray
- train_set_in_splits_generator(n_splits, shuffle=True, seed=42)¶
Generator that yields one split of the train set each time. The train set is divided in N splits while keeping the same label distribution as in the complete set.
- Parameters
n_splits (int) – Number of partitions the train set will be split into.
shuffle (bool) – Whether to shuffle the dataset prior to partitioning or not. Optional, default: True
seed (int) – Seed to initialize the random number generator for the shuffling. It must be set to the same value between executions to ensure the generated subsets are the same. Optional, default: 42
- Returns
Yields a split of the original train set on each iteration, with shape (~ N_EXAMPLES // N_SPLITS, 2). The actual number of examples differs among splits if the total number of examples in the training set is not a multiple of the number of splits. The two columns are the example’s file path and its manual label.
- Return type
numpy.ndarray


Presentation
Principle
Well, I bought that small BC05B module on eBay some times ago, in those moments of "hmmm I might need that one day". And guess what: I need it now (^_^).
Points of interrest
* Soldering those tight ass microscoping 0.7mm connectors on the side of the module and taking the decision of **never ever again use that kind of modules** :) No seriously, it works, it's doable with a bit of time and patience, but it's really not fun.
* It worked for a morning, quite well even on my [[http://www.kenwood.com/jp/products/audio/systemcompo/clx_70/index_end.html|Kenwood CLX-70]]. I used the jack in and also the red/white composite ones at the back, no problem. Then I tried to stuff it inside, steal some power from the stereo (using a LM317 to feed it with 3.7v) and there catastrophe! Sound went from great to awful, to nothing. Now it's out of the beast, and I just can't get any sound from it. Like zero, just static.
**EDIT:** it's fixed!! There was an invisible short between VOL+ and BACK pins, and that rendered the VOL+ button inefficient and added a big bunch of noise to the board. Now I have it run next to me for one hours to confirm it's all good in term of power regulation.
Schematics
//Recommended setup (taken from elecfreaks.com site)//
Bill of materials
Implementation
Source code
Pictures
//The infamous module. Pitch between those pins is very small, it's 0.7mm ! Don't shake while soldering.//
//Top view: ugliest air wire soldering, a few buttons (blue=power VREG, red=VOL+, yellow=VOL-)//
//Side view: where that S.o.B. of BACK and VOL+ were shorted.
Links
Helpful sources
* All one needs to know [[http://www.elecfreaks.com/wiki/index.php?title=Audio_Bluetooth_Speaker_Module]]
Inspiration
* [[http://www.instructables.com/id/BluetoothBox-for-Stereo-Headphones-and-Speakers/?ALLSTEPS#step7|A project that implemented it]]


My Pets
Kayla's Pets
Ruger
is a year old blue heeler, austrailian shepard, and lab mix/mutt.
Rob's Pets
Lucy
, the codependant live-in doggo.
Dahlia
, the starving dog.
Gigi
, the cat.


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
Butcher tableau for Runge-Kutta method. More...
#include <butcher_tableau.h>
Public Member Functions | |
Life Cycle | |
| virtual | ~ButcherTableau ()=default |
| Destructor. More... | |
Operations | |
| std::tuple< RowType::const_iterator, RowType::const_iterator > | GetMatrixRow (const unsigned int SubStepIndex) const |
| RowType::const_iterator | GetMatrixRowBegin (const unsigned int SubStepIndex) const |
| RowType::const_iterator | GetMatrixRowEnd (const unsigned int SubStepIndex) const |
| constexpr const VectorType & | GetWeights () const |
| constexpr double | GetIntegrationTheta (const unsigned int SubStepIndex) const |
Protected Attributes | |
Protected member Variables | |
| const MatrixType | mA = Derived::GenerateRKMatrix() |
| const VectorType | mB = Derived::GenerateWeights() |
| const VectorType | mC = Derived::GenerateThetasVector() |
Type Definitions | |
| using | VectorType = std::vector< double > |
| using | RowType = std::vector< double > |
| using | MatrixType = std::vector< RowType > |
| static constexpr unsigned int | Order () |
| static constexpr unsigned int | SubstepCount () |
Input and output | |
| static std::string | Name () |
| virtual std::string | Info () const =0 |
Butcher tableau for Runge-Kutta method.
Contains all info necessary of a particular RK method. It specifies the coefficients of the particular Runge-Kutta method.
| using Kratos::ButcherTableau< Derived, TOrder, TSubstepCount >::MatrixType = std::vector<RowType> |
| using Kratos::ButcherTableau< Derived, TOrder, TSubstepCount >::RowType = std::vector<double> |
| using Kratos::ButcherTableau< Derived, TOrder, TSubstepCount >::VectorType = std::vector<double> |
|
virtualdefault |
Destructor.
|
inlineconstexpr |
|
inline |
The runge kutta must perform for all substeps 1...N-1:
du^(i) = dt * A_ij*k_j
This method return the coefficients A_i[1...i]. The rest of coefficients A_i[i+1...n] are skipped. This is they are always zero for explicit Runge-Kutta.
| SubstepIndex | The i in the formula (the row of the matrix). Note that it counts from 1 to n. |
| rK | The k in the formula (the reaction) |
|
inline |
|
inline |
|
inlineconstexpr |
|
pure virtual |
|
inlinestatic |
|
inlinestaticconstexpr |
|
inlinestaticconstexpr |
|
protected |
|
protected |
|
protected |


boustrophedon
`Prev `__
B
`Next `__
--------------
**boustrophedon**: n.
[from a Greek word for turning like an ox while plowing] An ancient
method of writing using alternate left-to-right and right-to-left lines.
This term is actually philologists' techspeak and typesetters' jargon.
Erudite hackers use it for an optimization performed by some computer
typesetting software and moving-head printers. The adverbial form
‘boustrophedonically’ is also found (hackers purely love constructions
like this).
--------------
+-----------------------------------+----------------------------+------------------------+
| `Prev `__ | `Up <../B.html>`__ | `Next `__ |
+-----------------------------------+----------------------------+------------------------+
| bounce message | `Home <../index.html>`__ | box |
+-----------------------------------+----------------------------+------------------------+


import scrawl from '../source/scrawl.js'
import scrawl from '../source/scrawl.js'
let artefact = scrawl.library.artefact,
stack = artefact.mystack,
cube = artefact.cube;
The ‘faces’ Element artefacts are defined in the demo’s html
file (they are children of the Stack element). To make controlling them easier, we can create a Group object linked to the Stack and shift them into it.
let faces = scrawl.makeGroup({
name: 'faces',
host: 'mystack',
}).moveArtefactsIntoGroup('leftface', 'rightface', 'topface', 'bottomface', 'frontface', 'backface');
Give the Stack element some perspective depth
stack.set({
perspectiveX: '50%',
perspectiveY: '50%',
perspectiveZ: 1200
});
The ‘cube’ Element artefact (also defined in html, but not shifted into our new group) will act as the reference artefact for our ‘faces’ Elements
cube.set({
order: 1,
width: 0,
height: 0,
startX: 'center',
startY: 'center',
handleX: 'center',
handleY: 'center',
lockTo: 'start',
Give the Element a border, so we can see it
css: {
border: '20px solid black',
},
Delta animate the cube Element’s rotation:
pitch
represents rotation along the x-axisyaw
represents rotation along the y-axisroll
represents rotation along the z-axisValues are in degrees, not radians!
delta: {
pitch: 0.8,
yaw: 1.2,
roll: 0.4,
},
});
Give our ‘faces’ Elements some common values, via their Group
faces.setArtefacts({
Make sure the Elements get processed after the reference Element, so that they pick up changes to its positioning
order: 2,
width: 200,
height: 200,
startX: 'center',
startY: 'center',
handleX: 'center',
handleY: 'center',
Offset each Element from the reference Element
offsetZ: 100,
Set each Element to pivot
to the reference Element
lockTo: 'pivot',
pivot: 'cube',
Automatically add the reference Element’s rotation values to each Element’s initial rotation value (set below)
addPivotRotation: true,
Styling common to all the ‘faces’ Elements
css: {
border: '1px solid blue',
textAlign: 'center',
backfaceVisibility: 'hidden',
}
});
Give each ‘faces’ Element its own color and initial rotation
artefact.frontface.set({
css: { backgroundColor: 'rgba(255, 0, 0, 0.4)' },
});
artefact.rightface.set({
css: { backgroundColor: 'rgba(0, 0, 127, 0.4)' },
yaw: 90,
});
artefact.topface.set({
css: { backgroundColor: 'rgba(0, 255, 0, 0.4)' },
pitch: 90,
});
artefact.backface.set({
css: { backgroundColor: 'rgba(127, 0, 0, 0.4)' },
pitch: 180,
});
artefact.leftface.set({
css: { backgroundColor: 'rgba(0, 0, 255, 0.4)' },
yaw: 270,
});
artefact.bottomface.set({
css: { backgroundColor: 'rgba(0, 127, 0, 0.4)' },
pitch: 270,
});
Function to check whether mouse cursor is over the Stack element, and lock the cube Element accordingly
let stackCheck = function () {
let active = false;
return function () {
if (stack.here.active !== active) {
active = stack.here.active;
cube.set({
lockTo: (active) ? 'mouse' : 'start'
});
}
};
}();
Function to display frames-per-second data, and other information relevant to the demo
let report = function () {
let testTicker = Date.now(),
testTime, testNow, text,
testMessage = document.querySelector('#reportmessage');
return function () {
testNow = Date.now();
testTime = testNow - testTicker;
testTicker = testNow;
testMessage.textContent = `Screen refresh: ${Math.ceil(testTime)}ms; fps: ${Math.floor(1000 / testTime)}`;
};
}();
Create the Display cycle animation
scrawl.makeRender({
name: 'demo-animation',
target: stack,
commence: stackCheck,
afterShow: report,
});
console.log(scrawl.library);


WITNESS LIST Criminal Jurisprudence Committee April 24, 2001-2:00P HB 553 For: Anderson, II, L.E. (Fort Worth Police Department) Jack, Christy (Tarrant County District Attorney's Office) HB 653 For: Autry, Greg (Self and Loco - Pet Puppy) Byrne, Elaine (Self) Flynn, Clifton P. (Self) French, Robert E. (SPCA of Texas) Gilleland, Ellis (Texas-Animals (Internet)) Hale, Jef (Humane Society of the United States) Lizarraga, Marcos (Self and Jaime Esparza, Dist. Atty - 34th Judicial Dist. (El Paso, Hudspeth, Culberson, Cos.)) McCullough, Missy (Self) Palafox, Patricia L. (Self and El Paso Animal Shelter Advisory Committee) Short, Dianne W. (Self and Texas Humane Legislative Network, Humane Society of Navarro County) Valls-Trelles, Pat (Self) Winder, Merceda J. (Self) Against: Doubet, Frederick P. (Responsible Pet Owners Alliance) Registering, but not testifying: For: Briscoe, Judy (Self) Broussard, Kelle (Self) Byrne, Brian M. (Self) Campbell, Virginia (Self) Gratehouse, Pamela (Self) Hummer, Elizabeth (Self) Love, Ann (Self) Maher, Heidi (Self) Melancon, Billie (Self) Montford McCracken, Mindy (Self) Rosu, Nancy (Self) Schotz, Charles (Self) Shen, Gloria (Self) Whitaker, Sarah (Self) Wood, Suzanne (Self) Yancey, Stephanie (Self) Against: Burkey, Robert R. (Self and CPRL - Citizens for the Preservation of a Rural Lifestyle) Keith, Lanny (CPRL Citizens for the Preservation of a Rural Lifestyle) Killingsworth, David (CPRL - Citizens for the Preservation of a Rural Lifestyle) Patterson, Jerry (Houston Safari Club, Houston Gun Collectors Assoc) White, Dwain (CPRL Citizens for the Preservation of Rural Lifestyles) On: Sinclair, David (Texas Parks and Wildlife) HB 653Committee Substitute (Hinojosa) For: Patterson, Jerry (Houston Safari Club, Houston Gun Collectors Club) HB 1126 Registering, but not testifying: Against: Harrell, William (ACLU) HB 1395 For: Hartmann, Calvin A. (Harris County District Attorney's Office) Smith, Lt. Murray (City of Houston, Houston Police Department) Registering, but not testifying: For: Clark, Mark (Houston Police Officers' Union, Texas Union of Police and Sheriffs, South Texas Organization of ...) Granger, J.D. (Tarrant County District Attorney's Office) Jones, Claude T. (Texas Police Chiefs Association) Nafe, Chris (Abilene Police Dept, Abilene Police Officer's Assn) Oakley, Keith (TMPA Texas Municipal Police Assn.) Vickers, Sgt. Kim (Abilene Police Dept.) Against: Harrell, William (ACLU) HB 1955 For: Porter, Quentin B. (Texas Municipal Courts Assn) HB 2090 For: Gilleland, Ellis (Texas-Animals (Internet)) Registering, but not testifying: For: Montford McCracken, Mindy (Self) On: Sinclair, David (Texas Parks and Wildlife) HB 2317 For: Hartmann, Calvin A. (Harris County District Attorney's Office) Against: Hampton, Keith S. (Texas Criminal Defense Lawyers Assn) Registering, but not testifying: Against: Harrell, William (ACLU) HB 2515 For: Watson, Gregory (Self) Against: Liu, Livia (Self) Registering, but not testifying: Against: Jack, Christy (Tarrant County District Attorney's Office) Noll, Chuck (Harris County District Attorney's Office) HB 2798 Registering, but not testifying: For: Oakley, Keith (TMPA Texas Municipal Police Assn.) HB 3442 For: Breen, Terry (Self and 24th Judicial Dist. Atty's Ofc.) Lizarraga, Marcos (Self and District Attorney Jaime Esparza, 34th Jud. Dist. (El Paso, Culberson, Hudspeth)) Registering, but not testifying: For: Nafe, Chris (Abilene Police Dept.; Abilene Police Officers Assn.) Oakley, Keith (TMPA Tx Municipal Police Assn.) Vickers, Sgt. Kim (Abilene Police Department) Against: Hampton, Keith S. (Texas Criminal Defense Lawyers Association) Harrell, William (ACLU) SB 7 Registering, but not testifying: For: Beardall, Bill (Texas Appleseed) Hampton, Keith S. (Texas Criminal Defense Lawyers Assn.) Harrell, William (ACLU of TX) On: DeVasto, Diane (Self) SB 7mmittee Substitute (Hinojosa) Lee, Donald (Texas Conference of Urban Counties) SB 17 For: Buchanan, Bree (Texas Council on Family Violence) Registering, but not testifying: For: Hampton, Keith S. (Texas Criminal Defense Lawyers Assn.) Liu, Livia (Dallas County DA's Office) Nafe, Chris (Self and Abilene Police Dept. & Abilene Police Officer's Assn) Vickers, Sgt. Kim (Self and Abilene Police Dept.) Against: Harrell, William (ACLU) SB 251 Registering, but not testifying: Against: Kirk, Sheriff Chris (Self and Sheriff's Association of Texas) On: Granger, J.D. (Tarrant County District Attorney's Office) SB 600 Registering, but not testifying: For: Hutto, Kathy (Baylor University) McDonald, Carol (Independence Colleges and Universities of Texas) SB 814 Against: Oncken, Denise (Harris County District Attorney's Office) On: Bybee, Alice (Office of Attorney General; Sexual Assault & Crisis Prevention Services Division) Liu, Livia (Self) Registering, but not testifying: For: Craven, Susan Hopkins (Texans Care for Children) Sanchez, Ellen (Self and SafePlace) SB 1126 For: Granger, J.D. (Tarrant County District Attorney's Office) Registering, but not testifying: For: Whalen, Ken (Tx Daily Newspaper Assoication; Tx Press Association) Against: Hampton, Keith S. (Texas Criminal Defense Lawyers Assn.) SB 1262 Registering, but not testifying: For: Lyons, Steve (Houston Police Dept.) SB 1747 Registering, but not testifying: For: Ward, Don (Livestock Marketing Association of Texas)


Google to offer checking accounts next year - source
Alphabet Inc's Google will offer personal checking accounts from sometime next year in partnership with Citigroup Inc and a small credit union at Stanford University, a source familiar with Google's plans said on Wednesday.
REUTERS: Alphabet Inc's Google will offer personal checking accounts from sometime next year in partnership with Citigroup Inc and a small credit union at Stanford University, a source familiar with Google's plans said on Wednesday.
The details of the project, named Cache, were first reported by the Wall Street Journal and follow moves by tech heavyweights Apple Inc and Facebook Inc into the financial industry this year.
Facebook’s plan to launch the "Libra" digital coin has met with a chorus of skepticism from regulators, worried about money laundering and the security of transactions and user data.
But there are also broader concerns about how the big tech companies' will use their massive digital influence in other areas of business and economic infrastructure.
"Our approach is going to be to partner deeply with banks and the financial system," Caesar Sengupta, general manager and vice-president of payments at Google, told the Journal in an interview.
"It may be the slightly longer path, but it's more sustainable," Sengupta was quoted as saying in the report.
On Tuesday, Facebook also launched a unified payment service through which users across its platforms can make payments without exiting the app, named Facebook Pay, which is separate from its Libra project.
(Reporting by Neha Malara and Munsif Vengattil in Bengaluru; Editing by Maju Samuel and Patrick Graham)


0
xBitcoin.Store
Menu
Buy
Mine
Discover
Buy
0
xBitcoin directly from this
smart contract
.
Own your 0xBitcoin within seconds.
{loading metamask}
0xBitcoin are in stock
How many 0xBTC you want to buy
Ethereum Cost
400 0xBTC = 1 Eth (1 0xBTC = 0.0025 Eth)
Purchase
Want even more 0xBitcoin?
Start mining.
Let's Get Started!
Discover what all the buzz is about
Reddit
Discord
Github
Telegram


Test your integration (Simulator)
| Card |
PAN |
CVC |
Exp date |
| VISA Non-3DS |
4012001038443335 |
123 |
12/50 |
| VISA 3DS 1.0 |
4149011500000147 |
123 |
12/50 |
| VISA 3DS 2.0 - Frictionless Flow |
4111111111111111 |
123 |
12/50 |
| VISA 3DS 2.0 - Challenge Flow |
4000007000000031 |
123 |
12/50 |
| Master Card Non-3DS |
5399999999999999 |
123 |
12/50 |
| Master Card 3DS 1.0 |
5232569007637831 |
123 |
12/50 |
| Master Card 3DS 2.0 - Frictionless Flow |
5556011778787485 |
123 |
12/50 |
| Master Card 3DS 2.0 - Challenge Flow |
2720992593319364 |
123 |
12/50 |
| American Express Non-SafeKey |
374111111111111 |
123 |
12/50 |
| American Express SafeKey 1.0 |
373953192351004 |
123 |
12/50 |
| American Express SafeKey 2.0 - Frictionless Flow |
371449635398431 |
123 |
12/50 |
| American Express SafeKey 2.0 - Challenge Flow |
346018484777573 |
123 |
12/50 |
| Maestro Non-3DS |
6400051234567895 |
- |
- |
| VISA Debit Non-3DS |
4462030000000000 |
123 |
12/50 |
Example scenarios:
Without 3DS:
Scenario: MoTo credit card transaction without 3DS with amount of 1,00€
Scenario: MoTo credit card transaction without 3DS with amount of 1,11€
Scenario: Ecom credit card transaction without 3DS with amount of 1,00€
Scenario: Ecom credit card transaction without 3DS with amount of 1,11€
With 3DS:
Scenario: Check Ecom credit card transaction with 3DS with amount of 1,00€
Scenario: Check Ecom credit card transaction with 3DS with amount of 1,11€


Summary of course
Morten Hjorth-Jensen Email<EMAIL_ADDRESS>[1, 2]
[1] Department of Physics and Center of Mathematics for Applications, University of Oslo
[2] National Superconducting Cyclotron Laboratory, Michigan State University
Nov 29, 2018
© 1999-2018, Morten Hjorth-Jensen Email<EMAIL_ADDRESS>Released under CC Attribution-NonCommercial 4.0 license
What? Me worry? No final exam in this course!
Topics we have covered this year
The course has two central parts
- Statistical analysis and optimization of data
- Machine learning
Statistical analysis and optimization of data
The following topics will be covered
- Basic concepts, expectation values, variance, covariance, correlation functions and errors;
- Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
- Central elements of Bayesian statistics and modeling;
- Central elements from linear algebra
- Gradient methods for data optimization
- Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;
- Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;
- Practical optimization using Singular-value decomposition and least squares for parameterizing data.
- Principal Component Analysis.
Machine learning
The following topics will be covered
- Linear methods for regression and classification;
- Boltzmann machines;
- Neural networks;
- Decisions trees and nearest neighbor algorithms
- Support vector machines
Learning outcomes and overarching aims of this course
The course introduces a variety of central algorithms and methods
essential for studies of data analysis and machine learning. The
course is project based and through the various projects, normally
three, you will be exposed to fundamental research problems
in these fields, with the aim to reproduce state of the art scientific
results. The students will learn to develop and structure large codes
for studying these systems, get acquainted with computing facilities
and learn to handle large scientific projects. A good scientific and
ethical conduct is emphasized throughout the course.
- Understand linear methods for regression and classification;
- Learn about neural network;
- Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
- Be capable of extending the acquired knowledge to other systems and cases;
- Have an understanding of central algorithms used in data analysis and machine learning;
- Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Metropolis and Gibbs samplers and their possible applications;
- Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.
Best wishes to you all and thanks so much for your heroic efforts this semester


Vendor filter
[Select a vendor]
_ping_
didomi
Clear localStorage
AMP Consent CMP
Please open your devtools an enable the consent v2 experiment: `AMP.toggleExperiment('amp-consent-v2', true)`
Image that is blocked by both consent
Image that is NOT blocked by consent
Container for amp-consent tags.
Post Prompt UI
Manage
Post Prompt UI
Manage


val signatureName: String
a signature-scheme name as required to create Signature objects (e.g. "SHA256withECDSA")


{% extends 'pointofsale/mainapp_layout.html' %} {% load crispy_forms_tags %} {% block title %} Inventory Management {% endblock title %} {% block content %} Inventory Management Add Category Add New Product # Name Category Description Quantity Selling Price Action {% for product in products %} {{ product.id }} {{ product.name }} {{ product.category }} {{ product.description }} {{ product.quantity }} {{ product.selling_price }} Stock Edit Delete Edit this Product × {% csrf_token %} Name Description Cost Price Selling Price Discount Price {{ update_form.category |as_crispy_field}} Staff Update Product Stock the Product × {% csrf_token %} Stock Product Quantity Add Stock Do you want to Delete? × Clicking Delete Removes Permanently. It cannot be reversed. {% endfor %} Add New Product × {% csrf_token %} {{product_form|crispy}} Add Product Add New Category × {% csrf_token %} {{ category_form|crispy }} Add Category {% endblock %}
Clicking Delete Removes Permanently. It cannot be reversed.


SweetFreeStuff.com
///////////////////////////////////////////
Free Welchs Refreshingly Simple Juice at Kroger
Posted: 04 Dec 2015 10:29 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Load the coupon to your shoppers card today only (12/40) and use by 12/20.
Free Welch’s Refreshingly Simple Juice at Kroger
The post Free Welch’s Refreshingly Simple Juice at Kroger appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free Wrapped In Red by Kelly Clarkson MP3 Album Download
Posted: 04 Dec 2015 09:29 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Click the “Free” button. If you do not have a Google Play account you will
need Paypal or a credit card in order to create one. Free Wrapped In Red by
Kelly Clarkson MP3 Album Download
The post Free Wrapped In Red by Kelly Clarkson MP3 Album Download appeared
first on SweetFreeStuff.com.
///////////////////////////////////////////
Free Redbox DVD, Blu-Ray, or Video Game Rental
Posted: 04 Dec 2015 09:21 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Enter code: 22Y6LH49 on their website. Valid today only. Free Redbox DVD,
Blu-Ray, or Video Game Rental
The post Free Redbox DVD, Blu-Ray, or Video Game Rental appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free Water Wipes Baby Wipes Sample (Expired)
Posted: 04 Dec 2015 08:00 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Complete one of the tasks (Tweet, follow on Instagram, follow on Pinterest
or store locator question) and fill out the form to receive a Free Water
Wipes Baby Wipes Sample
The post Free Water Wipes Baby Wipes Sample (Expired) appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free blu Plus+ Electronic Cigarette Xpress Kit
Posted: 04 Dec 2015 12:40 AM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Free blu Plus+ Electronic Cigarette Xpress Kit
The post Free blu Plus+ Electronic Cigarette Xpress Kit appeared first on
SweetFreeStuff.com.
///////////////////////////////////////////
Free Glossier Priming Moisturizer Sample
Posted: 03 Dec 2015 07:13 PM PST
http://feedproxy.google.com/&edburner&utm_medium=email
Enter your e-mail and fill out the form for a free sample. Free Glossier
Priming Moisturizer Sample
The post Free Glossier Priming Moisturizer Sample appeared first on
SweetFreeStuff.com.


Scoring
The score.t implements a scoring system that is identical to that in the adv3 library (and uses exactly the same code). If your game doesn't use scoring, exclude the score.t module from your build.
The quick and dirty way to keep score is to use the addToScore(points, desc) function to add points to the player's total score along with a description of what you're awarding points for. A cleaner and more secure way (secure, for example, in that it makes it easier to ensure you don't keep awarding point for the same deed) is to use Achievement objects.
An Achievement is an object used to award points in the score. For most purposes, an achievement can be described simply by a string, but the Achievement object provides more flexibility in describing combined scores when a set of similar achievements are to be grouped.
There are two ways to use the scoring system.
- You can use a mix of string names and Achievement objects for scoring items; each time you award a scoring item, you call the function addToScore(points, desc) to specify the achievement (by name or by Achievement object) and the number of points to award. You can also call the method addToScoreOnce() on an Achievement object to award the scoring item, ensuring that the item is only awarded once in the entire game (saving you the trouble of checking to see if the event that triggered the scoring item has happened before already in the same game). If you do this, you MUST set the property gameMain.maxScore to reflect the maximum score possible in the game.
- You can use EXCLUSIVELY Achievement objects to represents scoring items, and give each Achievement object a 'points' property indicating the number of points it's worth. To award a scoring item, you call the method awardPoints() on an Achievement object. If you use this style of scoring, the library AUTOMATICALLY computes the gameMain.maxScore value, by adding up the 'points' values of all of the Achievement objects in the game. For this to work properly, you have to obey the following rules:
- use ONLY Achievement objects (never strings) to award points;
- set the 'points' property of each Achievement to its score;
- define Achievement objects statically only (never use 'new' to create an Achievement dynamically)
- if an Achievement can be awarded more than once, you must override its 'maxPoints' property to reflect the total number of points it will be worth when it is awarded the maximum number of times;
- always award an Achievement through its awardPoints() or awardPointsOnce() method;
- there exists at least one solution of the game in which every Achievement object is awarded
If there is more than one route through your game, it may be a bit tricky using the second of these approaches. One possible workaround is to use method 2 for the main route through your game (which you count as the 'main' route may, of course, be an arbitrary choice: you could choose the longest route, the shortest route, the most interesting route or anything else that makes sense to you). Then use Achievement objects for the other routes but call their addToScoreOnce(points) method instead of awardPoints() or awardPointsOnce() if they're not on the main route; and don't define their points property. That way the points from these additional off-the-main-route Achievements won't be included the calculation of the maximum available points.


Are you sure you want to remove your app (id: {{clientAppId}})?
All your chat history related to this app will be deleted.


Add initial spec for gcheap dump tool by josalem · Pull Request #512 · dotnet/diagnostics This is an initial specification for a commandline tool capable of collecting GC Heap Dumps of dotnet processes on Linux, Mac, and Windows. CC - @caslan
Why online identities are smart career moves The final catalyst for me eventually taking the leap into the blogosphere came from an unexpected source. It was actually my own response to a Stack Overflow Question where I’d suggested that one of the best ways to make yourself more marketable as...
Firing mods and forced relicensing: is Stack Exchange still interested in cooperating with the community? The last weeks and days have seen some erratic behaviour by Stack Exchange Inc., such as likely illegal changes to the content license and the firing of an upstanding community moderator with no
v5.7 Release Notes Ordinarily we’d have a longer release cadence in between releases with more features, but with the recent release of .NET Core 3 we’ve cut this release cycle short so we can release a version of ServiceStack compatible with .NET Core 3+. Other than that t...
No, White MAGA Friend—You Weren't "Embarrassed" by Barack Obama I remember the day after the Election, a friend of mine who happens to be white, remarked on social media that he “finally wasn’t embarrassed of America and our President.”  Since then I’...
Devirtualization: in need of some clarifications · Issue #13450 · dotnet/runtime I can't figure out which devirtualization cases are implemented at the moment of .NET Core 3.0 RC. Here is a quick and dirty example of some common (for me at least) use-cases: namespace Consol...
Native image lazy-loading for the web! In this post, we'll look at the new loading attribute which brings native <img> and <iframe> lazy-loading to the web!. For the curious, here&...
Using native DLL and resource files in BenchmarkDotNet projects How to use native DLL and resource files in benchmark projects.
Blazor Simple CQRS with MediatR Strong Recommendation I strongly recommend you take the time to watch NDC 2018 session entitled Vertical Slice Architecture – Jimmy Bogard at the bottom of this blog post. Unless you give you…
CppCon 2019: Olivier Giroux “The One-Decade Task: Putting std::atomic in CUDA.” http://CppCon.org — Discussion & Comments: https://www.reddit.com/r/cpp/ — Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2019 — Some tasks are projected to take a long time to comple...
A computer built from NOR gates: inside the Apollo Guidance Computer We recently restored an Apollo Guidance Computer 1 , the computer that provided guidance, navigation, and control onboard the Apollo flight...
Impeachment.fyi - the latest impeachment news A quick snapshot of what's happening now in impeachment news.


Toggle navigation
IMGS
About
Contact
Sign up
Login
The Llamas Image Gallery
A bunch of pics I found on unsplash.com for the purpose of this project


CC Catalog is a project that gathers information about images from around the internet, and stores the information so that these images can eventually be indexed in CC Search. A portion of the process is directed by Apache Airflow, which is a tool commonly used to organize workflows and data pipelines.
The nature of Airflow leads to some particular challenges when it comes to testing, and special care must be taken to make tests independent from the global state of the system where they are run. This blog post will describe a few of the challenges we faced when writing tests for Airflow jobs, and some tricks we used to solve those challenges.
Brief description of Apache Airflow
Apache Airflow is an open source piece of software that loads Directed Acyclic
Graphs (DAGs) defined via python files. The DAG is what defines a given
workflow. The nodes are pieces of jobs that need to be accomplished, and the
directed edges of the graph define dependencies between the various pieces. By
default, the Airflow daemon only looks for DAGs to load from a global location
in the user's home folder: ~/airflow/dags/
. When a DAG is 'run', i.e., the
tasks defined by the nodes of the DAG are each performed in the order defined by
the directed edges of the DAG, the Airflow daemon stores information about the
dag run in ~/airflow/
. The daemon also stores general information about what
DAGs exist on the system, and all of their current statuses in that directory.
For more details, please see the documentation
Challenge: Localize Airflow to the project directory
Even when installed using pip
within a virtualenv
environment,
all airflow commands will be run against the default locations in the user's
home directory. In particular, if you want to test a DAG from your project
directory, the method given in the Airflow documentation is to
copy the dag into the default location ~/airflow/dags/
, and use the
command-line airflow
tool to run the tasks defined by the nodes. The
information about success and failure of the tests will be stored by the Airflow
daemon in the ~/airflow/
directory. We'd rather keep all input and output
from our tests to the project directory instead. This helps avoid any side
effects which might arise by running tests for different projects, and also
ensures that tests can't affect anything in the default directory, which may be
used for production in many cases.
The solution is to choose a directory in your project, and set the environment
variable $AIRFLOW_HOME
whenever you run the tests, or use the airflow
command on the project DAGs. I recommend you add the command
export AIRFLOW_HOME=/your/desired/full/path/
to a script (ours is called env.sh
) that will be run in any shell dealing with
the 'localized' Airflow instance, because forgetting to set the variable for
even one airflow
command will corrupt the DAG states stored in the global
area. Note that setting this variable is necessary even when running in a
virtualenv
environment.
Now that you have $AIRFLOW_HOME
set, you'll likely want to load some DAGs that
you've written. This is made easier if you put the files defining them into a
dags
directory in the directory denoted by $AIRFLOW_HOME
. I.e., it's wise
to structure the project sub-directory dealing with Airflow and Airflow DAGs
similarly to the default location, but in your project directory. At this
point, you should have some $AIRFLOW_HOME
directory as a subdirectory of your
project directory, and then some $AIRFLOW_HOME/dags
directory, where you keep
any python files defining Airflow DAGs, and their dependencies. Another
advantage of this structure is it's likely the directory structure you'll use in
production, and replicating simplifies deployment.
Finally, Airflow will leave a number of files in the $AIRFLOW_HOME
directory
which you are not likely to want to track in source control (e.g., git
).
These files are:
$AIRFLOW_HOME/airflow.cfg
$AIRFLOW_HOME/airflow.db
$AIRFLOW_HOME/logs/
$AIRFLOW_HOME/unittests.cfg
Add these files to .gitignore
or the equivalent.
Smoketesting: Can the Airflow daemon load the DAGs?
Note that we're using pytest
for our unit testing, and so most examples assume
this.
The most basic test you'll want is to determine whether your DAGs can load without errors. To do this, you can use the following function:
from airflow.models import DagBag def test_dags_load_with_no_errors(): dag_bag = DagBag(include_examples=False) dag_bag.process_file('common_api_workflows.py') assert len(dag_bag.import_errors) == 0
We initialize a DagBag
(this loads DAG files). With the process_file
method,
we instruct the Airflow daemon to attempt to load any DAGs defined in the
common_api_workflows.py
file. We then check to make sure loading the DAGs
didn't produce any errors.
Hint: Use functions to create DAGs.
This will increase testability. You can test the function, bypassing the need to
load the DAG into the DagBag
(except when you're actually testing that it
can be loaded). This may seem obvious, but none of the Airflow documentation
uses this pattern. Here is an example of a function that creates a simple dag,
and a test of the function:
from airflow import DAG from airflow.operators.bash_operator import BashOperator def create_dag( source, script_location, dag_id, crontab_str=None, default_args=DAG_DEFAULT_ARGS): dag = DAG( dag_id=dag_id, default_args=default_args, schedule_interval=crontab_str, catchup=False ) with dag: start_task = BashOperator( task_id='{}_{}'.format(source, status), bash_command='echo Starting {} workflow'.format(status), dag=dag ) run_task = BashOperator( task_id='get_{}_images'.format(source), bash_command='python {} --mode default'.format(script_location), dag=dag ) start_task >> run_task return dag def test_create_dag_creates_correct_dependencies(): dag = create_dag( 'test_source', 'test_script_location', 'test_dag_id' ) start_id = 'test_source_starting' run_id = 'get_test_source_images' start_task = dag.get_task(start_id) assert start_task.upstream_task_ids == set() assert start_task.downstream_task_ids == set([run_id]) run_task = dag.get_task(run_id) assert run_task.upstream_task_ids == set([start_id]) assert run_task.downstream_task_ids == set([])
Here, we assume that DAG_DEFAULT_ARGS
is defined earlier in the file. See the
Airflow documentation for details about default DAG arguments. Now, this
function is testable (great!) but it doesn't acutally make the DAG it creates
known to the Airflow daemon. To do that, we have to create the created dag into
the global scope of the module defined by the file, which can be done with the
following snippet:
globals()[dag_id] = create_dag( source, script_location, dag_id )
Here, it's assumed that source
, script_location
, and dag_id
are defined
earlier in the python file.
We hope that these hints are helpful to the reader. For more, and for the context around the snippets shown here, please take a look at the repo.


 Using Blade style syntax, you can easily setup tasks for deployment, Artisan commands, and more.html. Currently, Envoy only supports the Mac and Linux operating systems. However, Windows support is achievable using WSL2.
Installation
First, install Envoy into your project using the Composer package manager:
composer require laravel/envoy --dev
Once Envoy has been installed, the Envoy binary will be available in your application's vendor/bin
directory:
php vendor/bin/envoy
Writing Tasks
Defining Tasks
Tasks are the basic building block of Envoy. Tasks define the shell commands that should execute on your remote servers when the task is invoked. For example, you might define a task that executes the php artisan queue:restart
command on all of your application's queue worker servers.
All of your Envoy tasks should be defined in an Envoy.blade.php
file at the root of your application. Here's an example to get you started:
@servers(['web' =><EMAIL_ADDRESS>'workers' =><EMAIL_ADDRESS>@task('restart-queues', ['on' => 'workers'])
cd /home/user/example.com
php artisan queue:restart
@endtask
As you can see, an array of @servers
is defined at the top of the file, allowing you to reference these servers via the on
option of your task declarations. The @servers
declaration should always be placed on a single line. Within your @task
declarations, you should place the shell commands that should execute on your servers when the task is invoked.
Local Tasks
You can force a script to run on your local computer by specifying the server's IP address as <IP_ADDRESS>
:
@servers(['localhost' => '<IP_ADDRESS>'])
Importing Envoy Tasks
Using the @import
directive, you may import other Envoy files so their stories and tasks are added to yours. After the files have been imported, you may execute the tasks they contain as if they were defined in your own Envoy file:
@import('vendor/package/Envoy.blade.php')
Multiple Servers
Envoy allows you to easily run a task across multiple servers. First, add additional servers to your @servers
declaration. Each server should be assigned a unique name. Once you have defined your additional servers you may list each of the servers in the task's on
array:
@servers(['web-1' => '<IP_ADDRESS>', 'web-2' => '<IP_ADDRESS>'])
@task('deploy', ['on' => ['web-1', 'web-2']])
cd /home/user/example.com
git pull origin {{ $branch }}
php artisan migrate --force
@endtask
Parallel Execution
By default, tasks will be executed on each server serially. In other words, a task will finish running on the first server before proceeding to execute on the second server. If you would like to run a task across multiple servers in parallel, add the parallel
option to your task declaration:
@servers(['web-1' => '<IP_ADDRESS>', 'web-2' => '<IP_ADDRESS>'])
@task('deploy', ['on' => ['web-1', 'web-2'], 'parallel' => true])
cd /home/user/example.com
git pull origin {{ $branch }}
php artisan migrate --force
@endtask
Setup
Sometimes, you may need to execute arbitrary PHP code before running your Envoy tasks. You may use the @setup
directive to define a block of PHP code that should execute before your tasks:
@setup
$now = new DateTime;
@endsetup
If you need to require other PHP files before your task is executed, you may use the @include
directive at the top of your Envoy.blade.php
file:
@include('vendor/autoload.php')
@task('restart-queues')
# ...
@endtask
Variables
If needed, you may pass arguments to Envoy tasks by specifying them on the command line when invoking Envoy:
php vendor/bin/envoy run deploy --branch=master
You may access the options within your tasks using Blade's "echo" syntax. You may also define Blade if
statements and loops within your tasks. For example, let's verify the presence of the $branch
variable before executing the git pull
command:
@servers(['web' =><EMAIL_ADDRESS>@task('deploy', ['on' => 'web'])
cd /home/user/example.com
@if ($branch)
git pull origin {{ $branch }}
@endif
php artisan migrate --force
@endtask
Stories
Stories group a set of tasks under a single, convenient name. For instance, a deploy
story may run the update-code
and install-dependencies
tasks by listing the task names within its definition:
@servers(['web' =><EMAIL_ADDRESS>@story('deploy')
update-code
install-dependencies
@endstory
@task('update-code')
cd /home/user/example.com
git pull origin master
@endtask
@task('install-dependencies')
cd /home/user/example.com
composer install
@endtask
Once the story has been written, you may invoke it in the same way you would invoke a task:
php vendor/bin/envoy run deploy
Running Tasks
To run a task or story that is defined in your application's Envoy.blade.php
file, execute Envoy's run
command, passing the name of the task or story you would like to execute. Envoy will execute the task and display the output from your remote servers as the task is running:
php vendor/bin/envoy run deploy
Confirming Task Execution
If you would like to be prompted for confirmation before running a given task on your servers, you should add the confirm
directive to your task declaration. This option is particularly useful for destructive operations:
@task('deploy', ['on' => 'web', 'confirm' => true])
cd /home/user/example.com
git pull origin {{ $branch }}
php artisan migrate
@endtask
Notifications
Slack
Envoy supports sending notifications to Slack after each task is executed. The @slack
directive accepts a Slack hook URL and a channel / user name. You may retrieve your webhook URL by creating an "Incoming WebHooks" integration in your Slack control panel.
You should pass the entire webhook URL as the first argument given to the @slack
directive. The second argument given to the @slack
directive should be a channel name (#channel
) or a user name (@user
):
@finished
@slack('webhook-url', '#bots')
@endfinished
Discord
Envoy also supports sending notifications to Discord after each task is executed. The @discord
directive accepts a Discord hook URL and a message. You may retrieve your webhook URL by creating a "Webhook" in your Server Settings and choosing which channel the webhook should post to. You should pass the entire Webhook URL into the @discord
directive:
@finished
@discord('discord-webhook-url')
@endfinished
Telegram
Envoy also supports sending notifications to Telegram after each task is executed. The @telegram
directive accepts a Telegram Bot ID and a Chat ID. You may retrieve your Bot ID by creating a new bot using BotFather. You can retrieve a valid Chat ID using @username_to_id_bot. You should pass the entire Bot ID and Chat ID into the @telegram
directive:
@finished
@telegram('bot-id','chat-id')
@endfinished


Magnus Opus - All vendors
All different types of cuisine to fulfill our customer's needs
Search Vendors
Add New Vendor
Vendor Database


Add new userAdd a new user to the user repositoryUser IDThe user account login IDEmailThe user's primary email addressGiven NameThe user's first nameSurnameThe user's family namePasswordPasswordThe user's initial password.Confirm PasswordType password again to detect accidential typos.Session SettingsOverride the default session timeout for this user.
The default session timeout defaults to one hour and can be set by the jwt.ttl environment variable.
Access Token Expiration PeriodIdle user session expiration period
UnitExpiration period temporal unitMinutesHoursDays
RolesSelect the roles of the user.{{name}}{{description}}Add userCancel


| hidden |
Illuminating, Isn't It?
- Standard incandescent bulbs convert 90
percent of electricity to heat and only 10 percent to visible light
- The electricity we use for our lighting
is generated by the burning of fossil fuels, adding tons of pollutants
to the atmosphere
- The U.S. is responsible for almost 25%
of the world's total energy consumption. We use one million gallons
of oil every two minutes.
- Energy currently wasted by U.S. cars,
homes and appliances equals more than twice the known energy reserves
in Alaska and the U.S. Outer Continental Shelf.
- We could cut our nation's energy consumption
in half by the year 2030 by using more efficiently and by using more
renewable energy sources. In the process, we would promote economic
growth by saving consumers $2.3 trillion and by producing one million
new jobs.
- When just 1% of America's 140 million
car owners tune up their cars, we eliminate nearly a billion pounds
of CO2 the key cause of global warming from entering the atmosphere.
|


Police meet Department of Health chiefs over Dr Watt neurology scandal
by Seanín GrahamPOLICE have confirmed they met with Department of Health officials to discuss the neurology recall scandal.
Around 3,000 patients of consultant Dr Michael Watt were recalled in May last year after safety concerns were raised about his work.
Earlier this week, BBC's Spotlight programme claimed that a procedure known as an epidural blood patch was carried out on many of former patients who didn't require it.
Last week The Irish News reported that than than 600 patients caught up in the recall were given an unreliable diagnosis or received the wrong drug treatment.
It also emerged that uncertainty exists around a further 300 cases, who "may" have been given inappropriate care or an incorrect diagnosis.
The figures are detailed in an 'outcomes' report, which was due to be published by the department in June but was cancelled due to "unforeseen circumstances".
Read More: 30 per cent of Dr Watt recall patients may have received unreliable diagnosis or drugs
In a statement, the PSNI said: "We are aware of the recall of neurology patients by the Belfast Health and Social Care Trust and have met with senior officials within the Department of Health to discuss the issue.
"The department has agreed to provide us with further information so that we can assess how best to move forward and to enable us to determine if any potential criminal offences can be identified."
Dr Watt remains suspended from his employer, the Belfast health trust, but applied to retire on medical grounds in August. He remains on full pay.
Read More: Belfast health trust boss issues first letter of apology to Dr Watt patients
Last June, The Irish News revealed that the consultant neurologist did not receive an annual appraisal by his medical bosses in the trust for two years prior to a whistleblower raising the alarm.
Appraisals for his work in 2014, 2015 and 2016 did not take place - but these were instead "completed" in late 2017, five months after he was stopped from seeing his patients.


how_to_contribute.Rmd
This repository is for internal use, so any kind of script or template will be helpful to others. They don’t need to be a final product, so go ahead and add anything you did that we don’t have automatized.
Scripts are being stored in inst/rmarkdown/Rscripts
:
# this is for alternative splicing with IRFinder
, it will be used as a description of the script in the page where all templates/scripts are being listed.How to add new templates to HBC repository in 30 lines.
First you need to clone hbcABC repo to your computer.
After that, the easiest way to create templates is to follow this structure.
Inside the folder inst/rmarkdown/templates
create a folder with the name of the RMD template you want to add, for instance: hello_word_rmd
.
Then, create a YAML file named template.yaml
with the following information:
name: Name of the template
description: >
Description that will be shown in Rstudio.
create_dir: false
Create another folder named: skeleton
and inside this a RMD file named skeleton.Rmd
containing the rmarkdown you want to add.
At the end you’ll should have this folders tree under inst/rmarkdown
:
templates
-- hello_world_rmd
---- template.yaml
---- skeleton
------- skeleton.Rmd
To test it, install the package and go to the File
-> New File
-> R Markdown
and click on templates. Your new template should be there.


| Home | About | Experience | Education | Blog | Projects | Contact |
This week's technical blog post is about everyone's favorite enumerable method, map. Enumerable map is an extremely useful method that gives Rubyists' the flexibility to move outside of the typical each method constraints. One of the advantages of map is the flexibility it gives you with arrays. If you use each on an array and try to take the square root of the argument, it will simply spit out the same unaltered array. In this case, the following code isn't very helpful:
arr = [4,16,64,256] arr.each{ |x| x = Math.sqrt(x).to_i } puts arr.join(", ") #=> 4, 16, 64, 256 # Displays 1,2,3,4,5
In case you're wondering, making arr.each! destructive confuses Ruby even more. Don't try it unless you like seeing error messages. What is a poor Rubyist to do when each can't get the job done? Map, my friend, map is the answer. The cool thing about map is that it gives you the flexibility to either create a new modified array (non-destructive) or to modify the existing array. If we try that same code with map instead of each, we get a result that actually makes sense:
arr = [4,16,64,256] arr = arr.map!{|x| Math.sqrt(x).to_i } puts arr.join(", ") #=> 4, 16, 64, 256 #=> 2, 4, 8, 16
Of course, map isn't constrained to the world of curly braces. You can also define a method before map, and then use that method to modify each element in the array as you iterate through it. I'm still getting the hang of symbols, but the example below includes a symbol combined with ampersand to reference the previously defined method. At first, you may be tempted to put parenthesis around the "&:x2". The code below stays dry and functions correctly without them:
class Numb def x2 self * 2 end end [2,5,10].map &:x2
That's all for this week's DBC technical blog. Hopefully I can manage to write something next week even if I'm in a turkey-induced coma.


class UntrustedAttachmentsException : CordaRuntimeException
Thrown during classloading upon encountering an untrusted attachment (eg. not in the TRUSTED_UPLOADERS list)
<init> |
Thrown during classloading upon encountering an untrusted attachment (eg. not in the TRUSTED_UPLOADERS list) UntrustedAttachmentsException(txId: SecureHash, ids: List<SecureHash>) |
ids |
val ids: List<SecureHash> |
txId |
val txId: SecureHash |


{% extends 'dashboard/base.html' %} {% load static account %} {% block title %}Home{% endblock %} {% block header %} {% endblock %} {% block content %}
logout
We're sorry, but there are no data files available for you to view at this time


Code
a blog which enables one to write anew blog
Invoice that allows
regestration form that allows student to regester
Design
Magazine,Adobe indesign
tigger girl,adobe photoshop
diamonds,Adobe photshop
different fruits in a basket,adobe photoshop


Personal Information
Name : Aswin Sukumran
Email<EMAIL_ADDRESS>Address : Plot no -69-a,a2,5th street,sudarshan nagar,madambakkam,chennai-126
Phone number :<PHONE_NUMBER>
Qualifications :
Qualifications
Name of the institute
Marks
Btech
Hindustan University
7.5 CGPA
12th HSC
Zion Matriculation Higher Secondary School
85%
10TH SSLC
Zion Matriculation Higher Secondary School
97%
Hobbies : Cricket,Video games


Hi, I am Hanezu.
This is a
blog of my thoughts on
deep learning, math, programming, and else.
I recently started to write some
non-technical posts in Chinese
too.
About me
-
Developing Crypko @ PFN, 2018 -
now.
-
Exchange Student @ Kyoto University, 2016 - 2017.
-
Bachelor of Math @ Fudan University, 2014 - 2018.
Contact
If you enjoy reading my posts, feel free to comment (in English, Japanese or Chinese) or
email me.
{% include footer.html %}


Marketing is everything an organization does to build a relationship between the company and the customer. It is the process by which a product or service is introduced and promoted to potential customers and sustaining relationships over time.
A marketing plan and content strategy will help you figure out what message you want to send to potential customers, how to get the word out about your business, and often includes a call to action - like buying buying a product or service.
 A well crafted message will highlight your unique benefits, target a specific audience, support your mission.
Once you’ve devised a plan, you will need to develop a strategy for creating and distributing content (i.e. text, images, videos) that inform your customers about what you're selling and how they can buy it.
{{ cms:partial:shared/link_box:"https://moz.com/blog/content-strategy-template":"VIEW":"Content Strategy Template" }}Here’s a list of common marketing platforms to distribute content.
The SBDC offers a Marketing 101 course to help you get started. See the training events calendar for details:
{{ cms:partial:shared/link_box:"http://lasbdcnet.ecenterdirect.com/events?reset=1":"VISIT":"LB SBDC Events Calendar" }}The Long Beach Small Business Development Center (SBDC) has several blogs about marketing that offer tips and steps for different marketing techniques:
{{ cms:partial:shared/link_box:"http://longbeachsbdc.org/?s=Marketing":"VISIT":"LB SBDC Marketing Blog" }}


The Second City
City of a Big Shoulders
Chicago is home of the blues and the truth jazz, the heart of comedy and the idea of the skyscraper. It's a city with a swagger, but without the surliness or even the fake smiles found in other cities of its size.
The Mother City
City of a Hundred Spires
Prague is the capital and largest city of the Czech Republic. The city's historic buildings and narrow, winding streets are testament to its centuries-old of the historic region of Bohemia.


Archaeological Site
Memorial
Monument
Other
Closest toilet
Drag the blue point over the map to show points at location and populate listing.Increases or decreases the radius of search is done by slider in left menu.