This the implementation of structural SVM for training complex alignment models for protein sequence alignment, especially for homology Modeling. The structural SVM algorithm can incorporate many relevant features like secondary structure, relative exposed surface area, profiles and their various interaction into the alignment model. It was developed under Linux and compiles under gcc, built upon the svm^light software by Thorsten Joachims.
This submission includes the presentation and model files that were used in delivering a webinar on 12-15-05 that covered the topic of Modeling Hybrid Electric Vehicles.
Hybrid electric vehicles (HEVs) have proven they can substantially improve fuel economy and reduce emissions. Because HEVs combine an electric drive with the internal combustion engine (ICE) in the powertrain, the vehicle?s kinetic energy can be captured during braking and transformed into electrical energy in the battery. The dual power source also means that the ICE can be reduced in size and can operate at its most efficient speeds.
Carrier-phase synchronization can be approached in a
general manner by estimating the multiplicative distortion (MD) to which
a baseband received signal in an RF or coherent optical transmission
system is subjected. This paper presents a unified Modeling and
estimation of the MD in finite-alphabet digital communication systems. A
simple form of MD is the camer phase exp GO) which has to be estimated
and compensated for in a coherent receiver. A more general case with
fading must, however, allow for amplitude as well as phase variations of
the MD.
We assume a state-variable model for the MD and generally obtain a
nonlinear estimation problem with additional randomly-varying system
parameters such as received signal power, frequency offset, and Doppler
spread. An extended Kalman filter is then applied as a near-optimal
solution to the adaptive MD and channel parameter estimation problem.
Examples are given to show the use and some advantages of this scheme.
Data mining (DM) is the extraction of hidden predictive information from large databases
(DBs). With the automatic discovery of knowledge implicit within DBs, DM uses
sophisticated statistical analysis and Modeling techniques to uncover patterns and relationships
hidden in organizational DBs. Over the last 40 years, the tools and techniques to
process structured information have continued to evolve from DBs to data warehousing
(DW) to DM. DW applications have become business-critical. DM can extract even more
value out of these huge repositories of information.
From helping to assess the value of new medical treatments to evaluating the
factors that affect our opinions and behaviors, analysts today are finding
myriad uses for categorical data methods. In this book we introduce these
methods and the theory behind them.
Statistical methods for categorical responses were late in gaining the level
of sophistication achieved early in the twentieth century by methods for
continuous responses. Despite influential work around 1900 by the British
statistician Karl Pearson, relatively little development of models for categorical
responses occurred until the 1960s. In this book we describe the early
fundamental work that still has importance today but place primary emphasis
on more recent Modeling approaches. Before outlining
Detecting Network Intrusions via Sampling_A Game Theoretic Approach
Internet Quarantine_Requirements for Containing Self-Propagating Code
Modeling Malware Spreading Dynamics
Modeling the Spread of Active Worms
