Stanford University

About: Stanford University is a education organization based out in Stanford, California, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 125751 authors who have published 320347 publications receiving 21892059 citations. The organization is also known as: Leland Stanford Junior University & University of Stanford.

Support vector machine active learning with applications to text classification

Abstract: Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.

Route packets, not wires: on-chip interconnection networks

Abstract: Using on-chip interconnection networks in place of ad-hoc glo-bal wiring structures the top level wires on a chip and facilitates modular design. With this approach, system modules (processors, memories, peripherals, etc...) communicate by sending packets to one another over the network. The structured network wiring gives well-controlled electrical parameters that eliminate timing iterations and enable the use of high-performance circuits to reduce latency and increase bandwidth. The area overhead required to implement an on-chip network is modest, we estimate 6.6%. This paper introduces the concept of on-chip networks, sketches a simple network, and discusses some challenges in the architecture and design of these networks.

Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling

Abstract: Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks.

Immersed boundary methods

Abstract: The term “immersed boundary method” was first used in reference to a method developed by Peskin (1972) to simulate cardiac mechanics and associated blood flow. The distinguishing feature of this method was that the entire simulation was carried out on a Cartesian grid, which did not conform to the geometry of the heart, and a novel procedure was formulated for imposing the effect of the immersed boundary (IB) on the flow. Since Peskin introduced this method, numerous modifications and refinements have been proposed and a number of variants of this approach now exist. In addition, there is another class of methods, usually referred to as “Cartesian grid methods,” which were originally developed for simulating inviscid flows with complex embedded solid boundaries on Cartesian grids (Berger & Aftosmis 1998, Clarke et al. 1986, Zeeuw & Powell 1991). These methods have been extended to simulate unsteady viscous flows (Udaykumar et al. 1996, Ye et al. 1999) and thus have capabilities similar to those of IB methods. In this review, we use the term immersed boundary (IB) method to encompass all such methods that simulate viscous flows with immersed (or embedded) boundaries on grids that do not conform to the shape of these boundaries. Furthermore, this review focuses mainly on IB methods for flows with immersed solid boundaries. Application of these and related methods to problems with liquid-liquid and liquid-gas boundaries was covered in previous reviews by Anderson et al. (1998) and Scardovelli & Zaleski (1999). Consider the simulation of flow past a solid body shown in Figure 1a. The conventional approach to this would employ structured or unstructured grids that conform to the body. Generating these grids proceeds in two sequential steps. First, a surface grid covering the boundaries b is generated. This is then used as a boundary condition to generate a grid in the volume f occupied by the fluid. If a finite-difference method is employed on a structured grid, then the differential form of the governing equations is transformed to a curvilinear coordinate system aligned with the grid lines (Ferziger & Peric 1996). Because the grid conforms to the surface of the body, the transformed equations can then be discretized in the

Social Exchange Theory

Abstract: Much of social life involves interactions between individuals or corporate actors in dyads, groups, organizations or networks that can be viewed as social exchanges. This chapter presents an overview of the main theories of social exchange focusing on the key contributors in sociology, including George Homans, Peter Blau, Richard M. Emerson and those whose work subsequently built on their original formulations. The theories that have been developed in recent decades have focused on the social structures created by repeated exchanges and the ways in which these structures both constrain and enable actors to exercise power and influence. Other related social processes addressed within the exchange tradition include interpersonal commitment, trust, fairness, procedural and distributive justice, coalition formation and collective action. Recent work also focuses on emotions and their role in social exchange. The methodological challenges of studying social exchange in the laboratory and in the world outside the lab are addressed as well as links between exchange theory and topics under study by economic sociologists and network scholars more broadly, including Internet-mediated exchanges and their growing significance.