Sparse Coding (SC), which models the data vectors as sparse linear combinations over basis vectors (i.e., dictionary), has been widely applied in machine learning, signal processing and neuroscience. Recently, one specific SC technique, Group Sparse Coding (GSC), has been proposed to learn a common dictionary over multiple different groups of data, where the data groups are assumed to be pre-defined. In practice, this may not always be the case. In this paper, we propose Automatic Group Sparse Coding (AutoGSC), which can (1) discover the hidden data groups; (2) learn a common dictionary over different data groups; and (3) learn an individual dictionary for each data group. Finally, we conduct experiments on both synthetic and real world data sets to demonstrate the effectiveness of AutoGSC, and compare it with traditional sparse coding and Nonnegative Matrix Factorization (NMF) methods.

Various operational control systems (OCS) are naturally modeled as Markov Decision Processes. OCS often enjoy access to predictions of future events that have substantial impact on their operations. For example, reliable forecasts of extreme weather conditions are widely available, and such events can affect typical request patterns for customer response management systems, the flight and service time of airplanes, or the supply and demand patterns for electricity. The space of exogenous events impacting OCS can be very large, prohibiting their modeling within the MDP; moreover, for many of these exogenous events there is no useful predictive, probabilistic model. Realtime predictions, however, possibly with a short lead-time, are often available. In this work we motivate a model which combines offline MDP infinite horizon planning with realtime adjustments given specific predictions of future exogenous events, and suggest a framework in which such predictions are captured and trigger real-time planning problems. We propose a number of variants of existing MDP solution algorithms, adapted to this context, and evaluate them empirically.

The rapid uptake of mobile phones, cheaper and more Given the potentially large number of users of the Spoken widespread mobile connectivity, and increasing familiarity Web system and the likelihood of shared information needs with technology are driving Internet adoption in developing and significant user similarities, we expect considerable improvements nations, but major hurdles still remain. First, today's Internet in audio navigation from using CF. is mostly in English and is thus largely inaccessible to A useful distinction among CFbased approaches arises billions of people for whom English is not a native or second from the types of data used to associate users to products language. Second, today's Internet is accessible largely and other items. In some scenarios, users may provide explicit through text-based technologies (web browsing, email, text feedback about their interest in products through ratings.