Current working professional and an OMSCS student here. It highly depends on the context. Biggest pros are: 1. This is perhaps the cheapest Computer Science masters in the United States from a premier school. The degree is exactly the same as offered to the residential program and the credits acquired are all legit and transferable to other universities. I had friends who transferred from OMSCS to a regular school and skipped one full semester due to the credits earned. 2. An OMSCS qualification holds way more water than if you do random MOOC qualifications on Coursera and others. 3. The coursework is the same as the residential program.
MongoDB has filed confidentially for IPO, sources tell TechCrunch. The company has submitted an S-1 filing in the past few weeks and is aiming to go public before the end of the year.
New York-based MongoDB helps companies including Adobe, eBay and Citigroup manage databases. Some of its offerings include its name-bearing MongoDB open source database and the Atlas database-as-a-service offering.
Scientists from the National Institute of Standards and Technology (NIST) and Michigan State University report that they have developed an algorithm that automates a key step in the fingerprint analysis process. Their research has been published in IEEE Transactions on Information Forensics and Security.
“We know that when humans analyze a crime scene fingerprint, the process is inherently subjective,” said Elham Tabassi, a computer engineer at NIST and a co-author of the study. “By reducing the human subjectivity, we can make fingerprint analysis more reliable and more efficient.”
Optimizing deliveries in the real world is thornier than the traveling-salesman problem on several levels. First, distances between locations need to be calculated, and as anyone who’s used Google Maps will know, there’s always more than one way to make a journey. Will Salter, managing director at Paragon, which provides routing and scheduling software in 60 countries for clients like the U.K. supermarket giant Tesco, says path-finding algorithms used in delivery planning are “highly customized” compared with a Google Maps algorithm. They’ll take into account road conditions, changing traffic flows at different times of day—even the marginal benefits of making a right turn at a junction.
The benefits and limitations of genetic studies using this technology (often confusingly called genome-wide association studies [1]) have been debated since before anyone even tried one (see examples from 2000, 2008, and 2017). But it’s generally believed that, if one accepts the basic goals and assumptions of a genome-wide association study, the most cost-effective and powerful technology to use is a genotyping array.
In this post, I argue that this is no longer the case: genotyping arrays are now less effective and more expensive than sequencing technologies.
In addition to $250,000 in seed funding for startups, the Allen Institute for Artificial Intelligence is offering office workspace at its Wallingford headquarters, and counsel from its experts in machine learning, computer vision and hardware engineering.
How does a town go from logging and livestock to bits and bytes? Tiny Prineville, Oregon, is finding out as huge data centers from Apple and Facebook transform the timber town into a recreational hub of mountain bikers and craft brewers.
Applied Network Science; Nikos Salamanos, Elli Voudigari and Emmanuel J. Yannakoudakis
from
The problem of identifying the influential spreaders – the important nodes – in a real world network is of high importance due to its theoretical interest as well as its practical applications, such as the acceleration of information diffusion, the control of the spread of a disease and the improvement of the resilience of networks to external attacks. In this paper, we propose a graph exploration sampling method that accurately identifies the influential spreaders in a complex network, without any prior knowledge of the original graph, apart from the collected samples/subgraphs. The method explores the graph, following a deterministic selection rule and outputs a graph sample – the set of edges that have been crossed. The proposed method is based on a version of Rank Degree graph sampling algorithm. We conduct extensive experiments in eight real world networks by simulating the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) epidemic models which serve as ground truth identifiers of nodes spreading efficiency. Experimentally, we show that by exploring only the 20% of the network and using the degree centrality as well as the k-core measure, we are able to identify the influential spreaders with at least the same accuracy as in the full information case, namely, the case where we have access to the original graph and in that graph, we compute the centrality measures. Finally and more importantly, we present strong evidence that the degree centrality – the degree of nodes in the collected samples – is almost as accurate as the k-core values obtained from the original graph. [full text]
Atlanta, GA StatFest 2017—a one-day event aimed at encouraging undergraduate students from under-represented groups to consider graduate studies and careers in the statistical sciences—will be held September 23. [free]
Portland, OR We invite you to submit your abstract for an electronic poster presentation at the Conference on Statistical Practice 2018. The submission deadline is August 31.
Vancouver, Canada ICLR 2018 will be held in Vancouver Convention Center, from April 30 to May 3, 2018 (notice the extra day!). The paper submission deadline is October 27.
A major challenge facing the field, however, is creating biosynthetic materials that can not only deliver targeted drug therapy or tissue engineering to a damaged area, but also non-invasively visualize the surrounding cells so that doctors can monitor their patient’s treatment progress. Although some advances have been made, a full solution has not yet been discovered — but it looks like Associate Professor Jin Montclare from NYU Tandon, CDS’s Director Richard Bonneau, and Associate Professor Youssef Zaim Wadghiri from NYU School of Medicine are on their way to cracking the case.
Since its launch in 2016, businesses have used the Google Cloud Speech API to improve speech recognition for everything from voice-activated commands to call center routing to data analytics. And since then, we’ve gotten a lot of feedback that our users would like even more functionality and control. That’s why today we’re announcing Cloud Speech API features that expand support for long-form audio and further extend our language support to help even more customers inject AI into their businesses.
The secret for reading algebra-heavy papers is NOT trying to follow the algebra on the first read. This is a mistake most students do. You don’t need to understand all steps of a long calculation on the first read. You skim through the algebra and assume it is correct, take a deep look at key steps along the way. Read that thing written in English between the equations. Read the results. Read the conclusion. When you made sense of what this fucking paper is talking about generally, than you decide if you’re going to waste your time with the algebra. Don’t get bogged down on the steps you don’t understand. Assume they are correct and carry on. Go back to them later. Repeat until you get it all.
An API represents a contract for communication that can’t be changed without considerable cooperation and effort. Because so many businesses rely on Stripe as infrastructure, we’ve been thinking about these contracts since Stripe started. To date, we’ve maintained compatibility with every version of our API since the company’s inception in 2011. In this article, we’d like to share how we manage API versions at Stripe.
Aimed specifically at developers who already have a TensorFlow model successfully working in a desktop environment, this report shows you through hands-on examples how to deploy mobile AI applications that are small, fast, and easy to build. You’ll explore use cases for on-device deep learning—such as speech, image, and object recognition—and learn how to deliver interactive applications that complement cloud services. [free eBook]
The split between the analytics (compute) and reliable storage worlds is clear and breaching the two commonly includes data copies to fit the needs. But with data growth, the copies become a significant overhead with increasingly high associated costs of data extraction, redistribution, and copy-back operations.
We will show how in-database analytics addresses this problem directly with the use of modern database systems and established numerical algorithms.