Groundbreaking simulations by Google Exacycle Visiting Faculty

Posted by David Konerding, Staff Software Engineer

In April 2011, we announced the Google Exacycle for Visiting Faculty, a new academic research awards program donating one billion core-hours of computational capacity to researchers. The Exacycle project enables massive parallelism for doing science in the cloud, and inspired multiple proposals aiming to take advantage of cloud scale. Today, we would like to share some exciting results from a project built on Google’s infrastructure.

Google Research Scientist Kai Kohlhoff, in collaboration with Stanford University and Google engineers, investigated how an important signalling protein in the membrane of human cells can switch off and on by changing its three-dimensional structure following a sequence of local conformational changes. This research can help to better understand the effects of certain chemical compounds on the human body and assist future development of more potent drug molecules with fewer side effects.

The protein, known as the beta-2 adrenergic receptor, is a G protein-coupled receptor (GPCR), a primary drug target that plays a role in several debilitating health conditions. These include asthma, type-2 diabetes, obesity, and hypertension. The receptor and its close GPCR relatives bind to many familiar molecules, such as epinephrine, beta-blockers, and caffeine. Understanding their structure, function, and the underlying dynamics during binding and activation increases our chances to decode the causes and mechanisms of diseases.

To gain insights into the receptor’s dynamics, Kai performed detailed molecular simulations using hundreds of millions of core hours on Google’s infrastructure, generating hundreds of terabytes of valuable molecular dynamics data. The Exacycle program enabled the realization of simulations with longer sampling and higher accuracy than previous experiments, exposing the complex processes taking place on the nanoscale during activation of this biological switch.

The paper summarizing the results of Kai’s and his collaborators’ work is featured on the January cover of Nature Chemistry, with artwork by Google R&D UX Creative Lead Thor Lewis, to be published on December 17, 2013. The online version of his paper was published on their website today.

We are extremely pleased with the results of this program. We look forward to seeing this research continue to develop.

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Googler Moti Yung elected as 2013 ACM Fellow

Posted by Alfred Spector, VP of Engineering

Yesterday, the Association for Computing Machinery (ACM) released the list of those who have been elected ACM Fellows in 2013. I am excited to announce that Google Research Scientist Moti Yung is among the distinguished individuals receiving this honor.

Moti was chosen for his contributions to computer science and cryptography that have provided fundamental knowledge to the field of computing security. We are proud of the breadth and depth of his contributions, and believe they serve as motivation for computer scientists worldwide.

On behalf of Google, I congratulate our colleague, who joins the 17 ACM Fellow and other professional society awardees at Google, in exemplifying our extraordinarily talented people. You can read a more detailed summary of Moti’s accomplishments below, including the official citations from ACM.

Dr. Moti Yung: Research Scientist
For contributions to cryptography and its use in security and privacy of systems

Moti has made key contributions to several areas of cryptography including (but not limited to!) secure group communication, digital signatures, traitor tracing, threshold cryptosystems and zero knowledge proofs. Moti's work often seeds a new area in theoretical cryptography as well as finding applications broadly. For example, in 1992, Moti co-developed a protocol by which users can commonly compute a group key using their own private information that is secure against coalitions of rogue users. This work led to the growth of the broadcast encryption research area and has applications to pay-tv, network communication and sensor networks.
Moti is also a long-time leader of the security and privacy research communities, having mentored many of the leading researchers in the field, and serving on numerous program committees. A prolific author, Moti routinely publishes 10+ papers a year, and has been a key contributor to principled and consistent anonymization practices and data protection at Google.

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Free Language Lessons for Computers

Posted by Dave Orr, Google Research Product Manager

Not everything that can be counted counts.

Not everything that counts can be counted.

- William Bruce Cameron

50,000 relations from Wikipedia. 100,000 feature vectors from YouTube videos. 1.8 million historical infoboxes. 40 million entities derived from webpages. 11 billion Freebase entities in 800 million web documents. 350 billion words’ worth from books analyzed for syntax.

These are all datasets that we’ve shared with researchers around the world over the last year from Google Research.

But data by itself doesn’t mean much. Data is only valuable in the right context, and only if it leads to increased knowledge. Labeled data is critical to train and evaluate machine-learned systems in many arenas, improving systems that can increase our ability to understand the world. Advances in natural language understanding, information retrieval, information extraction, computer vision, etc. can help us tell stories, mine for valuable insights, or visualize information in beautiful and compelling ways.

That’s why we are pleased to be able to release sets of labeled data from various domains and with various annotations, some automatic and some manual. Our hope is that the research community will use these datasets in ways both straightforward and surprising, to improve systems for annotation or understanding, and perhaps launch new efforts we haven’t thought of.

Here’s a listing of the major datasets we’ve released in the last year, or you can subscribe to our mailing list. Please tell us what you’ve managed to accomplish, or send us pointers to papers that use this data. We want to see what the research world can do with what we’ve created.

50,000 Lessons on How to Read: a Relation Extraction Corpus

What is it: A human-judged dataset of two relations involving public figures on Wikipedia: about 10,000 examples of “place of birth” and 40,000 examples of “attended or graduated from an institution.”
Where can I find it: https://code.google.com/p/relation-extraction-corpus/
I want to know more: Here’s a handy blog post with a broader explanation, descriptions and examples of the data, and plenty of links to learn more.

11 Billion Clues in 800 Million Documents

What is it: We took the ClueWeb corpora and automatically labeled concepts and entities with Freebase concept IDs, an example of entity resolution. This dataset is huge: nearly 800 million web pages.
Where can I find it: We released two corpora: ClueWeb09 FACC and ClueWeb12 FACC.
I want to know more: We described the process and results in a recent blog post.

Features Extracted From YouTube Videos for Multiview Learning

What is it: Multiple feature families from a set of public YouTube videos of games. The videos are labeled with one of 30 categories, and each has an associated set of visual, auditory, and and textual features.
Where can I find it: The data and more information can be obtained from the UCI machine learning repository (multiview video dataset), or from Google’s repository.
I want to know more: Read more about the data and uses for it here.

40 Million Entities in Context

What is it: A disambiguation set consisting of pointers to 10 million web pages with 40 million entities that have links to Wikipedia. This is another entity resolution corpus, since the links can be used to disambiguate the mentions, but unlike the ClueWeb example above, the links are inserted by the web page authors and can therefore be considered human annotation.
Where can I find it: Here’s the WikiLinks corpus, and tools can be found to help use this data on our partner’s page: Umass Wiki-links.
I want to know more: Other disambiguation sets, data formats, ideas for uses of this data, and more can be found at our blog post announcing the release.

Distributing the Edit History of Wikipedia Infoboxes

What is it: The edit history of 1.8 million infoboxes in Wikipedia pages in one handy resource. Attributes on Wikipedia change over time, and some of them change more than others. Understanding attribute change is important for extracting accurate and useful information from Wikipedia.
Where can I find it: Download from Google or from Wikimedia Deutschland.
I want to know more: We posted a detailed look at the data, the process for gathering it, and where to find it. You can also read a paper we published on the release.

Note the change in the capital of Palau.

Syntactic Ngrams over Time

What is it: We automatically syntactically analyzed 350 billion words from the 3.5 million English language books in Google Books, and collated and released a set of fragments -- billions of unique tree fragments with counts sorted into types. The underlying corpus is the same one that underlies the recently updated Google Ngram Viewer.
Where can I find it: http://commondatastorage.googleapis.com/books/syntactic-ngrams/index.html
I want to know more: We discussed the nature of dependency parses and describe the data and release in a blog post. We also published a paper about the release.

Dictionaries for linking Text, Entities, and Ideas

What is it: We created a large database of pairs of 175 million strings associated with 7.5 million concepts, annotated with counts, which were mined from Wikipedia. The concepts in this case are Wikipedia articles, and the strings are anchor text spans that link to the concepts in question.
Where can I find it: http://nlp.stanford.edu/pubs/crosswikis-data.tar.bz2
I want to know more: A description of the data, several examples, and ideas for uses for it can be found in a blog post or in the associated paper.

Other datasets

Not every release had its own blog post describing it. Here are some other releases:

• Automatic Freebase annotations of Trec’s Million Query and Web track queries.
• A set of Freebase triples that have been deleted from Freebase over time -- 63 million of them.

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