Nitish Srivastava

Enriching textbooks through data mining

Textbooks play an important role in any educational system. Unfortunately, many textbooks produced in developing countries are not written well and they often lack adequate coverage of important concepts. We propose a technological solution to address this problem based on enriching textbooks with authoritative web content. We augment textbooks at the section level for key concepts discussed in the section. We use ideas from data mining for identifying the concepts that need augmentation as well as to determine the links to the authoritative content that should be used for augmentation. Our evaluation, employing textbooks from India, shows that we are able to enrich textbooks on different subjects and across different grades with high quality augmentations using automated techniques.

Modeling Inter-individual Variability in Sugar Beet Populations

Modeling heterogeneity in field crops is a key issue for a better characterization of field production. This paper presents some experimental data on sugar beet illustrating this heterogeneity. Several sources of individual variability within plant populations are identified: namely, initial condition (seed biomass, emergence delay), genetic variability (including phyllochron) and environment (including spacing and competition). A mathematical framework is introduced to integrate the different sources of variability in plant growth models. It is based on the classical method of Taylor Series Expansion, which allows the propagation of uncertainty in the dynamic system of growth and the computation of the approximate means and standard deviations of the model outputs. The method is applied to the GreenLab model of plant growth and more specifically to sugar beet. It opens perspectives in order to assess the different sources of variability in plant populations and estimate their parameters from experimental data. However important issues like optimization of data collection and system identifiability have to be resolved first, since the uncertainty effects may be mixed in an inextricable way or may necessitate a too huge amount of experimental data for their estimation.

Flexible and Dynamic Power Allocation in Broadband Multi-Beam Satellites

For multi-beam broadband satellites operating at 10 GHz and above frequencies, rain attenuation is the dominant impairment factor. Using a stochastic model for rain attenuation prediction and a greedy approach, dynamic power allocation has been recently shown to increase the number of users served than the static technique. This letter proposes a new dynamic power allocation algorithm the novelty of which lies in treating users with similar power requirement as a group, instead of individuals. Thus, without resorting to exhaustive search we are able to serve more number of users than the existing technique.