Home About us Contact
|
Home About us Contact | ||
|
|
||
Vector Representations (vector + representation)
Selected AbstractsPerspectives of data analysis of enzyme inhibition and activation, Part 1: Use of the three-dimensional Km, V,I coordinate system for data analysis of enzyme inhibition and activationJOURNAL OF BIOCHEMICAL AND MOLECULAR TOXICOLOGY, Issue 2 2009Vladimir I. Krupyanko Abstract The possibility of construction of the three-dimensional (unfolded and folded) Km, V,I rectangular coordinate systems convenient for vector representation of inhibited and activated enzymatic reactions as well as of a two-dimensional Km, V, scalar rectangular coordinate system convenient for diagrammatic representation of enzymatic reactions is considered. The perspectives of using the properties of the three-dimensional L vectors and their scalar L projections for data analysis of enzyme inhibition and activation are analyzed. © 2009 Wiley Periodicals, Inc. J Biochem Mol Toxicol 23:97,100, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/jbt.20273 [source] Visualizing polysemy using LSA and the predication algorithmJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 8 2010Guillermo Jorge-Botana Context is a determining factor in language and plays a decisive role in polysemic words. Several psycholinguistically motivated algorithms have been proposed to emulate human management of context, under the assumption that the value of a word is evanescent and takes on meaning only in interaction with other structures. The predication algorithm (Kintsch, 2001), for example, uses a vector representation of the words produced by LSA (Latent Semantic Analysis) to dynamically simulate the comprehension of predications and even of predicative metaphors. The objective of this study was to predict some unwanted effects that could be present in vector-space models when extracting different meanings of a polysemic word (predominant meaning inundation, lack of precision, and low-level definition), and propose ideas based on the predication algorithm for avoiding them. Our first step was to visualize such unwanted phenomena and also the effect of solutions. We use different methods to extract the meanings for a polysemic word (without context, vector sum, and predication algorithm). Our second step was to conduct an analysis of variance to compare such methods and measure the impact of potential solutions. Results support the idea that a human-based computational algorithm like the predication algorithm can take into account features that ensure more accurate representations of the structures we seek to extract. Theoretical assumptions and their repercussions are discussed. [source] On Floating-Point Normal VectorsCOMPUTER GRAPHICS FORUM, Issue 4 2010Quirin Meyer Abstract In this paper we analyze normal vector representations. We derive the error of the most widely used representation, namely 3D floating-point normal vectors. Based on this analysis, we show that, in theory, the discretization error inherent to single precision floating-point normals can be achieved by 250.2 uniformly distributed normals, addressable by 51 bits. We review common sphere parameterizations and show that octahedron normal vectors perform best: they are fast and stable to compute, have a controllable error, and require only 1 bit more than the theoretical optimal discretization with the same error. [source] Analogy retrieval and processing with distributed vector representationsEXPERT SYSTEMS, Issue 1 2000Tony A. Plate Holographic reduced representations (HRRs) are a method for encoding nested relational structures in fixed-width vector representations. HRRs encode relational structures as vector representations in such a way that the superficial similarity of the vectors reflects both superficial and structural similarity of the relational structures. HRRs also support a number of operations that could be very useful in psychological models of human analogy processing: fast estimation of superficial and structural similarity via a vector dot-product; finding corresponding objects in two structures; and chunking of vector representations. Although similarity assessment and discovery of corresponding objects both theoretically take exponential time to perform fully and accurately, with HRRs one can obtain approximate solutions in constant time. The accuracy of these operations with HRRs mirrors patterns of human performance on analog retrieval and processing tasks. [source] | ||||||||||