With a vast number of items, web-pages, and news to choose from, online services and the customers both benefit tremendously from personalized recommender systems. Such sys- tems however provide great opportunities for targeted adver- tisements, by displaying ads alongside genuine recommendations. We consider a biased recommendation system where such ads are displayed without any tags (disguised as genuine recommendations), rendering them indistinguishable to a single user. We ask whether it is possible for a small subset of collaborating users to detect such a bias.
Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. Each graph is a job, where nodes represent compute tasks and edges indicate data-flows between these compute tasks. Jobs (graphs) arrive randomly over time, and upon completion, leave the system. When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations.
Joydeep Ghosh and student Suriya Gunasekar work on the generalization of standard matrix completion in various aspects. In previous work, we have proposed tractable estimators for matrix completion with observations arising from heterogeneous datatypes and heterogeneous noise models. In a more recent work, we focus on consistency results for the collective matrix completion problem of jointly recovering a collection of matrices with shared structure.
Several real-life high dimension datasets can be reasonably represented as a
linear combination of a few sparse vectors. Succinct representation of such data with a few selected variables is highly desirable for such cases. A Bayesian setup is useful because the limitation of knowing a limited number of high dimensional data points can be alleviated by well-designed domain-specific priors.
A vast majority of the increased mobile data throughput has been enabled by ever-increasing densification, i.e. adding more base stations and access points that have a wired backhaul connection. This trend is set to continue for the next decade at least, primarily through the provisioning of small cells such as pico and femtocells. What if we reached a point where adding more infrastructure did not allow increased wireless network throughput? This would be comparable to the impending end of "Moore's Law"; a cataclysmic event having far-reaching consequences.
In future computing systems, such as the Internet-of-Things (IoT), functionality is increasingly defined by the networked connectivity of spatially distributed devices. This, however, poses fundamentally new design challenges and tradeoffs. Computation and communication need to be tightly coupled and jointly explored, e.g. to determine whether a functionality should be performed locally or remotely over the network in order to achieve the best performance and energy consumption.
An incubator of cutting-edge technologies and digital creativity, SXSW Interactive 2015 featured five days of presentations and panels from the brightest minds in emerging technology. Special programs showcased new websites, video games and startup ideas from the community.
WNCG Faculty and students met on Sunday with researchers from the Center for Transportation Research (CTR) and other UT Austin Cockrell School Engineers during the first-ever UT Village at SXSW Interactive 2015.
This year's event featured panels and interactive research demonstrations and was open to all SXSW Interactive 2015 Badge Holders. Click the image below to view the complete slideshow from the day's events.
In 2013, 32,719 fatalities resulted from traffic crashes, most of which were caused by driver error. Across the globe, people are facing longer commutes and five Texas communities are in the top 26 most congested cities in the United States. Traffic congestion creates about 4.8 billion hours of travel delay and affects the environment through increased carbon footprints and higher fuel consumption.
AUSTIN, Texas — The University of Texas at Austin’s graduate schools in engineering, law, education and nursing are ranked among the Top 15 in the nation, and the university’s accounting program is No. 1 in the nation, according to U.S. News & World Report's 2016 edition of “Best Graduate Schools,” released this morning.