All events will take place at Brown University April 9-15 and are free and open to the public. The full schedule including times/locations and ticket reservations will be available online in April.
Important note: Please do no reserve a ticket unless you plan on using it. There will be a standby line that starts when the doors to each event open (30 minutes prior to start time). If you have a ticket, please arrive 15 minutes prior to start time in order to secure seating as every ticket will go VOID at this point.
In mid-2016, Shia LaBeouf (@thecampaignbook),
Nastja Säde Rönkkö (@nastjaronkko),
and
Luke Turner (@luke_turner)
tweeted out their GPS coordinates, asking the public to pick them
up and
take them anywhere they chose. This launched #TAKEMEANYWHERE,
an art project that would
yield a film of staggering scale and beauty,
as the
artists took a cross-country trip that illuminated the beauty
of humanity. It’s a film about the artists, about the participants,
about the country. It’s a film about all of us.
For a Keynote event, we will screen #TAKEMEANYWHERE. Afterwards, Mr. LaBeouf and Mr. Turner will conduct a Q&A (with Ms. Ronkko Skyping in). You don’t want to miss it.
IFF is proud to announce its first-ever partnership with the Aegean Film Festival, based in Patmos, Greece. IFF will feature the Aegean Film Festival’s Short Program, made up of award-winning short films from across the world. A program of IFF’s official selection will be screened at the Aegean Film Festival this summer. This partnership aims to build lasting relationships between IFF and the international film community and provide new opportunities for students to engage with the globally-practiced craft of short filmmaking.
RBG, an intimate portrait of the life and work of Justice Ruth Bader Ginsburg, premiered at Sundance earlier this year and is due in theaters on May 4th. With unprecedented access, the film explores how her early legal battles changed the world for women. This revelatory documentary screening will be followed by a dynamic Q&A with Director Betsy West.
Based on the beloved Ian McEwan novel, “On Chesil Beach” is a beautiful and important new film due in theaters on May 18th. In 1962, newlyweds Florence (Academy Award nominee Saoirse Ronan) and Edward (Billy Howle), both in their early 20s and also both virgins, spend their honeymoon preoccupied and terrified by the upcoming consummation of their marriage. Afterwards, join us for a Q&A with director Dominic Cooke (The Hollow Crown).
Please join us after the screening and discussion of "On Chesil Beach" in the lobby of the Granoff! Refreshments will be served.
Patrick Raleigh, the Data Visualization of Coordinator of the Brown University Library, will lead this crash course in creating simple VR content that can be posted to the web and is cardboard-ready.
As one of the first student-run film festivals in the world to pioneer an initiative geared towards virtual reality, IFF’s New Media Initiative aims to foster the next generation’s dialogue around VR storytelling. This year, IFF will showcase student-made VR and AR projects on Google Daydream and HTC Vive, as well as provide a hands-on environment for attendees to explore cutting-edge new media technologies.
IFF’s 2018 Official Selection of undergraduate and graduate student films features a curated program of 25 of the strongest films. This year’s Official Selection of screenplays will honor the top three scripts from four categories: undergraduate short, graduate short, features and TV pilots. The students on our programming and screenplay departments created these selections from over 500 submissions, representing a total of 27 countries around the world.
“This year’s “Moonlight.” That’s what IndieWire had to say about this deeply moving Sundance standout. Starring Raul Castillo and Josiah Gabriel, and directed by documentary veteran Jeremiah Zagar, this film follows three brothers through their troubled and formative childhood in upstate New York. Based on the novel by Justin Torres.
This year we received 100+ screenplays to our year-long Screenplay Contest, including shorts, features and pilots written by undergraduate and graduate students from all over the world. This year’s Final Selection will honor the top 3 scripts from 4 categories: undergraduate short, graduate short, features and TV pilots. This year’s screenplays stand out for their high quality writing and storytelling, making this one of the most competitive submission pools to date.
From the impact of the #MeToo movement, to the success of films like Lady Bird, Wonder Woman, and Mudbound, it is impossible to deny that the future of film is female. We feature two incredible actors to talk and answer questions about their lives in front of the camera in this two-day event.
Victoria Tennant trained at the Elmhurst Ballet School and at the Central School for Speech and Drama in London. In her first film, she starred in the title role of The Ragman’s Daughter. Subsequent films include All of Me with Steve Martin and Lily Tomlin, LA Story with Steve Martin, Stranger’s Kiss, The Plague, The Handmaid’s Tale with Natasha Richardson and Robert Duvall, The Holcroft Covenant with Michael Caine, and Louder Than Words with David Duchovny and Timothy Hutton. Ms. Tennant has also worked extensively in television including the miniseries and television movies The Winds of War and War and Remembrance, Chiefs, Dempsey, Under Siege, and The Man From Snowy River. She also wrote and co-produced the film Edie and Pen for HBO and produced Sister Mary Explains It All with Diane Keaton for Showtime.
As one of the first student-run film festivals in the world to pioneer an initiative geared towards virtual reality, IFF’s New Media Initiative aims to foster the next generation’s dialogue around VR storytelling. This year, IFF will showcase student-made VR and AR projects on Google Daydream and HTC Vive, as well as provide a hands-on environment for attendees to explore cutting-edge new media technologies.
IFF’s 2018 Official Selection of undergraduate and graduate student films features a curated program of 25 of the strongest films. This year’s Official Selection of screenplays will honor the top three scripts from four categories: undergraduate short, graduate short, features and TV pilots. The students on our programming and screenplay departments created these selections from over 500 submissions, representing a total of 27 countries around the world.
Written and starring lifelong friends Daveed Diggs (Hamilton) and Rafael Casal, Blindspotting is about the intersection of race and class in increasingly-gentrified Oakland.
Daveed Diggs (Collin/Co-Writer/Producer) is a Tony Award®, GRAMMY®, and Lucille Lortel Award-winning actor and rapper who is widely known for originating the dual roles of Thomas Jefferson and Marquis de Lafayette in the Broadway sensation, Hamilton. Diggs most recently appeared in the hit Lionsgate feature "Wonder", opposite Julia Roberts and Jacob Tremblay. On television, Diggs will next star in TNT’s Snowpiercer and lend his voice to Fox’s Bob’s Burgers, Netflix’s Bojack Horseman, and Josh Gad’s new animated musical comedy for Apple TV, Central Park. Most recently he was seen on ABC’s Emmy®-nominated series, Black-ish, and Netflix’s hit comedy, Unbreakable Kimmy Schmidt. He also starred in HBO’s Tour de Pharmacy, alongside Andy Samberg and Orlando Bloom. Diggs executive produced ABC’s The Mayor and is creating original content for ESPN.
Celebrate the 2018 Ivy Film Festival with us! More details to come!
IFF’s 2018 Official Selection of undergraduate and graduate student films features a curated program of 25 of the strongest films. This year’s Official Selection of screenplays will honor the top three scripts from four categories: undergraduate short, graduate short, features and TV pilots. The students on our programming and screenplay departments created these selections from over 500 submissions, representing a total of 27 countries around the world.
No tickets necessary! Come one, come all!
From the impact of the #MeToo movement, to the success of films like Lady Bird, Wonder Woman, and Mudbound, it is impossible to deny that the future of film is female. We feature two incredible actors to talk and answer questions about their lives in front of the camera in this two-day event.
Isabelle Fuhrman is best known for her roles in films like "The Hunger Games" and "Orphan", and the critically acclaimed Showtime TV series, "Masters of Sex". Considered one of her generation’s most promising young talents, the versatile performer also writes and directs her own material. Upcoming, Isabelle will star in Lionsgate’s "Down A Dark Hall" opposite AnnaSophia Robb and Uma Thurman, and the much-buzzed about indie "Shoplifters of the World" opposite Sasha Lane. Isabelle began her career at the age of seven and received her training at the highly selective Shakespeare and Contemporary Drama summer programs at the Royal Academy of Dramatic Art in London.
Bo Burnham’s directorial debut premiered at the 2018 Sundance Film Festival to audience and critical acclaim, featuring breakout star Elsie Fisher. The story follows thirteen-year-old Kayla as she endures the tidal wave of contemporary suburban adolescence as she makes her way through the last week of middle school--the end of her thus far disastrous eighth grade year before she begins high school. Eighth Grade will be the final screening of IFF 2018.


Chapter 6 - Finding datasets in publications: The Allen Institute for Artificial Intelligence approach¶
author:
| Daniel King, Waleed Ammar, Iz Beltagy, Christine BettsSuchin Gururangan and Madeleine van Zuylen
Allen Institute for Artificial Intelligence, Seattle, WA, USAdaniel @allenai.orgbibliography:
‘acl2015.bib’ title: Finding datasets in publications: The Allen Institute for AI Approach
Introduction¶
The Allen Institute for Artificial Intelligence (AI2) is a non-profit research institute founded by Paul G. Allen with the goal of advancing artificial intelligence research for the common good. One of the major undertakings at AI2 is to develop an equitable, unbiased software platform Semantic Scholar1 for finding relevant information in the scientific literature. Semantic Scholar extracts meaningful structures in a paper (e.g., images, entities, relationships) and links them to other artifacts when possible (e.g., knowledge bases, GitHub repositories), hence our interest in the rich context competition (RCC). In particular, we participated in the RCC in order to explore methods for extracting and linking datasets used in papers. At the time of this writing, Semantic Scholar comprehensively covers the computer science and biomedical literature, and we plan to expand our coverage in 2019 to other scientific areas, including social sciences.
In the following sections, we describe our approach to the three tasks of the RCC competition, which are described in more detail in Chapter 5:
extracting the datasets used in publications,
predicting the field of research of publications
extracting the methods used in publications
Methods¶
Dataset Extraction and Linking¶
This task focuses on identifying datasets used in a scientific paper. Datasets which are merely mentioned but not used in the research paper are not of interest. This task has two sub-tasks:
Citation prediction: extraction and linking to a provided knowledge base of known datasets, and
Mention prediction: extraction of both known and unknown dataset mentions.
Provided Data¶
The provided knowledge base of known datasets includes approximately 10K datasets used in social science research. The high textual similarity between different datasets in the knowledge base informs our approach for linking dataset mentions to their dataset in the knowledge base. Approximately 10% of the datasets in the knowledge base were linked one or more times in the provided corpus of 5K papers. To attempt to generalize mention discovery beyond those present in the knowledge base, we train a named entity recognition (NER) model on the noisy annotations provided by the labeled mentions in the knowledge base.
image Figure 6.1: A high-level overview of our appraoch to dataset mention detection and linking.
We provide a high-level overview of our approach in Figure 6.1. First, we use an NER model to predict dataset mentions. For each mention, we generate a list of candidate datasets from the knowledge base. We also developed a rule based extraction system which searches for dataset mentions seen in the training set, adding the corresponding dataset IDs in the training set annotations as candidates. We then use a binary classifier to predict which of these candidates is a correct dataset extraction.
Next, we describe each of the sub-components in more detail.
Mention and Candidate Generation¶
We first constructed a set of rule based candidate citations by exact string matching mentions and dataset names from the provided knowledge base. We found this to have high recall and low precision, both on the provided development fold and our own development fold that we created. High recall and low precision was the desired outcome for this candidate generation step. However, after our test submission, it became clear that there were many datasets in the actual test set that did not have mentions in the provided knowledge base. If the provided development fold had been representative of the test set (rather than the train set) in terms of what datasets were mentioned in it, we could have discovered this issue sooner. In this case, it would have been more representative of the test set if it included more datasets that did not have example mentions in the provided knowledge base. The importance of reliable evaluation and training data is discussed further in Chapter 12.
To address this limitation, we developed an NER model to predict additional dataset mentions. For NER, we use a bi-LSTM model with a CRF decoding layer, similar to Peters et al., 2018, and implemented using the AllenNLP framework2. In order to train the NER model, we automatically generate mention labels by string matching mentions in the provided annotations against the full text of a paper. This results in noisy labeled data, because it was not possible to find all correct mentions this way (e.g., some dataset mentions were not annotated), and the same string can appear multiple times in the paper, while only some are correct examples of dataset usage.
We limit the percentage of negative examples (i.e., sentences with no mentions) used in training to 50%, and use 40 words as the maximum sentence length. We use 50-dimensional Glove word embeddings (Pennington et al., 2014), 16-dimensional character embeddings with 64 CNN filters of sizes (2, 3, 4). The CNN character encoder outputs 128-dimensional vectors. We optimize model parameters using ADAM (Kingma and Ba, 2014) with a learning rate of 0.001. Training the model took approximately 12 hours on a single GPU.
In order to generate linking candidates for the NER mentions, we score each candidate dataset based on TF-IDF weighted token overlap between the mention text and the dataset title. For a given mention, many dataset titles can have a non-zero overlap score, so we take the top 30 scoring candidates for each mention as the linking candidates for that mention.
Candidate Linking¶
The linking model takes as input a dataset mention, its context, and one of the candidate datasets in the knowledge base, and outputs a binary label. We use a gradient boosted trees classifier using the XGBoost3 implementation. The model takes as input the following features:
prior probability of entity, estimated based on number of occurrences in the training set (float between 0 and 1)
prior probability of entity given mention, estimated based on number of occurrences in the training set (float between 0 and 1)
prior probability of mention given entity, estimated based on number of occurrences in the training set (float between 0 and 1)
whether the same year appears both in the mention context and in the dataset title (binary)
mention length (int)
mention sentence length (int)
whether the mention is an acronym, computed by checking if it is one token that is all upper case (binary)
estimated section title of the mention, computed by searching backwards from the mention for the nearest section header (binary one-hot)
count of overlapping words between the mention context and dataset keywords provided in the knowledge base (int)
We note that it is possible to predict zero, one or multiple dataset IDs for the same mention, and each dataset candidate is scored independently.
We performed a randomized hyperparameter search with 100 iterations over the following hyperparameters and ranges and used a learning rate of 10^-1:
max_depth
:range(2, 8)
n_estimators
:range(1, 50)
colsample_by_tree
:numpy.linspace(0.1, 0.5, 5)
min_child_weight
:range(5, 11)
Each model took a negligible amount of time to train, and the entire hyperparameter search took a few minutes to train on a machine with 8 CPUs.
Research Area Prediction¶
Data¶
The second task of the competition is to predict research areas of a paper. The task does not specify the set of research areas of interest, nor is training data provided for the task. After manual inspection of a subset of the papers in the provided test set, the SAGE taxonomy of research, and the Microsoft Academic Graph (MAG) (Shen et al., 2018), we decided to use a subset of the fields of study in MAG as labels. In particular, we included all fields related to social science or papers from the provided training corpus. However, since the abstract and full text of papers are not provided in MAG, we only use the paper titles for training our model. The training data we ended up with included approximately 75K paper titles along with their fields of study as specified in two levels of the MAG hierarchy. We held out about 10% of the titles for development data. The coarse level (L0) has 7 fields while the more granular one (L1) has 32. Fields associated with less than 100 papers were excluded.
Methods¶
For each level, we trained a bi-directional LSTM which reads the paper title and predicts one of the fields in this level. We additionally incorporate ELMo embeddings (Peters et al., 2018) to improve performance. In the final submission, we always predict the most likely field from the L0 classifier, and only report the most likely field from the L1 classifier if its prediction exceeds a score of 0.4. It takes approximately 1.5 and 3.5 hours for the L0 and L1 classifiers to converge, respectively.
Research Method Extraction¶
Data¶
The third task in the competition is to extract the scientific methods used in the research paper. Since no training data was provided, we started by inspecting a subset of the provided papers to get a better understanding of what kind of methods are used in social science and how they are referred to within papers. This limitation, and the difficulty of working on an undefined task, is discussed in Chapter 2.
Methods¶
Based on the inspection, we designed regular expressions which capture common contextual patterns as well as the list of provided SAGE methods. In order to score candidates, we used a background corpus to estimate the salience of candidate methods in a paper. Two additional strategies were attempted but proved unsuccessful: a weakly-supervised model for named entity recognition, and using open information extraction (openIE) to further generalize the list of candidate methods.
Results¶
Dataset Extraction and Linking¶
First, we report the results of our NER model in Table 6.1. Since it is easy for the model to memorize the dataset mentions seen at training time, we created disjoint train, development, and test sets based on the paper–dataset annotations provided for the competition. In particular, we sort datasets by the number of papers they appear in, then process one dataset at a time. For each dataset, we choose one of the train, development, or test splits at random and add the dataset to it, along with all papers which mention that dataset. When there is a conflict, (e.g., a paper p has already been added to the train split when processing an earlier dataset d1, but it is also associated with a later dataset d2), the later dataset d2 along with all papers associated with it are added to the same split as d1. For any further conflicts, we prefer to put papers in the development split over the train split, and the test split over the development split.
We also experimented with adding ELMo embeddings (Peters et al., 2018), but it significantly slowed down training and decoding which would have disqualified our submission due to the runtime requirements of the competition. As a result, we decided not to include ELMo embeddings in our final model. If the requirements for the competition had permitted the use of a GPU at evaluation time, neural network based embeddings like ELMo could be leveraged.
| | prec. | recall | F1 | | ———- | ——- | ——- | —— | | dev set | 53.4 | 50.3 | 51.8 | | test set | 50.7 | 41.8 | 45.8 |
Table 6.1: NER precision, recall and F1 performance (%) on the development and test sets.
| | prec. | recall | F1 | | ——————————— | ——- | ——– | —- | | baseline | 28.7 | 58.0 | 38.4 | | + p(d|m), p(m|d) | 39.6 | 42.0 | 40.7 | | + year matching | 35.1 | 57.0 | 43.5 | | + aggregated mentions, tuning, and other features | 72.5 | 45.0 | 55.5 | | + dev set examples | 77.0 | 47.0 | 58.3 | | + NER mentions | 56.3 | 62.0 | 59.0 |
Table 6.2: End-to-end precision, recall and F1 performance (%) for citation prediction on the development set provided in phase 1 of the competition.
| | prec. | recall | F1 | | —————– | ——- | ——– | —— | | phase 1 holdout | 35.7 | 19.6 | 25.3 | | phase 2 holdout | 39.6 | 18.8 | 25.5 |
Table 6.3: End-to-end precision, recall, and F1 performance (%) for dataset prediction on the phase 1 and phase 2 holdout sets. Note that the phase 1 holdout results are for citation prediction, while the phase 2 holdout results are for mention prediction.
We report the end-to-end performance of our approach (on the development set provided by the organizers in the first phase) in Table 6.2. This is the performance after using the linking classifier to predict which candidate mention–dataset pairs are correct extractions. We note that the development set provided in phase 1 ended up having significantly more overlap with the training data than the actual test set did. As a result, the numbers reported in Table 6.2 are not indicative of test set performance. End to end performance from our phase 2 submission can be seen in Table 6.3. This performance is reflective of our focus on the linking component of this task. Aside from the competition development set, we also used a random portion of the training set as an additional development set. The initial model only uses a dataset frequency feature, which gives a baseline performance of 38.4 F1. Adding p(d $\mid$ m) and p(m $\mid$ d), which are the probability of entity given mention and probability of mention given entity improves the performance ($\Delta = 2.3$ F1). Year matching helps disambiguate between different datasets in the same series, which was found to be a major source of errors in earlier models ($\Delta = 2.8$ F1). Aggregating mentions for a given dataset, adding mention and sentence length features, adding an is acronym feature, and further hyper-parameter tuning improve the results ($\Delta = 12.5$ F1). Adding examples in the development set while training the model results in further improvements ($\Delta = 2.8$ F1). Finally, adding the NER-based mentions significantly improves recall at the cost of lower precision, with a positive net effect on F1 score ($\Delta = 0.7$ F1).
Two clear limitations of our model are its difficulty in generalizing to unseen datasets, and its inability to effectively distinguish between datasets that are used in a publication and datasets that are merely referenced. These limitations are the main causes of the low recall (due to difficulty generalizing to unseen datasets) and low precision (due to difficulty distinguishing between used datasets and referenced datasets). An interesting approach to improving recall is presented in Chapter 7, and could potentially be leveraged in future work.
Research Area Prediction¶
To select a model, we performed a 100 trial random search across model hyper-parameters, evaluated on a held out development set of papers from the Microsoft Academic Graph. Our final model contained 512 hidden dimensions, 2 layers and 0.5 dropout prior to classification. The top performing classifier achieved 84.4% accuracy on our development set on L0 fields, and 65.2% accuracy on our development set on L1 fields. The main limitation of using MAG for this problem is that our model cannot find new fields of research, and is limited to those provided by MAG. Additionally, our method performs classification based only on the titles of papers, while there are other pieces of information about the paper that would be useful for classifying the field of research. Other resources that could have been used to help with this task are presented in Chapters 7 and 8.
Research Method Extraction¶
We evaluated performance by manually evaluating the output of our extractor for a subset of 50 papers from the provided test set to compute precision. Since evaluating recall requires a careful annotation, we resorted to using yield as an alternative metric. Our final submission for method extraction has 95% precision and yield of 1.5 methods per paper on the manually inspected subset of papers. Similarly to research area prediction, the main limiation here is the difficulty our model has finding new methods, as it is limited to the SAGE ontology and a few hand-crafted patterns. One potential way to alleviate this issue is to leverage external resources, as presented in Chapter 8.
Conclusion¶
This report summarizes the AI2 submission at the RCC competition. We identify dataset mentions by combining the predictions of an NER model and a rule-based system, use TF-IDF to identify candidates for a given mention, and use a gradient boosted trees classifier to predict a binary label for each candidate mention–dataset pair. To identify research fields of a paper, we train two multi-class classifiers, one for each of the top two levels in the MAG hierarchy for fields of study. Finally, to extract research methods, we use a rule-based system utilizing a dictionary and common patterns, followed by a scoring function which takes into account the prominence of a candidate in foreground and background corpora.
We now provide some possible directions of improvement for each component of our submission. For dataset extraction, the most promising avenue of improvement is to improve the NER model, and the most promising avenue to improve the NER model is to collect less noisy data. We effectively have distantly supervised training data for the NER model, and the first thing to try would be directly annotating papers with dataset mentions to provide a clearer signal for the NER model. As mentioned previously and discussed in Chapters 5 and 8, the dataset mentions provided are not located in the text, and are simply extracted strings. Given these strings, labels in the actual text can be created by searching for the provided string. However, this is a noisy process, as the string may occur multiple times in the document, and all occurrences may or may not be correct dataset mentions. This is especially problematic when the string is a common word (e.g. “time”). Therefore, directly annotating the strings in the full text (i.e. providing character offsets for the strings) would help to reduce the noise in the NER data. For research area prediction, it would help to include signals beyond just the paper title for predicting the field of study. The difficulty here is finding labeled training data that includes richer signals like abstract text and paper keywords. For method prediction, exploring the use of open information extraction is a potential avenue of future research. Additionally, it would be helpful to clarify what exactly is meant by a method, as it is currently unclear what a successful method extraction looks like. The main lesson learned is that, when presented with noisy, distantly supervised, real-world data, to produce a production-quality system, it becomes very important to (1) have a high-confidence evaluation dataset, and (2) look for other data sources that are similar enough to the task at hand to be useful. Taking steps towards both of these objectives are promising avenues of future work.
As discussed in Chapter 1 and throughout, the dataset extraction discussed in this report is intended to be part of a broader effort to create infrastructure and tools that will aid in the discovery and usage of datasets in social science research. This is critical to enable reproduciblity, collaboration, and effective use of data. We look forward to seeing what comes out of this project as a whole, and how AI techniques can be leveraged to have positive impact in the social sciences.
Acknowledgments¶
We would like to thank the competition organizers for their tireless efforts in preparing the data, answering all our questions, doing the evaluations, and providing feedback. We also would like to thank Zhihong (Iris) Shen for helping us use the MAG data.
References¶
Diederik P. Kingma and Jimmy Ba. 2014. Adam:A method for stochastic optimization.CoRR,abs/1412.6980.
Jeffrey Pennington, Richard Socher, and Christo-pher D. Manning. 2014. Glove: Global vectors forword representation. InEMNLP.
Matthew E. Peters, Mark Neumann, Mohit Iyyer,Matt Gardner, Christopher Clark, Kenton Lee, andLuke S. Zettlemoyer. 2018. Deep contextualizedword representations. InNAACL 2018.
Zhihong Shen, Hao Ma, and Kuansan Wang. 2018.A web-scale system for scientific knowledge explo-ration. InACL


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Review
Although HTML and CSS are not the core focus of this class, we will spend a week reviewing their fundamental concepts.
HTML
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--- layout: post title: "My Week in Review" date: 2018-04-22 09:10:00 +0000 tags: image: https://s3.amazonaws.com/audreywatters/2018-04-22-egg.jpg imagecredits: https://www.flickr.com/photos/8522151@N07/16786604663 ---
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Create a new binding source, routeBindingSource, and assign it to the dataGridView1.DataSource property.
Assign the DataSet to the routeBindingSource.DataSource property to bind the results to the dataGridView1 control.
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This documentation helps you to get started. It explains what tools you need to install, to compile and to run Piclet on an iOS Device or iOS Simulator. The Piclet iOS App was created with the open source programming language Swift 2.0 and supports all iPhone / iPod Touch Devices running iOS 8.0 and later.
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You need a computer which runs Mac OS X as your Operation System. This constraint comes from Apple and applies for every iOS Application.
Xcode
Xcode is an Integrated Development Environment (IDE) which also contains the iOS software development kit (SDK) which enables iOS development. To compile the Piclet Application you need to install Xcode.
There are two ways to obtain a copy. The easiest is to install Xcode through the Mac App Store. The Mac App Store comes pre installed in Mac OS X v10.6.6 or later.
The other way is to create an Apple Developer Account and download Xcode from the Apple Developer Website. Here you can also get a copy of a Beta version of Xcode.
Carthage
Carthage is a decentralized dependency manager. I used Carthage to simplify the process of obtaining and managing frameworks that I added to the Piclet project. There are two ways to install Carthage on your system:
Download the Carthage.pkg and install it on your machine. You can get the file from the github repository. https://github.com/Carthage/Carthage
You can also install Carthage through Homebrew. The only requirement is that Homebrew and Xcode 7.1 or newer is installed on your machine. To install execute the following commands on your command line.
brew update
brew install carthage
For more details on how this dependency manager works, please check the Carthage Github project Site.
Run the App
The following steps describe how to download and compile the project in Xcode. Before you start make sure that you fulfil all the requirements listed above.
Download the Project
First you need to push or download the iOS Piclet repository. To pull the repository just execute the following on your command line:
git pull<EMAIL_ADDRESS>The repository should now be downloaded and saved to your selected destination.
Install the dependencies
The next step is to download and install the required dependencies for the project. This process is simple thanks to Carthage. Just navigate on your command line to the Piclet destination path you selected above and execute:
carthage bootstrap
When the process finishes, all the existing project dependencies should be installed.
Execute Piclet
Open Xcode and select ‘open another Project’ and select the path where you saved the Piclet Project. After Xcode finished loading you should see the Piclet Project and all files which it includes.
To compile the App press the ‘Run’ (triangle) Button and select the iOS Simulator or your connected Device as the target.
After compiling the project, the Piclet App should be open on your selected target device.
You should now have a running copy on your target device.


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Streaming BSON¶
bson_reader_t provides a streaming reader which can be initialized with a filedescriptor or memory region. bson_writer_t provides a streaming writer which can be initialized with a memory region. (Streaming BSON to a file descriptor is not yet supported.)
Reading from a BSON Stream¶
bson_reader_t provides a convenient API to read sequential BSON documents from a file-descriptor or memory buffer. The bson_reader_read() function will read forward in the underlying stream and return a bson_t that can be inspected and iterated upon.
#include <stdio.h>
#include <bson/bson.h>
int
main (int argc, char *argv[])
{
bson_reader_t *reader;
const bson_t *doc;
bson_error_t error;
bool eof;
reader = bson_reader_new_from_file ("mycollection.bson", &error);
if (!reader) {
fprintf (stderr, "Failed to open file.\n");
return 1;
}
while ((doc = bson_reader_read (reader, &eof))) {
char *str = bson_as_canonical_extended_json (doc, NULL);
printf ("%s\n", str);
bson_free (str);
}
if (!eof) {
fprintf (stderr,
"corrupted bson document found at %u\n",
(unsigned) bson_reader_tell (reader));
}
bson_reader_destroy (reader);
return 0;
}
See bson_reader_new_from_fd(), bson_reader_new_from_file(), and bson_reader_new_from_data() for more information.
Writing a sequence of BSON Documents¶
bson_writer_t provides a convenient API to write a sequence of BSON documents to a memory buffer that can grow with realloc()
. The bson_writer_begin() and bson_writer_end() functions will manage the underlying buffer while building the sequence of documents.
This could also be useful if you want to write to a network packet while serializing the documents from a higher level language, (but do so just after the packets header).
#include <stdio.h>
#include <bson/bson.h>
#include <assert.h>
int
main (int argc, char *argv[])
{
bson_writer_t *writer;
bson_t *doc;
uint8_t *buf = NULL;
size_t buflen = 0;
bool r;
int i;
writer = bson_writer_new (&buf, &buflen, 0, bson_realloc_ctx, NULL);
for (i = 0; i < 10000; i++) {
r = bson_writer_begin (writer, &doc);
assert (r);
r = BSON_APPEND_INT32 (doc, "i", i);
assert (r);
bson_writer_end (writer);
}
bson_free (buf);
return 0;
}
See bson_writer_new() for more information.


|
Secret Agents of IslamWhen Ramzi Yousef was captured and brought to trial, many people were surprised to hear about his lifestyle.Yousef was an inveterate womanizer. He drank, he went to all-night discos, he took scuba diving vacations. He was not known to pray much (outside of prison), nor to observe Islamic holidays. Nevertheless, he presented himself -- in court and in life -- as a warrior for Islam. He took money from strict Muslim fundamentalists, and he worked to help advance their aims. He wrote poems about his love for God. In 1997 and 1998, Osama bin Laden praised him in interviews as a man devoted to protecting the Muslim community. He also warned that Yousef was not unique. "[I]f the American government is serious about avoiding the explosions inside the U.S., then let it stop provoking the feelings of 1,250 million Muslims," bin Laden warned, four years before the September 11 attack. "Those hundreds of thousands who have been killed or displaced in Iraq, Palestine, Lebanon, do have brothers and relatives. They would make of Ramzi Yousef a symbol and a teacher. The U.S. will drive them to transfer the battle into the United States." This was not idle talk, it was the open declaration of a war which had already been underway for nearly a decade. When bin Laden spoke those words to CNN's Peter Arnett in 1997, al Qaeda had already moved dozens of sleeper agents inside the United States -- dozens at minimum. Like Ramzi Yousef, these men were enigmas. Some devoutly followed the strictures of Islam. Others seemed to flaunt them. Yousef's explosive genius might have been unique, but his status as an infiltrator of Western society was not. al Qaeda's No. 3 official Khalid Shaikh Mohammed was a well-known womanizer. Khalid Shaikh, Yousef and other accomplices frequented seedy clubs in Manila's red-light district, such as the Manila Bay Club and Sir Williams Karaoke, according to CNN reporter Maria Ressa, writing in Seeds of Terror. Later, in Florida and Massachusetts, several of the September 11 hijackers even patronized strip clubs and prostitutes. Western observers were quick to point out the seeming hypocrisy -- killing in the name of Islam, while failing to follow Islamic traditions and laws. When the topic was raised, however, it was usually as a propaganda tool, a rhetorical device to diminish the terrorists by deriding them as perverts, hypocrites and bad Muslims. But it's more complicated than that. al Qaeda operatives routinely take on deep cover roles in order to infiltrate an enemy's society. This isn't simply a pragmatic decision to disregard the tenets of Islam in order to accomplish a goal. Rather, it's a specific, traditional practice, justified through an elaborate theological framework. The approach was first employed in the 11th century, by the Assassins, a notorious cult of Islamic extremists. Today, the practice is continued by a similar sect, known as Al Takfir Wal Hijra, which represents a key faction in the top leadership of al Qaeda. There are numerous similarities between the Assassins and the Takfiri -- including operational structure, strategy and theology. But the sects are separated by a centuries-long chasm of deep sectarian conflict. But, then and now, these men shared a common vocation -- secret agents for an extreme vision of Islam. They were -- and are -- the ultimate infiltrators, unbound by moral restraint. These men are not subject to any meaningful cultural repercussions for their acts. Nor are they subject to an inner moral conflict. The are amoral machines by design, licensed to kill. al Qaeda's AssassinsThe modern Sunni Islamic sect known as Al Takfir Wal Hijira is a nearly perfect ideological vehicle for terrorism. The Arabic name translates roughly as "anathema and exile" or "excommunication and emigration." A theologically extreme extension of fundamentalist Islamic doctrines, Takfir is dedicated to restoring the Caliphate, the Islamic political empire that once spanned the Near and Middle East. Takfir differs from establishment fundamentalists (such as Saudi Arabian Wahabbism) in two major respects. First, Takfiri cultists practice an extreme form of assimilation. And second, they openly embrace of violent, evangelistic jihad. An essential part of Takfiri religious practice is the infiltration of enemy societies, usually meaning Western society. Like the Assassins before them, Takfiri members are permitted to take any measure necessary to assimilate, a stipulation which amounts to a blanket exemption from virtually every aspect of Islamic law. To outsiders, Takfiri can appear to be entirely secular, frequently engaging in sexual activity and drinking alcohol in order to hide their religious beliefs. This behavior has been seen repeatedly among key al Qaeda operatives such as Yousef and Khalid Shaikh (who have not previously been identified as Takfiri). The other element that distinguishes Takfir from simple fundamentalism is the sect's violence -- specifically its obligation to violent jihad. Even among extremely rigid Islamist movements such as Wahabbism, jihad can be read to include any number of nonviolent activities. But Takfiris believe that violent struggle is essential to jihad. Going further still, the sect believes violence may justly be directed toward any non-Muslim, or even toward observant Muslims whose activities are perceived as undercutting the establishment of Islamic governments. Targets can include governments that actively oppose Takfir efforts, those perceived to be collaborators, competing sects whose beliefs are considered heretical, and innocent bystanders (Muslim or not) whose deaths advance the cult's political purpose. "(O)nce you're in, you never get out," said Roland Jacquard, an expert on Islamic terrorism, in a 2001 interview with Time Magazine. "The Takfir rely on brainwashing and an extreme regime of discipline to weed out the weak links and ensure loyalty and obedience from those taken as members."
Attacked From An Egyptian BaseAyman Al-Zawahiri, Osama bin Laden's second-in-command, is believed to be a member of the Takfir sect. Zawahiri founded Egyptian Islamic Jihad (EIJ), an organization that in many ways authored the philosophy of jihad that now drives al Qaeda. EIJ helped assassinate Anwar Sadat in 1981.Starting in the late 1980s, a group of terrorist sleeper agents working for Zawahiri emigrated to the United States. The first wave consisted of at least a half dozen confirmed agents, but it's likely the total number was significantly higher. Made up mostly of Egyptian nationals, the group emigrated to the U.S. over the the course of several years, forming a sleeper cell that would wreak great damage before finally being exposed. On their arrival, the first-wave infiltrators did not turn immediately to plotting attacks. Instead, they started building an infrastructure, networks through which they would provide crucial financial and logistical support to plots on U.S. soil and abroad. Members of this Egyptian clique were linked through the al-Kifah mosque, in Brooklyn, a conduit for funds and personnel tied to al Qaeda and the jihad against the Soviet Union in Afghanistan. From Brooklyn, the sleepers recruited foreign nationals, U.S. citizens and even American war veterans to join their ranks. No one in this first wave of infiltrators fit any sort of predictable profile for an Islamic fundamentalist at the time, although some did portray themselves as devout Muslims. The first wave assimilated almost effortlessly into U.S. society. Mahmud Abouhalima joined the NRA. One member, Ali Mohammed, even joined the U.S. Army, rising to the rank of sergeant in the Special Forces. Later, Mohammed would train Osama bin Laden's personal bodyguards in the Sudan. Another member of this EIJ sleeper cell, El Sayyid Nosair, assassinated right-wing Rabbi Meir Kahane in 1990.  Several cell members assisted Ramzi Yousef in bombing the World Trade Center in February 1993. Other cell operatives, including some recruited on U.S. soil, attempted to bomb New York City landmarks in the spring of that year. Both plots were directly tied to al Qaeda. bin Laden is believed to have helped finance the attacks, and he may have had a role in directing them as well. Dozens of terrorist operatives and collaborators were exposed when the FBI finally broke the cell, with the help of another Egyptian national, an informant named Emad Salem. Even faced with hundreds of hours of testimony, and thousands of pages of documents, the United States failed to realize the depth of the infiltration or its meaning. While many members of that first wave of infiltrators have since been convicted of terrorism and related crimes, some members of the cell are still at large -- some evading arrest altogether, others cutting top-secret plea bargains in exchange for testimony.
Q PrimeIt's fair to ask what Takfir Wal Hijra and Egyptian Islamic Jihad have to do with al Qaeda. There are two ways to usefully answer that question, one simplistic, the other holistic.On a simplistic level, al Qaeda and Takfir and EIJ share key members and funding mechanisms. These can be drawn on a chart or listed. They are concrete connections, if not simple. On a holistic level, the issue becomes more complicated. There is a broad public debate about the organization of al Qaeda. Many terrorism experts have touched on this by defining a difference between "al Qaeda, the movement" (seemingly independent terror groups sharing al Qaeda's ideology) and the "corporate al Qaeda" (a cohesive operational group led by Osama bin Laden). As it is commonly understood, "corporate al Qaeda" is too rigid and limited to illustrate the real reach of the corresponding terror network. al Qaeda "the movement" implies the absence of a command-and-control relationship between AQ and a group like EIJ. When most people use the term "al Qaeda," they're talking about the broad international conspiracy focused on attacking the U.S. with a specific set of tactics and under the theological umbrella of Osama bin Laden. Think of this grouping as "QPrime," an inner circle command-and-control structure which effectively coordinates terrorist activity across many ethnic and organizational lines. QPrime is essentially a real network that most closely matches the popular understanding of al Qaeda. Osama bin Laden and Ayman al-Zawahiri are just two members of QPrime, part of the network's inner leadership circle. When people talk about al Qaeda, they're usually talking about the network, QPrime, which includes components such as EIJ and the Takfiri cult. All references to al Qaeda in this series of articles are inclusive of the broad QPrime network.
Coming To AmericaThe first wave of al Qaeda assassins represented a conspiracy of shocking scale. A component of al Qaeda for most meaningful purposes, the Egyptian Islamic Jihad cell in New York City had operatives within several mosques and Islamic centers around the country, even within the U.S. Army.EIJ's sleeper agents were stationed in at least seven states, including Arizona, Illinois, California, New York, Virginia, Texas and Florida, according to trial evidence and testimony in terrorist prosecutions from 1995 to the present. Before the attacks of September 11, most Americans -- even within the U.S. intelligence community -- would have dismissed as paranoid nonsense the very notion that hundreds of Islamic extremists could be living undercover in places such as Arizona, Oklahoma and Kansas. After September 11, Americans were instead asking: "How did this happen?" The answer to that question begins 900 years in the past, when the Assassins raised the act of religious violence to an art form. Next: Mind Control And Murder Cults |


Potted flowers can crack, or they can blossom and seek greater paths.
In whimsical color, Isabella Hilario's light illustrations on adolescence and growth tell of the stories and pain behind coming-of-age and the rise of heart and spirit that comes in all inevitability in this video series.
This animated comic is about two people whose mental disorders (represented by potted flowers) surface at the time of their adolescence. They initially don't know how to deal with their disorders, but eventually find ways to cope.


The Eleanor Wilson Museum tutorial shows how to trace an event from a newspaper listing to its authoritative source website, check that website for a calendar page, check the calendar page for an iCal feed, and if one exists (as in this example it does) capture the iCal URL for use in a syndication hub.
The Sapphire Dance tutorial again begins with an event in a newspaper listing, and traces back to an authoritative source with an iCal feed. In this case, the source uses Google Calendar to provide several feeds. The tutorial shows how to discover the URLs of those feeds, differentiate them, and add them to a hub with appropriate tags.
The Bootstrapping Floyd example shows how to process the feeds automatically discovered by the elmcity service when a new hub is first started.
The Christiansburg is not in the Caribbean example shows that non-authoritative sources can make, then amplify, errors. In this case Eventful confuses Christiansted, St. Croix, VI (Virgin Islands), a venue its robot found on ReverbNation, with Christiansburg VA.
The Christiansburg.org example shows again how non-authoritative sources can make, then amplify, errors. In this case Eventful correctly finds a kids' gymnastics event in August, but incorrectly locates it at the Recreation Center.
The Adaire Theater on EventBrite example shows how to process an EventBrite venue found by the auto-discovery process.
The NRV dark matter example lists sites, discovered while processing the auto-discovered feeds for the NewRiverValleyVA hubs, with online calendars that should be, but aren't, machine-readable. These represent "dark matter" that wants to be lit up.
The NRV bright lights example lists sites with online calendars that are machine-readable. These are community role models.


These are tests for execCommand(InsertHorizontalRule).
This test inserts an hr between 'foo' and 'bar'.
This test inserts an hr with id="hr" between 'foo' and 'bar', fetches the hr using its id, then removes it.


Simple x-hr Component
Create a custom element that draws a horizontal line. Nothing fancy, just to demonstrate how a basic web component looks like.
- Spec says element name MUST contain dash character somewhere.
- HTML is NOT XML. DO NOT self-close a custom element tag as it will lead to unpredictable results. Close each tag with a closing tag.


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Welcoming you, and all your thoughts to engage with us, as now, you’re one of us.
We hope, somewhere in between the lines, your thoughts and along with that, your feelings, will get threaded, to the events that took place in someone else’s life. Because, believe or not, all our lives are connected in some way or other. This connection may add more dimensions to your thinking, and may bring greater hopes to you.
Therefore, we are looking forward to seeing you with more feeds in our blog, “Knitting Thoughts”. Till then, live with all the cheer, every moment. Because you and your thoughts matter, each day.
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Source code for old.lib.auth
import simplejson as json
from decorator import decorator
from pylons import session, response
from utils import unauthorizedMsg
import logging
log = logging.getLogger(__name__)
[docs]def authenticate(target):
"""Authentication decorator. If user is not logged in and tries to call
a controller action with this decorator, then the response header status
will be '401 Unauthorized' and the response body will be
{error: '401 Unauthorized'}.
"""
def wrapper(target, *args, **kwargs):
if getattr(session.get('user'), 'username', None):
return target(*args, **kwargs)
response.status_int = 401
return {'error': 'Authentication is required to access this resource.'}
return decorator(wrapper)(target)
[docs]def authenticateWithJSON(target):
"""Identical to the authenticate decorator except that the response body is
json.dumped beforehand. This is decorator is only needed in the retrieve
action of controllers/files.py
"""
def wrapper(target, *args, **kwargs):
if getattr(session.get('user'), 'username', None):
return target(*args, **kwargs)
response.status_int = 401
return json.dumps({'error': 'Authentication is required to access this resource.'})
return decorator(wrapper)(target)
[docs]def authorize(roles, users=None, userIDIsArgs1=False):
"""Authorization decorator. If user tries to request a controller action
but has insufficient authorization, this decorator will respond with a
header status of '403 Forbidden' and a JSON object explanation.
The user is unauthorized if *any* of the following are true:
- the user does not have one of the roles in roles
- the user is not one of the users in users
- the user does not have the same id as the id of the entity the action
takes as argument
Example 1: (user must be an administrator or a contributor):
>@authorize(['administrator', 'contributor'])
>def actionName(self):
> ...
Example 2: (user must be either an administrator or the contributor with Id 2):
>@authorize(['administrator', 'contributor'], [2])
>def actionName(self):
> ...
Example 3: (user must have the same ID as the entity she is trying to affect):
>@authorize(['administrator', 'contributor', 'viewer'], userIDIsArgs1=True)
>def actionName(self, id):
> ...
"""
def wrapper(target, *args, **kwargs):
# Check for authorization via role.
role = getattr(session.get('user'), 'role', None)
if role in roles:
id = getattr(session.get('user'), 'id', None)
# Check for authorization via user.
if users:
if role != 'administrator' and id not in users:
response.status_int = 403
return unauthorizedMsg
# Check whether the user id equals the id argument given to the
# target action. This is useful, e.g., when a user can only edit
# their own personal page.
if userIDIsArgs1:
if role != u'administrator' and int(id) != int(args[1]):
response.status_int = 403
return unauthorizedMsg
return target(*args, **kwargs)
else:
response.status_int = 403
return unauthorizedMsg
return decorator(wrapper)


Content is usually represented using HTML
HyperText Markup Language
Servers make content available through HTTP
HyperText Transfer Protocol
Web scraping tools use a website's HTML structure to navigate the page and identify the content to scrape.
Sites with well organized and descriptive structure are usually easier to scrape.
Source: Anatomy of an HTML element
Browser "inspect" tools allow you to explore the HTML structure of a web page.
Right-click and select Inspect or Inspect element to reveal how the selected content is coded in HTML.
A simple site listing buyer names and item prices
Buyer names are structured like this in the HTML
Moe Dess
The XPath expression that identifies all buyer-name nodes is
//div[@title="buyer-name"]


Reports an unresolved reference to a
CSS Module Value
(
@value
declaration).
Example:
@value foo from unknown;


--- layout: page category: spec title: "Payment Method: Basic Card" ---
| Current Status: | Working Draft (WD) |
|---|---|
| Canonical URI: |
http://www.w3.org/TR/payment-method-basic-card
|
| Abstract: | This specification describes data structures and formats, and a simple processing model, to facilitate card-based payments on the Web. It is used by other specifications to facilitate monetary transactions with a "basic card", such as credit, debit, or prepaid card. |
Payment Method: Basic CardUpdates:


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You can deactivate your account by clicking the button below. Your current assignment & assignees will not be cancelled. However, you will not be receiving further assignments or assignees. You will be immediately logged out, and will need to login again to reactivate. This will not delete your data.
You can delete your account by clicking the button below. This is the same as deactivating your account, and it will schedule a deletion for your account in 30 days. If you change your mind, you can login before the scheduled date and reactivate your account. You can sign up from scratch once your data is deleted.


Caracterograf
Data menu
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I am a dynamic and motivated person and am always up for learning new technologies and working in new and exciting environments.
<EMAIL_ADDRESS>Villeurbanne, France
slokuge.github.io
SOFTWARE ENGINEERING CO-OP - Ireland (Remote) | 2021 - 6 months
SOFTWARE ENGINEERING INTERN - France (Remote) | 2020 - 3½ months
SOFTWARE ENGINEERING INTERN - France | 2019 - 2½ months
INSA Lyon | 2016-2018
School of Advanced Level Studies, Seychelles | 2014-2015
WEB DEVELOPER - SYS ADMIN | 2018-2021


No documentation for 3PDT Breakout Board (for Buffered effects)
No documentation for 3PDT Breakout Board for Kliché Overdrive
No documentation for Illuminated 3PDT Breakout Board (Single Color)
https://www.pedalpcb.com/docs/PedalPCB-IntelligentRelayBypass.pdf
https://www.pedalpcb.com/docs/125B-DrillTemplate_MiniHeterodyne.pdf
https://www.pedalpcb.com/docs/PedalPCB-SuperHeterodyneReceiver.pdf


Tookuw
Jalu
Profile
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Catalog
Receipt
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Total
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Total
0
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Customers
Add New
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Welcome to Char7.
This program has been made to help people to replace some ASCII code to HTML codes.
The Main Purpose
This program has already been made in PHP, but it was one of the first codes that I developed so, now I would like to create something better, creating to restore back, restore those backups and customize with things that I want to replace. I have developed the new Char7 (or c7) using GOlang.
Authors and Contributors
The owner and programer of Char7 is Rodrigo Lopes ( @rodkranz ), start the program when he went to Amsterdam to participate in ReactNL conference on 11th October.
Support and Contact
Having trouble with Char7? Check out our documentation or issues and we will help.


--- date: 2010-06-22T00:00:00Z published: true title: Nicholas Zambetti – LiveView for iPhone & iPad url: /2010/06/22/nicholas-zambetti-liveview-for-iphone-ipad/ ---
LiveView is a specialized remote screen viewing application intended as a tool to help designers create graphics for mobile applications, it has also proven to be useful for creating quick and dirty simulations, demos, and experience prototypes.
For visual designers — Develop pixel–perfect graphics for the iPhone and iPad quickly and easily with a live view of your canvas/artboard while you work.
For interaction designers — With your iPhone or iPad tethered via WiFi, you can interact with software prototypes and demos running on your Mac to communicate and iterate your concepts quickly.
For everyone — If you've ever needed to press a button from afar or wished that you could take a piece of your monitor with you across the room, this app may prove useful from time to time.


Features:
Overview
Build your own Dual 15" 3 way line array cabinet! Plans available
The LA215 is a large format 3 way line array speaker system that can be ground stacked, subwoofer stacked, or single pole mounted on two SAW118 subwoofer cabinet or other subwoofers with a 24" T-hat pole spacing. It is a "open" design based on proven and practical line array cabinet design that uses off-the-shelf drivers for simplicity, maintainability and affordability. It is designed to be a cost effective solution to the much more expensive commercial units costing 3 to 4 times as much. The speaker components are provided by Eminence which are readily available from a number of sources including US Speaker and Parts Express. Using commercial, off-the-shelf components helps keep the price down and eliminates costly proprietary replacement parts often required by other line array speakers. The LA215 is a 3 way active crossover system and contains two 15" LF drivers, four 6.5" closed back midrange drivers and two high energy 1.4" HF driver. It is designed to be used with a subwoofer or can be used full-range with an F3 of 62Hz. The cabinet can be built from a single 4x8' sheet of 1/2" plywood and a small amount of 3/4" (for the driver panels) and 1/8" (for the MF covers). The following specifications are based on the stock plans with the Eminence drivers : Kappalite 3015, LA-6CBMR, and the N314T-8 1.4" compression drivers. Other drivers can be substituted and would result in different spcifications.
Specifications |
|---|
Power handling: |
|
LF: 900 Watts RMS |
|
LF: 1800 Watts Program |
|
MF: 400 Watts RMS |
|
MF: 800 Watts Program |
|
HF: 200 Watts RMS |
|
HF: 400 Watts Program |
Frequency Response: |
|
LF: 62Hz - 500Hz |
|
MF: 500Hz - 1600Hz |
|
HF: 1600Hz - 20kHz |
Weight: |
|
150Lbs |
Dimensions: |
|
Height: 16.125" |
|
Width: 48" |
|
Depth: 20.5" |


POST /markdown/raw
You must send Markdown as plain text (using a Content-Type
header of text/plain
or text/x-markdown
) to this endpoint, rather than using JSON format. In raw mode, GitHub Flavored Markdown is not supported and Markdown will be rendered in plain format like a README.md file. Markdown content must be 400 KB or less.
curl http(s)://[hostname]/api/v3/markdown/raw -X "POST" -H "Content-Type: text/plain" -d "Hello world github/linguist#1 **cool**, and #1!"
Status: 200 OK
Content-Type: text/html
Content-Length: 68
X-CommonMarker-Version: 0.17.4

Hello world github/linguist#1 cool, and #1!


#include <BL3_OakGame_structs.hpp>
◆ AreaWaypointIconSize
| float AreaWaypointIconSize |
◆ bCachedVisible
◆ bClampRadarEdge
◆ bIsAreaWaypoint
◆ bShouldBeVisibleIfInRange
| bool bShouldBeVisibleIfInRange |
◆ bStaticIconLocation
◆ CurrentTransitionTime
| float CurrentTransitionTime |
◆ IconFrameName
| class FString IconFrameName |
◆ IconInstance
◆ IconLocation
◆ MaxClampDistance
◆ TargetActor
| TWeakObjectPtr<class AActor> TargetActor |
◆ TargetGuid
◆ TargetMinimapIconComponent
| TWeakObjectPtr<class UObject> TargetMinimapIconComponent |
◆ TrackedType
◆ TransitionStartingLocation
◆ UnknownData00
| unsigned char UnknownData00[0x2] |
◆ UnknownData01
| unsigned char UnknownData01[0x3] |
◆ UnknownData02
| unsigned char UnknownData02[0x3] |
◆ UnknownData03
| unsigned char UnknownData03[0x2] |
◆ UnknownData04
| unsigned char UnknownData04[0x4] |
◆ WaypointRadius
◆ ZoneMapPOIData


{% extends "base.html" %} {% block content %}
This privacy policy sets out how OpenPSE uses and protects any information that you give OpenPSE when you use this website.


Before He Died He Left A Secret Note, What It Said… I’m Actually Jealous.
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We are a startup. We have some young and bright people from diverse backgrounds and tastes from some of the best universities of the world.
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We provide an opportunity to work on a great product and a great brand, so that your selling and marketing skills are put to best use.
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Writing beautiful code, thinking in solid abstractions, working synchronously in a micro-services based architecture - if all this gives you job satisfaction, then go on and check us out.
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Privacy Policy for fokatBank
This Privacy Policy document contains types of information that is collected and recorded by fokatdunia and how we use it.
If you have additional questions or require more information about our Privacy Policy, do not hesitate to contact us.
This Privacy Policy applies only to our online activities and is valid for visitors to our website with regards to the information that they shared and/or collect in fokatdunia. This policy is not applicable to any information collected offline or via channels other than this website.
Disclaimer for fokatBank
In our website "fokatdunia.store" , we hereby declare that :-
● Lorem ipsum dolor sit amet consectetur adipisicing elit. Sint, porro. Consequuntur iure tenetur, repellendus nisi ad a quo quaerat enim?
●Lorem ipsum dolor sit amet consectetur adipisicing elit. Laboriosam harum expedita officiis recusandae. Molestiae est deleniti, ad ullam nesciunt consectetur.
● Lorem ipsum dolor sit amet consectetur adipisicing elit. Tempora dolores praesentium distinctio eaque officiis sed natus non odio et nemo?
● Lorem ipsum dolor, sit amet consectetur adipisicing elit. Temporibus numquam quo nulla soluta omnis dolorem necessitatibus perferendis qui dolores hic?
In this manner, we are also responsible for maintaining copyright law.
● Lorem, ipsum dolor sit amet consectetur adipisicing elit. Debitis praesentium consequatur suscipit id, temporibus nostrum quasi commodi sit nobis obcaecati!
Information we collect
The personal information that you are asked to provide, and the reasons why you are asked to provide it, will be made clear to you at the point we ask you to provide your personal information.
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How we use your information
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Privacy Policy Changes
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My background story is just like any other kid and man since my childhood but since everyone is unique in a way i am kinda so
As my name suggests (Hamza Farah Arab) we are from the blood line of Arabs
But not Arabs but one of a well known somali tribe that lives in the north eastern part of kenya "Garissa" the Ogaden
Our family consists of 6 children
where by 4 of us are boys and two are girls
Being the second born in the family i always do my best and work hard to fulfill my dreams which is rather too big to describe it
My studies background traces me back to where i was born n brought up in Garissa
I studied my primary school in Garissa at AL-IBRAHIM primary school and did my kcpe there in the year 2013 and passed with flying colours of marks 446 marks overall position 3rd
I later joined my highschool in kiambu town , Limuru an islamic school called Highlands Boys High School, Limuru where i studied for 4 years and took my K.C.S.E and scored B+ in the year 2014
I later joind KCA university town campus where i study BICOM and computer sience which will take me roughly 4 years
I was born and brought up as a Muslim guy
Who is dedicated to his religion and deen
Our tribe is well known for its records of pure muslims
A whole country with non-beliver in islam in the whole globe is somalia


DELETE https://apis.wso2.com/api/am/admin/v0.15/throttling/policies/subscription/{policyId}
This operation can be used to delete a subscription-level throttling policy specifying the Id of the policy as a path paramter.
apim:tier_manage
i
DELETE https://localhost:9443/api/am/admin/v0.15/throttling/policies/subscription/3cc8902e-f7c7-49dc-987b-b146e9bd8ee8
Authorization: Bearer 0d63e133-7ad6-3aeb-9ca9-9299e0708122
Content-Type: application/json
HTTP/1.1 200 OK
curl -k -X DELETE -H "Authorization: Bearer 0d63e133-7ad6-3aeb-9ca9-9299e0708122" -H "Content-Type: application/json" https://localhost:9443/api/am/admin/v0.15/throttling/policies/subscription/3cc8902e-f7c7-49dc-987b-b146e9bd8ee8
| Type | Name | Description | Schema | Default |
|---|---|---|---|---|
| Path |
policyId required |
Thorttle policy UUID |
String | |
| Header |
If-Match optional |
Validator for conditional requests; based on ETag (Will be supported in future). |
String | |
| Header |
If-Unmodified-Since optional |
Validator for conditional requests; based on Last Modified header (Will be supported in future). |
String |
| HTTP Code | Description | Schema |
|---|---|---|
200 |
OK. Resource successfully deleted. |
|
404 |
Not Found. Resource to be deleted does not exist. |
Error |
412 |
Precondition Failed. The request has not been performed because one of the preconditions is not met (Will be supported in future). |
Error |
GET https://apis.wso2.com/api/am/admin/v0.15/throttling/policies/subscription/{policyId}
Retrieve a single subscription-level throttling policy. We should provide the Id of the policy as a path parameter.
apim:tier_view
i
GET https://localhost:9443/api/am/admin/v0.15/throttling/policies/subscription/6dd9fc11-bbe1-4daf-aee5-06a4d0e44476
Authorization: Bearer 0d63e133-7ad6-3aeb-9ca9-9299e0708122
HTTP/1.1 200 OK
Content-Type: application/json
{
"policyId": "6dd9fc11-bbe1-4daf-aee5-06a4d0e44476",
"policyName": "Platinum",
"displayName": "Platinum",
"description": "Allows 4000 requests per minute",
"isDeployed": true,
"defaultLimit": {
"type": "RequestCountLimit",
"timeUnit": "min",
"unitTime": 1,
"requestCount": 4000
},
"rateLimitCount": -1,
"rateLimitTimeUnit": "NA",
"customAttributes": [
{
"name": "customAttr1",
"value": "value1"
},
{
"name": "customAttr2",
"value": "value2"
}
],
"stopOnQuotaReach": true,
"billingPlan": "COMMERCIAL"
}
curl -k -H "Authorization: Bearer 0d63e133-7ad6-3aeb-9ca9-9299e0708122" https://localhost:9443/api/am/admin/v0.15/throttling/policies/subscription/6dd9fc11-bbe1-4daf-aee5-06a4d0e44476
| Type | Name | Description | Schema | Default |
|---|---|---|---|---|
| Path |
policyId required |
Thorttle policy UUID |
String | |
| Header |
If-None-Match optional |
Validator for conditional requests; based on the ETag of the formerly retrieved variant of the resource (Will be supported in future). |
String | |
| Header |
If-Modified-Since optional |
Validator for conditional requests; based on Last Modified header of the formerly retrieved variant of the resource (Will be supported in future). |
String |
| HTTP Code | Description | Schema |
|---|---|---|
200 |
OK. Policy returned |
SubscriptionThrottlePolicy |
304 |
Not Modified. Empty body because the client has already the latest version of the requested resource (Will be supported in future). |
|
404 |
Not Found. Requested Policy does not exist. |
Error |
406 |
Not Acceptable. The requested media type is not supported. |
Error |
PUT https://apis.wso2.com/api/am/admin/v0.15/throttling/policies/subscription/{policyId}
Updates an existing subscription-level throttling policy.
apim:tier_manage
i
PUT https://localhost:9443/api/am/admin/v0.15/throttling/policies/subscription/6dd9fc11-bbe1-4daf-aee5-06a4d0e44476
Authorization: Bearer 0d63e133-7ad6-3aeb-9ca9-9299e0708122
Content-Type: application/json
{
"policyName": "Platinum",
"displayName": "Platinum",
"description": "Allows 10000 requests per minute",
"defaultLimit": {
"type": "RequestCountLimit",
"timeUnit": "min",
"unitTime": 1,
"requestCount": 10000
},
"isDeployed": true,
"rateLimitCount": -1,
"rateLimitTimeUnit": "NA",
"customAttributes": [
{
"name": "customAttr0",
"value": "value1"
},
{
"name": "customAttr1",
"value": "value1"
}
],
"stopOnQuotaReach": true,
"billingPlan": "COMMERCIAL"
}
HTTP/1.1 200 OK
Content-Type: application/json
{
"policyId": "6dd9fc11-bbe1-4daf-aee5-06a4d0e44476",
"policyName": "Platinum",
"displayName": "Platinum",
"description": "Allows 10000 requests per minute",
"isDeployed": true,
"defaultLimit": {
"type": "RequestCountLimit",
"timeUnit": "min",
"unitTime": 1,
"requestCount": 10000
},
"rateLimitCount": -1,
"rateLimitTimeUnit": "NA",
"customAttributes": [
{
"name": "customAttr0",
"value": "value1"
},
{
"name": "customAttr1",
"value": "value1"
}
],
"stopOnQuotaReach": true,
"billingPlan": "COMMERCIAL"
}
curl -k -X PUT -H "Authorization: Bearer 0d63e133-7ad6-3aeb-9ca9-9299e0708122" -H "Content-Type: application/json" https://localhost:9443/api/am/admin/v0.15/throttling/policies/subscription/6dd9fc11-bbe1-4daf-aee5-06a4d0e44476 -d @data.json
| Type | Name | Description | Schema | Default |
|---|---|---|---|---|
| Path |
policyId required |
Thorttle policy UUID |
String | |
| Body |
body required |
Policy object that needs to be modified |
SubscriptionThrottlePolicy (SubscriptionThrottlePolicy) | |
| Header |
Content-Type required |
Media type of the entity in the body. Default is application/json. |
String |
application/json |
| Header |
If-Match optional |
Validator for conditional requests; based on ETag (Will be supported in future). |
String | |
| Header |
If-Unmodified-Since optional |
Validator for conditional requests; based on Last Modified header (Will be supported in future). |
String |
| HTTP Code | Description | Schema |
|---|---|---|
200 |
OK. Policy updated. |
SubscriptionThrottlePolicy |
400 |
Bad Request. Invalid request or validation error. |
Error |
404 |
Not Found. The resource to be updated does not exist. |
Error |
412 |
Precondition Failed. The request has not been performed because one of the preconditions is not met (Will be supported in future). |
Error |


TO END ALL WARS A film review by Mark R. Leeper
CAPSULE: This is a harrowing look at a rarely dramatized chapter of WWII, life in a Japanese prison camp.  The somewhat religious interpretation may not be to everyone's taste. Rating: 9 (0 to 10), +3 (-4 to +4)
More than any other people the Japanese seem capable of acting with one goal and not letting any other consideration get in their way. This may be a holdover from the code of Bushido when loyalty to ones master was the only law. During World War II, of course, the one goal was winning the war. This led them to do some very inhuman things in pursuit of that goal. When the Japanese had captured prisoners, they were very much treated in whatever way would be optimum for achieving the one goal. Minimum resources were to be spent in maintaining prisoners in keeping with maximal positive output. While the Germans, not known for their kindness in those days, had a 6% mortality rate among captured prisoners of war, the mortality rate of Japanese prisoners of war was 27%. The best thing for the war effort was working prisoners nearly to death on the Thailand to Burma railroad. That railroad was needed if Japan was to attack India as it planned to do. The best thing for the effort was not to waste much food on the prisoners so short and amazingly wretched food was the order of the day. And just being in the jungle without proper medical aid took its toll.
In the public mind Japan has never been held as accountable for war atrocities as was Germany. Filmmakers have been reticent to tell the story, perhaps for fear of offending the Japanese. There are comparatively few films about the Japanese POW camps. Certainly there was David Lean's THE BRIDGE ON THE RIVER KWAI. There were some low-budget British exploitation films and that was about it. Then there were TV series "A Town Like Alice" and "Tenko." Lest the experience be forgotten we have a new film TO END ALL WARS directed by David Cunningham and written by Brian Godawa. It is based on the account of Ernest Gordan who survived the horror of that World War II prison camp and went on to become for 26 years the Dean of the Chapel at Princeton University. The film while realistic shows the conditions in the camp as being considerably more brutal and sadistic than BRIDGE ON THE RIVER KWAI portrayed them.
The story opens with six or so soldiers being marched into the prison camp only to be immediately placed in front of a firing squad. It turns out to be a grim joke, one of many that the sadistic Japanese play to amuse themselves. Beating and torture are commonplace events. Men already imprisoned tell the new arrivals to enjoy the last of their health; it will not last long with parasites and disease almost inevitable. However, unlike as in KWAI, the prisoners want to avoid going to the hospital, called by the prisoners the Death House.
So goes a war within a war with the prisoners trying to maintain their humanity and with the Japanese trying to make them interchangeable and highly expendable cogs in a rail-laying machine. This is more than just a battle of who will win the war but a battle of ideologies. The Japanese believe that the individual is nothing, that conformity to group's norms is all that gives a life meaning. Conformity is purpose. Before the film is over there will be some surprising revelations about the character of the prisoners and the character of those running the camp. If this story showed nothing but sadism from the Japanese it would be one kind of story. If the British (with one American, by the way) and the Japanese learned to respect each other it would be another kind of story. It is neither. It is a stirring and believable account of camp life.
The color has been distorted in the film to give a washed out yellow. This serves a double purpose for Cunningham. It gives an effect of Technicolor film that has been left in heat. It also creates a distancing effect. The only touch that seems a little out of place is the use of Gaelic music.
This is a powerful and philosophical view of the prison camp experience. I rate it a 9 on the 0 to 10 scale and a +3 on the -4 to +4 scale.
Mark R. Leeper<EMAIL_ADDRESS>Copyright 2001 Mark R. Leeper
========== X-RAMR-ID: 29740 X-Language: en X-RT-AuthorID: 1309 X-RT-RatingText: 9/10
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


Class: Ovto::Middleware::Actions
- Defined in:
- lib/ovto/middleware.rb
Instance Attribute Summary collapse
-
#middleware_path ⇒ Object
readonly
Returns the value of attribute middleware_path.
Attributes inherited from Actions
Instance Method Summary collapse
-
#initialize(middleware_path) ⇒ Actions
constructor
A new instance of Actions.
-
#middleware_name ⇒ Object
The name of the middleware this Actions belongs to.
- #state ⇒ Object
Methods inherited from Actions
Constructor Details
#initialize(middleware_path) ⇒ Actions
Returns a new instance of Actions.
87 88 89 |
# File 'lib/ovto/middleware.rb', line 87 def initialize(middleware_path) @middleware_path = middleware_path end |
Instance Attribute Details
#middleware_path ⇒ Object (readonly)
Returns the value of attribute middleware_path.
90 91 92 |
# File 'lib/ovto/middleware.rb', line 90 def middleware_path @middleware_path end |
Instance Method Details
#middleware_name ⇒ Object
The name of the middleware this Actions belongs to
93 94 95 |
# File 'lib/ovto/middleware.rb', line 93 def middleware_name self.class::OVTO_MIDDLEWARE_NAME end |
#state ⇒ Object
97 98 99 100 |
# File 'lib/ovto/middleware.rb', line 97 def state app_state = super return Ovto::Middleware.dig_middleware_state(app_state, @middleware_path) end |


Spotlight
This page shows spotlight plugin usage. Click button to see the effect and source code.
Animate
Options
Global
Duration
Delay
Repeat
Easing
Linear
Ease
Ease In
Ease Out
Ease In Out
Direction
Normal
Alternate
Alternate Reverse
Reverse
Fill Mode
Forwards
Backwards
Both
*Count
*Radius
*Stop X
*Stop Y
Source Code