We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...
We investigate the performance of longest-queue-first (LQF) scheduling (i.e., greedy maximal scheduling) for wireless networks under the SINR interference model. This interference...
Long Bao Le, Eytan Modiano, Changhee Joo, Ness B. ...
Business performance modeling and model-driven business transformation are two research directions that are attracting much attention lately. In this study, we propose an approach...
—This paper considers the problem of designing scheduling algorithms for multi-channel (e.g., OFDM-based) wireless downlink systems. We show that the Server-Side Greedy (SSG) rul...
Shreeshankar Bodas, Sanjay Shakkottai, Lei Ying, R...
Sharing huge databases in distributed systems is inherently difficult. As the amount of stored data increases, data localization techniques become no longer sufficient. A more ef...
Rabab Hayek, Guillaume Raschia, Patrick Valduriez,...