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Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning
https://blogs.dropbox.com/tech/2017/04/creating-a-modern-ocr-pipeline-using-computer-vision-and-deep-learning/
Brad Neuberg April 12, 2017
In this post we will take you behind the scenes on how we built a state-of-the-art Optical Character Recognition (OCR) pipeline for our mobile document scanner. We used computer vision and deep learning advances such as bi-directional Long Short Term Memory (LSTMs), Connectionist Temporal Classification (CTC), convolutional neural nets (CNNs), and more. In addition, we will also dive deep into what it took to actually make our OCR pipeline production-ready at Dropbox scale.
In previous posts we have described how Dropbox's mobile document scanner works. The document scanner makes it possible to use your mobile phone to take photos and "scan" items like receipts and invoices. Our mobile document scanner only outputs an image — any text in the image is just a set of pixels as far as the computer is concerned, and can't be copy-pasted, searched for, or any of the other things you can do with text.
Hence the need to apply Optical Character Recognition, or OCR. This process extracts actual text from our doc-scanned image. Once OCR is run, we can then enable the following features for our Dropbox Business users:
Extract all the text in scanned documents and index it, so that it can be searched for later
Create a hidden overlay so text can be copied and pasted from the scans saved as PDFs
When we built the first version of the mobile document scanner, we used a commercial off-the-shelf OCR library, in order to do product validation before diving too deep into creating our own machine learning-based OCR system. This meant integrating the commercial system into our scanning pipeline, offering both features above to our business users to see if they found sufficient use from the OCR. Once we confirmed that there was indeed strong user demand for the mobile document scanner and OCR, we decided to build our own in-house OCR system for several reasons.
First, there was a cost consideration: having our own OCR system would save us significant money as the licensed commercial OCR SDK charged us based on the number of scans. Second, the commercial system was tuned for the traditional OCR world of images from flat bed scanners, whereas our operating scenario was much tougher, because mobile phone photos are far more unconstrained, with crinkled or curved documents, shadows and uneven lighting, blurriness and reflective highlights, etc. Thus, there might be an opportunity for us to improve recognition accuracy.
In fact, a sea change has happened in the world of computer vision that gave us a unique opportunity. Traditionally, OCR systems were heavily pipelined, with hand-built and highly-tuned modules taking advantage of all kinds of conditions they could assume to be true for images captured using a flatbed scanner. For example, one module might find lines of text, then the next module would find words and segment letters, then another module might apply different techniques to each piece of a character to figure out what the character is, etc. Most methods rely on binarization of the input image as an early stage, and this can be brittle and discards important cues. The process to build these OCR systems was very specialized and labor intensive, and the systems could generally only work with fairly constrained imagery from flat bed scanners.
The last few years has seen the successful application of deep learning to numerous problems in computer vision that have given us powerful new tools for tackling OCR without having to replicate the complex processing pipelines of the past, relying instead on large quantities of data to have the system automatically learn how to do many of the previously manually-designed steps.
Perhaps the most important reason for building our own system is that it would give us more control over own destiny, and allow us to work on more innovative features in the future.
In the rest of this blog post we will take you behind the scenes of how we built this pipeline at Dropbox scale. Most commercial machine learning projects follow three major steps:
Research and prototyping to see if something is possible
Productionization of the model for actual end users
Refinement of the system in the real world
We will take you through each of these steps in turn.
Research and Prototyping
Our initial task was to see if we could even build a state of the art OCR system at all.
We began by collecting a representative set of donated document images that match what users might upload, such as receipts, invoices, letters, etc. To gather this set, we asked a small percentage of users whether they would donate some of their image files for us to improve our algorithms. At Dropbox, we take user privacy very seriously and thus made it clear that this was completely optional, and if donated, the files would be kept private and secure. We use a wide variety of safety precautions with such user-donated data, including never keeping donated data on local machines in permanent storage, maintaining extensive auditing, requiring strong authentication to access any of it, and more.
Another important, machine learning-specific component for user-donated data is how to label it. Most current machine learning techniques are strongly-supervised, meaning that they require explicit manual labeling of input data so that the algorithms can learn to make predictions themselves. Traditionally, this labeling is done by outside workers, often using a micro-work platform such as Amazon's Mechanical Turk (MTurk). However, a downside to using MTurk is that each item might be seen and labeled by a different worker, and we certainly don't want to expose user-donated data in the wild like this!
Thus, our team at Dropbox created our own platform for data annotation, named DropTurk. DropTurk can submit labeling jobs either to MTurk (if we are dealing with public non-user data) or a small pool of hired contractors for user-donated data. These contractors are under a strict non-disclosure agreement (NDA) to ensure that they cannot keep or share any of the data they label. DropTurk contains a standard list of annotation task UI templates that we can rapidly assemble and customize for new datasets and labeling tasks, which enables us to annotate our datasets quite fast.
For example, here is a DropTurk UI meant to provide ground truth data for individual word images, including one of the following options for the workers to complete:
Transcribing the actual text in an image
Marking whether the word is oriented incorrectly
Marking whether it's a non-English script
Marking whether it's unreadable or contains no text
DropTurk UI for adding ground truth data for word images
Our DropTurk platform includes dashboards to get an overview of past jobs, watch the progress of current jobs, and access the results securely. In addition, we can get analytics to assess workers' performance, even getting worker-level graphical monitoring of annotations of ongoing jobs to catch potential issues early on:
DropTurk Dashboard
Using DropTurk, we collected both a word-level dataset, which has images of individual words and their annotated text, as well as a full document-level dataset, which has images of full documents (like receipts) and fully transcribed text. We used the latter to measure the accuracy of existing state-of-the-art OCR systems; this would then inform our efforts by telling us the score we would have to meet or beat for our own system. On this particular dataset, the accuracy percentage we had to achieve was in the mid-90s.
Our first task was to determine if the OCR problem was even going to be solvable in a reasonable amount of time. So we broke the OCR problem into two pieces. First, we would use computer vision to take an image of a document and segment it into lines and words; we call that the Word Detector. Then, we would take each word and feed it into a deep net to turn the word image into actual text; we call that the Word Deep Net.
We felt that the Word Detector would be relatively straightforward, and so focused our efforts first on the Word Deep Net, which we were less sure about.
Word Deep Net
The Word Deep Net combines neural network architectures used in computer vision and automatic speech recognition systems. Images of cropped words are fed into a Convolutional Neural Net (CNN) with several convolutional layers. The visual features that are output by the CNN are then fed as a sequence to a Bidirectional LSTM (Long Short Term Memory) — common in speech recognition systems — which make sense of our word "pieces," and finally arrives at a text prediction using a Connectionist Temporal Classification (CTC) layer. Batch Normalization is used where appropriate.
OCR Word Deep Net
Once we had decided on this network architecture for turning an image of a single word into text, we then needed to figure out how to collect enough data to train it. Deep learning systems typically need huge amounts of training data to achieve good recognition performance; in fact, the amount of training data is often the most significant bottleneck in current systems. Normally, all this data has to be collected and then labeled manually, a time-consuming and expensive process.
An alternative is to programmatically generate training data. However, in most computer vision problems it's currently too difficult to generate realistic-enough images for training algorithms: the variety of imaging environments and transformations is too varied to effectively simulate. (One promising area of current research is Generative Adversarial Networks (GANs), which seem to be well-suited to generating realistic data.) Fortunately, our problem in this case is a perfect match for using synthetic data, since the types of images we need to generate are quite constrained and can thus be rendered automatically. Unlike images of natural or most manmade objects, documents and their text are synthetic and the variability of individual characters is relatively limited.
Our synthetic data pipeline consists of three pieces:
A corpus with words to use
A collection of fonts for drawing the words
A set of geometric and photometric transformations meant to simulate real world distortions
The generation algorithm simply samples from each of these to create a unique training example.
Synthetically generated word images
We started simply with all three, with words coming from a collection of Project Gutenberg books from the 19th century, about a thousand fonts we collected, and some simple distortions like rotations, underlines, and blurs. We generated about a million synthetic words, trained our deep net, and then tested our accuracy, which was around 79%. That was okay, but not good enough.
Through many iterations, we evolved each piece of our synthetic data pipeline in many ways to improve the recognition accuracy. Some highlights:
We noticed that we weren't doing well on receipts, so we expanded our word corpus to include the Uniform Product Code (UPC) database, which has entries like "24QT TISSUE PAPER" which commonly occur on receipts.
We noticed the network was struggling with letters with disconnected segments. This revealed a deeper problem: receipts are often printed with thermal fonts that have stippled, disconnected, or ink smudged letters, but our network had only been given training data with smooth continuous fonts (like from a laser printer) or lightly bit-mapped characters (like in a screenshot). To address this shortcoming, we eventually tracked down a font vendor in China who could provide us with representative ancient thermal printer fonts.
Synthetically generated words using different thermal printer fonts, common in receipts
Our font selection procedure was too naive initially. We ended up hand-selecting about 2,000 fonts. Not all fonts are used equally. So, we did research on the top 50 fonts in the world and created a font frequency system that allowed us to sample from common fonts (such as Helvetica or Times New Roman) more frequently, while still retaining a long tail of rare fonts (such as some ornate logo fonts). In addition, we discovered that some fonts have incorrect symbols or limited support, resulting in just squares, or their lower or upper case letters are mismatched and thus incorrect. We had to go through all two thousand fonts by hand and mark those that had invalid symbols, numbers, or casing, so that we didn't inadvertently train the network with incorrect data.
The upstream Word Detector (described later) was tuned to provide high recall and low precision. It was overzealous in finding text in images so that it wouldn't miss any of the actual text (high recall) at the expense of often "finding" words that weren't actually there (low precision). This meant that the Word Deep Net had to deal with a very large number of essentially empty images with noise. So we had our synthetic data pipeline generate representative negative training examples with empty ground truth strings, including common textured backgrounds, like wood, marble countertops, etc.
Synthetically generated negative training examples
From a histogram of the synthetically generated words, we discovered that many symbols were underrepresented, such as / or &. We artificially boosted the frequency of these in our synthetic corpus, by synthetically generating representative dates, prices, URLs, etc.
We added a large number of visual transformations, such as warping, fake shadows, and fake creases, and much more.
Fake shadow effect
Data is as important as the machine learning model used, so we spent a great deal of time refining this data generation pipeline. At some point, we will open source and release this synthetically generated data for others to train and validate their own systems and research on.
We trained our network on Amazon EC2 G2 GPU instances, spinning up many experiments in parallel. All of our experiments went into a lab notebook that included everything necessary to replicate experiments so we could track unexpected accuracy bumps or losses.
Our lab notebook contained numbered experiments, with the most recent experiment first. It tracked everything needed for machine learning reproducibility, such as a unique git hash for the code that was used, pointers to S3 with generated data sets and results, evaluation results, graphs, a high-level description of the goal of that experiment, and more. As we built our synthetic data pipeline and trained our network, we also built many special purpose tools to visualize fonts, debug network guesses, etc.
Example early experiment tracking error rate vs. how long our Word Deep Net had trained, against an evaluation dataset that consisted of just single words (Single Word Accuracy)
Our early experiments tracked how well Word Deep Net did on OCR-ing images of single words, which we called Single Word Accuracy (SWA). Accuracy in this context meant how many of the ground truth words the deep net got right. In addition, we tracked precision and recall for the network. Precision refers to the fraction of words returned by the deep net that were actually correct, while recall refers to the fraction of evaluation data that is correctly predicted by the deep net. There tends to be a tradeoff between precision and recall.
For example, imagine we have a machine learning model that is designed to classify an email as spam or not. Precision would be whether all the things that were labeled as spam by the classifier, how many were actually spam? Recall, in contrast, would be whether of all the things that truly are spam, how many did we label? It is possible to correctly label spam emails (high precision) while not actually labeling all the true spam emails (low recall).
Week over week, we tracked how well we were doing. We divided our dataset into different categories, such as register_tapes (receipts), screenshots, scanned_docs, etc., and computed accuracies both individually for each category and overall across all data. For example, the entry below shows early work in our lab notebook for our first full end-to-end test, with a real Word Detector coupled to our real Word Deep Net. You can see that we did pretty terribly at the start:
Screenshot from early end-to-end experiments in our lab notebook
At a certain point our synthetic data pipeline was resulting in a Single Word Accuracy (SWA) percentage in the high-80s on our OCR benchmark set, and we decided we were done with that portion. We then collected about 20,000 real images of words (compared to our 1 million synthetically generated words) and used these to fine tune the Word Deep Net. This took us to an SWA in the mid-90s.
We now had a system that could do very well on individual word images, but of course a real OCR system operates on images of entire documents. Our next step was to focus on the document-level Word Detector.
For our Word Detector we decided to not use a deep net-based approach. The primary candidates for such approaches were object detection systems, like RCNN, that try to detect the locations (bounding boxes) of objects like dogs, cats, or plants from images. Most images only have perhaps one to five instances of a given object.
However, most documents don't just have a handful of words — they have hundreds or even thousands of them, i.e., a few orders of magnitude more objects than most neural network-based object detection systems were capable of finding at the time. We were thus not sure that such algorithms would scale up to the level our OCR system needed.
Another important consideration was that traditional computer vision approaches using feature detectors might be easier to debug, as neural networks as notoriously opaque and have internal representations that are hard to understand and interpret.
We ended up using a classic computer vision approach named Maximally Stable Extremal Regions (MSERs), using OpenCV's implementation. The MSER algorithm finds connected regions at different thresholds, or levels, of the image. Essentially, they detect blobs in images, and are thus particularly good for text.
Our Word Detector first detects MSER features in an image, then strings these together into word and line detections. One tricky aspect is that our word deep net accepts fixed size word image inputs. This requires the word detector to thus sometimes include more than one word in a single detection box, or chop a single word in half if it is too long to fit the deep net's input size. Information on this chopping then has to be propagated through the entire pipeline, so that we can re-assemble it after the deep net has run. Another bit of trickiness is dealing with images with white text on dark backgrounds, as opposed to dark text on white backgrounds, forcing our MSER detector to be able to handle both scenarios.
Combined End-to-End System
Once we had refined our Word Detector to an acceptable point, we chained it together with our Word Deep Net so that we could benchmark the entire combined system end-to-end against document-level images rather than our older Single Word Accuracy benchmarking suite. However, when we first measured the end-to-end accuracy, we found that we were performing around 44% — quite a bit worse than the competition.
The primary issues were spacing and spurious garbage text from noise in the image. Sometimes we would incorrectly combine two words, such as "helloworld", or incorrectly fragment a single word, such as "wo rld".
Our solution was to modify the Connectionist Temporal Classification (CTC) layer of the network to also give us a confidence score in addition to the predicted text. We then used this confidence score to bucket predictions in three ways:
If the confidence was high, we kept the prediction as is.
If the confidence was low, we simply filtered them out, making a bet that these were noise predictions.
If the confidence was somewhere in the middle, we then ran it through a lexicon generated from the Oxford English Dictionary, applying different transformations between and within word prediction boxes, attempting to combine words or split them in various ways to see if they were in the lexicon.
We also had to deal with issues caused by the previously mentioned fixed receptive image size of the Word Deep Net: namely, that a single "word" window might actually contain multiple words or only part of a very long word. We thus run these outputs along with the original outputs from the Word Detector through a module we call the Wordinator, which gives discrete bounding boxes for each individual OCRed word. This results in individual word coordinates along with their OCRed text.
For example, in the following debug visualization from our system you can see boxes around detected words before the Wordinator:
The Wordinator will break some of these boxes into individual word coordinate boxes, such as "of" and "Engineering", which are currently part of the same box.
Finally, now that we had a fully working end-to-end system, we generated more than ten million synthetic words and trained our neural net for a very large number of iterations to squeeze out as much accuracy as we could. All of this finally gave us the accuracy, precision, and recall numbers that all met or exceeded the OCR state-of-the-art.
We briefly patted ourselves on the back, then began to prepare for the next tough stage: productionization.
Productionization
At this point, we had a collection of prototype Python and Lua scripts wrapping Torch — and a trained model, of course! — that showed that we could achieve state of the art OCR accuracy. However, this is a long way from a system an actual user can use in a distributed setting with reliability, performance, and solid engineering. We needed to create a distributed pipeline suitable for use by millions of users and a system replacing our prototype scripts. In addition, we had to do this without disrupting the existing OCR system using the commercial off the shelf SDK.
Here's a diagram of the productionized OCR pipeline:
Overall Productionized OCR Pipeline
We started by creating an abstraction for different OCR engines, including our own engine and the commercial one, and gated this using our in-house experiments framework, Stormcrow. This allowed us to introduce the skeleton of our new pipeline without disrupting the existing OCR system, which was already running in production for millions of our Business customers.
We also ported our Torch based model, including the CTC layer, to TensorFlow for a few reasons. First, we'd already standardized on TensorFlow in production to make it easier to manage models and deployments. Second, we prefer to work with Python rather than Lua, and TensorFlow has excellent Python bindings.
In the new pipeline, mobile clients upload scanned document images to our in-house asynchronous work queue. When the upload is finished, we then send the image via a Remote Procedure Call (RPC) to a cluster of servers running the OCR service.
The actual OCR service uses OpenCV and TensorFlow, both written in C++ and with complicated library dependencies; so security exploits are a real concern. We've isolated the actual OCR portion into jails using technologies like LXC, CGroups, Linux Namespaces, and Seccomp to provide isolation and syscall whitelisting, using IPCs to talk into and out of the isolated container. If someone compromises the jail they will still be completely separated from the rest of our system.
Our jail infrastructure allows us to efficiently set up expensive resources a single time at startup, such as loading our trained models, then have these resources be cloned into a jail to satisfy a single OCR request. The resources are cloned Copy-on-Write into the forked jail and are read-only for how we use our models so it's quite efficient and fast. We had to patch TensorFlow to make it easier to do this kind of forking. (We submitted the patch upstream.)
Once we get word bounding boxes and their OCRed text, we merge them back into the original PDF produced by the mobile document scanner as an OCR hidden layer. The user thus gets a PDF that has both the scanned image and the detected text. The OCRed text is also added to Dropbox's search index. The user can now highlight and copy-paste text from the PDF, with the highlights going in the correct place due to our hidden word box coordinates. They can also search for the scanned PDF via its OCRed text on Dropbox.
At this point, we now had an actual engineering pipeline (with unit tests and continual integration!), but still had performance issues.
The first question was whether we would use CPUs or GPUs in production at inference time. Training a deep net takes much longer than using it at inference time. It is common to use GPUs during training (as we did), as they vastly decrease the amount of time it takes to train a deep net. However, using GPUs at inference time is a harder call to make currently.
First, having high-end GPUs in a production data center such as Dropbox's is still a bit exotic and different than the rest of the fleet. In addition, GPU-based machines are more expensive and configurations are churning faster based on rapid development. We did an extensive analysis of how our Word Detector and Word Deep Net performed on CPUs vs GPUs, assuming full use of all cores on each CPU and the characteristics of the CPU. After much analysis, we decided that we could hit our performance targets on just CPUs at similar or lower costs than with GPU machines.
Once we decided on CPUs, we then needed to optimize our system BLAS libraries for the Word Deep Net, to tune our network a bit, and to configure TensorFlow to use available cores. Our Word Detector was also a significant bottleneck. We ended up essentially rewriting OpenCV's C++ MSER implementation in a more modular way to avoid duplicating slow work when doing two passes (to be able to handle both black on white text as well as white on black text); to expose more to our Python layer (the underlying MSER tree hierarchy) for more efficient processing; and to make the code actually readable. We also had to optimize the post-MSER Word Detection pipeline to tune and vectorize certain slow portions of it.
After all this work, we now had a productionized and highly-performant system that we could "shadow turn on" for a small number of users, leading us to the third phase: refinement.
With our proposed system running silently in production side-by-side with the commercial OCR system, we needed to confirm that our system was truly better, as measured on real user data. We take user data privacy very seriously at Dropbox, so we couldn't just view and test random mobile document scanned images. Instead, we used the user-image donation flow detailed earlier to get evaluation images. We then used these donated images, being very careful about their privacy, to do a qualitative blackbox test of both OCR systems end-to-end, and were elated to find that we indeed performed the same or better than the older commercial OCR SDK, allowing us to ramp up our system to 100% of Dropbox Business users.
Next, we tested whether fine-tuning our trained deep net on these donated documents versus our hand chosen fine tuning image suite helped accuracy. Unfortunately, it didn't move the needle.
Another important refinement was doing orientation detection, which we had not done in the original pipeline. Images from the mobile document scanner can be rotated by 90° or even upside down. We built an orientation predictor using another deep net based on the Inception Resnet v2 architecture, changed the final layer to predict orientation, collected an orientation training and validation data set, and fine-tuned from an ImageNet-trained model biased towards our own needs. We put this orientation predictor into our pipeline, using its detected orientation to rotate the image to upright before doing word detection and OCRing.
One tricky aspect of the orientation predictor was that only a small percentage of images are actually rotated; we needed to make sure our system didn't inadvertently rotate upright images (the most common case) while trying to fix the orientation for the smaller number of non-upright images. In addition, we had to solve various tricky issues in combining our upright rotated images with the different ways the PDF file format can apply its own transformation matrices for rotation.
Finally, we were surprised to find some tough issues with the PDF file format containing our scanned OCRed hidden layer in Apple's native Preview application. Most PDF renderers respect spaces embedded in the text for copy and paste, but Apple's Preview application performs its own heuristics to determine word boundaries based on text position. This resulted in unacceptable quality for copy and paste from this PDF renderer, causing most spaces to be dropped and all the words to be "glommed together". We had to do extensive testing across a range of PDF renderers to find the right PDF tricks and workarounds that would solve this problem.
In all, this entire round of researching, productionization, and refinement took about 8 months, at the end of which we had built and deployed a state-of-the-art OCR pipeline to millions of users using modern computer vision and deep neural network techniques. Our work also provides a solid foundation for future OCR-based products at Dropbox.
Interested in applying the latest machine learning research to hard, real–world problems and shipping to millions of Dropbox users? Our team is hiring!
Filed under: Machine Intelligence, MobileTags: computer vision, deep learning, doc scanner, machine learning, ocr, optical character recognition


Population growth in large cities in Indonesia increased demand for housing in urban areas. It might cause in interference to natural landscape of the city. Cities are required to be a comfortable inhabited place and concern to environmental sustainability. Ecodesign concept approach planning is an alternative that could be done to improve quality of the environment. The principle of ecodesign concept is the integration design in designing environment that integrates man-made design with natural design. The method used was collecting residents' perception in three urban housing in West Bogor District, Bogor City, which are Bumi Menteng Asri, Griya Melati, and Pakuan Regency. There are two aspects related with ecodesign concept: ecodesign concept micro scale and ecodesign concept macro scale. Based on the survey (n = 90), only 39% of the respondents know about the definition of ecodesign concept of settlement landscape. Although most of respondents don't know about the concept, there are still evidence of behaviors related with ecodesign concept. Generally, the ecological and esthetical condition in those three study location are in good condition. The result of correlation test shows that there is no any significant correlation between perception, preference, and behavior. Ecology and esthetic aspect are correlated positively only at Bumi Menteng Asri. Participation from the user is the main factor in order to create ecodesign concept of settlement landscape.
Arifin, Hadi Susilo dan Nakagoshi, Nobukazu, 2011. Landscape Ecology and Urban Biodiversity in Tropical Indonesian Cities. Landscape and Ecological Engineering, 7(2011), 33-43.
BPS Kota Bogor, 2017. Kota Bogor dalam Angka 2017. Bogor: BPS Kota Bogor.
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Project acronym NAMO
Project Narrative Modes of Historical Discourse in Asia
Researcher (PI) Ulrich Timme Kragh
Host Institution (HI) UNIWERSYTET IM. ADAMA MICKIEWICZA W POZNANIU
Summary Modern historiography produced in Asia belongs to the history-paradigm of the European humanities and it is from within these epistemological confines that Western as well as Eastern scholars of Asian studies view the Asian writing of the past. While source criticism and historicism have today become key parts of historical consciousness in Asia, Asian historical representations are nonetheless firmly embedded in pre-modern Asian literary traditions via specific uses in historical writing of traditional rhetorical structures of narrative, emplotment, tropes, and literary imagery. Taking such linkage between present and past Asian traditions of historiography as its premise, project NAMO – with four team members consisting of the PI and three Postdocs – will examine the literary features of Asian historiography in India, China, and Tibet across the longue durée of the classical, medieval, and modern periods. First, a new method for the study of the literary forms that characterize historiography in Asia will be established by adapting basic analytical principles from Asian literary theories drawn from twelve classical Indian and Chinese works on poetics. Next, the team will determine the specific literary characteristics of narrative, plot, tropes, and historical explanation found in seventeen classical and medieval histories composed in China, India, and Tibet. Finally, it will be examined to which extent those traditional literary features still function as constitutive rhetorical elements in modern Asian history writing. This will be done by analyzing the literary forms used in a selection of twenty representative histories written in the People's Republic of China and the Republic of India during the period 1980-2010. The outcome will be a novel approach for the empirical study of Asian history that will open up a new level of comparative work in the theory of history across non-Western and Western traditions.
Modern historiography produced in Asia belongs to the history-paradigm of the European humanities and it is from within these epistemological confines that Western as well as Eastern scholars of Asian studies view the Asian writing of the past. While source criticism and historicism have today become key parts of historical consciousness in Asia, Asian historical representations are nonetheless firmly embedded in pre-modern Asian literary traditions via specific uses in historical writing of traditional rhetorical structures of narrative, emplotment, tropes, and literary imagery. Taking such linkage between present and past Asian traditions of historiography as its premise, project NAMO – with four team members consisting of the PI and three Postdocs – will examine the literary features of Asian historiography in India, China, and Tibet across the longue durée of the classical, medieval, and modern periods. First, a new method for the study of the literary forms that characterize historiography in Asia will be established by adapting basic analytical principles from Asian literary theories drawn from twelve classical Indian and Chinese works on poetics. Next, the team will determine the specific literary characteristics of narrative, plot, tropes, and historical explanation found in seventeen classical and medieval histories composed in China, India, and Tibet. Finally, it will be examined to which extent those traditional literary features still function as constitutive rhetorical elements in modern Asian history writing. This will be done by analyzing the literary forms used in a selection of twenty representative histories written in the People's Republic of China and the Republic of India during the period 1980-2010. The outcome will be a novel approach for the empirical study of Asian history that will open up a new level of comparative work in the theory of history across non-Western and Western traditions.
Project acronym OurMythicalChildhood
Project Our Mythical Childhood... The Reception of Classical Antiquity in Children's and Young Adults' Culture in Response to Regional and Global Challenges
Researcher (PI) Katarzyna Marciniak
Host Institution (HI) UNIWERSYTET WARSZAWSKI
Summary The project aims at developing a pioneering approach to the reception of Classical Antiquity in children's and young adults' contemporary culture. This newly identified research field offers valuable insights into the processes leading to the formation of the culture recipients' identities along with their initiation into adulthood. However, the most vital potential of this phenomenon remains unexploited, for the research is still selective, focused mainly on Western culture. With my project, I intend to overcome these limitations by applying regional perspectives without the pejorative implication of regional as parochial or inferior. I recognize regions as extremely valuable contexts of the reception of Antiquity, which is not only passively taken in, but also actively reshaped in children's and young adults' culture in response to regional and global challenges. Thus, the essence of this innovative approach consists in comparative studies of differing reception models not only across Europe but also America, Australia & New Zealand and – a bold but necessary step – in parts of the world not commonly associated with Graeco-Roman tradition: Africa and Asia. The shared heritage of Classical Antiquity, recently enhanced by the global influence of popular culture (movies, Internet activities, computer games inspired by the classical tradition), gives a unique opportunity – through the reception filter – to gain deeper understanding of the key social, political and cultural transformations underway at various locations. The added value of this original research, carried out by an international team of scholars, will be its extremely broad impact on the frontiers of scholarship, education and culture: we will elaborate a supra-regional survey of classical references, publish a number of analyses of crucial reception cases, and prepare materials on how to use ancient myths in work with disabled children, thus contributing to integration and stimulating cultural exchange.
The project aims at developing a pioneering approach to the reception of Classical Antiquity in children's and young adults' contemporary culture. This newly identified research field offers valuable insights into the processes leading to the formation of the culture recipients' identities along with their initiation into adulthood. However, the most vital potential of this phenomenon remains unexploited, for the research is still selective, focused mainly on Western culture. With my project, I intend to overcome these limitations by applying regional perspectives without the pejorative implication of regional as parochial or inferior. I recognize regions as extremely valuable contexts of the reception of Antiquity, which is not only passively taken in, but also actively reshaped in children's and young adults' culture in response to regional and global challenges. Thus, the essence of this innovative approach consists in comparative studies of differing reception models not only across Europe but also America, Australia & New Zealand and – a bold but necessary step – in parts of the world not commonly associated with Graeco-Roman tradition: Africa and Asia. The shared heritage of Classical Antiquity, recently enhanced by the global influence of popular culture (movies, Internet activities, computer games inspired by the classical tradition), gives a unique opportunity – through the reception filter – to gain deeper understanding of the key social, political and cultural transformations underway at various locations. The added value of this original research, carried out by an international team of scholars, will be its extremely broad impact on the frontiers of scholarship, education and culture: we will elaborate a supra-regional survey of classical references, publish a number of analyses of crucial reception cases, and prepare materials on how to use ancient myths in work with disabled children, thus contributing to integration and stimulating cultural exchange.

redpajama_set_name: RedPajamaCommonCrawl


On the other hand, the American Psychological Association defines schizophrenia as a "serious mental illness", which is not true, either, as it conveys information about irreversibility of changes, and we know already that patients with schizophrenia have periods of remission and that 15% of them may be mentally healthy till the end of their life after the first psychotic episode.
The reason for the occurrence of schizophrenia symptoms are abnormalities in brain functionality, what will be discussed in a more detail in the subsequent article.
The particular types of schizophrenia differ among one another with the intensity of symptoms and the course of diseases, however, this division does not significantly affect the way how the disease is diagnosed or treated, so, from the perspective of the patient or his/her caregiver, it has no major practical relevance. The American Psychiatric Society, in their recent DSM-V classification, departs from the identification of types, moving towards "area identification", what will be discussed in the chapter "The symptoms of schizophrenia".


Royal LePage Learning Services has introduced an exclusive new monthly webinar series ̶ Sales Success ̶ with topics to enhance sales development.
Topics will be released each quarter. Watch for the registration email each month, which will be sent prior to each webinar or follow the Learning Services Google+ page where you can sign up directly. One-hour webinars on or near the third Thursday of every month.
← HEADSTART 2015 – Let's cruise!


Manage the implementation of Quality system procedures, test and record results of final & intermediate products.
Operate Proportional and carbonator(for conventional lines) & to monitor (for advanced lines) - Start-up Shutdown as per Standard Operating procedure provided by Quality Assurance Manager & CO's manuals.
Ensure caustic residue and methylene blue test in washed bottle.
To maintain required strength of Caustic & temperature in washer.


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You are here: Home / Latest Research / Press Release: Analytics by and for Business Users for Fast Information...
Press Release: Analytics by and for Business Users for Fast Information Flow
September 7, 2020 /in Latest Research, News /by admin
PRESS RELEASE – WÜRZBURG, September 7th, 2020
Analyst firm publishes the first edition of 'BARC Score Analytics for Business Users'
– For the first time, BARC evaluates 17 market-relevant platforms for self-service analytics
– Business users expect to be able to create everything from data source to dashboard in one integrated tool
– Comprehensive platforms can do more than specialized tools, but their usability needs to improve
The analyst firm BARC (Business Application Research Center) presents BARC Score Analytics for Business Users – Platforms for Governed Self-Service Analytics. This new report – written by BARC experts Robert Tischler, Larissa Baier and Dr. Carsten Bange – examines and compares 17 globally relevant manufacturers of self-service analytics platforms. The evaluation of these providers and accompanying analyst commentary are available now.
"We at BARC see that neither 'centralistic' BI suites nor isolated tools for data visualization fully meet the requirements of modern business management. BARC Score Analytics for Business Users addresses exactly this issue. We evaluate tools according to their flexibility for business users, their ease of use and their suitability for enterprise-wide use – for the democratization of data usage," said author Robert Tischler.
Today's business users expect maximum flexibility and – at the same time – support from professionals, especially for maintenance and operation. Modern platforms for analytics by business users must be very easy to use, but at the same time support the entire analytical cycle seamlessly within one tool. In order to securely share and reuse data sets and dashboards, a unified platform must be available – this is the only way to ensure that information can circulate quickly within the company and reach the relevant decision-makers at the right time, where it can then be used profitably. Access to important data without barriers is essential for the democratization of data use.
Positioning of providers in BARC Score Analytics for Business Users 2020, © BARC
The first issue of BARC Score Analytics for Business Users examines the solutions of the following 17 market-relevant manufacturers based on their strengths and weaknesses: Domo, Dundas Data Visualization, IBM, Infor, Information Builders, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sisense, Tableau, ThoughSpot, TIBCO and Yellowfin.
For more information and to purchase the complete software evaluation, please visit:
http://barc-research.com/barc-score/barc-score-analytics-business-users/
About BARC Score
Since 2015, BARC has been classifying providers of business software in various market segments and economic regions. BARC analysts evaluate each vendor meeting a number of technical and financial criteria according to their "Portfolio Capabilities" and "Market Execution". Behind these two dimensions lie detailed, weighted criteria that determine the placement of the vendors in the BARC Score chart. In addition, BARC draws on the results of user surveys such as The Planning Survey and The BI & Analytics Survey.
For a concise overview of the market, all vendors are classified in the BARC Score chart in one of five categories: "Dominators", "Market Leaders", "Challengers", "Specialists" and "Entrants". For more information on BARC Score and the methodology behind it, please visit:
www.barc-research.com/barc-score/.
BARC (Business Application Research Center) is one of Europe's leading analyst firms for business software, focusing on the areas of data, business intelligence (BI) and analytics, enterprise content management (ECM) and customer relationship management (CRM). The company was founded in 1999 as a spin-off of the Chair of Business Administration and Information Systems at the Julius-Maximilians-University in Würzburg. Today, BARC combines empirical and theoretical research, technical expertise and practical experience, including a constant exchange with all market participants.
Mark Handford
E-Mail: mhandford@barc-research.com
http://barc-research.com/wp-content/uploads/2020/09/barc-score-analytics-business-users-blacked-out.png 1782 1442 admin http://barc-research.com/wp-content/uploads/2015/04/Logo_BARC_header.jpg admin2020-09-07 10:41:072020-10-27 10:11:45Press Release: Analytics by and for Business Users for Fast Information Flow
Press Release: The Time Has Come for the CloudNovember 11, 2020 - 9:00 am
Briefing Insights: The Talend Business User OffensiveOctober 26, 2020 - 9:32 am
Briefing Insights: Zeenea – The Adaptive Data CatalogOctober 1, 2020 - 2:57 pm
Infographic: "Sound Decisions in Dynamic Times – Forecasts and Simulations Support Modern Corporate Management"September 28, 2020 - 4:02 pm
The Data Catalog – The "Yellow Pages" for Business-Relevant DataJuly 2, 2020 - 1:23 pm
BARC powerBImap Creates Transparency in the Market Around Microsoft Analyti... The Software Works Fine – Organizational Problems Are The Main Barrier to...


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Home | Magazine | Spring 2021 | Translator's Note
Chip Rossetti
Photo by Wendy Tanner / Flickr
THE PEOPLE IN DIAA JUBAILI'S STORIES have a confounding habit of turning themselves into other things: a eucalyptus tree, a dung beetle, a lighthouse, a butterfly, or even water. These stories—from his flash-fiction collection, No Windmills in Basra—read at times like modern folktales or urban legends, presenting the reader with inexplicable, even magical, events, even as they foreground the violence and war that have overshadowed Iraqis' daily lives over the past several decades. It's what first drew me to Jubaili's work.
His story "The Hat Stand" demonstrates this blend of fairytale metamorphosis and grim reality perfectly: the protagonist, 'Ubayd, has become the neighborhood scapegoat, blamed for everything bad, from broken streetlights to the US occupation and Iraq's burgeoning civil war. When rumors fly about a vicious testicle-eating creature at large in the city, 'Ubayd's neighbors, like the torch-wielding mob in Frankenstein, descend on his apartment to kill him once and for all. Having already served as a metaphorical hat stand on which they can hang all their problems, he transforms into an actual hat stand, which conveniently saves him from the clutches of his pursuers, who now focus their attention elsewhere.
I first came across this collection in December 2018, not long after it won the Almultaqa Prize, a relatively new award specifically for Arabic short-story collections. I was on a work trip to Abu Dhabi, and as on other occasions, I took advantage of access to brick-and-mortar Arabic-language bookstores to buy a pile of books and stuff them in my suitcase. I hadn't read flash fiction in Arabic before, but I was struck by Jubaili's stories, which ended with a dark twist and a surprise emotional wallop.
It wasn't until the late spring of 2020—still early on in the Covid pandemic, which now seems like eons ago—that I turned to the idea of translating the book. I naïvely thought these stories would be straightforward to work on. But, as I should have known, there is no such thing as an "easy translation": every literary text reveals its challenges only after you've started working on it. In this case, the compactness of the flash-fiction genre makes it closer to poetry than to short stories: every word has to cohere and drive the reader seamlessly toward the final line.
There is no such thing as an "easy translation": every literary text reveals its challenges only after you've started working on it.
Even more challenging from a translator's perspective, many of the pieces in No Windmills in Basra hinge on a pun in Arabic. One story, for example, turns on the word mismar (meaning both a carpenter's nail and a skin tag) and its close relation mismari (referring to cuneiform script) for its central motif—a neat vocabulary overlap that English can't replicate. Other pieces offer even trickier conundrums outside an Iraqi context: in the story "A Hundred Laylas and One Wolf," some alert English readers may catch Jubaili's allusion to the popular Majnun and Layla legend, but far fewer will realize that "Layla" is also the name used in Arabic for Little Red Riding Hood without some translatorial handholding.
While working on "The Hat Stand," I began thinking about the story as a metaphor for translation itself. Like a man becoming a lighthouse or a woman turning into water, translation is always an act of metamorphosis. At the same time, I have often felt the impulse to become as unobtrusive as 'Ubayd, to adopt the "invisibility" that can't (and shouldn't) always be the goal of translation. But unlike 'Ubayd, I have to confront the text and make decisions. As a translator, I don't have the option of turning myself into a hat stand.
Read Rossetti's translation of Diaa Jubaili's "The Hat Stand," from this issue.
Chip Rossetti has a doctorate in modern Arabic literature from the University of Pennsylvania. His published translations include Beirut, Beirut, by Sonallah Ibrahim; Metro: A Story of Cairo, by Magdy El Shafee; and Utopia, by Ahmed Khaled Towfik. He is currently editorial director for the Library of Arabic Literature at NYU Press.
More Translator's Note
Notes for a Paleface Museum, by Enza García Arreaza
Translator's Note: Translating Ch'ol: A Bridge Between Linguistics and Poetry, by Carol Rose Little & Charlotte Friedman
Translator's Note: Translating a Modern Zoroastrian Tale, by Michelle Quay
Translator's Note: Translating the Wandering Birds of Shuri Kido, by Tomoyuki Endo
Translator's Note: Translation as Detective Work, by Jozefina Komporaly
Translator's Note: Exchanging Different Versions of Jokes with Ariel Magnus, by Kit Maude
Translator's Note: Translating Toshiko Hirata's Ars Poetica, by Eric E. Hyett & Spencer Thurlow
Translator's Note: The Tragic, the Absurd in Sadaa al-Daas's "Zoo Syndrome," by Omar Qaqish
Translator's Note: Hauntings, by John Bengan


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Africa Renewal
"Once a peacekeeper, always a peacekeeper"
Women in Peacekeeping
— Major Monira Mowri, from Bangladesh, serving in South Sudan
Monira Mahjabeen Mowri
Major Monira Mahjabeen Mowri, from Bangladesh, serving in South Sudan
Our peacekeeper of the day is Major Monira Mahjabeen Mowri Staff Officer at the United Nations Mission in South Sudan (UNMISS)
My name is Major Monira Mahjabeen Mowri. I have been serving with the Bangladesh Army for some 11 years. I am the first from my family to serve in the Armed Forces, though my educational background was focused on aeronautical engineering. I'm married to an army officer and I love travelling, meeting new people and learning about different cultures across the world.
What are your responsibilities in the mission and what is your typical day like?
I work as a Staff Officer at the United Nations Mission in South Sudan (UNMISS) headquarters in the capital Juba and my daily responsibilities involve coordinating and liaising with different contingents, military observers and, on occasion, our humanitarian partners serving across South Sudan, for a varied range of operational tasks, including regular patrols.
Despite the fact that I have a largely desk-oriented role here, I make it a point to accompany ground patrols in Central Equatoria state whenever I find the time—I believe that the only way I can ensure that the operational requirements of our peacekeepers on patrol are met swiftly and efficiently is if I actually experience on-ground realities with them!
How long have you been a UN peacekeeper and how did you become one?
This is my first deployment as a UN peacekeeper; I joined UNMISS a year ago. As you may know, Bangladesh has been contributing troops to UN peace operations since 1988 and ever since I joined the Bangladesh Army, it has been my dream to serve under a blue helmet.
I went through a stringent selection procedure in my own country and was very proud to be deployed as a staff officer to UNMISS. Originally, I was supposed to serve for a year; however, with COVID-19, the mission has granted me an extension.
What did your family and friends back home think about your decision to leave your country and work for a UN peacekeeping mission?
Honestly speaking, I think it's a difficult decision for anyone to move abroad, leaving loved ones. It was the same for me. However, I've been very lucky--my husband who, as I mentioned earlier,
also comes from a uniformed personnel background, my parents, my in-laws and my siblings were extremely supportive. They all knew how much being a UN peacekeeper meant to me and understood my need to serve a cause that transcends national and personal boundaries.
What are three things you like most about the mission and the country?
My role at UNMISS puts me in daily contact with a cross-section of military, police and civilian peacekeepers from different countries. It has been a life-altering experience. I think it has made me a better officer and I'm going to carry much of my enhanced skillset when I return home to share with my colleagues in Bangladesh.
On the personal front, I have managed to forge genuine bonds with colleagues who live across the world. Additionally, I've had many opportunities to interact with our host communities since we organise a regular cleaning campaign in Juba as part of the mission's civilian-military cooperation activities.
I have discovered that the South Sudanese people are very friendly and supportive of us as peacekeepers. They are genuinely committed to making their country a better place. This is what inspires and motivates me to do my best for this country every single day.
What part of your job do you find most challenging and why?
I think a major challenge any newcomer being deployed to a peace operation faces is learning the breadth and scope of how the task at hand contributes to the overall mandate of the mission.
I have spoken about the multicultural work environment – this too can, at times, come with its own set of challenges because as military personnel we are used to working within country-specific rules and regulations which may differ widely from each other. It takes time, dedication and a willingness to embrace change if one wants to be successful peacekeeper.
Do you think female peacekeepers serve as role models for the local population?
Yes, I firmly believe that. Female peacekeepers often forge a deeper connection with women and children in the communities we serve. I've experienced this first-hand in my interactions with local women here—we can forge real connections based on our shared experiences of the range of feminine roles we fulfil as wives, daughters, sisters…Moreover, women constitute 50 per cent of any society and I'm glad that peace operations worldwide are looking to recruit more women.
What would you say to female soldiers considering a career in peacekeeping?
Go for it! I acknowledge that it's an uncommon choice of career for women in many parts of the world, but it is extremely rewarding as we get the chance to not only empower ourselves but make lives safer and, hopefully, a little better for people who need our help the most. Once you have served as peacekeeper, you'll always be a peacekeeper. In my opinion, it's a value system, an ethic that will stay with me long after my deployment with UNMISS ends!
Africa's free trade area opens for business
AfCFTA: Africa readying for free trade come January 2021
Protecting Africa's children from child labour
The Secretary-General: New Year's message 2021
UN launches 2020 Class of 17 Young Leaders for the Sustainable Development Goals
AfCFTA: Experts, traders call for robust participation of women and youth
More from Africa Renewal
Community Health Workers: The Unsung Heroes in Africa's COVID-19 response
Why AU should push for equitable access to COVID-19 vaccines
Central Africans went to the polls
UN chief urges peaceful, credible elections in Central African Republic
About Africa Renewal
The Africa Renewal information programme provides up-to-date information and analysis of the major economic and development challenges facing Africa today. It examines the many issues confronting the people of Africa, its leaders and its international partners: economic reform, debt, education, health, women's advancement, conflict and civil strife, democratization, aid, investment, trade, regional integration, rural development and many other topics. It works with the media in Africa and beyond to promote the work of the United Nations, Africa and the international community to bring peace and development to Africa.
The Office of the Special Adviser on Africa (OSAA)
New Partnership for Africa's Development (AUDA-NEPAD)
United Nations Economic Commission for Africa (ECA)
© United Nations Africa Renewal


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Gaza power plant runs out of fuel amid longstanding electricity crisis
Ma'an News Agency | – –
GAZA (Ma'an)– The besieged Gaza Strip's only power plant announced on Thursday that it was unable to operate on more than one generator after exhausting its fuel reserves.
In a statement, the electricity supplier noted that efforts were being made to obtain fuel in the coming week.
Even at full capacity, Egyptian and Israeli electricity grids, together with Gaza's sole power plant, fail to cover the Gaza Strip's energy needs. The power plant has not run at full capacity in years, with Israel's crippling blockade severely limiting fuel imports into the coastal enclave.
The enclave has experienced severe electricity shortages over the years, exacerbating already dire living conditions in the small Palestinian territory.
Gaza's usual electricity schedule alternates eight hours of power followed by eight hours without.
Gaza's electricity crisis made headlines in May when three small children died in a house fire caused by candles that the family used during a power cut.
War has also taken its toll, and during Israel's 50-day offensive on Gaza in 2014, the power plant was targeted, completely knocking it out of commission.
The UN has warned that the Gaza Strip would become uninhabitable for residents by 2020, pointing to the devastation of war and nearly a decade of Israel's blockade.
Via Ma'an News Agency
Filed Under: Israel/ Palestine


Bitcoin exchange giant Coinbase is introducing a new space for investing by launching a program that offers institutional investors the option to earn interest on specific cryptocurrencies they hold.
The San-Francisco based exchange allows customers to buy and sell several cryptocurrencies. It also operates Coinbase Custody, which provides fund managers and institutional investors cold storage for their digital assets.
Staking makes it possible to earn passively against your holdings – all without moving anything from offline storage.
Investors are expected to earn between 5 percent and 8 percent, and the payments will be made in the same crypto asset they hold.
Tezos is a new platform that focuses on smart contracts and decentralized platforms and uses the proof-of-stake consensus algorithm. As of this writing, XTZ had jumped over 18 percent in the last 24 hours, with a capitalization market of $571,381,877 USD.
The launch of Tezos staking through Coinbase Custody serves an acute need that existed up until now: a way for institutional participants who rely on a secure, offline custodian to take an active role in the network.
The Maker protocol, creator of the DAI stablecoin, will follow. DAI is based on the Ethereum network, and it is pegged to the dollar. DAI market cap totals $87,957,606 USD.
Bitcoin is not included in this program because it does not use proof-of-stake. Instead, the Bitcoin network uses the proof-of-work algorithm.
Coinbase holds a license to process payments through banks under the state money-transmission licenses in the U.S. And, the Department of Financial Services granted the exchange a "Virtual Currency License" on January 17, 2017. Moreover, Coinbase introduced a system for making cryptocurrency payments easier, faster, and safer. As a result, the U.S. Patent and Trademark Office (USPTO) granted protection on August 14, 2018.
What do you think about Coinbase's Staking Program to help financial investors earn interest on certain cryptocurrencies? Let us know in the comments below.
The post Biggest US Exchange Will Pay Institutional Investors Interest on 'Staked' Cryptocurrencies appeared first on Bitcoinist.com.


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Welcome day for German students and teachers of the exchange with Weinheim
The French Alliance explains the characteristics of the DELF exams to ESO students
The Cambridge and Trinity diplomas award certifies the great level of our students
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Former students Alejandro Pérez and Yago Fernández visited the 1º high school students at noon, explaining the characteristics of this American university center with which the School maintains a collaboration agreement.
Alejandro Pérez and Yago Fernández, former students of the School San Fernando who are studying at the Tusculum University of Tennessee (United States), are these days in Avilés enjoying the Christmas holidays. This afternoon they made a parenthesis to visit their old center and explain their experience in American lands to the students of the different 1º Bachillerato groups.
Both highlighted the characteristics of Tusculum, a university whose size allows a personal and close treatment between student and teacher, but who is living a process of development and growth in both its facilities and equipment. This moment of change coincides with the step taken by Tusculum this course, since it has gone from being College to University, with all the conditioning factors that represent such an important step forward.
Alejandro and Yago explained the facilities that exist when accessing different types of scholarships (international, academic and sports) that facilitate the possibility of living the experience of studying abroad. In the case of the students of the School San Fernando, the facilities increase by virtue of the agreement reached with Tusculum last year. Both highlighted especially the possibilities that athletes have, to which the center gives all kinds of facilities to continue their studies and excel in their respective disciplines. "They treat us almost like professionals," commented Alejandro.
This way of understanding the university experience makes the two have chosen to prepare two simultaneous careers, favored by the validations that are given between the subjects of careers in the same branch.
Both Alejandro and Yago highlighted the kindness of the people and how easy it has been to adapt, so they recommended without hesitation the experience to our 1º students, who followed their explanations with attention and raised their doubts.
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The Jill Bloomberg Case Asks Where Do Official Duties of a Principal and Politics Intersect, if at all?
The case of Jill Bloomberg is turning into a media event which will help analyze the very important issue of where politics, public employment, and student learning intersect.
Her fight is our fight, collectively speaking. Be the change you wish to see in the world.
Jill Bloomberg
DOE is bullying me because I spoke out: principal
Jill Bloomberg, a loud critic of school segregation in NYC, is suing DOE officials for what she describes as a bizarre retaliation campaign.
The NYC Department of Education Gets A Green Light To Retaliate Against Park Slope Collegiate Principal Jill Bloomberg
Department of Education Surr Reply
Defendants' Memorandum of Law in Opposition
Park Slope Collegiate Principal Jill Bloomberg Sues the NYC Department of Education For Retaliation Against Her For Speaking Out Against Racial Injustice
Investigation of activist principal has free-speech advocates asking what politics are allowed at school
By Cassi Feldman CFELDMAN@CHALKBEAT.ORG
The strange saga of a Park Slope principal accused of promoting communism took another turn Wednesday, when her request for a temporary halt to the probe against her was denied.
Jill Bloomberg, principal of Park Slope Collegiate, is known for her activism, particularly around the issue of school segregation. But the Department of Education says now she's gone too far by sharing her political views at school and "actively recruiting" students into a communist organization.
"We lost the battle, not the war," said Bloomberg's attorney Jeanne Mirer after the judge's decision to allow the investigation to proceed.
The war, it seems, will partly depend on whether Bloomberg violated D-130 — a Chancellor's regulation that prohibits school employees from "being involved in any activities, including fundraising, on behalf of any candidate, candidates, slate of candidates or political organization/committee during working hours."
The city claims, among other allegations, that Bloomberg violated the regulation by advocating on behalf of the Progressive Labor Party, a political organization with communist ties, at school. Bloomberg denies that and says she isn't a member herself. But the case raises a larger question of what the regulation is meant to cover.
Mirer says a close read suggests it only bars election-related political activity — campaigning for a candidate, for instance — and not the type of organizing of which Bloomberg is accused.
If it did cover non-electoral politics, she said in court Wednesday, that would create a slippery slope for any educator who dared to voice a political view. "Any ideological belief could be the subject of a violation," she warned.
Judge Paul Gardephe seemed unmoved by her argument. "I read the relevant parts [of D-130]," he said. "This lawsuit is not about whether D-130 is fair."
But Mirer is not alone in worrying about how the regulation is being applied. Arthur Eisenberg, legal director at the New York Civil Liberties Union, is advising Mirer and has his own concerns about the free speech issues at play.
According to Eisenberg, the rules are the same for students, teachers and principals: "It's well-established that school officials do not lose their First Amendment rights to speak out as citizens even when they are in school," he said. "The standard is they can't speak out in ways that are disruptive to the functioning of the school."
Eisenberg declined to speculate on whether or not Bloomberg might have done that, but he said he was confident that D-130 could only apply to electoral politics.
A broader interpretation, he said, "puts the DOE in the position of having to regulate issue-oriented speech in ways that make it difficult to know how and where to draw the line." Limiting free speech on issues that are political in nature, he said, could potentially impact student clubs that deal with gay rights, for instance, environmental causes, or racism. "And we know that can't be right," he said.
Eisenberg also questioned another line in the regulation quoted in the city's court documents, which calls for a "posture of complete neutrality" on political candidates. Even if that were possible, he said, it wouldn't be desirable.
"The obligation of an academic or teacher is to engage in critical judgment and to support those judgements with reasoning and fact," Eisenberg said. "And that may be inconsistent with a principle of absolute neutrality."
We asked the city's law department what it made of Mirer's argument that D-130 was meant to be more narrow in scope. Nick Paolucci, a spokesman for the department, said he wasn't familiar with argument and couldn't comment.
Posted by Betsy Combier at 5:55 PM
Labels: freedom of speech, Jill Bloomberg, Park Slope Collegiate, politics, protected speech, retaliation
Jill Bloomberg is the principal at Park Slope Collegiate.
Park Slope Collegiate's web site was used to promote a showing of the movie "Profiled" along with a panel discussion and Q&A session moderated by Jill Bloomberg.
The movie "Profiled" was shown June 3, 2016 at 7 PM in the auditorium of Park Slope Collegiate.
Jill Bloomberg did moderate a panel discussion and a Q&A session after the movie.
Merchandise was sold at the school with proceeds going toward the film makers.
The event was sponsored by the Len Ragozin Foundation, who funded the production of the film.
The Len Ragozin Foundation is run by Jill Bloomberg's husband, Adam Lessuck. The Len Ragozin Foundation shares the same address as Jill Bloomberg & Adam Lessuck, residents & owners of that address. (2014)
Ragozin was a Stalinist associated with the Progressive Labor Party, a splinter group of the Communist Party USA.
Jill Bloomberg abused her position and school property to promote her politics, and possibly her financial interests.
The Jill Bloomberg Case Asks Where Do Official Dut...


This class for more advanced beginners and intermediate students will emphasize strength, stamina and deeper understanding of the poses. The poses learned in Level I or Mixed level classes are explored more in depth and more challenging postures are introduced, including inversions and back bends Various modifications are taught to make the more challenging poses accessible or working deeper in poses. Special attention is paid to discovering how breath work can support you in a pose and practicing the connection between the physical and energetic body.
Prerequisite: Intro to Yoga workshop, strong background in Yoga practice, or two private sessions.
Discover the incredible benefits that a regular yoga practice can bring to your pregnancy.
This is a series of classes is designed to empower and honor the changing body, increase your body awareness and teach you how to modify movements during this unique, beautiful and challenging time.
You will learn appropriate yoga postures, modifications and techniques to develop strength and flexibility, cultivate a calm and confident mind, and deepen your relationship with your baby; preparing for the birthing process with ease and joy.
Enhance your comfort, enjoyment, and relaxation during all prenatal trimesters!
Please eat a light meal one hour before class and bring a snack and water.


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Thursday morning draft links
March 10th, 2011 | Written by Rob Staton
Later today I'm going to look at four 'under the radar' prospects – a couple of guys from small schools and two others I think could be mid-round sleepers. Also don't forget to check out the latest mock draft which you can discuss here.
Mike Mayock was at the Alabama pro-day and had strong praise for Mark Ingram and Marcell Dareus. Ingram runs a better shirtless forty yard dash than former Crimson Tide top-ten pick Andre Smith.
The NFL Network team grade Mark Ingram. It's a mixed review really and while Ingram is clearly a talented player, he may last until the late first round.
Charles Davis and Michael Lombardi discuss who stood-out in the pro-days so far. Lombardi had strong words of support for Marcell Dareus, while Davis talked up Ryan Mallett. Included in the clip is an all-time classic – Tom Brady's forty yard dash footage.
Bucky Brooks was at the Wisconsin pro-day, featuring JJ Watt and Gabe Carimi.
Charley Casserly offers a mock draft without Cam Newton in the top ten. The Panthers take Da'Quan Bowers first overall.
Jake Locker was also on set and heads to the chalk board to talk coverages with Trent Green.
Kendall Hunter ran in the 4.5's during the Oklahoma State pro-day. It was a similar time to the one he clocked at the combine.
Arizona pass rusher Ricky Elmore talks draft to ESPN:
12 Responses to "Thursday morning draft links"
Jerry Nice says:
Hey Rob,
I'm a little disappointed at how locked in you are with The Redskins taking Jake Locker at #10. I don't think you've changed this prediction in over a year.
In my opinion, Locker is a project. Mallett is actually more ready to go with the accuracy and arm to boot. Remember another QB that Shanahan drafted with a great arm, solid accuracy, and character concerns? Another reason I'm speculative is that Shanahan has gone out of his way recently to talk about Locker. This could be a strong sign that he's looking to either move down for more picks, or is sending mixed signals to get the guy that he wants.
I believe that Of the 4 big names at QB, Locker may be the least ready to start. It's my belief that the perfect situation for him would be to go with a team that has the ability to let him sit for at least a year. We know Washington does not have this luxury. McNabb wants to leave and Shanahan hasn't made him feel like he wants him around either. I believe the Redskins get another stop-gap in FA and go with a playmaker on defense or offense (i.e. Julio Jones). They have so many holes to fill, a project QB doesn't seem like it would be wise here.
I remember you had Clausen going as high as #6 last year and kept him in the top 10 for almost all year, and he seemed to be more pro-ready than even Locker does right now.
Hi Jerry,
I've mixed Locker around, but admittedly he's been at #10 ever since the end of the regular season. During the 2010 college season I regularly moved him around though, going in the 20's and dropping out of the first round.
I have a really good feeling about the pick and while I do like to mix things around, I also think you have to stand by certain things you're sure about. The 'Skins didn't bench McNabb lightly and for me just set themselves up to go QB in the draft. If they can somehow sort out this CBA situation before April we'll get a better view of how Washington plans to move forward at QB, but for now I can't see them going in any other direction. That doesn't mean the pick won't change in the future, but I'm standing by it for now.
As for Clausen – I moved him around a lot. From day one I had him down as a second round pick (google: The Jimmy Clausen debate). I also appreciated that my opinion on a prospect won't necessarily be shared by NFL scouts, GM's and Head Coaches. It only took one team to believe he was a franchise QB to make him a top ten pick. Clearly that didn't happen. When I watched him, I saw a limited talent playing in a favorable system. For all Locker's issues, I still think he's got so much more potential than Clausen. As for being pro-ready – I think Joe Flacco was possibly the least pro-ready QB I've ever seen and he's taken to the NFL very well indeed because of his physical talents, good coaching and a willingness to learn. I wouldn't write off Locker being able to do the same, but there are things he needs to improve on.
Not sure if you ever listen to Brock and Salk, Rob, but today John Clayton was on and the debate arose around "trading down," as Brock and Salk thought was the best thing to do. Clayton mentioned the fact that teams who continually trade down have a set base of upper echelon talent in place (always at QB). He outright said the Hawks are in no position to "get cute and trade down." I was relieved to hear this because you simply need great talent to win. Build a team of average players and you will have an average team. Difference makers are vital.
Now, it could just be due diligence, but the actions of JS and PC thus far (not resigning Hass, lower tender on Mebane, JS at the Mallett pro-day, Newton workout scheduled) lead me to believe that we are positioning ourselves to move up and get "the guy we want." Now, I must say that "our guy" might not necessarily be a QB, but a player at a key position in our system that we see as a building block.
Rob, will you be surpised if they don't make an aggressive move come draft day? Always curious to hear your thoughts. Secondly, who might be a realistic target of theirs considering what they have to trade?
I could see Jimmy Smith, Muhammad Wilkerson (I think goes much higher than most expect), Jake Locker as guys they feel could be cornerstone players at valuable positions in their system.
Kudos to John Clayton because he's absolutely right.
I wouldn't say I'd necessarily be surprised if they don't make an aggressive move, because obviously if it doesn't happen we'll never know what happened behind the scenes. The Seahawks don't have a lot of trade stock – there's no third round pick and they don't have mulitple picks in any round until R5. Essentially they'll be dealing with #25 and #57 to move up and get a guy they really want, plus obviously any future picks in 2012. Someone needs to be willing to make that move down the board.
At the same time, I suspect there will be genuine interest in moving up. The names you've listed are all logical fits. Just on Locker – who I still expect to go #10 to Washington – we've now way of knowing how they feel about him. There's talk out there that Seattle isn't that interested. There's also a line of thought that would suggest there are plenty of connections and that if Carroll buys into what he brings to the table, that could be a target. I understand why fans love to talk about trading down – it happens on every forum/blog for every team. As Clayton says – this team can't get cute. The Seahawks need to be pro-active to acquire cornerstone talent at key positions.
What are your thoughts on Muhammad Wilkerson? He seems somewhat underrated and has such versatility that could make him a cornerstone type guy on the D-line. He seems like he could become a good 5 tech or 3 tech which would be extremely valuable in Carroll's hybrid defense.
Secondly, how high do you think we could move simply using the 25th and 57th pick? I know there are the draft trade charts, but I think we all know that doesn't determine whether or not a team makes the trade. 25 + 57 = 12-15 maybe?
From what I've seen of Wilkerson, he flashes top-10 talent at times. You look at a guy +300lbs rushing the edge at Temple like he does and it's unnatural. He could play 5-tech, he could play 3-tech. He's not always playing at that high level, but I think he's going to go between 10-20.
As for how high we could go – it's tough to say. NYJ and Jacksonville moved up a lot without a real blockbuster package. San Diego and Philly had stock to move up last year. I think 8-10 spots with the #25 and #57 is achievable but then who moves down? New England? They already have a large quota of picks. Miami? Jacksonville? It's hard to find a partner.
Oh completely agree. Takes 2 to tango. And yes, looking at the teams in the teens, doesn't look like a terrible amount of suitors. Long shot, but maybe the Rams would trade down if Julio Jones isn't there. They could take a Jonathan Baldwin or another DT at 25. Long shot obviously. Just thinking in erms of who might be looking at DT, WR, or OT that would move down.
I am really intrigued by Wilkerson. His frame is great and he has amazing athleticism for his size. Just seems like a versatile guy who would be worthy of a trade up. Does Liuget fit that mold as well? Or is he more of a 3 tech?
Aldon Smith may be a guy they target as well. Fits the Leo mold, but like you have said, no slouch against the run either. I'm just thinking of names they might trade up for being since we know who might be around at 25. Thanks for the feedback Rob. By far my favorite sports site on the web.
I totally agree with the notion that we shouldn't try to be cute here…If we have a QB or play maker in mind, GO GET HIM. I honestly think we can and will this year. Only problem is, without a 3rd rounder, we're stuck with a ton of 4-7 round picks…Now while Tate isn't necessarily killing it for us, he is a superior talent to most, and could turn out to be an incredible slot guy.
Is it me or is Mallett the most exciting prospect this year? Gabbert doesn't really excite me, and Newton is a little bit of a question mark. Also, while I love Locker, his decision-making makes me worried sometimes. If you combined Mallet and Locker though, it would be the greatest QB of all time.
GO GET THE FUTURE PETE! RAKE MOCKER!
I heard Jyan Lockett had a slightly higher wonderlic – maybe he's the answer?
Liuget is definitely more of a three-technique, but a very good one. I don't think it's beyond the realms of possibility that if they do want to move up, Liuget, Smith or Wilkerson could be the reason. Three very good defensive lineman.
Thanks also for the kind words about the blog Matt, really appreciated.
Kip says:
Anyone else catch it when Mayock called Stephen Paea a 3 technique? I was under the impression that Paea's gifts lay more in a 1 tech role, but Paea is better suited for the 3 as Mayock believes, then he'd suddenly make sense for Seattle.
I like Mayock, but I'm not buying that one bit. He has three technique size, but he's absolutely a one-tech. I can only presume it's one of the guys Mayock hasn't had a chance to have a real good look at yet – after all, he had Robert Quinn at #5 in his DE rankings initially, then bumped him up to a top five pick a couple of weeks later.