text:
Thank you to all webinar attendees for the active feedback and questions about BigML's Fall 2016 Release that includes Topic Models, our latest resource that helps you find thematically related terms in your text data. Our implementation of the underlying Latent Dirichlet Allocation (LDA) technique, one of the most popular probabilistic methods for topic modeling tasks, is now available from the BigML Dashboard and API. As is the case for any Machine Learning workflow, you can also automate your Topic Model workflows with WhizzML!
If you missed the webinar, it's not a problem. Now you can watch the complete session on the BigML Youtube channel.
Please visit our dedicated Fall 2016 Release page for more resources, including:
The Topic Models documentation to learn how to create, interpret and make predictions from the BigML Dashboard and the BigML API.
The series of six blog posts that explain Topic Models step by step, starting with the basics and wrapping up with the mathematical insights of the LDA algorithm.
Many thanks for your time and attention. We are looking forward to bringing you our next release!
In our recent series of blog posts on Topic Models, we've tried to explore this powerful new resource in the BigML Dashboard, in the API, using WhizzML, and we have also suggested some uses for it. But we've left a nuts and bolts description of how Latent Dirichlet Allocation (LDA) works until the end. Within this post, the last of a series of six posts, we'll try here to give you exactly that: A high-level overview of the internal mathematics that underlies Topic Models, and what that mathematics might imply for you, the modeler.
While I'll explain a few things here, a more precise and technical explanation given by the inventor of the technique, David Blei, is available. Where there seems to be conflict between his explanation and mine, rest assured, his is correct!
My Generation
A crucially important aspect of the topic models learned by Latent Dirichlet Allocation is that they are generative models. This is different from many of our models at BigML. Decision trees and logistic regressions are discriminative models. This means essentially that they spend their effort using the data to model the boundary between classes, directly approximating the function of interest without much concern about exactly why the data is the way it is.
Generative models are a bit different. Typically, generative models posit a statistical structure that is said to have generated the data. The modeling process is then a process of using the data to fit the parameters of that structure so that the structure is likely to have generated the data that we see.
More concretely, Latent Dirichlet Allocation imagines that each document is a distribution over topics in your dataset, like
{Topic 1: 0.8, Topic 2: 0.0, Topic 3: 0.1, Topic 4: 0.1}. Each of those topics, as we know, is a distribution over words, like
{President: 0.5, united: 0.25, states: 0.25}. Now, to generate a document from that topic distribution, we first choose a random topic according to those probabilities (so we'd be very likely to choose topic 1, unlikely to choose topics 3 or 4, and would never choose topic 2). Once we've chosen our topic, we choose a word from that topic (so we'd choose "President" with high probability, and "united" or "states" with lower probability). That gives us a single word in our document. Then we repeat the process over and over again until we have however long of a document we'd like. Et Voilà, we've generated a document from a topic distribution!
The astute reader will notice we've glossed over where the topic distribution for our document came from. There's actually another meta-distribution from which we choose these topic distributions. So each document in our collection is a random choice of a topic distribution from the overlying distribution over topic distributions. With that, we've got a structure that could possibly generate our entire document collection, if all of those distributions are just right.
Another Tricky Day
As we mentioned earlier, learning proceeds in a somewhat backwards way from how we think about the generative structure: That is, the model gives a a procedure to generate documents given the distributions, but we already have the documents and need to infer the parameters of the model that probably generated them.
This is a tricky proposition so I won't bore you with the details, but there are techniques like collapsed Gibbs sampling and variational methods that allow you to do this sort of "reverse inference" in generative models, and that's exactly what we use in practice for our Topic Models.
The Seeker
So how can this knowledge affect the way you use BigML topic models? One of the key things to remember when using Topic Models is that the learning process is trying to use the generative structure above to explain how your document collection came to be. This means that if there's a certain bunch of terms that occur all the time in your documents, the model will spend a lot of effort trying to explain how they got there, and might end up ignoring terms that are less frequent.
Why might this be bad? Mostly because text data often tends to be "dirty", with a lot of cruft that you don't care about modeling. A great example is web pages. There's a lot of nice information on web pages, but you'll find if you pass raw web page source to Topic Models, the terms it finds most important will be things like "html", "span", "div" and "href": Because these formatting directives appear all the time in web pages and in different quantities, the model will spend lots of effort trying to explain the differences in the occurrence rates of these tokens. Maybe that's what you're looking for; if you've got a dataset that is half web pages and half e-mail messages, the tokens that denote web pages might be useful indeed. It might also be nice to know which pages have more links. Then again, maybe you don't care about HTML tags at all.
BigML attempts to remove tokens that occur "too frequently to be useful" when doing topic modeling, but it's often specific to the use case whether or not something that's frequently occurring is very important or just noise. But this is a problem that's easy enough for you to fix: You can just exclude those useless terms from your dataset, either by pre-processing before you upload it, using Flatline, or using the excluded_terms parameter during the modeling process. You'll often find that eliminating the half-dozen or so most common terms will yield a very different model; you'll have changed the composition of your collection so much that other terms will have become far more relevant on a relative basis than they were the first time around.
Eyesight to the Blind
With a little understanding of how topic models work, you can create them with open eyes and maybe even improve your results by tweaking the data just a bit. Give them a try and, as usual, please drop us a line at email@example.com if you're having trouble, or even if you just have some interesting data and want to show off the results. Good luck!
If you want to know more about this new resource, please visit the Topic Models release page and check our documentation to create Topic Models, interpret them, and predict with them through the BigML Dashboard and API, as well as the six blog posts of this series.
P.S. – Extra credit trivia question: What is notable about the names of all the subsections in this post?
Democratizing Machine Learning has always been BigML's founding mission, so we are continually searching for new opportunities. As such, when a company is interested in our technology and is willing to help us further our cause of "Machine Learning for everyone", we feel the urge to collaborate. This is exactly what happened with our new education partner. Today we are happy to announce our educational collaboration with CICE, the Leading School in New Technologies Training in Madrid, Spain.
CICE, the only Official Training Center in Spain for more than 20 multinational companies, is already a community of 70,000+ students from 30 different countries. With 35 years of experience, the school provides High Quality and Official Training Programs and the homologations from the leading companies thanks to certified teaching professionals recruited from the most prestigious Spanish production companies. In fact, the CICE Team is currently going through BigML's new certifications program and they will soon be among the first batch of BigML Certified Engineers.
CICE aims to create efficacy through constant educational innovation by being protagonists and agents of the deep digital transformation our society is going through. They have already set exemplary standards for the fast-growing market segment of New Technologies Education through a mix of:
- The best instructors: certified professionals with wide experience and proven ability to teach.
- The best facilities: a heterogeneous system of professional Apple and DELL workstations along with a set of cloud services that provide the best educational experience.
- The best homologations: a set of alliances that make CICE to provide a unique learning environment that guarantees a learning process that will fulfill the student's expectations, in a way only the leaders of the Educational Sector can do.
- The greatest quantity and quality of production: facts to demonstrate objectively that an excellent education at CICE plus the effort and talent of the students they train, turns into a harvest of projects that make CICE the winner of the high profile national and international competitions.
CICE's enthusiasm for the BigML platform and their eagerness to improve Machine Learning education were big reasons why we agreed to collaborate. CICE has already joined our education program, which has more than 100 ambassadors and 620+ universities affiliated around the world that actively promote BigML. They have started spreading the word through their students, social media, and at the education events they run in Madrid.
We are excited about the official partnership and are looking forward to make a big difference together in graduating the future data-driven leaders of the 21st century 's global digital economy.
This series of posts started by introducing Topic Models as BigML's implementation of Latent Dirichlet Allocation (LDA) to help discover thematically related terms in unstructured text data. We later explained how to use it through the BigML Dashboard, showed how to apply Topic Models in a real-life use case, and how to program Topic Models using the BigML API. This post will focus on automating LDA workflows by using WhizzML, a DSL for Machine Learning that helps automate workflows, program high-level algorithms, and share workflows and algorithm with others.
Let's dive in by creating a Topic Model and making a prediction with it. In BigML, you can perform single instance predictions (referred to as a Topic Distribution) or in batch mode, which is called Batch Topic Distribution.
Firstly, we will create a Topic Model without specifying any particular configuration option, that is, relying on default settings. For that, you just need to create a script with the source code below:
BigML's API is mostly asynchronous, so the above creation function will return a response before the Topic Model creation is completed. This implies that the Topic Model information is not ready to make predictions right after the above code snippet is executed, so it is convenient to wait for its completion before you can predict with it. You can do just that by using the directive "create-and-wait-topicmodel". See the example below:
Now let's try configuring a Topic Model via WhizzML. The properties to configure can be easily added in the mapping by using property pairs like <property_name> and <property_value>. For instance, in order to calculate a Topic Model with a dataset that contains one or more text fields, BigML automatically determines the number of topics, but if you prefer the maximum number of topics that BigML allows, you should add the property "number_of_topics" and set it to 64. Additionally, if you want your Topic Model to be case sensitive, then you need to set the "case_sensitive" property as true. Property names always need to be between quotes and the value should be expressed in the appropriate type. The code for our example can be seen below:
For more details about all available properties please check the API documentation.
You now know how to create a Topic Model, let's see how to create predictions from it. The code is similar to the one used to create any resource. You just need the ID of the Topic Model you want to predict with and provide the input data as a map with the new text (or texts) that you want to predict for. The input_data property is a map that uses the field ID as key. Here's an example:
This is one of the exceptions to the asynchronous behavior of BigML's API, therefore the Topic Distribution gets ready without stopping the source code progress in this case.
In many working scenarios, a batch prediction that allows predictions from a set of new data is more useful than a single prediction. In other words, a Batch Topic Distribution is usually preferred to a Topic Distribution.
It is pretty straightforward to create a Batch Topic Distribution from an existing Topic Model, where the dataset with name ds1 represents a set of rows with the text data to analyze:
It's likely that before creating a Batch Topic Distribution you will need to configure properties including fields mapping between Topic Model and the Dataset with the input data, or you may need to set another property to include the importance of each field as columns in your results. The way you should configure these properties is the same as for creating a Topic Model. The full list of all available properties is listed under API documentation. It's also important to know that contrary to single predictions, batch predictions are asynchronous in nature. Below is such an example:
Up to this point we were covering source code of scripts. In order to write the code in the BigML Dashboard, you can also use a pretty editor that supports colored syntax highlighting, auto-complete functions, and code format utility. Take it for a spin and build your scripts quickly.
Nevertheless, if you are at home with APIs and you can find more script creation answers here. To obtain results from WhizzML scripts we need to first run them, so let's see how to execute a Script in the Dashboard and how to carry out the same process by calling the right endpoint directly in the API.
First, the BigML Dashboard option: Look for your new script in the scripts list and click on it.
You will see a page like the one below, that is, the inputs you need to fill before you run. For instance, in the Topic Model script creation that we described at the beginning of this post, you just need to select the dataset you want to use to build your Topic Model from the dropdown.
It's mandatory to fill all the input fields with a grey icon so they are validated with a green icon (empty values are not accepted except for text inputs).
Finally, we will focus on how to execute a script through the API. To do this you need to compose a POST request with JSON content to /execute endpoint with two parameters: one will be the ID of the script you previously created, and the other one will be the "inputs", which is a list of pairs that follow the schema <input_name> <input_value>. These include all the inputs without a defined default value in your script. Let's see an example to showcase this idea: for the first script we used above, the input you need to fill is "ds1" with the dataset identifier you want to use to create a Topic Model. The complete request to the BigML API should be similar to this curl example:
curl "https://bigml.io/execution?$BIGML_AUTH" -X POST -H 'content-type: application/json' -d '{"script": "script/55f007d21f386f5199000003", "inputs": [["ds1", "dataset/55f007d21f386f5199000000"]]}'
We hope you enjoyed reading this quick tour on executing a script using the BigML API. For a more extensive list of execution parameters and how to access to the execution results, please visit the corresponding section in the API documentation. Notice that we didn't dive into the authentication description, but it is described here. Finally, for an extensive description of WhizzML you can visit the WhizzML page.
In the next blog post we will discover the internal mathematics that underlies Topic Models and what these mathematics might imply for you, the modeler.
Would you like to know more about Topic Models? Visit the Topic Models release page and check our documentation to create Topic Models, interpret them, and predict with them through the BigML Dashboard and API, as well as the six blog posts of this series.
In this post, the fourth one of our Topic Model series, we will briefly demonstrate how you can create a Topic Model by using the BigML API. As mentioned in our introductory post, Topic Modeling is an unsupervised learning method to discover the different topics underlying a collection of documents. You can also read the detailed process to create Topic Models using the BigML Dashboard and a real use case to predict the sentiment of movie reviews in the second and third posts respectively.
The API workflow to create a Topic Model is composed of four steps:
Any resource created with the API will automatically be created in your Dashboard too, so you can take advantage of BigML's intuitive visualizations at any time. In case you never used the BigML API before, all requests to manage your resources must use HTTPS and be first authenticated by using your username and API key to verify your identity. For instance, here is a base URL example to manage Topic Models.
For more details, check the API documentation related to Topic Models here.
1. Upload your Data
Upload your data in your preferred format from a local file, a remote file (using a URL) or from your cloud repository e.g., AWS, Azure etc. This will automatically create a source in your BigML account.
To do this, you need to open up a terminal with curl or any other command-line tool that implements standard HTTPS methods. In the example below we are creating a source from a remote file containing almost 110,000 Airbnb reviews of accommodations in Portland, Oregon.
curl "https://bigml.io/source?$BIGML_AUTH"
-H 'content-type: application/json'
-d '{"remote":"http://data.insideairbnb.com/united-states/or/portland/2016-07-04/data/reviews.csv.gz"}'
Topic Models only accept text fields as inputs so your source should always contain at least one text field. To find out how Topic Models tokenize and analyze the text in your data, please read our previous post.
2. Create a Dataset
After the source is created, you need to build a dataset, which computes basic statistics for your fields and gets them ready for the Machine Learning algorithm to take over.
curl "https://bigml.io/dataset?$BIGML_AUTH"
-H 'content-type: application/json'
-d '{"source":"source/68b5627b3c1920186f123478900"}'
3. Create a Topic Model
When your dataset has been created, you need its ID to create your Topic Model. Once again, although you may have many different field types in your dataset, the Topic Model will only use the text fields.
curl "https://bigml.io/topicmodel?$BIGML_AUTH"
-H 'content-type: application/json'
-d '{"dataset":"dataset/98b5527c3c1920386a000467"}'
If you don't want to use all the text fields in your dataset you can use either the argument input_fields (to indicate which fields you want to use as inputs) or the argument excluded_fields (to indicate the fields that you don't want to use). In this case, since we don't want to use the text field that contains the reviewer name, we define as our input data just the field containing the text of the reviews.
curl "https://bigml.io/topicmodel?$BIGML_AUTH"
-H 'content-type: application/json'
-d '{"dataset":"dataset/98b5527c3c1920386a000467",
Apart from the dataset and the input fields, you can also include additional arguments like the parameters that we explained in our previous post to configure your Topic Model.
4. Make Predictions
The main goal of creating a Topic Model is to find the topics in your dataset instances. Predictions for Topic Models are called Topic Distributions in BigML since they return a set of probabilities (one per topic) for a given instance. The sum of all topic probabilities for a given instance is always 100%.
BigML also allows you to perform a Topic Distribution for one single instance or for several instances simultaneously.
Topic Distribution
To get the topic probability distributions for a single instance, you just need the ID of the Topic Model and the values for the input fields used to create the Topic Model. In most cases, it may be just a text fragment.
curl "https://bigml.io/topicdistribution?$BIGML_AUTH"
-H 'content-type: application/json'
-d '{"topicmodel":"topicmodel/58231122983efc15d400002a",
"000005": "Lovely hosts, very accommodating - I was unable to
meet at the original check-in time so were flexible and let me
come an hour earlier. Clean, tidy, very cute room! Perfect -
thanks very much!"
Batch Topic Distribution
To get the topic probability distributions for multiple instances, you need the ID of the Topic Model and the ID of the dataset containing the values for the instances you want to predict.
curl "https://bigml.io/batchtopicdistribution?$BIGML_AUTH" -X POST -H 'content-type: application/json' -d '{"topicmodel":"topicmodel/58231122983efc15d400002a", "dataset":"dataset/98b5527c3c1920386a000467"}'
When the Batch Topic Distribution has been performed, you can download it as a CSV file simply by appending "download" to the Batch Topic Distribution URL.
If you want to use the topics as inputs to build another model (as we explain in the third post of our series), we recommend that you create a dataset from the Batch Topic Distribution. You can easily do so by using the argument output_dataset at the time of the Batch Topic Distribution creation as indicated in the snippet below.
curl "https://bigml.io/batchtopicdistribution?$BIGML_AUTH" -X POST -H 'content-type: application/json' -d '{"topicmodel":"topicmodel/58231122983efc15d400002a", "dataset":"dataset/98b5527c3c1920386a000467", "output_dataset": true}'
In the next post, we will explain how to use Topic Models with WhizzML.
Would you like to know more about Topic Models? Visit the Topic Models release page and check our documentation to create Topic Models, interpret them, and predict with them through the BigML Dashboard and API, as well as the six blog posts of this series.
In this blog post, the third one of our Topic Models series, we are showcasing how you can use BigML Topic Models to improve your model performance. We are using movie reviews extracted from the IMBD database to predict if a given review has a positive or a negative sentiment. Notice that in this post we will not dive into all the configuration options that BigML offers for Topic Models, for that we recommend that you read our previous post.
The Data
The dataset contains 50,000 highly polarized movie reviews labeled with their sentiment class: positive or negative. This dataset was built by Stanford researchers for their paper from 2011, which achieves an accuracy of 88.89%. The reviews belong to many different movies (a movie can only have up to 30 reviews) to avoid situations where the algorithm may learn non-generalizable patterns for specific plots, directors, actors or characters. The dataset is first split in two: 50% for training and the other 50% for testing.
As in any other dataset, the IMBD dataset, needs a bit of pre-processing so it can be used to properly train a Machine Learning model. Fortunately, BigML handles the major tasks of text pre-processing at the time of the Topic Model creation so you don't need to care about text tokenization, stemming, lower and upper cases or stop words. When you create the Topic Model, the text is automatically tokenized so each single word is considered a token by the model vocabulary. Non-word tokens such as symbols and punctuation marks are automatically removed. One interesting task for further study would be to replace symbols that may indicate sentiment to test how it improves the model e.g., ":-)" becomes "SMILE". BigML also removes stop words by default, however, in this case negative stop words may indicate sentiment so we can opt to leave them. Finally, stemming is applied over all the words so terms with the same lexeme are considered the same unique term (e.g., "loving" and "love").
The only data cleansing task that we need to perform beforehand for this dataset is removing the HTML tag "<br>" which is seen frequently in the review content as seen below. You can do it at the time of the model creation using the configuration option "Excluded terms" explained below.
Single Decision Tree
To get a first sense of whether the text in the reviews have any power to predict the sentiment, we are going to build a single decision tree by selecting the "sentiment" as the objective field and using the reviews as input.
We assume in this post that you already know the typical process to build any model in BigML, i.e., how to upload the data to BigML and how to create a dataset. If you are not familiar then take a peak here.
When you are done creating your dataset, you can see that the movie reviews dataset is composed of two fields: sentiment (positive or negative) and reviews (the review text). Not surprisingly, the words "movie" and "film" are the most frequent ones in the collection (see image below).
If we perform a 1-click model, we obtain the decision tree in a few seconds. As you mouse over the root node and go down the tree until you reach the leaf nodes, you can see the different prediction paths. For example, in the image below you can see that if the review contains the terms "bad" and "worst" then it is a "negative" review with 92.99% of confidence. As expected, we can find words in the nodes such as "awful", "boring", "wonderful", "amazing", which are the terms that best split the data to best predict sentiment.
Confidence values seem pretty high for most prediction paths in this tree, but to measure its predictive power we need to evaluate it by using data it has not seen before. We use the previously set aside 50% of the dataset, which contains the remaining 25,000 movie reviews that have not been used to train our model.
The evaluation yields an overall accuracy of 75.52%, which is not that bad for a 1-click model. However, we can definitely improve on this!
Discovering the Topics in the Reviews
We already saw that single terms are not very bad predictors of movie review sentiment. But having a particular term in a given review may not always be applicable, so the model gets quite complex as it uses lots of possible term combinations in order to get to the best prediction. This is because each person has his/her own way of expressing ideas with different vocabulary although the reviews may transmit very similar concepts. What if we could group those terms thematically related so that the model wouldn't have to always look for single terms but instead groups of terms? You can now implement that approach in BigML by using Topic Models!
In the interest of time, we will neither go over the step by step process to create a Topic Model in BigML Dashboard nor cover all Topic Model configuration options here. We will just explain the relevant options that suffice in solving our problem of predicting the sentiment.
As we mentioned at the beginning of this post, the only data cleansing needed is the removal of the HTML tag "<br>", so instead of using the 1-click Topic Model option we need to configure the model first. From the dataset, we select the "Configure Topic Model" option.
When the configuration panel has been displayed, use the "Excluded terms" option to type in the term "<br>" and click in the "Create Topic Model" green button below.
When our Topic Model is created, we can filter and inspect it by using the two visualizations provided by BigML.
In the first view, you will be able to see at-a-glance all the topics represented by circles sized in proportion to the topic importances. Furthermore, the topics are displayed in a map layout, which depicts the relationship between them such that closer topics are more thematically related.
However, probably the best way to get an overview of your topics is to take a look at the second visualization: the bar chart. In this chart, you can view the top terms (up to 15) by topic. Each term is represented by a bar whose length signifies the importance of the term within that topic (i.e., the term probability). As you can see in the image below, you can use the top menu filters to better visualize the most important terms by topic.
The Topic Model finds the terms that are more likely to appear together and groups them into different topics. This probabilistic method yields quite accurate groupings of terms that are highly thematically related. If we inspect the model topic by topic, we can easily see that Topics 35 and 38 reveal terms like "bad", "poor", "boring", "stupid", "waste", etc., all of which are clearly related to negative reviews. On the other hand, we can see positive sentiment topics like Topic 20 that includes words like "love", "beautiful", "perfect", "excellent", "recommend", etc. Then there are other more neutral topics that indicate the genre of the film or its target audience e.g., Topic 21 containing "kids", "animation", "cartoons", etc. We expect such topics that are not apparently correlated with any kind of sentiment to be identified as less important in our predictive model.
Including Topics in our Model
Now that we have seen that the topics discovered in our dataset seem to follow a general logic that may be useful to predict sentiment, we can include them as predictors. You can easily achieve this by performing a Batch Topic Distribution. Simply click in the corresponding option found within the 1-click menu and select the dataset used to train the Topic Model.
The Batch Topic Distribution calculates the probability of each topic for a given instance, so we will obtain a distribution of probabilities, one per topic, for all our dataset instances.
When the Batch Topic Distribution is created, we can access the dataset that contains the topic distributions. As seen below, a new field per topic is created whereby its values represent the probabilities for each of the instances.
Now we can recreate a model by using the topics as input fields. Amazingly, the resulting tree is very different from the one created before. Now the most important values to predict sentiment are not the single terms anymore, but the topics!
If we click in the Summary Report to see the field importances for our model, we can see that topics are much more important than full review texts. Not only that, we can also see that topics containing terms that may transmit sentiment are more important than neutral ones related to film genres or plots.
Time to evaluate our model!
We first need to perform a Batch Topic Distribution over the test dataset so it contains the same topic fields that the model uses to calculate predictions. We need to follow the same steps explained before, but this time we will select our test dataset instead of the training dataset. Once the Batch Topic Distribution has been performed, we can use the output dataset to evaluate our model.
As you can see, by including the topics in our model, we are able to bump up model accuracy to 80.04%, an improvement of 5% compared to the previous model without topics. This may sound small, but in real terms it means an increase of 1,250 reviews that are now correctly classified. Add to that the fact that the increase in performance has been realized without any fancy model configuration or complex feature engineering.
Using Random Decision Forests
So far we used a single tree to be able to easily visualize the differences between the modeling approaches we took (i.e., with or without topics). But it is well known that ensembles of trees usually perform significantly better in great majority of cases. Thus, using the same dataset, we build a Random Decision Forest and evaluate how it does. As expected, a Random Decision Forest with 100 trees swiftly reaches 84.05% accuracy.
We have seen how anyone working with text data can achieve significant performance enhancements by using topics as additional input fields in their models. With more time, one can still improve on our Random Decision Forests, but maybe that is best left for another day.
In the next post, we will cover how to programming Topic Models.
Would you like to know more about Topic Models? Visit the Topic Models release page and check our documentation to create Topic Models, interpret them, and predict with them through the BigML Dashboard and API, as well as the six blog posts of this series.
BigML is bringing a new resource called Topic Models to help you discover the topics underlying a collection of documents. The main goal of Topic Modeling is finding significant thematically related terms ("topics") in your unstructured text data. You can find an English example in the image below, which shows the topics found in a newspaper article about unemployment. BigML Topic Models can analyze any type of text in seven different languages: English, Spanish, Catalan, French, Portuguese, German and Dutch.
The resulting topics list can be used as a final output for information retrieval tasks, collaborative filtering, or for assessing document similarity among others. Topics can also be very useful as additional input features in your dataset towards other modeling tasks (e.g., classification, regression, clustering, anomaly detection).
In the first blog post of our series of six posts about Topic Models, we made a high-level introduction of Topic Modeling and the BigML implementation. In this post we will cover more in detail the fundamental steps required to find the topics hidden in your text fields by using the BigML Dashboard:
1. Upload your Data
Upload your data to your BigML account. You can drag and drop a local file, connect BigML to your cloud repository (e.g., S3 buckets) or copy and paste a URL containing your data. Once you have uploaded your data to BigML, the type for each field in your dataset will be automatically recognized by BigML.
Topic Models will only use the text fields in your dataset to find the relevant topics, so you need at least one text field. If you have a dataset with several types of fields, only the text fields will be considered as inputs to build the Topic Model. If multiple text fields are given as inputs they will be automatically concatenated so the content for each instance can be considered as a "bag of words" by the Topic Model.
BigML provides several configuration parameters for Text Analysis at the source level so you can decide the tokenization strategy e.g., whether you want to keep stop words. This configuration is used to create the vocabulary that will be used to build your models (see image below). However, Topic Models don't take into account this configuration, you can configure most of these options at the time of the model creation from the Topic Model configuration panel explained in the 3rd step below. Tokenization and stemming are the only two options not configurable for Topic Models. Regarding tokenization, Topic Models will always tokenize your text fields by terms. On the other hand, stemming will be always active by default (e.g., the words "play", "played", "playing" will always be considered one unique term "play").
2. Create the Dataset
From your Source view, use the 1-click Dataset option to create a dataset. This is a structured version of your data ready to be used by a Machine Learning algorithm.
For each of your text fields you will get a distribution histogram of your terms ordered by their frequency in the dataset. Next to the histogram, you will find an option to open the tag cloud of your text field as shown in the image below.
3. Create a Model
If you want to use the default parameter values, you can create a Topic Model from the dataset by using the 1-click Topic Model option. Alternatively you can tune the parameters by using the Configure Topic Model option.
BigML allows you to configure the following parameters:
- Number of topics: the total number of topics to be discovered in your dataset. You can set this number manually or you can let the algorithm find the optimal number of topics according to the number of instances in your dataset. The maximum number of topics allowed is 64.
- Number of top terms: the total number of top terms within each topic to be displayed in your Topic Model. By default it is set to 10, the maximum is 128 terms.
- Term limit: the total number of unique terms to be considered for the Topic Model vocabulary. By default it is set to 4,096, the maximum is 16,384 unique terms.
- Text analysis options:
- Case sensitivity: whether the Topic Model should differentiate terms with lower and upper cases. If you activate this option, "Grace" and "grace" will be considered considered two different terms. By default Topic Models are case insensitive.
- Bigrams: whether the Topic Model, apart from single terms, should include pairs of terms that typically go together (e.g.: "United States" or "mobile phone"). By default this option is not active.
- Stop words: whether the Topic Model should remove stop words (i.e., words such as articles, prepositions, conjunctions, etc.). By default stop words are removed.
- Excluded Terms: you can select specific terms to be excluded from the model vocabulary.
- Sampling options: if you have a very large dataset, you may not need all the instances to create the model. BigML allows you to easily sample your dataset at the model creation time.
4. Analyze your Topic Model
BigML provides two different views so you can analyze the topics discovered in your text.
Topic Map
The topic map shows the topics in a map view, where you can get a sense of both the topic importances and the relationship between them. Each circle represents a different topic, and its size depicts the "Topic probability" — this is the average probability of the topic to appear in a given instance of the original dataset. The distance between the circles represents how close or far topics are thematically speaking.
By mousing over each topic you can see the top most important terms for that topic (see "Number of top terms" in the third step above). The terms are ordered by their importance within each topic as measured by the "Term probability". All term probabilities for a given topic should sum up to 100%. A given term can be attributed to more than one topic, e.g., the word "bank" may be found in a topic related to finances, but also in a topic related to geology (river bank).
If you mouse over each term within a topic, you will find all the stemmed forms for that term. Stemming is the process of taking just the lexeme for the terms so e.g. the terms "great", "greatness" and "greater", are considered the same term, since they all have the same lexeme: "great".
The map has the following options found in the top menu:
- Filtering options:
- Topic probability slider to filter the topic circles by size (importance) i.e., the average probability of a topic in a given instance in the original dataset used to create the model.
- Search box to filter topics and terms.
- Include/exclude labels for topics.
- Reset filters option.
- Export options:
- Export map in PNG format.
- Export model in CSV format.
- Edit topic names by clicking in a Topic circle, and you can change its name by using the edit icon. This is very useful to interpret the main theme of each topic, when you make predictions afterwards.
- Tag cloud to see the terms composing a topic in a tag cloud.
Term Chart
This view shows the topics and their terms in a bar chart to give you a quick overview of the terms composing the topics as well as their importances. In the horizontal axis, you can see each term probability for each of the top terms of a given topic (you can select up to 15 terms).
The chart has the following options found in the top menu:
- Filtering options:
- Term probability slider to filter terms by their probability in a given topic.
- Search box to filter topics and terms.
- Number of terms to select the maximum terms shown per topic in the chart.
- Dynamic axis option to adjust the axis scale to the current filters.
- Reset filters option.
- Export options:
- Export chart in PNG format (with or without legends).
- Export model in CSV format.
5. Make Predictions
The main objective of creating a Topic Model is to find the relevant topics for your dataset instances. Topic Model predictions are called Topic Distributions in BigML. For each instance you will get a set of probabilities (one per topic) indicating their relevance for that instance. For any given instance, topic probabilities should sum up to 100%.
BigML also allows you to make predictions for a single instance, Topic Distribution, or for several instances simultaneously, Batch Topic Distribution.
Topic Distribution
To get one single prediction for an input text, click the Topic Distribution option as shown in the image below.
You will get a form containing the input fields used to create the Topic Model. Include any text in the corresponding input field boxes. BigML automatically computes each topic probability for that input text. You can see topic probabilities in a histogram like the one shown in the image below. You can also see the terms within each topic by mousing over each topic.
Batch Topic Distribution
If you want to make predictions for several instances simultaneously, click the Batch Topic Distribution option as shown in the image below.
Then you need to select a dataset containing the instances for which you want to make the predictions. It can be the same dataset used to create the Topic Model or a different dataset.
When your batch prediction finishes, you will be able to download the CSV file and see the output dataset.
In the next post, we will cover a real Topic Modeling use case to uncover the hidden topics of movie reviews from the IMBD database to predict the sentiment behind the reviews.
Would you like to know more about Topic Models? Visit the Topic Models release page and check our documentation to create Topic Models, interpret them, and predict with them through the BigML Dashboard and API, as well as the six blog posts of this series.
At BigML we're fond of celebrating every season by launching brand new Machine Learning resources, and our Fall 2016 creation will be headlined by our Latent Dirichlet Allocation (LDA) implementation. After months of meticulous work, BigML's Development Team is making the LDA primitive available in the Dashboard and API simultaneously under the name of Topic Models. With Topic Models, words in your text data that often occur together are grouped into different "topics". With the model, you can assign a given instance a score for each topic, which indicates the relevance of that topic to the instance. You can then use these topic scores as input features to train other models, as a starting point for collaborative filtering, or for assessing document similarity, among many other uses.
This post gives you a general overview of how LDA has been integrated into our platform. This will be the first of a series of six posts about Topic Models that will provide you with a gentle introduction to the new resource. First, we'll get started with Topic Models through the BigML Dashboard. We'll follow that up with posts on how to apply Topic Models in a real-life use case, how to create Topic Models and make predictions with the API, how to automate this process using WhizzML, and finally a deeper, slightly more technical explanation of what's going on behind the scenes.
Why implement the Latent Dirichlet Allocation algorithm?
There are plenty of valuable insights hidden in your text data. Plain text data can be very useful for content recommendation, information retrieval tasks, segmenting your data, or training predictive models. The standard "bag of words" analysis BigML performs when it creates your dataset is often useful, but sometimes it doesn't go far enough as there may be hidden patterns in text data that are difficult to discover when you're only considering occurrences of a single word at a time. Often, the Latent Dirichlet Allocation algorithm is able to organize your text data in such a way that it causes some of this hidden information to spring to the fore.
There are three key vocabulary words we need to know when we're trying to understand the basics of Topic Models: documents, terms, and topics. Latent Dirichlet Allocation (LDA) is an unsupervised learning method that discovers different topics underlying a collection of documents, where each document is a collection of words, or terms. LDA assumes that any document is a combination of one or more topics, and each topic is associated with certain high probability terms.
Here are some additional useful pointers on our Topic Models:
- Topic Models work with text fields only. BigML is able to analyze any type of text in seven different languages: English, Spanish, Catalan, French, Portuguese, German and Dutch.
- Each instance of your dataset will be considered a document (a collection of terms) and the text input field the content of that document.
- A term is a single lexical token (usually one or more words, but can be any arbitrary string).
- A topic is a distribution over terms. Each term has a different probability within a topic: the higher the probability, the more relevant is a term for that topic.
- Several topics may have a high probability associated with the same term. For example, the word "house" may be found in a topic related to properties but also in a topic related to holiday accommodation.
- Each document will have a probability associated with each topic, according to the terms in the document.
Professor David Blei, the inventor of LDA, gives a very nice tutorial on it here:
To use Topic Models, you can specify a number of topics to discover, or let BigML pick a reasonable number of topics according to the amount of training data that you have. Another parameter allows you to also use consecutive pairs of words (bigrams) in addition to single words when fitting the topics. You may also specify case sensitivity. By default, Topic Models automatically discard stop words and high frequency words that occur in almost all of the documents as they typically do not help determine the boundaries between topics. You can also make predictions with your Topic Models both for individual instances and in batch in which case BigML will assign topic probabilities to each instance you provide; the higher the probability, the greater the association between that topic and the given instance.
For instance, imagine that you have a telecommunications company and you want to predict customer churn at the end of the month. For that, we will use all the information available in the customer service data collected when they call or send emails asking for help. Thanks to Topic Models you can automatically organize your data in a way that let's you define exactly what a client's correspondence was "about". Your Topic Model will then return a list of top terms for each topic found in the data. By analyzing your text data you can also use these topics as input features in order to better cluster your customer correspondences into distinct groups you can devise actionable relationship management strategies for. The image below reveals three potential topics that might be extracted from a dataset of such correspondence. One may easily name the first topic "complaints", the second "technical issues", and the third "pricing concerns".
Want to know all about Topic Models?
Visit the Topic Models release page and check our documentation to create Topic Models, interpret them, and predict with them through the BigML Dashboard and API, as well as the six blog posts of this series.
BigML's Fall 2016 Release is here! Join us on Tuesday, November 29, at 10:00 AM PST (Portland, Oregon / GMT -08:00) / 07:00 PM CET (Valencia, Spain / GMT +01:00) for a FREE live webinar to get a first look at the latest version of BigML! We'll be focusing on Topic Models, the latest resource that helps you find thematically related terms in your unstructured text data.
The BigML Team has been working hard to implement the underlying Latent Dirichlet Allocation (LDA) technique into the BigML Dashboard and API. With Topic Models you can use your identified topics as final output for information retrieval tasks, collaborative filtering, or for assessing document similarity, among other use cases. You can also use those topics as input features to train other models, such as Classification or Regression models, Cluster Analysis, Anomaly Detection, or Association Discovery.
Topic Models come with two visualizations to better analyze the topics discovered in your text data: a Topic Map and a Term Chart. The Topic Map presents a map view for you to both see the topic importances at a glance and the relationship between them, whereas the Term Chart displays the topics and their terms in a bar chart view to give you a quick overview of the terms composing the topics as well as their importances. While BigML's Topic Models only use the text fields in your dataset, they can analyze any type of text in seven different languages: English, Spanish, Catalan, French, Portuguese, German and Dutch.
BigML offers Topic Distributions to make predictions for a given single instance, and Batch Topic Distributions to predict several instances simultaneously. In the related prediction, BigML provides a set of probabilities for each instance (one probability per topic), which indicates the relevance of a certain topic for that instance.
BigML has been democratizing Machine Learning since 2011, and today marks an important milestone to more systematically do so: we are happy to announce the BigML Certifications for partners that want to master BigML to successfully deliver real-life Machine Learning projects on behalf of their customers. Regardless if you are a software developer, systems integrator, analyst, or scientist our certifications programs pay for themselves by taking your skill set and ability to deliver data-driven solutions to a whole new level. We invite all of you to register with one of our upcoming BigML Engineer and BigML Architect certification waves.
Would you like to know more about Topic Models? Join us on Tuesday, November 29, at 10:00 AM PST (Portland, Oregon / GMT -08:00) / 07:00 PM CET (Valencia, Spain. GMT +01:00). Be sure to reserve your free spot today as space is limited! Following our tradition, we will also be giving away BigML t-shirts to those who submit questions during the webinar. Don't forget to request yours!
Innovative technology, leading edge startups and brilliant entrepreneurship were at display at London's Tobacco Dock for the 2016 edition of Wired conference this week, on November 3 and 4. The inspiring venue and a great line up of speakers in this two-day event made for a truly special gathering along with the Test Lab, which was organized by Telefónica. PreSeries, the joint venture between Telefónica Open Future_ and BigML, has been invited to participate, and made the best of it by interactively demoing its capabilities thanks to our Alexa integration. Nothing better to capture the moment than a few pictures!
In addition to the PreSeries booth, Telefónica presented an impressive array of seven more companies from its portfolio:
- Saffe, a mobile payment app that leverages world-class facial recognition technology
- Knomo, a company that creates fashionable accessories to get life organized
- Pzizz, an app that helps people beat insomnia and get great sleep
- Cru Kafe, a company that offers one of the best ethical and organic coffee labels in the world
- Voicemod, a platform that lets you add real time audio modification to your app with a simple and free SDK
- 52 MasterWorks, the first crowdfunding company investing in art
- Pulsar, an audience intelligence platform that provides insights on customer behaviour from social media data
Over 700 people visited the Test Lab, which was even further expanded the evening of the first exhibition day, when 13 additional companies joined the event.
The Test Lab and WIRED2016 event will close its doors today (Friday, November 4, at 06:45 PM GMT). We sincerely thank the Wired community and Telefónica for this great opportunity to showcase the value of PreSeries to fellow early stage startups and investors that are thirsty for quantifiable and objective feedback about their growth potential.


text:
- Home
- News
- Columnists
- Reviews
- Special Reports
- Blogs
- Literature
- Poetry
- Lyrics
- Authors
- Community Events
Football can be safer
At first glance, it almost seems ridiculous to think in a day and age where football players are bigger and faster than ever and showing no signs of letting up that the game could be made safer. NFL commissioner Roger […]
Football can be safer
By James Moore
At first glance, it almost seems ridiculous to think in a day and age where football players are bigger and faster than ever and showing no signs of letting up that the game could be made safer. NFL commissioner Roger […]
Baltimore Arena League team unveils identity
Co-owner Roger Mody, Mayor Catherine Pugh, and head coach Omarr Smith announced that the new Arena Football team will be called the Baltimore Brigade with blue, black, and white
Super Bowl LI will be the Patriots vs the Falcons
Since last week's divisional round, we have seen Donald Trump sworn in as president and countless women take to the streets in unity, over what, I am not exactly
NFL Conference Championships are next
Before we get to the games from this past weekend, lets look at what has become proof the NFL, both its owners and commissioner, do not give a rat's
What's the best name for Baltimore's new Arena Football League team?
Baltimore Arena Football League Facebook page With the schedule being released to the general public and the identity of the team set to be revealed shortly, here's a list of possible
NFL Wild Card Weekend whittles down the field
Wild Card weekend finally arrived and for some teams, Oakland and Detroit, it was a few weeks late while for others, Seattle and Houston, it provided them with a
#NFL Week 16: Merry Christmas Cleveland
Yes, Cleveland, there is a Santa Claus and he wears lightning bolts. The Mistake by the Lake can finally call themselves winners after beating the soon to be Los
NFL Week 15: Ravens keep hope alive
Dear Mr. Kroenke, I am officially tossing in my hat and applying for the head coaching position of the Los Angeles Rams. I realize just how important it is for
NFL Week 13: A leap over the lines and the first coach fired
With most of the NCAA football regular season over, this looked to be a week that the NFL would enjoy without worrying about viewer distractions like the more exciting
NFL Week12: Ravens looking better and the Browns don't lose
Wow, let's hear it for the NFL league office for allowing players to wear custom painted cleats this past week. Nothing shows support of a cause like 53 grown

subset: commoncrawl


text: Hot ringtones on Audiko.net! It's easy to download a free bonde ringtones for your iPhone or mobile. Cut your favorite measures from your favorite hit - Love from Paddington, By Michael Bond, Read by Hugh Bonnevil, Paddington and the Grand Tour, By Michael Bond, Read by Paul, MC Kelvinho - Bonde Do Cifrão 2, or any other track. On Audiko you can have your ringtone both in the MP3 and m4a formats, played by both mobile phones and iPhones. Just select a track, and choose free bonde ringtones download option.


text:
Xiamen Warmness Seaview Hotel
Find Hotel Rates
Recent Searches
Wego searches multiple travel sites so you can find and compare the best rates.
Xiamen Warmness Seaview Hotel Hotel Reviews
Based on reviews from across the web.
Xiamen Warmness Seaview Hotel Hotel Details
- Highspeed Internet
- Parking
- Restaurant
- Airconditioning
- Non-smoking Rooms
- Safe/Vault
- Babysitting/Child Services
- Bar/Lounge
- Parking
- Restaurant
- Baby sitting
- Boutiques/stores
- Coffee shop
- Dry cleaning
- Internet services
- Lounges/bars
- Pets allowed
- Room service
- Safe deposit box
- Valet parking
- Water sports
- Air conditioning
- Bathtub
- Coffee/Tea maker
- Hairdryer
- Internet access
- Kitchenette
- Minibar
- Non-smoking
- Outdoor space
- Safe
- Separate tub and shower
- Shower
Check-in: 15:00
Check-out: 11:00
Ideally located in the prime touristic area of Si Ming District - Zeng Cuo An / Huang Cuo, Xiamen Warmness Seaview Hotel promises a relaxing and wonderful visit. Both business travelers and tourists can enjoy the hotel's facilities and services. Shops, safety deposit boxes, Wi-Fi in public areas, pets allowed, tours are there for guest's enjoyment. Comfortable guestrooms ensure a good night's sleep with some rooms featuring facilities such as refrigerator, non smoking rooms, microwave, laptop safe box, separate shower and tub. Enjoy the hotel's recreational facilities, including garden, water sports (non-motorized), before retiring to your room for a well-deserved rest. No matter what your reasons are for visiting Xiamen, Xiamen Warmness Seaview Hotel will make you feel instantly at home.


text:
Crescent Sail YC Regatta August 25, 2007 deedsphotos
More by this Author
- 29
More than 100 sailboat racing pictures and videos taken on Lake St. Clair, San Francisco Bay and other places by Ralph Deeds and others.
- 10
Lake St. Clair links Lake Huron and Lake Erie between Canada and the U.S. with a deep water port on the Detroit River. It's a veritable paradise for sailors, fishermen, duck hunters swimmers and other water sportsmen.
- 6
Diego Rivera Murals This web page tells the story of Diego Rivera's ill-fated mural commissioned by Nelson Rockefeller in Rockefeller Center in New York City. In a poem entitled "I Paint What I See, "E.B....
No comments yet.


text:
Yeah I'm thinking that's just what it is - been 8 days since my last dose so hopefully O is in the next couple of days.
Bloating and tenderness has subsided. Twinges from both sides today
Mamasupial (01-03-2016),sarah1502 (01-03-2016)
I'm feeling more side effects although im not sure if its from the clomid or metaformin feeling bloated today and also have been waking up between 3-4am every night really hot as a result I've been tired through out the day and very lethargic.. I really hope this is our month sabochan!
I'm not on metformin so i don't know the side effects for that but hot flushes is a side effect I've had from the start with the clomid. I have to go to sleep with the fan on flat out or I just toss and turn all night. Poor DH is freezing when he wakes up
Fingers crossed sarah! Though, if this isn't the month, I'd be happy with a xmas baby too lol
I am in the exact same boat for both! I always sleep with the aircon every night without fail and DP is freezing
I also wouldn't mind if I didn't fall pregnant this month I've got my brothers wedding early October so I've been worried that I would miss it! And im in the bridal party.. That being said my sister gets married in a few weeks and I was worried I would be heavily pregnant or in hospital on her wedding day too and here I am not even pregnant
I'll be 36 weeks at my best friends wedding lol
I know what you mean. Sort of hoping I do end up with a xmas baby because then we won't be expected to travel anywhere for xmas day lol.
Mamasupial (02-03-2016),sarah1502 (04-03-2016)
I'm CD 16 still no sign of ovulation but I'm still feeling cramping/pinching sensation on and off also feeling really tired and lethargic.. How are you going sabochan
Pregnant for the first-time?
Not sure where to start? We can help!
Our Insider Programs for pregnancy first-timers will lead you step-by-step through the 14 Pregnancy Must Dos!


text:
This is a digital painting made from a digital photo of ships in Penn Cove near Coupeville, Whidbey Island, Washington in the Pacific Northwest.
boat coupeville island landscape lawler ocean sailboat sea washington whidbey
Very cool, Boats are just awesome, love the sails
Loading more work by Rick Lawler...
10% off
for joining the Redbubble mailing list
Receive exclusive deals and awesome artist news and content right to your inbox.
Free for your convenience.


text:
'Symon S.' Stripe Sweater
Product not available
Have questions?
Chat with us or call 1.888.282.6060.
HideDetails & Care
Smooth, allover ribbing contours the stretchy fit of a soft-touch crewneck sweater with side slits.
- Approx. length from shoulder: 22".
- Measurements taken from size Medium and may vary slightly by size.
- Viscose rayon/polyester; dry clean.
- By Theory; imported.
- Individualist.


text:
Set on a lovely island between downtown Sarasota and the Gulf of Mexico is St. Armand's Circle. This is a unique spot just two minutes walking from the beach which blends the vestiges of classical design and architecture with trendy shops restaurants and storefronts. The little island itself has pristine beaches, handsome hotels and quiet neighborhoods, but the central circle is the hub around which the social activity of St. Armand's thrives.
Historically much of the land around Sarasota's waterfronts was purchased and developed by John Ringling. He envisioned an urban setting informed by the classic structures of high art and architecture as even his own property is a complete Venetian mansion. St. Armand's key is very much a testament to the vision of John Ringling in that it balances quality retail developments amidst fine sculptures, manicured lawns and a laid back atmosphere.
St. Armand's Circle is a roundabout which has a small park in the center. Much of this area is filled in by statues representing figures from Greek and Roman Myths and the embodiment of virtues. Tall palms are mixed in with ornate lamp stands and a display of rectangular monuments recounts the role many played in the history of the American Circus and early forms of public entertainment. The circle is best reached by direct causeway from downtown Sarasota and parking in the circle itself tends to be difficult. However many side streets fan out in all directions from this circle and they are full of shops, restaurants and more parking spaces.
A popular time of day for visitors is late afternoon to early evening. Many come for a sunset view and to enjoy the restaurants in the evenings. There are around 130 different stores offering everything from boutique fashion, to spa treatments, to specialty stores to restaurants, to jewelers, to travel agencies to medical offices. One of the most coveted restaurant experiences is the Columbia Restaurant that has strong roots historically. Many of the dishes here have a strong Spaniard/Cuban influence and are quite memorable. The Daquiri Deck is also a great place for a refreshing beverage. A well run public bus system serves St. Armand's connecting it with Sarasota and Long Boat Key.
Being a center for artistic intrigue and retail attracts quite a few events to St. Armand's. The Art festival has been running for over a decade now, there are live music performances, classic car shows, parades and holiday activities and many exciting displays both in and out of season. Christmas decorations are a spectacular sight too. St. Armand's Circle is a unique place to shop, eat, rest and abide in the refined comforts of culture.


text:
Easy Hexes in Madrid, Spain
Welcome! My name is Phelan; I am a white witch and root worker. I am located in Atlanta, GA where I practice white witchcraft, I specialize in uniting or reuniting lovers, I can also help you with spell caster located in Atlanta, spiritual consultations or psychic readings in person. Feel free to create an account if you have questions or give me a call at my Atlanta number: 678-918-3333, I can help in all aspects of your life through magic even if you are located in Madrid, Spain.
ContactPhelan.com has an ethical commitment to provide our clients with the best service in Madrid, Spain from our Atlanta location. If you are thinking about our "Easy Hexes" in Madrid, Spain or " Easy Love Spells " in Chandler, Arizona create an account and I will be more than happy to discuss your situation or any other questions that you may have.
Spirit Guides speak to me through the means of clairaudience, which is my ability to hear clearly on a psychic level, through the art of clairaudience much can be heard to change the path of a esoteric client, meet with me today to learn how you can change your path.
May your journey be safe and pleasant, may your lessons however hard they may be, be learned well. Carry with you in mind and in heart love and compassion of self, self-respect and a desire to learn and to grow spiritually. May the Mother bless you with light, love, abundance and prosperity!
Blessed Be, Phelan
Easy Hexes | Past Life Lovers | Madrid, Spain


text:
Search Results: (1-15 of 3138 records)
|Pub Number
|Revenues and Expenditures for Public Elementary and Secondary School Districts: School Year 2013-14 (Fiscal Year 2014) (NCES 2016-303)
This First Look report presents data on public elementary and secondary education revenues and expenditures at the local education agency (LEA) or school district level for fiscal year (FY) 2014. Specifically, this report includes findings from the following types of school finance data:
|Enrollment and Employees in Postsecondary Institutions, Fall 2015; and Financial Statistics and Academic Libraries, Fiscal Year 2015: First Look (Provisional Data)
This provisional First Look report includes fully edited and imputed data from the Integrated Postsecondary Education Data System (IPEDS) spring 2016 data collection, which included four survey components: Enrollment for fall 2015; Finance for fiscal year 2015; data on employees in postsecondary education for Fall 2015; and data for Academic Libraries for fiscal year 2015.
|IPEDS Media Primer
IPEDS brochures inform data users (e.g., researchers, policy makers, members of the media, the general public, etc.) about the Integrated Postsecondary Education Data System. The Media Primer describes data collected through IPEDS and when they become available. It also lists ways in which members of the press can interact with the data.
|Instructional Time for Third- and Eighth-Graders in Public and Private Schools: School Year 201112
This Statistics in Brief examines the amount of time that students in grades 3 and 8 spent on different activities in 201112 and compares how, if at all, this time varied by activity, school sector, and grade.
|IPEDS Graduation Rates Brochure
IPEDS brochures inform data users (e.g., researchers, policy makers, members of the media, the general public, etc.) about the Integrated Postsecondary Education Data System. The Graduation Rates (GR) Brochure explains to a non-technical audience how cohorts are established and graduation rates are calculated in IPEDS. It also provides timelines for the release of GR data and a list of key terms.
|Best Practices for Determining Subgroup Size in
Accountability Systems While Protecting Personally
Identifiable Student Information
The Every Student Succeeds Act (ESSA) of 2015 (Public Law 114-95) requires each state to create a plan for its statewide accountability system. In particular, ESSA calls for state plans that include strategies for reporting education outcomes by grade for all students and for economically disadvantaged students, students from major racial and ethnic groups, students with disabilities, and English learners. In their plans, states must specify a single value for the minimum number of students needed to provide statistically sound data for all students and for each subgroup, while protecting personally identifiable information (PII) of individual students. This value is often referred to as the "minimum n-size."
Choosing a minimum n-size is complex and involves important and difficult trade-offs. For example, the selection of smaller minimum n-sizes will ensure that more students' outcomes are included in a state's accountability system, but smaller n-sizes can also increase the likelihood of the inadvertent disclosure of PII. Similarly, smaller minimum n-sizes enable more complete data to be reported, but they may also affect the reliability and statistical validity of the data.
To inform this complex decision, Congress required the Institute of Education Sciences (IES) of the U.S. Department of Education to produce and widely disseminate a report on "best practices for determining valid, reliable, and statistically significant minimum numbers of students for each of the subgroups of students" (Every Student Succeeds Act of 2015 (ESSA 2015), Public Law 114-95). Congress also directed that the report describe how such a minimum number "will not reveal personally identifiable information about students." ESSA prohibits IES from recommending any specific minimum number of students in a subgroup (Section 9209).
IES produced this report to assist states as they develop accountability systems that (1) comply with ESSA; (2) incorporate sound statistical practices and protections; and (3) meet the information needs of state accountability reporting, while still protecting the privacy of individual students.
As presented in this report, the minimum n-size refers to the lowest statistically defensible subgroup size that can be reported in a state accountability system. Before getting started, it is important to understand that the minimum n-size a state establishes and the privacy protections it implements will directly determine how much data will be publicly reported in the system.
|NATES 2013: Nonresponse Bias Analysis Report
The 2013 National Adult Training and Education Survey (NATES) was a pilot study that tested the feasibility of using address-based sampling and a mailed questionnaire to collect data on the education, training, and credentials of U.S. adults. This report presents study findings related to nonresponse bias. Nonresponse adjustments corrected for bias on key outcome measures, but not for many background variables. Auxiliary data were found to be of potential use in correcting this bias.
|NCES-Barron's Admissions Competitiveness Index Data Files: 1972, 1982, 1992, 2004, , 2008, 2014
The NCES−Barrons Admissions Competitiveness Index Data Files: 1972, 1982, 1992, 2004, 2008, 2014 (NCES 2015-332) contain the Barrons college admissions competitiveness selectivity ratings for 1972, 1982, 1992, 2004, 2008, 2014 along with the NCES Higher Education Information System (HEGIS) FICE ID and Integrated Postsecondary Education Data System (IPEDS) UNITID codes and the Office of Postsecondary Education OPEID codes of each postsecondary institution included. Also included are the city and state of each institution included in the Barrons lists. The years selected correspond to the years that students in the longitudinal studies (NLS-72, HS&B, NELS:88, ELS-2000,HSLS:09, and BPS) initially attended the 4-year postsecondary institutions. Each of the six NCES−Barrons index files is available in a separate worksheet in an Excel workbook file that is in Excel 19972003 compatible format.
|Public-Use Data Files and Documentation (FRSS 107): Programs and Services for High School English Learners
This file contains data from a fast-response survey titled "Programs and Services for High School English Learners." This survey provides the first nationally representative data on programs and services for high school English learners (ELs). NCES released the results of this survey in the First Look report Programs and Services for High School English Learners in Public School Districts: 2015 16 (NCES 2016-150).
Questionnaires and cover letters were mailed to the superintendent of each sampled district in September 2015. The letter stated the purpose of the study and requested that the questionnaire be completed by the person(s) in the district most knowledgeable about programs and services for English learners at the high school level. Respondents were asked to respond for the current 201516 school year. Respondents were offered options of completing the survey on paper or online. Telephone follow-up for survey nonresponse and data clarification was initiated in October 2015 and completed in February 2016. The weighted response rate was 89 percent.
Respondents reported about programs and services for high school ELs, including instructional approaches, newcomer programs, online or computer-based programs, and programs or services (e.g., tutoring) designed specifically for high school ELs.
|Student Reports of Bullying: Results From the 2015 School Crime Supplement to the National Crime Victimization Survey
These Web Tables use data from the 2015 School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS) to show the relationship between bullying victimization and other variables of interest such as the reported presence of gangs, guns, drugs, and alcohol at school; select security measures; student criminal victimization; and personal fear, avoidance behaviors, fighting, and weapon-carrying at school.
|Split-Half Administration of the 2015 School Crime Supplement to the National Crime Victimization Survey
The 2015 School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS) administration contained an embedded, randomized split-half experiment to compare two versions of an updated series of questions on bullying. This report outlines the development, methodology, and results of the split-half experiment.
|Student Victimization in U.S. Schools: Results From the 2011 School Crime Supplement to the National Crime Victimization Survey
This document reports data from the 2011 School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS).1 The Web Tables show the extent to which students with different personal characteristics report being the victims of crime at school. Estimates include responses by student characteristics: student sex, race/ethnicity, grade, and household income.
|Digest of Education Statistics, 2015
The 51st in a series of publications initiated in 1962, the Digest's purpose is to provide a compilation of statistical information covering the broad field of education from prekindergarten through graduate school. The Digest contains data on a variety of topics, including the number of schools and colleges, teachers, enrollments, and graduates, in addition to educational attainment, finances, and federal funds for education, libraries, and international comparisons.
|Performance of U.S. 15-Year-Old Students in Science, Reading, and Mathematics Literacy in an International Context: First Look at PISA 2015
This report provides international comparisons of student performance in science, reading, and mathematics literacy from the PISA 2015 assessment. In 2015, 70 education systems, including the United States, participated in PISA. In addition, Massachusetts, North Carolina, and Puerto Rico participated in PISA separately from the nation.
The report includes average scores in the three subject areas; score gaps across the three subject areas between the top (90th percentile) and low performing (10th percentile) students; the percentages of students reaching selected PISA proficiency levels; and trends in U.S. performance in the three subjects over time.
Additional findings from PISA 2015 are available on the NCES PISA website at http://nces.ed.gov/surveys/pisa/pisa2015/.
|Use of Private Loans by Postsecondary Students: Selected Years 200304 through 201112
This Statistics in Brief examines the use of private education loans by both undergraduate and graduate students in the 200304, 200708, and 201112 academic years. The data used in this study are nationally representative of U.S. postsecondary students and are drawn from three administrations of the National Postsecondary Student Aid Study (NPSAS)NPSAS:04, NPSAS:08, and NPSAS:12.


text:
Alberta Waste and Recycling
Construction, Waste Management, Recycling
Our depots are set up to handle recyclable construction and demolition waste. They provide contractors, builders and haulers with a cost-effective, well-managed, environmentally sustainable waste and recycling solution. Our outcomes exceed the province of Alberta's proposed C&D program waste diversion criteria. The depot's waste diversion rates will meet or exceed LEED requirements and will capture both Waste Management credits and potentially a third point for innovation. A successful waste management and recycling program will also result in cost savings in garbage tipping fees.
Print this page
Create your free Hotfrog business profile
Follow Hotfrog on:
Share on:
Hotfrog Small Business Hub


text:
Wanna Be Star PT 2
- Posted April 1, 2012 by Dave Viewed 4347 times
- This is a migrated legacy post. Image resolution is low. Info
4 There are 4 comments, add yours!
2012.04.09 Edited
So funny!
2012.04.01 Edited
a new rock star is born nice fun set....
2012.03.29 Edited
This should be on the American Idol Show
2012.03.29 Edited
I can see this one on pop idol anytime soon, a star in the making!


text:
- Location Details
- Hours
- Reviews
- Services
- Photos/Videos
- Careers
FedEx Office Print & Ship Center
400 Capitol St
Charleston, WV 25301 US
Printing with FedEx OfficeStart Online Order
Hours of OperationMonday 7:00 AM–11:00 PM Tuesday 7:00 AM–11:00 PM Wednesday 7:00 AM–11:00 PM Thursday 7:00 AM–11:00 PM Friday 7:00 AM–11:00 PM Saturday 8:00 AM–9:00 PM Sunday 9:00 AM–9:00 PM
Latest Express DropoffMonday – Friday 6:00 PM Saturday 1:00 PM Sunday No Pickup
Latest FedEx Ground® DropoffMonday – Friday 6:00 PM Saturday No Pickup Sunday No Pickup
Reviews for this FedEx Office Print & Ship Center2/8/2017Jason was very helpful and supportive in making my complicated printing requirement with high level and accuracy and skill. Read more2/6/2017They made it very easy for me to blow up a drawing that I needed bigger. However, it would be nice if you could change the store police… Read more2/6/2017Ashley recommended I have her add paper filler in one of my boxes I was shipping. I ship paintings from your store, so I appreciated her… Read more2/1/2017I needed over 2,000 1099's folded and they completed it before the pick up deadline. I use them every year for my folding and I have never… Read more1/31/2017Bob waited on me that day. All the team members--Angie, Ashley, Jason, Tim, Emmett and Kent are always friendly, helpful and professional.… Read more1/19/2017Kent worked closely with me to help me create exactly the product I needed, within my budget, in a quick fashion. I was extremely pleased… Read more12/21/2016Took the time to check with me if the address was all good and to my approval. Read more12/15/2016The lady named Ashley was extremely helpful with my questions & packing the box to be shipped. She told me when my friend should receive the… Read more12/13/2016Team member gave me personalized recommendations, printed prototype photos for comparing color, size. I was able to make an educated… Read more12/7/2016Tim was the team member who provided the service. He was very polite, personable and competent. Read more12/4/2016I had a very complicated and bulky order of 75 binders that required individual packaging and shipping. I was unable to print labels from my… Read more12/1/2016Jason was extremely helpful in answering questions and making recommendations for my project. He was friendly and had a positive attitude… Read more12/1/2016He was very interactive with me in making my order the best possible to suit my needs. Went out of his way to get it right. Read more11/14/2016They always print what I need very quickly. I can make an order and have it ready for pickup usually within 30 minutes of the order… Read more11/12/2016Robert H. assisted me in getting started which was very helpful. He also made sure I knew how to operate the machine I was using. He was… Read more10/15/2016As soon as I walked in the door, Tim greeted me and joked that he had lost my e-mail (order). I loved that he did that because it meant he… Read more9/27/2016Kent went out of his way to help me navigate through some technical difficulties and he completed my order in half the time it should have… Read more9/14/2016Angie and Kent in the Charleston, WV office located on Capital Street WENT ABOVE AND BEYOND FOR OUR SMALL TOWN FESTIVAL. So thankful for… Read more8/25/2016Angie is a terrific manager. Read more6/23/2016Ashley was amazing! She helped me with completing my work by making suggestions that were spot on. Tim was also very helpful, but appeared… Read more
M–F Express Hold at FedEx Location Saturday Service Sunday Service Ground Hold at Location Saturday Hold at LocationExpress Services Ground Services Copy and Print Services Signs and Banners Sony PictureStationDirect Mail Services eBay Dropoff Center International Shipping
Store PhotoGoogle StreetviewLearn How to Print On the Go to FedEx Office
Job Openings at this FedEx Office Print & Ship Center
There are no careers available for this location yet.
FedEx Locations Nearby
1206 Greenbrier St
Charleston, WV 25311
Track the Status of Your FedEx® Package
Enter a FedEx tracking or door tag number below. You can also track FedEx Office orders by entering order numbers.
eBay Valet Drop-Off
Make money the easy way when you let an eBay Valet sell your stuff. Bring your electronics, apparel, sporting goods and more to an eBay Drop-Off Center at FedEx Office, and a valet will list, sell and securely pack and ship them for you.
Copy & Print Services
Upload your documents online and pick them up at FedEx Office on 400 Capitol St or print to any of our 1800 locations. We offer self-service color copying, high-volume digital printing, presentations and postcards to name a few.
Packing & Shipping Supplies
Get your packing supplies and let our shipping experts help you ship with FedEx. Our professionals can even help you pack your specialty items like luggage or golf clubs and ship them FedEx Express or FedEx Ground to meet you at your destination.
Computer Access & Wi-Fi
Rent a computer with internet access or get online with free AT&T Wi-Fi access at FedEx Office in Charleston. Check email, browse the web, print copies or scan photos. All in one place.
Photo Printing Services
FedEx Office® Print & Ship Center also offers a full range of photo printing services that can help you make the most of your photos. Photo posters and canvases to cards, calendars and passport photos, we've got it all.


text:
You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Chromosomal Drive and the Evolution of Meiotic Nondisjunction and Trisomy in Humans
Troy Day and Peter D. Taylor
Proceedings of the National Academy of Sciences of the United States of America
Vol. 95, No. 5 (Mar. 3, 1998), pp. 2361-2365
Published by: National Academy of Sciences
Stable URL: http://www.jstor.org/stable/44055
Page Count: 5
Topics: Trisomy, Chromosomes, Mortality, Evolution, Menopause, Trisomics, Maternal age, Age, Ova, Meiosis
- Item Type
- Article
- Thumbnails
- References
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
Trisomy is a genetic abnormality of considerable medical importance. The most familiar example is trisomy 21, which causes Down Syndrome [Cummings, M. R. (1988) Human Heredity: Principles and Issues (West Publishing Company, New York)]. In a classic paper, Axelrod and Hamilton [Axelrod, R. & Hamilton, W. D. (1981) Science 211, 1390-1396] offered a chromosomal drive (CD) hypothesis based on the game iterated prisoner's dilemma (IPD) to explain the evolution of an increased frequency of trisomic pregnancies with maternal age. In this paper we explore this hypothesis and its predictions in detail. On closer examination we find that IPD does not provide an adequate model for the CD hypothesis. Therefore, we develop a more suitable model and explore the conditions necessary for it to explain maternal age-dependent trisomy. Our results demonstrate that a relationship between the decay of a female's reproductive potential and chromosomal drive must exist for the CD hypothesis to work. With appropriate parameter values, a comparison of model predictions with empirical estimates for the age-dependence of trisomy reveals a striking correspondence. We point out a close correspondence between other predictions made by the CD hypothesis and empirical observations, as well.
Page Thumbnails
Proceedings of the National Academy of Sciences of the United States of America © 1998 National Academy of Sciences


text:
Morecambe 1 Hereford Untied 1
THE FACES OF THE HEREFORD UNITED supporters making the short walk from the coach park to Christie Road bore that haunted look of those who have seen it all before. For the third consecutive season Hereford have finished runners-up in the Nationwide Conference, but have yet to translate that league form into progress to the final of the play-offs.
The fans' foreboding appeared justified as Hereford fell behind to a powerful far-post header from Jim Bentley, the Morecambe centre back. However, the visiting team grabbed a fortuitous lifeline early in the second half when Fraser McLachlan was harshly adjudged to


text:
Male aficionados of classical music have long enjoyed their eye candy. There was the sight of willowy Vanessa Mae swaying her way through Vivaldi, then came the scantily dressed Bond. Recent days have seen the emergence of the crop-topped singing sensation Keedie Babb, aged just 22.
Now four singers are offering some visual thrills for women fans of "crossover" music - an expanding multimillion-pound niche where pop meets classical. The group Il Divo (male diva) have been nicknamed the "phwoar tenors" for their smouldering Latino sex appeal.
The group was assembled by Simon Cowell, the British pop Svengali, after a two year, international search. Cowell, the Mr Nasty of Pop Idol and architect of the television phenomenon X Factor, has described the group as his finest achievement. He thinks they are going to be very big.
And this weekend Il Divo look set to reward his optimism by knocking Robbie Williams off the top of the British album charts.
The critics may have dismissed them as a boy band - the Westlife of opera: part Julio Iglesias, part Europop - but the public appears to love them.
Despite having never released a single, they appeared on Parkinson - a crucial showcase for a new easy listening artist. They went on to sell 30,000 copies of their eponymous debut album in just 24 hours - 5,000 more than Robbie's Greatest Hits. So popular have they proved that an extra 100,000 copies of the album have been ordered.
Their first single, a classical arrangement of Toni Braxton's Unbreak My Heart will follow and the band will switch on this year's Oxford Street Christmas lights.
Cowell's search united the talents of Carlos Marin, 35, from Spain, Urs Buhler, 33, from Switzerland, Sebastien Izambard, 31, from France and 31-year-old David Miller, from America.
All four singers have had successful solo careers, Marin was a child singing star while Buhler was taught at the Lucerne Academy in Switzerland, moving on to classical after an early flirtation with rock.
Izambard is an accomplished composer, while Miller, a graduate of the Oberlin conservatory in Ohio, appeared on American Idol after singing for 10 years in the US. Their music has been described as a cross between Eros Ramazzotti and Andrea Bocelli and the repertoire includes a version of My Way in Italian.
The men are already getting used to being sex symbols. In their latest interview Buhler was playing it coy: "Girls in the UK are so much more friendly," he said. "When you meet them you are able to talk to them, just have a chat. That is very refreshing." But Marin had no such reticence. "We can't wait for women to start throwing themselves at us," he said. "We are prepared for everything. We are more than prepared."
- More about:
- Estelle
- Fergie
- Music
- Paul Smith
- Singers
- Socks
- Style
- Styles And Clothes
- Violin
- Violinists


text: Pinch to zoom Leather Bucket Bag by The Fairground £65$111.55€77.90$83.29 7 customer reviews(7) Favourite Favourite View your favourites colour of leathercolour of leather (required)Chocolate BrownNatural Tan colour of leather (required)Chocolate BrownNatural Tan colour of leather (required)Chocolate BrownNatural Tan colour of leather (required)Chocolate BrownNatural Tan quantity personalise & pay by The Fairground Location: Kent, GB read seller's profile see seller's complete range more items from this seller Block Printed Blue Rajastani Quilt by The Fairground £100$171.62€119.85$128.14 Personalised Girls Name Bunting by The Fairground £20$34.32€23.97$25.62 Personalised Blue Bunting by The Fairground £25$42.90€29.96$32.03 Personalised Girls Floral Bunting by The Fairground £20$34.32€23.97$25.62 Personalised Patchwork Red Blue Quilt by The Fairground £55$94.39€65.91$70.47 Spring Gingham And Floral Bunting by The Fairground £22$37.75€26.36$28.19 view all you may also like PRODUCT DETAILS CUSTOMER REVIEWS DELIVERY RETURNS exclusive eco-friendly ask seller a question save for later Simple, Stylish and Practical leather bag . Perfect size to fit all the essentials! This leather bucket bag is lined with a floral block printed fabric. There is a handy inside pocket. This bag is handcrafted in Rajasthan by a co-operative that helps generate rural income through the traditional skills of the village artisans. There is no machinery involved and follows an environmentally friendly method of dying the leather. Comes in Chocolate Brown or Natural Tan. dimensions: Dimensions: Width 32cm Height 26cm product code: 162817 7 customer reviews read about our product rating system ratings guide trusted independent customer feedbackforum excellent bad good poor This product is dispatched to you by The Fairground. You have 28 days, from receipt of cancellable goods, to notify the seller if you wish to cancel your order or exchange an item. Please note: goods that are personalised, bespoke or made-to-order to your specific requirements, perishable products and personal items sold with a hygiene seal (cosmetics, underwear) in instances where the seal is broken are non-refundable, unless faulty. Read more about our returns policy.Should you choose to return or exchange your order you will need to deliver the item(s) to the UK, where this seller is based. The Fairground's return policy Please see notonthehighstreet.com's returns policy.


text:
SolarTown visited a middle school in Washington, DC today. We let the kids borrow a solar oven for the next month so that they can cook up their favorite foods. On SolarTownKids, we have an activity to make your own solar cooker. If you are the parent of a school age kid, you might ask your child to cook you up some food with a solar cooker. And if you have a favorite solar activity to share on SolarTownKids, let us know.
Posts Tagged 'Solar Decathlon'
SolarTown Goes Back to SchoolMonday, March 5th, 2012
Solar Home ExtravaganzaMonday, October 3rd, 2011
You think that the solar industry is just emerging. Well, you may be right, but don't tell that to the people who have been on the solar home tours for the past 21 years. The 21st Annual Metro Washington, D.C. Tour of Slar and Green Homes took place this past weekend, and if you missed this solar home tour or the one in your area, then you missed out on seeing some of the vibrant solar homes that have taken the solar challenge. This year's solar home tours, like the Solar Decathlon on the National Mall a week before, was not blessed with sunny weather, but that did not deter the spirits of those who wanted to check out the solar home panels, and solar water heater systems throughout the DC metro region. Despite the rain, solar homeowners were eager to show off their energy efficient houses and to show that, even when it is not sunny, their solar arrays help save on energy costs.
Solar Decathlon Shines on the National MallSaturday, September 24th, 2011
The Solar Decathlon is an event sponsored by the U.S. Department of Energy, in which students from around the world design, build and operate solar-powered and energy-efficient houses. The team with the house that best incorporates elements such as design excellence, affordability, customer appeal and maximum energy efficiency, wins the competition. The event is meant to educate both the public and the students involved in the project of energy and energy efficiency. The first day of the Solar Decathlon was rainy, which did not deter the visitors as they waited in line to visit each house and discover what made it so special. Fortunately, the rain did not dampen the spirits of the crowds and was no deterrent to this solar event.
Solar Towns All Over AmericaTuesday, December 1st, 2009
OK, the residential solar market did not exactly explode in 2009 as many had hoped in 2008 when the price of oil was approaching $150 a barrel. The world economic crisis, lack of financing, cheap oil, constrained governmental budgets all contributed to a less than lackluster year as measured by the expectations that were set in a rosier time a year earlier. Nevertheless, there are some bright spots and probably the brightest of them all is that as prices for solar panels have come down, and the landscape for economic incentives has stabilized, the solar industry has hit mainstream and more importantly, Main Street.
The Solar Crown Goes to GermanyFriday, October 16th, 2009
Congratulations to Team Germany for winning the 2009 Solar Decathlon. With a massive 11.1 kW array, and thin film on the walls, there was no one who could match their net metering prowess. Illinois finished second; California, third, and Ontario fourth. Make no mistake about it. Germany won based on the strength of its PV array. It finished in the top five of the other nine categories, except for communications. On net metering, it received 150—out of a total of 150 points. This is the photo of the winning entry.
SolarTown Greets Solar Decathlon ParticipantsTuesday, October 13th, 2009
Sunny Day Fries Solar Decathlon in the CapitalFriday, October 9th, 2009
The day belonged not to Barack Obama, but to the visionary students on the National Mall who came from not only the US, but also from Canada, Germany and Spain to showcase their solar homes in the Solar Decathalon. There were lines at every house and some of the lines were not short—some people waited for as long as 30 minutes to get into some of the houses.


text:
84 S State Street, Concord, NH 03301Request info Add to list
To view building contacts please sign in with your Pro account or create a pro account.
Wendy E. K.
Company: requires Pro account
Email: requires Pro account
Phone: requires Pro account
Building Details:
84 S State Street is aretail property for lease in Concord, NH.
Highly visible and affordable retail commercial space for lease. This ideal commercial space is located on the corner of South State Street and Downing Street. Two (2) on-site parking spaces included in the rear parking lot.
There is ample street parking as well on both South State Street and Downing Street.
900 +/- Square Feet consisting of new handicap bathroom, storage and office in rear and large open retail area. This space was all newly redone in May which included new electrical, plumbing, bathroom, suspended ceiling with 2 x 4 LED lighting, plank vinyl flooring and portable A/C units!
Rent is $750 per month. Tenant to pay separately metered electricity, natural gas heat, interior cleaning, telephone and internet. Landlord pays the water/sewer, real estate taxes and common area maintenance. Lots of glass!
This property consisting of fully leased (4) one-bedroom apartments (two Tenants are on a TAW basis) is also available for sale for $250,000. The main address of the building is 84 South State Street.
Create a Property List
Please describe your commercial real estate requirements. We'll introduce you to local properties that match your criteria.


text:
Status: Pre-Production
Pre-Production: Key talent and film crew being hired; budgeting, preparing and planning of production schedule; approving final shooting script.
- September 2011: Venice, Italy
- September 2011: Hungary
- September 2011: Germany
- September 2011: Bruges, Belgium
No Poster Available
TBA | Drama Romance
A forbidden romance develops between the young composer priest Antonio Vivaldi and his muse Anna, which inspired his masterpiece "The Four Seasons." more
In Movie Theaters U.S.To Be Announced (TBA)
Cast more cast
Neve Campbell
(In Talks)
Continues Below Ad
Production CompaniesMechaniks
Questions & Answers Have a question about this movie? Ask it!
Submit additional information and/or report errors. Send Us Feedback


text:
Product Details
- 9780446383110
- Publisher:
- Grand Central Publishing
- Publication date:
- 10/01/1984
- Pages:
- 32
Customer Reviews
Average Review:
Most Helpful Customer Reviews
See all customer reviews
Uh-oh, it looks like your Internet Explorer is out of date.
For a better shopping experience, please upgrade now.
Average Review:
See all customer reviews


text:
Yes, Chec is a safe and secure way to sell and accept payments online. We hold ourselves to the highest standard when it comes to the security of your private information and transactions. All transactions are fully encrypted, SSL secured and are processed via a PCI Compliant Server.
Fraud Protection
Chec has partnered with Sift Science to protect all merchants from fraudulent customers. Sift Science uses machine learning to determine the real intent of your customers by score.
You can learn more about Chec's fraud protection here.


text:
Lost Password?
We offer you this space to share your knowledge about Magento and learn from our experienced customers.
Home › Forums › "How do I" questions › Removing Welcome MSG box with links on hellominty › Reply To: Removing Welcome MSG box with links on hellominty
Hello Tina, to remove that links, go to: appdesignfrontenddefaulthellomintytemplatepagehtmlheader.phtml
Find the <div class="quick-access"> ,there you can remove or edit that links.