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.

C-store: a column-oriented DBMS

Abstract: This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of column-oriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures.We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.

Neurocomputing: Foundations of Research

Abstract: ion for Knowledge Acquisition” by T. Bylander and B. Chadrasekaran. Chandrasekaran’s papers are usually illuminating, and this one does not fail: He and Bylander re-examine such traditional beliefs as knowledge should be uniformly represented and controlled and the knowledge base should be separated from the inference engine. The final 10 papers in volume 1 discuss generalized learning and ruleinduction techniques. They are interesting and informative, particularly “Generalization and Noise” by Y. Kodratoff and M. Manango, which discusses symbolic and numeric rule induction. Most rule-induction techniques focus on the use of examples and numeric analysis such as repertory grids. Kodratoff’s and Manango’s exploration of how the two complement each other is refreshing. Because of their technical nature and the amount of work it would take to put their content to use, most of the papers in this section of the volume are more appropriate for a specialized or research-oriented group. For those just getting involved in knowledge-based–systems development, Knowledge Acquisition Tools for Expert Systems is the more useful volume. In addition to discussing the tools themselves, most of the papers contain details of the knowledgeacquisition techniques that are automated, thus providing much of the same information which is available in the first volume. As an added benefit, they also often discuss the underlying architectures for solving domain-specific problems. For instance, the details of the medical diagnostic architecture laid out in “Design for Acquisition: Principles of Knowledge System Design to Facilitate Knowledge Acquisition” by T. R. Gruber and P. R. Cohen are almost as useful as the discussion of how to build a knowledge-acquisition system. Volume 2 is particularly germane given the rise in commercial interest about automated knowledge acquisition following this year’s introduction of Neuron Data’s NEXTRATM product and last year’s introduction of Test Bench by Texas Instruments. Test Bench is actually discussed in “A Mixed-Initiative Workbench for Knowledge Acquisition” by G. S. Kahn, E. H. Breaux, P. De Klerk, and R. L. Joseph. This volume provides the background necessary to evaluate knowledge-acquisition tools such as NEXTRA, Test Bench, and AutoIntelligence (IntelligenceWare). The vendors of knowledge-based–systems development tools, for example, Inference, IntelliCorp, Aion, AI Corp., and IBM, would do well to pay heed to these books because they point the way to removing the knowledge bottleneck from knowledge-based–systems development. Overall, the papers in both volumes are comprehensive and well integrated, a sometimes difficult state to achieve when compiling a collection of papers resulting from a small conference. The collection is comparable to Anna Hart’s Knowledge Acquisition for Expert Systems (McGraw-Hill, 1986), but it is broader in scope and not as structured. The arrangement of the papers is marred only by an overly brief index. Few readers can be expected to read a collection from beginning to end, and a better index would facilitate more enlightened use. Less important—but nevertheless distracting—is the large number of typographical errors in both volumes. In conclusion, the set is recommended for both the commercial and research knowledge-based–systems practitioner. Reading the volumes in reverse order might be more useful to the commercial developer given the extra information available in volume 2. Neurocomputing: Foundations of Research

A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.

Abstract: We propose that children employ specialized cognitive systems that allow them to recover an accurate causal map of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

The Effect of Financial Development on Convergence: Theory and Evidence

Abstract: We introduce imperfect creditor protection in a multi-country version of Schumpeterian growth theory with technology transfer. The theory predicts that the growth rate of any country with more than some critical level of financial development will converge to the growth rate of the world technology frontier, and that all other countries will have a strictly lower long-run growth rate. The theory also predicts that in a country that converges to the frontier growth rate, financial development has a positive but eventually vanishing effect on steady-state per-capita GDP relative to the frontier. We present cross-country evidence supporting these two implications. In particular, we find a significant and sizeable effect of an interaction term between initial per-capita GDP (relative to the United States) and a financial intermediation measure in an otherwise standard growth regression, implying that the likelihood of converging to the U.S. growth rate increases with financial development. We also find that, as predicted by the theory, the direct effect of financial intermediation in this regression is not significantly different from zero. These findings are robust to alternative conditioning sets, estimation procedures and measures of financial development.

Settling velocity and concentration distribution of heavy particles in homogeneous isotropic turbulence

Abstract: The average settling velocity in homogeneous turbulence of a small rigid spherical particle, subject to a Stokes drag force, has been shown to differ from that in still fluid owing to a bias from the particle inertia (Maxey 1987). Previous numerical results for particles in a random flow field, where the flow dynamics were not considered, showed an increase in the average settling velocity. Direct numerical simulations of the motion of heavy particles in isotropic homogeneous turbulence have been performed where the flow dynamics are included. These show that a significant increase in the average settling velocity can occur for particles with inertial response time and still-fluid terminal velocity comparable to the Kolmogorov scales of the turbulence. This increase may be as much as 50% of the terminal velocity, which is much larger than was previously found. The concentration field of the heavy particles, obtained from direct numerical simulations, shows the importance of the inertial bias with particles tending to collect in elongated sheets on the peripheries of local vortical structures. This is coupled then to a preferential sweeping of the particles in downward moving fluid. Again the importance of Kolmogorov scaling to these processes is demonstrated. Finally, some consideration is given to larger particles that are subject to a nonlinear drag force where it is found that the nonlinearity reduces the net increase in settling velocity.