Oversight of human gene transfer research ("gene therapy") presents an important model with potential application to oversight of nanobiology research on human participants. Gene therapy oversight adds centralized federal review at the National Institutes of Health's Office of Biotechnology Activities and its Recombinant DNA Advisory Committee to standard oversight of human subjects research at the researcher's institution (by the Institutional Review Board and, for some research, the Institutional Biosafety Committee) and at the federal level by the Office for Human Research Protections. The Food and Drug Administration's Center for Biologics Evaluation and Research oversees human gene transfer research in parallel, including approval of protocols and regulation of products. This article traces the evolution of this dual oversight system; describes how the system is already addressing nanobiotechnology in gene transfer: evaluates gene therapy oversight based on public opinion, the literature, and preliminary expert elicitation; and offers lessons of the gene therapy oversight experience for oversight of nanobiotechnology.

We initiate the study of sparse recovery problems under the Earth-Mover Distance (EMD). Specifically, we design a distribution over m × n matrices A, for m n, such that for any x, given Ax, we can recover a k-sparse approximation to x under the EMD distance. We also provide an empirical evaluation of the method that, in some scenarios, shows its advantages over the "usual" recovery in the p norms.

This paper aims at collecting and analysing temperature, rainfall, soil, seed, crop production, humidity and wind speed data (in a few regions), which will help the farmers improve the produce of their crops. Firstly, we pre-process the data in a Python environment and then apply the MapReduce framework, which further analyses and processes the large volume of data. Secondly, kmeans clustering is employed on results gained from MapReduce and provides a mean result on the data in terms of accuracy. After that we use bar graphs and scatter plots to study the relationship between the crop, rainfall, temperature, soil and seed type of two regions (Ahmednagar, Maharashtra and, Andaman and Nicobar Islands). Further, a self-designed recommender system has been used to predict the crops and display them on a Graphic User Interface designed in a Flask environment. The system design is scalable and can be used to find the recommended crops of other states in a similar manner in the future.

We propose a framework for compressive sensing of images with local geometric features. Specifically, let x ∈ R N be an N -pixel image, where each pixel p has value xp. The image is acquired by computing the measurement vector Ax, where A is an m × N measurement matrix for some m N . The goal is then to design the matrix A and recovery algorithm which, given Ax, returns an approximation to x.In this paper we investigate this problem for the case where x consists of a small number (k) of "local geometric objects" (e.g., stars in an image of a sky), plus noise. We construct a matrix A and recovery algorithm with the following features: (i) the number of measurements m is O(k log k N ), which undercuts currently known schemes that achieve m = O(k log(N/k)) (ii) the matrix A is ultra-sparse, which is important for hardware considerations (iii) the recovery algorithm is fast and runs in time sub-linear in N . We also present a comprehensive study of an application of our algorithm to a problem in satellite navigation.

We propose a framework for compressive sensing of images with local geometric features. Specifically, let x ∈ R N be an N -pixel image, where each pixel p has value xp. The image is acquired by computing the measurement vector Ax, where A is an m × N measurement matrix for some m N . The goal is then to design the matrix A and recovery algorithm which, given Ax, returns an approximation to x.In this paper we investigate this problem for the case where x consists of a small number (k) of "local geometric objects" (e.g., stars in an image of a sky), plus noise. We construct a matrix A and recovery algorithm with the following features: (i) the number of measurements m is O(k log k N ), which undercuts currently known schemes that achieve m = O(k log(N/k)) (ii) the matrix A is ultra-sparse, which is important for hardware considerations (iii) the recovery algorithm is fast and runs in time sub-linear in N . We also present a comprehensive study of an application of our algorithm to a problem in satellite navigation.