In many learning problems, the decision maker is provided with various (types of) context information that she might utilize to select actions in order to maximize performance/rewards. But not all information is equally relevant: some context information may be more relevant to the decision problem at hand. Discovering and exploiting the most relevant context information speeds up learning, red...

A recently introduced technique for pathogen recognition called BARDOT (BActeria Rapid Detection using Optical scattering Technology) belongs to the broad class of optical sensors and relies on forward-scatter phenotyping (FSP). The specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system...

Increasing demand for personalized tours tourists travel in an urban area motivates more attention to points of interest (POI) and tour recommendation services. Recently, the granularity POI has been discussed provide detailed information planning, which supports both inside outside routes that would improve tourists' experience. Such systems require a predefined database with different granula...

Stormwater management has significant consequences for urban hydrology, water quality, and flood risk, and has changed substantially over history, but it is unknown how these paradigm shifts play out at the local scale and whether local changes in stormwater infrastructure use follow similar trajectories across cities. This research addressed: (1) How does current infrastructure use and past in...

The majority of humanity now lives in cities or towns, with this proportion expected to continue increasing for the foreseeable future. As novel ecosystems, urban areas offer an ideal opportunity to examine multi-scalar processes involved in community assembly as well as the role of human activities in modulating environmental drivers of biodiversity. Although ecologists have made great strides...

Discovering underlying low dimensional structure of a high-dimensional matrix is traditionally done through rank approximations in the form sum rank-one matrices. In this article, we propose new approach. We assume can be approximated by small number Kronecker products matrices with potentially different configurations, named as hybrid outer Product Approximation (hKoPA). It provides an extreme...

Predicting financial markets is of particular importance for investors who intend to make the most profit. Analysing reasonable and precise strategies predicting has a long history. Deep learning techniques include analyses predictions that can assist scientists in discovering unknown patterns data. In this project, application noise elimination such as Wavelet transform Kalman filter combinati...

Discovering the latent structure from many observed variables is an important yet challenging learning task. Existing approaches for discovering latent structures often require the unknown number of hidden states as an input. In this paper, we propose a quartet based approach which is agnostic to this number. The key contribution is a novel rank characterization of the tensor associated with th...

This work presents a recommender system that helps travel agents in discovering options for customers, especially those who do not know where to go and what to do. The system analyzes textual messages exchanged between a travel agent and a customer through a private Web chat. Text mining techniques help discover interesting areas in the messages. After that, the system searches a database and r...