A reference for the scale modeler and aviation enthusiast. General and detail photographs and drawings, five-view drawings, technical data and facts on the actual aircraft, kit and product reviews for the scale modeler, reference listings.
All geographic information systems (GIS) are built
using formal models that describe how things are
located in space. A formal model is an abstract and
well-defined system of concepts. It defines the
vocabulary that we can use to describe and reason
about things. A geographic data model defines the
vocabulary for describing and reasoning about the
things that are located on the earth. Geographic data
models serve as the foundation on which all
geographic information systems are built.
We are all familiar with one model for geographic
Systems Analysis and Design: Chapter 6 - Agile Modeling and Prototyping's Objectives is Understand the roots of agile modeling in prototyping and the four main types of prototyping; Be able to use prototyping for human information requirements gathering.
This module provides students with an introduction to the Microsoft Solutions
Framework (MSF) Team Model, including the team goals for success, team
roles of the model, how to scale the model for small or large projects, principles
of a successful team, and how to apply the model to different types of projects.
Silicon technology continues to progress, but device scaling is rapidly
taking the metal oxide semiconductor field-effect transistor (MOSFET) to its
limit. When MOS technology was developed in the 1960's, channel lengths
were about 10 micrometers, but researchers are now building transistors with
channel lengths of less than 10 nanometers. New kinds of transistors and
other devices are also being explored. Nanoscale MOSFET engineering
continues, however, to be dominated by concepts and approaches originally
developed to treat microscale devices...
In this research monograph, we explain the development of a mechanistic, stochastic
theory of nonfickian solute dispersion in porous media. We have included sufficient
amount of background material related to stochastic calculus and the scale dependency
of diffusivity in this book so that it could be read independently.
Modern complex dynamical systems1 are highly interconnected and mutually
interdependent, both physically and through a multitude of information
and communication network constraints. The sheer size (i.e., dimensionality)
and complexity of these large-scale dynamical systems often necessitates
a hierarchical decentralized architecture for analyzing and controlling these
systems. Specifically, in the analysis and control-system design of complex
large-scale dynamical systems it is often desirable to treat the overall system
as a collection of interconnected subsystems.
In the quest for knowledge, it is not uncommon for researchers to push the limits
of simulation techniques to the point where they have to be adapted or totally new
techniques or approaches become necessary. True multiscale modeling techniques
are becoming increasingly necessary given the growing interest in materials and
processes on which large-scale properties are dependent or that can be tuned by their
low-scale properties. An example would be nanocomposites, where embedded nanostructures
completely change the matrix properties due to effects occurring at the
Clearly, man-made projects are not new: monuments surviving from the earliest civilizations testify
to the incredible achievements of our forebears and still evoke our wonder and admiration. Modern
projects, for all their technological sophistication, are not necessarily greater in scale than some
of those early mammoth works.
This paper presents an attempt at building a large scale distributed composite language model that simultaneously accounts for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content under a directed Markov random ﬁeld paradigm.
This paper presents an exponential model for translation into highly inﬂected languages which can be scaled to very large datasets. As in other recent proposals, it predicts targetside phrases and can be conditioned on sourceside context. However, crucially for the task of modeling morphological generalizations, it estimates feature parameters from the entire training set rather than as a collection of separate classiﬁers.
Cross-document coreference, the task of grouping all the mentions of each entity in a document collection, arises in information extraction and automated knowledge base construction. For large collections, it is clearly impractical to consider all possible groupings of mentions into distinct entities.
In this paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language models.
We present a novel probabilistic classiﬁer, which scales well to problems that involve a large number of classes and require training on large datasets. A prominent example of such a problem is language modeling. Our classiﬁer is based on the assumption that each feature is associated with a predictive strength, which quantiﬁes how well the feature can predict the class by itself. The predictions of individual features can then be combined according to their predictive strength, resulting in a model, whose parameters can be reliably and efﬁciently estimated.
Large-scale discriminative machine translation promises to further the state-of-the-art, but has failed to deliver convincing gains over current heuristic frequency count systems. We argue that a principle reason for this failure is not dealing with multiple, equivalent translations. We present a translation model which models derivations as a latent variable, in both training and decoding, and is fully discriminative and globally optimised. Results show that accounting for multiple derivations does indeed improve performance.
This paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. While previous work assumed the effects of word co-occurrence statistics to be constant over a window of several hundred words, we show that their influence is nonstationary on a much smaller time scale.
The Santa Fe Institute (SFI) is interested in understanding evolving complex
social, biological, and physical adaptive systems in a most general sense
(see Cowan et al. 1994). Those of us at SFI interested in the evolution of
social behavior have tended to focus on either small-scale societies or on specific
aspects of more complex societies, such as the economy.
We propose a simple generative, syntactic language model that conditions on overlapping windows of tree context (or treelets) in the same way that n-gram language models condition on overlapping windows of linear context. We estimate the parameters of our model by collecting counts from automatically parsed text using standard n-gram language model estimation techniques, allowing us to train a model on over one billion tokens of data using a single machine in a matter of hours.