In 2008, Political Analysis published a groundbreaking special issue on the analysis of political text, examining some of the initial efforts in political science to consider text as a data source and to develop methods for analyzing text data. In their introduction to the special issue, Monroe and Schrodt (2008) note that text one of the most common mediums through which political phenomenon are documented is underutilized in the social sciences and they argue for further research. They suggest the research discussed in the special issue should be a jumping-off point, or "departure lounge" for future text as data research. Answering their call, in the last eight years, the fi eld of "text as data" in social science has grown dramatically. As the number of sources and types of textual data documenting social science phenomenon has exploded, so too have methods for, and the use of, text analysis in social science research.

We've put together an introductory lecture on TensorFlow as part of CS 224D, Stanford's deep-learning for NLP class. As far as we can tell, this is one of the first academic lectures on TensorFlow (aside from Google's official docs of course). We hope it'll prove useful to the ML community. Feel free to ask us questions on this thread, and we'll answer to the best of our ability.

Used to be you knew which city you were in from the food, the sports team, the historic sites, even the local brew. Now a team of microbiologists discovered they can tell cities apart by their unique bacterial fingerprints. The surprising finding was made after an intense study led by John Chase of Northern Arizona University's Department of Biological Sciences and Center for Microbial Genetics and Genomics. He and his colleagues spent a year swabbing for samples at nine offices in San Diego, Flagstaff, and Toronto. They wanted to find out what kind of impact factors like geography, location in a room, seasons, and human interaction have on the microbial communities we spread around, called microbiomes.

Ugh, that is not art, it is not even good design! Paradoxically, claiming that non-art is art is an artistic act, but the act of doing so is the art, not the subject.

Undergraduate Winners for their invention SignAloud, gloves that translate sign language into text and speech. Researchers have floated possibilities in this space that could end up transforming health care, like using Google Glass to give doctors more treatment information in real time. And health care wearables promise to be a billion-dollar industry. Just this month, IBM announced the latest in a string of health and tech partnerships, a project with pharmaceutical giant Pfizer to us wearable sensors to help study and treat patients with Parkinson's disease. But last week, two new wearable technologies gained a spotlight when their creators were awarded the Lemelson-MIT student prize, a yearly cash award to a handful of collegiate inventors.

This is a good question, and one that seems to get raised time and time again. Myself and a colleague (Sven Crone from Lancaster University in the UK) published a paper on this issue last year in the International Journal of Forecasting. A summary of our findings can also be found in the book "Credit Scoring, Response Modeling and Insurance Rating. There are also some very good papers by G. Weiss from 2004/5 which are highly cited and referenced in our paper/book. What we found was that for some methods of model construction sample imbalance was not an issue at all – not even a tiny amount.

A team of scientists from the Moscow Institute of Physics and Technology (MIPT) have created prototypes of "electronic synapses" based on ultra-thin films of hafnium oxide (HfO2). These prototypes could potentially be used in fundamentally new computing systems. The paper has been published in the journal Nanoscale Research Letters. The group of researchers from MIPT have made HfO2-based memristors measuring just 40x40 nm2. The nanostructures they built exhibit properties similar to biological synapses.

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Cancer is responsible for one-in-three deaths in Canada, according to the Canadian Cancer Society. To patients who are diagnosed, early detection can mean the difference between life and death. A microscope using AI is being touted as a powerful new instrument in the diagnostic toolkit--one that manages to snap an astounding 36 million images per second to catch cancer cells and identify their characteristics. The microscope was designed by a team at UCLA's California NanoSystems Institute, who say it's a way to identify cancer cells in patients' blood samples faster and more accurately than current methods. In a new study published in the journal Nature Scientific Reports, they describe how, using a patented microscope outfitted with a camera, they're able to photograph cells without destroying them.

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words. While unsupervised techniques have been proposed to overcome this data sparsity problem, such techniques have not outperformed supervised methods. In this paper, we propose a new approach to building semi-supervised WSD systems that combines a small amount of sense-annotated data with information from Word Sense Induction, a fully-unsupervised technique that automatically learns the different senses of a word based on how it is used. In three experiments, we show how sense induction models may be effectively combined to ultimately produce high-performance semi-supervised WSD systems that exceed the performance of state-of-the-art supervised WSD techniques trained on the same sense-annotated data. We anticipate that our results and released software will also benefit evaluation practices for sense induction systems and those working in low-resource languages by demonstrating how to quickly produce accurate WSD systems with minimal annotation effort.