Stony Brook University

About: Stony Brook University is a education organization based out in Stony Brook, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32534 authors who have published 68218 publications receiving 3035131 citations. The organization is also known as: State University of New York at Stony Brook & SUNY Stony Brook.

Volume Visualization

Abstract: Volume visualization is a method of extracting information from volumetric datasets through interactive graphics and imaging, and is concerned with the representation, manipulation, and rendering of these datasets [Gallagher 1995; Kaufman 1991; Rosenblum 1994]. Volume data are 3D entities that may have information inside them, may not consist of surfaces and edges, or may be too voluminous to be represented geometrically. Volume visualization encompasses an array of techniques for peering inside the dataset and for interactively extracting meaningful information from it using transformations, cuts, segmentation, translucency, measurements, and the like. The primary sources of volume data are three: sampled data of real objects or phenomena, computed data produced by a computer simulation, and modeled data generated from a geometric model. Examples of applications generating sampled data are medical imaging (e.g., CT, MRI), biology (e.g., confocal microscopy), geoscience (e.g., seismic measurements), industry (e.g., nondestructive inspection), and chemistry (e.g., electron density maps) [Kaufman 1991]. Some examples of applications generating computed datasets, typically by running a simulation on a supercomputer, are meteorology (e.g., storm prediction), computational fluid dynamics (e.g., water flow), and materials science (e.g., new materials). Recently, many traditional computer graphics applications, such as computer-aided design and flight simulation [Cohen and Shaked 1993; Kaufman et al. 1993], have been exploiting the advantages of volumetric techniques for modeling, manipulation, and visualization, an approach called volume graphics [Kaufman et al. 1993]. Volumetric data is typically a set S of samples (x, y, z, v), representing the value v of some property of the data at a 3D location (x, y, z). If v is simply a 0 or a 1, with 0 indicating background and 1 indicating the object, the data is called binary data. The data may instead be multivalued, with v representing some measurable property of the data, such as density, color, heat, or pressure. The value v may even be a vector, representing, for example, velocity at each location. In general, the samples may be taken at random locations in space, but in many cases S is isotropic, containing samples taken at regularly spaced intervals along three orthogonal axes. Since S is defined on a regular grid, a 3D array (called volume buffer, cubic frame buffer, 3D raster) is typically used to store the values. S is therefore referred to as the array of values S(x, y, z), which is defined only at grid locations. A function may be defined to describe the value at any continuous location by approximating v at a location (x, y, z) using some interpolation function to S, such as zero-order (nearest-neighbor), piecewise function known as first-order (trilinear), or higher-order interpolation. The region of constant value that surrounds each sample in zero-order interpolation is known as a volume cell (voxel for short), with each voxel being a rectangular cuboid having six faces, twelve edges, and eight corners. The terms, voxel, grid location,

Controlled diffusion models for optimal dividend pay-out

Abstract: The reserve r ( t ) of an insurance company at time t is assumed to be governed by the stochastic differential equation dr ( t ) = ( μ − a ( t )) d t + σ d w ( t ), where w is standard Brownian motion, μ , σ > 0 constants and a ( t ) the rate of dividend payment at time t (0 acts as absorbing barrier for r ( t )). The function a ( t ) is subject to dynamic allocation and the objective is to find the one which maximizes EJ x ( a (·)), where J x = ∫ 0 ∞ e − ct a ( t ) d t is the total (discounted) pay-out of dividend and x refers to r (0) = x . Two situations are considered: 1. (a) The dividend rate is restricted so that the function a ( t ) varies in [0, a 0 ] for some a 0 a 0 is smaller than some critical value, the optimal strategy is to always pay the maximal dividend rate a 0 . Otherwise, the optimal policy prescribes to pay nothing when the reserve is below some critical level m , and to pay maximal dividend rate a 0 when the reserve is above m . 2. (b) The dividend rate is unrestricted so that a ( t ) is allowed to vary in all of (0, ∞). Then the optimal strategy is of singular control type in the sense that it prescribes to pay out whatever amount exceeds some critical level m , but not pay out dividend when the reserve is below m .

Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

Abstract: As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve prediction accuracy. Few of them, however, have studied how to select input parameters carefully to achieve desired results. After introducing a Back Propagation neural network based on Particle Swam Optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection. The IS-PSO-BP is short for Input parameter Selection (IS)-PSO-BP, where IS stands for Input parameter Selection. To evaluate the forecast performance of proposed approach, this paper uses daily average wind speed data of Jiuquan and 6-hourly wind speed data of Yumen, Gansu of China from 2001 to 2006 as a case study. The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model.

Optical production of ultracold polar molecules.

Abstract: We demonstrate the production of ultracold polar RbCs molecules in their vibronic ground state, via photoassociation of laser-cooled atoms followed by a laser-stimulated state transfer process. The resulting sample of X1Sigma+ (nu = 0) molecules has a translational temperature of approximately 100 microK and a narrow distribution of rotational states. With the method described here it should be possible to produce samples even colder in all degrees of freedom, as well as other bialkali species.