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On Monday, March 18, 2013, Microsoft rolled out the latest release of the Kinect for Windows software development kit (SDK). This represents the largest update to the technology since the SDK was first commercially released in February last year, and it includes the Kinect Fusion technology that originated in Microsoft Research.
Kinect Fusion, an implementation of Microsoft Research’s 3-D surface reconstruction technology, can create highly accurate 3-D renderings of people and objects in real time.
The new release has a number of features that will benefit the academic and research community:
Another helpful development: earlier this month, Kinect for Windows announced broader availability of academic pricing through Microsoft Authorized Educational Resellers (AERs). Most of these resellers can now offer academic pricing directly to educational institutions; academic researchers; and students, faculty, and staff of public or private K-12 schools, vocational schools, junior colleges, colleges, universities, and scientific or technical institutions. Academic pricing on the Kinect for Windows sensor is currently available through AERs in the United States, Taiwan, and Hong Kong SAR. We eagerly look forward to a seeing what the academic community does with the new features!
—Stewart Tansley, Director, Microsoft Research Connections—Kenji Takeda, Solutions Architect and Technical Manager, Microsoft Research Connections EMEA
Can scientists predict what happens when they introduce a change into a living system—for example, if they change the structure of a gene or administer a drug? Just as changing one letter can completely change the meaning of a word, the change of a single letter of the genetic code (referred to as a single nucleotide polymorphism, or SNP) can subtly affect the meaning of a gene’s instructions or alter them completely, making the effect of any change extremely hard to predict. Such changes are thought to be responsible for much of the variation between members of a single species—for example, in susceptibility to different diseases. The ability to successfully predict the effect of such changes would accelerate drug discovery and provide a deeper understanding of the processes of life.
In collaboration with Jasmin Fisher at Microsoft Research Cambridge, professor Yanay Ofran and his colleagues at Bar Ilan University have embarked on a program of scientific research that aims to resolve some of the questions underlying this overall goal, and some of their early results have now been published.
One of the researchers’ first tasks was to determine whether it is possible to predict how a complex network of biochemical interactions will change when a SNP (pronounced “snip”) alters the function of one of the network’s components. In an August 2012 paper entitled, “Static Network Structure Can Be Used to Model the Phenotypic Effects of Perturbations in Regulatory Networks” (available at Bioinformatics with paid subscription), the authors describe their success in analyzing static models of biological networks and correctly predicting the response to changes more than 80 percent of the time. This enables the functions of the network to be deduced, the foundation for building a more expressive dynamic model.
Building static networks is a challenge in itself; before beginning this work, the researchers needed to understand which genes are active in a particular cell and what they do. In their latest publication entitled, “Assessing the Relationship between Conservation of Function and Conservation of Sequence Using Photosynthetic Proteins” (available at Bioinformatics with paid subscription), the Ofran lab has shown that, while sets of related genes with similar structure diverge in function more quickly than previously thought, selected smaller pieces of each gene may still be useful in predicting function.
There are many unresolved challenges along the way to the eventual goal of predicting the effect of a SNP—understanding which genes are switched on in which cells and how drugs interact with proteins are just two active areas of investigation—but once the goal is reached, an understanding of the functions of all genes and how changes affect biological systems could lead to the development of computational models to predict and cure many diseases.
—Simon Mercer, Director of Health and Wellbeing, Microsoft Research Connections
For baby boomers who grew up watching The Jetsons, the idea of the fully automated home was the futuristic stuff of cartoons. Today, the technology is available to make a Jetsonesque home a reality, by using inexpensive network devices that remotely control locks, lights, thermostats, cameras, and motion sensors. In theory, we should be able to monitor our home security cameras remotely from a smartphone or customize the climate of each room based on occupancy patterns. In practice, however, the high overhead of managing and extending home automation technology has restricted such “smart home” scenarios to expert hobbyists, who enjoy grappling with the technical challenges, and the wealthy, who can hire someone to handle the tech chores.
HomeMaestro: a platform that helps end users program their home appliances
To simplify the management and development of smart-home applications, Microsoft Research has developed HomeOS. When coupled with smartphones and cloud services (by using Project Hawaii and Windows Azure), HomeOS makes the smart home a reality for the rest of us. Unlike past home technology models, which rely either on an “appliance abstraction,” in which a closed, monolithic system supports a fixed set of tasks over a fixed set of devices, or a “network of devices abstraction,” in which a decentralized collection of devices relies on interoperability protocols, our HomeOS provides users and developers with a PC-like abstraction. It presents network devices as peripherals, enables cross-device tasks via applications, and gives users a management interface that is designed for the home environment. By so doing, the HomeOS overcomes the extensibility limitations of the appliance model and the manageability hassles of the network of devices model. At the same time, it brings the “app store” to the home environment, allowing users to extend the functionality of their home by downloading applications.
To date, the HomeOS research prototype has been running in more than a dozen homes. We’ve also made it freely available to academic institutions for teaching and research purposes. Nearly 50 students, across several institutions, have already built some exciting applications for HomeOS.
For example, HomeMaestro from the MIT Media Lab shows the power of the HomeOS approach. HomeMaestro is a platform for intuitively defining home appliance behavior. The key concept in HomeMaestro is a repository of rules defined by other users, which can be mashed into interesting scenarios. These rules could be simple if-then statements, such as “if my bedroom window is open, then switch off the heater.” The rules can be defined on Windows Phone 7 and uploaded to the cloud (Project Hawaii web services and Windows Azure) for later use and sharing.
In another example, students at the University of Washington recently used HomeOS with Windows Phone 7 and cloud services (from Project Hawaii) to create a door-monitoring system and networked alarm, and to control various home devices using the Kinect sensor.
Student demos of HomeOS applications
You can check out some potential applications of the HomeOS in these student demos. A paper describing HomeOS will be presented at the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI '12), which runs from April 25 to 27, 2012, in San Jose, California.
With HomeOS, we feel we’re on the way toward that Jetson home—now, if only we could make George Jetson’s nine-hour workweek a reality!
—Arjmand Samuel, Senior Research Program Manager, Microsoft Research Connections