Brown University

About: Brown University is a education organization based out in Providence, Rhode Island, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35778 authors who have published 90896 publications receiving 4471489 citations. The organization is also known as: brown.edu & Brown.

Intelligence quotient scores of 4-year-old children: social-environmental risk factors.

Abstract: Verbal IQ scores in a socially heterogeneous sample of 215 4-year-old children were highly related to a cumulative environmental risk index composed of maternal, family and cultural variables. Different combinations of equal numbers of risk factors produced similar effects on IQ, providing evidence that no single factor identified here uniquely enhances or limits early intellectual achievement and that cumulative effects from multiple risk factors increase the probability that development will be compromised. The multiple risk index predicted substantially more variance in the outcome measure than did any single risk factor alone, including socioeconomic status. High-risk children were more than 24 times as likely to have IQs below 85 than low-risk children.

Convergence? Inferences from Theoretical Models

Abstract: This essay suggests that the convergence controversy may reflect, in part, differences in perception regarding the viable set of competing testable hypotheses generated by existing growth theories. It argues that in contrast to the prevailing wisdom, the traditional neo-classical growth paradigm generates the club convergence hypothesis as well as the conditional convergence hypothesis. Furthermore, the inclusion of empirically significant variables such as human capital, income distribution, and fertility in conventional growth models, along with capital market imperfections, externalities, and non-convexities, strengthens the viability of club convergence as a competing hypothesis with conditional convergence.

Dynamic provable data possession

Abstract: We consider the problem of efficiently proving the integrity of data stored at untrusted servers. In the provable data possession (PDP) model, the client preprocesses the data and then sends it to an untrusted server for storage, while keeping a small amount of meta-data. The client later asks the server to prove that the stored data has not been tampered with or deleted (without downloading the actual data). However, the original PDP scheme applies only to static (or append-only) files.We present a definitional framework and efficient constructions for dynamic provable data possession (DPDP), which extends the PDP model to support provable updates to stored data. We use a new version of authenticated dictionaries based on rank information. The price of dynamic updates is a performance change from O(1) to O(logn) (or O(nelog n), for a file consisting of n blocks, while maintaining the same (or better, respectively) probability of misbehavior detection. Our experiments show that this slowdown is very low in practice (e.g. 415KB proof size and 30ms computational overhead for a 1GB file). We also show how to apply our DPDP scheme to outsourced file systems and version control systems (e.g. CVS).

On pole assignment in multi-input controllable linear systems

Abstract: It is shown that controllability of an open-loop system is equivalent to the possibility of assigning an arbitrary set of poles to the transfer matrix of the closed-loop system, formed by means of suitable linear feedback of the state. As an application of this result, it is shown that an open-loop system can be stabilized by linear feedback if and only if the unstable modes of its system matrix are controllable. A dual of this criterion is shown to be equivalent to the existence of an observer of Luenberger's type for asymptotic state identification.

Results of Initial Low-Dose Computed Tomographic Screening for Lung Cancer

Abstract: Background Lung cancer is the largest contributor to mortality from cancer. The National Lung Screening Trial (NLST) showed that screening with low-dose helical computed tomography (CT) rather than with chest radiography reduced mortality from lung cancer. We describe the screening, diagnosis, and limited treatment results from the initial round of screening in the NLST to inform and improve lung-cancer-screening programs. Methods At 33 U.S. centers, from August 2002 through April 2004, we enrolled asymptomatic participants, 55 to 74 years of age, with a history of at least 30 pack-years of smoking. The participants were randomly assigned to undergo annual screening, with the use of either low-dose CT or chest radiography, for 3 years. Nodules or other suspicious findings were classified as positive results. This article reports findings from the initial screening examination. Results A total of 53,439 eligible participants were randomly assigned to a study group (26,715 to low-dose CT and 26,724 to chest radiography); 26,309 participants (98.5%) and 26,035 (97.4%), respectively, underwent screening. A total of 7191 participants (27.3%) in the low-dose CT group and 2387 (9.2%) in the radiography group had a positive screening result; in the respective groups, 6369 participants (90.4%) and 2176 (92.7%) had at least one follow-up diagnostic procedure, including imaging in 5717 (81.1%) and 2010 (85.6%) and surgery in 297 (4.2%) and 121 (5.2%). Lung cancer was diagnosed in 292 participants (1.1%) in the low-dose CT group versus 190 (0.7%) in the radiography group (stage 1 in 158 vs. 70 participants and stage IIB to IV in 120 vs. 112). Sensitivity and specificity were 93.8% and 73.4% for low-dose CT and 73.5% and 91.3% for chest radiography, respectively. Conclusions The NLST initial screening results are consistent with the existing literature on screening by means of low-dose CT and chest radiography, suggesting that a reduction in mortality from lung cancer is achievable at U.S. screening centers that have staff experienced in chest CT. (Funded by the National Cancer Institute; NLST ClinicalTrials.gov number, NCT00047385.).